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.     

含复合储能的冷热电联供系统多目标运行优化研究

冷热电联供（combined cooling, heating and power, CCHP）系统是分布式能源系统发展的主流趋势,针对CCHP系统的能量调度问题,提出了储电、储热相结合的复合储能技术;为实现CCHP系统的运行优化控制,建立了CCHP系统拓扑架构、系统模型、多目标函数及约束条件,采用线性加权和法将多目标函数转化为单目标函数,利用遗传算法进行优化求解,并与不含复合储能的CCHP系统进行对比分析。结果表明：将复合储能引入CCHP系统,能有效降低系统运行成本和一次能源消耗量,提高系统节能率和削峰填谷能力,为CCHP系统的优化运行策略提供了较好的参考方法。     

Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems

In this paper we address the optimal sizing and scheduling of isolated hybrid systems using an optimization framework. The hybrid system features wind and photovoltaic conversion systems, batteries and diesel backup generators to supply electricity demand. A Mixed-Integer Linear Programming formulation is used to model system behavior over a time horizon of one year, considering hourly changes in both the availability of renewable resources and energy demand. The optimal solution is achieved with respect to the minimization of the levelized cost of energy (LCOE) over a lifetime of 20 years. Results for a case study show that the most economical solution features all four postulated subsystems.     

Optimal sizing of stand-alone hybrid systems based on PV/WT/FC by using several methodologies

This paper presents a comparative study of four sizing methods for a stand-alone hybrid generation system integrating renewable energies (photovoltaic panels and wind turbine) and backup and storage system based on battery and hydrogen (fuel cell, electrolyzer and hydrogen storage tank). Two of them perform a technical sizing. In one case, the sizing is based on basic equations, and in the other case, an optimal technical sizing is achieved by using Simulink Design Optimization. The other two methods perform an optimal techno-economical sizing by using the hybrid system optimization software HOMER and HOGA, respectively. These methods have been applied to design a stand-alone hybrid system which supplies the load energy demand during a year. A MATLAB-Simulink model of the hybrid system has been used to simulate the performance of hybrid system designed by each method for the stand-alone application under study in this work. The results are reported and discussed in the paper.     

基于风-光-蓄-火联合发电系统的多目标优化调度

针对风电、光伏出力的随机性、间歇性和波动性而导致其在大规模接入电网时对电网发电计划制定和调度产生的影响,提出了含风-光-蓄-火联合发电系统的多目标优化调度模型。利用抽水蓄能的抽蓄特性,将风电和光伏出力进行时空平移,使风-光-蓄联合出力转变为稳定可调度电源,具备削峰填谷的功能,与火电机组共同参与系统优化调度。以风-光-蓄联合出力最大、广义负荷波动最小和火电机组运行成本最小作为目标函数,建立多目标优化调度模型,通过多目标处理策略,使目标函数简化为2个,以降低问题维数;在求解阶段,利用分层求解思想,将模型划分为两层,分别采用混合整数规划方法和机组组合优化方法进行求解。10机测试系统仿真结果表明：所建模型可以提高风能和太阳能的利用率,缓解火电机组的调峰压力,大幅降低风电反调峰特性对电网的影响,从而保证电力系统安全、稳定、经济运行。     

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.     

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.     

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.     

Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach

Recently, the increasing energy demand has caused dramatic consumption of fossil fuels and unavoidable raising energy prices. Moreover, environmental effect of fossil fuel led to the need of using renewable energy (RE) to meet the rising energy demand. Unpredictability and the high cost of the renewable energy technologies are the main challenges of renewable energy usage. In this context, the integration of renewable energy sources to meet the energy demand of a given area is a promising scenario to overcome the RE challenges. In this study, a novel approach is proposed for optimal design of hybrid renewable energy systems (HRES) including various generators and storage devices. The ε-constraint method has been applied to minimize simultaneously the total cost of the system, unmet load, and fuel emission. A particle swarm optimization (PSO)-simulation based approach has been used to tackle the multi-objective optimization problem. The proposed approach has been tested on a case study of an HRES system that includes wind turbine, photovoltaic (PV) panels, diesel generator, batteries, fuel cell (FC), electrolyzer and hydrogen tank. Finally, a sensitivity analysis study is performed to study the sensibility of different parameters to the developed model.     

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%).     

SIZING AND TECHNO-ECONOMICAL OPTIMIZATION FOR HYBRID SOLAR PHOTOVOLTAIC/WIND POWER SYSTEMS WITH BATTERY STORAGE

A general methodology is presented for the sizing and optimization of renewable power supply systems, including hybrids such as those with solar photovoltaic and wind power components. The technical and economic optimum configurations are found by reference to periods over which the average resource (e.g. wind/solar) is least or the average load demand is greatest. For stand-alone systems, the annual autonomy is an important further design factor. This is the fraction of time for which the specified load can be met. The optimization seeks the least expensive system configuration which achieves the required autonomy level. It is the autonomy level which largely determines the size of battery storage capacity required. A system performance simulation procedure, with an hourly time-step, is used to obtain the autonomy levels of potentially optimum arrangements as the battery size is varied. Illustrative examples of the use of the method employ annual and monthly averaging periods, although any other period may be used. Data refer to the particular location and load pattern for an existing hybrid system, but the method is quite generally applicable. © 1997 by John Wiley & Sons, Ltd.     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

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.     

Design and control strategies of PV-Diesel systems using genetic algorithms

Hybrid photovoltaic systems (PV-hybrid) use photovoltaic energy combined with other sources of energy, like wind or Diesel. If these hybrid systems are optimally designed, they can be more cost effective and reliable than PV-only systems. However, the design of hybrid systems is complex because of the uncertain renewable energy supplies, load demands and the non-linear characteristics of some components, so the design problem cannot be solved easily by classical optimisation methods. When these methods are not capable of solving the problem satisfactorily, the use of heuristic techniques, such as the Genetic Algorithms, can give better results.The authors have developed the HOGA program (Hybrid Optimisation by Genetic Algorithms), a program that uses a Genetic Algorithm (GA) to design a PV-Diesel system (sizing and operation control of a PV-Diesel system). The program has been developed in C++.In this paper a PV-Diesel system optimised by HOGA is compared with a stand-alone PV-only system that has been dimensioned using a classical design method based on the available energy under worst-case conditions. In both cases the demand and the solar irradiation are the same. The computational results show the economical advantages of the PV-hybrid system. HOGA is also compared with a commercial program for optimisation of hybrid systems.Furthermore, we show a number of results and conclusions about hybrid systems optimised by HOGA.     

纯电动船复合储能系统的设计与优化

针对纯电动船中的动力电池组易受瞬态大电流的冲击、使用寿命短等问题,提出以磷酸铁锂电池和超级电容为核心的复合储能系统方案,进行理论分析,并采用DC/DC变换器以更好地发挥超级电容性能。采用带精英策略的非支配排序遗传算法进行多目标优化选型,结合能量管理策略,实现储能系统的优化配置。仿真结果表明,得到的选型方案结合以模糊控制为核心的能量管理策略能够很好地应对波动性负载。     

Techno‐economic optimization of hybrid power system using genetic algorithm

This paper presents an optimum sizing methodology to optimize the hybrid energy system (HES) configuration based on genetic algorithm. The proposed optimization model has been applied to evaluate the techno‐economic prospective of the HES to meet the load demand of a remote village in the northern part of Saudi Arabia. The optimum configuration is not achieved only by selecting the combination with the lowest cost but also by finding a suitable renewable energy fraction that satisfies load demand requirements with zero rejected loads. Moreover, the economic, technical and environmental characteristics of nine different HES configurations were investigated and weighed against their performance. The simulation results indicated that the optimum wind turbine (WT) selection is not affected only by the WT speed parameters or by the WT rated power but also by the desired renewable energy fraction. It was found that the rated speed of the WT has a significant effect on optimum WT selection, whereas the WT rated power has no consistent effect on optimal WT selection. Moreover, the results clearly indicated that the HES consisting of photovoltaics (PV), WT, battery bank (Batt) and diesel generator (DG) has superiority over all the nine systems studied here in terms of economical and environmental performance. The PV/Batt/DG hybrid system is only feasible when wind resource is very limited and solar energy density is high. On the other hand, the WT/Batt/DG hybrid system is only feasible at high wind speed and low solar energy density. It was also found that the inclusion of batteries reduced the required DG and hence reduced fuel consumption and operating and maintenance cost. Copyright © 2014 John Wiley & Sons, Ltd.     

Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

Increasing penetration of wind power in power systems causes difficulties in system planning due to the uncertainty and non dispatchability of the wind power. The important issue, in addition to uncertain nature of the wind speed, is that the wind speeds in neighbor locations are not independent and are in contrast, highly correlated. For accurate planning, it is necessary to consider this correlation in optimization planning of the power system. With respect to this point, this paper presents a probabilistic multi-objective optimal power flow (MO-OPF) considering the correlation in wind speed and the load. This paper utilizes a point estimate method (PEM) which uses Nataf transformation. In reality, the joint probability density function (PDF) of wind speed related to different places is not available but marginal PDF and the correlation matrix is available in most cases, which satisfy the service condition of Nataf transformation. In this paper biogeography based optimization (BBO) algorithm, which is a powerful optimization algorithm in solving problems including both continuous and discrete variables, is utilized in order to solve probabilistic MO-OPF problem. In order to demonstrate performance of the method, IEEE 30-bus standard test case with integration of two wind farms is examined. Then the obtained results are compared with the Monte Carlo simulation (MCS) results. The comparison indicates high accuracy of the proposed method.     

基于混合算法的配电网中电池储能的多目标优化配置研究

基于随机会约束规划理论,计及系统的不确定性因素提出配电网储能电站多目标选址定容模型。首先分析配电网的经济性和可靠性特征,接着基于随机机会约束规划建立储能电站的优化模型。采用二进制粒子群算法和改进粒子群算法的混合算法对模型进行求解。最后利用研究模型,结合IEEE 33标准节点系统建立配电网储能电站优化算例,对离网模式和并网模式2种模式进行仿真,对优化配置结果进行对比和分析。     

Particle swarm optimization for redundant building cooling heating and power system

An optimal and redundant building cooling heating and power (BCHP) system can yield economical savings, but more importantly can save energy as well as reduce the emission of pollutants. This paper presents the energy flow analysis of the conventional separation production (SP) system and the redundant BCHP system. Four decision variables (the capacity of power generation unit (PGU), the capacity of heat storage tank, the on–off coefficient of PGU and the ratio of electric cooling to cool load) to be optimized are selected in consideration of the design and the operation strategy of BCHP system. An objective function to simultaneously measure the energetic, economical and environmental benefits achieved by BCHP system in comparison to SP system is constructed and maximized. Particle swarm optimization algorithm (PSOA) is employed to search the optimal solutions. A case study of BCHP system with thermal storage unit and hybrid cooling system is presented to ascertain the feasibility and validity of the optimization method.     

State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)

The current research aims to present an inclusive review of latest research works performed with the aim of improving the efficiency of the hybrid renewable energy systems (HRESs) by employing diverse ranges of the optimization techniques, which aid the designers to achieve the minimum expected total cost, while satisfying the power demand and the reliability. For this purpose, a detailed analysis of the different classification drivers considering the design factors such as the optimization goals, utilized optimization methods, grid type as well as the investigated technology has been conducted. Initial results have indicated that among all optimization goals, load demand parameters including loss of power supply probability (LPSP) and loss of load probability (LLP), cost, sizing (configuration), energy production, and environmental emissions are the most frequent design variables which have been cited the most. Another result of this paper indicates that almost 70% of the research projects have been dedicated towards the optimization of the off-grid applications of the HRESs. Furthermore, it has been demonstrated that, integration of the PV, wind and battery is the most frequent configuration. In the next stage of the paper, a review concerning the sizing methods is also carried out to outline the most common techniques which are used to configure the components of the HRESs. In this regard, an analysis covering the optimized indicators such as the cost drivers, energy index parameters, load indicators, battery’s state of charge, PV generator area, design parameters such as the LPSP, and the wind power generation to load ratio, is also performed.