photovoltaic energy system developments: Critical review and findings

Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

Contents lists available at ScienceDirect

Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Hybrid wind/photovoltaic energy system developments: Critical review and findings Aeidapu Mahesh n, Kanwarjit Singh Sandhu Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana, India.

art ic l e i nf o

a b s t r a c t

Article history: Received 18 September 2014 Received in revised form 10 June 2015 Accepted 1 August 2015

Renewable energy sources are certain to play a key role in the future energy generation due to the rapid depletion of conventional sources of energy. The solar and wind energy are the major renewable energy sources which have the potential to meet the energy crisis to some extent. However, such sources when explored independently are not completely reliable because of the unpredictable nature. Whereas their use as hybrid energy systems seems to be more reliable and cost effective, due to the complementary nature of these two resources. In this paper an attempt has been made to discuss a systematic review related to hybrid PV/wind energy systems with battery storage. The work as presented in the manuscript will help the researchers to explore such hybrid energy systems for further improvements in terms of designing, analyzing and integrating such systems into the power network. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Hybrid energy systems Size optimization Wind Photovoltaic

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 Unit sizing of PV/wind HRES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 2.1. Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 2.2. Load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 2.3. Modeling of photovoltaic system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 2.4. Modeling of wind energy system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 2.5. Modeling of battery storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1138 2.6. Constrains for optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1138 2.7. Reliability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1138 2.8. Economic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 2.9. Optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 3. Converter and controller design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1141 4. Performance assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 5. Software tools for hybrid systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 6. Location specific studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 7. Critical review and observations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 7.1. Review points and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 7.2. Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145

1. Introduction

n

Corresponding author. E-mail addresses: [email protected] (A. Mahesh), [email protected] (K.S. Sandhu). http://dx.doi.org/10.1016/j.rser.2015.08.008 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

Conventional fuel resources which are rapidly depleting day by day do not seem to be capable to meet the future increasing load demand. In addition to that the pollution caused by such resources is also alarming the environmental concerns. Thus, it necessitated

1136

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

the search for the alternate energy sources in order to generate more sustainable and non-pollutant energy. As far as the available alternate energy sources are concerned the solar energy and wind energy systems are widely used and the others include biomass, geothermal, tidal and wave energy. Due to the nature of omnipresence and ease of availability solar and wind energy systems are considered as the most promising of all alternate energy systems and use of solar and wind power has become very significant and cost effective [1]. However, the common problem of solar and wind energy systems the dependence on weather and climatic changes and the unpredictable nature. Due to this nature, there is always a mismatch between the generated energy and the load demand. This will make the system less reliable and arises need for storage or other source in the system. But, the optimal mix of two or more sources can overcome the drawbacks completely or partially due to the complementary nature of these sources. This optimal mix of the resources emerges as new field of study can be called as the hybrid energy system. In general a hybrid renewable energy system (HRES) can be defined as an energy system, which consists of two or more renewable sources, such that the power generated is more reliable and cost effective. It can be connected to grid or work in isolation and it may or may not have the storage capability or a conventional source in it. Typically the size of the HRES varies form few kW to hundreds of kW depending on the load it is serving. The HRES, with the capacity less than 5 kW can be treated as the small systems, this kind of systems are generally used to serve the loads of a remotely located home or a telecommunication relay systems. Then the systems with the capacity more than 5 kW and less than 100 kW can be treated as the medium systems, these are used to power remotely located community which contains several homes and other required amenities. The medium systems in most cases work in stand-alone mode and sometimes may be connected to utility grid, if it is nearby. The other type of the system, which is capable to power a region, with the capability of more than 100 kW can be called as the large system. These systems are generally connected to grid, to enable the power exchange between the grid and the system in case of surplus or deficiency. The HRES based on different sizes can be summarized by using Table 1.

Table 1 Classification of the HRES based on size. Type

Size

Typical load

Small Medium Large

Less than 5 kW 5 kW to 100 kW More than 100 kW

Remote home or a telecommunication system Remotely located community Regional loads

The major areas of research in the HRES according to the recent trends can be classified into the categories of size optimization, controller design, converter design and performance assessment as shown in Fig. 1. Presently there are few review articles [2–5] available in the literature regarding the HRES. They have mainly concentrated on the various optimal sizing methodologies, configurations and control strategies used in the context of the HRES. But, there is a need for an article which presents the comprehensive review of the entire PV, wind energy systems which includes some of the important areas in the HRES research like performance assessment and converter design. This article is aimed at filling the gap by incorporating these areas to present the reader with the complete review of the various facets of research in the field of HRES. For any hybrid system sizing of various systems is very much necessary to ensure the reliability of the supply while keeping the cost of the system low. The next section of this work includes the various size optimization techniques which are available in the literature and may be employed. For the proper operation of system a controller is required and converters play an important role in improving the power quality by reducing the effect of harmonics. Different controllers and converters for this purpose are discussed in Section 3. Any system which is designed needs to be assessed for checking the proper functioning. As the sources are not predictable the system should work in the environment for which it may not be prepared to. Numerous assessment methods available in the literature are given in Section 4. Various software packages available for analyzing the hybrid energy systems from cost, reliability and design aspects have been presented in Section 5 and a brief overview of various locations where, these studies have been carried out is given in Section 6. Later in Section 7, the critical review points and observations of the following review have been presented with the concluding remarks.

2. Unit sizing of PV/wind HRES A hybrid PV/wind system consists of a wind energy system, solar energy system, controllers, battery and an inverter for either connecting to the load or to integrate the system with a utility grid as shown in Fig. 2. Here, the solar and wind sources are the main energy sources, and the battery gets charged when the generated power is in surplus. And when the power demand is more than the actual generation the battery discharges and provides support for meeting the load demand. The performance of hybrid systems is mainly dependent on the performance of its individual components. For analyzing the system performance these components need to be modeled individually. The accuracy of individual component’s model decides the accuracy of the entire system.

Fig. 1. Research areas for hybrid energy systems.

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

1137

Fig. 2. PV/wind hybrid system representation.

Along with the models of the components, an understanding of local weather patterns and load patterns are very much important in optimal sizing of hybrid system. 2.1. Meteorological data The climatic conditions play a major role as the entire power generation is dependent on this. For every different location the weather conditions will be different. So, for a feasibility study or for optimal sizing of the hybrid systems, weather data is a very important tool for analyzing the climatic conditions thoroughly before setting up a plant. Such data is mostly available at the local meteorological stations, for some potential sites the space research agencies like national aeronautics and space administration (NASA) have made the data available through the web resources. The solar radiation, ambient temperature and the wind speed are the relevant parameters required for such study. As per the literature the data is collected for an interval of 2 years to 30 years in various articles for getting a clear understanding of the patterns at that particular site. In some areas the data might not be available for entire required duration of study; in this case the data can be generated by using statistical methodologies [6]. If the data is available for a nearby location, then it can be extrapolated for a particular site by making necessary adjustments [7]. The data which is generated synthetically generally saves time and can be easily used in simulations. The analysis of the data for several years is a cumbersome task and more error prone. There are few techniques which are employed for simplifying this job. Arabali et al. [8] have used fuzzy C-Means to group the days with similar patterns of solar irradiance, wind speed and load data. Celik [9] has divided the total year into three groups namely: wind-biased months, solar-biased months and even month. The concept of typical metrological year (TMY) the year which represents the best characteristics of the weather patterns of the selected region has been used in [10,11]. A typical day of the month also found based on hourly average power generated is presented in [12]. Based on only the data patterns and also mismatch in energy concept Heide et al. [13], have found the share of solar and wind energy for hundred percent renewable Europe. 2.2. Load profile In addition to the weather data profiles, the local electrical load profiles are also required to find whether the generated energy is matching the load demand. The hourly average load demand is required for the purpose of this study, which is generally available

at local load dispatch centers. In case it is not available it can also be generated synthetically. Pillai et al. [14] have attempted a method for generating the load profiles and weather data patterns synthetically by using ANN. Arabali et al. [8], proposed a method in which, the hourly load variation was modeled by using a Gaussian distribution with specific lower and upper limits. The statistical methods are also widely used for the prediction of the residential energy consumption. Few authors [15–17] have used liner, nonlinear and multiple linear regression analysis for the prediction of the residential energy consumption. 2.3. Modeling of photovoltaic system Systematic literature review reveals that a lot of research has been carried out in modeling of photovoltaic systems. Overstraeten and Mertens [18] have introduced an equivalent model of a solar cell, which was considered as the basis for any advanced study. Zhou et al. [19] presented a simulation model based on the I–V curves of a solar cell. There are various mathematical models available for simplifying the analysis. Xu et al. [20] have considered a simple model of a solar cell with one useful approximation that they have considered is the effect of tilt angle at the time of calculation of solar radiation itself. This has reduced the complexity of the system model while maintaining its accuracy. Arabali et al. [8] have taken the PV output as a function of sky clearness index. Mohammed Alsayed et al. [21] have used a model in terms of open circuit voltage and short circuit current to find out the power output of solar models. Wang and Yang [22] used HDKR model which considers the effect of diffused, scattered, reflected and incident radiation on an inclined surface to estimate the power output of PV panels. Diaf et al. [23] found out that the solar generator efficiency can be calculated in terms of overall heat loss coefficient as stated in [24,25]. To estimate the diffused solar radiation on any tilted surface Perez model [26] has been used by Yang et al. [1]. This model offers the reduced brightness coefficients to estimate the radiation on the surface. 2.4. Modeling of wind energy system Mathematical modeling of wind energy system includes the dynamics of wind turbine and the model of wind generator. Different wind turbines have different power curves as per the available literature there are several assumptions regarding the power curves: some authors [27–30] have considered the power curve to be linear, quadratic and cubic form, and some consider it as a piece wise linear function with few nodes. Diaf et al. [23]

1138

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

considered the model based on the data provided by the manufacturers the wind power is estimated through interpolation and the approximation used is a cubic spline interpolation. A wind turbine model is based on Weibull distribution was presented in [8], [22]. Mohammed Alsayed et al. [21] modeled the wind system based on wind speed distribution later on using the standard model. Abbes et al. [31] considered a wind turbine model interms of rotor swept area. Giuseppe Marco Tina and Gagliano [32] considered the power generated in a HRES is sum of power generated by PV and wind power, and by an assumption of both the powers are statistically independent the total power is considered to be the convolution of both the powers. The limits for the convolution are taken from [33]. The wind turbine curve doesn’t actually represent the exact characteristics of the wind turbine as they are modeled with the average wind speeds. But the instantaneous wind speed variations can cause a considerable effect on the performance of the wind turbine. Zamani and Riahy [34] proposed a new methodology for calculating power of a wind turbine by considering the effect of instantaneous wind speed variations. They have introduced two factors namely energy pattern factor (EPF) and turbine controllability (Ca) for modifying the power curve to track the instantaneous changes in wind speed. Based on the electric load, average wind speed and the wind turbine power curve a methodology of modeling wind energy system was presented in [35]. A detailed review of various statistical, parametric and non-parametric and reference models of predicting the wind and the power are presented in [36]. 2.5. Modeling of battery storage As the PV and wind systems are completely dependent on the climatic conditions, which are intermittent in nature. This causes a great deal of reliability concern. To improve the system reliability and to store the excess energy batteries are much needed in any HRES. To analyze the battery performance various models have been developed by the researchers. The battery can be modeled in the form of an equivalent circuit with components as the resistances of internal components of the battery [37]. Some researchers have adopted Kinetic battery model (KiaNM) which gives the amount of energy stored/released at each time step by the battery [20,22]. In KiaNM modeling method the battery is modeled as a two tank system one with readily available energy which can be converted to dc output and the other is a bound energy which cannot be converted instantly. Another empirical approach for

PV •Array capacity •Panel tilt angle •Area •complementary characteristics

Wind •Wind capacity •Area •Installation height •Complementary characteristics

modeling the battery is by using the parameter called the state of charge (SOC) of a battery [38], [23]. The battery can be modeled in terms of instantaneous state of charge (SOC), which is a function of SOC at the previous instant, self-discharge rate and efficiency of battery while charging and discharging [39]. They have also considered battery floating point voltage as a polynomial. The coefficients are found using the least square fitting of the battery performance data. In Zhou et al. [40] adopted a simple mathematical model for a lead-acid battery to predict its SOC in a hybrid system. The semi-empirical Shepherd Model of battery has been adopted by Kaiser in [41]. This model gives the accuracy upto 2% in case of stand-alone systems. Effect of the charge controller performance and columbic efficiency on the off grid hybrid system have been presented in [42]. An effective probabilistic modeling of battery states to evaluate the system reliability in an effective manner has been presented by Priyanka et al., in [43]. Different models for the prediction of the lifetime of the lead-acid batteries have been presented in [44,45], and a review of such methods has been presented by Dufo-Lpez et al. in [46]. 2.6. Constrains for optimization For any optimization study, the constraints are always going to be the key part in determining the optimal solution, as these are going to define the boundaries for the search space. Each constraint included in the problem specifies a parameter and there are several constraints used for the optimal sizing of HRES by various researchers. Fig. 3 shows the info-graphic of the constraints used for different systems. For the PV system, Number of PV panels or the installation capacity, PV panel tilt angle and the area of PV installations are the main constraints in many of the studies. As far as the wind systems are concerned, the main constraints are the wind capacity, wind turbine installation height and installation area. There are several constraints associated with the battery in order to improve its life span, like depth of discharge (DOD), SOC, number of charge/discharge cycles and power. The environmental constraints include the emissions and social acceptance. There are few grid integration factors also included such as the fluctuations of power injected and cost of line extension. 2.7. Reliability analysis Due to the unpredictable nature of the power produced by a PV and wind energy system an analysis for the reliability plays a vital role for a hybrid system design.

Battery •State of charge •Depth of discharge •Charge/discharg e power and rate •No. of cycles

Environmental factors •Pollutant emissions •Social acceptability

Fig. 3. Constraints for HRES optimization.

Grid integration •flucuations of power injected •Cost of line extension

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

There are various methods for carrying out the reliability analysis; the loss of power supply probability (LPSP) is one of the widely used methodologies for this purpose. LPSP can be defined as the probability that loss of power supply occurs [26]. Which means the combined hybrid system is unable to supply the power to the load. The mathematical form of the LPSP is given by eq. 1.   PTn time if P supplied o P demand LPSP ¼ n ¼ 1 ð1Þ Th There are numerous publications [1,10,12,20,22,23,31,38,58,59,62,69] available, in which they have incorporated LPSP for the reliability analysis. The loss of load probability (LOLP) is also another important parameter for the study [48,57,64,65,70]. It can be defined as the probability that the system load demand has exceeded the system capacity. There are other methodologies for the analysis of the system reliability, such as loss of load risk (LOLR) [52], deficiency in power supply probability (DPSP) [56] and expected energy not supplied (EENS) [31,71]. The system autonomy level [11] is also considered as a reliability index in some of the optimization studies. 2.8. Economic analysis The system cost analysis also plays an important role in analyzing a hybrid system. There are several ways of performing system cost analysis; Levelised cost of energy (LCE), sometimes referred to as Levelised unit electricity cost, can be defined as, the ratio of total annualized system cost to the annualized energy generated by the system, given by LCE ¼

C A_total Etotal

ð2Þ

Several authors, [1,2,20,23,38,56,58,59,64] have used LCE for the cost analysis. Another important parameter used by the researchers is the annualized life cycle cost as presented in [72,73], given by ALCC ¼ CRFnLCC

ð3Þ

where LCC is the life cycle cost, which is the total cost of equipment purchase and maintenance over its useful life by considering the influence of cost increment on the annual maintenance and running costs and CRF is called as the capital recovery factor given by, dð1 þdÞt  CRF ¼  ð1 þdÞt  1

ð4Þ

where d is the discount rate and t is the life time of the project. The next parameter used most by the researchers is the net present value (NPV) [64]. It can be defined as, the total present value of all the time series cash flows, which include, the system initial cost, replacement cost of spare parts and the maintenance cost. The NPV compares the present value of money to the present value of money in the future, so that the inflation is taken care of. According to [74], the NPV can be defined as " #q  1=q P NPV ¼ 1þ  1 1 ð5Þ A where q ¼ log ½1 þð1=NÞ= log 2, P is the payment amount, A is the initial cost and N is the number of payments. 2.9. Optimization techniques The size optimization is one of the key areas of the hybrid energy system, to provide a balance between the system cost and the reliability. The survey indicates that there are various methods

1139

adopted for achieving the optimal configuration of the systems such as, iterative techniques, graphical methods, stochastic approaches and artificial intelligent techniques. Various studies [12,20,47,56], have reportedly used the iterative techniques. An iterative technique to find out the deficiency of power supply probability (DPSP) and LCE for reliability analysis and cost analysis has been used by Kaabeche et al. [56]. The algorithm developed allows calculating excess energy, which can be used to create hydrogen by using an electolyzer for the long-term storage. Another iterative technique was presented by Kellogg et al. [47] to optimize the hybrid system, with the constraints being; wind turbine size and number of PV modules required to minimize the objective function, which is the difference between power generated and power demanded. Several combinations of the PV and wind sizes are obtained and the combination with the less overall system cost is chosen to be the best system. In addition to these they have also analyzed whether a line extension or a new installation of hybrid system is going to be more cost effective. To find out the optimum mix of renewable system components another iterative technique has been used by Borowy and Salameh [12]. Another iterative approach has been used by Diafa et al. [58], the main objective of this study is to optimize the system for the given load pattern, for the sites which are located near the Corsica islands. In this study the wind power output has been fed through the uninterruptable power supply, due to which the wind energy generated cannot be sent to the load directly. A simple graphical method has been presented by Markvart [49] to identify the optimal mix of the PV and wind generation. The least squares method is used to find number of PV panels and wind turbines required to achieve an optimal solution is presented in [50]. By using the local weather data a suitable hybrid system comprising PV and wind energy resources was suggested in [51]. Simulated annealing has been used to optimize the total system cost [65]. ARENA 12.0 software was used to fit the available meteorological data to the PDFs. Various combinations of the possible hybrid energy systems, have been listed in the order of the net present value for electrifying a remotely located community in Ethiopia by Bekele and Palm in [61]. An optimization method to optimize the hybrid energy system with wind rotor swept area, solar panel area and battery bank size as the constraints was presented in [64], along with this a breakeven analysis has been performed to analyze whether a line extension from an available utility grid or a new hybrid system is cost effective. Diafa et al. [59] presented a comparative study between two system configurations for the sites located in Corsica islands. Yang et al. [1] have optimized the system based on 1 day, half day and 3 days storage battery banks to find out the optimum solution. While sizing the hybrid system components it is a common practice to consider the worst month scenario [57] in order to make system more reliable. But, this practice has been found to be less cost effective and considerable oversizing has been done. This scenario was addressed by a new method of incorporating a third energy source; the system has become more cost-effective [66]. An analysis of choosing between a hybrid and a single PV or a single wind system has been carried out by Celik [9]. The analysis is carried out by using two ratios namely; energy to load ratio (ELR) and battery to load ratio (BLR) and they have found the hybrid system to be more cost effective. A convolution approach to combine the solar and wind model is presented by Karaki et al. [75], and also in order to access the EENS a methodology to combine hybrid model to load model was presented. Artificial intelligent techniques are extensively used in optimizing the hybrid renewable systems. The genetic Algorithm (GA) is the most preferred algorithm among the other methodologies [8,20,21,31,53,54,60,62]. The main advantage of the GA is the algorithm execution technique doesn’t depend on the error surface, which makes it more suitable for the multi-criteria optimizations. Xu et al. [20] proposed a method, in which size optimization

1140

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

Table 2 A Summary of recent optimal sizing studies. Ref.

Parameters optimized

Main objectives

Method

Standalone/grid connected

Description

[79]

PV, wind and battery capacity and diesel generator PV, wind and battery capacity PV, wind and battery capacity

Cost, renewable energy penetration, pollutant emission Cost and reliability

GA

SA

The optimization has been done using genetic algorithm, according to different modes of operation of the system described in the work

Iterative method

SA

Cost and reliability

Iterative method

SA

[82]

PV and wind capacity

Reliability

Analytical method

GC

[83]

PV and wind capacity

[84]

PV, wind and battery capacity

Total energy deficit, total Iterative method net present cost and energy cost NPC, Cost of Energy and HOMER based reliability

[85]

Battery energy storage Total system cost

Improved bat algorithm

GC

[86]

PV, wind and Fuel cell Total system cost

SA

[87]

PV, wind and battery

PSO, Taboo search, harmony search and Simulated Annealing GA

A new software tool has been presented in this work to optimize the PV, wind and battery system techno-economically New optimization strategy has been proposed based on critical period LPSP, so that the loss of power supply can be made uniform throughout the year A MATLAB based program has been developed to analyze the effect of available water resources on the sizing of PV and wind hybrid systems The combinations PV/wind/DG/battery and PV/wind/battery are compared and the optimality between the battery storage and DG set has been found out in terms of cost and pollutant emissions A pre-feasibility study has been carried out for a remotely located island using HOMER software and sensitivity analysis has been performed on load variations and renewable sources An efficient algorithm has been proposed for optimizing the size of BESS and also for efficient energy management in the micro-grid Performance of various algorithms for optimizing the size of PV, wind, FC systems has been analyzed in this work

[88]

Battery energy storage Total system cost system (BESS)

Enhanced gravitational search algorithm (EGSA)

GC

[89]

PV, wind and fuel cell Cost and LPSP

Artificial bee swarm algorithm

SA

[90]

PV, wind battery and DG

Cost and LPSP

Iterative method

SA

[91]

PV, wind

Total cost

SA

[92]

PV, wind and battery

Cost and reliability

Ant colony optimization (ACO) based linear programming Sequential Monte Carlo simulation(SMCS)

[93]

Battery storage

[94]

PV, wind

Minimizing the battery storage Cost of energy and LPSP

Electricity system cascade SA analysis Iterative method SA

[95]

PV, wind and battery

Cost and EENS

[96]

PV, wind, battery and DG

Reliability, social factors and cost

Non-dominated sorting genetic algorithm II (NDSA II) PSO

[97]

PV, wind

Minimize the energy loss Modified Big bang-big crunch

[98]

PV, wind, battery and DG PV, wind and battery

Cost and emissions

[80] [81]

[99]

20-year system cost

System cost

Improved fruit fly algorithm (IFFA) Biogeographic based algorithm (BBO)

SA

SA

SA

SA

GC

SA

GC

SA SA

Life cycle cost and reliability

PSO

SA

Power loss, voltage stability

Weighted aggregation PSO

GC

[102] PV, wind and battery

Cost and reliability

iHOGA

SA

[103] PV, wind and battery

Capital annualized cost

Iterative method

SA

[104] PV, wind and battery

Cost and reliability

A-Strong

SA

[100] PV capacity, Wind rotor swept area, battery capacity [101] PV and wind

Optimal combination of PV, wind and battery has been found using a new approach called Differential flatness approach. Efficient utilization of BESS have been achieved through the EGSA. A rainflow method has been used to relate the increase in cost to battery life expectancy The system has been optimized using ABSA, to find out the better combination between PV/wind/FC and WT/FC at different levels of LPSP A MATLAB code has been developed for optimizing the PV, wind and battery system. The results have been compared with the HOMER results. Effect of adding DG on the LPSP and cost has been presented A strategy based on ACO for continuous domains (ACOR) and LP has been proposed. The total cost for various combinations like solar alone, wind alone and both combined together have been presented The uncertainties in load and sources have been modeled using auto regressive moving average (ARMA) model and optimization model is developed using SMCS. The effect of load shifting on the performance of the system has been investigated A new optimization strategy has been developed by using ESCA A combination of PV, wind and a pumped hydro system has been investigated The optimal allocation of PV, wind and energy storage in the micro grid using NDSAII have been presented An analytical model has been developed to identify the optimal resource mix for the particular location. Various storage criterions have been analyzed for proper sizing Optimal size and placement of PV and wind generation in the distribution network have been found using the models of stochastic dependence between PV and wind A multi-objective optimization problem has been formulated and solved using IFFA Instead of using the past data patterns the solar irradiation and wind speed are forecasted using ANN. Then the system has been optimized using BBO An optimization algorithm using PSO has been formulated and different variants of PSO algorithms have been used and compared Optimal placement of renewable sources in the distribution has been done through WAPSO to improve the distribution network performance An assessment of the hybrid renewable power sources in Malaysia has been performed and optimal sizing is performed for a location using iHOGA software tool A deterministic approach has been formulated to find the optimal mix of the renewables. A break-even analysis has been performed for wind only, PV only and PV/wind hybrid systems An efficient optimal sizing method using a meta-model based algorithm has been developed to find the optimal mix

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

1141

Table 2 (continued ) Ref.

Parameters optimized

[105] PV, wind and battery

Main objectives

Method

Standalone/grid connected

Description

Cost and reliability

Non-dominated sorting genetic algorithm II (NDSA II)

SA

Different types of PV panes and wind turbines and batteries have been considered and a multi-objective optimization has been carried out

is done using GA, taking the cost as the objective, extra objectives included are the operating reserve capacity, battery discharge/ charge rate and cycles, as these parameters have a great impact on the power supply reliability and the lifetime of the battery [76]. Arabali et al. [8] used a GA based optimization approach together with a two-point method is used. A novel optimization technique based on multi criteria decision analysis (MCDA) to find out the optimal size by ranking the various alternatives available is presented in [21]. This MCDA gives the flexibility to add any extra constraint to see the effect of it in sizing the system. In addition to the standard LPSP analysis they have included parameters to include the effect of the complementary characteristics of wind and solar, for grid connected system the fluctuations of power injected into the grid and total cost of the system. GA is applied to minimize the 20-year total system cost with the constraint of zero load rejection [53]. Emphasis has been given on different storage schemes based on the net present cost for a solar/wind/diesel system by Khan et al. [54]. They have found the wind/diesel/battery system was more cost effective, but if the fuel cell cost was reduced by 15% then wind/fuel-cell system become more cost effective. Yang et al. [62] have used GA with the objective function, total cost of the system should be minimum; the concept of a TMY is used to model the system. Abbes et al. [31] have used two different optimization techniques for achieving optimal configuration, non-linear SQP algorithm for single objective optimization and genetic algorithm for multiobjective optimization. A methodology to optimize the size of a HRES for residential purpose has been presented by Senjyu et al. [60]. The particle swarm optimization (PSO) was also used by some researchers [22], [48]. Wang and Yang [22] used improved particle swarm optimization method by taking the total cost of the system as the objective function. They have included restart and disturbance to avoid the trap in the local maxima by including the taboo list with the PSO. Without incorporating system uncertainties a grid connected hybrid system is designed and by accounting for equipment failures, energy sources whose output is time dependent and stochastic generation/load variations have been analyzed by an adequacy evaluation using PSO [48]. A fuzzy analytic hierarchy process associated with benefits, opportunities, costs and risks, to help select a suitable solar–wind power generation project has been presented in [77]. A review of artificial neural networks used for PV wind systems has been presented by Karabacak et al. [78]. A summary of the most recent studies related to the optimal sizing of HRES is presented in Table 2.

3. Converter and controller design Such renewable resources of intermittent nature, if not monitored properly may cause severe problems of power quality and stability. For this purpose several supervisory controllers have been proposed by the researchers. The first step in the control part is the maximum power extraction, which is generally done by a dc–dc converter, and then the power can be transmitted to the load or a grid by means of proper circuitry.

A controller has been proposed by Cheldid and Rahman [68], for a hybrid system, which gives the state of the system such as unit cost, unmet or spilled energies and state of all generating units and the battery discharge or charge losses. A novel controller for the hybrid system was designed by Valenciaga et al. [106], which use both passivity and sliding mode control, with the objective of complementing the wind system with the solar system. And a dynamic model for the system was also developed in the rotor reference frame for the purpose of designing the controller. An intelligent energy management scheme using fuzzy logic has been proposed by Lagorse et al. [107]. This controller was designed based on a multi-agent system which considers the entire system as various small blocks working for a global maximum. The advantage of this system is whenever there is a change in the system it can be done very easily without any change in the control strategy. A supervisory predictive control method for optimal management and operation of hybrid system is proposed in [108]. An optimal controller for efficient energy management for a hybrid PV, wind and battery system has been proposed in [109]. The control strategy and the hybrid system have been programmed in MATLAB/Simulink environment for different load scenarios have found to be working efficiently. An energy dispatch model based on the Model Predictive control techniques for the management and control of the energy flow in the PV/wind/battery/DG system have been developed by Tazvinga et al. in [110]. An open loop and closed loop models have been developed and the closed loop model is found to be giving better results when the system is subjected to the uncertainties and disturbances. A model predictive control based energy management strategy for a HRES employing hydrogen and battery storage has been presented in [111]. Daniel and Ammasaigounden [112] have proposed a scheme of integrating the PV array with an induction generator through a simple three-phase square wave inverter. And in addition to that a dynamic mathematical model in synchronous reference frame has been developed for the hybrid system. The disadvantage of this method is it cannot perform any efficiency optimization because of inevitable copper loss in the rotor of an induction machine. A supervisory control system has been designed for the hybrid system comprising solar and wind energy system with a battery bank as well as an ac load has been given in [113]. The main objective of this control system is to maintain the continuity of power and also maintaining the SOC of batteries. A dc/dc converter has been used for the MPPT of PV output and also another dc/dc converter was used for the maximum power point tracking (MPPT) of wind power in combination to a 3phase bridge rectifier. Liu et al. [114] have Developed an efficient hybrid system by incorporating a permanent magnet brushless machine instead of an induction machine by improving the drawbacks of the methodologies presented in [112,113]. The wind side MPPT was performed by the flux control of the PM machine, and the PV side MPPT was done by the duty cycle control of a single-ended primary inductance converter (SEPIC). The system given by Kim et al. [115], employs a three-phase space-vector PWM ac/dc converter to achieve a maximum power output from the wind system by regulating the d-axis and q-axis currents. The two individual converts for both PV and wind systems is bulky and less cost-effective, due to the fact that the extra converter always increases the system size and also it needs extra control mechanism

1142

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

which will increase the cost. To avoid the use of two converters the following works [116–119] have used the concept of multi-input converter. A multi-input inverter for connecting the hybrid PV/Wind system to the grid has been proposed by Chen et al. [116]. In this topology the MPPT was used for both solar and wind systems and the power can be delivered to grid independently or simultaneously. The authors claim that as the even though solar insolation and wind speed are variable this topology can accept wide range of voltage variations. An effective way of integrating the PV and wind systems to the grid has been presented in [117] by Rupesh and Aggarwal. This method uses a single rotor side converter to integrate the power of both PV and wind power into the grid. The proposed scheme also prevents the circulating currents during the sub synchronous speed of operation of wind turbine. Also the converters utilization has been improved due to the complementary nature of PV and wind sources. A semi-isolated multi-input converter for a stand-alone PV/wind hybrid system for the battery charging application has been presented in [120]. This converter can support the MPPT of both PV and wind systems and also it can operate well even though the voltage difference between PV and wind output is large due to the semiisolated nature. A multi-input dc–dc buck boost converter using the sliding mode control strategy has been presented in [119]. This will reduce the cost of an extra converter and controller. Another configuration of a stand-alone hybrid system has been given by Lin et al. [121], in which Artificial Neural Network has been used for the MPPT for both wind as well as PV sides. A radial base function network (RBFN) was used to extract maximum power form the solar side and an Elman neural network was used for wind side. The superconducting magnetic energy storage (SMES) was used as an energy storage medium for a hybrid system in [122]. As the SMES have the characteristics of high energy density and the very quick response it can be used to improve the dynamic security of the distribution system under abnormal conditions by maintaining the levels of current or the reactive power fluctuations. A spiral spring as the auxiliary energy generating system has been used in [118]. For smoothing the power fluctuations in the hybrid system battery energy storage station (BESS) is a necessary requirement. The analysis by incorporating such station to the hybrid system is presented by Li et al. [123]. The control strategy for the smoothing of the fluctuations was also proposed and the results are also verified through simulations under normal operating conditions. Kabalci [124] designed a complete hybrid system by using simulation, which comprises of wind and PV resources with MPPT schemes as well as inter-phase

transformers to connect the both systems to a three-phase full bridge inverter. The inverter was controlled by SPWM modulation technique. A current source multiple input dc–dc converter to integrate the renewable system to the grid was proposed in [125]. A permanent magnet synchronous generator with a controller has been to extract maximum power for the wind speed below rated speeds. Various multilevel converters and their control strategies for integrating the renewable sources into the electric grid are presented in [126–132]. They have mainly concentrated on the active and reactive power control, reduction in harmonics and voltage recovery post fault conditions.

4. Performance assessment Any hybrid system must be assessed properly in order to know its real time performance. Hence, the performance assessment of a hybrid system is also emerging as a research area for the researchers. A hybrid system which was set up in Lowell, MA has been assessed by Giraud and Salameh [70]. The system was assessed on the basis of 2-year actual system data, on the parameters of LPSP, actual energy generated, sufficiency of battery storage and actual cost of the energy per unit generated and the optimal sizing. Also the fluctuations in the power on the grid side were also assessed. Instead of time step simulations a statistical approach for the performance assessment has been given by Tina et al. [71]. The energy index of reliability (EIR) has been taken as a parameter for the sake of reliability analysis, which is directly related to energy expected not supplied (EENS). The result of the case study presented shows a good agreement between the results of the method and the time step simulations. A megawatt assessment model was developed using a stochastic approach by Subhadarshi and Venkataramana [133]. This model can be used to observe the effect of varying wind speeds, ratings of the generator, efficiencies of various components in the system and the ratio of rating of the system to the maximum load on the system. An economic analysis of a hybrid system based on payback period of the initial capital cost and life cycle savings was presented by Bakos and Tsagas [134]. A feasibility study for having a hybrid system installed at a location named Dhahran, Saudi Arabia, comprising a wind, PV and a diesel backup system has been carried out by Elhadidy [135]. The energy generated by the hybrid system was estimated mathematically by using the local meteorological data.

Table 3 Summary of few software tools for HRES. Software

Developed by

Key features

✓ Can perform optimal sizing with all renewable sources along with diesel generators, battery or hydrogen storage and both electrical and thermal loads✓ Both technical and economic analysis can be performed ✓Can perform technical, financial and environmental RETscreen National analysis✓ Also performs risk and sensitivity analysis✓ Resources Canada in 1996 Can compare the proposed case to the base case to show the benefit ✓ Can perform size optimization, Technical and financial HYBRID2 RERL, evaluation✓ Inter time-step variations are accounted University of Massachusetts, using probabilistic methods USA

HOMER

NREL , USA in 1992

iHOGA

University of ✓ Single/multi-objective problems can be solved✓ Low Zaragoza, Spain computational burden✓ Technical, economical and environmental analysis can be performed

Hybrids

Solaris Homes

✓ Can simulate the particular combination at a time✓ Design can be improved

Limitations ✓ Cannot perform Multi-objective optimization as the only objective is minimizing the NPC.✓ Battery DOD is not considered ✓ No provision for time-series data import✓ The effect of temperature on PV module performance is not considered ✓ Less flexible✓ Large data set is required

Availability 30-Day free trial is available from www. homerenergy.com Free Can be downloaded from www.retscreen.net

Free Can be downloaded from www.ceere. org/rerl/ rerl_hydripower. html ✓ No sensitivity analysis✓ No probability Edu version is free and analysis✓ Limitation on the daily load. Pro version is priced. Can be downloaded from www. Unizar. es/rdufo/grhyso.htm ✓ No optimal sizing Commercial

Case study [54],[63],[61]

[142], [143]

[144]

[145]

–

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

The performance of the hybrid system by changing different combinations of PV array sizes as well as storage has been presented on monthly basis and the energy which needs to be produced by the diesel generator was also estimated. By taking the outages caused by hardware failure and primary energy fluctuations into consideration a probabilistic model was developed to assess the hybrid system by Karaki et al. [75]. This model considers various capacities of PV panels and different storage capacities for assessment. A simple model to evaluate the performance of a stand-alone hybrid system was presented by Hashem et al. [35] by making use of weather data. This work presents a computer simulation approach for the assessment of hybrid system; so that it can be assessed even before it was built. The water heating system has been used as a dump load to make use the excess power available. As there is no internationally accepted standard for the assessment of hybrid system an attempt was made to create some standards by Arribas et al. [136]. Those standards were created by modifying the existing standards for PV system (IEC-61724) by adding a wind system. Yang et al. [38] have analyzed the parameters of a telecommunication relay system for one year by using an iterative approach which uses GA, and they have found a good complementary characteristics between solar and wind power systems and the battery state of charge was also within the specified limits. Bekele et al. [137] introduced a numerical model which estimates the power output of the hybrid system under variable weather conditions. This model is a combination of various mathematical models of PV, wind, battery as well as energy balance. The local database of meteorological data and load data can be used to estimate the power output of the system on monthly as well as yearly basis. A hybrid power system with active/reactive power control and dump power control was proposed by Hirose et al. [138]. The advantage of this scheme is without actually applying a dump load the dump power control is achieved. The performance of a HRES has been evaluated by varying the tilt angle of the PV cell at different time periods of the day, which will expose the different radiation patterns has been presented in [139]. A method of estimating the reliability of the HRES using a probabilistic model for the battery has been proposed by Priyanka et al. [43].

1143

5. Software tools for hybrid systems The simulation is the most common practice, which saves more time and cost for the analysis and the assessment of hybrid systems. Various software tools are available for this purpose such as, HOMER, AEOLIUS, BALMOREL, BCHP Screening tool, COMPOSE, E4cast, EMCAS, EMINENT, EMPS, EnergyPLAN, energyPRO, ENPEP-BALANCE, GTMax, H2RES, HYDROGEMS, IKARUS, INFORSE, invert, LEAP, MARKEL/TIMES, MesapP PlaNet, MESSAGE, MiniCAM, NEMS, ORCED, PERSEUS, PRIMES, ProdRisk, RAMSES, RETScreen, simREN, SIVAEL, STREAM, TRNSYS16, UniSyD3.0, WASP, WILMAR Planning Tool. A detailed review about these softwares about their applicability, abilities and short comings are presented by Connolly et al. [140] and Sinha et al. [141]. The Hybrid Optimization Model for Electric Renewables (HOMER) is a tool developed by National Renewable Energy Laboratory, USA, which is a most used tool for the system optimization by the researchers [54,61,63]. ARENA 12.0 is the other tool used in [64,,65]. Other tools are also being used by the researchers are; RAPSODY [68], GAMS [8]. A short summary of the few of the simulation tools is presented in Table 3.

6. Location specific studies The hybrid system studies are presently going worldwide, some specific locations where the studies are being carried out include, Montana [47], Turkey [65], Corsica-Islands [58,59], Hong Kong [10,146,147], Newfoundland, Canada [54], Dhahran, Saudi Arabia [67,135], Greece [134], India [55], China [38], Addis Ababa [61], Algeria [56], Spain [136]. A short summary of few studies have been reported in Table 4.

7. Critical review and observations 7.1. Review points and discussion 1. From the available literature it can be seen that, the researchers have started initially with the graphical and iterative

Table 4 Summary of few location specific studies. Case Location study

Parameters optimized

[47]

Montana

PV capacity, wind capacity and battery Remotely located capacity residential load

[65]

Turkey

[58]

Corsica Islands

PV capacity, wind capacity and battery GSM base station capacity PV capacity, wind capacity and battery Residential load capacity

[10]

[134]

Hong Kong (surrounding islands) Newfoundland, Canada Dhahran, Saudi Arabia Greece

[55]

India

[38]

China

[54] [67]

Load type

PV capacity, wind capacity and battery Telecommunication capacity system Wind capacity, battery capacity and diesel generator PV capacity, wind capacity and battery capacity PV capacity and wind capacity

Remotely located house Residential load Residential load

PV capacity, wind capacity and battery Residential load capacity Telecommunication PV capacity, wind capacity, battery capacity, PV tilt angle and wind turbine relay system height

Description

A simple numerical algorithm has been developed to optimize the system including PV/wind/DG systems and also a break-even analysis is being carried out An optimal sizing method using simulated Annealing has been proposed and the results are compared to the results form ARENA software For three different locations, the optimal sizing is being done with different storage capacities have been tested. An iterative method has been proposed for this purpose The sizing method based on LPSP has been developed and also the complementary characteristics for PV and wind are considered It is a HOMER based study to find out the optimal combination of renewable sources including wind source, battery bank, hydrogen storage and fuel cell The optimal combination is found out using the previous meteorological data parameters The optimal sizing has been done using Monte-Carlo simulation and the closed cycle natural gas power plant has been used as the third source Several combinations of PV and wind systems have been tried and found the optimal combination based on the local meteorological data A genetic algorithm based optimal sizing method has been applied to find the optimal sizing of the PV, wind and battery combination

1144

2.

3.

4.

5.

6.

7.

8.

9.

10.

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

methods for solving the optimal sizing problems. But, as the constraints and the number of objectives have increased, the researchers shifted their focus towards the multi-objective optimization algorithms to help in solving the problems. Even though, there are several optimization strategies available, the researchers have clearly chosen the artificial intelligent algorithms. Table 2 gives the strong evidence to this fact that, the majority of the optimal sizing studies in recent times have used algorithms like GA, modified GA called as NDSA II, PSO and its variants, Ant colony optimization, artificial bee colony and improved bat algorithm. The reason behind this is the fact that, these algorithms are heuristic/metaheuristic in nature. One of the main advantages of using the metaheuristic algorithms is their ability to find the near-optimal solutions efficiently. Because the size optimization problems deal with the situation, where there are many solutions possible, but it is not required to find out the exact optimal solution as the near optimal solution is also one of the potential solution provided that it satisfies all of the constraints. Along with the size optimization, the allocation of the various components in the microgrid at the optimal locations so as to keep the entire system loss at the minimum level is also one of the challenges that the researchers have taken up. Most of the researchers have rightly pointed out the battery as the most vulnerable source in the entire system due to the issue of life time and cost. In a typical plant life cycle the batteries have to be replaced at least 3–4 times and also they require very frequent maintenance. There are many studies that have included the battery life constraints, like the number of charge or discharge cycles and depth of discharge in the optimization process. By imposing these constraints the batteries can be maintained in the healthy charge and reduced number of cycles, which will improve the life time of the battery. But these measures can extend a little more, but a deep research in the field of batteries is the need of the hour to reduce the cost and replacements in the HRES. Due to the above mentioned reasons, some of the studies have considered a study that whether the battery or the diesel generator is most cost effective third source in the HRES to improve the reliability. The DG is found to be cost effective solution but again this is going to cause the environment pollution from the emissions. There are different storage systems namely superconducting magnetic energy storage (SMES), Compressed air energy storage (CAES), ultra-capacitors, flywheel storage, pumped hydro storage and hydrogen storage. In which the CAES and pumped hydro have more storage capacity but their applicability to the systems is limited by the specific site. The greatest advantage of SMES is that, it can store large amount of power with an efficiency of around 98%, but the drawback is that it can store only for shorter duration and the cost is also high. The use of hydrogen storage requires high pressure hydrogen tanks, but the efficiency is around 40–60%. Ultra-capacitors have long lifecycle but they also can store for shorter duration. There were several energy management strategies for the HRES available in the literature. Most of them have addressed the issues of flow and control of power in the system and efficient battery utilization to increase its life. But, the analysis of the literature gives us the idea that a comprehensive control strategy which looks after all of the issues of energy management is due in this field. One of the key issues in the HRES is the intermittent nature of the power generated. As far as the grid integration is concerned, this kind of power fluctuations can be a serious issue

for the future, where the energy share of the renewables is certain to increase. Very few researchers have identified this issue of fluctuations of the power injected into the grid, which requires more attention to be paid on. 11. The converters play a huge role in transferring power form one side to the other side in the HRES. There are several converters have been designed and employed for this purpose. Few of them are individual converters for PV and wind systems and some are single multi-input converters which are having their own merits and demerits. For solving the issues of power quality and grid integration there is a definite need for efficient converters and their control strategies. 12. There is a need of local dc-micro grids to address the issues like power quality, stability at the user end as they require less number of converters in the system. 13. The cost of the electricity produced using the HRES is still way above than that of the cost of power from the conventional sources, there must be significant improvement required in terms of efficiency and cost of the system components. The governments have a key responsibility to encourage the research in these areas and also encourage the power production through the HRES as the renewables are the only sustainable and pollution free sources of energy for the empowered and clean tomorrow.

7.2. Observations Extensive literature survey, as presented leads to the following major outcomes:

 Several size optimization studies have been conducted world-











wide and mostly the locations were coastal areas where the winds are high or remotely located hilly areas where the transmission extension may not be feasible or cost-effective. Out of all the available literature, 55–60% of researchers have concentrated only on the size optimization, including PV, Wind and battery storage. This indicates that the size optimization as the one of the major research area in PV wind hybrid energy systems. The constraints used for the optimization by most of researchers are the PV capacity, Wind capacity and battery bank capacity. Some of the researchers used PV array area, PV tilt angle and Wind turbine rotor swept area as additional and very few people have also considered the environmental constrains such are reduction in pollutant emissions and social acceptability factors. Only few studies considered a break-even analysis whether, setting up a new plant or transmission line extension is going to be a cost-effective solution. This may be considered as one of the research area yet to be explored. Nearly 10% of the total studies have assessed the performance of the existing plant over few years of operational data based on reliability indices. This is also one of the major areas for future research in hybrid wind and PV systems. A controller which is one of the major parts of the hybrid system, but it has been paid a little less attention than other areas of the field. Only 6% of the total studies have designed novel controllers for effective power management, battery SOC management and dump power control. Artificial intelligent techniques like fuzzy logic have also been used for this purpose. Also about 4% studies also considered modeling of battery for analyzing its states. Multilevel converters play a key role in integrating the power into the utility grid. About 10% of the works presented various

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

Hybrid System Review 6% 7% Size optimization

7%

Converter design Controller design

10% 60% 10%

Battery SOC Reviews tools and data analysis

Fig. 4. Graphical representation of research areas related to wind/PV/battery hybrid systems.

converters and their control strategies for effective power transfer. Pie chart as shown in Fig. 4 gives the percentage distribution of research areas as identified after extensive literature survey.

8. Conclusion Hybrid renewable sources have been widely appreciated all around the globe as the sustainable source for the future energy needs. Keeping this in mind, an extensive literature review on the stand-alone and grid connected PV/wind/battery hybrid energy system has been presented in this work. More than 140 journal articles have been considered in the areas of mathematical modeling, size optimization, converter design for integration into the grid, controller design and performance assessment to present the reader with the comprehensive review of the research in the field of HRES. In size optimization, the importance of meteorological data, load profiles, modeling of various components in the system and cost and reliability aspects have been covered in detail. Different size optimization techniques used by the researchers: analytical, iterative and artificial intelligent techniques have been identified and discussed clearly. It is also observed that the artificial intelligent techniques like GA, PSO and ACO reduce the computational burden to achieve the global optimum solution. Various converter topologies for integrating the HRES to the grid and also different control strategies have been covered as per the available literature. Along with the above mentioned areas various computer tools for analyzing the HRES have been discussed and their relative merits and demerits have been brought out. The observations drawn and the critical review points as presented in text makes this work helpful for any researcher interested to explore the research related to the PV/wind/battery hybrid energy systems. References [1] Yang Hongxing, Lu Lin, Zhou Wei. A novel optimization sizing model for hybrid solar–wind power generation system. Sol Energy 2007;81(1):76–84. [2] Zhou Wei, Lou Chengzhi, Li Zhongshi, Lu Lin, Yang Hongxing. Current status of research on optimum sizing of stand-alone hybrid solarwind power generation systems. Appl Energy 2010;87(2):380–9. [3] Upadhyay Subho, Sharma MP. A review on configurations, control and sizing methodologies of hybrid energy systems. Renewable Sustainable Energy Rev 2014;38(0):47–63. [4] Chauhan Anurag, Saini RP. A review on integrated renewable energy system based power generation for stand-alone applications: configurations, storage options, sizing methodologies and control. Renewable Sustainable Energy Rev 2014;38(0):99–120. [5] Mohammed YS, Mustafa MW, Bashir N. Hybrid renewable energy systems for off-grid electric power: review of substantial issues. Renewable Sustainable Energy Rev 2014;35(0):527–39.

1145

[6] Gansler Robert A, Klein SA, Beckman WA. Assessment of the accuracy of generated meteorological data for use in solar energy simulation studies. Sol Energy 1994;53(3):279–87. [7] AbdelWahab M, Essa KSM. Extrapolation of solar irradiation measurements: case study over Egypt. Renewable Energy 1998;14(14):229–39. [8] Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS, Baghzouz Y. Geneticalgorithm-based optimization approach for energy management. IEEE Trans Power Delivery 2013;28(1):162–70 Jan. [9] Celik AN. Optimisation and techno-economic analysis of autonomous wind hybrid energy systems in comparison to single photovoltaic and wind systems. Energy Convers Manage 2002;43(18):2453–68. [10] Yang HX, Lu L, Burnett J. Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong. Renewable Energy 2003;28(11):1813–24. [11] Brinkworth BJ, Protogeropoulos C, Marshall RH. Sizing and technoeconomical optimization for hybrid solar photovoltaic/wind power systems with battery storage. Int J Energy Res 1997;21(0):465–79. [12] Borowy BS, Salameh ZM. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Trans Energy Convers 1996;11(2):367–75. [13] Heide Dominik, von Bremen Lueder, Greiner Martin, Hoffmann Clemens, Speckmann Markus, Bofinger Stefan. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renewable Energy 2010;35 (11):2483–9. [14] Pillai Gobind G, Putrus Ghanim A, Pearsall Nicola M. Generation of synthetic benchmark electrical load profiles using publicly available load and weather data. Int J Electr Power Energy Syst 2014;61(0):1–10. [15] Fumo Nelson, Rafe Biswas MA. Regression analysis for prediction of residential energy consumption. Renewable Sustainable Energy Rev 2015;47 (0):332–43. [16] Asadi Somayeh, Shams Amiri Shideh, Mottahedi Mohammad. On the development of multilinear regression analysis to assess energy consumption in the early stages of building design. Energy Build 2014;85(0):246–55. [17] Braun MR, Altan H, Beck SBM. Using regression analysis to predict the future energy consumption of a supermarket in the fUKg. Appl Energy 2014;130 (0):305–13. [18] Mertens RP, Overstraeten RJV. Physics, technology and use of photovoltaics. Bristol and Boston: Adam Hilger; 1986. [19] Zhou Wei, Yang Hongxing, Fang Zhaohong. A novel model for photovoltaic array performance prediction. Appl Energy 2007;84(12):1187–98. [20] Xu Lin, Ruan Xinbo, Mao Chengxiong, Zhang Buhan, Luo Yi. An improved optimal sizing method for wind–solar–battery hybrid power system. IEEE Trans Sustainable Energy July 2013;4(3):774–85. [21] Alsayed M, Cacciato M, Scarcella G, Scelba G. Multicriteria optimal sizing of photovoltaic/wind turbine grid connected systems. IEEE Trans Energy Convers June 2013;28(2):370–9. [22] Wang Jidong, Yang Fan. Optimal capacity allocation of standalone wind/ solar/battery hybrid power system based on improved particle swarm optimisation algorithm. IET Renewable Power Gener Sept 2013;7(5):443–8. [23] Diaf S, Diaf D, Belhamel M, Haddadi M, Louche A. A methodology for optimal sizing of autonomous hybrid PV/wind system. Energy Policy 2007;35 (11):5708–18. [24] Chedid R, Saliba Y. Optimization and control of autonomous renewable energy systems. Int J Energy Res 1996;20(0):609–24. [25] Beckman WA, Duffie JA. Solar engineering of thermal of thermal process. 2nd ed. New York, NY: Wiley; 1991. [26] Abouzahr I, Ramakumar R. Loss of power supply probability of stand-alone wind electric conversion systems: a closed form solution approach. IEEE Trans Energy Convers 1990;5(3):445–52 Sep. [27] Lu Lin, Yang Hongxing, Burnett John. Investigation on wind power potential on Hong Kong Islands an analysis of wind power and wind turbine characteristics. Renewable Energy 2002;27(1) 1 12. [28] Chedid R, Akiki H, Rahman. S. A decision support technique for the design of hybrid solarwind power systems. IEEE Trans Energy Convers 1998;13 (1):76–83 Mar. [29] Koutroulis Eftichios, Kolokotsa Dionissia, Potirakis Antonis, Kalaitzakis Kostas. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol Energy 2006;80(9):1072–88. [30] Lysen EH. Introduction to wind energy. 2nd ed. Holland: SWD 821; 1983. [31] Abbes D, Martinez A, Champenois G. Eco-design optimisation of an autonomous hybrid wind/photovoltaic system with battery storage. IET Renewable Power Gener Sept 2012;6(5):358–71. [32] Marco Tina Giuseppe, Gagliano Salvina. Probabilistic modelling of hybrid solar/wind power system with solar tracking system. Renewable Energy 2011;36(6):1719–27. [33] Tina G, Gagliano S, Raiti S. Hybrid solar/wind power system probabilistic modelling for long-term performance assessment. Sol Energy 2006;80 (5):578–88. [34] Zamani MH, Riahy GH. Introducing a new method for optimal sizing of a hybrid (wind/PV/battery) system considering instantaneous wind speed variations. Energy Sustainable Dev 2008;12(2):27–33. [35] Nehrir MH, LaMeres BJ, Venkataramanan G, Gerez V, Alvarado. LA. An approach to evaluate the general performance of stand-alone wind/photovoltaic generating systems. IEEE Trans Energy Convers Dec 2000;15 (4):433–9.

1146

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

[36] Jung Jaesung, Broadwater Robert P. Current status and future advances for wind speed and power forecasting. Renewable Sustainable Energy Rev 2014;31(0):762–77. [37] Noviello EI Bernieri A. A method for leadacid battery performance prediction. In: Proc. of the 10th IASTED international symposium on modelling identification and control; 1991. p. 439–41. [38] Yang Hongxing, Wei Zhou, Chengzhi Lou. Optimal design and technoeconomic analysis of a hybrid solarwind power generation system. Appl Energy 2009;86(2):163–9. [39] Guasch D, Silvestre S. Dynamic battery model for photovoltaic applications. Prog Photovoltaics: Res Appl 2003;11:193–206. [40] Zhou Wei, Yang Hongxing, Fang Zhaohong. Battery behavior prediction and battery working states analysis of a hybrid solarwind power generation system. Renewable Energy 2008;33(6):1413–23. [41] Kaiser Rudi. Optimized battery-management system to improve storage lifetime in renewable energy systems. J Power Sources 2007;168(1):58–65. [42] Lujano-Rojas Juan M, Dufo-Lpez Rodolfo, Bernal-Agustn Jos L. Technical and economic effects of charge controller operation and Coulombic efficiency on stand-alone hybrid power systems. Energy Convers Manage 2014;86 (0):709–16. [43] Paliwal Priyanka, Patidar NP, Nema RK. A novel method for reliability assessment of autonomous PV–wind–storage system using probabilistic storage model. Int J Electr Power Energy Syst 2014;55(0):692–703. [44] Schiffer Julia, Uwe Sauer Dirk, Bindner Henrik, Cronin Tom, Lundsager Per, Kaiser Rudi. Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous powersupply systems. J Power Sources 2007;168(1):66–78. [45] Achaibou N, Haddadi M, Malek A. Modeling of lead acid batteries in fPVg systems. Energy Procedia 2012;18(0):538–44. [46] Dufo-Lpez Rodolfo, Lujano-Rojas Juan M, Bernal-Agustn Jos L. Comparison of different leadacid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems. Appl Energy 2014;115(0):242–53. [47] Kellogg WD, Nehrir MH, Venkataramanan G, Gerez V. Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems. IEEE Trans Energy Convers 1998;13(1):70–5 Mar. [48] Wang Lingfeng, Singh C. Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Trans Energy Convers 2009;24(1):163–72 March. [49] Markvart Tomas. Sizing of hybrid photovoltaic–wind energy systems. Sol Energy 1996;57(4):277–81. [50] Borowy BS, Salameh ZM. Optimum photovoltaic array size for a hybrid wind/ PV system. IEEE Trans Energy Convers Sep 1994;9(3):482–8. [51] Alhusein MA, Abu-Leiyah O, Inayatullah. G. A combined system of renewable energy for grid-connected advanced communities. Renewable Energy 1993;3 (67):563–6. [52] Al-Ashwal AM, Moghram IS. Proportion assessment of combined PV–wind generating systems. Renewable Energy 1997;10(1):43–51. [53] Koutroulis Eftichios, Kolokotsa Dionissia, Potirakis Antonis, Kalaitzakis Kostas. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol Energy 2006;80(9):1072–88. [54] Khan MJ, Iqbal MT. Pre-feasibility study of stand-alone hybrid energy systems for applications in Newfoundland. Renewable Energy 2005;30 (6):835–54. [55] Bhave AG. Hybrid solar wind domestic power generating system a case study. Renewable Energy 1999;17(3):355–8. [56] Kaabeche A, Belhamel M, Ibtiouen R. Sizing optimization of gridindependent hybrid photovoltaic/wind power generation system. Energy 2011;36(2):1214–22. [57] Beyer Hans Georg, Langer Christian. A method for the identification of configurations of PV/wind hybrid systems for the reliable supply of small loads. Sol Energy 1996;57(5):381–91. [58] Diaf S, Belhamel M, Haddadi M, Louche. A. Technical and economic assessment of hybrid photovoltaic/wind system with battery storage in Corsica Island. Energy Policy 2008;36(2):743–54. [59] Diaf S, Notton G, Belhamel M, Haddadi M, Louche A. Design and technoeconomical optimization for hybrid PV/wind system under various meteorological conditions. Appl Energy 2008;85(10):968–87. [60] Senjyu T, Hayashi D, Urasaki N, Funabashi T. Optimum configuration for renewable generating systems in residence using genetic algorithm. IEEE Trans Energy Convers June 2006;21(2):459–66. [61] Bekele Getachew, Palm Bjrn. Feasibility study for a standalone solarwindbased hybrid energy system for application in ethiopia. Appl Energy 2010;87 (2):487–95. [62] Yang Hongxing, Zhou Wei, Lu Lin, Fang Zhaohong. Optimal sizing method for stand-alone hybrid solarwind system with fLPSPg technology by using genetic algorithm. Sol Energy 2008;82(4):354–67. [63] Bernal-Agustn Jos L, Dufo-Lpez Rodolfo, Rivas-Ascaso David M. Design of isolated hybrid systems minimizing costs and pollutant emissions. Renewable Energy 2006;31(14):2227–44. [64] Ekren Orhan, Ekren Banu Y, Ozerdem Baris. Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage a case study. Appl Energy 2009;86(78):1043–54. [65] Ekren Orhan, Ekren Banu Y. Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Appl Energy 2010;87(2):592–8.

[66] Celik Ali Naci. Techno-economic analysis of autonomous PV–wind hybrid energy systems using different sizing methods. Energy Convers Manage 2003;44(12):1951–68. [67] Elhadidy MA, Shaahid SM. Parametric study of hybrid (wind þ solarþ diesel) power generating systems. Renewable Energy 2000;21(2):129–39. [68] Chedid R, Rahman S. Unit sizing and control of hybrid wind–solar power systems. IEEE Trans Energy Convers Mar 1997;12(1):79–85. [69] Wang Kai, Ciucu F, Lin Chuang, Low SH. A stochastic power network calculus for integrating renewable energy sources into the power grid. IEEE J Sel Areas Commun July 2012;30(6):1037–48. [70] Giraud F, Salameh ZM. Steady-state performance of a grid-connected rooftop hybrid wind photovoltaic power system with battery storage. IEEE Trans Energy Convers Mar 2001;16(1):1–7. [71] Tina G, Gagliano S, Raiti S. Hybrid solar/wind power system probabilistic modelling for long-term performance assessment. Sol Energy 2006;80 (5):578–88. [72] Esen Hikmet, Inalli Mustafa, Esen Mehmet. Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energy Convers Manage 2006;47(910):1281–97. [73] Esen Hikmet, Inalli Mustafa, Esen Mehmet. A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build Environ 2007;42(5):1955–65. [74] Esen Mehmet, Yuksel Tahsin. Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build 2013;65 (0):340–51. [75] Karaki SH, Chedid RB, Ramadan R. Probabilistic performance assessment of autonomous solar–wind energy conversion systems. IEEE Trans Energy Convers Sep 1999;14(3):766–72. [76] Bindner H. Life time modeling of lead-acid batteries. Denmark: Riso Nat. Lab; 2005. [77] Chen Hsing Hung, Kang He-Yau, Lee Amy HI. Strategic selection of suitable projects for hybrid solar–wind power generation systems. Renewable Sustainable Energy Rev 2010;14(1):413–21. [78] Karabacak Kerim, Cetin Numan. Artificial neural networks for controlling wind/PV power systems: a review. Renewable Sustainable Energy Rev 2014;29(0):804–27. [79] Zhao Bo, Zhang Xuesong, Li Peng, Wang Ke, Xue Meidong, Wang Caisheng. Optimal sizing, operating strategy and operational experience of a standalone microgrid on Dongfushan Island. Appl Energy 2014;113(0):1656–66. [80] Belmili Hocine, Haddadi Mourad, Bacha Seddik, Almi Mohamed Faycal, Bendib Boualem. Sizing stand-alone photovoltaic-wind hybrid system: techno-economic analysis and optimization. Renewable Sustainable Energy Rev 2014;30(0):821–32. [81] Camargo Nogueira Carlos Eduardo, Vidotto Magno Luiz, Niedzialkoski Rosana Krauss, de Souza Samuel Nelson Melegari, Incio Chaves Luiz, Edwiges Thiago, dos Santos Darlisson Bentes, van Werncke. Sizing and simulation of a photovoltaic-wind energy system using batteries, applied for a small rural property located in the South of Brazil. Renewable Sustainable Energy Rev 2014;29(0):151–7. [82] Amos Madhlopa Debbie Sparks, Keen Samantha, Moorlach Mascha, Krog Pieter, Dlamini Thuli. Optimization of a PV/wind hybrid system under limited water resources. Renewable Sustainable Energy Rev 2015;47 (0):324–31. [83] Abdelhamid Kaabeche, Rachid Ibtiouen. Techno-economic optimization of hybrid photovoltaic/wind/diesel/battery generation in a stand-alone power system. Sol Energy 2014;103(0):171–82. [84] Tao Ma Hongxing Yang, Lin Lu. A feasibility study of a stand-alone hybrid solar wind battery system for a Remote Island. Appl Energy 2014;121 (0):149–58. [85] Bahman Bahmani-Firouzi, Rasoul Azizipanah-Abarghooee. Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int J Electr Power Energy Syst 2014;56(0):42–54. [86] Akbar Maleki, Alireza Askarzadeh. Comparative study of artificial intelligence techniques for sizing of a hydrogen-based stand-alone photovoltaic/wind hybrid system. Int J Hydrog Energy 2014;39(19):9973–84. [87] Tgani I, Aboubou A, Ayad MY, Becherif M, Saadi R, Kraa O. Optimal sizing design and energy management of stand-alone photovoltaic/wind generator systems. Energy Procedia 2014;50(0):163–70. [88] KeheWu Huan Zhou, Sicheng An, Ting Huang. Optimal coordinate operation control for wind photovoltaic battery storage power-generation units. Energy Convers Manage 2015;90(0):466–75. [89] Akbar Maleki, Alireza Askarzadeh. Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering fLPSPg concept. Sol Energy 2014;107(0):227–35. [90] Mokheimer Esmail MA, Al-Sharafi Abdullah, Habib Mohamed A, Iyad Alzaharnah. A new study for hybrid PV/wind off-grid power generation systems with the comparison of results from homer. Int J Green Energy 2015;12(5):526–42. [91] Abdolvahhab Fetanat, Ehsan Khorasaninejad. Size optimization for hybrid photovoltaic wind energy system using ant colony optimization for continuous domains based integer programming. Appl Soft Comput 2015;31 (0):196–209. [92] Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS. Stochastic performance assessment and sizing for a hybrid power system of solar/wind/energy storage. IEEE Trans Sustainable Energy 2014;5(2):363–71.

A. Mahesh, K.S. Sandhu / Renewable and Sustainable Energy Reviews 52 (2015) 1135–1147

[93] H Zahboune, FZ Kadda, S Zouggar, E Ziani, JJ Klemes, PS Varbanov, Y Zarhloule. The new electricity system cascade analysis method for optimal sizing of an autonomous hybrid PV/wind energy system with battery storage. In: Fifth international renewable energy congress (IREC); 2014, , p. 1–6, March 2014. [94] Tao Ma Hongxing Yang, Lu Lin, Jinqing Peng. Optimal design of an autonomous solar wind pumped storage power supply system. Appl Energy, (Article 2014;0 in press. [95] Sheng Wanxing, Liu Ke yan, Meng Xiaoli, Ye Xueshun, Liu Yongmei. Research and practice on typical modes and optimal allocation method for PV–wind– ES in microgrid. Electr Power Syst Res 2015;120(0):242–55. [96] Paliwal Priyanka, Patidar NP, Nema RK. Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using particle swarm optimization. Renewable Energy 2014;63(0):194–204. [97] Abdelaziz AY, Hegazy YG, Walid El-Khattam, Othman MM. Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks. Electr Power Syst Res 2015;119 (0):34–44. [98] Jingyi Zhao, Yuan Xiaofang. Multi-objective optimization of stand-alone hybrid PV–wind–diesel battery system using improved fruit fly optimization algorithm. Soft Comput 2015:1–13. [99] Gupta RA, Kumar Rajesh, Bansal Ajay Kumar. Bbo-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting. Renewable Sustainable Energy Rev 2015;41 (0):1366–75. [100] Alireza Askarzadeh, dos Santos Coelho Leandro. A novel framework for optimization of a grid independent hybrid renewable energy system: a case study of Iran. Sol Energy 2015;112(0):383–96. [101] Farivar Fazelpour, Majid Vafaeipour, Rahbari Omid, Marc A Rosen. Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics. Energy Convers Manage 2014;77(0):250–61. [102] Fadaeenejad M, Radzi MAM, AbKadir MZA, Hizam H. Assessment of hybrid renewable power sources for rural electrification in Malaysia. Renewable Sustainable Energy Rev 2014;30(0):299–305. [103] Souheil El Alimi Taher Maatallah, Sassi Ben Nasrallah. Break-even analysis and optimization of a stand-alone hybrid system with battery storage for residential load consumption a case study. Renewable Sustainable Energy Rev 2014;37(0):408–23. [104] Kuo-Hao Chang, Lin Grace. Optimal design of hybrid renewable energy systems using simulation optimization. Simul Modell Pract Theory 2015;52 (0):40–51. [105] Jahromi Mohammad Ali Yazdanpanah, Farahat Said, Barakati Seyed Masoud. Optimal size and cost analysis of stand-alone hybrid wind/photovoltaic power-generation systems. Civ Eng Environ Syst 2014;31(4):283–303. [106] F Valenciaga, PF Puleston, PE Battaiotto, RJ Mantz. Passivity/sliding mode control of a stand-alone hybrid generation system. In: Control theory and applications, IEE proceedings-147, (vol. 6):680–686; Nov 2000. [107] Lagorse J, Simoes MG, Miraoui. A. A multiagent fuzzy-logic-based energy management of hybrid systems. IEEE Trans Ind Appl 2009;45(6):2123–9 Nov. [108] Wei Qi Jinfeng Liu, Xianzhong Chen, Christofides PD. Supervisory predictive control of standalone wind/solar energy generation systems. IEEE Trans Control Syst Technol 2011;19(1):199–207 Jan. [109] Kumaravel S, Ashok S. Optimal power management controller for a standalone solar PV/wind/battery hybrid energy system. Energy Sources Part A: Recovery Util Environ Eff 2015;37(4):407–15. [110] Tazvinga Henerica, Zhu Bing, Xia Xiaohua. Energy dispatch strategy for a photovoltaic wind diesel battery hybrid power system. Sol Energy 2014;108 (0):412–20. [111] Torreglosa Juan P, Garca Pablo, Fernndez Luis M, Jurado Francisco. Energy dispatching based on predictive controller of an off-grid wind turbine/ photovoltaic/hydrogen/battery hybrid system. Renewable Energy 2015;74 (0):326–36. [112] Daniel SA, Ammasaigounden. N. A novel hybrid isolated generating system based on PV fed inverter-assisted wind-driven induction generators. IEEE Trans Energy Convers 2004;19(2):416–22 June. [113] Valenciaga F, Puleston PF. Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy. IEEE Trans Energy Convers 2005;20(2):398–405 June. [114] Chunhua Liu KT Chau, Zhang Xiaodong. An efficient wind photovoltaic hybrid generation system using doubly excited permanent-magnet brushless machine. IEEE Trans Ind Electron 2010;57(3):831–9 March. [115] Seul-Ki Kim Jin-Hong Jeon, Cho Chang-Hee, Ahn Jong-Bo, Sae-Hyuk Kwon. Dynamic modeling and control of a grid-connected hybrid generation system with versatile power transfer. IEEE Trans Ind Electron 2008;55(4):1677–88 April. [116] Yaow-Ming Chen Yuan-Chuan Liu, Hung Shih-Chieh, Chung-Sheng Cheng. Multi-input inverter for grid-connected hybrid PV/wind power system. IEEE Trans Power Electron 2007;22(3):1070–7 May. [117] Wandhare RG, Agarwal. V. Novel integration of a PV–wind energy system with enhanced efficiency. IEEE Trans Power Electron 2015;30(7):3638–49 July. [118] SJ Park, BB Kang, JP Yoon, IS Cha, JY Lim. A study on the stand-alone operating or photovoltaic/wind power hybrid generation system. In: Power electronics

[119]

[120]

[121]

[122]

[123]

[124] [125]

[126]

[127]

[128]

[129]

[130]

[131]

[132]

[133]

[134] [135] [136]

[137] [138]

[139]

[140]

[141] [142] [143] [144] [145]

[146]

[147]

1147

specialists conference, 2004. PESC 04. 2004 IEEE 35th Annual, vol. 3; June 2004. p. 2095–99. AR Gautam, OP Mahela, S Singh, RN Mahia. A double input SMV controlled dc/dc buckboost converter for solar PV/wind energy sources. In: Recent advances and innovations in engineering (ICRAIE), 2014, p. 1–6; May 2014. Cheng-Wei Chen, Kun-Hung Chen, Yaow-Ming Chen. A semi-isolated multiinput converter for hybrid PV/wind power charger system. In: International power electronics conference (IPEC Hiroshima 2014—ECCE-ASIA), 2014; May 2014. p. 3592–97. Whei-Min Lin, Chih-Ming Hong, Chiung-Hsing Chen. Neural-network-based MPPT control of a stand-alone hybrid power generation system. IEEE Trans Power Electron 2011;26(12):3571–81 Dec. Seung-Tak Kim, Byung-Kwan Kang, Sun-Ho Bae, Jung-Wook Park. Application of smes and grid code compliance to wind/photovoltaic generation system. IEEE Trans Appl Supercond, 2013;23(3):5000804 –5000804, June. Xiangjun Li, Dong Hui, Xiaokang Lai. Battery energy storage station (bess)based smoothing control of photovoltaic (PV) and wind power generation fluctuations. IEEE Trans Sustainable Energy 2013;4(2):464–73 April. Kabalci Ersan. Design and analysis of a hybrid renewable energy plant with solar and wind power. Energy Convers Manage 2013;72(0):51–9. Sungwoo Bae, Kwasinski A. Dynamic modeling and operation strategy for a microgrid with wind and photovoltaic resources. IEEE Trans Smart Grid 2012;3(4):1867–76 Dec. Pouresmaeil Edris, Montesinos-Miracle Daniel, Gomis-Bellmunt Oriol, Bergas-Jan Joan. A multi-objective control strategy for grid connection of fDGg (distributed generation) resources. Energy 2010;35(12):5022–30. Edris Pouresmaeil, Gomis-Bellmunt Oriol, Montesinos-Miracle Daniel, Bergas-Jan Joan. Multilevel converters control for renewable energy integration to the power grid. Energy 2011;36(2):950–63. Abbes Mohamed, Belhadj Jamel. New control method of a robust fNPCg converter for renewable energy sources grid connection. Electr Power Syst Res 2012;88(0):52–63. Dionisio Ramirez, Sergio Martinez, Carmelo Carrero, Platero Carlos A. Improvements in the grid connection of renewable generators with full power converters. Renewable Energy 2012;43(0):90–100. Carrasco JM, Franquelo LG, Bialasiewicz JT, Galvan E, Guisado RCP, Prats MaAM, Leon JI, Moreno-Alfonso N. Power-electronic systems for the grid integration of renewable energy sources: a survey. IEEE Trans Ind Electron 2006;53(4):1002–16 June. Pouresmaeil E, Montesinos-Miracle D, Gomis-Bellmunt O. Control scheme of three-level NPC inverter for integration of renewable energy resources into ac grid. IEEE Syst J 2012;6(2):242–53 June. Vasiladiotis M, Rufer A. Analysis and control of modular multilevel converters with integrated battery energy storage. IEEE Trans Power Electron 2015;30(1):163–75 Jan. Sarkar S, Ajjarapu V. Mw resource assessment model for a hybrid energy conversion system with wind and solar resources. IEEE Trans Sustainable Energy 2011;2(4):383–91 Oct. Bakos GC, Tsagas NF. Technoeconomic assessment of a hybrid solar/wind installation for electrical energy saving. Energy Build 2003;35(2):139–45. Elhadidy MA. Performance evaluation of hybrid (wind/solar/diesel) power systems. Renewable Energy 2002;26(3):401–13. Luis Arribas Luis Cano, Cruz Ignacio, Montserrat Mata, Ermen Llobet. PV/wind hybrid system performance: a new approach and a case study. Renewable Energy 2010;35(1):128–37. Ching Lee Shun. Numerical estimation model of energy conversion for small hybrid solarwind system. Sol Energy 2012;86(11):3125–36. Hirose T, Matsuo H. Standalone hybrid wind–solar power generation system applying dump power control without dump load. IEEE Trans Ind Electron 2012;59(2):988–97 Feb. Erdinc O, Elma O, Uzunoglu M, Selamogullari US. Real-time performance analysis of an optimally sized hybrid renewable energy conversion unit. Energy Build 2014;75(0):419–29. Connolly D, Lund H, Mathiesen BV, Leahy. M. A review of computer tools for analysing the integration of renewable energy into various energy systems. Appl Energy 2010;87(4):1059–82. Sunanda Sinha, Chandel SS. Review of software tools for hybrid renewable energy systems. Renewable Sustainable Energy Rev 2014;32(0):192–205. Himri Y, Boudghene Stambouli A, Draoui B. Prospects of wind farm development in Algeria. Desalination 2009;239(13):130–8. Houri Ahmad. Solar water heating in Lebanon: current status and future prospects. Renewable Energy 2006;31(5):663–75. Andrew Mills, Al-Hallaj Said. Simulation of hydrogen-based hybrid systems using hybrid2. Int J Hydrog Energy 2004;29(10):991–9. Dufo-Lpez Rodolfo, Bernal-Agustn Jos L, Yusta-Loyo Jos M, Domnguez-Navarro Jos A, Ramrez-Rosado Ignacio J, Juan Lujano, Ismael Aso. Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV/wind/diesel systems with batteries storage 2011;88(11):4033–41Appl Energy 2011;88(11):4033–41. Ma Tao, Yang Hongxing, Lu Lin, Peng Jinqing. Technical feasibility study on a standalone hybrid solar–wind system with pumped hydro storage for a remote island in Hong Kong. Renewable Energy 2014;69(0):7–15. Ai B, Yang H, Shen H, Liao X. Computer-aided design of PV/wind hybrid system. Renewable Energy 2003;28(10):1491–512.