Evaluation of Electrical Energy Storage (EES) technologies for renewable energy: A case from the US Pacific Northwest

Journal of Energy Storage 11 (2017) 25–54

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Journal of Energy Storage journal homepage: www.elsevier.com/locate/est

Review article

Evaluation of Electrical Energy Storage (EES) technologies for renewable energy: A case from the US Pacific Northwest Jisun Kim, Yulianto Suharto, Tugrul U. Daim* Portland State University, Dept of Engineering and Technology Management, Portland, USA

A R T I C L E I N F O

Article history: Received 8 August 2016 Received in revised form 12 January 2017 Accepted 16 January 2017 Available online 26 April 2017 Keywords: Technology evaluation Multiple criteria decision making Review

A B S T R A C T

Increase in use of renewable energy such as solar and wind has created challenges in balancing load. Renewable energy intermittency can be addressed with different solutions and technologies. Using Electric Energy Storage (EES) has been an approach which has been studied extensively in the recent years. This paper reviews the storage technologies leveraging both technical papers on technologies as well as other reviews of such technologies done by other researchers. The contribution of this paper is in two areas. First the use of a case study demonstrates how different approaches can address different challenges. Second contribution is the review of evaluation factors and methods of such technologies resulting in a proposed framework. © 2017 Elsevier Ltd. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Challenges in renewable energy electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1. Renewable energy intermittency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision making in renewable energy intermittency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Distributed generation (at the end user level) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Interconnect dispersed Variable Generation (VG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Combination of Variable Generation (VG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3. Forecasting model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4. Demand response method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5. Electric Energy Storage (EES) approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.6. Electric Energy Storage (EES) – current development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Classification of Electrical Energy Storage (EES) technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Battery Energy Storage (BES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. 2.2.3. Non-battery technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Landscape analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Electric Energy Storage (EES) – application demonstration through a case study: Bonneville Power Administration (BPA) 2.4. Multi-Criteria Decision Making (MCDM) in sustainable energy planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Multi objective programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multi-attribute utility theory (MAUT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Analytical Hierarchy Process (AHP)/Hierarchical Decision Model (HDM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Preference Ranking Organization Method For Enrichment Evaluation (PROMETHEE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Elimination and Choice Translating Reality (ELECTRE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. 3.6. Decision Support Systems (DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graphical model methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.

* Corresponding author. E-mail address: [email protected] (T.U. Daim). http://dx.doi.org/10.1016/j.est.2017.01.003 2352-152X/© 2017 Elsevier Ltd. All rights reserved.

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3.7.1. Analytical Network Process (ANP) Cognitive or Causal Maps (CM) . . 3.7.2. Fuzzy Cognitive Maps (FCM) . . . . 3.7.3. Bayesian Networks (BN) . . . . . . . . 3.7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction 1.1. Research background Energy is essential for sustainable economic development and prosperity of a society. The literature agrees that there are options for supplying bulk low-carbon electricity: fossil with carbon capture and sequestration (CCS), nuclear, and renewable sources [1]. Each option has challenges such as waste disposal with CCS and nuclear. Fossil fuels draw on finite resources and they are becoming too costly from both economic and expensive or too environmental perspectives. Renewable sources, particularly wind and solar are challenged by the intermittency of the resource. Therefore, it is crucial for the decision makers in electricity industry to formulate a diverse and comprehensive energy policy and increase the share of renewable technologies [2]. 1.1.1. Challenges in renewable energy electricity In most scenarios, the problem on the demand growth is generally addressed by using the low cost conservation and energy efficiency resources. However, renewable energy sources are getting more attention to address the issue of demand growth due to several reasons [3]. Strong political support for renewable energy per se, which is due to the attendant environmental advantages, associated the absence of greenhouse gas emissions, as well as national energy security advantages in the US avoiding reliance on imported fossil fuels is one of the driving reasons behind this growth. Renewable Portfolio Standard (RPS) – also known as Renewable Energy Target (RET) – laws require the States’ power producers to generate a significant percentage of their electricity from specifically designated, low-impact renewable energy sources by a specific date. RPS generally require utilities to produce a certain portion of their electricity from renewable energy. This is 20% by 2020 for European Union [4]. The United States has a region wide RPS of 20% by 2030, with different targets and years depending on the state [5]. For example, in Oregon, 25% of generation should be from approved renewable energy sources by 2025 [6] [7] Government policy has been the key driver for renewable energy expansion globally, including in EU. U.S. and Canada resulting in over 50% of (non-hydro) renewable capacity additions in the US from the late 1990s through 2007 [8]. Federal, provincial and state tax incentives, renewable energy investment funds, economic competitiveness, voluntary green power markets, public support, and hedging against fuel price increases and carbon regulation have been other positive influencing factors [8–10]. Rate payer selected willingness to pay a premium for “green power” as a personal means to advance renewable energy is another strong reason behind the growth [11,12]. In the U.S. as of the end of 2014 [13], more than 500 utilities, including cooperatives, in 34 States offer green pricing programs. Altogether, more than 418,000 customers participate in utility green pricing programs Improved renewable sources–such as wind power another major driver. Despite the increasing interest as mentioned above,

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renewable energy sources are facing something of a challenge in term of their power output. The variability of power output showed by numerous renewable energy sources represents something of a challenge to keeping up secure supplies in the incorporated electricity systems of industrialized countries – particularly if, as broadly foreseen, the commitment of renewable energy sources to national grids rises to exceptionally considerable levels [14]. Electricity produced from sustainable energy resources have demonstrated astounding development worldwide. However the availability for variable resources often does not positively correlate with the power demand [8,9]. Thus, the development implies greater network load stability problems. In particular the United Kingdom, Ireland and Denmark possess favorable wind conditions. In comparison Eastern Mediterranean countries seem to be less favorable for the use of onshore wind energy [15]. Intermittent renewable generators (mainly wind, solar photovoltaic without storage) are not like traditional ones [1]. The yield of renewable electricity is portrayed by steep “slope/ramp” rather than the controlled, progressive “slope” up or down experienced with electricity demand and the yield of traditional generation [8–10,14,16]. Dealing with these slopes can be challenging for network operators, especially if “down” slope happen as demand increases and the other way around. Inadequate ramping and dispatchable capability on the remainder of the bulk power system can increase these challenges [8,14]. The penetration of renewable sources (especially wind, solar, and wave power plants) into the power system network has been increasing in the recent years [5,17]. The United States ranked third in annual wind additions in 2014, but was well behind the market leaders in wind energy penetration [17]. Several countries have much higher levels of wind energy penetration in their electricity grids ranging from 20% to 40% while about 5% in the US [17]. 1.1.2. Renewable energy intermittency The above facts have led into a discussion over reliable and satisfactory operation of the power grid systems. In United States, the limits of wind and solar are not resource based. Wind and solar resource are fundamentally more prominent than the total electric demand [10,16]. The major technical challenge is resource intermittency, or the fact that the supply of variable renewable generation does not equal the demand for electricity during all hours of the year [1]. The difficulty associated with integrating renewable sources of electricity stems from the fact that the power grid was designed around the concept of large, controllable electric generators, while intermittency is an inherent characteristics of renewable energybased electricity generation systems [18]. Intermittent renewable sources disrupt the traditional methods for planning the daily operation of the electric grid. Their power fluctuates over multiple time horizons, forcing the grid operator to change its day-ahead, hour-ahead, and real-time operating procedures [19]. Reliably integrating high levels of intermittent resources into the North American bulk power system will require significant changes to traditional methods [8]. Power balancing requirements resulting from the intermittency of renewable sources suggest using intermittency support

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

alternatives to improve overall generation and load characteristics. Renewable energy intermittency can be addressed on different levels [20–23] including distributed generation (at the end user level), e.g.: the use of solar panel batteries [20]. At the utility level using non-storage approach, the intermittency and variability of renewable energy sources can be reduced and accommodated by [8,10,21–34]: interconnecting geographically dispersed and/or technologically diverse Variable Generation (VG) types (such as wind, solar, and tidal) to a common transmission grid to smooth out daily supply variability [8,10,24–35]; using complementary and non-variable sources to help supply match demand [31,36–38]; forecasting their variation, and integrating them with dispatchable renewable sources (such as hydropower, geothermal, and biomass) to fill energy deficits between demand and intermittent resource generation [8,10,14,21,31,39]; demand response (or demand-side management) to shift flexible loads to a time when more renewable energy is available, and away from times when renewable generation is low [21,31,36,37]; utility level using electric energy storage (EES) approach [20–23]. While the “cost-optimal” solution may require all the approaches mentioned above, it is beneficial to evaluate the “limiting case” of using EES scenario. Additionally, according to Chen, et al. [18], Luo, et al. [40], Mohd, et al. [41], Suberu, et al. [42], Ibrahim, et al. [43], and Daim, et al. [44]. amongst all the possible solutions, EES has been recognized as one of the most promising approaches. The historical backdrop of the stationary EES goes back to the turn of the twentieth century, when power stations were regularly closed down overnight, with lead-acid accumulators supplying the residual loads on the immediate current systems [43,45,46]. Utility companies eventually perceived the significance of the adaptability that energy storage provides in networks and the first central station for energy storage, a Pumped Hydroelectric Storage (PHS), was put to put use in 1929 [43,45]. The case of Switzerland is a success case of using pumping water systems for energy storage [44]. However this is no option for a small utility organizations. In addition, pressures from deregulation and ecological concerns prompt financing in major PHS facilities tumbling off, furthermore enthusiasm toward the

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useful of EES is at present enjoying somewhat of a revival, for a variety of reasons including [18,40,47]: changes in the worldwide utility regulatory environment, increasing dependence on electricity supply issues, the growth of renewable energy sources, and all combined with ever more strict environmental requirements [18]. These factors, combined with new advances in storage are creating options with smaller outputs using a variety of practices and approaches, ranging from batteries of all types to flywheels and compressed air storage. Despite the anticipated benefits and needs, there are relatively few EES installations in operation in the United States. Only 2.5% of the total electric power passes through energy storage, which is largely pumped hydroelectric. This is higher in Europe and Japan, at 10% and 15%, respectively [48]. In general, there are five main application areas of EES, which including [44,49]: Electricity supply applications; Ancillary services; Grid support applications; Renewables integration applications; End-user applications. These applications are found in essentially all forms of energy: mechanical, chemical, and thermal. The investigation of distinctive procedures has lead to the development of the emerging technology in energy storage. The technologies are many, but a comparative study is rendered difficult by the fact that, among others, their levels of development vary greatly. For example, pumped hydro and compressed air which have high storage capacity are used for energy arbitrage storing of large amounts of cheap energy during off-peak time period [8,50]. Other technologies like flywheels and battery technologies are applicable to frequency regulation and load following which require an energy storage device to release small to medium amounts of stored electricity in seconds on demand. For the most part lead batteries has been used as a potential solution. However, lead batteries cannot withstand high cycling rates, nor can they store large amounts of energy in a small volume. Other types of storage technologies are currently emerging [51]. The above facts has led to the emergence of EES technologies as a crucial element in an attempt to deal with renewable energy intermittency, allowing energy to be released into the grid during

Fig. 1. Storage in the power grid.

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peak hours when it is more valuable [49]. Accordingly, it is convenient to generate the energy, transmit it, convert it, and then store it if need be. More than ever then, the storage of electrical energy has become a necessity (Fig. 1). But renewable electricity is difficult to store as this requires large and costly equipment. Therefore, from the decision making point of view, it requires that investment and operating costs be kept to an acceptable level, and that the environmental issues be considered [8]. It is very important for organizations involved in overseeing sustainable energy resources to review their positions and form a strategy to decide how to act on future necessities. The analysis will review multiple perspectives [52,53]. 2. Literature review A comprehensive literature review on EES technology was conducted during independent studies which lead to publication of several papers. The literature reviewed in preparation for this proposed research revealed gaps in the comprehensive evaluation of EES technologies and by focusing on these gaps may improve the overall evaluation with respect to the five STEEP perspectives. 2.1. Decision making in renewable energy intermittency The literature review has revealed that several studies has been conducted in plausible solutions to cope with renewable energy intermittency, including: distributed generation, interconnect dispersed Variable Generation (VG) studies, combination of Variable Generation (VG) studies, forecasting model studies, demand response method studies, and EES studies. Table 1 summarized different approach that can be used for intermittent renewable electricity. 2.1.1. Distributed generation (at the end user level) Customer-sited storage, e.g.: the use of solar panel batteries, is typically used to increase self-consumption of distributed energy resources such as photovoltaic panels, to shift grid power consumption towards off-peak hours, and to reduce demand charges [1,13,14]. The smaller scale involved, and therefore the lower capital outlays, can encourage the use of battery storage systems and eventually reduce the need for more complex commercial arrangements like net metering. Currently, lithium-ion and leadacid batteries are becoming popular for residential users, while sodium-sulfur and advanced lead-acid batteries with higher capacity, are being deployed in commercial buildings. Where power quality is a concern, such as for commercial data servers or in health care facilities, flywheels provide a viable option for smoothing out power variations [67]. 2.1.2. Interconnect dispersed Variable Generation (VG) Interconnecting geographically dispersed and/or technologically diverse Variable Generation (VG) types (such as wind, solar, and tidal) to a common transmission grid will smooth out daily

supply variability [21]. Kahn [28] addressed the reliability of geographically dispersed wind turbine generators using a “Loss-ofLoad Probability (LOLP) calculation. LOLP analysis is typically performed on a system to determine the amount of capacity that needs to be installed to meet the desired reliability target, commonly expressed as an expected value, or LOLP of 0.1 days/ year [10,26,29–31,34,35]. Palutikof, et al. [33] analyzed simulated wind turbine output from four sites in England in order to investigate the effect of geographical dispersion. Milligan and Factor [32] researched the best way to distribute wind-generating capacity among several sites by using an electricity-production, cost and reliability model. DeCarolis and Keith [27] developed a mathematical model to provide an economic characterization of large-scale wind when intermittency and remoteness cannot be ignored. Archer and Jacobson [24], [25] investigated whether winds from a network of farms can provide a steady and reliable source of electric power. 2.1.3. Combination of Variable Generation (VG) As discussed before, integrating a large share of intermittent renewable energy into our daily electricity operations will require a mix of sources such as continental wind energy peaking at night, coastal wind energy during the day, and solar at various times over the day, depending on which way it is oriented [1,9,17,38]. According to Zhou, et al. [54] with the complementary characteristics between solar and wind energy for certain locations, the hybrid systems with storage can be a highly reliable source and is suitable to electrical loads that need higher reliability. In order to support their hypothesis, Zhou, et al. [54] looked at different optimization techniques for hybrid solar–wind system. 2.1.4. Forecasting model This is done by forecasting their variation, and integrating them with dispatchable renewable sources (such as hydropower, geothermal, and biomass) to fill energy deficits between demand and intermittent resource generation [8,10,14,31,39]. Electricity markets can only trade with more certainty if the error in forecasting can be reduced. Forecasting models for wind power can either be based on analysis of historical time series of wind or on forecasted values from a numerical weather prediction (NWP) model as an input [14,21,55]. Foley, et al. [55] were able to capture the development of current wind power forecasting & prediction models in their paper. 2.1.5. Demand response method Reliable operation of the electricity system requires an immaculate balance between supply and demand continuously. Demand response (or demand-side management) is designed to shift flexible loads to a time when more renewable energy is available, and away from times when renewable generation is low [31]. The main idea of demand-response management is to manage demand so that flexible loads are shifted to times when more variable generation is available [31,36,37]. Albadi and El-Saadany [56] argue that electricity system infrastructure is highly capital

Table 1 Plausible solutions for handling intermittent renewable electricity. Plausible solutions for handling intermittent renewable electricity

References

Distributed generation (at the end user level) Interconnect dispersed Variable Generation (VG) Combination of Variable Generation (VG) Forecasting model Demand response method Electric Energy Storage (EES) approach

[1,20,21] [8,10,24–35] [1,9,17,38,54] [8,10,14,21,31,39,55] [31,36,37,56–61] [18,20–23,40–44,62–66]

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

intensive and demand response is one of the cheaper resources available for operating the system. Different demand response programs can be classified into two main categories: Incentive-Based Programs (IBP) and Price-Based Programs (PBP) [56,58]. In classical IBP, participating customers are reported receive participation payments, in the form of a bill credit or discount rate while in market-based IBP, they are compensated with money for their performance, depending on the amount of load reduction during critical conditions [56]. PBP depend on dynamic pricing rates in which electricity tariffs are not level; the rates fluctuate following the real time cost of electricity. A definitive target of these programs is to smooth the demand curve by offering a high cost during peak periods and lower cost during off-peak periods [56,57]. These rates include the Time of Use (TOU) rate, Critical Peak Pricing (CPP), Extreme Day Pricing (EDP), Extreme Day CPP (ED-CPP), and Real Time Pricing (RTP). In TOU plan, the rates of power cost per unit utilization is contrast in various squares of time. The rate amid top periods is higher than the rate amid off-top periods [31,56]. CPP rates incorporate a pre-determined higher power use cost superimposed on TOU rates or ordinary level rates. CPP costs are utilized amid possibilities or high wholesale power costs for a predetermined number of days or hours every year [57,59]. EDP is like CPP in having a higher cost for power and contrasts from CPP in the way that the cost is as a result for the entire 24 h of the extreme day, which is obscure until a day-ahead [56,60]. RTP are schemes in which clients are charged hourly fluctuating costs mirroring the real expense of power in the wholesale market. RTP clients are educated about the costs on a day-ahead or hour-ahead premise. Numerous business analysts are persuaded that RTP projects are the most immediate and effective DR programs appropriate for aggressive power advertises and ought to be the center of policymakers [56,61]. 2.1.6. Electric Energy Storage (EES) approach To deal with the variability of wind and solar power electricity generation at large scale, several methods that have been discussed above are proposed, where each attempt to solve one aspect of integration challenges. However, these attempts have some drawbacks, for example: - Interconnecting a dispersed Variable Generation (VG) and Combination of Variable Generation (VG) allow higher renewable sources penetration by providing higher flexibility, but is expensive due to the magnitude of energy exchange required to make them profitable [68,69]. - Improving VG forecasting reduces system dispatch errors, but does not give full economic opportunity to the VG power generator [70,71]. - Increasing dispatchable back-up power generation may improve the system's ability to cope with dispatch errors at the cost of greenhouse gas emissions, since these units generally require fossil fuels for power [16,72,73]. Alternatively, hydro power responds quickly and can absorb some of the fluctuations in wind power output; however, hydro resources are limited [74]. - Decoupling VG from the grid removes power quality problems associated to VG at the cost of reducing clean energy sources feeding the grid [75–78]. Hence, the proposed approaches are insufficient to mitigate every challenge [16,68–78]. EES, although generally expensive, has the ability to address several VG integration issues [18,40–44,79]. Thus, the research will be focusing on EES evaluation. Electrical power generation is changing significantly over the world in light of the need to diminish greenhouse gas emissions and to present blended energy sources. The power system

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confronts extraordinary difficulties in transmission and distribution to meet demand with daily and occasional varieties [41]. EES is perceived as supporting advances to have great potential in meeting these difficulties, whereby energy is put away in a specific state, as indicated by the technology used, and is changed over to electrical energy when required [62]. Be that as it may, the wide assortment of alternatives and characteristic matrices make it hard to evaluate a particular EES technology for a specific application [21,40,41,43]. In general, EES technology refers to the procedure of converting energy from one form (fundamentally electrical energy) to a storable shape and reserving it in different mediums; then the stored energy can be changed over again into electrical energy when required [18,40]. Such a procedure enables electricity to be created now and again of either low demand, low generation cost or from intermittent energy sources and to be utilized on occasion of high demand, high generation cost or when no other generation means is accessible [63–65]. 2.1.6.1. Role of Electric Energy Storage (EES). EES can be applied at the power plant, in support of the transmission system, at various points in the distribution system and on particular appliances and equipments on the customer’s side of the meter [41,43,66]. There are many potential applications of EES and they vary across a full spectrum ranging from larger scale, generation and transmission-related systems, to those primarily related to the distribution network and even ‘beyond the meter’, into the customer/end-user site [18,80]. Mears [3] identified EES functions, in the applications of EES to enhance renewable power generation, as: i Transmission Curtailment (TC): mitigation of power delivery constraint imposed by insufficient transmission capacity. Wind power generation is generally at a remote area which lack proper transmission and distribution systems. This leads to curtailment, loss of energy production opportunity, and investment in expanding the transmission capability. ii Time-Shifting (TS): Wind turbines are not dispatchable and EES can support to store energy generated during periods of low demand and deliver it during periods of high demand. This is called ‘firming and shaping’ as it changes the power profile of the wind to allow greater control over dispatch. [81]. iii Forecast Hedge (FH): Mitigation of shortfalls in renewable energy bids into the market prior to required delivery to reduce volatility of spot prices. iv Grid Frequency Support (GFS): EES supports grid frequency during sudden, large decreases in renewable generation over a short discharge interval. Spinning reserve at the transmission level can handle such imbalances, but EES can provide prompter response to such imbalances without any emissions. v Fluctuation suppression (FS): Renewable sources, such as wind farm, generation frequency can be stabilized by suppressing fluctuations (absorbing and discharging energy during short duration variations in output). Power electronic, information and communication systems may be highly sensitive to power related fluctuation which EES facilities can provide protection against [40]. vi “Combined function applications”, which address EES adapted to serve multiple functions (e.g., combined transmission curtailment, grid frequency support and regulation control applications). Future development of renewable energy technologies will help reduce cost as already seen in wind and photovoltaic power generation. Nonetheless, the widespread deployment of solar, wind and wave power in the future will have to deal with the

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J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

Fig. 2. Types of Storage Applications.

intermittency challenge. [18,40,49]. Fig. 2 summarizes major storage applications. 2.1.6.2. Current studies of Electric Energy Storage (EES) evaluation. Recent developments in advanced EES, including a number of demonstration and commercial projects, are providing new opportunities to use energy storage in grid stabilization [40,82]. According to report from Electric Power Research Institute (EPRI) [82] and a current research by Luo, et al. [40], broad technical, economic and social factors suggest a promising future for EES technologies. The subsequent development of the electricity supply industry, with the quest of economy of scale, at large central generating stations, with their complementary and extensive transmission and distribution networks, essentially transferred interest in storage systems up until relatively recent years [18]. Pumped Hydroelectric Storage (PHS) is the largest-capacity form of grid energy storage available, and, as of March 2012, the Electric Power Research Institute (EPRI) reports that PHS accounts for more than 99% of bulk production capacity worldwide, representing around 127 GW [83]. Although PHS facilities have been built worldwide as a mature and commercially available technology, it is considered that the potential for further major PHS schemes is restricted due to pressures from deregulation and environmental concerns [18,40,83]. In addition, interest in the practical application of EES systems is presently getting a charge out fairly a new start, for a variety of reasons including changes in the worldwide utility regulatory environment, an ever-increasing reliance on electricity in industry, power quality/quality-of-supply issues, the growth of renewable as a major new source of electricity supply, and all combined with ever more stringent environmental requirements [18,40]. Technology development on top of the factors listed above with cost reductions will make EES attractive [18] increasing the storage

levels by 10–15% the delivered inventory for the U.S. and European countries, and even higher for Japan in the near future [18,84]. Initial economic studies of EES systems focused on applications for peak shaving and as capacity resources [85]. In recent years there has been increased attention to evaluating the economics of EES systems as backup for intermittent renewable sources. Some examples include wind and CAES [27], wind and hydro or batteries [86], solar photovoltaic and batteries [87,88]. Prior studies evaluated these technologies through their economic impacts. These studies as reported by Walawalkar [81] include a ranking of potential opportunities [89], life-cycle costs for batteries, CAES, and flywheels [90], a general calculation of potential revenues in California and PJM without regard to technologies [91], pumped hydroelectric storage using PJM market data [92] and comparison of energy arbitrage revenues (from storing power purchased at off-peak times and selling it on-peak) in North American and European energy markets [93]. Since the emergence of deregulated electric energy markets, several studies have appeared to compare the performance of the various EES technologies in different categories based on lists of criteria from technical and economic perspectives. For instance, life-cycle cost analysis to compare EES technologies [90,94], comparative analysis for large-scale EES [47], a multiple objective optimization method [51,95], economic analysis to estimate the profitability of EES systems [96], technical characteristics of different EES technologies [18,49,97–99], financial and environmental analysis of EES technologies [99–102]. Table 2 summarized EES technology assessment studies with respect to the research focus. As we can see from Table 4, most of these studies considered only some aspects focusing on the technical and economic influences of EES. However, other criteria like regulatory/environmental, social, and political (such as federal, provincial and state tax incentive; green power programs, carbon tax, etc.) effects also

Table 2 EES technology assessment studies with respect to the research focus. Research Focus

References

Life-cycle cost analysis to compare EES technologies Comparative analysis for large-scale EES Multiple objective optimization method Economic analysis to estimate the profitability of EES systems Technical characteristics of different EES technologies Financial and environmental analysis of EES technologies

[90,94] [47,89,93] [51,95] [27,65,85–88,91,92,96] [18,49,97–99] [99–102]

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need to be considered comprehensively. Therefore, this research attempts to incorporate comprehensive evaluation criteria comprising social, technical, economic, regulatory/environmental and political perspectives – as indicated by Chen, et al. [18] and Luo, et al. [40] that pressures from deregulation and environmental concerns have significant contributions in regard to utilization of EES technologies. Overall, the various EES applications with different sizes will use different factors to consider when choosing the most appropriate one. At the national level, the stage of technological maturity, reliability and environmental impacts (such as landscape damage and toxic chemical materials used in batteries) will be important [40]. 2.2. Electric Energy Storage (EES) – current development The development and use of renewable energy, such as solar and wind power plants, has experienced rapid growth over the past few years. In the next 20–30 years all sustainable energy systems will have to be based on the rational use of traditional resources and greater use of renewable energy [49]. These rapid development causes problems in reliable and sustainable energy supply in US due to its intermittency. EES is considered as an important solution for the problem as it provides an inventory for the un expected surplus or deficit from renewable generation. One of the biggest obstacles in regard to integration of renewable energy sources into the grid: it fluctuate independently from demand. Yet they are ample and conversion systems are turning out to be more reasonable and affordable. Their significant contribution to sustainable energy use will however require significant further advancement of storage methods. This will open up another field of utilization, particularly because of the

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development of electrical generation from renewable energy, alongside decentralized production [49]. Several research and projects have been conducted to investigate potential solutions for managing high levels of renewable intermittency. The California ISO [103], established its Participating Intermittent Resource Program (PIRP) to integrate wind generators into its Hour-Ahead markets. They also started a new initiative with the FERC called the Remote Resource Interconnection Program, which provides a mechanism for transmission upgrades and supports renewable resources permitting construction of transmission to renewable energy resources in areas that are remote from the existing transmission network. California, like any other states, is expecting rapid growth of energy generated from renewable sources. Renewable Portfolio Standard (RPS) laws in California require the States’ power producers to generate 20% of all electricity must come from renewable in 2012 and 33% in 2020. CAISO [103] acknowledges that the role of innovative resource options, such as demand response and EES, will be important as the state’s energy providers work toward these targets. Daim and Suharto [51] reported that Bonneville Power Administration (BPA), which markets electricity to public utility customers in Washington, Oregon, Montana, Wyoming, Utah, Nevada and some parts of California posed a challenge in wind energy intermittency and has been conducted several studies in respect to EES technologies. Northwest US is reported to be very focused on the adoption of renewable energy and specifically wind energy. 2.2.1. Classification of Electrical Energy Storage (EES) technologies There are a wide range of energy storage devices currently available, and many have been in operation for decades. There are

Fig. 3. Comparison of maturity levels for various EES technologies.

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several ways to sort EES technologies, such as, in terms of their functions, response times, and suitable storage durations [18,40,104]. The most common way is based on the form of energy stored in the system [15,16], which can be mechanical (pumped hydroelectric storage, compressed air energy storage and flywheels), electrochemical (conventional rechargeable batteries and flow batteries), electrical (capacitors, super-capacitors and superconducting magnetic energy storage), thermochemical (solar fuels), chemical (hydrogen storage with fuel cells) or thermal energy storage (sensible heat storage and latent heat storage). Analysts expect advancements in EES to occur with the maturation of new technologies, such as metal air batteries, and the application of new materials and designs to proven technologies, like lead-acid [105]. In general, EES technologies are divided into battery technology (electrochemical) and non-battery technology (mechanical and thermal). Each technology has its strengths, limitations, and appropriateness for the large and diverse set of applications for EES. Fig. 3 provides an insight into the range of maturity level among these technologies. 2.2.2. Battery Energy Storage (BES) The rechargeable battery is used widely in industry and daily life. Many types of batteries are under development. Some are available commercially while some are still in the experimental stage. The batteries currently in use in power system applications are deep cycle batteries with energy capacity ranging from 17 to 40 MWh and having efficiencies of about 70–80% of the various battery technologies [106]. Many different battery types exist while the lead–acid and the sodium–sulfur batteries are the most common for large-scale installations. Batteries are a mature technology type with many commercial MW-scale installations worldwide. Currently, battery applications covering durations of less than one second to around five hours are commercially feasible. Low power related costs and high storage related costs compared to many of the other electricity storage technologies makes batteries mainly suitable for electricity storage of shorter durations [107]. Many kinds of battery cannot be fully discharged because of dependence on the cycle Depth-of-Discharge (DoD) [108]. Recent outlooks from GTM Research [109] forecast rapid growth for BES over the balance of this decade, with penetration of BES solutions accelerating in grid-scale utility markets, residential markets, and commercial building markets. There are available technologies today for applications in all three of these sectors, but as technologies mature, we can expect growth curves to continue for a much longer period, leading to novel business models and strategic choices for utilities and investors in technology. 2.2.2.1. Lead-acid batteries. Lead-acid batteries are the most mature of all EES technologies that exist today and offer an immediate solution. Lead acid batteries are especially attractive for early use due to their (compared to other things) low cost, ease of manufacture, fast, and good cycle life under controlled conditions [110]. The maximum theoretical specific energy (the energy content per unit mass) of fully compounded active material in a lead-acid battery is reported to be 171 Watthours per kilogram (Wh/kg) [110]. However, conventional lead-acid batteries are reported to lose much of this potential due to the need for internal connectors and grids to structurally support the heavy lead material. In summary, the advantages of lead-acid batteries are low price, high practicality, high efficiencies and low self-discharge rate. On the other hand, there are also disadvantages of lead-acid batteries such as low energy density, low life cycle by deep discharge, and requirement of long charging time [63,111,112].

Lead–acid batteries are reported to be used in stationary devices as back-up power supplies for data and telecommunication systems, and energy management applications. They have been used as power sources for hybrid or full electric vehicles as well. However, there are still a few installations around the world as utility-scale EES, because of their low cycling times (up to 2000), energy density (50–90 Wh/L) and specific energy (25–50 Wh/kg) [18,63]. In addition, they are reported to perform poorly at low temperatures so a costly thermal management system is required [40]. 2.2.2.2. Lithium-ion batteries. Lithium based battery is mainly consisted of two main types: lithium-ion and lithium-polymer cells. Lithium-ion batteries are reported to have the highest power density of all batteries on the commercial market on a per-unit-ofvolume basis. Its self-discharge rate is very low at maximum 5% per month and battery lifetime can reach to more than 1500 cycles. Typical life of a lithium-ion battery is 300–500 discharge/charge cycles or two years from time of manufacturing. The lifetime of a lithium-ion battery is reported to depend on temperature with aging taking its toll much faster at high temperatures, and can be severely shortened due to deep discharges[114]. Lithium ion batteries offer high-power densities, typically 110–160 W h per kilogram (Wh/kg) [63]. However, one of the big drawbacks is safety because the explosion of lithium-ion batteries in laptop computer has been reported. In addition it is inherently unsafe and extremely sensitive to over temperature, over-charge and internal pressure build-up [111]. Even though the lithium-ion batteries are affordable for storage technology in many ways, the possibility of the large size for a plant is not very high [115]. A Li-ion BES system has been operated commercially (8 MW/ 2 MWh in 2010, enlarged 16 MW in 2011) in New York for supplying frequency regulation [116,117]. The same company also installed a 32 MW/8 MWh Li-ion BES system (Laurel Mountain) to support a 98 MW wind generation plant in 2011 [116,118]. The largest European Li-ion battery EES pilot is in process in the UK [40]. It will deploy a 6 MW/10 MWh Li-ion battery at a primary substation. The project can be used to balance the intermittency of wind and other renewables [119]. In addition, in December 2013 Toshiba announced a project to install a 40 MW/20 MWh Li-ion battery project in Tohoku, Japan which will help integrate renewables into the grid [120]. 2.2.2.3. Sodium sulfur batteries. Sodium Sulfur (NaS) batteries present one of the best options for energy management, including peak-shaving and load curve balancing but, its greatest disadvantage is the cost [114]. It can operate at high temperatures over 300  C [121]. NaS batteries are high capacity battery systems developed for application in electrical power systems. NaS batteries are reported to be in use in applications including peak shaving and improving power quality [112]. NaS batteries has long cycle life over 15 years, but the expected period would be dependent on depth of discharge (DOD). NaS batteries are highly energy efficient (89–92%) but, its high operating temperature and strong corrosion electrolyte would be the disadvantages of NaS batteries [112]. In addition, According to the NGK Insulators, Ltd., the company is developing a 34 MW plant of NaS batteries for a wind farm [122]. The NaS battery is reported to be one of the most promising candidates for high power EES applications. The “Wind to Battery” project led by Xcel Energy was presented by Tewari and Mohan [123], regarding the field results and analyses quantifying the ability and the value of NaS battery EES toward wind generation integration support [123,124]. There has been several pilot studies of NaS batteries in bulk electric utility applications some resulting in dangerous situations like the NGK fire (http://www.ngk.co.jp/english/

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

announce/). There has been several lessons taken from this incident shaping the future design practice. 2.2.2.4. Nickel–cadmium (NiCd) batteries. A NiCd battery has nickel hydroxide and metallic cadmium as the two electrodes and an aqueous alkali solution as the electrolyte. It is reliable and does not require high maintenance. However cadmium and nickel are toxic heavy metals, causing environmental hazards [127,128]. These kinds of batteries’ capacity decreases after repetitive charges [106]. There has not been many commercial successes using NiCd batteries for utility-scale EES applications. One successful example is at Golden Valley, Alaska, in the US [117]. This type of NiCd facility, which was started in 2003 was not pursued further after the Golden Valley installation [116,117]. It does not seem that in the future NiCd batteries will be widely used in large-scale EES projects [40,116]. 2.2.2.5. Lithium metal polymer batteries. After the first introduction of Li-ion battery, the energy density of Li-ion battery is developing year by year, while new type battery of Liion polymer battery is being produced with the adaptable light components. Another effort is also made to realize the battery with the anode of Li metal. Despite the continuous research and development, the energy densities are yet not so high for the Li ion polymer battery compared with the Li-ion batteries. However, the Li-ion polymer battery is thin and light weight making the ideal for cellular phones which require thinner shape [129]. 2.2.2.6. Vented lead-acid batteries. Vented cell system, also known as “flooded cell”, require substantial investment upon installation due to safety issue. Vented cell continuously vent gases and must be installed in controlled-access area such as specially ventilated battery rooms with spill containment [130]. 2.2.2.7. Valve regulated lead-acid batteries. A VRLA battery (valveregulated lead-acid battery), also known as a sealed battery (SLA) or maintenance free battery, is a lead-acid rechargeable battery which can be mounted in any orientation, and do not require constant maintenance. They require very long charge time due to the two-stage process: bulk charge and float charge. While all other lead acid batteries are quick to charge to 70% of capacity within 2–3 h, this technology requires another 9–10 h to “float charge, after the initial charge. If users fail to float charge, lead acid battery-type life is reported to be dramatically reduced to possible 1/10 of its potential [130]. 2.2.2.8. Regenerative metal-air batteries. In zinc-air batteries, energy is released by oxidizing zinc (which is held within the battery case) with oxygen from the air. Zinc-air batteries, in many ways, are similar to fuel cells, in which neither the oxidizer nor the fuel is packaged inside the cell. Metal-air batteries reduce atmospheric oxygen to form oxides and/or perox-des with various metals [105]. Metal-air batteries have high theoretical energy densities such as 1300, 8100 and 11,100 Wh/kg for zinc, aluminum and lithium, respectively [131,132]. These densities are much higher than the current Li-ion batteries that generate only 120 Wh/kg from a theoretical energy density of just 450 Wh/kg. Given this fact, many researcher and investors have shown significant interest in metalair batteries [105]. Con Edison and City University of New York are reported to be testing a zinc-based battery from Urban Electric Power as part of a New York State Energy Research and Development Authority program. Eos Energy System battery

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[133] projects that the cost of storing electricity with zinc-air batteries is U$160/kwh and that it will provide electricity cheaper than a new natural-gas peaking power station. 2.2.2.9. Other candidates of battery energy storage. The Nickel– metal Hydride (NiMH) battery which is similar to the NiCd battery uses a hydrogen-absorbing alloy as the electrode instead of cadmium. Its moderate specific energy (70–100 Wh/kg) and relatively high energy density (170–420 Wh/L), makes it much better than the NiCd battery [128,134,135]. The high rate of selfdischarge, losing 5–20% of its capacity within the first 24 h after fully charging [134,135] is a major weakness for this kind of battery which is also sensitive to deep cycling. Its performance decreases after a few hundred full cycles [134]. 2.2.2.10. Flow Battery Energy Storage (FBES). Flow batteries are consist of two different electrolyte containers, one anode and cathode, and a separator while the most electrochemical energy storage technologies use one electrolyte container [63,98,121]. It also has over 20 years’ development history and there are many cases to set up the facilities of energy storage in the around the world [49,136]. Flow batteries can be classified into the categories of redox flow batteries and hybrid flow batteries, depending on whether all electro active components can be dissolved in the electrolyte [40]. Low performance resulting from non-uniform pressure drops and the reactant mass transfer limitation, high manufacturing costs and more complicated system requirements compared to traditional batteries are disadvantages for flow batteries [46,49,137]. 2.2.2.10.1. Vanadium Redox Flow Battery (VRB). VRB technology, which is one of the most mature types of flow battery systems uses one common electrolyte compared to other flow batteries which store energy as charged ions in two separate tanks of electrolytes, one of which stores electrolyte for positive electrode reaction while the other stores electrolyte for negative electrode reaction [138]. Several studies are undertaken in respect to bulk energy storage applications of Zn/Br batteries. For instance, the Energy Commission’s PIER program helped fund a Primus Power Corporation 25 MW/75 MWh grid-connected Zinc-based flow battery energy storage system to provide renewable firming, strategic local peak shaving, automated load shifting, and ancillary services. Recent studies project the cost of these projects at $290-$350/kWh [138,139]. VRBs can be used in a large number of applications including enhancing power quality used for stationary applications and UPS devices, improving load leveling and power security, supporting the intermittent nature of renewable energy-based power generation [40,46,49]. 2.2.2.10.2. Zinc Bromine (ZnBr) flow battery. ZnBr flow batteries are hybrid flow batteries which have high energy density (30– 65 Wh/L) and cell voltage (1.8 V) [40,46,49], and deep discharge capability with good reversibility [49]. They can range from 3 kW to 500 kW, with estimated lifetimes of 10–20 years and discharge durations of up to 10 h [4,112,113]. Material corrosion, dendrite formation and relatively low cycle efficiencies (around 65–75%) are their disadvantages. [40,46,49]. Utility EES applications using ZnBr batteries are in demonstration stage [40,49]. ZBB Energy Corporation and Premium Power Corporation are reported to have developed this technology for commercial purposes (50 kW h, recently tested up to 2 MW) [140]. In 2011, U.S. electric utilities conducted early trials of 0.5 MW/2.8 MWh transportable ZnBr systems for grid support and reliability [46,80], when Sacramento Municipal Utility District

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(SMUD) planned to demonstrate a 1 MW ZnBr flow battery system for multi EES applications [141]. 2.2.2.10.3. Polysulfide Bromine (PSB) flow battery. Polysulfide Bromide battery (PSB) is a regenerative fuel cell technology. It provides a reversible electrochemical reaction between two salt solution electrolytes (sodium bromide and sodium polysulfide) [18,49]. PSB electrolytes are brought close together in the battery cells which are electrically connected in series and parallel to obtain the desired voltage and current levels. There the electrolytes are separated by a polymer membrane that only allows positive sodium ions to go through, producing about 1.5 V across the membrane [46,142]. Regenesys Technologies, a large-scale PSB manufacturer, tried to build a 15 MW/120MWh energy storage plant at a power station

in the UK. Another demonstration plant to be located at Tennessee Valley in the U.S. was designed with a 12 MW/120MWh capacity for EES to support a wind power plant operation [18]. Both projects were not completed due to engineering difficulties and financial constraints [117,143,144]. 2.2.2.10.4. Summary of battery storage technologies. According to Daim, et al. [44], US Department of Energy (DOE) is leading research on several technologies including liquid metal batteries, advanced superconducting magnetic storage, lead-acid flow batteries, hydrogen flow bromine batteries, iron-air rechargeable batteries, and alkaline membrane fuel cells. These projects are expected to improve technical performance and reduce costs significantly, but the associated technologies will not be commercially available in the near future. A comparison of

Table 3 A summary of battery storage technologies [18,44,49,65,98,105,145,146]. Technology

Advantage

Disadvantage

Application

Lead-Acid Batteries

1 The most mature of all battery-EES systems that exist today 2 Offer an immediate solution while engineers continue to develop other storage systems. 3 Attractive for early deployment due to their relatively low cost, ease of manufacture, rapid electrochemical reaction kinetics, and good cycle life under controlled conditions

1 conventional lead acid batteries lose much of this potential due to the need for internal connectors and grids to structurally support the heavy lead material. 2 Need for careful thermal management, including a high likelihood for active cooling. 3 Need for custom-designed grid storage system using lead acid batteries.

Lithium ion (Li-ion)

1 High energy and power density 2 Long life 3 High efficiency

1 High initial cost 2 Requires sophisticated management (balancing and charge control electronics)

Sodium sulfur batteries (NAS) Zinc– bromine batteries

1 High energy and power density 2 High efficiency and excellent cycle life 3 Relatively matured

1 High initial cost 2 Requires high temperature 3 Safety concerns

1 Peak shaving and TD upgrade deferral 2 Small load leveling

1 High energy and power density 2 Flat voltage profile 3 Long electrolyte life

1 2 3 4

1 Peak shaving and TD upgrade deferral 2 Small load leveling

Regenerative metal-air

1 High energy and power density 2 Low cost

1 Relatively new and untested 2 Voltage drop at start of discharge 3 Limited shelf and cycle life

Lithium metal polymer batteries

1 High energy and power density 2 Relatively tolerant to temperature extremes.

1 Relatively new and untested 2 High initial cost 3 Requires balancing and charge control electronics

Vanadium redox (VRB)

1 Relatively high energy and power density for large system. 2 Easily upgradeable

1 Not yet proven for cycle life or maintenance cost. 2 Relatively expensive

Vented leadacid

1 Mature and well-known 2 Low cost 3 High reliability and efficiency

1 2 3 4 5 6

Valve regulated lead-acid Nickel metal hydride (NiMH)

1 Low maintenance 2 Low initial cost

1 Relatively intolerant of temperature extremes NA 2 Short life

1 2 3 4

Mature technology High energy and power density Better cycle life than lead-acid Less toxic than Ni–Cd

1 2 3 4

Low cell voltage Intolerant of temperature extremes Float effect makes capacity testing difficult More expensive than lead-acid

NA

1 2 3 4 5

Mature and well-known High energy and power density High efficiency Better cycle life than lead-acid Relatively tolerant to temperature extremes

1 2 3 4

Low cell voltage Float effect makes capacity testing difficult More expensive than lead-acid Toxic components (cadmium)

NA

Vented nickel cadmium (Ni–Cd)

Relatively new and untested Mechanical parts require maintenance May require occasional stripping cycles Safety hazard (corrosive and toxic materials)

1 Peak shaving and TD upgrade deferral 2 Small load leveling 3 Balancing services

Balancing services

1 Energy arbitrage 2 Peak shaving

NA

1 Peak shaving 2 Transmission lines upgrade deferral 3 Small load leveling

Relatively intolerant of temperature extremes NA Significant environmental effects Relatively short life cycle Low energy density Poor low temperature performance High maintenance cost

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current and emerging battery technologies (such as flow batteries: polysulphide bromide (PSB), vanadium redox (VRB), and zinc bromine (ZnBr)) can be seen in Table 3. 2.2.3. Non-battery technologies A few non-battery technologies have been widely used for decades. Bulk storage systems such as pumped hydro systems (PHS). Bulk systems such as CAES, are lesser known, however two large systems, Huntorf Germany, 290 MW and McIntosh, Alabama Electric Cooperative 110 MW have been operating successfully and reliably for over 34 and 20 years, respectively [102]. 2.2.3.1. Pump Hydro Storage (PHS). PHS technology is readily available and uses the power of water which is a highly concentrated renewable energy source. This technology is in use for high-power applications (a few tens of GWh or 100 of MW) [49]. PHS is the most ready and best developed for energy storage purposes. However, pressures from deregulation and ecological concerns prompt financing in major PHS facilities tumbling off, furthermore enthusiasm toward the useful of EES is at present enjoying somewhat of a revival, for a variety of reasons including [18,40,47]: changes in the worldwide utility regulatory environment, increasing dependence on electricity supply issues, the growth of renewable energy sources, and all combined with ever more strict environmental requirements [18]. Interestingly, even with the deregulation and ecological concerns, PHS technology is still getting recognized and being developed in some part of the world. Recent technology has made it possible so that some PHS plants using flooded mine shafts, underground caves and oceans as reservoirs have been planned or are in operation, such as the Okinawa Yanbaru in Japan, Upper Cisokan Pumped Storage (UCPS) Hydro-Electrical Power (1040 MW) Project in Indonesia, 300 MW seawater-based PHS plant in Hawaii, the Summit project in Ohio and the Mount Hope project in New Jersey [105,147–149]. In addition, wind or solar power generation coupled with PHS which could help the adoption of renewable energy in isolated or distributed networks is reported to be under development [75,150]. PHS stores energy by pumping water from a lower level up to a high reservoir and the water is run back down through hydroelectric turbines when power is needed. However building new systems is expensive ($1000e$2000/kW). Alternative opportunities to upgrade old systems could add large amounts of EES for as little as $250/kW and generally can operate inexpensively for decades [18,49]. Installing advanced pumps, turbines, impellers, control systems, and variable speed drives is reported to have a potential to increase storage capacities by 15–20% at existing multi-purpose facilities and PHS plants [151]. The main shortcoming of this technology is the need for a site with different water elevations. A map issued by The Federal Energy Regulatory Commission (FERC) lists potential sites for the pumped storage [153]. FERC is an independent agency that regulates the interstate transmission of natural gas, oil, and electricity. FERC also regulates natural gas and hydropower projects. PHS applications are reported to involve energy management in the fields of time shifting, frequency control, non-spinning reserve and supply reserve while the restriction of site selection, long construction time and high capital investment makes them unattractive [18,40]. 2.2.3.2. Compressed Air Energy Storage (CAES). Compressed Air Energy Storage (CAES) is the term given to the technique of storing energy as the potential energy of a compressed gas [154]. CAES relies on relatively mature technology with several high-power projects in place. CAES is a way to store energy generated at one time for use at another time. This approach is reported to have been used to provide the grid with a variety of ancillary services

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and it is getting more attention recently as a means of addressing the intermittency problems associated with wind turbine electrical generators [154]. When energy is available, it is used to run air compressors which pump air into the storage cavern and it is expanded through conventional gas turbine expanders when power is needed [49,154,155]. 2.2.3.3. Flywheel Energy Storage (FES). A kinetic energy storage, flywheels with a long working lifetime (>20 years) are already available. However, there are no commercial applications for FES in power management, the technology still being at the demonstration stage [98]. FES works by accelerating a rotor (flywheel) to a very high speed and maintaining the energy in the system as rotational energy. Most FES systems use electricity to accelerate and decelerate the flywheel, but researchers are developing devices that directly use mechanical energy [105]. Modern FES systems rely on magnetically levitated bearings that mitigate wear and tear on bearings, thereby extending the system’s life. Up to date, the most common applications of FES technology store 2 kWh–6 kWh for small telecommunication backup power applications. However, the University of Texas at Austin completed research and development of a FES that had 133 kWh of energy storage. This is said to be the record for carbon-composite flywheels. Beacon Power also successfully developed a FES network consisting of 40 25-kWh wheels capable of storing 1 MW of energy. Yet, FES typically experience far lower capacity than other storage applications [156]. 2.2.3.4. Supercapacitors. Supercapacitors havevery high energystorage efficiencies (>95%) and can be cycled hundreds of thousands of times without significant loss of energy-storage capacity making them the energy-storage solution with the greatest lifetime in terms of cycling ability [98]. The majority of commercially available supercapacitors are reported to incorporate activated carbon electrodes and either an aqueous or organic electrolyte that may be operated at potential differences of 1 V and 3 V respectively and therefore offer increased energy density [49,98]. Superconducting Magnetic Energy Storage 2.2.3.5. (SMES). Superconducting magnetic energy storage (SMES) systems are reported to have been used for several years at utility and industrial sites worldwide to provide both transmission voltage support and power quality to customers vulnerable to fluctuating power quality and it is estimated [102] that since the 1970s over 100 MW of these units in these two markets are in operation worldwide (the average rating of 3 MW or less per unit). While these units can respond within a few milliseconds at high power output (similar to flywheels) only for short periods of time, their costs are reported as high compared to other technologies [18,40,49]. There are several advantages of SMES, for instance, this storage system has a great immediate (wasting very little while working or producing something), close to 95% for a chargedischarge cycle. According to Ibrahim, et al. [49], these systems are also capable of discharging the near totality of the stored energy, as opposed to batteries. They are very useful for applications requiring continuous operation with a great number of complete charge–discharge cycles making them ideal for regulating network stability (load leveling) leveraging the fast response time (under 100 ms) of these systems. As reported [40], there are demonstration projects in US and UK. In the US, SuperPower Inc., together with ABB Inc., Brookhaven national laboratory and the Texas center for superconductivity at the University of Houston developed a 20 kW ultra-high field SMES system with a capacity up to 2 MJ [157]. The University of Bath in

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the UK also works on SMES technology development funded by the UK EPSRC [158,159]. 2.2.3.6. Solar fuels. Solar fuel technology is currently at the development stage and relatively new technology to EES. The power rating of solar fuels is potentially up to 20 MW and the specific energy estimate is from 800 Wh/kg to 100,000 Wh/kg [18,40,43]. The storage duration can range from a few hours to several months [18]. According to Chen, et al. [18], a number of fuels can be produced by solar energy, such as solar hydrogen, carbon-based fuels, and solar chemical heat pipe. These fuels can be stored and subsequently provide the basis for later electricity generation [18,40]. Research in solar fuels has experienced significant advances, making it possible for it feasible for it to wind up financially savvy for utility EES applications sooner rather than later. There are on-going exploration ventures in the U.S., the Netherlands, South Korea, Singapore, Japan and China. In the US, there are a few associations concentrating on this area, such as Energy Innovation Hub at DoE, the MIT spinout Sun Catalytix and the Princeton University spin-out Liquid Light [40].

2.2.3.7. Hydrogen storage and fuel cell. Fuel cells are different from batteries which store electrical energy chemically in a closed system, whereas fuel cells consume reactants, which must be replenished [18]. Electrodes of a fuel cell are catalytic and relatively stable while a battery react and change when a battery is charged or discharged [49]. The utilization of a water electrolysis unit is a typical approach to deliver hydrogen which can be put away in high pressure compartments and/or transmitted by pipelines for later utilize [108,116]. When using the stored hydrogen for electricity generation, the fuel cell (otherwise called regenerative energy unit) is embraced, which is the key innovation in hydrogen EES [40,116]. The world’s first utilityscale test of a stand-alone renewable energy system integrated with hydrogen storage and fuel cells was introduced in Norway, which conveyed power with required quality and high unwavering quality [40]. One of the world’s largest biogas fuel cell power plants, which converts biogas into electricity and usable highquality heat is reported to have been launched in 2012 in California (2.8 MW) [40,116].

Table 4 A summary of non-battery storage technologies [18,44,49,65,98,102,145,146]. Technology

Advantage

Disadvantage

Application

Pumped hydro Storage (PHS)

1 2 3 4

Mature technology High energy and power capacity Least cost for large scale power Long life

1 Require special sites for upper and lower water reservoirs 2 Large scale requires large capital investment and collaborations 3 Long construction time

1. Energy arbitrage 2. Frequency regulation 3. Balancing services

Compressed Air Energy storage (CAES)

1 2 3 4

High energy and power capacity Least cost Long life Mature technology

1 2 3 4

Large scale requires large capital investment Need gas fuel input Long construction time. Require special sites such as caverns left behind when miners finish mining and clearing salt domes

1 Frequency regulation 2 Balancing services 3 Energy arbitrage

Flywheels

1 High power density 2 Flat voltage profile 3 Better cycle life than batteries

1 2 3 4

Low energy density Short term power Relatively high initial cost per KWh Large standby losses

1 Frequency regulation 2 Emergency bridging power 3 Fluctuation smoothing

Supercapacitors

1 2 3 4

1 Short term power 2 High initial cost 3 Relatively low energy density

1 Power quality 2 Emergency bridging power 3 Fluctuation smoothing

1 Low energy density 2 Expensive

1 Power quality 2 Emergency bridging power

1 The storage duration can range from a few hours to several months. 2 High power output. 3 The change in solar to electric conversion efficiency of a solar energy system with and without storage can be close to zero

1 Solar fuels are at an earlier 2 stage of development. 3 solar fuel facilities need a large area to place devices to concentrate sunlight, especially when using the thermochemical approach to produce solar fuels.

1 Utility EES applications

Hydrogen storage and fuel cell

1 High energy density 2 ability to implement systems over a wide range of scales, from kW scale to multi-MW capacity.

1 High cost ($6–20/kWh) 2 Low efficiency (20–50%)

1 Distributed generation 2 Backup power

Thermal Energy Storage (TES)

1 TES system can store large quantities of energy without any major hazards. 2 Daily self-discharge loss is small (0.05–1%). 3 The reservoir offers good energy density and specific energy (80–500 Wh/L, 80–250 Wh/ kg). 4 The system is economically viable with relatively low capital cost (3–60$/kW h).

1 overall round trip efficiency of TES is low (30–60%).

1 Load shifting 2 Electricity generation for heat engine cycles

Superconducting Magnetic Energy Storage (SMES) Solar fuels

High power density Long cycle life High efficiency Quick recharge

High power output

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

2.2.3.8. Thermal Energy Storage (TES). TES includes an assortment of technologies that store accessible heat energy utilizing distinctive methodologies as a part of protected storehouses [40,49]. Heat/cold recovered can then be applied for electricity generation using heat engine cycles [18]. TES systems are reported to be classified into low-temperature or high-temperature based on the operating temperature of the energy storage material is higher than the room temperature [18]. The TES system is reported to be capable of storing large quantities of energy without any major hazards with a small daily self-discharge loss (0.05–1%). While the reservoir offers good energy density and specific energy (80–500 Wh/L, 80–250 Wh/kg) and the system is economically viable with relatively low capital cost (3–60 $/kW h) [18,40,46,49,105,108,133], the cycle efficiency of TES systems is normally low (30–60%) [18]. TES has been in use in a wide spectrum of applications, including load shifting and electricity generation for heat engine cycles [18,40]. 2.2.3.9. Summary of non-battery storage technologies. A comparison of different non-battery technologies is presented in Table 4. 2.3. Landscape analysis This section provides insight into the actual projects under taken in the US. Table 5 lists these projects undertaken in the US for storage or to address intermittency by a nonstorage approach. These projects are identified through ARPA-E (https://arpa-e. energy.gov) and WEIL (http://www.weilgroup.org). 2.4. Electric Energy Storage (EES) – application demonstration through a case study: Bonneville Power Administration (BPA) BPA is under the Department of Energy and operates as a nonprofit organization in the Pacific Northwest. The agency provides transmission and markets wholesale electrical power to five states in the Pacific Northwest. The source of electrical power comes from 31 federal hydro projects in the Columbia River Basin, one nonfederal nuclear plant, and several other small non-federal power plants. In total, one-third of the electric power used in the Northwest is provided by BPA. Related to transmission, BPA operates and maintains approximately three-fourths of the high voltage transmission lines in the region, approximately 15,300 circuit miles. BPA’s service territory includes Idaho, Oregon, Washington, and parts of Montana, California, Nevada, Utah and Wyoming. Overall, the area serviced by BPA covers approximately 300,000 square miles. The BPA pamphlet provides specifics on their mission, vision, and values (www.bpa.gov). The authors have worked with BPA experts to develop this analysis. The work was accomplished in weekly meetings with the key experts in the organization and through consulting with experts throughout the industry. As the share of the wind power in BPA’s portfolio increased, it brought new challenges to the Columbia River system. The flexibility of the system was reduced by the balancing requirements for the wind generators. As a result the organization identified use cases for the storage technology. These cases are listed in Table 6 and are plotted in Figs. 4 and 5. Furthermore these cases are plotted in Fig. 6 representing a Storage Landscape Analysis for BPA’s purposes. The landscape analysis integrates the cost, capacity, discharge time and maturity of storage technologies as well as a comparison with no storage options. Review of this case analysis should give the readers better comprehension of the storage technology review provided in the prior sections.

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3. Multi-Criteria Decision Making (MCDM) in sustainable energy planning Energy planning problems are complex and require multicriteria analysis for the decision maker(s). Multi-Criteria Decision Making (MCDM) techniques are gaining popularity in sustainable energy management. The techniques provide solutions to the problems involving conflicting and multiple objectives. Technological systems are always intertwined with human beings, organizations, and society. Therefore, the methodologies and approach of technology evaluation have been diversely developed in each area such as economical assessment, organizational assessment, social/environmental impact assessment, political and ethical impact analysis, which are employing various methodologies. As in any systematic problem, one must define the holistic approach of the decision making process to ensure that the optimal results are the function of many decision criteria. One classic method that is still being used today is economic analysis, which includes: cost/benefit analysis, life-cycle cost assessment, payback period analysis, cost effectiveness analysis, and real option analysis. Basically, all economic analysis is based on the funds available to the organization. However, if the decision is to be made only using the economic benefit, the model would likely favor EES technology alternatives that requires the minimum amount of funds. This model would be very limited because it would only give information based on the cost and would not include other valuable information like the perceived benefits of the technology [51]. One can also use decision trees to model the decision of the renewable energy technology [160]. The major challenge with the application of a decision tree model is that increasing the number of criteria and sub criteria will cause the model to become very complicated and extremely challenging to follow. This is a very important factor because the complexity of the model will not allow the users to extract the necessary information they need to reach the final decision. Table 7 summarizes the number of existing publications on MCDM in sustainable energy planning [161–167]. Several methods are used based on weighted averages, priority setting, outranking, fuzzy principles and their combinations. The application areas of MCDM in energy planning presented in Table 7 is renewable energy planning, energy resource allocation, building energy management, transportation energy management, planning for energy projects, electric utility planning and other miscellaneous areas. The commonly applied MCDM methods out of the above are multi-objective optimization, Analytical Hierarchy Process (AHP), Preference Ranking Organization Method For Enrichment Evaluation (PROMETHEE), Elimination and Choice Translating Reality (ELECTRE), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), Compromise Programming (CP), Multi-attribute utility theory (MAUT), fuzzy methods and Decision Support Systems (DSS). More than one MCDM method is also applied in many application areas to validate the results [163,165,167]. It can be observed from the table that AHP/HDM is the most popular method for prioritizing the alternatives, followed by Multi-objective programming and PROMETHEE. Fuzzy MCDM methods are also adopted for considering the uncertainties in energy planning. The methodologies fall under the others categories are Decision Support Systems (DDS) and the use of graphical model such as Analytical Network Process (ANP), Cognitive Maps (CM), Fuzzy cognitive Maps (FCM) and Bayesian Networks (BN). Decision Support Systems (DDS) are becoming popular in energy planning and resource allocation with the advent of the latest computational aids.

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Table 5 Selected Storage Projects through the US. Project Title

Type(s) storage

Use

Storage size (MW or KW)

Project size ($) Beginning/ Business Case or Research End Dates Objective

Lead/Partner

Fluidic Energy, Inc.: Enhanced Metal-Air Energy Storage System with Advanced GridInteroperable Power Electronics Enabling Scalability and Ultra-Low Cost

Advanced Multifunction Energy Storage (AMES)

Prototype for intermittent renewable energy ramp support

scalable to the megawatthour

$3,000,000

2011

Fluidic Energy Inc.

General Atomics: GRIDS Soluble Lead Flow Battery Technology

Flow battery based on lead-acid

Overcome limitation of Scalable to the the conventional lead grid-scale energy storage acid battery. Potential for multiple applications

$1,986,308

2011

General Compression: FuelFree, Ubiquitous, Compressed Air Energy Storage and Power Conditioning

Compressed Air Energy Storage Process (GCAESTM)

Prototype for renewable Round-trip $750,000 integration electrical efficiency up to 75% and response time of less than one second

2011

Lawrence Berkeley National Laboratory: HydrogenBromine Flow Batteries for Grid-Scale Energy Storage

HydrogenBromine (H2Br2) Flowbattery System

Prototype for grid applications

High power capabilities reducing the cost of stack components

$1,592,730

2011

Flow battery Primus Power: Low-Cost, High Performance 50 Year Electrodes

New electrode and production process of flow battery

N/A (Electrode and manufacturing process)

$2,000,000

2011

Proton Energy: Transformative Renewable Energy Storage Devices Based on Neutral

Regenerative Electrolyzer and Fuel Cell (REFC) based on alkaline membrane

Prototype for grid-level 20 kW electrical energy storage

$2,148,719

2011

United Technologies Research Center: Transformative

Flow battery

Prototype for grid-scale energy storage solution

$3,000,000

2011

20-kW

Address traditional challenges for grid storage deployment, including limited rechargeability, low power density, and poor roundtrip efficiency. The AMES device will provide energy storage at low cost, in part by developing domestically-sourceable and geologically abundant active materials in an advanced battery chemistry. Apply electrode materials that greatly increase the surface area available for chemical reactions, minimizing the amount of excess lead in the battery. It will result in a battery that easily can be scaled for grid-scale energy storage, but which costs less than existing technologies to accelerate the adoption and integration or renewable energy sources. Develop a new CAES process that is highly efficient and requires no fossil fuel. This innovative compressed air energy storage technology could accelerate the integration of renewable electricity resources, particularly wind, into the grid. Apply novel technical approaches to deliver a proofof-concept cell that will demonstrate the potential of this chemistry in grid-scale energy storage applications, so it meets the most stringent demands of costs, performance, lifetimes, and safety. Develop an extremely durable, highly active, conductive, and inexpensive metal electrode for flow batteries. The low cost electrode and volume manufacturing process will result in a significant decrease in energy storage costs for the proposed flow battery technology, while simultaneously increasing the power density of the system. Transitioning to an alkaline membrane in the proposed REFC will eliminate the highest-cost materials and enable higher efficiency through reductions in current density. An inexpensive, alkaline membrane will be developed and then utilized in a 20 kW reversible electrochemical advanced storage system that converts water to fuel and then back to water to for grid-level electrical energy storage. Develop a flow battery system that uses a novel cell design to deliver 10 higher power

General Atomics (UC San Diego)

General Compression

Lawrence Berkeley Nat. Lab. (DuPnt; Bosch; 3 M; Proton Energy)

Primus Power

Proton Energy (Penn State University)

United Technologies Research

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

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Table 5 (Continued) Project Title

Type(s) storage

Use

Storage size (MW or KW)

Project size ($) Beginning/ Business Case or Research End Dates Objective

Electrochemical Flow Storage System (TEFSS)

Iron-air rechargeable battery

Prototype for large-scale N/A energy storage for renewable integration

$1,459,324

2011

Forecasting for Wind Energy Non-Storage Grid Integration (Wind forecasting)

Forecast wind speeds N/A accurately using WRF at either 1 km or 200 m resolution.

$323,116

Dec. 2008–Jan. 2010

PG&E’s Renewable Integration Model (RIM)

Non-Storage (Renewable Integration)

RIM estimates amount of flexibility services required.

N/A

N/A

N/A

Dynamic Line Rating Using Real Time Weather Data

Non-Storage (Weather forecasting)

Determine where N/A weather can be reliably modeled to allow potential dynamic rating of a power line.

N/A

N/A

Quantifying the Full Value of Non-Storage (hydropower Hydropower in the grid service) Transmission Grid

Determine effects of alternative polices on value of hydropower

$3.2M

2 years

California ISO 20% RPS and 33% RPS Study

Non-storage (current system, DR, wind feathering, solar, fast regulation, etc.) SMUD’s Regional Smart Grid Zinc bromine emonstrations flow batteries (SupportARRA FOA 36 Topic 2.3)

Identify and compare N/A resource options that can be used to meet the power system needs under 20% RPS in 2012 and 33% RPS in 2020.

N/A

N/A

Support for smart grid

Two 500 kW– 6h

$12.6M

3 years

SMUD’s High Penetration PV Non-storage Initiative (solar generation)

Address key integration barriers in visualizing, monitoring, and controlling highpenetration PV on the grid.

N/A

$3,697,091

Jun. 2010– Jun. 2012

University of Southern California: A Robust and Inexpensive Iron-Air Rechargeable Battery for Grid-Scale Energy Storage

N/A

density than current state-ofthe art flow batteries enabling a dramatic reduction in the size and cost of the cell-stack. It will lay the scientific and technical foundation for development of a commercially-available gridscale energy storage solution. Overcome the limitations of the current iron-air battery technologies, low round-trip energy efficiency and poor cycle life by using additives at the iron electrode, the application of nano-structured electrodes, and a unique cathode catalyst. It will develop an iron-air proof of concept rechargeable battery, the first step in the commercialization of this promising, low-cost battery chemistry. Develop a methodology to accurately and reliably forecast power outputs (MW) from wind farms fro periods 5 min near Boise. Evaluate the accuracy of the forecasting methodology. Assesses the incremental integration requirements of different intermittent renewable portfolio and calculate the flexible integration resources to address the variability and forecast uncertainty of intermittent renewables. During favorable weather conditions additional power can be carried by the power line. The study develops a weather forecasting model of wind speed, direction and ambient temperature for area line sections based on historic weather data. Conduct industry case studies and evaluate hydropower participation in ancillary services market and.

Lead/Partner Center (Univ. of TX; Clipoper Windpower; Pratt & Whitney; SNL)

Univ. of So. California (Jet Propulsion Laboratory)

BPA, IPC, INL, JDRE, RE (Contractor: Boise State University)

PG&E

Idaho Power

EPRI, funded through DOE FOA DE0000069, SDL, ORNL Understand what is needed to CISO, PNNL, operate the California power PLEXOS, system under 20% RPS in 2012 Nexant and 33% RPS in 2020 under a range of renewable generation mixes.

Quantifying costs and benefits of this storage deployment to gain insights to broader application for SMUD

Premium Power, National Grid, SAIC, NREL, Syracuse University SMUD, HECO, Development and testing of hardware and software to SunPower, NEO Virtus evaluate the impact of high penetrations of PV systems on Engineering, BEW our grid. Engineering

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Table 5 (Continued) Project Title

Type(s) storage

Use

Storage size (MW or KW)

Project size ($) Beginning/ Business Case or Research End Dates Objective

SCE’s Large Scale Battery Storage, “Tehachapi Storage Project”

Lithium-ion battery

Grid support and renewable integration

8 MW for 4 h

$53.5M

SCE’s Large Transportable Battery System

Lithium-ion battery

Distributed generation storage service

Two 2 MW/ $3M 500 kWh units

SCE’s Community Energy Storage

Community storage

SCE’s Residential Home Energy Storage

Home battery

Distributed units 25 kW/ 50 kWh 4 kW/10 kWh

N/A

DOE & CEC Evaluate a utility scale lithium-ion battery’s ability to (Contractor: SCE/A 123) increase grid performance & integrate wind generation 2010– Evaluate transportable, 2013 containerized Li-Ion battery systems in field & laboratory 2011–2013 Enhance circuit efficiency, resilience and reliability 2010– 2014

2010–  $3 million 2013 Part of Irvin Smart Grid Demonstration n/a analysis Spring 2010 Complete, but will be expanded

Energy Storage Analysis Economics and Technical Potential for the NWPP under High Renewable Energy Assumptions

Batteries (NaS, Li-ion) combined with DR and Pumped Hydro

Storage for Renewable Gen Arbitrage

1 20 MW (batt.) 1000 MW (p. hyd)

Compressed Air Energy Storage Demonstration

Compressed Air

Large-scale storage

300 MW for 10 h

$50 M (incl. ARRA $25 M match)

Large-scale Sodium-Sulfur Battery Energy Storage Demonstration

NaS Batteries

Large-scale storage

4 MW/28M Wh

$17.0 M CEC PIER Grant

Intelligent Agent Project

Flywheel

Integration of renewables and matching wind output to transmission system capability

100 KW flywheel

$1.15M

Lead/Partner

Evaluate home storage integration with customer Han, EE, smart appliances, solar PV, PEV, etc. The study focused on the following items: *Determined the balancing requirements for a Northwest power pool in a high wind penetration scenario *Determined the most costeffective means to meet the balancing requirements, looking at lifecycle costs *Analyzed technology options consisting of batteries, pumped hydro, combustion turbines, Demand Response, and a combination of those options for optimal portfolios design *Analyzed the business case of storage for purely arbitrage purposes * Analyzed the locational sensitivity of single storage or multiple storage systems Phase 1 project will confirm Starting July 2010 the feasibility of the geology in Project the area, confirm next length: 7– generation design costs and 10 years, confirm the benefits/costs of CAES technology in mitigating intermittency and providing ancillary services. Phase 2 will involve a market Request For Offers to construct the project if Phase 1 results are acceptable. Oct. 2009– Project will install batteries on Oct. 2010 a 21 kV distribution circuit to demonstrate the system's ability to mitigate reliability and power quality issues on the circuit as well as quantifying the benefits of the technology to shape intermittent resources, provide ancillary services in the CAISO markets and determine the overall cost effectiveness of the technology. Feb 2009/ Research objective is to Sept. 2010 demonstrate that intelligent agent technology can be used to integrate wind, storage and transmission system assets. The near-term business objective is to demonstrate the viability of a distributed decision making solution, when applied to a known problem. This will help to facilitate increased acceptance

BPA (Contractor: PNNL)

PG&E

CEC (Contractor: PG&E)

CEC (Contractor: Alternative Energy Systems Consulting Inc.)

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

41

Table 5 (Continued) Project Title

PV Plus Battery for Simultaneous Voltage Smoothing and Peak Shifting

Type(s) storage

Advanced Lead Acid

Use

Integreation of PV

Microgrid project

Distributed Generation Storage Services Evaluation

Li-ion Batteries

Community Energy Storage System Research SANO Battery

Li Ion Battery

Regulation Service

Storage size (MW or KW)

Project size ($) Beginning/ Business Case or Research End Dates Objective

of distributed grid control solution by the utility industry. 500 kW/ Q3 2010$5M Create a firm peaking resource 2 MWH Q1 2014 from a renewable resource, target a 15% reduction in feeder size, mitigate voltage fluctuations introduced by PV intermittancy 1 MW/7 MWh Coupled with PV in Borrego Springs, project will demonstrate islanding ability, address intermittency of renewables, and provide voltage regulation on a 4 MW feeder. There will be interaction with customer homes and have automatic control of distributed generation and customer loads. Two 2 MW/ 2010– Evaluate transportable, $3 million, 2013 500 kWh units part of Irvine containerized Li-Ion battery Smart Grid systems in field & laboratory Demonstration trials 2011–2013 Enhance circuit efficiency, Part of Irvine Distributed Smart Grid resilience and reliability units (25 kW/ 50 kWh) Demonstration 2 MW/ Est. $3 Million May 2009/ 500 KWH March 2010

3.1. Multi objective programming In many cases, several objectives need to be addressed by the decision makers to assure that the organizations can optimize those objectives. Therefore, Goal Programming (GP) is used in such a case. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. This can be a vector or a weighted sum dependent on the goal programming variant used. In the case when target value is not given or unknown, other mathematical methods can be used. The method called Multiple Objective Linear Programming (MOLP) and can be viewed as special type of GP problem. One must also determine target values for each goal or objective. Analyzing these problems effectively also requires the use of another type of objective function, called the MINIMAX (Q) objective to minimize the maximum deviation from any goal [51,279]. 3.2. Multi-attribute utility theory (MAUT) Multi-attribute utility theory (MAUT) is a popular multi-criteria decision making tool developed by Keeney and Raiffa [280]. The objective of MAUT is to transform diverse criteria into a common utility or value scale. In MAUT poor scores on criteria can be compensated for by high scores on other criteria and hence MAUT is also referred to a ‘compensatory’ method [183]. MAUT takes into consideration the decision maker’s preferences in the form of the utility function which is defined over a set of attributes. The utility value can be determined by determination of single attribute utility functions followed by verification of preferential and utility independent conditions and derivation of multi-attribute utility functions [166].

Lead/Partner

PNM

SDG&E

SCE

SCE

AES/CAISO (AES)

In several cases where qualitative decision attributes are used, MAUT is often difficult to define definitive scenarios and corresponding desirability values that could best represent the range of conditions associated with the alternatives [166]. 3.3. Analytical Hierarchy Process (AHP)/Hierarchical Decision Model (HDM) As mentioned earlier that AHP and its variant, HDM, are the most popular methods for prioritizing the alternatives in the area of sustainable energy decision making. It should be noted that AHP is also the most popular multi-criteria decision making model in management science research and applications [281]. AHP was developed by Saaty [282] and it is subjective tool for analyzing qualitative criteria to generate priorities between alternatives. In AHP, a decision-making process is constructed as a hierarchical structure. At each level of hierarchy, AHP uses the pairwise comparison matrix to identify and prioritize the criteria and alternatives for decision-making. AHP used scale of 1–9 to evaluate the intensity of preference between two criteria. The value of 1 indicates equal importance, 3 moderately more, 5 strongly more, 7 very strongly and 9 indicates extremely more importance. The values of 2, 4, 6, and 8 are selected to indicate compromise values of importance. Ratio scale and the use of verbal comparisons are used for weighting of quantifiable and non-quantifiable criteria. AHP computes and aggregates their eigenvectors until the composite final vector of weight coefficients for alternatives is obtained [166,283]. HDM is the most popular method for prioritizing the alternatives. HDM is a clear approach that is straightforward and easy to execute. HDM has been used to address many complex, real-world multi-criteria problems [284,285]. HDM breaks down complex decision problems into smaller sub problems and

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Table 6 Storage Use Cases for BPA. Case Name

Current Response

Opportunity Cost or Risk

CASE 1: Congestion occurs on electric Transmission transmission Congestion facilities when actual or scheduled flows of electricity across a line or piece of equipment are restricted below desired levels.

Case Triggering Event

Planning planners develop grid reenforcements (new lines) to relieve congestion Operations restrict flows and schedules

New lines are difficult to permit and  Performance: size (50–800 MW); expensive, public is preferring “nondischarge time (hours) wire” solutions.  Economic: may not be economic Operations have difficult time taking unless utilized as a regional relines out for maintenance. source between BAs to lower costs Wind generation is expected to in coordination with dispatchable make congestion issues worse. loads (Smart Grid)  Operation: the energy can be stored on the right side of the transmission congestion during low demand hours, and then can be made available during peak hours, thereby reducing transmission use during peak demand hours.

Storage Solution

CASE 2: Oscillation Damping

The problem is when the oscillations grow in magnitude. In extreme cases, the growing oscillations can cause lines to open, generators to trip, and ultimately to cascading outages.

Conventional solutions include generator controls, such as Power System Stabilizers and hydrogovernor controls

BPA is relying on the generators to  Performance: size (20–50 MW); tune their controls properly to discharge time (less than seconds) provide damping. Most of these  Operation: Modulated to absorb/ generators are operated by other, in supply. No technology exists to many cases foreign, utilities and deliver energy this fast except merchants. super caps and Li-Ion, which have not been applied at this magnitude. May need to be a shared resource between several BA’s and meet other transmission needs to be economic.

CASE 3: AC & DC Intertie (Current)

Both AC and DC intertie capacity is fully subscribed by long-term contract holders. The long-term contract holders have a roll over right on the capacity. There are several thousands of long-term transmission requests in the queue 2900 MW on AC and 1875 MW on DC. Most congestion occurs during the spring, high run-off months.

BPA and other transmission providers are in the process of reviewing the historical data, costs, and certain scheduling and reservation practices to identify ways to increase intertie utilization.

There is an increasing demand for a  Performance: size (200– firm transmission service and for 800 MW); discharge time (hours) future wind and other power plants  Economic: storage cost should be planned in the NW region. Despite of compared to the transmission the many long transmission requests rates/costs of building new. in the queue, non-firm transmission  Operation: The PNNL Wide Area capacity is available most times of Energy Management System the year. (WAEMS)* proved that a shared resource to provide balancing energy between two BAs would substantially lower costs by fully utilizing the resource and not demand a firm transmission path on the COI. Increasing capacity to provide energy imbalance with the WAEMS would need to be studied to see if feasible.

CASE 4: Generation imbalance (Current, 3– 6GW Wind)

Currently, the FCRPS can absorb 2000 MWh of energy on a rolling 24 h basis without negatively impacting the Hydro Objectives of the FCRPS most of the time. Above 2000 MWh, the flexibility of the FCRPS might be limited and require the use of real time marketing to maintain Hydraulic Objectives.

BPA engages in market and operate hydro system to the level of risking non-power object. The current solution for integrating 3–6GWh of wind capacity in the BPA BA are the Wind Integration Team (WIT) Initiatives.

the market depth is not unlimited.  Performance: size (800– There is a risk that when the rolling 1500MW); discharge time (hours) 24 h imbalance exceeds 4000  Economic: the price of the caMWh at certain times of the year, the pacity service would need to be on flexibility and market depth will be par with a thermal resource to be a exhausted. Under this type of competitive solution for BPA. For scenario BPA would be at risk of not BPA to consider, the storage projmeeting all its Hydraulic Objectives. ect would need to be competitive with a natural gas thermal resource.  Operation: The storage project would need to be dispatchable within hour and provide power/ load for multiple hours. An energy storage project could fit into the Third Party Supply Pilot as an augmentation option. The WAEMS may be utilized with other high capacity energy storage technologies (such as NaS, Li-Ion and ultra batteries, for example) to provide balancing support through out the operating hour and potent-ially energy imbalance next hour.

CASE 5: Generation imbalance (10 GW Wind)

With the WIT Initiative exiting their Pilot status and be full BPA programs, BPA should be able to handle the forecasted 4–6 GW of penetration. But it is unlikely that BPA will be able to balance 10 GW of

To accommodate this much wind The risks are still the same as listed  Performance: size (50–1500 MW); there would need to be some at 3–6 GW wind case. discharge time (hours) fundamental changes in the Pacific  Economic: (see the Case 4) Northwest. Specifically, there would  Operation: Energy storage could need to be a mature and robust certainly be part of the new balancing market. BPA could not be resources stack and participate in

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

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Table 6 (Continued) Case Name

Case Triggering Event

Current Response

Opportunity Cost or Risk

wind penetration under the present the sole provider of the balancing WIT Initiatives solution. services. Secondly, the PNW would need to adopt a ten minute market. This would enable the PNW to sink up with the CAISO. Then California could participate in the balancing market of the PNW.

the PNW balancing market. An energy storage technology shared between several BAs to increase utilization may substantially lower costs, but only pumped hydro, CAES, NaS and Smart Grid (dispatchable loads) may have the needed capacity today. The FCRPS has the ability and more  Operation: rapid response energy than enough capacity to provide this storage would be redundant and is service. not needed.

CASE 6: Regulation and Frequency Response CASE 7: Load Following

N/A

Provides second-to-second generation support for voltage and frequency variance.

N/A

provides within-the-hour balancing BPA has enough balancing reserves reserves with 10 min dispatch. with 10 min dispatch., but existing hydro based systems may not be adaptable to 10 min schedules.

CASE 8: Ancillary (Current, 3– 6 GW Wind)

There is a limit to the amount of generation inputs (Spinning and non-spinning reserves) available from current Federal Resources to provide some Ancillary Services (VAR & freq. support, for ex.). The limit is dependant on other capacity uses and non-power constraints (such as ESA obligations). We expect to come up short, possibly in FY14.

BPAT Operations, BPAT Ancillary Services and Power (long-term sales and purchasing) will work together to identify sources and implement additional generation supply of capacity reserves.

CASE 9: Ancillary (10 GW Wind)

Insufficient Balancing Resources for that level of wind generation under status quo operating protocols (forecasting, scheduling, balancing reserve obligation)

provides decision makers a systematic way to evaluate multiple decision alternatives. 3.4. Preference Ranking Organization Method For Enrichment Evaluation (PROMETHEE) PROMETHEE has been used for decision making in multiple applications and was introduced by Brans, et al. [286]. PROMETHEE is an outranking method for a finite set of alternative actions to be ranked and selected among criteria, which are often conflicting. Brans, et al. [286] proposed six generalized criteria functions for reference: usual criterion; quasi criterion; criterion with linear preference; level criterion; criterion with linear preference and indifference area; and Gaussian criterion [166]. PROMETHEE is also a quite simple ranking method in conception and application compared with the other methods for multi-criteria analysis[209]. As mentioned earlier, in the area of sustainable energy planning, PROMETHEE is considered one of the most favorable approach given its simplicity. Gilliams, et al. [287] argued that PROMETHEE is slightly preferable compared to both Elimination and Choice Translating Reality (ELECTRE) and AHP, based on user friendliness, simplicity of the model strategy, variation of the solution, and implementation.

Storage Solution

 Operation: WAEMS and other storage technologies such as pumped hydro, NaS, Li-Ion and Smart Grid may substitute for hydro based load following, which would free reserves to meet other power system needs.

Cost is somewhat unknown, but we  Performance: size (800– are gaining some experience with 1500 MW); discharge time (hours) purchasing Decs from generation  Economic: should be cost-com(Calpine). For market purchases petitive to other resources. there is minimal risk – the revenue  Operation: WAEMS, NaS, Li-Ion requirement will go into rates. More and Smart Grid are potential stranded asset risk with acquisition solutions to meet this need if used of physical generating resources. as a shared resource between BAs to be economic. Hydro reserves to support wind intermittency would then be released to serve other power system needs.  Performance: (see the Case 8)  Economic: (see the Case 8)  Operation: An evolving energy storage solution starting with perhaps WAEMS, in concert with other large capacity energy storage solutions such as NaS, Li-Ion, Smart Grid (short term), which would then lead to long term solutions such as CAES and pumped hydro.

3.5. Elimination and Choice Translating Reality (ELECTRE) The basic concept of ELECTRE is how to deal with outranking relation by using pair-wise comparisons among alternatives under each criteria separately. It is based on the study of outranking relations, exploitation notions of concordance. These outranking relations are built in such a way that it is possible to compare alternatives [187]. ELECTRE involves less mathematical computations when compared to other MCDM methods, so ranking can be easily achieved. This method focuses on ranking the alternatives by performing the pair wise comparison among the alternatives [288]. ELECTRE works by reducing the number of alternatives from a group of alternatives by setting up the concordance and discordance threshold limits [289]. Since the number of alternatives is becoming less, the particular step of checking for the value within the limit is eliminated in this context. It examines both the concordance and discordance among the alternatives by computing their respective indices. Up to date, the concept of threshold values in ELECTRE has been debatable and criticized. It is stated to be logical at first sight; however, it is criticized for not having clearly defined physical or psychological explication. Moreover, ELECTRE methods are

44

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

Fig. 4. BPA’s Storage Needs (adopted from ESA).

Fig. 5. Solutions for BPA’s Storage Needs (adopted from ESA).

sometimes unable to identify the preferred alternative, and in this case, they produce a core of leading alternatives [187]. 3.6. Decision Support Systems (DSS) The concept of Decision Support Systems (DSS) began to evolve in the late 1970s. A number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semi-structured problems [290]. A DSS is an interactive system that helps decision-makers utilize

data and models to solve unstructured or semi-structured problems [291]. DSS is sophisticated, interactive and computer aided techniques for aiding the decisions. DSS can support complex problems that would be otherwise difficult to handle. Knowledge based DSS can support the decision makers in selecting criteria, alternatives and trade-offs, thus making the renewable energy planning simple. The identified DSS use MCDM methods for arriving at interim results [166,291].

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45

Fig. 6. Storage Landscape Analysis for BPA.

Ford [291] argued that DSS do not provide answers to structured problems; rather, they emphasize direct support for decision-makers in order to enhance the professional judgments required in their decision-making. Implementing DSS may reinforce the rational perspective and overemphasize decision processes and decision making. It is important to educate decision makers regarding the broader context of decision making and the social, political and environmental factors that impact organizational success. 3.7. Graphical model methods Many graphical model methods have already been employed to model and encode relationships among decision elements, such as: Analytical Network Process (ANP) [283,292–294], Cognitive Maps (CM) [295,296], Fuzzy cognitive Maps (FCM) [297], and Bayesian Networks (BN) [298,299].

3.7.1. Analytical Network Process (ANP) Analytical Network Process (ANP) is a more general type of AHP to handle more confounded interrelationships, Including dependences, interactions, inputs and so forth, among components, both at the same level and in distinctive levels. In spite of the above advantages, there are constraints concerning ANP. For example, ANP can just express connections among distinctive components through relative weights created from pair-examinations and the convergence of a so-called supermatrix. It cannot quantify or expressly show impacts among those components. What's more, it is hard for ANP to update results because of progressions of data and decision factors in an environment in which such changes may happen every now and then [283,292–294]. 3.7.2. Cognitive or Causal Maps (CM) Cognitive or Causal Maps (CM) is powerful graphical models for knowledge representation. They offer an easy means to express

Table 7 MCDM Approaches in Renewable Energy From Literature Review. Application

Multi Objective Programming

MAUT

AHP/HDM

PROMETHEE

ELECTRE

Renewable energy Policy Energy Resource Allocation

[51,168–174] [181,200–202]

[44,177–184] [50,161,201,202]

[215–222]

[185–190] [188–190,206– 210] [233,234]

[185,187,191] [178,192–199] [211–214]

Building Energy Management Transportation Energy Systems Project Planning in Renewable Energy Electric Utility Planning

[175,176] [203– 205] [223,224]

[259–267]

[268,269]

Total Number

29

10

[249]

[225–232] [113,237–247] [250–255] [177,181,270– 275] 48

[196,235]

OTHERS (DSS, ANP, CM, FCM, BN)

[236] [248]

[256–258] [177,276–278] 16

5

18

46

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individual’s judgments, thinking or beliefs about a given problem. However, drawing inferences in cognitive maps, especially when the problem is complex, may not be an easy task. The main reason of this limitation in cognitive maps is that they do not model uncertainty with the variables [295,296]. In addition, the variables in cognitive maps are represented in a static way. In other words, the way in which the beliefs of decision-makers about

some target variables change when they learn additional information about the concepts of the map is not represented. 3.7.3. Fuzzy Cognitive Maps (FCM) Fuzzy Cognitive Maps (FCM) is an emerging technique for knowledge elicitation and data synthesis. The technique can capture the cause and effect relationships that subject matter

Table 8 Key research areas and findings in the literature. Key Research Area

Research findings

References

Fossil fuels vs. renewable energy sources

1 There are three main technology pathways for supplying large amounts of low-carbon electricity: fossil [1,3,6,301–303] with carbon capture and sequestration (CCS), nuclear, and renewable sources. 2 In most scenarios, the problem on the demand growth is generally addressed by using the low cost conservation and energy efficiency resources. However, renewable energy sources are getting more attention to address the issue of demand growth due to several reasons: - Strong political support for renewable energy per se, which is due to the attendant environmental advantages, associated the absence of greenhouse gas emissions, as well as National energy security advantages. - Rate payer selected willingness to pay a premium for “green power” as a personal means to advance renewable energy. - Improved renewable sources–such as wind power–system economics due in large part to ever increasing power ratings, larger manufacturing operations and improved performance capabilities of the wind turbine systems.

Renewable energy intermittency

1 The penetration of renewable sources (especially solar, wave and wind power plants) into the power [16,18,40– 44,49,51,68– system network has been increasing in the recent years. 2 Electricity produced from sustainable energy resources have demonstrated astounding development 79,105] worldwide, however it can rarely provide immediate response to demand as these resources do not deliver a regular supply easily adjustable to consumption needs. 3 The problems in generation imbalance for renewable sources, such as solar and wind power, require multi-criteria analysis for the decision makers. 4 In addition to the required multi-criteria analysis, there is also a problem of uncertainty inherent in future changes as a result of interdependence among these criteria. 5 To deal with the variability of wind and solar power electricity generation at large scale, several methods have been proposed, which including: (i) Distributed generation (at the end user level), (ii) Interconnect dispersed Variable Generation (VG), (iii) Combination of Variable Generation (VG), (iv) Forecasting model, (v) Demand response method,and (vi) Electric Energy Storage (EES) approach. 6 Methods (i) to (v) are not sufficient to mitigate the challenges of the variability due to several reasons: - Interconnecting dispersed Variable Generation (VG) and Combination of Variable Generation (VG) allow higher renewable sources penetration by providing higher flexibility, but is expensive due to the magnitude of energy exchange required to make them profitable. - Improving VG forecasting reduces system dispatch errors, but does not give full economic opportunity to the VG power generator. - Increasing dispatchable back-up power generation may improve the system's ability to cope with dispatch errors at the cost of greenhouse gas emissions, since these units generally require fossil fuels for power. - Alternatively, hydro power responds quickly and can absorb some of the fluctuations in wind power output; however, hydro resources are limited. - Decoupling VG from the grid removes power quality problems associated to VG at the cost of reducing clean energy sources feeding the grid. 7 EES, although generally expensive, has the ability to address several VG integration issues described above.

EES technology

[44,46,48,49] 1 The use of EES for the real time and short notice (milliseconds to a few minutes) support and optimization of the generation, transmission and distribution (G, T&D) system has been limited to date to Pumped Hydro Systems (PHS) and, in a couple of instances, lead-acid or nickel cadmium batteries. 2 Despite numerous advances in EES technologies and technical benefits offered, markets have not yet adopted EES applications other than pumped hydro on a large scale. 3 The pace of development and deployment of new EES technologies is accelerating and these solutions could play an important role as more electric grid (such as in the U.S.) incorporates more intermittent renewable sources. This proposed research will be conducted for case of Indonesia which is targeting more integration of intermittent renewable sources into the grid. 4 These changes provide opportunities for new players as the technological and business landscape evolves to incorporate more EES. 5 As an emerging element of electricity delivery systems, EES faces both great opportunity and considerable challenges. 6 Participants wishing to enter this market or expand their presence need to carefully consider many factors, related to choice of technology, type of applications they target, the regulatory framework, and the characteristics of the markets they wish to serve.

Multi-criteria decision making in renewable energy planning

Literature reviews of energy decision model studies with partial or conceptual-level STEEP perspectives covered.

[18,47,49,51,95– 102]

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experts believe to exist about a problem. The unquestionable advantages of FCMs lie in the simplicity and adaptability to a certain application domain. However, it seems that their further development and is somewhat constrained by deficiencies that are present in their underlying theoretical framework. Disadvantages related to the manual development recently encouraged researchers to work on semi-automated or automated tools for learning FCM models from historical data [297]. 3.7.4. Bayesian Networks (BN) Bayesian Networks (BN) is a well-established method for reasoning under uncertainty and making inferences, the elicitation

47

of the structure and parameters of the network in complex domains might be a tedious and time-consuming task. Additionally, the notion of probability is often not perceived or understood by domain experts. The task is hence more tiresome and time intensive for generating conditional probabilities [298,299]. 4. Conclusions A comprehensive literature review of more than 350 academic journal articles, government reports, white papers, magazine articles, web articles, etc. has been conducted in the following areas:

Table 9 Evaluation criteria for EES technologies. Perspectives

Criteria

Description

Social

Job Intensity

The opportunity of new jobs creation from the energy storage technologies and related business. [111,337] Job creation is a top priority for many communities. Certain EES technologies may be produced locally within the utility’s service area. Jobs are created for production, installation, and operations. The condition of being protected from danger, risk, or injury under normal operation. Hence, [49,111,112] expert is required to assess the negative health effects of EES technologies The ability to accept the new EES technology by public [338,339]

Health and Safety Public Acceptance Technical

Economic

Efficiency Maturity

References

The ratio between released energy and stored energy The level of development stage of the energy technology such as idea verification, prototype, demonstration, and commercialization. This variable measures how long has this EES technology been in the market and field tested Capacity The quantity of available energy in the storage system after charging. For example: Lead acid (valve regulated) at Milwauke, Wisconsin has capacity of 300 kW or 580KWh after charging The length of time that the storage unit can be used. For example: the lifetime for lead acid Lifetime batteries is 13 years while sodium sulfur batteries can last for over 15 years The number of times the storage unit can release the energy level it was designed for after each Durability recharge. Durability is measured in number of cycles. Lead acid batteries have up to 2000 cycles while redox flow batteries have up to 13,000 cycles Autonomy The maximum amount of time the system can continuously release energy. It is defined by the ratio between the energy capacity (restorable energy) and maximum discharge power. The autonomy of a system depends on the type of storage and the type of application. For small systems (a few kWh) in an isolated area relying on intermittent renewable energy, autonomy is a crucial variables The interval time a storage unit takes to react to a given input Response time Power density The rated output power divided by the volume of the storage device Energy density The stored energy divided by the volume of the storage device Power transmission rate The ratio of the quantity of electricity removed from a storage device to its rated capacity

[49,90,98,111,112,337] [18,90,98,101,111,112]

Capital costs

[49,90,95,96,340,342]

Operations and maintenance costs Recurrent costs Disposal costs

The total costs include the purchase of land, buildings, construction and equipment to be used in the storage unit. Like any other investment, a storage system is an interesting venture when total gains exceed total expenses. The capital invested and operational costs (maintenance, energy lost during cycling, aging) are the most important factors to consider for the entire life of the system. For example: Sodium sulfur battery has an initial capital cost between 1000–2700 ($/kW) while Li-ion battery has a higher cost up to 4000 ($/kW) The costs include two main parts: a fixed one, rated power and a variable part depending on its annual discharged energy. Sodium sulfur battery has O&M cost of 15–90 $/kW per year The cost per unit energy divided by the cycle life Cost for getting rid of end-of-life equipment.

Environmental Air pollution Water pollution Wildlife impacts Landscape damage

The The The The

Regulatory/ Policy

Security for energy supply issue and define it as consistent availability of sufficient secure supplies of energy A national accreditation program that sets environmental and reporting standards for renewable electricity products offered by energy suppliers to households. The Green Power Program also purchases renewable energy generated by wind, landfill gas and agricultural waste. This criterion consists of mainly three aspects or factors: support by government national laboratories, increased technology transfer activity to the private sector, and the execution of a strategic technology plan or roadmap. Refers to tools that government agencies can take advantage to promote specific energy technologies.

Security Green power programs

Public/Government R&D Framework Federal, Provincial and State tax incentives

contamination of the atmosphere by noxious gases and particulates drainage of the waste water into natural water bodies effects on nature and wildlife potential damage to the landscape where EES will be sited

[49,90,97,98,101,111,340] [18,90,95,97,98,111,112,337] [18,49,97,98,101,111]

[49,341]

[90,141,337,339] [40,47,49,90,141,337,339] [18,40,49,98,112] [18,40,49,98,112]

[18,49,90,95,96,300] [90,94] [18,40,49] [47,337,343] [18,47,337,343] [18,47,337,343] [18,40,49] [301,344–347] [348–350]

[18,40,49,183]

[301,344–350]

48

   

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Renewable energy intermittency EES technology development and implementation Evaluation of EES program for renewable energy intermittency Multi-criteria decision making methodologies in renewable energy planning (energy resource allocation, building energy management, transportation energy management, planning for energy projects, electric utility planning and other miscellaneous areas)

Table 7 summarizes the key research areas and findings derived from the literature review. Table 8 gives an overview of the key research areas and the findings in EES technology evaluation. Several gaps have been identified based on the above Table Several scholars in their research papers also confirmed the gaps. Those gaps are:  Typically all five STEEP perspectives are not considered in one evaluation. Journal papers tend to be focused around 3 clusters of perspectives: (1) Technical and Economical (TE), (2) Social and Political (SP), and (3) Social, Environmental, and Political (SEP) [18,47,49,51,95–102].  The use of EES for the real time and short notice (milliseconds to a few minutes) support and optimization of the generation, transmission and distribution (G, T&D) system has been limited to date to Pumped Hydro Systems (PHS) and, in a couple of instances, lead-acid or nickel cadmium batteries, primarily due to a lack of cost-effective options. However, recent developments in advanced energy storage technology, including several wind power related demonstration projects and assessments, are providing new opportunities to use energy storage to optimize grid connected wind power in curtailment mitigation; time shifting of firmed and shaped wind generation from night to day; forecast hedging; grid frequency support and fluctuation suppression applications [6,51,304].

 There is no general conceptual framework to evaluate existing and emerging EES alternatives in a more reliable, qualitative and quantitative, systematic and effective way.  The impacts of changing priorities on EES technology alternatives have not been fully explored [49,63,95,112,131,305]. Each EES technology has unique characteristics and is different in terms of its appropriate application field and energy storage scale as discussed in great detail in Section 2. Ibrahim, et al. [49] considered seven criteria as the main criteria of the different EES technologies and their fields of application: storage capacity, available power, efficiency, durability, self-discharge, autonomy and costs. Bo et al. (2008) evaluated several utility-scale EES technology options for the wide area energy management system based on these criteria: energy efficiency, capacity, duration, maturity of technology, lifetime and cost. Hadjipaschalis, et al. [112] provided an overview of the current and future EES technologies used for electric power applications, and made a comparison between the various technologies in terms of the most important technological criteria, which included energy density, power density, lifetime, self-discharge, maturity and efficiency of each technology. Chen, et al. [18] provided a comprehensive assessment and comparison of EES technologies based on these criteria: technical maturity, power rating and discharge time, storage duration, capital cost, cycle efficiency, energy and power density, lifetime and cycle life, and influence on the environment. Based on these previous studies, a comprehensive evaluation of EES was developed using HDM model covering multiple perspectives, which including: social, technical, economic, environmental and regulatory/policy. Table 9 indicates the criteria descriptions for each perspective. The model hierarchy is presented in Fig. 7. A limited version of this model [44] was demonstrated using fuzzy

Fig. 7. Proposed Model to evaluate Storage Technologies.

J. Kim et al. / Journal of Energy Storage 11 (2017) 25–54

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