Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

Electrical Power and Energy Systems 54 (2014) 26–37

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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review Wei Gu a,⇑, Zhi Wu b, Rui Bo c, Wei Liu a, Gan Zhou a, Wu Chen a, Zaijun Wu a a

School of Electrical Engineering, Southeast University, Nanjing, China School of Electronic, Electrical & Computer Engineering, University of Birmingham, Birmingham, UK c Midwest Independent Transmission System Operator (Midwest ISO), St. Paul, USA b

a r t i c l e

i n f o

Article history: Received 10 March 2013 Received in revised form 21 June 2013 Accepted 28 June 2013

Keywords: CCHP Solid oxide fuel cell Microgrid Modeling Planning Energy Management

a b s t r a c t A combined cooling, heating and power (CCHP) microgrid with distributed cogeneration units and renewable energy sources provides an effective solution to energy-related problems, including increasing energy demand, higher energy costs, energy supply security, and environmental concerns. This paper presents an overall review of the modeling, planning and energy management of the CCHP microgrid. The performance of a CCHP microgrid from the technical, economical and environmental viewpoints are closely dependent on the microgrid’s design and energy management. Accurate modeling is the first and most important step for planning and energy management of the CCHP microgrid, so this paper first presents an review of modeling of the CCHP microgrid. With regard to planning of the CCHP microgrid, several widely accepted evaluation methods and indicators for cogeneration systems are given. Research efforts on the planning methods of the CCHP microgrid are then introduced. Finally, the energy management of the CCHP microgrid is briefly reviewed in terms of cogeneration decoupling, control strategies, emission reduction and problem solving methods. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The urgent need to meet increasing energy demands and concerns over air pollution have led to the rapid development of microgrid technology in recent years. Development of the combined cooling, heating and power (CCHP) microgrid by using distributed cogeneration equipment and renewable energy sources has drawn considerable research attention recently. Compared with conventional CCHP systems, the CCHP microgrid has novel and greater functionality, because the CCHP microgrid not only satisfies the cooling, heating and power demands of certain types of customer (such as residential buildings, schools, department stores, and industrial loads), but also interacts with the main grid to provide reserve, peak-saving and demand response services, and provides improved capability for integration of renewable energy sources. The concept of ‘‘intelligent energy networks’’ (IENs) has been proposed to represent an intelligent management system for a complete set of energy sources, including electricity, heat, hydrogen, biofuels and non-biofuels [1]. The CCHP microgrid is set to play a vital role in this type of energy synthesis system. In Ref. [2], CCHP systems are mainly classified into two groups: traditional large-scale CCHP applications, and distributed CCHP

⇑ Corresponding author. Tel.: +86 13814005169; fax: +86 25 87796196. E-mail address: [email protected] (W. Gu). 0142-0615/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2013.06.028

units with relatively small capacity and advanced technologies designed to meet multiple demands in the commercial, institutional, residential and small industrial sectors [3–7]. The distributed CCHP systems have two obvious advantages over the larger centralized power plants. First, overall fuel energy use is dramatically improved, ranging from 70% to more than 90% in comparison to the 30–45% achieved by typical centralized power plants. Second, these systems would contribute to emission reduction. The application of new technologies, such as fuel cells (FCs) and micro-turbines (MTs), mean much lower exhaust emissions of NOx and CO2 than the traditional technologies used in centralized power plants. The possible benefits and vast potential of these cogeneration systems mean that they have drawn a great deal of attention from governments, industries, and researchers over the last two decades. The emergence of distributed energy resources (DERs) has also promoted the development of these cogeneration systems. For example, the US Department of Energy (DOE), in cooperation with the Environmental Protection Agency (EPA) and the Combined Heat & Power Association (CHPA), put a ‘‘CHP Challenge’’ into effect in 1998. In 1999, the DOE then published the ‘‘Combined Cooling Heating & Power for Buildings 2020 Vision’’, which presented a timetable for CCHP development. By 2020, 50% of new buildings and 25% of existing commercial institutional buildings in the USA will be equipped with CCHP [2]. At present, 11% of the energy generated in Europe is supplied by cogeneration systems; distributed energy systems in Denmark have taken up nearly

W. Gu et al. / Electrical Power and Energy Systems 54 (2014) 26–37

60% of the country’s total power market. It is estimated that by 2020, the market share of alternative energy systems in Germany will increase from the current 12.5–25%. CCHP microgrid structures vary from site to site, with diverse prime movers, cooling options, rated size ranges, load characteristics, and operational objectives. As shown in Fig. 1, a typical CCHP microgrid has a prime mover (e.g. FCs or MTs); the cooling and heating demands are satisfied by an absorption chiller and a thermal boiler, respectively, by using waste gas or natural gas; heat storage is used to store the excess heat; a gas boiler is used as auxiliary heating equipment when the waste heat is insufficient. The proportion of renewable energy in the CCHP microgrid is high, and energy storage devices are therefore used to smooth any fluctuations rooted in the renewable energy (photovoltaic (PV) generation varies with the solar radiation, and the wind power changes with the wind speed) and the loads. It should be noted that, in comparison with traditional power systems, the CCHP microgrid has the following features: (1) the volatility of the cooling and heating loads and the intermittent nature of renewable energy lead to great randomness within the system; (2) the prime movers have different operational characteristics under different operating conditions, e.g. the power efficiency and heating efficiency of the MT, which vary with the capacity and the load rate; (3) the waste heat can be used not only for heating, but also for cooling, and thus there are strong coupling relationships between the three types of energy in the system; (4) the inertia of the system is small, because of the widespread use of power electronic technologies, and the response speeds of the various devices used differ; and (5) the energy balances are complex, and the system requires a variety of scheduling strategies. For instance, waste heat can be given alternative priorities to meet heating demands or cooling demands; the power of the prime mover can be determined based on the different operating modes. In summary, a CCHP microgrid with a renewable energy source is a complex system with many uncertainties, a variety of structures, and highly coupled characteristics. At present, the use of the CCHP microgrid is growing dramatically around the world, making this area a research hotspot. Be-

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cause of the complex nature of the system, it is necessary to understand the existing and ongoing development and research around CCHP microgrid. Therefore, in this paper, a comprehensive review of the modeling, planning and energy management strategies of the CCHP microgrid will be presented in sequence. 2. Modeling of the CCHP microgrid Accurate mathematical models of the distributed units of the microgrid must be established to analyze the various characteristics of the distributed devices and the power flow among them. It should be emphasized that the efficiency of each device, and especially that of the prime mover, is not a constant value. Instead, it varies with the capacity, the load ratio and other system factors. The stochastic characteristics of the renewable energy, the coupling of the electricity, heating and cooling, and the diverse characteristics of the distributed units mean that it is very difficult to model these systems. Although many researchers have focused on building dynamic models for each separate unit, few research achievements with regard to the static characteristics of the distributed units have been reported. As a result, the accuracy of CCHP microgrid day-ahead scheduling and CCHP microgrid planning is unsatisfactory because of the lack of consideration of the static characteristics. It is therefore very important to know the pros and cons of the static models of the distributed units in the literature. In this section, a review of the static modeling of the distributed units is presented. A typical CCHP microgrid consists of four main elements: the prime mover, the storage system, the load and the renewable energy source. Each of these four elements will be described in detail in this section. Fig. 2 is the scheme of CCHP microgrid, showing the energy flows within the microgrid. In Fig. 2, Ep and Eboiler represent the chemical energy consumed by the prime mover and the boiler respectively; Eex is the exchange energy between the main grid and the microgrid; Ebt_ch, Ebt_dis, Etst_ch and Etst_dis denote the charge energy and discharge energy of the battery and the thermal storage tank respectively; Eres represents the energy of the renewable en-

Fig. 1. Structure of CCHP microgrid.

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W. Gu et al. / Electrical Power and Energy Systems 54 (2014) 26–37

Fuel Boiler

Eboiler

Ep

Prime mover

Ep·ηth

Ep·ηel

Main Grid

Heat Recovery System Thermal Storage Tank

Eex

Eboiler·ηboiler

Ep·ηth·ηhrs

Etst_ch /ηtst_ch Etst_dis·ηtst_dis

Echiller

Battery Renewable Energy

Ebt_ch /ηbt_ch Ebt_dis·ηbt_dis Eres

Chiller Echiller·ηchiller

Electricity Energy

Cooling Energy

Heating Energy

Fig. 2. Mathematical modeling of the CCHP microgrid.

ergy source; Echiller is the energy of the chiller. All energy flows depend on the efficiency parameter g of each of the components. 2.1. Modeling of prime movers The prime mover plays a critical role in the cogeneration system, and the precision of the prime mover’s modeling directly affects the accuracy of the whole system. At present, there are several types of prime mover, including steam turbines, combustion turbines, MTs and FCs, and the latter two technologies have a promising future in prime mover roles, especially in small-scale cogeneration systems. The MT has great flexibility in that smallscale units can be combined together into larger systems; and the MT has the environmental advantage of low emissions. Similarly, FCs are quiet and environmentally friendly, and produce high energy efficiencies under varying load rates. There are five major fuel cell technologies [2], of which the proton exchange membrane fuel cell (PEMFC) and the solid oxide fuel cell (SOFC) are most widely used. The dynamic characteristics of FCs and MTs with their complex heat transfer and balancing relationships have been studied many times in the literature. For example, El-Sharkh et al. established a model of an SOFC system including the heat exchanger, the reformer, the after-burner and other components; steady-state and dynamic simulations of this FC system were then presented [8]. In a research report published by the New Energy and Industrial Technology Development Organization (NEDO) in Japan in 2011, the SOFCs of various manufacturers (MHI, Kyocera, TOTO, MMC) were found to show different internal impedance and voltage characteristics after analysis of 5000–10,000 h of operational results, and thus the parameters for their fitting models were different from each other. However, the complicated thermodynamic transfer processes and the variable operational conditions of FCs and MTs make the research into their static characteristics difficult to carry out [9,10]. Gunes studied the static characteristics of PEMFCs, and summarized by stating that the power efficiency of the PEMFC first increases and then decreases as the load rate rises to the rated power. Graphs (a) and (b) in Fig. 3 show the characteristics of the fitting models for the PEMFC electric efficiency and thermal efficiency respectively; these models were presented in [11]. This fitting model was adopted to study the economical

operation of a combined heat and power microgrid composed of a renewable energy source and the PEMFC [12,13]. In [14], the empirical validation of the mathematical models that represent the thermal and electrical performance of one particular prototype 2.8 kWAC SOFC is presented. In [15], the electrical efficiency and thermal efficiency of the PEMFC are assumed to be constant. The ramp rate of the PEMFC is also considered in two Refs. [12,15]. The electrical efficiency, thermal efficiency, and the pollutant gas emission characteristics of the MT are decided by its capacity and its load factor [16,17]. In [17], empirical formulas for the MT’s electrical efficiency and thermal efficiency are established. However, for the emission characteristics of MTs, there is little available data because of limited operational experience. Based on current test results, it would be difficult to create a generalized model, but the model could consider a specific MT with a given capacity. For example, graphs (c) and (d) in Fig. 3 shows the partial-load emission factor characteristics for NOx, CO and total hydrocarbon (THC) emission of a 60 kWe MT, according to experimental data at 100%, 75%, 50% and 25% of the full load [18]. Graphs (c) and (d) in Fig. 3 shows that the minimum value of the electrical output of the MT is 50%. This is because, as the output of the MT drops from the rated power, the quantity of pollutant gases increases rapidly, and manufacturers recommend that the MT should stop running if its power is less than half of the rated power [17]. Although these fitting models could reflect the FC and miniature gas turbine power generation characteristics to some extent, their accuracy is unacceptable. Another more detailed modeling method for both the prime mover and the overall CCHP microgrid is to establish the system by thermodynamic modeling based on the first and second laws of thermodynamics, which would provide a powerful tool for the energy, exergy and thermo-economic analysis of the CCHP system [19]. In summary, as the core of the CCHP microgrid, the modeling of the prime mover has great importance for the modeling of the entire system. However, because of the complexity of the operating conditions, few advances have been made with regard to the modeling of the electrical efficiency, thermal efficiency, ramp rate, emissions and other static characteristics of the prime movers. Therefore, more research should be focused on this issue to improve the accuracy of CCHP microgrid modeling.

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100

90

90

Thermal efficiency [%]

100

Characteristics of FC

Power efficiency [%]

W. Gu et al. / Electrical Power and Energy Systems 54 (2014) 26–37

80 70 60 50 40 30 20 10 0

20

40

60

80

80 70 60 50 40 30 20 10 20

0

100

18000

70 60 50 40 30 20

Power efficiency

10 50

60

70

80

90

Mg·kWh

Efficiency [%]

Thermal efficiency

-1

20000

90

Characteristics of MT

100 80

40

60

80

100

90

100

Load ratio [%] (b)

Load ratio [%] (a)

16000 14000 12000 10000

NO

THC

CO

8000 6000 4000 2000

100

50

Load ratio [%] (c)

60

70

80

Load ratio [%] (d)

Fig. 3. Characteristics of FC and MT.

2.2. Modeling of storage systems Economic evaluation of the electrical, thermal or fuel storage always poses a challenge because of the inter-temporal nature of storage, where the operation in one time step affects the operation in the subsequent time steps. Difficulties also arise from the need to make decisions despite the great uncertainty that surrounds the future circumstances [20]. The fluctuations of the renewable energy and the multiple loads in the CCHP microgrid lead to fastchanging power, heat and cooling balances. To deal with this situation, the energy storage and thermal storage systems are vital components to reduce power fluctuations within the cogeneration plant. More importantly, when the CCHP microgrid is isolated, the storage system could then provide voltage and frequency references for the microgrid alone or together with other sources. In some instances, such as nighttime in solar generation systems and periods without wind in wind generation systems, the storage system can supply energy to consumers. Therefore, the rational use of storage systems is an important factor in the safe, economical, and reliable operation of CCHP microgrids. Until now, the energy storage systems have had various working principles and forms, which could generally be divided into chemical energy storage and physical energy storage. Battery storage and electrochemical capacitors provide the chemical energy storage; while pumped hydroelectric storage, flywheel storage, compressed air energy storage (CAES), and superconducting magnetic energy storage (SMES) are forms of physical energy storage [21]. The literature review on various energy storage technologies can be found in [22]. Although the storage system plays a significant role in the microgrid, the high cost of storage restricts its widespread application. Among these storage technologies, the battery technologies are the oldest and most mature, and thus battery storage systems are the most common storage devices in

microgrids [23]. There are several types of battery, and comparisons of the battery types are listed in Table 1. At present, the lead acid battery is the most commonly used type of battery in large-scale energy storage systems. Therefore, in this section, modeling of the lead acid battery is presented. Over the years, many different battery models have been developed for various applications, and they can generally be divided into three groups: (1) chemical mechanism modeling, based on the internal battery [24]; (2) circuit modeling, based on an equivalent circuit [25,26]; and (3) a numerical model [27]. Chemical mechanism modeling is the most accurate method available to date, but it contains many coupled differential equations, and thus involves great computational complexity. In most studies, the battery is modeled by using equivalent circuits. The circuit model is easy to understand because of its simple structure and it is widely applied, but this model has certain defects and needs a large quantity of experimental data to estimate the circuit parameters. In the numerical model, taking the kinetic battery model (KiBaM) as an example, the battery cell is regarded as being in two parts: the bound charge and the available charge. The available charge can provide electrical power to the load, and the bound charge turns into available charge at a certain rate [28,29]. The KiBaM is expressed using two differential equations, and through appropriate discretization, these differential equations can be transformed into five sub-models, each of which have explicit meanings and are easy to calculate. Another important issue with this type of battery is the estimation of the state of charge (SOC) and state of health (SOH) of lead acid batteries. Details of how to calculate the SOC and SOH can be found in the literature [29,30]. A thermal storage tank is used for space heating and space cooling, while a hot water tank is used to supply hot water. In most cases, the thermal tank is a water-based system, and for both tanks, it is often assumed that the heat loss depends on the heat

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Table 1 Comparisons of different batteries. Type

Specific energy (W h kg1)

Specific power (W h l1)

Efficiency (%)

Life span (Year)

cycle numbers

Lead-Acid Nickel–Cadmium Nickel–Metal Hydride Lithium-Ion

2040 3050 4090 90150

50120 100150 150320 230330

8090 6070 8090 95

320 325 25

250500 300700 300600 5001000

radiation from the tank and the total heat [17]. The corresponding parameters are always set on the basis of the manufacturer’s specifications. In some research in the literature, energy losses in thermal storage are considered to be negligible [15,31]. 2.3. Modeling of loads Accurate load modeling is vitally important to both optimal system planning and optimal energy management, because customer demand is a prerequisite for system planning and energy management. In the load modeling process, the expected maximum loads, load profiles and yearly energy demands are all divided into heat, cold and electricity loads, which are important input parameters to provide the most economically, technically and environmentally optimal energy distribution system for each planning area. Energy consumption by the building energy sector is increasingly high, and numerous studies of building load modeling have been carried out. Building load modeling can be established by either physical or statistical methods [32]. Physical models are based on the analysis of physical processes such as heat transfer, while statistical models rely on a survey of the more apparent end effects without much emphasis on the detail of the processes. For example, a statistic analysis of residential, commercial and industrial consumers load profiles was presented in [33]. Crawley et al. summarized several building energy performance simulation programs that are based on physical reasoning [34]. A diverse range of methods has been used to investigate building-load characteristics, including those found in [35–38]. For example, Pedersen et al. applied piece-wise regression analyses and probability distribution analyses to estimate the hourly measured district heat and electricity consumption in various buildings [39]. Different aspects of the building loads (including the floor area, the number of rooms, and geographical factors) have been studied by many research groups, and numerous studies have been devoted to the particular types of buildings that exist in many countries [40–44]. In the future, customer energy consumption will be affected by real-time energy prices, which should be included in the load modeling. Also, in the CCHP microgrid, customers may also own the renewable energy generation devices themselves, and so the fluctuations in the renewable energy availability also affect the customers’ behavior, which is another factor to be considered when modeling the loads. 2.4. Modeling of renewable energy resources The use of distributed power generation systems, especially those using PV and wind power, is increasing because of the inexhaustible and environmentally friendly nature of renewable energy. Because of the intermittent nature of renewable energy output, many studies have focused on building forecasting models for the PV and wind power output. In general, in the study of dayahead energy management, the output power of PV and wind power systems is modeled by two main methods. The first and most common method is the deterministic model, giving the forecast output power over a certain period. The other method is the

Temperatures (°C) Charging

Discharging

1040 2050 045 040

1550 4550 2060 2060

probabilistic model, which considers the random nature of the PV and wind power sources, by using chance-constrained programming or an expectancy model. Many researchers focused on prediction of the PV power with good accuracy, and most of them used two-stage methods. In the first stage, the solar irradiance is obtained on different time scales by various methods, such as artificial neural networks (ANNs) [45– 47], auto-regression [48], or hybrid methods [49]. In the literature [50,51], a deterministic model based on the Hottel–Liu–Jordan formula is applied to calculate the solar radiation, for which the latitude and longitude are required, along with long term solar data. In the second stage, the forecast irradiance and temperature data are used as inputs for commercial PV simulation software, such as TRNSYS [52], PVFORM [53], and HOMER [54]. Beside these twostage approaches, there is also a single-stage approach, which predicts the PV power directly based on prior information or some readily accessible data [55–57]. The output power of a wind power source is determined by the wind speed, so many methods (including ANNs [58,59], Kalman filtering methods [60,61], and time series methods [62]) have been studied to forecast the wind speed. Unlike the conventional single modeling approaches for wind speed forecasting, the hybrid forecasting approach typically consists of an ARIMA (autoregressive integrated moving average) prediction model for the linear component of a time series and a nonlinear prediction model for the nonlinear component [63,64]. The hybrid forecasting methodology does offer another approach to short-term forecasting, but it does not always produce a better performance under differing conditions [63].

3. Planning of the CCHP microgrid Compared with the traditional CCHP system planning approach, there are many more factors that must be considered for CCHP microgrid planning. In future power systems, the CCHP microgrid will form one part of an IEN, and thus the CCHP microgrid will have more functions, including improvement of the stability of the whole system and provision of auxiliary services. The challenges of planning a CCHP microgrid are not easy to overcome, because many factors affect the planning performance, including the choices of the equipment and control strategies, the fluctuations of the renewable energy sources and the multi-loads, which are illustrated in Fig. 4. The economic benefits (EcB), the environmental benefits (EnB) and the energy utilization efficiency benefits (EUEB) of CCHP microgrids are greatly affected by its design [65]. A reasonable plan should have these merits: adaptability to the diurnal and seasonal load characteristics of multi-loads, smoothing ability for the output power fluctuations of the renewable energy source, multiple energy balancing capability, and ability to improve the overall system efficiency. A large amount of research work has been carried out to date on CCHP system planning, which can be classified into two categories: (1) evaluation methods and indicators for the CCHP microgrid; and (2) planning methods for the CCHP microgrid.

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Fig. 4. Factors in the planning of CCHP microgrid.

3.1. Evaluation methods and indicators As a result of the increasing variety of distributed equipment types, the evaluation of the CCHP microgrid is becoming a critical issue and requires considerable effort to establish the evaluation methods and indicators, which can assess the CCHP microgrid performance in terms of economic benefits, energy use efficiency and environmental benefits. The concept of energy saving is widely adopted in the evaluation of single power plant configurations. Many methods and indicators have been proposed, including the energy utilization factor (EUF), the artificial thermal efficiency (ATE), the fuel energy saving ratio (FESR) and the exergy efficiency (ExEff) [66–68]. It should be emphasized that each of these four indicators only considers one particular aspect of the CCHP microgrid. Although the FESR and ExEff, which consider the different values of the energy flows, might produce a better performance than the other parameters, all four of these parameters are inappropriate for selection of the best plant [66,69]. Different countries have developed various indicators based on the requirements for their practical applications [70], e.g., Deliberation No. 42/02 of the Italian AEEG (2002), and the European Directive 2004/8/EC (2004) for the establishment of a common framework for regulation of cogeneration at a continental level. Apart from the four indicators mentioned above, there are four more indicators, for which preferences have been expressed in the literature [65,71–76]. The performances of alternative systems (ASs), including the CCHP, are usually compared with those of a conventional energy system (CS), including the electric grid, gas boilers, and electric heat pumps (EHPs), based on separate energy ‘‘production’’. Both the alternative and conventional systems must satisfy the electrical and thermal requirements of the users (e.g. space heating and cooling, domestic hot water). In the following four equations, ‘‘AS’’ and ‘‘CS’’ will be used as subscripts to represent these two systems. (1) The primary energy ratio (PER) is defined as the ratio of the required electricity and thermal output (Eel, Eth) to the primary energy demand (Ep).

PER ¼

Eel þ Eth EP

ð1Þ

(2) The primary energy saving (PES) is defined as the ratio of the energy saved by the CCHP system over the conventional system to the actual energy consumption of the conventional system.

PES ¼

Ep;CS  Ep;AS Ep;CS

ð2Þ

(3) The carbon dioxide emission reduction (CDER) is defined as the ratio of the reduction in CO2 emission from the CCHP system over the conventional system to the CO2 emission from the conventional system.

CO2;CS  CO2;AS CO2;CS

DCO2 ¼

ð3Þ

(4) The annual total cost saving (ATCS) is defined as the ratio of the annual cost saving of the CCHP system in comparison to the conventional system to the annual cost of the conventional system.

ATCS ¼

ATCCS  ATCAS ATCCS

ð4Þ

The annual total cost, which includes the annual capital cost of equipment (Ce) and the annual energy charge (Cm), is calculated as follows:

ATC ¼ C e þ C m

ð5Þ

However, each of the above indicators can only describe one aspect of the system, and none of them is perfect for evaluation of the performance of the CCHP system. There is also a trend where environmental policies and in particular cost-internalizing actions are being empowered to favor improvements in the conversion efficiencies and promote low environmental impact technologies. A comprehensive integration of these indicators may thus be necessary.

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Along with the above indicators, the reliability assessment must also be considered during the system design. Until now, only a few studies in the literature have investigated the reliability of CHP systems. In [77], the cogeneration reliability model was based on the state space approach and the continuous Markov method with electricity generation, fuel distribution and heat generation subsystems. Mean-time-to-failure indices based on the interactions between the subsystems, from the generation site to consumer delivery, were proposed. In the literature [78], a Monte-Carlo simulation has also been applied to assess CHP system reliability. In Ref. [79], optimal deployment with respect to the locations, capacity sizes, and types of DER were studied, while also considering the reliable supply and quality of both power and heat for the customers. The reliability assessment of the CCHP microgrid is even more complex than that of the CHP systems, and few reports can be found that address this issue. We shall therefore present more studies to establish reliability assessment indicators and methods for the CCHP microgrid. 3.2. Planning methods for the CCHP microgrid Electricity, cold and heat demands fluctuate both daily and seasonally. Therefore, taking operational strategies into account during the design stage should prove beneficial for the candidate CCHP system. Consequently, a robust technique for sizing and identification of the optimum operational strategies for such systems is needed. There are several methods that can be used to size energy systems and optimize their operational strategies, such as

Loads Demand

Operation Objectives

the maximum rectangle method (MR), linear programming (LP), non-linear programming (NLP), mixed-integer non-linear programming (MINLP), fuzzy logic (FL), and genetic algorithms (GA). These methods are also applied in the planning of CCHP systems. For instance, Shaneb et al. developed a generic deterministic LP model to minimize the expected annual cost of the system, which would be capable of determining the optimal size (electrical rating) of a micro CHP unit and the optimal size (thermal rating) of a backup heater for any given residential demand, regardless of the type of cogeneration technology [80]. Kavvadias et al. addressed the problem of optimal trigeneration plant design, discussed the factors that affect the operation and the feasibility of the investment, and analyzed the effects of various operational parameters and energy tariff structures [81]. Sanaye and Ardali applied energy-economic analysis to select the type and number of the required prime movers for specific load curves and proposed a method to determine the operational strategy and the payback period of the system [82]. Commercial software for CCHP system planning is also available. Weber and Shah introduced the District Energy System Design and Optimization (DESDOP) tool, based on mixed integer linear optimization techniques, with the aim of lowering emissions while simultaneously guaranteeing the resilience of the supply [83]. RETscreen has developed an Excel-based clean energy project analysis software tool that helps decision makers to determine the technical and financial viability of potential renewable energy, energy efficiency and cogeneration projects quickly and inexpensively. However, this software does not include complicated tariff schemes, thermal storage options, or trigeneration

Weather Forecasting

Energy Management

Characteristics of Equipment

Maintenance Schedule

Interaction with Main Grid

Real-time Energy Price

Optimal Operation Schedule Fig. 5. Energy management of the CCHP microgrid.

W. Gu et al. / Electrical Power and Energy Systems 54 (2014) 26–37

operational strategies. Building energy simulation tools, such as TRNSYS, can also assist in the design of CCHP systems [84]. As increasing numbers of renewable energy sources will be used in microgrids, studies have been conducted to study the effects of the integration of renewable energy power technologies in cogeneration systems [85,86]. Lund and Clark studied the effects of the integration of large scale wind turbines on the operating strategy of back-up thermal power plants in Denmark [86]. The strategic selection of DERs’ optimal locations, optimal sizes and optimal technologies within the CHP microgrid was considered in [87]. They showed that the introduction of the renewable energy sources means that thermal plants will have to follow new operating strategies in the future. Compared with the traditional CCHP system, the CCHP microgrid will play an active role in future IENs, including interaction with the main grid, responding to demands, and providing auxiliary services. The CCHP microgrid also emphasizes the integration of renewable energy sources. The randomness of the renewable energy sources causes great difficulty in the planning of CCHP microgrids, and is a hot topic in microgrid design research [88– 91]. The multi-energy balancing requirements of the CCHP system and the numerous device choices make CCHP microgrid design even more complicated. Given the importance of proper planning of the system, further research is needed. 4. Energy management of the CCHP microgrid To a great extent, the CCHP microgrid performance largely depends on the energy management. Considering the complexity of the CCHP microgrid, the energy management is not an easy task, and consists of energy coupling (cold, heat, and electricity), multiple operational objectives (running costs, energy efficiency, emissions) and multiple time scales (short term and long term). As Fig. 5 shows, many information sources including weather forecasts, equipment characteristics, real-time energy prices, and operational objectives must be considered to produce an optimal operation schedule under the actual operating conditions. Also, because the microgrid will be a vital part of an integrated energy system, the energy management of the CCHP microgrid will form an integral part of the management of the entire community energy system. The energy management of the CCHP system is a hot research topic, and there are many studies that focus on different aspects of the optimal energy management of the CCHP microgrid. In this section, both the energy management models and the solution methods are discussed. 4.1. Optimization of CCHP microgrid energy management The purpose of CCHP microgrid energy management is to find the optimal allocation of the power among the online units of the system to meet certain operational objectives, while meeting the demand, after the unit commitment is performed. The economic dispatch of the CCHP microgrid is the most widely considered objective in day-ahead energy management optimization problems. For example, Kong et al. presented a basic linear programming model to determine the optimum energy combination for a CCHP system with a gas turbine [92]. El-Sharkh et al. considered a short-term scheduling scheme for multiple grid-parallel PEM fuel cell power plants (FCPPs) and formed a cost-based optimization problem [8]. It can be seen from these studies that introduction of the structure of the specific CCHP system is the first common step toward establishment of the economic dispatch model, followed by the modeling of each device and formation of the cost objective. The corresponding optimization algorithm and the case studies are then presented. Finally, in some studies

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[77,93–97], a sensitivity analysis or parametric analysis of the control variables or of some other factors (e.g. loads, renewable energy errors, fuel prices) would also be conducted. Another common point in these studies is that the optimization result is simply an optimal solution of the established optimization problem, with the given loads and renewable energy output. Therefore, when the loads and the renewable energy output deviate from the forecast values, the optimization results might not be sufficiently flexible to accommodate these deviations. To deal with this dilemma, operational strategies or modes are proposed in the literature. Two of the simplest operational modes are to run the prime mover in accordance with either the electrical or the thermal demand. Cardona and Piacentino refer to these two styles as electric demand management (EDM) and thermal demand management (TDM) [66]. The choice between EDM and TDM is determined by several factors, including the loading of the prime mover, the ability to store any extra energy via the storage system, and the price of the fuel versus that of electricity purchased from the main grid. In Ref. [98], a non-dimensional analysis of the energy costs and the primary energy consumption (PEC) of CCHP systems using gas fired micro-turbines was presented together with a comparison of the two main operational modes (following the electric load (FEL) and following the thermal load (FTL)). Fang et al. [99] proposed a comprehensive indicator and an integrated performance criterion (IPC), which was a weighted value of the PEC, the carbon dioxide emission (CDE) and the operational cost (COST). Based on the IPC, an optimal operational strategy for the CCHP system based on the two typical operating modes (FEL and FTL) was presented. Apart from the EDM and TDM operating modes, there are three more common operating modes, which are summarized as follows [65,81,100,101]: (1) Continuous operation: the system operates for a predetermined time at maximum power. This applies to certain types of engine which are not allowed to operate on partial load. (2) Peak saving: the system operates for a limited amount of time to cover a predetermined part of the load during peak electricity conditions. The peak power purchased from the grid is thus reduced. (3) Base load operation: the system is designed to cover a constant amount of the electrical load. This approach is used when larger groups have a poor dynamic performance. Increasing emphasis is currently being placed on reduction of the emissions from power generation, so the environmental constraints/costs must also be included in the power dispatch problem. Many studies focus on the environmental dispatch to minimize the emission of gases such as CO2, SO2 and NOx [76,102–107]. There are two main methods used to deal with emissions reduction. The first is to introduce the emissions as a constrained dispatch [105]; the other is to introduce a new objective, form multi-objective functions, and then use trade-offs with the cost objective functions [104]. Environmental concerns normally lead to priority dispatch and thus to increased penetration of renewable energy sources into the traditional electric power networks, under the government’s high feed-in tariff for renewable energy. Another difficult issue with CCHP microgrid energy management is the random nature of the loads and the renewable energy [108]. Under these conditions, the uncertainties of the stochastic parameters (wind power, solar radiation and loads) cannot be expressed by using the deterministic models of the power system applications. Most current studies ignore or simplify the randomness, and simply use the predicted value, which may deviate considerably from the actual value and lead to low effectiveness in the results. Also, some researchers have studied the uncertainty of the cogeneration system. The expec-

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tancy model is commonly used to deal with stochastic problems, and the expected value of the objective is calculated by analyzing the probability distribution of the stochastic variable or by sampling the stochastic variable for enough times. For example, the stochastic multi-objective model for cogeneration dispatch is formulated to minimize the expected production costs, the expected power generation deviation, and the expected heat generation deviation [105,109]. Another method that is commonly used to deal with uncertainty problems is the application of chance-constrained programming (CCP), which was applied in several studies [78,108,110–112]. For CCP models, the main difficulty is that the optimal decisions have to be made ahead of the realization of random parameters. So, it is hardly to ensure that constraints with random parameters could be definitely satisfied in all conditions, and constraints might be violated in some cases. Such constraint violation can be considered by some compensation decisions in a second stage, which leads to the two-stage or multi-stage stochastic programs [113]. In summary, CCHP microgrid energy management raises several major challenges, and more attention should be focused on the following aspects. First, the uncertainties of the renewable energy sources have a considerable effect on energy management, and improvement of the forecasting accuracy for the renewable energy resources in different time scales is one important task. Another method to reduce the effects of the fluctuations induced by the renewable energy sources and the loads is the development of operational strategies. For instance, renewable energy is given a high priority to produce hydrogen or to simply be stored by the storage system, so the intermittent nature of the renewable energy source can be mitigated. Third, as mentioned above, the multiple time scales of energy management (e.g. day-ahead, 15-min, 1 min) should be combined to form a comprehensive time scale, which could then be adapted to suit the complexity of the CCHP microgrid. 4.2. Methods to solve the CCHP microgrid energy management model The CCHP microgrid energy management model is complex, with the uncertainties of the load and the renewable energy source, the variability of the operating modes, multiple operational objectives, and the instantaneity of the control variables; all of this increases the difficulty of solving this optimization problem. Many methods have been proposed to deal with this problem, and they are reviewed here as follows. The energy management models of CCHP microgrid vary quite widely in the literature, and thus various methods are proposed to solve the different models. For instance, the LP model was applied in the literature [92,114] to study the economic dispatch of the cogeneration system. In Ref. [114], with the aim of reducing the difficulty of solving the problem, the LP model was divided into two subsystems: the electrical subsystem, and the steam subsystem. The two subsystems were solved separately, with the solutions being coordinated to achieve optimality in the combined system. The methods used to decompose the cogeneration system economic dispatch (ED) problem into two sub-problems are also presented in the literature [115,116], where the combination of the two sub-problems gives the heat-power constraints of the cogeneration units. The two-layer algorithm was proposed, in which the outer layer uses a Lagrangian relaxation (LR) technique to solve for the power dispatch, and the inner layer uses the gradient searching method to solve for the heat dispatch with the unit heat capacities passed by the outer layer. In addition to the LP methods, many intelligent algorithms are also applied in this problem. For example, El-Sharkh et al. proposed a hybrid technique based on evolutionary programming (EP) and hill climbing (HC) techniques to deal with the short term scheduling problems of

multiple grid-parallel PEM FCPPs, where EP was used to search for the optimal solution, while the HC technique was used to monitor the feasibility of the solution [8]. The novel direct search approach was proposed to deal with the mutual dependency of multiple-demand and heat-power capacity of cogeneration units [117]. Some other algorithms are also used to solve the cogeneration ED problems, including GAs [118,119], the colony search algorithm [120], a hybrid of a GA and the tabu search algorithm [121], the artificial immune system algorithm [122], the harmony search algorithm [123], the particle swarm optimization algorithm [105,109,124], and the non-dominated sorting genetic algorithmII(NSGA-II) [125,126]. It is difficult to determine which algorithm produces the best performance, because each algorithm is particularly suitable for certain types of models or problems. Because the energy management problem of the CCHP microgrid consists of random variables (renewable energy, loads), several objectives (e.g. operational costs, emission reduction) and mixed binary and continuous variables, greater efforts should be made to improve the flexibility and stability of these algorithms, while simultaneously reducing the computation time to satisfy the application requirements. 4.3. Perspectives of CCHP microgrid energy management It is foreseeable that interactions between multiple CCHP microgrids in a single distribution network will improve the network’s ability to achieve higher instantaneous efficiency and lower emission levels; at the same time, however, methods to coordinate the energy management of multiple CCHP microgrids and the distribution network require further research. Also, in the future development of the power grid, the distribution grid will develop into an IEN, which not only includes the bi-directional flow of single electrical energy, but also consists of the bi-directional flow of some other energy types (heat, hydrogen, and others). As a vital part of the IEN, the energy management of the CCHP microgrid should be integrated into the energy dispatch of the whole IEN, and will act as a basic but crucial level of the multilevel energy management of the IEN. Another great challenge in future energy scenarios is the process of vehicle electrification, which is likely to bring significant change to energy management patterns [127,128]. Electric drive vehicles, whether battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), plug-in hybrid vehicles (PHEVs), or plug-in electric vehicles (PEVs), could be connected to the grid not only to be recharged by filling up their traction batteries but also to give energy back to the grid, which is the meaning of ‘‘vehicle to grid’’, or V2G. Another new concept is ‘‘vehicle to building’’ or V2B, which enables electric vehicles to feed power back to the charging facility into which they are plugged. As CCHP systems are widely applied in various buildings, the implementation of V2B will definitely lead to variations in the system energy management. 5. Conclusions The increasing problems of energy shortages and environmental concerns have boosted the development of the CCHP microgrid. In the face of likely widespread development of CCHP microgrids, there is an urgent need to improve the available technical skills for design and operation of the system to realize energy savings, environmental protection, and economical operation. In this context, this paper presents an overview of the modeling, planning and energy management of CCHP microgrid systems. First, the static models used for the distributed units in the CCHP microgrid are not fully satisfactory for application to microgrid planning and energy management for the following reasons:

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(1) more operational data and experimental results need to be collected to study the characteristics of the prime movers fully; (2) the modeling of the storage system must balance the accuracy and the complexity of the model; (3) real-time energy prices and the renewable energy devices affect the modeling of multiple loads, especially from the electricity viewpoint; (4) while singlestage, two-stage or hybrid approaches can be used, it is essential that the forecasting accuracy of the renewable energy is increased for different time scales (day-ahead, short term, long term) to enable accurate modeling of the renewable energy resources. Second, many methods and indicators have been applied to evaluate the CCHP microgrid from the perspectives of energy usage, operating costs, and emission reduction. However, research efforts into the reliability assessment of the CCHP microgrid are both rare and insufficient, and must be strengthened. There is a tendency for comprehensive evaluation of the CCHP microgrid to provide greater flexibility for its planning and energy management. Further, as the penetration of renewable energy into conventional power systems is rising dramatically, the problem of how to integrate a renewable energy source into the CCHP microgrid becomes important when planning the structure and size of the system. Finally, the energy management of the CCHP microgrid is complex because of the coupling relationships among the three kinds of energies. The uncertainties of the renewable energy resources and greater interaction with the main grid make the energy management even more difficult, especially when the proportion of the renewable energy is relatively high. The real-time control strategies and day-ahead optimal scheduling should be combined to establish a robust energy management method that provides good performance even when the forecast renewable energy value deviates significantly from the real value. The emergence of new concepts, such as IEN, V2G, and V2B, will also definitely enrich the energy management requirements of the CCHP microgrid. The interactions of the CCHP microgrid with the main grid, the consumers, and electric vehicles are likely to become hot research topics. Acknowledgments This work was supported by the National High Technology Research and Development Program of China (863 Program, Grant No. 2011AA05A107), the National Science Foundation of China (Grant No. 51277027), the Natural Science Foundation of Jiangsu Province of China (Grant No. SBK201122387), the Science and Technology Foundation of State Grid Corporation (Energy Efficiency Standards System and Test Method Research for Distributed Energy Using Natural Gas). References [1] Orecchini F, Santiangeli A. Beyond smart grids – The need of intelligent energy networks for a higher global efficiency through energy vectors integration. Int J Hydrog Energy 2011;36:8126–33. [2] Wu DW, Wang RZ. Combined cooling, heating and power: a review. Prog Energy Combust Sci 2006;32:459–95. [3] Lin L, Wang YD, Al-Shemmeri T, Ruxton T, Turner S, Zeng SC, et al. An experimental investigation of a household size trigeneration. Appl Therm Eng 2007;27:576–85. [4] Angrisani G, Rosato A, Roselli C, Sasso M, Sibilio S. Experimental results of a micro-trigeneration installation. Appl Therm Eng 2012;38:78–90. [5] Maidment GG, Zhao X, Riffat SB, Prosser G. Application of combined heat-andpower and absorption cooling in a supermarket. Appl Energy 1999;63:169–90. [6] Tse LKC, Wilkins S, McGlashan N, Urban B, Martinez-Botas R. Solid oxide fuel cell/gas turbine trigeneration system for marine applications. J Power Sources 2011;196:3149–62. [7] Maeda K, Masumoto K, Hayano A. A study on energy saving in residential PEFC cogeneration systems. J Power Sources 2010;195:3779–84. [8] El-Sharkh MY, Rahman A, Alam MS. Short term scheduling of multiple gridparallel PEM fuel cells for microgrid applications. Int J Hydrog Energy 2010;35:11099–106.

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