- Email: [email protected]
Optimal sizing of renewable hybrids energy systems: A review of methodologies
Available online at www.sciencedirect.com
Solar Energy 86 (2012) 1077–1088 www.elsevier.com/locate/solener
Optimal sizing of renewable hybrids energy systems: A review of methodologies R. Luna-Rubio a,⇑, M. Trejo-Perea b, D. Vargas-Va´zquez b, G.J. Rı´os-Moreno b a
Divisio´n de Investigacio´n y Posgrado, Facultad de Ingenierı´a, Universidad Auto´noma de Quere´taro, Cerro de las Campanas s/n, C.P. 76010 Santiago de Quere´taro, Qro., Mexico b Departamento de Edificios Inteligentes, Facultad de Ingenierı´a, Universidad Auto´noma de Quere´taro, Cerro de las Campanas s/n, C.P. 76010 Santiago de Quere´taro, Qro., Mexico Available online 3 December 2011 Communicated by: Associate Editor C. Estrada-Gasca
Abstract Taking into account oil depletion, increasing population, and increasing energy demand, electrical power generation has entered into a new phase of evolution, which can be characterized mainly by increasing concerns about climate change, by a transition from a hydrocarbon-based economy, and by an efficient utilization of energy. In this sense, it seems that alternative energies have gathered considerable momentum since 1970s oil crisis. Moreover, Earth seems to have enough power to cover World’s electrical power demand but not by a single source; for this reason, recent researches have been carried out in order to design in an optimal way system’s configuration. Nevertheless, because of the randomized nature of alternative energy sources, electrical load profile, as well as the non-linear response of system components, to mention a few, is not an easy to assess the hybrid energy system performance; therefore, hybrid energy system designing has been a complex task. For this reason, the aim of this paper is to present a brief review about the sizing methodologies developed in the recent years. Ó 2011 Elsevier Ltd. All rights reserved. Keywords: Hybrid energy systems; Design; Sizing methods; Optimization
1. Introduction Energy is a vital factor for social and economic development of any country. Nowadays 80% of worldwide energy demand is met by means of fossil fuels (MullerFurstenberger and Wagner, 2007). Taking into account oil depletion, increasing population, and increasing energy demand, electrical power generation has entered in a new evolution phase, which can be characterized mainly by increasing concerns about climate changes, by a transition from a hydrocarbon-based economy, as well as by an efficient utilization of energy (Al-Saleh, 2009; Roth et al., 2009; Eastin et al., 2010; Baza´n-Perkins and Ferna´ndez-
⇑ Corresponding author. Tel.: +52 442 192 12 00x6015.
E-mail address: [email protected] (R. Luna-Rubio). 0038-092X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.solener.2011.10.016
Zayas, 2008; Bouffard and Kirschen, 2008; Matutinovic, 2009; Midilli et al., 2006). Recently, in order to reach sustainable development, humankind needs to be steady on the path of low-carbon society (Sioshansi, 2010; Kammen, 2010). For this reason, in order to make an efficient use of electrical energy there is a growing interest in optimizing the design of urban settlements by means of the exploitation of natural sources of energy (i.e. solar and wind energy) and the development of building management systems (Gugliermetti and Bisegna, 2006; Kampf and Robinson, 2009; Kolokotsa et al., 2011; Luna-Rubio et al., 2009; di Stefano, 2000; Wong et al., 2005, 2008). Additionally, electrical power nets are in a transition stage where these need to be more flexible and dynamical at all levels, from power generation plant to customer level in order to enable distributed generation (mainly based on renew-
1078
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Nomenclature AC alternating current (VCA) ACS annualized cost of system BLR battery to load ratio Cinvestment total initial investment cost CRF capital recovery factor DC direct current (VCD) DE deficit energy EENS expected energy not supplied ELR energy to load ratio Etot annual total energy fPH(Ph) probability density function for the hybrid energy power output GA genetic algorithms GC grid connected GHG green house gas HLOL hours which loss of load occurs (h) Htot total hours operation system (h) I current (A) Ibat(t) charging current at hour, t (A) L electrical load LCE levelized cost of energy LOL loss of load LPSP loss of power supply probability NPV net present value NPVend_k discounted present values of incomes from the residual value of the component k at the end of the lifetime of the hybrid energy system discounted present costs of future costs
able sources), to promote efficient use of energy at customer level, and to reach an intelligent demand response (Castillo-Cagigal et al., 2011; Ja¨rventausta et al., 2010). The generation of electrical energy through of alternative sources such wind and solar, has become more attractive (Kaldellis and Zafirakis, 2011; Michalak and Zimny, 2011; Razykov et al., 2011; Solangi et al., 2011) and is widely used for substituting fossil fuels in the process of electrical power energy since 1970s because of the crisis oil (Cancino-Solo´rzano et al., 2010; Jebaraj and Iniyan, 2006; Omer, 2008); nevertheless, such alternative energy sources have a slow development (Devezas et al., 2008; Sovacool, 2010), and the transition into a new phase of evolution in the electrical power generation sector appears to be a complex task because of the different insights of the problem (Floyd and Zubevich, 2010; Vasileiadou and Safarzynska, 2010; Xu et al., 2006), not only due to environmental, and economic issues, also because of social and psychological impacts on people’s behavior (Hondo and Baba, 2010). Technology for power production from alternative energy sources has experimented a considerable development (Michalak and Zimny, 2011; Razykov et al., 2011).
NPVO&M_k discounted present costs of future costs of operation and maintenance of component k throughout the life of the system NPVr_k are the discounted present cost of future costs of replacing the components throughout the life of the system NPVsale_k discounted present values of incomes from the sale k PDF probability density function Ph power generated by hybrid energy systems Phmax maximum power generated by the hybrid energy system (W) Phmin minimum power generated by the hybrid energy system (W) Pload load power (W) PSO particle swarm optimization PV photovoltaic SA stand-alone SA system’s autonomy SOC battery state of charge (A h) T time (h) TAC total annualized cost TMY typical meteorological year r self-discharge rate of battery bank, percentage g(Ibat(t)) charging current efficiency of the battery bank with the charging current at hour, t Dt sample period (usually 1 h) (h)
Moreover, a new tendency to increase the efficiency and reliability of electrical power nets in alternative energy sources include the use of information and communication technology (ICT), in this way electrical utility is improved by becoming more dynamical and flexible and promote the distributed energy storage (Rahimi and Ipakchi, 2010). Nowadays, Earth appears to have enough power to cover World’s electrical power demand (15 TW) with the current technology (Cho, 2010). Nowadays, it is technically possible to extract 1.6 TW form hydro, 3.8 TW from geothermal, 9 TW from biomass, 20 TW from wind, and more than 50 TW from solar power (Cho, 2010). However, due to the stochastic nature in some of those alternative energies (e.g. solar and wind energy), the transition to low-carbon society will require of a non-single solution. Because the aforementioned, in order to displace fossil fuels by means of alternative energy sources, most of experts foresee that it will be necessary an integration of multiple energy sources working together (hydro, geothermal, biomass, wind, solar, hydrogen, nuclear, and fossil fuels) at bulk energy generation and customer level into decentralized energy systems (Devezas et al., 2008; Moslehi and Kumar, 2010; Kaundinya et al., 2009).
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
When an energy system includes two or more energy sources is named hybrid; it is broadly known that hybrid energy systems (which include alternative energy sources) sometimes present lower costs and higher reliability than systems which use only one source of alternative energy (Dalton et al., 2009; Ylmaz et al., 2008). Nevertheless, a proper sizing of every single component in the hybrid energy system is a key factor for its techno-economical feasibility. Therefore, the penetration of renewable sources into the energy market depends mainly on the sizing methodologies used for designing this kind of hybrid energy systems in an optimal way. These optimization problems refer to the choice of the “best” set of system’s components from a search space or set of possible solutions (i.e. set of possible components and their size in the hybrid energy system); this involves selecting one or more optimization problem, choosing an objective function and identifying a set of constraints. The objective function and the constraints must all be functions of one or more optimization variables. Additionally, the problem increases its complexity due to non-linear characteristic response in system’s components, stochastic availability in some renewable sources (e.g. solar or wind), and the number of the design constraints and optimization variables. In order to make the system economically feasible and technically reliable, the designing process of these energy systems is solidly based on an optimization stage. Because the aforementioned, along the last years it has been developed a large number of optimization methodologies applied to design process of hybrid energy systems. The simplest sizing methodologies can be those which are based in average values of weather variables (Celik, 2003; Morgan, 1996; Protogeropoulos et al., 1997) or the worst scenario (e.g. the month with lower solar or wind availability); however, designs obtained by this kind of methodologies trends to be oversized because of the worst case has a low occurrence probability or the average value is not a constant value all the time. In this sense, usually energy system performance is assessed using long time series of weather or electrical load profile records (commonly along 1 year of data records with 1 h sampling period) increasing in this way calculus complexity. In order to deal with the multi-objective functions, non-linear characteristic response of system’s components, as well as long time series of weather variables, several mathematical tools have been proposed such as probabilistic approaches (Celik, 2003; Tina, 2006; Yang et al., 2003), artificial neural networks (ANNs) (Mellit et al., 2005a, 2007), genetic algorithms (GAs) (Dufo-Lo´pez and Bernal-Agustı´n, 2005; Kaldellis et al., 2009; Yang et al., 2009), or particle swarm optimization (PSO) (Hakimi and Moghaddas-Tafreshi, 2009; Kornelakis, 2010) to mention a few. The aim of this paper is to give a brief overview of the current state of methodologies used to size hybrid energy systems, with energy storage components for both standalone (SA) and grid connected (GC) architectures.
1079
The present paper is structured in five additional sections. Section 2 provides general information about hybrid energy systems. Section 3 presents a summary of indicators used in order to assess the energy system performance. Section 4 includes the hybrid energy system sizing methodologies state of the art. Section 5 presents a brief summary and findings of the methodologies reviewed. Finally, Section 6 summarizes the conclusions and discusses issues for future research. 2. Hybrid energy system architectures In general, a hybrid energy system could be integrated as Fig. 1 shows. This kind of energy systems are called “hybrid” because they include more than one energy source in order to cover a determined electrical load, commonly an AC load; however, it may also supply a DC load or both at the same time. Energy sources may be alternative (i.e. renewable) as well as conventional (i.e. electrical grid or diesel generator), or energy storage components (i.e. battery bank or fuel cells); in this way, weakness of some energy sources is complemented by strengths of the other sources in a natural or controlled way. To illustrate that, despite the unpredictable availability of some alternative energy sources (like solar and wind), usually, they present complementary patterns (Tina and Gagliano, 2010; Yang et al., 2003). Hybrid energy systems can operate either in presence of grid (grid-connected) where the main priority of the system is to cater the local energy demand and occasionally to feed the grid with any energy surplus, or as a stand-alone system for producing energy independently of the grid utility in isolated areas. When a hybrid energy system includes solar or wind energy, most of times is required an auxiliary source of energy (e.g. coming from battery banks, fuel cells, or utility grid), the aforementioned is in order to overcome the stochastic availability of those energies (Celik, 2003). Sometimes, depending on the availability of an energy source, it is necessary to find
ENERGY STORAGE COMPONENTS (BATTERY BANK, AND/OR FUEL CELLS) ALTERNATIVE ENERGY SOURCE 1 DC LOAD ALTERNATIVE ENERGY SOURCE 2
CONTROL UNIT
ALTERNATIVE ENERGY SOURCE N
DC/AC INVERTER
AC LOAD CONVENTIONAL SOURCE OF ENERGY (GRID, AND/OR DIESEL) GENERATORS)
Fig. 1. General hybrid energy system architecture.
1080
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
a balance; in other words, a control unit which decides which energy source will supply the electrical load. In order to assess the performance of the hybrid energy system, there are several indexes used in the last years, some of them are presented in the next section.
CRF ¼
dð1 þ dÞt ; ð1 þ dÞt 1
ð3Þ
where d is a discount rate, and t is the useful lifetime of the energy system.
3. Hybrid energy metrics
3.3. Battery’s state of charge (SOC)
There are several performance indicators for hybrid energy systems, which can help us to assess its reliability and/or feasibility; in this way, designer can size in adequate way the system components. Some of them are described briefly in next.
SOC in batteries is related to the storage energy in the energy system, it can be computed as follows (Yang et al., 2003):
3.1. Loss of power supply probability (LPSP) Due to stochastic solar radiation and wind speed in nature, it is important to assess the energy system reliability. An electrical power system is reliable when it is able to supply enough power to the electrical load during a certain period. One parameter that help us to assess system’s reliability is the LPSP (Diaf et al., 2008; Yang et al., 2009), which, can be defined as the ratio of all energy deficits to the load demand during the considered period PT DEðtÞ LPSP ¼ PT t¼1 ; ð1Þ t¼1 P load ðtÞDt
SOCðt þ 1Þ ¼ SOCðtÞ r þ I bat ðtÞ Dt gðI bat ðtÞÞ
ð4Þ
where r is the self-discharging rate of battery bank, Ibat(t) is the battery’s charging current, Dt is the sampling period, and g(Ibat(t)) is the charging current efficiency. SOC can help in choosing of the energy storage capacity ensuring the constraints about system reliability (DufoLo´pez and Bernal-Agustı´n, 2005; Yang et al., 2003). 3.4. Level of autonomy (LA) LA is defined as one minus the ratio between of the total number of hours in which loss of load (LOL) occurs and the total hours of operation (Celik, 2003): LA ¼ 1
H LOL ; H tot
ð5Þ
where DE(t) represents the deficit energy at hour t. 3.5. Expected energy not supplied (EENS) 3.2. Levelized cost of energy (LCE) The levelized cost of energy can be defined as the constant price per unit of energy that causes the investment to just break even. It is an economic assessment of the energy generated by the energy system used in designing of hybrid energy systems (Diaf et al., 2008; Yang et al., 2007) which includes all the costs over its lifetime. LCE is defined as the ratio of the total annualized cost of the system to the annual electricity delivered by the system; it can be calculated by means of the following expression (Lazou and Papatsoris, 2000): TAC LCE ¼ ; Etot
ð2Þ
where TAC represents the total annualized cost, and Etot the annual total energy. TAC is calculated taking into account the present value of costs (the value on a given date of a future payment or series of future payments, discounted to reflect the time value of money, for a hybrid energy system, the present value of costs can be composed of the initial cost, the present value of maintenance cost, and the present value of replacement cost), and the capital recovery factor (CRF) of the hybrid energy system. The CRF can be defined as (Lazou and Papatsoris, 2000)
EENS is a probabilistic reliability index used in Tina (2006); this indicator measures the expected energy that will not be supplied because of load exceeds availability in the energy system. According to electrical load (L), and power generated by hybrid energy system (Ph), EENS can be calculated as follows: 8 R P hmax > L P hmin P h fPh ðP h ÞdP h L > P hmax > < RL EENSðL;P h Þ ¼ P hmin 6 L 6 P hmax ðL P h Þ fPh ðP h ÞdP h P hmin > > : L < P hmin 0 ð6Þ where Phmax is the maximum power generated by the hybrid energy system, Phmin is the minimum energy generated by the hybrid energy system and is assumed to be 0, and fPh(Ph) is the probability density function for the power output of the hybrid energy system. 3.6. Net present value (NPV) System’s NPV can be calculated by adding the discounted present values of incomes and by subtracting the discounted present costs along the useful lifetime of the system (Dufo-Lo´pez et al., 2009), this value can be defined as follows:
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
NPV ¼
X
X NPVsale k þ NPVend k C investment X X NPCr k NPCO&M k ;
ð7Þ
where NPVsale_k are discounted present values of incomes from the sale k (e.g. electrical energy sold to the grid), NPVend_k are discounted present values of incomes from the residual value of the component k at the end of the lifetime of the hybrid energy system, Cinvestment is the total initial investment cost, NPVr_k are the discounted present cost of future costs of replacing the components throughout the life of the system, NPVO&M_k are the discounted present costs of future costs of operation and maintenance of component k throughout the life of the system. 3.7. Annualized cost of system (ACS) The ACS is composed of the annualized capital cost (Cacap), the annualized replacement cost (Carep), and the annualized maintenance cost (Camain) (Yang et al., 2009). ACS ¼ C acap þ C arep þ C amain ;
ð8Þ
4. Sizing methods Several methodologies had been used in order to design hybrid energy systems. The simplest way to classify them could be done according their complexity level as we can see in Fig. 2.
1081
annual average of monthly system performance, and the second one on the worst monthly scenario. Based on the total system cost, in Protogeropoulos et al. (1997) optimum configuration was PV alone system. Taking as reference (Morgan, 1996; Protogeropoulos et al., 1997), in Celik (2003) those methodologies were compared with a new one called “Scenario 4”. The proposed alternative methodology is an iterative process, where standard deviations of system performance are used to find an optimal system configuration. This alternative methodology leads to optimized techno-economically systems in a similar way that (Morgan, 1996; Protogeropoulos et al., 1997); furthermore, in Celik (2003) was suggested that a third energy source (auxiliary source) should be incorporated to the system instead of increasing the hardware sizes excessively for the worst monthly methodologies previously reported. Another probabilistic approach is suggested in Yang et al. (2003), where authors pointed that in order to obtain an accurate assessment of performance in a hybrid PV– wind energy system, a suitable typical meteorological year ´s (TMY) should be studied and utilized to assess system performance. Finally, a novel probabilistic approach based on convolution technique using probability density function (PDF) in order to assess the long-term performance of hybrid solar–wind power systems (Tina and Gagliano, 2010; Tina, 2006). 4.2. Analytical methods
4.1. Probabilistic methods Probabilistic methods may be the simplest sizing methodologies; however, results obtained by these techniques are not the most suitable to find out the best solution. Usually, they take into account one or two system performance indicators to be optimized in order to size components of the studied system. Table 1 summarizes some examples of probabilistic methods. These methods involve the development of appropriate models for generation and/or load followed by a combination of these models to create a risk model. For instance, in Protogeropoulos et al. (1997) are presented two sizing methods called “Scenario 1” and “Scenario 2” used for sizing autonomous PV–wind hybrid energy systems. Both methods assess systems performance by means of the energy to load ratio (ELR), and battery to load ratio (BLR); however, the first one is based on the
In these methods (Table 2), hybrid energy systems are represented by means of computational models which describe hybrid system size as function of its feasibility. ´ s performance can be assessed for a Consequently, system set of possible system architecture and/or a particular size of components. Best configuration of a hybrid energy system is determined due to a single or a multiple performance index of the systems analyzed. This kind of methodologies allows the designer to simulate the performance of several hybrid system configurations; nevertheless, they need long time series, usually 1 year, of weather variables (solar, and wind) for the simulations. The performance assessment of hybrid system can be carried out by computational models (i.e. commercial software tools and/or numerical approximations of system components). Recently, several computer tools have been developed in order to assess hybrid energy performance, which aids the designer to
Sizing methodologies
Probabilistic
Analytical
Iterative
Fig. 2. Classification of optimization methodologies.
Hybrid
1082
Table 1 Summary of probabilistic methodologies. Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Yang et al. (2003)
Probabilistic
Wind, PV, and battery banks
SA
LPSP, SOC
LPSP
Celik (2003)
Technoeconomic
SA
Cost-per-kW h-electricity (unit cost)
Level of autonomy
Tina and Gagliano (2010)
Probabilistic
Solar, and wind with battery storage Solar and wind
SA
Energy index of reliability which is directly related to EENS and the internal rate of return
A battery bank with an energy storage capacity of 3 days was suitable for ensuring the desired LPSP of 1%, and a LPSP of 0% can be achieved with a battery bank of 5 days storage capacity Author suggests that a third energy source (auxiliary source) be incorporated into the system instead of increasing the hardware sizes excessively The results inform the design of a pre-processing stage for the input of an algorithm that probabilistically optimizes the design of hybrid (solar and wind) power systems
Table 2 Summary of analytical methodologies. Reference
Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Dufo-Lo´pez et al. (2009)
Economic
Solar, wind, and H2
GC
NPV
The land surface taken up by the system and the initial investment cost
Kamel and Dahl (2005) Diaf et al. (2008)
Economic
Solar, wind, and diesel generator
SA
Annualized cost
LPSP
Only in places with a high wind speed rate and with the current prices of the components, the intermittent production of hydrogen could be economically viable if the selling price is a minimum 10€/kg Optimization results show that hybrid systems are less costly than diesel generation from a net present cost perspective
Technoeconomic
Wind, PV, battery banks, and UPS
SA
LCE
LPSP
Technoeconomic
PV and grid
GC
PV electricity cost
Fraction of load met by a PV system
Economic
PV
SA
Embodied energy
Available power from individual units, wind power generation to load ratio, and generationdemand balance
Economic
Solar, wind, and battery banks
SA
Production cost
Mondol et al. (2009) Kaldellis et al. (2009) Khatod et al. (2010)
The results indicate that the hybrid energy system is the best option for all the sites considered in the study. Additionally, it is shown that the LCE depends largely on the renewable energy potential quality The profitability of a grid connected PV system increases if the PV system is sized to reduce excess PV electrical energy feed to grid when the fed-in tariff is lower than electricity buying price The most interesting finding concerns the fact that the contribution of the battery components exceeds 27% of the lifecycle energy requirements, reflecting the difference between GC and SA configurations Results shows that the developed technique require less computational time than Monte Carlo simulation method
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Reference
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
analyze the integration of renewable sources. In a recent and extensive review Connolly et al. (2010), different computational simulation tools of hybrid energy systems were analyzed and compared. According to literature reviewed, a simulation tool broadly used in performance assessment of hybrid energy systems is the Hybrid Optimization Model for Electric Renewable (HOMER), developed by the National Renewable Energy Laboratory (NREL), US, this simulation tool was used in Dalton et al. (2008, 2009), where it is presented a feasibility study for a small to medium-sized tourist accommodations.
Initialization
Constraints evaluation & chromosomes repair
Fitness function evaluation
All generations finished?
Yes
Output & store optimal parameters
No
Selection
4.3. Iterative methods Performance assessment of hybrid energy systems in iterative methodologies is done by means of a recursive process which stops when the best configuration is reached according to design specifications. An iterative method is reported by Ashok (2007) where an optimal hybrid system was obtained among different renewable energy combinations for a rural community, ´ s reliminimizing the total life cycle cost, ensuring system ability. In this work, a numerical algorithm based on QuasiNewton method was used to solve the optimization problem (Rao, 1996). Genetic algorithms (GAs) are stochastic global search and optimization technique bio-inspired in the process of natural evolution species, which is generally robust in finding global optimal solutions in multi-modal and multi-optimization process. Fig. 3 shows a flowchart for the proposed optimization methodology in Kaldellis et al. (2009). In this Battery chargers specs.
1083
PV module specs.
WGs specs.
Batteries specs.
DC/AC Inverters specs.
Select a combination of PV modules, battery charger, WG and DC/AC inverter types. Consumer power requirements. Optimal sizing. No Store the optimal parameters.
Daily irradiation and hourly mean values of temperature and wind speed.
All combinations optimized?.
Yes Select the combination of components whit the lowest cost.
Fig. 3. Flowchart of the proposed optimization methodology (Koutroulis et al., 2006).
Simple crossover
Simple arithmetic Crossover
Whole arithmetic crossover
Crossover operators
Non-uniform mutation
Mutation operators
Constraints evaluation & chromosomes repair
Uniform mutation
Boundary mutation
Constraints evaluation & chromosomes repair
Fig. 4. GA used for optimization process (Koutroulis et al., 2006).
study, the system cost was minimized by means of electric load; according with design constraints, optimal sizing is achieved by means GA process, showed in Fig. 4. The results reported in Kaldellis et al. (2009) show that hybrid PV–wind systems present lower cost than either PV or wind energy system. Similar works can be found in Dufo-Lo´pez and Bernal-Agustı´n (2005) and Yang et al. (2007, 2008, 2009), where PV–wind energy sizing methodologies based on GA were used in order to reach an optimum system configuration. In these cases, optimal configuration was calculated by a required LPSP with a minimum annualized cost of system (ACS) (Yang et al., 2008, 2009), levelized cost of energy (LCE) technique (Yang et al., 2007). Moreover, other stochastic optimization method applied to sizing hybrid energy systems is the called particle swarm optimization (PSO) (Kennedy and Eberhart, 1995). This procedure is inspired by certain social behavior. For a brief introduction to this method, consider a swarm of p particles, where each particle´s position represents a possible solution point in the design problem space D. Every single particle is denoted by its position and speed; in an iterative process, each particle continuously records the best solution thus far during its flight. As an example of optimal sizing of hybrid energy systems by means of PSO, refers to Hakimi and Moghaddas-Tafreshi (2009).
The optimal configurations of the hybrid system are obtained in terms of different desired system reliability requirements and the LCE
Finally, in Ekren and Ekren (2009) was used OptQuest, available on software tool ARENA 12.0 to solve optimization problem. OptQuest is also an iterative heuristic method that combines the meta-heuristics of tabu search, neural networks, and scatter search into a single search. As a summary of iterative methods above mentioned, Table 3 is presented. 4.4. Hybrid methods Due to multidimensional nature in optimization problem, a suitable methodology to deal this problem will be one able to solve multi-objective optimizing based on heuristics methods as GA, neural networks, and tabu search to mention a few. Nevertheless, in hybrid methods results can be improved by using combining optimization techniques. For instance, in Bernal-Agustı´n et al. (2006), Katsigiannis et al. (2010), and Shi et al. (2007), it was modified GA in order to obtain a set of non-dominating Pareto set solutions, which, by means of a selection criteria, the designer can choose the optimal configuration. In Katsigiannis et al. (2010) optimization objective was twofold, to minimize the system’s cost of energy, and greenhouse gas (GHG) emissions during its lifetime by six different design constraints. The main novelty of this work is the assessment of GHG based on life cycle analysis. A similar approach was proposed in Wang and Singh (2009). However, in this work, the set of non-dominated Pareto solutions was obtained by means of a PSO modified algorithm. In this study (see Fig. 5), optimization objectives were twofold (techno-economical), and threefold (technical, economical, and environmental). Artificial neural networks (ANNs) can be considered as simplified mathematical models of brain-like systems because they inspired in biological neural systems operation (Hoppfield, 1982; McCulloch and Pitts, 1943). An
Technoeconomic Yang et al. (2007)
Solar and wind
SA
LPSP
Resource availability and equipment constraints LCE Life cycle cost SA Solar, wind, and hydro Technoeconomic Ashok (2007)
Load energy requirements Total cost Solar and wind Technoeconomic
SA
Total net present cost SA Solar and diesel Technoeconomic
Dufo-Lo´pez and Bernal-Agustı´n (2005) Koutroulis et al. (2006)
SA Solar and wind
Annualized cost
SA
Technoeconomic Technoeconomic
Technoeconomic
Hakimi and MoghaddasTafreshi (2009) Ekren and Ekren (2009) Yang et al. (2009)
Unmet load and battery capacity
The methodology described in this study is especially helpful if there are various auxiliary energy sources or loads This model can be used to calculate the system output configuration which can achieve the desired LPSP with a minimum annualized cost of system This methodology presented finds the optimal SOC set point whether the cycle charging or combined strategies are the optimal ones The simulations results verify that hybrid systems present lower cost compared with the systems with only one renewable energy source The developed model helps in sizing hybrid energy system hardware and in selecting the operating options Auxiliary energy unit cost and electric load LPSP
Stored energy in the hydrogen tank
Capital cost, operations and maintenance cost, efficiency lifetime, and produced wasted Investment cost system SA
Wind, hydrogen, and biomass PV, and, wind
Design constraints Indicators optimized SA– GC Energy sources Study Reference
Table 3 Summary of iterative methodologies.
The hybrid system used has a high reliability because fuel cells are as a backup for wind turbines
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Findings
1084
Fig. 5. Pareto fronts for bi and tri-objective scenarios (Wang and Singh, 2009).
Table 4 Summary of hybrid methodologies. Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Katsigiannis et al. (2010)
Economic and environmental
SA
Cost of energy (COE) and total green house gas (GHG) emissions
Electrical load
The main novelty of this work is that the calculation of GHG emission is based on life cycle analysis of each system’s component
Wang and Singh (2009) BernalAgustı´n et al. (2006) Shi et al. (2007)
Technoeconomic and environmental Technoeconomic and environmental
Solar, wind, diesel, biodiesel, and battery banks (lead acid and fuel cells) Solar, wind, and batteries banks
Total cost, energy index of reliability (EIR), and pollutant emissions (PEs) Total net present cost, and kg CO2 produced by the system throughout 1 year
LPSP, system area
A set of tradeoff solution is obtained using multi-criteria meta-heuristic method that offers many design alternatives to decision marker
Solar, wind, and diesel
SA and GC SA
Unmet load and battery capacity
Technoeconomical
Solar and wind
SA
Hontoria et al. (2005)
Technical
Solar and batteries banks
SA
Total system cost, autonomy level, and wasted energy rate Capacity of the generator
Mellit et al. (2005b) Mellit et al. (2007)
Technical
Solar and batteries banks
SA
Technical
Solar and batteries banks
SA
The design is posed as an optimization problem whose solution allows obtaining the configuration of the system as well as the control strategy that simultaneously minimizes both the total cost through the useful life of system and the pollutant emissions The method presented can handle the optimal design of hybrid energy system effectively and facilitate the designer with a range of the design solutions and the tradeoff information The proposed methodology works without any information of the relationships between the different variables and sources of information studied. The presented method reports an improvement in results respect to an analytical method studied The developed method can estimate the optimal sizing components from a minimum of input data compared The proposed procedure has an advantage compared to the classical models as it can predict the future optimal configuration or sizing of energy system’s components
PV generator area (m2) and useful accumulator capacity PV generator area (m2) and useful accumulator capacity
Accumulator capacity, LOLP, and the clearness index yearly average LPSP LPSP
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Reference
1085
1086
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
ANN is a set of individually interconnected processing units named neurons. Every single interconnection in the network has two values associated with it: an interconnection value, and an interconnection weight. The neuron’s output is a function of the input summed values. Every single interconnection in the network has two values associated with it: an interconnection value, and an interconnection weight. The neuron’s output is a function of the input values summed. Once ANN has been trained or calibrated by means of a recursive algorithm (i.e. interconnection weights are adjusted), new patterns can be presented for prediction or classification purposes (Paliwal and Kumar, 2009). In design of energy systems, ANN has been used in prediction of total daily solar radiation in isolated areas where data records are not always available (Hontoria et al., 2002; Mellit et al., 2005b) or in prediction of the performance of a hybrid energy system (Mellit and Pavan, 2010). In energy systems sizing, ANN has been used for improving the analytical method of loss of load probability (LOLP) curves (Hontoria et al., 2005; Mellit et al., 2005a, 2007) analytical method of the loss of load probability (LOLP) curves proposed by Egido and Lorenzo (1992), as well as combined with GA for improving the design and calibration process of the ANN and then, generating the sizing curve of the power system based in the LOLP concept. Finally, an emerging approach is the well-being hybrid energy performance assessment, which is a robust tool that includes the capacity of energy reserve (deterministic criterion) and the system risk criterion (probabilistic criterion), as example of this approach can be found in Khatod et al. (2010). In order to present a brief summary of hybrid methods above mentioned Table 4 is developed. 5. Summary and findings Sizing methodologies based in average values (e.g. monthly solar radiation) or worst scenarios (e.g. the month with lower solar or wind availability) present a tendency to over-sizing system components because of the worst case has a low occurrence probability or the average value is not a constant value all the time. When a hybrid energy system includes solar or wind energy, most of times adding an auxiliary source of energy (e.g. coming from battery banks, fuel cells, or utility grid) can help in reducing energy costs and ensuring system’s reliability instead of increasing excessively sizing in energy generators components. Bio-inspired methodologies such as GA, ANN, and PSO can require considerable computing processing; however, they can deal with non-linear behavior of systems’ components or stochastic variability in solar or wind energy. These methodologies can deal with a lack of information in data records of weather variables, can be adjusted in real time, can deal with multi-objective optimization functions, and can works without any information
of the relationships between the different variables and sources of information studied. Hybrid optimization methods most of times have been proposed to combine two or more methodologies in order to improve them, for increasing its convergence time in the optimization process. These methodologies can be characterized because its flexibility and dynamic in the sizing process. Therefore, they are the most powerful sizing methodologies.
6. Conclusions This paper summarizes existing research of optimal sizing of renewable hybrids energy systems. Some probabilistic methods involve the development of appropriate models for generation and/or load, followed by a combination of these models to create a risk model; therefore, it can be assessed the long-term performance of hybrid energy system. However, according to the amount of weather data considered and the probabilistic model, results of sizing methodologies may not be accurate. By means of analytical methods, system performance can be assessed in an accurate way. Nevertheless, when multi-objectives function needs to be solved, sometimes these methods do not offer accurate solutions. For this type of problems, iterative and hybrid methods improve multi-objective results. From this brief literature review, it can be observed that hybrid energy systems appears to be a good alternative in order to integrate alternative energy sources in the process of electrical power generation. Almost in all reviewed systems that include more than one alternative and/or conventional energy sources, like diesel generator or conventional grid, it can be noticed that they present better performance than systems which only include one alternative energy source. In order to become reliable, feasible, and/or environmental friendly hybrid energy systems, we can see a current trend in development of multi-objective sizing methodologies; thereby, it is possible to ensure the assessment of different insights of the system performance.
Acknowledgments The first author would like to thank to Consejo Nacional de Ciencia y Tecnologı´a (CONACYT) for Ricardo Luna Rubio Ph.D. scholarship Support No. 230783 as well as Ph.D. Klavdia Oleschko Lutova for her kindly supporting. Additionally, Ricardo Luna Rubio wishes to thank to Ph.D. Irineo Torres Pacheco (I. Torres-Pacheco) for his helpful advises, Ph.D. Moise´s Alejandro Va´zquez Cruz (M.A. Vazquez-Cruz) in Mexico and to Professor Fernanda Amaral do Nascimento in Brazil for their kind help during the writing process, and the respective reviewers for their valuable comments and criticisms.
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
References Al-Saleh, Y., 2009. Renewable energy scenarios for mayor oil-producing nations: the case of Saudi Arabia. Futures 41 (9), 650–662. Ashok, S., 2007. Optimised model for community-based hybrid energy system. Renewable Energy 32 (7), 1155–1164. Baza´n-Perkins, S.D., Ferna´ndez-Zayas, J.L., 2008. Evaluation of Mexico´s 1975–2000 energy plan. Energy Economics 30 (5), 2569–2586. Bernal-Agustı´n, J.L., Rodolfo, Dufo-Lo´pez., Rivas-Ascaso, D., 2006. Design of isolated hybrid systems minimizing costs and pollutant emissions. Renewable Energy 31 (14), 2227–2244. Bouffard, F., Kirschen, D.S., 2008. Centralised and distributed electricity systems. Energy Policy 36 (12), 4504–4508. Cancino-Solo´rzano, Y. et al., 2010. Electricity sector in Mexico: current status. Contribution of renewable energy sources. Renewable and Sustainable Energy Reviews 14 (1), 454–461. Castillo-Cagigal, M. et al., 2011. A semi-distributed electric demand-side management system with PV generation for self-consumption enhancement. Energy Conversion and Management 52 (7), 2659–2666. Celik, A.N., 2003. Techno-economic analysis of autonomous PV–wind hybrid systems using different sizing methods. Energy Conversion and Management 44 (12), 1951–1968. Cho, A., 2010. Energy´s tricky tradeoffs. Science 329 (5993), 786–787. Connolly, D. et al., 2010. A review of computer tools for analysing the integration of renewable energy into various energy systems. Applied Energy 87 (4), 1059–1082. Dalton, G.J., Lockington, D.A., Baldoc, T.E., 2008. Feasibility analysis of stand-alone renewable energy supply options for a large hotel. Renewable Energy 33 (7), 1475–1490. Dalton, G.J., Lockington, D.A., Baldock, T.E., 2009. Case study feasibility of renewable energy supply options for small to mediumsize tourist accommodations. Renewable Energy 34 (4), 1134–1144. Diaf, S. et al., 2008. Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions. Applied Energy 85 (10), 968–987. Devezas, T. et al., 2008. Energy scenarios toward a new energy paradigm. Futures 40 (1), 1–16. di Stefano, J., 2000. Energy efficiency and the environment: the potential for energy efficient lighting to save energy and reduce carbon dioxide emissions at Melbourne University, Australia. Energy 25 (9), 823–839. Dufo-Lo´pez, R., Bernal-Agustı´n, J.L., Mendoza, F., 2009. Design and economical analysis of hybrid PV–wind systems connected to the grid for the intermittent production of hydrogen. Energy Policy 37 (8), 3082–3095. Dufo-Lo´pez, R., Bernal-Agustı´n, J.L., 2005. Design and control strategies of PV–diesel systems using genetic algorithms. Solar Energy 79 (1), 33– 46. Eastin, J., Grundmann, R., Prakash, A., 2010. The two limits debates: “Limits to Growth” and climate change. Futures 43 (1), 16–26. Egido, M.A., Lorenzo, E., 1992. The sizing of stand alone PV systems: a review and proposed method. Solar Energy Materials and Solar Cells 26 (1–2), 51–69. Ekren, B.Y., Ekren, O., 2009. Simulation based size optimization of a PV/ wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Applied Energy 86 (9), 1387–1394. Floyd, J., Zubevich, K., 2010. Linking foresight and sustainability: an integral approach. Futures 42 (1), 59–68. Gugliermetti, F., Bisegna, F., 2006. Daylighting with external shading devices: design and simulation algorithms. Building and Environment 41 (2), 135–149. Hakimi, S.M., Moghaddas-Tafreshi, S.M., 2009. Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran. Renewable Energy 34 (7), 1855– 1862. Hondo, H., Baba, K., 2010. Socio-psychological impacts of the introduction of energy technologies: change in environmental behavior of
1087
households with photovoltaic systems. Applied Energy 87 (1), 229– 235. Hontoria, L., Aguilera, J., Zufiria, P., 2002. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Solar Energy 72 (5), 441–446. Hontoria, L., Aguilera, J., Zufiria, P., 2005. A new approach for sizing stand alone photovoltaic systems based in neural networks. Solar Energy 78 (2), 313–319. Ja¨rventausta, P. et al., 2010. Smart grid power system control in distributed generation environment. Annual Reviews in Control 34 (2), 277–286. Jebaraj, S., Iniyan, S., 2006. A review of energy models. Renewable and Sustainable Energy Reviews 10 (4), 281–311. Kaldellis, J.K., Zafirakis, D., 2011. The wind energy (r)evolution: a short overview of a long history. Renewable Energy 36 (7), 1887–1901. Kaldellis, J.K., Zafirakis, D., Kondili, E., 2009. Optimum autonomous stand-alone photovoltaic system design on the basis of energy payback analysis. Energy 34 (9), 1187–1198. Kamel, S., Dahl, C., 2005. The economics of hybrid power systems for sustainable desert agriculture in Egypt. Energy 30 (8), 1271–1281. Kammen, D.M., 2010. 2020 Visions. Nature 463 (7), 26–32. Kampf, J.H., Robinson, D., 2009. A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential. Applied Soft Computing 9 (2), 738–745. Katsigiannis, Y.A., Georgilakis, P.S., Karapidakis, E.S., 2010. Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power system with renewables. IET Renewable Power Generation 4 (5), 404–419. Kaundinya, D.P., Balachandra, P., Ravindranath, N.H., 2009. Gridconnected versus stand-alone energy systems for decentralized power. Renewable and Sustainable Energy Reviews 13 (8), 2041–2050. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proc. Int. Conf. Neural Netw., pp. 1942–1948. Khatod, D., Kumar, Pant V., Sharma, J., 2010. Analytical approach for well-being assessment of small autonomous power system with solar and wind energy sources. IEEE Transactions on Energy Conversion 25 (2), 535–545. Kolokotsa, D., et al., 2011. A roadmap towards intelligent net zero- and positive-energy buildings. Solar Energy 85 (12), 3067–3084. Kornelakis, A., 2010. Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems. Solar Energy 84 (12), 2022–2033. Koutroulis, E. et al., 2006. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy 80 (9), 1072–1088. Lazou, A.A., Papatsoris, A.D., 2000. The economics of photovoltaic stand-alone residential households: a case study for various European and Mediterranean locations. Solar Energy Materials and Solar Cells 62 (4), 411–427. Luna-Rubio, R. et al., 2009. Lighting automatic control system for intelligent buildings. International Review of Automatic Control 2 (4), 469–476. Matutinovic, I., 2009. Oil and the political economy of energy. Energy Policy 37 (11), 4251–4258. McCulloch, W.S., Pitts, 1943. A logical calculus of the ideas imminent in nervous activity. Bulletin of Mathematical Biophysics 5 (4), 115–133. Hoppfield, J.J., 1982. Neural networks and to physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the Unites States of America 79 (8), 2554–2558. Mellit, A., Pavan, A.M., 2010. Performance prediction of 20 kWp grid connected photovoltaic plant at Trieste (Italy) using artificial neural network. Energy Conversion and Management 51 (12), 2431–2441. Mellit, A., Benghanem, M., Hadj Arab, A., Guessoum, A., 2005a. An adaptative artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria. Renewable Energy 30 (10), 1501–1524.
1088
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Mellit, A., Benghanem, M., Hadj Arab, A., Guessoum, A., 2005b. A simplified model for generating sequences of global radiation for isolated sites: using artificial neural network and a library of transition Markov Matrices. Solar Energy 79 (5), 469–482. Mellit, A., Benghanem, M., Kalogirou, S.A., 2007. Modeling and simulation of a stand-alone photovoltaic system using an adaptative artificial neural network: proposition for a new sizing procedure. Renewable Energy 32 (2), 285–313. Michalak, P., Zimny, J., 2011. Wind energy development in the world, Europe and Poland from 1995 to 2009; current status and future perspectives. Renewable and Sustainable Energy Reviews 15 (5), 2330– 2341. Midilli, A., Dincer, I., Ay, M., 2006. Green energy strategies for sustainable development. Energy Policy 34 (18), 3623–3633. Mondol, J.D., Yohanis, Y.G., Norton, B., 2009. Optimising the economic viability grid-connected photovoltaic systems. Applied Energy 86 (7– 8), 985–999. Morgan, T.R., 1996. The Performance and Optimization of Autonomous Renewable Energy Systems. Division of Mechanical Engineering and Energy Studies, University of Wales, Cardiff: [s.n.] (Chapter 5). Moslehi, K., Kumar, R., 2010. A reliability perspective of the smart grid. Transactions on Smart Grids 1 (1), 57–64. Muller-Furstenberger, G., Wagner, M., 2007. Exploring the environmental Kuznets hypothesis: theoretical and economical problems. Ecological Economics 62 (3–4), 648–660. Omer, A.M., 2008. Energy, environment and sustainable development. Renewable and Sustainable Energy Reviews 12 (9), 2265–2300. Paliwal, M., Kumar, U.A., 2009. Neural networks and statistical techniques: a review of applications. Expert Systems with Applications 36 (1), 2–17. Protogeropoulos, C., Brinkworth, B.J., Marshall, R., 1997. Sizing and techno-economical optimization for hybrid solar PV–wind power systems with battery storage. International Journal of Energy Research 21 (6), 465–479. Rao, S.S., 1996. A text book on Engineering optimization, theory and practice, third ed. Wiley, New York. Rahimi, R., Ipakchi, A., 2010. Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid 1 (1), 82–88. Razykov, T.M., et al., 2011. Solar photovoltaic electricity: Current status and future prospects. Solar Energy 85 (8) 1580–1608. Roth, S. et al., 2009. Sustainability of electricity supply technology portfolio. Annals of Nuclear Energy 36 (3), 409–416. Shi, J.-H., Zhu, X.-J., Cao, G.-Y., 2007. Design and techno-economical optimization for stand-alone hybrid power systems with multi-objec-
tive evolutionary algorithms. International Journal of Energy Research 31 (3), 315–328. Sioshansi, F.P., 2010. Generating Electricity in a Carbon-Constrained World. Academic Press. Solangi, K.H. et al., 2011. A review on global solar energy policy. Renewable and Sustainable Energy Reviews 15 (4), 2149–2163. Sovacool, B.K., 2010. Exploring the hypothetical limits to nuclear and renewable electricity future. International Journal of Energy Research 34 (13), 1183–1194. Tina, G., Gagliano, S., 2010. Probabilistic analysis of weather data for a hybrid solar/wind energy system. International Journal of Energy Research 35 (3), 221–232. Tina, G., 2006. Hybrid solar/wind power system probabilistic model for long-term performance assessment. Solar Energy 80 (5), 578– 588. Vasileiadou, E., Safarzynska, K., 2010. Transitions: taking complexity seriously. Futures 42 (10), 1176–1186. Wang, L., Singh, C., 2009. Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Transactions on Energy Conversion 24 (1), 163–172. Wong, J.K.W., Li, H., Wang, S.W., 2005. Intelligent building research: a review. Automation in Construction 14 (1), 143–159. Wong, J., Li, H., Lai, J., 2008. Evaluating the system intelligence of the intelligent building systems. Part 1: development of key intelligent indicators and conceptual analytical framework. Automation in Construction 17 (3), 284–302. Xu, F.-L. et al., 2006. A triangle model for evaluating the sustainability status and trends of economic sustainability. Ecological Modelling 195 (3–4), 327–337. Yang, H. et al., 2008. Optimal sizing method for stand-alone hybrid solar– wind system with LPSP technology by using genetic algorithm. Solar Energy 82 (4), 354–367. Yang, H., Zhou, W., Lou, C., 2009. Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy 86 (2), 163–169. Yang, H.X., Lu, L., Burnett, J., 2003. Weather data and probability analysis of hybrid photovoltaic-wind power generation systems in Hong Kong. Renewable Energy 28 (11), 1813–1824. Yang, H., Lu, L., Zhou, W., 2007. A novel optimization sizing model for hybrid solar–wind power generation system. Solar Energy 81 (1), 76– 84. Ylmaz, P., Hakan Hocaoglu, M., Konukman, A.E.S., 2008. A prefeasibility case study on integrated resource planning including renewables. Energy Policy 36 (3), 1223–1232.
Solar Energy 86 (2012) 1077–1088 www.elsevier.com/locate/solener
Optimal sizing of renewable hybrids energy systems: A review of methodologies R. Luna-Rubio a,⇑, M. Trejo-Perea b, D. Vargas-Va´zquez b, G.J. Rı´os-Moreno b a
Divisio´n de Investigacio´n y Posgrado, Facultad de Ingenierı´a, Universidad Auto´noma de Quere´taro, Cerro de las Campanas s/n, C.P. 76010 Santiago de Quere´taro, Qro., Mexico b Departamento de Edificios Inteligentes, Facultad de Ingenierı´a, Universidad Auto´noma de Quere´taro, Cerro de las Campanas s/n, C.P. 76010 Santiago de Quere´taro, Qro., Mexico Available online 3 December 2011 Communicated by: Associate Editor C. Estrada-Gasca
Abstract Taking into account oil depletion, increasing population, and increasing energy demand, electrical power generation has entered into a new phase of evolution, which can be characterized mainly by increasing concerns about climate change, by a transition from a hydrocarbon-based economy, and by an efficient utilization of energy. In this sense, it seems that alternative energies have gathered considerable momentum since 1970s oil crisis. Moreover, Earth seems to have enough power to cover World’s electrical power demand but not by a single source; for this reason, recent researches have been carried out in order to design in an optimal way system’s configuration. Nevertheless, because of the randomized nature of alternative energy sources, electrical load profile, as well as the non-linear response of system components, to mention a few, is not an easy to assess the hybrid energy system performance; therefore, hybrid energy system designing has been a complex task. For this reason, the aim of this paper is to present a brief review about the sizing methodologies developed in the recent years. Ó 2011 Elsevier Ltd. All rights reserved. Keywords: Hybrid energy systems; Design; Sizing methods; Optimization
1. Introduction Energy is a vital factor for social and economic development of any country. Nowadays 80% of worldwide energy demand is met by means of fossil fuels (MullerFurstenberger and Wagner, 2007). Taking into account oil depletion, increasing population, and increasing energy demand, electrical power generation has entered in a new evolution phase, which can be characterized mainly by increasing concerns about climate changes, by a transition from a hydrocarbon-based economy, as well as by an efficient utilization of energy (Al-Saleh, 2009; Roth et al., 2009; Eastin et al., 2010; Baza´n-Perkins and Ferna´ndez-
⇑ Corresponding author. Tel.: +52 442 192 12 00x6015.
E-mail address: [email protected] (R. Luna-Rubio). 0038-092X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.solener.2011.10.016
Zayas, 2008; Bouffard and Kirschen, 2008; Matutinovic, 2009; Midilli et al., 2006). Recently, in order to reach sustainable development, humankind needs to be steady on the path of low-carbon society (Sioshansi, 2010; Kammen, 2010). For this reason, in order to make an efficient use of electrical energy there is a growing interest in optimizing the design of urban settlements by means of the exploitation of natural sources of energy (i.e. solar and wind energy) and the development of building management systems (Gugliermetti and Bisegna, 2006; Kampf and Robinson, 2009; Kolokotsa et al., 2011; Luna-Rubio et al., 2009; di Stefano, 2000; Wong et al., 2005, 2008). Additionally, electrical power nets are in a transition stage where these need to be more flexible and dynamical at all levels, from power generation plant to customer level in order to enable distributed generation (mainly based on renew-
1078
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Nomenclature AC alternating current (VCA) ACS annualized cost of system BLR battery to load ratio Cinvestment total initial investment cost CRF capital recovery factor DC direct current (VCD) DE deficit energy EENS expected energy not supplied ELR energy to load ratio Etot annual total energy fPH(Ph) probability density function for the hybrid energy power output GA genetic algorithms GC grid connected GHG green house gas HLOL hours which loss of load occurs (h) Htot total hours operation system (h) I current (A) Ibat(t) charging current at hour, t (A) L electrical load LCE levelized cost of energy LOL loss of load LPSP loss of power supply probability NPV net present value NPVend_k discounted present values of incomes from the residual value of the component k at the end of the lifetime of the hybrid energy system discounted present costs of future costs
able sources), to promote efficient use of energy at customer level, and to reach an intelligent demand response (Castillo-Cagigal et al., 2011; Ja¨rventausta et al., 2010). The generation of electrical energy through of alternative sources such wind and solar, has become more attractive (Kaldellis and Zafirakis, 2011; Michalak and Zimny, 2011; Razykov et al., 2011; Solangi et al., 2011) and is widely used for substituting fossil fuels in the process of electrical power energy since 1970s because of the crisis oil (Cancino-Solo´rzano et al., 2010; Jebaraj and Iniyan, 2006; Omer, 2008); nevertheless, such alternative energy sources have a slow development (Devezas et al., 2008; Sovacool, 2010), and the transition into a new phase of evolution in the electrical power generation sector appears to be a complex task because of the different insights of the problem (Floyd and Zubevich, 2010; Vasileiadou and Safarzynska, 2010; Xu et al., 2006), not only due to environmental, and economic issues, also because of social and psychological impacts on people’s behavior (Hondo and Baba, 2010). Technology for power production from alternative energy sources has experimented a considerable development (Michalak and Zimny, 2011; Razykov et al., 2011).
NPVO&M_k discounted present costs of future costs of operation and maintenance of component k throughout the life of the system NPVr_k are the discounted present cost of future costs of replacing the components throughout the life of the system NPVsale_k discounted present values of incomes from the sale k PDF probability density function Ph power generated by hybrid energy systems Phmax maximum power generated by the hybrid energy system (W) Phmin minimum power generated by the hybrid energy system (W) Pload load power (W) PSO particle swarm optimization PV photovoltaic SA stand-alone SA system’s autonomy SOC battery state of charge (A h) T time (h) TAC total annualized cost TMY typical meteorological year r self-discharge rate of battery bank, percentage g(Ibat(t)) charging current efficiency of the battery bank with the charging current at hour, t Dt sample period (usually 1 h) (h)
Moreover, a new tendency to increase the efficiency and reliability of electrical power nets in alternative energy sources include the use of information and communication technology (ICT), in this way electrical utility is improved by becoming more dynamical and flexible and promote the distributed energy storage (Rahimi and Ipakchi, 2010). Nowadays, Earth appears to have enough power to cover World’s electrical power demand (15 TW) with the current technology (Cho, 2010). Nowadays, it is technically possible to extract 1.6 TW form hydro, 3.8 TW from geothermal, 9 TW from biomass, 20 TW from wind, and more than 50 TW from solar power (Cho, 2010). However, due to the stochastic nature in some of those alternative energies (e.g. solar and wind energy), the transition to low-carbon society will require of a non-single solution. Because the aforementioned, in order to displace fossil fuels by means of alternative energy sources, most of experts foresee that it will be necessary an integration of multiple energy sources working together (hydro, geothermal, biomass, wind, solar, hydrogen, nuclear, and fossil fuels) at bulk energy generation and customer level into decentralized energy systems (Devezas et al., 2008; Moslehi and Kumar, 2010; Kaundinya et al., 2009).
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
When an energy system includes two or more energy sources is named hybrid; it is broadly known that hybrid energy systems (which include alternative energy sources) sometimes present lower costs and higher reliability than systems which use only one source of alternative energy (Dalton et al., 2009; Ylmaz et al., 2008). Nevertheless, a proper sizing of every single component in the hybrid energy system is a key factor for its techno-economical feasibility. Therefore, the penetration of renewable sources into the energy market depends mainly on the sizing methodologies used for designing this kind of hybrid energy systems in an optimal way. These optimization problems refer to the choice of the “best” set of system’s components from a search space or set of possible solutions (i.e. set of possible components and their size in the hybrid energy system); this involves selecting one or more optimization problem, choosing an objective function and identifying a set of constraints. The objective function and the constraints must all be functions of one or more optimization variables. Additionally, the problem increases its complexity due to non-linear characteristic response in system’s components, stochastic availability in some renewable sources (e.g. solar or wind), and the number of the design constraints and optimization variables. In order to make the system economically feasible and technically reliable, the designing process of these energy systems is solidly based on an optimization stage. Because the aforementioned, along the last years it has been developed a large number of optimization methodologies applied to design process of hybrid energy systems. The simplest sizing methodologies can be those which are based in average values of weather variables (Celik, 2003; Morgan, 1996; Protogeropoulos et al., 1997) or the worst scenario (e.g. the month with lower solar or wind availability); however, designs obtained by this kind of methodologies trends to be oversized because of the worst case has a low occurrence probability or the average value is not a constant value all the time. In this sense, usually energy system performance is assessed using long time series of weather or electrical load profile records (commonly along 1 year of data records with 1 h sampling period) increasing in this way calculus complexity. In order to deal with the multi-objective functions, non-linear characteristic response of system’s components, as well as long time series of weather variables, several mathematical tools have been proposed such as probabilistic approaches (Celik, 2003; Tina, 2006; Yang et al., 2003), artificial neural networks (ANNs) (Mellit et al., 2005a, 2007), genetic algorithms (GAs) (Dufo-Lo´pez and Bernal-Agustı´n, 2005; Kaldellis et al., 2009; Yang et al., 2009), or particle swarm optimization (PSO) (Hakimi and Moghaddas-Tafreshi, 2009; Kornelakis, 2010) to mention a few. The aim of this paper is to give a brief overview of the current state of methodologies used to size hybrid energy systems, with energy storage components for both standalone (SA) and grid connected (GC) architectures.
1079
The present paper is structured in five additional sections. Section 2 provides general information about hybrid energy systems. Section 3 presents a summary of indicators used in order to assess the energy system performance. Section 4 includes the hybrid energy system sizing methodologies state of the art. Section 5 presents a brief summary and findings of the methodologies reviewed. Finally, Section 6 summarizes the conclusions and discusses issues for future research. 2. Hybrid energy system architectures In general, a hybrid energy system could be integrated as Fig. 1 shows. This kind of energy systems are called “hybrid” because they include more than one energy source in order to cover a determined electrical load, commonly an AC load; however, it may also supply a DC load or both at the same time. Energy sources may be alternative (i.e. renewable) as well as conventional (i.e. electrical grid or diesel generator), or energy storage components (i.e. battery bank or fuel cells); in this way, weakness of some energy sources is complemented by strengths of the other sources in a natural or controlled way. To illustrate that, despite the unpredictable availability of some alternative energy sources (like solar and wind), usually, they present complementary patterns (Tina and Gagliano, 2010; Yang et al., 2003). Hybrid energy systems can operate either in presence of grid (grid-connected) where the main priority of the system is to cater the local energy demand and occasionally to feed the grid with any energy surplus, or as a stand-alone system for producing energy independently of the grid utility in isolated areas. When a hybrid energy system includes solar or wind energy, most of times is required an auxiliary source of energy (e.g. coming from battery banks, fuel cells, or utility grid), the aforementioned is in order to overcome the stochastic availability of those energies (Celik, 2003). Sometimes, depending on the availability of an energy source, it is necessary to find
ENERGY STORAGE COMPONENTS (BATTERY BANK, AND/OR FUEL CELLS) ALTERNATIVE ENERGY SOURCE 1 DC LOAD ALTERNATIVE ENERGY SOURCE 2
CONTROL UNIT
ALTERNATIVE ENERGY SOURCE N
DC/AC INVERTER
AC LOAD CONVENTIONAL SOURCE OF ENERGY (GRID, AND/OR DIESEL) GENERATORS)
Fig. 1. General hybrid energy system architecture.
1080
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
a balance; in other words, a control unit which decides which energy source will supply the electrical load. In order to assess the performance of the hybrid energy system, there are several indexes used in the last years, some of them are presented in the next section.
CRF ¼
dð1 þ dÞt ; ð1 þ dÞt 1
ð3Þ
where d is a discount rate, and t is the useful lifetime of the energy system.
3. Hybrid energy metrics
3.3. Battery’s state of charge (SOC)
There are several performance indicators for hybrid energy systems, which can help us to assess its reliability and/or feasibility; in this way, designer can size in adequate way the system components. Some of them are described briefly in next.
SOC in batteries is related to the storage energy in the energy system, it can be computed as follows (Yang et al., 2003):
3.1. Loss of power supply probability (LPSP) Due to stochastic solar radiation and wind speed in nature, it is important to assess the energy system reliability. An electrical power system is reliable when it is able to supply enough power to the electrical load during a certain period. One parameter that help us to assess system’s reliability is the LPSP (Diaf et al., 2008; Yang et al., 2009), which, can be defined as the ratio of all energy deficits to the load demand during the considered period PT DEðtÞ LPSP ¼ PT t¼1 ; ð1Þ t¼1 P load ðtÞDt
SOCðt þ 1Þ ¼ SOCðtÞ r þ I bat ðtÞ Dt gðI bat ðtÞÞ
ð4Þ
where r is the self-discharging rate of battery bank, Ibat(t) is the battery’s charging current, Dt is the sampling period, and g(Ibat(t)) is the charging current efficiency. SOC can help in choosing of the energy storage capacity ensuring the constraints about system reliability (DufoLo´pez and Bernal-Agustı´n, 2005; Yang et al., 2003). 3.4. Level of autonomy (LA) LA is defined as one minus the ratio between of the total number of hours in which loss of load (LOL) occurs and the total hours of operation (Celik, 2003): LA ¼ 1
H LOL ; H tot
ð5Þ
where DE(t) represents the deficit energy at hour t. 3.5. Expected energy not supplied (EENS) 3.2. Levelized cost of energy (LCE) The levelized cost of energy can be defined as the constant price per unit of energy that causes the investment to just break even. It is an economic assessment of the energy generated by the energy system used in designing of hybrid energy systems (Diaf et al., 2008; Yang et al., 2007) which includes all the costs over its lifetime. LCE is defined as the ratio of the total annualized cost of the system to the annual electricity delivered by the system; it can be calculated by means of the following expression (Lazou and Papatsoris, 2000): TAC LCE ¼ ; Etot
ð2Þ
where TAC represents the total annualized cost, and Etot the annual total energy. TAC is calculated taking into account the present value of costs (the value on a given date of a future payment or series of future payments, discounted to reflect the time value of money, for a hybrid energy system, the present value of costs can be composed of the initial cost, the present value of maintenance cost, and the present value of replacement cost), and the capital recovery factor (CRF) of the hybrid energy system. The CRF can be defined as (Lazou and Papatsoris, 2000)
EENS is a probabilistic reliability index used in Tina (2006); this indicator measures the expected energy that will not be supplied because of load exceeds availability in the energy system. According to electrical load (L), and power generated by hybrid energy system (Ph), EENS can be calculated as follows: 8 R P hmax > L P hmin P h fPh ðP h ÞdP h L > P hmax > < RL EENSðL;P h Þ ¼ P hmin 6 L 6 P hmax ðL P h Þ fPh ðP h ÞdP h P hmin > > : L < P hmin 0 ð6Þ where Phmax is the maximum power generated by the hybrid energy system, Phmin is the minimum energy generated by the hybrid energy system and is assumed to be 0, and fPh(Ph) is the probability density function for the power output of the hybrid energy system. 3.6. Net present value (NPV) System’s NPV can be calculated by adding the discounted present values of incomes and by subtracting the discounted present costs along the useful lifetime of the system (Dufo-Lo´pez et al., 2009), this value can be defined as follows:
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
NPV ¼
X
X NPVsale k þ NPVend k C investment X X NPCr k NPCO&M k ;
ð7Þ
where NPVsale_k are discounted present values of incomes from the sale k (e.g. electrical energy sold to the grid), NPVend_k are discounted present values of incomes from the residual value of the component k at the end of the lifetime of the hybrid energy system, Cinvestment is the total initial investment cost, NPVr_k are the discounted present cost of future costs of replacing the components throughout the life of the system, NPVO&M_k are the discounted present costs of future costs of operation and maintenance of component k throughout the life of the system. 3.7. Annualized cost of system (ACS) The ACS is composed of the annualized capital cost (Cacap), the annualized replacement cost (Carep), and the annualized maintenance cost (Camain) (Yang et al., 2009). ACS ¼ C acap þ C arep þ C amain ;
ð8Þ
4. Sizing methods Several methodologies had been used in order to design hybrid energy systems. The simplest way to classify them could be done according their complexity level as we can see in Fig. 2.
1081
annual average of monthly system performance, and the second one on the worst monthly scenario. Based on the total system cost, in Protogeropoulos et al. (1997) optimum configuration was PV alone system. Taking as reference (Morgan, 1996; Protogeropoulos et al., 1997), in Celik (2003) those methodologies were compared with a new one called “Scenario 4”. The proposed alternative methodology is an iterative process, where standard deviations of system performance are used to find an optimal system configuration. This alternative methodology leads to optimized techno-economically systems in a similar way that (Morgan, 1996; Protogeropoulos et al., 1997); furthermore, in Celik (2003) was suggested that a third energy source (auxiliary source) should be incorporated to the system instead of increasing the hardware sizes excessively for the worst monthly methodologies previously reported. Another probabilistic approach is suggested in Yang et al. (2003), where authors pointed that in order to obtain an accurate assessment of performance in a hybrid PV– wind energy system, a suitable typical meteorological year ´s (TMY) should be studied and utilized to assess system performance. Finally, a novel probabilistic approach based on convolution technique using probability density function (PDF) in order to assess the long-term performance of hybrid solar–wind power systems (Tina and Gagliano, 2010; Tina, 2006). 4.2. Analytical methods
4.1. Probabilistic methods Probabilistic methods may be the simplest sizing methodologies; however, results obtained by these techniques are not the most suitable to find out the best solution. Usually, they take into account one or two system performance indicators to be optimized in order to size components of the studied system. Table 1 summarizes some examples of probabilistic methods. These methods involve the development of appropriate models for generation and/or load followed by a combination of these models to create a risk model. For instance, in Protogeropoulos et al. (1997) are presented two sizing methods called “Scenario 1” and “Scenario 2” used for sizing autonomous PV–wind hybrid energy systems. Both methods assess systems performance by means of the energy to load ratio (ELR), and battery to load ratio (BLR); however, the first one is based on the
In these methods (Table 2), hybrid energy systems are represented by means of computational models which describe hybrid system size as function of its feasibility. ´ s performance can be assessed for a Consequently, system set of possible system architecture and/or a particular size of components. Best configuration of a hybrid energy system is determined due to a single or a multiple performance index of the systems analyzed. This kind of methodologies allows the designer to simulate the performance of several hybrid system configurations; nevertheless, they need long time series, usually 1 year, of weather variables (solar, and wind) for the simulations. The performance assessment of hybrid system can be carried out by computational models (i.e. commercial software tools and/or numerical approximations of system components). Recently, several computer tools have been developed in order to assess hybrid energy performance, which aids the designer to
Sizing methodologies
Probabilistic
Analytical
Iterative
Fig. 2. Classification of optimization methodologies.
Hybrid
1082
Table 1 Summary of probabilistic methodologies. Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Yang et al. (2003)
Probabilistic
Wind, PV, and battery banks
SA
LPSP, SOC
LPSP
Celik (2003)
Technoeconomic
SA
Cost-per-kW h-electricity (unit cost)
Level of autonomy
Tina and Gagliano (2010)
Probabilistic
Solar, and wind with battery storage Solar and wind
SA
Energy index of reliability which is directly related to EENS and the internal rate of return
A battery bank with an energy storage capacity of 3 days was suitable for ensuring the desired LPSP of 1%, and a LPSP of 0% can be achieved with a battery bank of 5 days storage capacity Author suggests that a third energy source (auxiliary source) be incorporated into the system instead of increasing the hardware sizes excessively The results inform the design of a pre-processing stage for the input of an algorithm that probabilistically optimizes the design of hybrid (solar and wind) power systems
Table 2 Summary of analytical methodologies. Reference
Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Dufo-Lo´pez et al. (2009)
Economic
Solar, wind, and H2
GC
NPV
The land surface taken up by the system and the initial investment cost
Kamel and Dahl (2005) Diaf et al. (2008)
Economic
Solar, wind, and diesel generator
SA
Annualized cost
LPSP
Only in places with a high wind speed rate and with the current prices of the components, the intermittent production of hydrogen could be economically viable if the selling price is a minimum 10€/kg Optimization results show that hybrid systems are less costly than diesel generation from a net present cost perspective
Technoeconomic
Wind, PV, battery banks, and UPS
SA
LCE
LPSP
Technoeconomic
PV and grid
GC
PV electricity cost
Fraction of load met by a PV system
Economic
PV
SA
Embodied energy
Available power from individual units, wind power generation to load ratio, and generationdemand balance
Economic
Solar, wind, and battery banks
SA
Production cost
Mondol et al. (2009) Kaldellis et al. (2009) Khatod et al. (2010)
The results indicate that the hybrid energy system is the best option for all the sites considered in the study. Additionally, it is shown that the LCE depends largely on the renewable energy potential quality The profitability of a grid connected PV system increases if the PV system is sized to reduce excess PV electrical energy feed to grid when the fed-in tariff is lower than electricity buying price The most interesting finding concerns the fact that the contribution of the battery components exceeds 27% of the lifecycle energy requirements, reflecting the difference between GC and SA configurations Results shows that the developed technique require less computational time than Monte Carlo simulation method
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Reference
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
analyze the integration of renewable sources. In a recent and extensive review Connolly et al. (2010), different computational simulation tools of hybrid energy systems were analyzed and compared. According to literature reviewed, a simulation tool broadly used in performance assessment of hybrid energy systems is the Hybrid Optimization Model for Electric Renewable (HOMER), developed by the National Renewable Energy Laboratory (NREL), US, this simulation tool was used in Dalton et al. (2008, 2009), where it is presented a feasibility study for a small to medium-sized tourist accommodations.
Initialization
Constraints evaluation & chromosomes repair
Fitness function evaluation
All generations finished?
Yes
Output & store optimal parameters
No
Selection
4.3. Iterative methods Performance assessment of hybrid energy systems in iterative methodologies is done by means of a recursive process which stops when the best configuration is reached according to design specifications. An iterative method is reported by Ashok (2007) where an optimal hybrid system was obtained among different renewable energy combinations for a rural community, ´ s reliminimizing the total life cycle cost, ensuring system ability. In this work, a numerical algorithm based on QuasiNewton method was used to solve the optimization problem (Rao, 1996). Genetic algorithms (GAs) are stochastic global search and optimization technique bio-inspired in the process of natural evolution species, which is generally robust in finding global optimal solutions in multi-modal and multi-optimization process. Fig. 3 shows a flowchart for the proposed optimization methodology in Kaldellis et al. (2009). In this Battery chargers specs.
1083
PV module specs.
WGs specs.
Batteries specs.
DC/AC Inverters specs.
Select a combination of PV modules, battery charger, WG and DC/AC inverter types. Consumer power requirements. Optimal sizing. No Store the optimal parameters.
Daily irradiation and hourly mean values of temperature and wind speed.
All combinations optimized?.
Yes Select the combination of components whit the lowest cost.
Fig. 3. Flowchart of the proposed optimization methodology (Koutroulis et al., 2006).
Simple crossover
Simple arithmetic Crossover
Whole arithmetic crossover
Crossover operators
Non-uniform mutation
Mutation operators
Constraints evaluation & chromosomes repair
Uniform mutation
Boundary mutation
Constraints evaluation & chromosomes repair
Fig. 4. GA used for optimization process (Koutroulis et al., 2006).
study, the system cost was minimized by means of electric load; according with design constraints, optimal sizing is achieved by means GA process, showed in Fig. 4. The results reported in Kaldellis et al. (2009) show that hybrid PV–wind systems present lower cost than either PV or wind energy system. Similar works can be found in Dufo-Lo´pez and Bernal-Agustı´n (2005) and Yang et al. (2007, 2008, 2009), where PV–wind energy sizing methodologies based on GA were used in order to reach an optimum system configuration. In these cases, optimal configuration was calculated by a required LPSP with a minimum annualized cost of system (ACS) (Yang et al., 2008, 2009), levelized cost of energy (LCE) technique (Yang et al., 2007). Moreover, other stochastic optimization method applied to sizing hybrid energy systems is the called particle swarm optimization (PSO) (Kennedy and Eberhart, 1995). This procedure is inspired by certain social behavior. For a brief introduction to this method, consider a swarm of p particles, where each particle´s position represents a possible solution point in the design problem space D. Every single particle is denoted by its position and speed; in an iterative process, each particle continuously records the best solution thus far during its flight. As an example of optimal sizing of hybrid energy systems by means of PSO, refers to Hakimi and Moghaddas-Tafreshi (2009).
The optimal configurations of the hybrid system are obtained in terms of different desired system reliability requirements and the LCE
Finally, in Ekren and Ekren (2009) was used OptQuest, available on software tool ARENA 12.0 to solve optimization problem. OptQuest is also an iterative heuristic method that combines the meta-heuristics of tabu search, neural networks, and scatter search into a single search. As a summary of iterative methods above mentioned, Table 3 is presented. 4.4. Hybrid methods Due to multidimensional nature in optimization problem, a suitable methodology to deal this problem will be one able to solve multi-objective optimizing based on heuristics methods as GA, neural networks, and tabu search to mention a few. Nevertheless, in hybrid methods results can be improved by using combining optimization techniques. For instance, in Bernal-Agustı´n et al. (2006), Katsigiannis et al. (2010), and Shi et al. (2007), it was modified GA in order to obtain a set of non-dominating Pareto set solutions, which, by means of a selection criteria, the designer can choose the optimal configuration. In Katsigiannis et al. (2010) optimization objective was twofold, to minimize the system’s cost of energy, and greenhouse gas (GHG) emissions during its lifetime by six different design constraints. The main novelty of this work is the assessment of GHG based on life cycle analysis. A similar approach was proposed in Wang and Singh (2009). However, in this work, the set of non-dominated Pareto solutions was obtained by means of a PSO modified algorithm. In this study (see Fig. 5), optimization objectives were twofold (techno-economical), and threefold (technical, economical, and environmental). Artificial neural networks (ANNs) can be considered as simplified mathematical models of brain-like systems because they inspired in biological neural systems operation (Hoppfield, 1982; McCulloch and Pitts, 1943). An
Technoeconomic Yang et al. (2007)
Solar and wind
SA
LPSP
Resource availability and equipment constraints LCE Life cycle cost SA Solar, wind, and hydro Technoeconomic Ashok (2007)
Load energy requirements Total cost Solar and wind Technoeconomic
SA
Total net present cost SA Solar and diesel Technoeconomic
Dufo-Lo´pez and Bernal-Agustı´n (2005) Koutroulis et al. (2006)
SA Solar and wind
Annualized cost
SA
Technoeconomic Technoeconomic
Technoeconomic
Hakimi and MoghaddasTafreshi (2009) Ekren and Ekren (2009) Yang et al. (2009)
Unmet load and battery capacity
The methodology described in this study is especially helpful if there are various auxiliary energy sources or loads This model can be used to calculate the system output configuration which can achieve the desired LPSP with a minimum annualized cost of system This methodology presented finds the optimal SOC set point whether the cycle charging or combined strategies are the optimal ones The simulations results verify that hybrid systems present lower cost compared with the systems with only one renewable energy source The developed model helps in sizing hybrid energy system hardware and in selecting the operating options Auxiliary energy unit cost and electric load LPSP
Stored energy in the hydrogen tank
Capital cost, operations and maintenance cost, efficiency lifetime, and produced wasted Investment cost system SA
Wind, hydrogen, and biomass PV, and, wind
Design constraints Indicators optimized SA– GC Energy sources Study Reference
Table 3 Summary of iterative methodologies.
The hybrid system used has a high reliability because fuel cells are as a backup for wind turbines
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Findings
1084
Fig. 5. Pareto fronts for bi and tri-objective scenarios (Wang and Singh, 2009).
Table 4 Summary of hybrid methodologies. Study
Energy sources
SA– GC
Indicators optimized
Design constraints
Findings
Katsigiannis et al. (2010)
Economic and environmental
SA
Cost of energy (COE) and total green house gas (GHG) emissions
Electrical load
The main novelty of this work is that the calculation of GHG emission is based on life cycle analysis of each system’s component
Wang and Singh (2009) BernalAgustı´n et al. (2006) Shi et al. (2007)
Technoeconomic and environmental Technoeconomic and environmental
Solar, wind, diesel, biodiesel, and battery banks (lead acid and fuel cells) Solar, wind, and batteries banks
Total cost, energy index of reliability (EIR), and pollutant emissions (PEs) Total net present cost, and kg CO2 produced by the system throughout 1 year
LPSP, system area
A set of tradeoff solution is obtained using multi-criteria meta-heuristic method that offers many design alternatives to decision marker
Solar, wind, and diesel
SA and GC SA
Unmet load and battery capacity
Technoeconomical
Solar and wind
SA
Hontoria et al. (2005)
Technical
Solar and batteries banks
SA
Total system cost, autonomy level, and wasted energy rate Capacity of the generator
Mellit et al. (2005b) Mellit et al. (2007)
Technical
Solar and batteries banks
SA
Technical
Solar and batteries banks
SA
The design is posed as an optimization problem whose solution allows obtaining the configuration of the system as well as the control strategy that simultaneously minimizes both the total cost through the useful life of system and the pollutant emissions The method presented can handle the optimal design of hybrid energy system effectively and facilitate the designer with a range of the design solutions and the tradeoff information The proposed methodology works without any information of the relationships between the different variables and sources of information studied. The presented method reports an improvement in results respect to an analytical method studied The developed method can estimate the optimal sizing components from a minimum of input data compared The proposed procedure has an advantage compared to the classical models as it can predict the future optimal configuration or sizing of energy system’s components
PV generator area (m2) and useful accumulator capacity PV generator area (m2) and useful accumulator capacity
Accumulator capacity, LOLP, and the clearness index yearly average LPSP LPSP
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Reference
1085
1086
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
ANN is a set of individually interconnected processing units named neurons. Every single interconnection in the network has two values associated with it: an interconnection value, and an interconnection weight. The neuron’s output is a function of the input summed values. Every single interconnection in the network has two values associated with it: an interconnection value, and an interconnection weight. The neuron’s output is a function of the input values summed. Once ANN has been trained or calibrated by means of a recursive algorithm (i.e. interconnection weights are adjusted), new patterns can be presented for prediction or classification purposes (Paliwal and Kumar, 2009). In design of energy systems, ANN has been used in prediction of total daily solar radiation in isolated areas where data records are not always available (Hontoria et al., 2002; Mellit et al., 2005b) or in prediction of the performance of a hybrid energy system (Mellit and Pavan, 2010). In energy systems sizing, ANN has been used for improving the analytical method of loss of load probability (LOLP) curves (Hontoria et al., 2005; Mellit et al., 2005a, 2007) analytical method of the loss of load probability (LOLP) curves proposed by Egido and Lorenzo (1992), as well as combined with GA for improving the design and calibration process of the ANN and then, generating the sizing curve of the power system based in the LOLP concept. Finally, an emerging approach is the well-being hybrid energy performance assessment, which is a robust tool that includes the capacity of energy reserve (deterministic criterion) and the system risk criterion (probabilistic criterion), as example of this approach can be found in Khatod et al. (2010). In order to present a brief summary of hybrid methods above mentioned Table 4 is developed. 5. Summary and findings Sizing methodologies based in average values (e.g. monthly solar radiation) or worst scenarios (e.g. the month with lower solar or wind availability) present a tendency to over-sizing system components because of the worst case has a low occurrence probability or the average value is not a constant value all the time. When a hybrid energy system includes solar or wind energy, most of times adding an auxiliary source of energy (e.g. coming from battery banks, fuel cells, or utility grid) can help in reducing energy costs and ensuring system’s reliability instead of increasing excessively sizing in energy generators components. Bio-inspired methodologies such as GA, ANN, and PSO can require considerable computing processing; however, they can deal with non-linear behavior of systems’ components or stochastic variability in solar or wind energy. These methodologies can deal with a lack of information in data records of weather variables, can be adjusted in real time, can deal with multi-objective optimization functions, and can works without any information
of the relationships between the different variables and sources of information studied. Hybrid optimization methods most of times have been proposed to combine two or more methodologies in order to improve them, for increasing its convergence time in the optimization process. These methodologies can be characterized because its flexibility and dynamic in the sizing process. Therefore, they are the most powerful sizing methodologies.
6. Conclusions This paper summarizes existing research of optimal sizing of renewable hybrids energy systems. Some probabilistic methods involve the development of appropriate models for generation and/or load, followed by a combination of these models to create a risk model; therefore, it can be assessed the long-term performance of hybrid energy system. However, according to the amount of weather data considered and the probabilistic model, results of sizing methodologies may not be accurate. By means of analytical methods, system performance can be assessed in an accurate way. Nevertheless, when multi-objectives function needs to be solved, sometimes these methods do not offer accurate solutions. For this type of problems, iterative and hybrid methods improve multi-objective results. From this brief literature review, it can be observed that hybrid energy systems appears to be a good alternative in order to integrate alternative energy sources in the process of electrical power generation. Almost in all reviewed systems that include more than one alternative and/or conventional energy sources, like diesel generator or conventional grid, it can be noticed that they present better performance than systems which only include one alternative energy source. In order to become reliable, feasible, and/or environmental friendly hybrid energy systems, we can see a current trend in development of multi-objective sizing methodologies; thereby, it is possible to ensure the assessment of different insights of the system performance.
Acknowledgments The first author would like to thank to Consejo Nacional de Ciencia y Tecnologı´a (CONACYT) for Ricardo Luna Rubio Ph.D. scholarship Support No. 230783 as well as Ph.D. Klavdia Oleschko Lutova for her kindly supporting. Additionally, Ricardo Luna Rubio wishes to thank to Ph.D. Irineo Torres Pacheco (I. Torres-Pacheco) for his helpful advises, Ph.D. Moise´s Alejandro Va´zquez Cruz (M.A. Vazquez-Cruz) in Mexico and to Professor Fernanda Amaral do Nascimento in Brazil for their kind help during the writing process, and the respective reviewers for their valuable comments and criticisms.
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
References Al-Saleh, Y., 2009. Renewable energy scenarios for mayor oil-producing nations: the case of Saudi Arabia. Futures 41 (9), 650–662. Ashok, S., 2007. Optimised model for community-based hybrid energy system. Renewable Energy 32 (7), 1155–1164. Baza´n-Perkins, S.D., Ferna´ndez-Zayas, J.L., 2008. Evaluation of Mexico´s 1975–2000 energy plan. Energy Economics 30 (5), 2569–2586. Bernal-Agustı´n, J.L., Rodolfo, Dufo-Lo´pez., Rivas-Ascaso, D., 2006. Design of isolated hybrid systems minimizing costs and pollutant emissions. Renewable Energy 31 (14), 2227–2244. Bouffard, F., Kirschen, D.S., 2008. Centralised and distributed electricity systems. Energy Policy 36 (12), 4504–4508. Cancino-Solo´rzano, Y. et al., 2010. Electricity sector in Mexico: current status. Contribution of renewable energy sources. Renewable and Sustainable Energy Reviews 14 (1), 454–461. Castillo-Cagigal, M. et al., 2011. A semi-distributed electric demand-side management system with PV generation for self-consumption enhancement. Energy Conversion and Management 52 (7), 2659–2666. Celik, A.N., 2003. Techno-economic analysis of autonomous PV–wind hybrid systems using different sizing methods. Energy Conversion and Management 44 (12), 1951–1968. Cho, A., 2010. Energy´s tricky tradeoffs. Science 329 (5993), 786–787. Connolly, D. et al., 2010. A review of computer tools for analysing the integration of renewable energy into various energy systems. Applied Energy 87 (4), 1059–1082. Dalton, G.J., Lockington, D.A., Baldoc, T.E., 2008. Feasibility analysis of stand-alone renewable energy supply options for a large hotel. Renewable Energy 33 (7), 1475–1490. Dalton, G.J., Lockington, D.A., Baldock, T.E., 2009. Case study feasibility of renewable energy supply options for small to mediumsize tourist accommodations. Renewable Energy 34 (4), 1134–1144. Diaf, S. et al., 2008. Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions. Applied Energy 85 (10), 968–987. Devezas, T. et al., 2008. Energy scenarios toward a new energy paradigm. Futures 40 (1), 1–16. di Stefano, J., 2000. Energy efficiency and the environment: the potential for energy efficient lighting to save energy and reduce carbon dioxide emissions at Melbourne University, Australia. Energy 25 (9), 823–839. Dufo-Lo´pez, R., Bernal-Agustı´n, J.L., Mendoza, F., 2009. Design and economical analysis of hybrid PV–wind systems connected to the grid for the intermittent production of hydrogen. Energy Policy 37 (8), 3082–3095. Dufo-Lo´pez, R., Bernal-Agustı´n, J.L., 2005. Design and control strategies of PV–diesel systems using genetic algorithms. Solar Energy 79 (1), 33– 46. Eastin, J., Grundmann, R., Prakash, A., 2010. The two limits debates: “Limits to Growth” and climate change. Futures 43 (1), 16–26. Egido, M.A., Lorenzo, E., 1992. The sizing of stand alone PV systems: a review and proposed method. Solar Energy Materials and Solar Cells 26 (1–2), 51–69. Ekren, B.Y., Ekren, O., 2009. Simulation based size optimization of a PV/ wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Applied Energy 86 (9), 1387–1394. Floyd, J., Zubevich, K., 2010. Linking foresight and sustainability: an integral approach. Futures 42 (1), 59–68. Gugliermetti, F., Bisegna, F., 2006. Daylighting with external shading devices: design and simulation algorithms. Building and Environment 41 (2), 135–149. Hakimi, S.M., Moghaddas-Tafreshi, S.M., 2009. Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran. Renewable Energy 34 (7), 1855– 1862. Hondo, H., Baba, K., 2010. Socio-psychological impacts of the introduction of energy technologies: change in environmental behavior of
1087
households with photovoltaic systems. Applied Energy 87 (1), 229– 235. Hontoria, L., Aguilera, J., Zufiria, P., 2002. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Solar Energy 72 (5), 441–446. Hontoria, L., Aguilera, J., Zufiria, P., 2005. A new approach for sizing stand alone photovoltaic systems based in neural networks. Solar Energy 78 (2), 313–319. Ja¨rventausta, P. et al., 2010. Smart grid power system control in distributed generation environment. Annual Reviews in Control 34 (2), 277–286. Jebaraj, S., Iniyan, S., 2006. A review of energy models. Renewable and Sustainable Energy Reviews 10 (4), 281–311. Kaldellis, J.K., Zafirakis, D., 2011. The wind energy (r)evolution: a short overview of a long history. Renewable Energy 36 (7), 1887–1901. Kaldellis, J.K., Zafirakis, D., Kondili, E., 2009. Optimum autonomous stand-alone photovoltaic system design on the basis of energy payback analysis. Energy 34 (9), 1187–1198. Kamel, S., Dahl, C., 2005. The economics of hybrid power systems for sustainable desert agriculture in Egypt. Energy 30 (8), 1271–1281. Kammen, D.M., 2010. 2020 Visions. Nature 463 (7), 26–32. Kampf, J.H., Robinson, D., 2009. A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential. Applied Soft Computing 9 (2), 738–745. Katsigiannis, Y.A., Georgilakis, P.S., Karapidakis, E.S., 2010. Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power system with renewables. IET Renewable Power Generation 4 (5), 404–419. Kaundinya, D.P., Balachandra, P., Ravindranath, N.H., 2009. Gridconnected versus stand-alone energy systems for decentralized power. Renewable and Sustainable Energy Reviews 13 (8), 2041–2050. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proc. Int. Conf. Neural Netw., pp. 1942–1948. Khatod, D., Kumar, Pant V., Sharma, J., 2010. Analytical approach for well-being assessment of small autonomous power system with solar and wind energy sources. IEEE Transactions on Energy Conversion 25 (2), 535–545. Kolokotsa, D., et al., 2011. A roadmap towards intelligent net zero- and positive-energy buildings. Solar Energy 85 (12), 3067–3084. Kornelakis, A., 2010. Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems. Solar Energy 84 (12), 2022–2033. Koutroulis, E. et al., 2006. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy 80 (9), 1072–1088. Lazou, A.A., Papatsoris, A.D., 2000. The economics of photovoltaic stand-alone residential households: a case study for various European and Mediterranean locations. Solar Energy Materials and Solar Cells 62 (4), 411–427. Luna-Rubio, R. et al., 2009. Lighting automatic control system for intelligent buildings. International Review of Automatic Control 2 (4), 469–476. Matutinovic, I., 2009. Oil and the political economy of energy. Energy Policy 37 (11), 4251–4258. McCulloch, W.S., Pitts, 1943. A logical calculus of the ideas imminent in nervous activity. Bulletin of Mathematical Biophysics 5 (4), 115–133. Hoppfield, J.J., 1982. Neural networks and to physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the Unites States of America 79 (8), 2554–2558. Mellit, A., Pavan, A.M., 2010. Performance prediction of 20 kWp grid connected photovoltaic plant at Trieste (Italy) using artificial neural network. Energy Conversion and Management 51 (12), 2431–2441. Mellit, A., Benghanem, M., Hadj Arab, A., Guessoum, A., 2005a. An adaptative artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria. Renewable Energy 30 (10), 1501–1524.
1088
R. Luna-Rubio et al. / Solar Energy 86 (2012) 1077–1088
Mellit, A., Benghanem, M., Hadj Arab, A., Guessoum, A., 2005b. A simplified model for generating sequences of global radiation for isolated sites: using artificial neural network and a library of transition Markov Matrices. Solar Energy 79 (5), 469–482. Mellit, A., Benghanem, M., Kalogirou, S.A., 2007. Modeling and simulation of a stand-alone photovoltaic system using an adaptative artificial neural network: proposition for a new sizing procedure. Renewable Energy 32 (2), 285–313. Michalak, P., Zimny, J., 2011. Wind energy development in the world, Europe and Poland from 1995 to 2009; current status and future perspectives. Renewable and Sustainable Energy Reviews 15 (5), 2330– 2341. Midilli, A., Dincer, I., Ay, M., 2006. Green energy strategies for sustainable development. Energy Policy 34 (18), 3623–3633. Mondol, J.D., Yohanis, Y.G., Norton, B., 2009. Optimising the economic viability grid-connected photovoltaic systems. Applied Energy 86 (7– 8), 985–999. Morgan, T.R., 1996. The Performance and Optimization of Autonomous Renewable Energy Systems. Division of Mechanical Engineering and Energy Studies, University of Wales, Cardiff: [s.n.] (Chapter 5). Moslehi, K., Kumar, R., 2010. A reliability perspective of the smart grid. Transactions on Smart Grids 1 (1), 57–64. Muller-Furstenberger, G., Wagner, M., 2007. Exploring the environmental Kuznets hypothesis: theoretical and economical problems. Ecological Economics 62 (3–4), 648–660. Omer, A.M., 2008. Energy, environment and sustainable development. Renewable and Sustainable Energy Reviews 12 (9), 2265–2300. Paliwal, M., Kumar, U.A., 2009. Neural networks and statistical techniques: a review of applications. Expert Systems with Applications 36 (1), 2–17. Protogeropoulos, C., Brinkworth, B.J., Marshall, R., 1997. Sizing and techno-economical optimization for hybrid solar PV–wind power systems with battery storage. International Journal of Energy Research 21 (6), 465–479. Rao, S.S., 1996. A text book on Engineering optimization, theory and practice, third ed. Wiley, New York. Rahimi, R., Ipakchi, A., 2010. Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid 1 (1), 82–88. Razykov, T.M., et al., 2011. Solar photovoltaic electricity: Current status and future prospects. Solar Energy 85 (8) 1580–1608. Roth, S. et al., 2009. Sustainability of electricity supply technology portfolio. Annals of Nuclear Energy 36 (3), 409–416. Shi, J.-H., Zhu, X.-J., Cao, G.-Y., 2007. Design and techno-economical optimization for stand-alone hybrid power systems with multi-objec-
tive evolutionary algorithms. International Journal of Energy Research 31 (3), 315–328. Sioshansi, F.P., 2010. Generating Electricity in a Carbon-Constrained World. Academic Press. Solangi, K.H. et al., 2011. A review on global solar energy policy. Renewable and Sustainable Energy Reviews 15 (4), 2149–2163. Sovacool, B.K., 2010. Exploring the hypothetical limits to nuclear and renewable electricity future. International Journal of Energy Research 34 (13), 1183–1194. Tina, G., Gagliano, S., 2010. Probabilistic analysis of weather data for a hybrid solar/wind energy system. International Journal of Energy Research 35 (3), 221–232. Tina, G., 2006. Hybrid solar/wind power system probabilistic model for long-term performance assessment. Solar Energy 80 (5), 578– 588. Vasileiadou, E., Safarzynska, K., 2010. Transitions: taking complexity seriously. Futures 42 (10), 1176–1186. Wang, L., Singh, C., 2009. Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Transactions on Energy Conversion 24 (1), 163–172. Wong, J.K.W., Li, H., Wang, S.W., 2005. Intelligent building research: a review. Automation in Construction 14 (1), 143–159. Wong, J., Li, H., Lai, J., 2008. Evaluating the system intelligence of the intelligent building systems. Part 1: development of key intelligent indicators and conceptual analytical framework. Automation in Construction 17 (3), 284–302. Xu, F.-L. et al., 2006. A triangle model for evaluating the sustainability status and trends of economic sustainability. Ecological Modelling 195 (3–4), 327–337. Yang, H. et al., 2008. Optimal sizing method for stand-alone hybrid solar– wind system with LPSP technology by using genetic algorithm. Solar Energy 82 (4), 354–367. Yang, H., Zhou, W., Lou, C., 2009. Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy 86 (2), 163–169. Yang, H.X., Lu, L., Burnett, J., 2003. Weather data and probability analysis of hybrid photovoltaic-wind power generation systems in Hong Kong. Renewable Energy 28 (11), 1813–1824. Yang, H., Lu, L., Zhou, W., 2007. A novel optimization sizing model for hybrid solar–wind power generation system. Solar Energy 81 (1), 76– 84. Ylmaz, P., Hakan Hocaoglu, M., Konukman, A.E.S., 2008. A prefeasibility case study on integrated resource planning including renewables. Energy Policy 36 (3), 1223–1232.