Computer-aided design of PV/wind hybrid system

A complete set of match calculation methods for optimum sizing of PV/wind hybrid system is presented. In this method, the more accurate and practical mathematic models for characterizing PV module, wind generator and battery are adopted; combining with hourly measured meteorologic data and load data, the performance of a PV/wind hybrid system is determined on a hourly basis; by fixing the capacity of wind generators, the whole year’s LPSP (loss of power supply probability) values of PV/wind hybrid systems with different capacity of PV array and battery bank are calculated, then the trade-off curve between battery bank and PV array capacity is drawn for the given LPSP value; the optimum configuration which can meet the energy demand with the minimum cost can be found by drawing a tangent to the trade-off curve with the slope representing the relationship between cost of PV module and that of the battery. According to this match calculation method, a set of match calculation programs for optimum sizing of PV/wind hybrid systems have been developed. Applying these match calculation programs to an assumed PV/wind hybrid system to be installed at Waglan island of Hong Kong, the optimum configuration and its hourly, daily, monthly and yearly performances are given.     

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%).     

Optimal design and techno-economic analysis of a hybrid solar–wind power generation system

Solar energy and wind energy are the two most viable renewable energy resources in the world. Good compensation characters are usually found between solar energy and wind energy. This paper recommend an optimal design model for designing hybrid solar–wind systems employing battery banks for calculating the system optimum configurations and ensuring that the annualized cost of the systems is minimized while satisfying the custom required loss of power supply probability (LPSP). The five decision variables included in the optimization process are the PV module number, PV module slope angle, wind turbine number, wind turbine installation height and battery capacity. The proposed method has been applied to design a hybrid system to supply power for a telecommunication relay station along south-east coast of China. The research and project monitoring results of the hybrid project were reported, good complementary characteristics between the solar and wind energy were found, and the hybrid system turned out to be able to perform very well as expected throughout the year with the battery over-discharge situations seldom occurred.     

Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm

System power reliability under varying weather conditions and the corresponding system cost are the two main concerns for designing hybrid solar–wind power generation systems. This paper recommends an optimal sizing method to optimize the configurations of a hybrid solar–wind system employing battery banks. Based on a genetic algorithm (GA), which has the ability to attain the global optimum with relative computational simplicity, one optimal sizing method was developed to calculate the optimum system configuration that can achieve the customers required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS). The decision variables included in the optimization process are the PV module number, wind turbine number, battery number, PV module slope angle and wind turbine installation height. The proposed method has been applied to the analysis of a hybrid system which supplies power for a telecommunication relay station, and good optimization performance has been found. Furthermore, the relationships between system power reliability and system configurations were also given.     

Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

High cost of renewable energy systems has led to its slow adoption in many countries. Hence, it is vital to select an appropriate size of the system in order to reduce the cost and excess energy produced as well as to maximize the available resources. The sizing of hybrid system must satisfy the LPSP (Loss of Power Supply Probability) which determines the ability of the system to meet the load requirements. Once the lowest configurations are determined, the cost of the system must then be taken into consideration to determine the system with the lowest cost. The optimization methodology proposed in this paper uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) to model the PV and wind sources. The algorithm developed is compared to HOMER (Hybrid Optimization Model for Electric Renewables) and HOGA (Hybrid Optimization by Genetic Algorithms) software and the results demonstrate an accuracy of 96% for PV and wind. The optimized system is simulated in PSCAD/EMTDC and the results show that low excess energy is achieved. The optimized system is also able to supply power to the load without any renewable sources for a longer period, while conforming to the desired LPSP.     

风光互补发电系统的优化设计(I) CAD设计方法

给出了一整套利用CAD进行风光互补发电系统优化设计的方法。为了精确确定系统每小时的运行状态,采用了更精确地表征组件特性及评估实际获得的风光资源的数学模型。为了寻找出以最小设备投资成本满足用户用电要求的系统配置,首先在风力发电机容量固定不变的前提下,计算了与该容量风力发电机匹配的不同容量的PV方阵和蓄电池所组成的风/光/蓄组合的全年功率供给亏欠率LPSP,根据总的设备投资成本最小化的原则筛选出一组与该容量风力发电机对应的满足用户给定系统供电可靠性即LPSP值的风/光/蓄组合;然后通过改变风力发电机的容量,优选出多个与不同容量风力发电机对应的既能满足用户用电要求同时总的设备购置成本又是最低的风/光/蓄组合,比较它们的成本最终唯一确定出以最小投资成本满足用户用电要求的优化的系统配置。     

Simulation model for sizing of stand-alone solar PV system with interconnected array

Solar PV arrays made of interconnected modules are comparatively less susceptible to shadow problem and power degradation resulting from the aging of solar cells. This paper presents a simulation model for the sizing of stand-alone solar PV systems with interconnected arrays. It considers the electricity generation in the array and its storage in the battery bank serving the fluctuating load demand. The loss of power supply probability (LPSP) is used to connote the risk of not satisfying the load demand. The non-tracking (e.g., fixed and tilted) and single-axis tracking aperture arrays having cross-connected modules of single crystalline silicon solar cells in a (6×6) modular configuration are considered. The simulation results are illustrated with the help of a numerical example wherein the load demand is assumed to follow uniform probabilistic distribution. For a given load, the numbers of solar PV modules and batteries corresponding to zero values of LPSP on diurnal basis during the year round cycle of operation are presented. The results corresponding to the surplus and deficit of energy as a function of LPSP are also presented and discussed to assess the engineering design trade offs in the system components.Furthermore, a simple cost analysis has also been carried out, which indicates that for Delhi the stand-alone solar PV systems with fixed and tilted aperture arrays are better option than those with single-axis tracking aperture (with north–south oriented tracking axis) arrays.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

A novel optimization sizing model for hybrid solar-wind power generation system

This paper develops the Hybrid Solar-Wind System Optimization Sizing (HSWSO) model, to optimize the capacity sizes of different components of hybrid solar-wind power generation systems employing a battery bank. The HSWSO model consists of three parts: the model of the hybrid system, the model of Loss of Power Supply Probability (LPSP) and the model of the Levelised Cost of Energy (LCE). The flow chart of the HSWSO model is also illustrated. With the incorporated HSWSO model, the sizing optimization of hybrid solar-wind power generation systems can be achieved technically and economically according to the system reliability requirements. A case study is reported to show the importance of the HSWSO model for sizing the capacities of wind turbines, PV panel and battery banks of a hybrid solar-wind renewable energy system.     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO‐simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well‐known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively. Copyright © 2014 John Wiley & Sons, Ltd.     

Comparative study of artificial intelligence techniques for sizing of a hydrogen-based stand-alone photovoltaic/wind hybrid system

As non-polluting reliable energy sources, stand-alone photovoltaic/wind/fuel cell (PV/wind/FC) hybrid systems are being studied from various aspects in recent years. In such systems, optimum sizing is the main issue for having a cost-effective system. This paper evaluates the performance of different artificial intelligence (AI) techniques for optimum sizing of a PV/wind/FC hybrid system to continuously satisfy the load demand with the minimal total annual cost. For this aim, the sizing problem is formulated and four well-known heuristic algorithms, namely, particle swarm optimization (PSO), tabu search (TS), simulated annealing (SA), and harmony search (HS), are applied to the system and the results are compared in terms of the total annual cost. It can be seen that not only average results produced by PSO are more promising than those of the other algorithms but also PSO has the most robustness. As another investigation, the sizing is also performed for a PV/wind/battery hybrid system and the results are compared with those of the PV/wind/FC system.     

Generation unit sizing and cost analysis for stand-alone wind,photovoltaic, and hybrid wind/PV systems

This paper presents the results of investigations on the application of wind, photovoltaic (PV), and hybrid wind/PV power generating systems for utilization as stand-alone systems. A simple numerical algorithm has been developed for generation unit sizing. It has been used to determine the optimum generation capacity and storage needed for a stand-alone, wind, PV, and hybrid wind/PV system for an experimental site in a remote area in Montana with a typical residential load. Generation and storage units for each system are properly sized in order to meet the annual load and minimize the total annual cost to the customer. In addition, an economic analysis has been performed for the above three scenarios and is used to justify the use of renewable energy versus constructing a line extension from the nearest existing power line to supply the load with conventional power. Annual average hourly values for load, wind speed, and insolation have been used     

太阳能电动汽车储能系统的优化配置

在太阳能电动汽车(SEEV)系统中,储能系统的优化配置是一个重要且具挑战性任务.太阳能电动汽车储能系统的优化配置可以看成一个具有约束的优化问题:以储能系统的成本最小为优化目标,以表达系统可靠性指标的负载失电率为约束.决策变量不仅包含传统方法中的蓄电池充电电流而且还包含储能飞轮的质量.优化算法是采用基于遗传算法和神经网络的组合优化方法,即把机会约束遗传算法中比较耗时的个体检验部分交给神经网络处理.研究结果表明,基于遗传算法和神经网络的组合优化算法在被应用于太阳能电动汽车储能系统的优化配置时,算法收敛良好,计算时间少且可行.     

Optimal sizing study of hybrid wind/PV/diesel power generation unit

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented.The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system.     

风光互补独立供电系统的优化设计

在设计风光互补独立供电系统时,系统中需要优化的不仅有光伏电池和蓄电池的容量,还应该有风力发电机种类和容量以及光伏电池的倾角。优化目标为系统安装成本,约束条件为供电可靠性,其指标负载缺电率LP- SP需经仿真运行得到。本问题属于非线性整数规划,也是一个NP-hard问题。用包含精英策略的遗传算法优化,以自适应罚函数法处理约束。计算和验证表明本文采用的算法收敛,能同时优化风力发电机类型和容量、光伏电池的容量和倾角以及蓄电池的容量,并且计算效率高。     

A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt

In this paper, a reliable methodology incorporated mine blast algorithm (MBA) is applied to solve the optimal sizing of a hybrid system consisting of photovoltaic modules, wind turbines and fuel cells (PV/WT/FC) to meet a certain load of remote area in Egypt. The main objective of the optimal sizing process is to achieve the minimum annual cost of the system with load coverage. The sizing process is performed optimally based on real measured data for solar radiation, ambient temperature and wind velocity recorded by the solar radiation and meteorological station located at national research institute of astronomy and geophysics, Helwan city, Egypt. Three other meta-heuristic optimization techniques, particle swarm optimization, cuckoo search and artificial bee colony are applied to solve the problem and the results are compared with those obtained by the proposed methodology. A power management strategy that regulates the power flow between each system component is also presented. The obtained results show that; applying the proposed methodology will save about 24.8% in the annual total cost of the proposed system compared with PSO, 8.956% compared with CS and 11.5576% compared with ABC. The proposed algorithm based on MBA is candidate for solving the presented optimization problem of optimal sizing the hybrid PV/WT/FC system.     

Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico

Renewable generating systems are alternative to produce electric energy in a clean manner. However, the high costs of the constituents limit their broad use. Thus, sizing is an important issue in the renewable generating systems design, in order to reach an efficient relationship between cost and benefit. Likewise, the random nature of the sources makes the sizing a complex task with regard to a conventional system. This paper is focused on calculate the optimal size of a wind-photovoltaic-fuel cell system to meet the power demand of an isolated residential load located in the south-east region of Mexico (Chetumal city 18°31′21.4″N 88°16′11.3″W), with a solar radiation range from 0 to 0.75 kW/m² and wind speed range from 5 to 7.8 m/s. Swarm intelligence techniques have been successfully applied in solving many combinatorial optimization problems in which the objective space possesses many local optimal solutions. This work employs the Particle Swarm Optimizer (PSO) algorithm to search the optimal sizing for the power plant minimizing the total costs of the system; as a metaheuristic procedure, the PSO was able to find the best configuration regardless the lack of a deep knowledge of the problem. Compared against the Differential Evolution (DE) technique, the PSO performance is faster and able to provide a configuration that saves around 10% of the total cost of the hybrid system.     

Optimization and sensitivity analysis of a PV/Wind/Diesel hybrid system for a rural community in the pacific

This work looks at the feasibility of a standalone hybrid power generation system for providing power to a rural community in the Pacific Islands. The optimization and sensitivity analysis of a proposed PV/Wind/Diesel hybrid System is performed together with economic analysis. We have used HOMER, a sizing and optimization tool for distributed power system, developed by National Renewable Energy Laboratory (NREL) for our analysis. The sensitivity analysis was done using wind speed data and diesel price as variables. An Ice storage facility having a corrected mean daily load of 60 kWh/day was considered as the stand-alone load. The life cycle cost analysis was done for a number of scenarios with different renewable energy contribution to the total electricity produced.     

Optimal configuration assessment of renewable energy in Malaysia

This paper proposes the use of a PV–wind–diesel generator hybrid system in order to determine the optimal configuration of renewable energy in Malaysia and to compare the production cost of solar and wind power with its annual yield relevant to different regions in Malaysia namely, Johor, Sarawak, Penang and Selangor. The configuration of optimal hybrid system is selected based on the best components and sizing with appropriate operating strategy to provide a cheap, efficient, reliable and cost-effective system. The various renewable energy sources and their applicability in terms of cost and performance are analyzed. Moreover, the annual yield and cost of energy production of solar and wind energy are evaluated. The Simulations were carried out using the HOMER program based on data obtained from the Malaysian Meteorological Centre. Results show that, for Malaysia, a PV–diesel generator hybrid system is the most suitable solution in terms of economic performance and pollution. However, the cost of production of solar and wind energy proved to be cheaper and more environmentally friendly than the energy produced from diesel generators.