PV-battery energy storage system operating of asynchronous motor driven by using fuzzy sliding mode control

This article tends on the designing of renewable-storage electrical system for asynchronous machine application. The machine requires a photovoltaic energy as a primary source of renewable energy. Such as the major drawback of solar sources are neither continuous nor regular in the time. To overcome this problem, a battery energy storage system will be added to make sure the continuity of the operation machine. Generally, electrical machine is driven by using sliding mode control where the major problem in this control technique design is chattering phenomenon which can be defeated using a sliding surface based on fuzzy logic. The proposed technique is performed with the proposed control system. A simulation test of the system for varied load motor conditions proved that the system is preferment. When, the generated power from PV is superior that the demands, the rest is stored in the battery. In the opposite condition, PV and battery required load. Moreover, the system which consists of machine and its control is enabling to produce electrical energy when it is functioning in the other sense of rotation. Four quadrant machine operations guarantee many possibilities of charging and discharging of the battery that assure the continuity of operating system.     

A hybrid optimization technique for proficient energy management in smart grid environment

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively.     

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the ‘best’ compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.     

A fuzzy programming approach to energy resource allocation

This paper presents a fuzzy multi‐objective linear programming approach to solve the energy allocation problem. For this, nine energy resources and six household end uses are considered. An optimal solution will be extracted, and an explicit interactive sensitivity analysis will be dealt with. As the results obtained depend on the fuzzy nature of the objective functions and on the conflicting nature of some of the objectives among themselves, the proposed method can be regarded as a decision support tool for the decision makers who can be guided by its results to arrive at an appropriate solution interactively. It will be shown that optimization using fuzzy logic can provide the decision‐makers with more flexibility that would assist them in the allocation of various resources to meet the various end‐uses by studying the effects of several factors such as price variations, membership function shapes and membership function reference values. Copyright © 1999 John Wiley & Sons, Ltd.     

基于平抑风光出力波动的主动配电网优化调度

因风电、光伏光出力波动特性,并网运行将对主动配电网运行的稳定性造成影响。结合储能系统,将主动配电网中功率波动幅度大的风电、功率具有间歇特性的光伏搭建为风光储联合发电系统。通过改变柔性负荷边际效益增强其参与调度意愿,提高主动配电网消纳分布式可再生能源的能力及运行的稳定性。为提高粒子群算法的收敛性,利用模糊推理规则对学习因子动态调整。基于IEEE 30节点仿真算例,以系统日运行综合经济性为目标,使用模糊自适应粒子群算法寻优。算例仿真与分析验证了该文模型有效性。     

Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network

Renewable energy sources are gaining more and more interest because they are nonpolluting and sustainable. Recently, a notable number of renewable distributed generations (DGs) having intermittent generation patterns are being interconnected with the distribution network to meet growing load demand and nullify environmental threats. Appropriate integration of renewable DGs in distribution networks is crucial to guarantee the qualitative network operational benefits. In this paper a simple but efficient approach has been proposed for optimal placement and sizing of solar and wind DGs in distribution territory by considering electrical network power loss minimization, voltage stability and network security improvement. The stochastic nature of solar irradiance and wind speed are accounted using suitable probabilistic models. Weighted aggregation particle swarm optimization technique is employed to optimize the objective functions considering bus voltage limit, line loading capacity, discrete size limit and penetration constraints of DGs. Strategic weight selection technique has been adopted to assess the well trade-off solution by persuasion of multiple objectives regarding the performance of distribution network. The proposed method has been applied to a typical Indian rural distribution network, and the satisfactory results are obtained.     

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.     

有源电力滤波器神经终端滑模控制

以电力电子装备为接口的高渗透率可再生能源并网已成为未来配电网的显著特性。可再生能源具有随机性和间歇性,作为其并网接口的电力电子装备也会导致电能质量恶化等问题。为提高电能质量,该文提出一种有源电力滤波器神经终端滑模控制方法。首先,结合分数阶思想和滑模控制理论设计一种分数阶终端滑模控制器,以保证误差有限时间收敛,并引入边界层技术降低抖振。然后,利用自组织模糊神经网络构造一种无模型控制方案以更好地应对各种不确定因素。所设计的自组织模糊神经网络控制器用于学习分数阶终端滑模控制器,不仅从根源上解决抖振问题,而且可继承原控制器的有限时间收敛性能,并满足李雅普诺夫理论框架下的稳定控制性能。仿真与实验结果表明：所提出的控制方法能有效解决可再生能源发电系统中的谐波问题。     

考虑源-荷发展不确定性的电网扩展规划模型

在城市电网的发展过程中,随着可再生能源及储能等可控负荷的大规模接入,系统内源-荷实时功率供需关系与未来容量发展的不确定性将会增加,导致可再生能源利用率降低,并给上级电网带来调峰压力。为解决这一问题,文章充分考虑源-荷发展不确定性对系统的影响,以总规划成本最小为目标,建立了基于多重不确定性的电网模糊扩展规划模型及其求解方法。通过仿真算例验证了所提模型可有效地提升电网规划的经济性。     

Energy management of a smart autonomous electrical grid with a hydrogen storage system

The problem of energy management in the smart autonomous electrical grids (SAEGs) is a main challenge in the active distribution networks. In such systems, the operator of the network decides on the optimal scheduling of the resources to supply the local demand. In this paper, a multi-objective optimization model is developed for a SAEG considering responsive consumers (RCs) and a hydrogen storage system (HSS). The objective functions are maximizing the reliability and minimizing both the operation cost and the gap between the energy consumption and its optimal value. The participation of the RCs is modeled through the demand shifting strategy and the local generation of the plug-in electric vehicles. To model the uncertainties of the renewable energy sources and the demand, the Monte Carlo simulation approach is used. The resulted model is solved using the shuffled frog leaping algorithm (SFLA) regarding which the non-dominated solutions are generated. Then, the best solution is obtained using the fuzzy and the weighted sum methods. To investigate the effectiveness of the proposed model, it is applied on a 24-node test system through defining four case studies. The results shown that in the presence of the RCs and the HSS, the operation cost and the reliability of the system both improve.     

A novel concept for a renewable network within municipal energy systems

Renewable energy sources are amongst the more widely acceptable options for the future transformations of existing energy systems. The complexities of such transformations call for various comprehensive preparatory actions. Amongst them, the organisation of a multifaceted renewable value-chain into a renewable network may well be crucial for the successful utilisation of renewable energy sources in the future. This paper proposes a novel concept for renewable network covering entire renewable value chain with division on supply, demand and technology sections. In the past, each section has been addressed separately. The organisation of renewable network covering all sections is deemed to be vital for accomplishing the optimal distribution and deployment of renewable energy sources. Constant technological advancements within the renewable sector indicate the significance of a technology section within a renewable network. The appropriate arrangement of various stakeholders involved throughout the entire value-chain, which includes all sections of a renewable network, is therefore crucial for the further development of a renewable sector. The proposed concept of a renewable network would support the effective operation of a renewable value-chain within a municipality through the establishment of local virtual energy cooperatives aimed at fulfilling energy needs and supporting the development of the community. The applicability of the proposed concept is demonstrated through the development of a biomass network within the municipality of Cirkulane, Slovenia.     

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.     

Operational performance of energy storage as function of electricity prices for on-grid hybrid renewable energy system by optimized fuzzy logic controller

Realization of benefits from on-grid distributed generation based on renewable energy sources requires employment of energy storage to overcome the intermittency in power generation by such sources, while accounting for time-varying electricity prices. The objective of this study is to examine the effects of time-varying electricity prices on the performance of energy storage components for an on-grid hybrid renewable energy system (HRES) utilizing an optimized fuzzy logic controller (FLC). To achieve the objective, FLC membership functions are optimized for minimizing the operational cost of the HRES based on weekly and daily prediction of data for grid electricity price, electrical load, and environmental parameters, including wind speed, solar irradiation, and ambient temperature, using shuffled frog leap algorithm. FLC three inputs include (a) grid electricity price, (b) net power flow as the difference between energy produced and energy consumed, and (c) state of charge (SOC) of battery stack. It is confirmed that accounting for grid electricity price has considerable effects on the performance of energy storage components for operation of on-grid HRES, as the weekly and daily optimized FLCs result in less working hours for fuel cell and electrolyzer and less fluctuations in SOC of battery stack.     

基于模糊决策法的10 kV变压器组经济运行

针对采用临界值法投切变压器会造成变压器的频繁投切、影响电气开关寿命、降低电力系统可靠性的问题,基于短期预测到的未来1 d的负荷数据,将给定的时间段划分为多个时间区间,引入改进后的模糊决策法对时间区间影响进行数学优度值计算,进而选出最优对象,并通过算例进行了仿真。结果表明,改进后的模糊决策法简单,适用于解决配电变压器经济运行时投切时机的选取问题,且在降低变压器自身综合功率损耗的同时,减少了变压器相关电气元件的投切次数,延长了使用寿命,提高了电力系统的可靠性,减少了投资,满足经济运行和工程应用的要求。     

可再生能源电力系统负荷平衡化分散调度方法研究

可再生能源电力系统负荷调度容易受到不确定性因素的影响,没有考虑备用调度因素,导致电力系统调度效果较差。针对该问题,提出可再生能源电力系统负荷平衡化分散调度方法研究。根据动态负荷平衡系统结构,建立单区域考虑多重不确定性负荷平衡化分散调度模型,并描述负荷在时间段内的变化值,以此确定调度目标函数。在相关约束条件下,使各个负荷预测误差相互独立,保证本区域负荷波动一致,完成多重不确定性负荷平衡化分散调度。在既定联络线功率下保持约束条件不变,通过引入协调优化乘子项,计算第二层下级优化目标函数,实现备用分散协调调度。仿真实验结果表明,该方法具有良好的抗干扰性,调度效果最高可达到99%。     

基于深度学习的配变停电电量损失预测

停电电量损失预测可为电网调度及规划提供参考,有利于为用户提供可靠供电服务。针对当前配变停电过程中的电量损失问题,先基于模糊C均值聚类算法实现对配变负荷曲线的分类处理及精细化分析,挖掘配变负荷数据规律;在此基础上,运用皮尔逊相关系数算法提取选择输入特征,构建基于门控循环单元神经网络的预测模型,从而得到停电时间负荷值,进而分析预测负荷曲线得到损失电量;最后,基于停电管理工作分析,实现基于粒子群优化的台区用电行为停电优化问题求解。算例测试验证了所提方法的正确性和有效性。     

Optimal economic operation of microgrids considering combined heat and power unit,reserve unit,and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.     

Scheduling coupled photovoltaic,battery and conventional energy sources to maximize profit using linear programming

To address the increase of electricity demand, the need for reducing carbon dioxide, and the reduction of available fossil fuel resources, renewable energy sources are being recruited. Specifically energy generated by photovoltaic (PV) cells is becoming one of the most promising alternatives. In this context, this paper presents an optimization model for the scheduling problem where conventional and photovoltaic sources of energy are scheduled to be delivered to satisfy energy demand. The optimization model is formulated as a Linear Program (LP) with a bounded number of variables and constraints. The respective solution can be obtained in polynomial time and provides the optimal combination or schedule of energy generated from different sources (conventional, renewable and battery storage) such that the total demand is satisfied and the profit is maximized. Numerical results demonstrate the effectiveness and the generality of the scheme.     

Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm

Deregulation and restructuring in power systems, the ever-increasing demand for electricity, and concerns about the environment are the major driving forces for using Renewable Energy Sources (RES). Recently, Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) have gained great interest by Distribution Companies (DisCos) as the most common RES. In fact, the connection of enormous RES to existing distribution networks has changed the operation of distribution systems. It also affects the Volt/Var control problem, which is one of the most important schemes in distribution networks. Due to the intermittent characteristics of WFs, distribution systems should be analyzed using probabilistic approaches rather than deterministic ones. Therefore, this paper presents a new algorithm for the multi-objective probabilistic Volt/Var control problem in distribution systems including RES. In this regard, a probabilistic load flow based on Point Estimate Method (PEM) is used to consider the effect of uncertainty in electrical power production of WFs as well as load demands. The objective functions, which are investigated here, are the total cost of power generated by WFs, FCPPs and the grid; the total electrical energy losses and the total emission produced by WFs, FCPPs and DisCos. Moreover, a new optimization algorithm based on Improved Shuffled Frog Leaping Algorithm (ISFLA) is proposed to determine the best operating point for the active and reactive power generated by WFs and FCPPs, reactive power values of capacitors, and transformers’ tap positions for the next day. Using the fuzzy optimization method and max-min operator, DisCos can find solutions for different objective functions, which are optimal from economical, operational and environmental perspectives. Finally, a practical 85-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method.