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.     

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.     

Study on optimal configuration of the grid-connected wind–solar–battery hybrid power system

The capacity allocation of each energy unit in the grid-connected wind–solar–battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind–solar–battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind–solar–battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.     

Optimization of micro-grid system using MOPSO

Access to a reliable source of electricity is a basic need for any community as it can improve the living standards characterized via the improvement of healthcare, education, and the local economy at large. There are two key factors to consider when assessing the appropriateness of a micro-grid system, the cost-effectiveness of the system and the quality of service. The tradeoff between cost and reliability of the system is a major compromise in designing hybrid systems. In this way, optimization of a Hybrid Micro-Grid System (HMGS) is investigated. A hybrid wind/PV system with battery storage and diesel generator is used for this purpose. The power management algorithm is applied to the load, and the Multi-Objective Particle Swarm Optimization (MOPSO) method is used to find the best configuration of the system and for sizing the components. A set of recent hourly wind speed data from three meteorological stations in Iran, namely: Nahavand, Rafsanjan, and Khash, are selected and tested for the optimization of HMGS. Despite design complexity of the aforementioned systems, the results show that the MOPSO optimization model produces appropriate sizing of the components for each location. It is also suggested that the use of HMGS can be considered as a good alternative to promote electrification projects and enhance energy access within remote Iranian areas or other developing countries enjoying the same or similar climatic conditions.     

Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach

An economic model and optimization procedure is developed in this paper for grid-connected hybrid wind–hydrogen combined heat and power systems for residential applications in northeastern Iran. The model considers various significant factors: energy production cost, electrical trade with local grid, electrical power generation from the wind/hydrogen energy system, thermal recovery from the fuel cell, and maintenance. Also, various tariffs for purchasing and selling electrical energy from the local grid are considered for the hybrid system operation. The optimization objective is to minimize the system total cost subject to relevant constraints for residential applications. To achieve this aim, an efficient optimization method is proposed based on particle swarm optimization. The proposed algorithm performance is compared with that for the imperialist competition algorithm. The results show that the hybrid system is the most cost-effective for the residential load, and the results of the proposed algorithm are more promising than those for the alternative algorithm.     

PES Meetings

This letter presents a hybrid genetic algorithm (GA) method to solve optimal power flow (OPF) in a power system incorporating flexible AC transmission systems (FACTS). In the solution process GA is integrated with conventional OPF to select the best control parameters to minimize the total generation fuel cost and keep the power flows within the security limits. A case study using an IEEE test system is presented to demonstrate its applicability.     

Technico-economic analysis of off grid solar PV/Fuel cell energy system for residential community in desert region

A technico-economic analysis based on integrated modeling, simulation, and optimization approach is used in this study to design an off grid hybrid solar PV/Fuel Cell power system. The main objective is to optimize the design and develop dispatch control strategies of the standalone hybrid renewable power system to meet the desired electric load of a residential community located in a desert region. The effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. The goal of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems. Simulation, modeling, optimization and dispatch control strategies were used in this study to determine the performance and the cost of the proposed hybrid renewable power system. The simulation results show that the distributed power generation using solar PV and Fuel Cell energy systems integrated with an electrolyzer for hydrogen production and using cycle charging dispatch control strategy (the fuel cell will operate to meet the AC primary load and the surplus of electrical power is used to run the electrolyzer) offers the best performance. The hybrid power system was designed to meet the energy demand of 4500 kWh/day of the residential community (150 houses). The total power production from the distributed hybrid energy system was 52% from the solar PV, and 48% from the fuel cell. From the total electricity generated from the photovoltaic hydrogen fuel cell hybrid system, 80.70% is used to meet all the AC load of the residential community with negligible unmet AC primary load (0.08%), 14.08% is the input DC power for the electrolyzer for hydrogen production, 3.30% are the losses in the DC/AC inverter, and 1.84% is the excess power (dumped energy). The proposed off-grid hybrid renewable power system has 40.2% renewable fraction, is economically viable with a levelized cost of energy of 145 $/MWh and is environmentally friendly (zero carbon dioxide emissions during the electricity generation from the solar PV and Fuel Cell hybrid power system).     

Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO

This study presents an optimized design of microgrid (MG) in distribution systems with multiple distributed generation (DG) units under different market policies such as pool/hybrid electricity market.Proposed microgrid includes various energy sources such as photovoltaic array and wind turbine with energy storage devices such as battery bank.In this study, microgrid is considered as independent power producer company (IPP) in power system. Price of selling/buying power in on-peak or off-peak for MG, DG and upstream power system (DISCO) under pool/bilateral/hybrid electricity market are different. In this study, particle swarm optimization (PSO) algorithm has been implemented for the optimization of the microgrid cost. The costs include capital cost, replacement cost, operation and maintenance costs and production cost for microgrid and DGs. Then, an objective function to maximize total net present worth (NPW) is presented. PSO approach is employed to obtain the minimum cost of microgrid, during interconnected operation by optimizing the production of local DGs and power exchanges with the main distribution grid. The optimization algorithm is applied to a typical LV network operating under different market policies.     

风光水互补发电系统的优化配置

为提高风光水互补发电系统的供电可靠性,降低经济成本,研究了电源容量的优化配置。首先提出独立和并网运行调度策略,然后考虑供电可靠性和蓄水库水量平衡等约束,建立了以全寿命周期成本最小为目标的优化配置模型,采用改进遗传算法进行求解。算例探讨了供电可靠性和联络线传输容量对优化配置的影响,验证了所提方法的有效性。由仿真结果可知,独立运行时,利用风光水互补发电即可保证较高的供电可靠性;并网运行时,采用联络线的双向交互电能能力,可进一步提高系统供电可靠性,有效降低经济成本。     

Metamodel based design optimization approach in promoting the performance of proton exchange membrane fuel cells

This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density.     

Dynamic modeling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology

Economic and environmental concerns over fossil fuels encourage the development of photovoltaic (PV) energy systems. Due to the intermittent nature of solar energy, energy storage is needed in a stand-alone PV system for the purpose of ensuring continuous power flow. Three stand-alone photovoltaic power systems using different energy storage technologies are studied in this paper. Key components including PV modules, fuel cells, electrolyzers, compressors, hydrogen tanks and batteries are modeled in a clear way so as to facilitate the evaluation of the power systems. Based on energy storage technology, a method of ascertaining minimal system configuration is designed to perform the sizing optimization and reveal the correlations between the system cost and the system efficiency. The three hybrid power systems, i.e., photovoltaic/battery (PV/Battery) system, photovoltaic/fuel cell (PV/FC) system, and photovoltaic/fuel cell/battery (PV/FC/Battery) system, are optimized, analyzed and compared. The obtained results indicate that maximizing the system efficiency while minimizing system cost is a multi-objective optimization problem. As a trade-off solution to the problem, the proposed PV/FC/Battery hybrid system is found to be the configuration with lower cost, higher efficiency and less PV modules as compared with either single storage system.     

A hybrid GA approach for OPF with consideration of FACTS devices

This letter presents a hybrid genetic algorithm (GA) method to solve optimal power flow (OPF) in power systems incorporating flexible AC transmission systems (FACTS). In the solution process, GA is integrated with conventional OPF to select the best control parameters to minimize the total generation fuel cost and keep the power flows within the security limits. A case study using an IEEE test system is presented to demonstrate its applicability     

基于改进拉格朗日算法的风光互补系统优化设计研究

从风光互补系统的经济性出发研究了风光互补系统的优化设计问题。以年度平均电量成本最小化作为目标,将太阳能光伏板面积、风电机组额定功率和蓄电池最大储能容量作为决策变量,构建了风光互补系统优化设计模型,并通过引入互补约束条件,将分段函数转化为连续函数,利用改进的拉格朗日分解法,由上而下分阶段求解模型。实例应用结果表明,利用优化模型计算所得结果在经济性和运算效率上有明显提高,验证了模型的可行性和算法的有效性。     

Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling

As the development of China's economy, environmental problems in China become more and more serious. Solar energy and wind energy are considered as ones of the best choices to solve the environmental problems in China and the hybrid wind/solar distributed generation (DG) system has received increasing attention recently. However, the instability and intermittency of the wind and solar energy throw a huge challenge on designing of the hybrid system. In order to ensure the continuous and stable power supply, optimal unit sizing of the hybrid wind/solar DG system should be taken into consideration in the design of the hybrid system. This paper establishes a multi-objective optimization framework based on cost, electricity efficiency and energy supply reliability models of the hybrid DG system, which is composed of wind, solar and fuel cell generation systems. Detailed models of each unit for the hybrid wind/solar/fuel cell system were established. Advanced ε-constraints method based on Hammersley Sequence Sampling was employed in the multi-objective optimization of the hybrid DG system. The approximate Pareto surface of the multi-objective optimization problems with a range of possible design solutions and a logical procedure for searching the global optimum solution for decision makers were presented. In this way, this work provided an efficient method for decision makers in the design of the hybrid wind/solar/fuel cell system.     

Full load synthesis/design optimization of a hybrid SOFC–GT power plant

In this paper, the optimization of a hybrid solid oxide fuel cell–gas turbine (SOFC–GT) power plant is presented. The plant layout is based on an internal reforming SOFC stack; it also consists of a radial gas turbine, centrifugal compressors and plate-fin heat exchangers. In the first part of the paper, the bulk-flow model used to simulate the plant is presented. In the second part, a thermoeconomic model is developed by introducing capital cost functions. The whole plant is first simulated for a fixed configuration of the most important synthesis/design (S/D) parameters in order to establish a reference design configuration. Next a S/D optimization of the plant is carried out using a traditional single-level approach, based on a genetic algorithm. The optimization determined a set of S/D decision variable values with a capital cost significantly lower than that of the reference design, even though the net electrical efficiency for the optimal configuration was very close to that of the initial one. Furthermore, the optimization procedure dramatically reduced the SOFC active area and the compact heat exchanger areas.     

风光接入下储能系统双层优化模型

提出一种风光接入下储能系统的双层优化模型,综合考虑规划和运行2个不同时间尺度问题的相互影响。外层考虑储能系统配置下的全网总成本,用改进粒子群算法迭代求解;内层考虑储能参与下的电网运行成本,采用CPLEX进行求解。在改进的IEEE 10机39节点系统进行仿真验证,结果表明所提模型可得到合理的储能配置方案,在对比不同情景下的经济指标可得到双储能配置为仿真系统的最佳方案。     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

A solar-driven combined cycle power plant

The main results of a feasibility study of a combined cycle electricity generation plant, driven by highly concentrated solar energy and high-temperature central receiver technology, are presented. New developments in solar tower optics, high-performance air receivers and solar-to-gas turbine interface, were incorporated into a new solar power plant concept. The new design features 100% solar operation at design point, and hybrid (solar and fuel) operation for maximum dispatchability. Software tools were developed to simulate the new system configuration, evaluate its performance and cost, and optimize its design. System evaluation and optimization were carried out for two power levels. The results show that the new system design has cost and performance advantages over other solar thermal concepts, and can be competitive against conventional fuel power plants in certain markets even without government subsidies.     

Techno-economic analysis of a hybrid energy system for CCHP and hydrogen production based on solar energy

A typical problem in Northeast China is that a large amount of surplus electricity has arisen owing to the serious photovoltaic power curtailment phenomenon. To effectively utilize the excess photovoltaic power, a hybrid energy system is proposed that uses surplus electricity to produce hydrogen in this paper. It combines solar energy, hydrogen production system, and Combined Cooling Heating and Power (CCHP) system to realize cooling, heating, power, and hydrogen generation. The system supplies energy for three public buildings in Dalian City, Liaoning Province, China, and the system configuration with the lowest unit energy cost (0.0615$/kWh) was obtained via optimization. Two comparison strategies were used to evaluate the hybrid energy system in terms of unit energy cost, annual total cost, fossil energy consumption, and carbon dioxide emissions. Subsequently, the annual total energy supply, typical daily loads, and cost of the optimized system were analyzed. In conclusion, the system is feasible for small area public buildings, and provides a solution to solve the phenomenon of photovoltaic power curtailment.     

Implementation of repowering optimization for an existing photovoltaic‐pumped hydro storage hybrid system: A case study in Sichuan,China

For a remote area or an isolated island, where the grid has not extended, a standalone hybrid energy system can provide cheap and adequate power for local users. However, with the development of society, the load demand will increase and the original system cannot completely meet the load demand. This situation occurs in Xiaojin, Sichuan, China. The existing photovoltaic‐pumped hydro storage (PV‐PHS) hybrid system in this area as the original system cannot completely meet the load requirements at present. The term “repowering” aims to maximize the reliability of power supply and the utilization of the PV‐PHS hybrid energy system that differs from traditional planning optimization to build all components. The repowering strategy is to integrate wind turbines (WTs) and battery into the original system. For the repowering system, a power management strategy is proposed to determine the operating modes of the PHS and battery. Three objectives, which are minimizing percentage of the demand not supplied, levelized cost of energy, and curtailment rate of renewable energy, are considered in the optimization model. Simulation is conducted by single‐objective, biobjective, and triobjective particle swarm optimization (PSO) techniques. For the single‐objective optimization, the comparison of PSO and genetic algorithm (GA) is made. For the double‐objective optimization, multiobjective PSO (MOPSO) is compared with weighted sum approach (WSA), and fuzzy satisfying method is utilized to find the win‐win solution. The results reveal that the repowering strategy can help to achieve maximum reliability of power supply after load demand increases significantly, and the battery plays an important role in such a hybrid system.