Hybrid renewable energy‐based distribution system for seasonal load variations

In recent scenario, there is abundant availability of renewable energy resources to satisfy the significant increase in residential, industrial, and commercial demand. This paper presents a novel framework to determine the preeminent size of renewable distributed generators (RDGs) by optimizing the system components such as area required for solar‐photovoltaic modules, swept area occupied by wind turbine blades, and area used by fuel cell. A microgrid with hybrid RDG (h‐RDG) is integrated in distribution system to minimize the distribution loss, substation energy requirement, and improve the voltage level of the load. The power loss minimization is formulated as a nonlinear problem and optimized by the proposed Hybrid Nelder Mead‐Particle Swarm Optimization algorithm. The microgrid location is identified by voltage stability index to improve the stability of system. Further, the system is analyzed for energy flow in different seasonal loading conditions with mixture of residential, industrial, and commercial load. The effective performance of the proposed technique is applied to standard 12‐bus, 69‐bus, and a practical Tamil Nadu (TN) 84‐bus radial distribution system (RDS) for different hybrid combinations of h‐RDG in microgrid. The result proves that the proposed method provides a simple and efficient tool for optimal and flexible use of h‐RDG in microgrid under different climatic changes by simultaneously reducing distribution energy loss and improving voltage profile.     

Modeling and performance analysis of renewable hydrogen energy hub connected to an ac/dc hybrid microgrid

This paper proposes a system modeling and performance analysis of a renewable hydrogen energy hub (RHEH) connected to an ac/dc hybrid microgrid (MG). The proposed RHEH comprises a photovoltaic (PV)-based renewable energy source (RES) as the primary source, a proton exchange membrane fuel cell (PEMFC) as the secondary power source, and a proton exchange membrane electrolyzer (PEMELZ) that can generate and store hydrogen in a hydrogen tank. All these resources are directly connected at the dc bus of the ac/dc microgrids. The PEMFC operates and utilizes the hydrogen from the hydrogen tank when the energy generated by RES cannot meet the load demand. A coordinated power flow control approach has been developed for the RHEH to mitigate the mismatch between generation and demand in the ac/dc microgrid and produce renewable hydrogen when renewable power is in excess. The paper also proposes a modified hybrid Perturb & Observe-Particle Swarm Optimization (Hybrid PO-PSO) algorithm to ensure the maximum power point tracking (MPPT) operation of the PV and the PEMFC. The operation of the proposed RHEH is validated through simulations under various critical conditions. The results show that the proposed RHEH is effective to maintain the system power balance and can provide power-to-hydrogen and hydrogen-to-power when required.     

基于虚拟储能的微网风光储容量优化配置方法研究

风光储互补发电系统能够提高微网系统的稳定性。为了提升微网储能资源的合理配置,文章基于虚拟储能和电力弹簧概念,提出了计及主配储能协同的微网风光储容量双层优化配置方法,并利用改进的粒子群算法对风光储容量双层优化配置方法求解。最后,通过算例分析表明,文章配置方法提高了微网系统调节能力,降低了电压偏移率。     

微网并网容量分析

将分布式发电以微网形式接入到主电网中并网运行,与主电网互为支撑,是充分发挥分布式发电的最有效方式之一.研究微网并网规模,明确主电网接纳微网的能力,将充分发挥新能源及可再生能源的优势,实现主电网与分布式新能源及可再生能源发电的协调发展,有利于引导与规范微网接入主电网,确保主电网的安全、稳定、经济、高效运行.从微网并网系统的特点出发,分析了微网并网的相关问题,研究了微网并网对主电网的影响,同时对微网并网容量即主电网接纳微网能力进行分析,最后结合算例针对微网并网的稳态分析,通过仿真实现了对微网并网容量的确定.     

Modeling and operation optimization of RE integrated microgrids considering economic,energy, and environmental aspects

Microgrids provide promising solution for integration of renewable energy sources in the electrical grid. To exploit the key benefits, achieving the economical operation of renewable aided microgrids has become necessary and is a challenging task. This paper presents an efficient optimization model to minimize the operational cost of a solar integrated microgrid. We formulate a joint optimization mixed integer problem for appropriate modeling of the system under various practical constraints. An efficient solution is obtained with a distributed approach such that the original problem is solved in two stages. Dual decomposition approach is adopted for cost, emissions, and solar share optimization. Lagrange relaxation, Lambda iteration method, and binary integer programming are employed to obtain the joint optimization solution. Finally, the performance of the proposed model is validated through simulations that show that an overall cost reduction of 4.2070e+04 $ and emission reduction of 7.2001e+03 kg are achieved with the proposed model.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Design of isolated renewable hybrid power systems

Isolated electrical power generating units can be used as an economically viable alternative to electrify remote villages where grid extension is not feasible. One of the options for building isolated power systems is by hybridizing renewable power sources like wind, solar, micro-hydro, etc. along with appropriate energy storage. A method to optimally size and to evaluate the cost of energy produced by a renewable hybrid system is proposed in this paper. The proposed method, which is based on the design space approach, can be used to determine the conditions for which hybridization of the system is cost effective. The simple and novel methodology, proposed in this paper, is based on the principles of process integration. It finds the minimum battery capacity when the availability and ratings of various renewable resources as well as load demand are known. The battery sizing methodology is used to determine the sizing curve and thereby the feasible design space for the entire system. Chance constrained programming approach is used to account for the stochastic nature of the renewable energy resources and to arrive at the design space. The optimal system configuration in the entire design space is selected based on the lowest cost of energy, subject to a specified reliability criterion. The effects of variation of the specified system reliability and the coefficient of correlation between renewable sources on the design space, as well as the optimum configuration are also studied in this paper. The proposed method is demonstrated by designing an isolated power system for an Indian village utilizing wind-solar photovoltaic-battery system.     

Microgrid composed of three or more SOFC combined cycles without accumulation of electricity

Large-scale centralization of the power supply system, consisting mainly of nuclear power generation and thermal power generation, has been adopted in Japanese electrical power system. Because Japan's centralized power supply system has little accommodation for changes in load, the amount of renewable energy that can be introduced is restricted substantially. The percentage of renewable energy introduced in Japan in 2012 was 1.6%; if this were to include hydraulic power generation, the percentage would be less than 10%. Accordingly, this study proposes the development of a microgrid that responds to the changes in output from a large-scale solar power system by using load from the operation of three or more solid oxide fuel cell hybrid power systems (PGSSs), and controlling the number of PGSS units in response to the magnitude of load. A storage battery is not used for the microgrid, developed in this study, for controlling the change in output from renewable energy. The proposal of a system with an introductory high rate of renewable energy is the purpose of this study. The study clarified the method of system operation and the rate at which renewable energy can be introduced at the time of distributed installation of the developed microgrid, using three or more PGSSs to supply all the cities in the Hokkaido region of Japan. From the results of the analysis, the control achieved with the PGSS units was confirmed to be effective. Furthermore, according to meteorological data and our proposed microgrid, the power supplied by renewable energy over the entire Hokkaido region in 2012 reached 48% on February 14 (winter), 49% on July 15 (summer), and 45% on October 15 (moderate season).     

基于改进下垂特性的微网功率控制方法

微网技术作为新能源及可再生能源接入智能电网的技术平台,可以有效整合新能源及可再生能源分布式发电的优势,实现能源的梯级利用,为智能电网的实现提供了必备的技术基础。针对微网的功率控制方法进行了研究,概述了现有的3类经典的控制方式,重点阐述并分析了微网功率下垂控制方法。结果表明,原常规有功功率-频率下垂控制的下垂系数固定无法保证微网的频率质量。提出微电源可采用一种改进的有功功率-频率的下垂控制方法,以有效保证微网运行的频率稳定。仿真结果表明了该控制方法的正确性和可行性。     

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi‐oppositional‐chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid‐connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two‐strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.     

针对并网型风储微网的飞轮储能阵列系统控制方法CSCD

由于新能源发电存在波动性和间歇性,其大量接入时会给电网运行带来新的困难,而利用储能技术提高新能源的可调度性是当前研究的热点。为缓解新能源并网对电力系统的不良影响,针对并网型风储微网提出了一种基于飞轮储能阵列系统的分层优化控制方法,上层优化中心根据功率缺额和各台飞轮的转速建立相应充/放电优化模型,并求解相应飞轮的功率参考值;下层飞轮控制器采用双模双环控制方法,实现飞轮转速和输出功率的控制,最后通过MATLAB/Simulink仿真验证了所提控制方法的有效性和可行性。     

Application of ANFASO for optimal power flow management of MG-connected system with energy storage

The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage. Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach.     

Simplified design of independent hydropower-PV-storage micro-grid system

含可再生能源的独立微电网为解决无电地区用电提供了一种因地制宜的可持续的电力解决方案,但目前微电网由于理论性强、可操作性差,市场推广困难。为了解决目前微电网项目开展的壁垒,提出了一种关于独立水光储微电网系统的简化设计。根据项目所在地的微电源与负荷特性,计算系统的需电量,得出光伏及水电等电源所需的配置容量。为合理地协调各个时段微电网系统内各电源的出力来满足各时段负荷的需求,确保微电网内各电源模块出力与负荷需求的实时功率平衡,最大程度保障系统供电可靠性,本文提出了系统的两个重点考核指标——系统缺电率及能量溢出比,根据这两个指标,确定微电网系统的电池储能系统的配置容量。本文总结了一套合理的关于微电网的通用初步设计方法,为从事微电网的工程设计人员提供了便利。     

Design and optimum energy management of a hybrid renewable energy system based on efficient various hydrogen production

Utilizing renewable energy resources is one of the convenient ways to reduce greenhouse gas emissions. However, the intermittent nature of these resources has led to stochastic characteristics in the generation and load balancing of the microgrid systems. To handle these issues, an energy management optimization for microgrids operation should be done to urge the minimization of total system costs, emissions, and fuel consumption. An optimization program for decreasing the operational cost of a hybrid microgrid consisting of photovoltaic array, wind unit, electrolyzer, hydrogen storage system, reformer, and fuel cell is presented. Two different methods of producing hydrogen are considered in this study to ensure the effectiveness of the developed methodology. In the microgrid system with high penetration of renewable energy resources, using storage technologies to compensate for the intermittency of these resources is necessary. To evaluate the functioning of the microgrid system, a mathematical model for each source is developed to coordinate the system operation involving energy conversion between hydrogen and electricity. Particle Swarm Optimization Algorithm is utilized to determine the optimum size and operational energy management within the system. It is evident from the results that there is about a 10% reduction in the amount of CH₄ consumption in reformer when the electrolyzer was employed in the system. It is observed that the CH₄ reduction in summer and fall is higher than other seasons (10.6% and 11.5%, respectively). The reason is that the highest RES production occurs in these seasons during a year. It is also worth mentioning that the electrolyzer technology would play a significant role in decreasing the CH₄ consumption in the microgrid system.     

Optimal operation of DC smart house system by controllable loads based on smart grid topology

From the perspective of global warming mitigation and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all-electric apartment houses or residence such as DC smart houses are increasing. However, due to the fluctuating power from renewable energy sources and loads, supply-demand balancing of power system becomes problematic. Smart grid is a solution to this problem. This paper presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuation. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuation, it is possible to reduce the electric power consumption and the cost of electricity. This system consists of photovoltaic generator, heat pump, battery, solar collector, and load. To verify the effectiveness of the proposed system, results are used in simulation presented.     

An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system

Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.     

Distributed generation hybrid AC/DC microgrid protection: A critical review on issues,strategies, and future directions

The integration of distributed energy resources (DERs) with conventional systems emerges as an intelligent solution for providing uninterrupted and secure power even at times of high load demand. Better load management with a mature fault handling mechanism makes AC a viable option which has an efficiency of 78.24%. In contrast with less power loss and slightly better efficiency of 84.6%, DC microgrid is a reliable option in a low power environment. In order to accommodate all operating conditions and load types, a hybrid system can be designed with a theoretical efficiency of more than 90%. Bidirectional power flows, low inertia, the transition between different modes of operations are the challenges for the protection of alternating current (AC) and direct current (DC) microgrid systems. Power balance fluctuation, absence of zero-crossing currents, selection of suitable grounding, and coordination between different rating devices restrict the hybrid system to achieve the said efficiency constantly. This paper reviews in detail of existing protection along with grid-connected algorithms for both modes of operation. Finally, the limitation, major hurdles, and future course of action for a reliable, efficient, and secure hybrid grid system are figured out.     

考虑需求侧响应的微网混合储能优化配置

在微网中配置混合储能并引入需求侧响应机制,有利于提高电网运行时的灵活性,降低分布式电源对电网带来的冲击。针对含风力发电机、光伏、储能的并网型微电网,引入需求侧响应机制,建立了以混合储能全寿命周期净现值、微网购电成本和需求侧响应成本为目标函数的微网混合储能优化配置模型,对混合储能容量进行优化配置,采用改进差分算法求解该模型。结合某地实际微网进行验证,结果表明,混合储能可有效改善分布式电源对微电网的影响,需求侧响应可显著降低混合储能成本,提高微网运行的经济效益,为类似微网混合储能优化配置提供了参考。     

Modeling of integrated renewable energy system for electrification of a remote area in India

Over the years, renewable energy based power generation has proven to be a cost-effective solution in stand-alone applications in the regions where grid extension is difficult. The present study focused on the development of models for optimal sizing of integrated renewable energy (IRE) system to satisfy the energy needs in different load sectors of four different zones considered in Chamarajanagar district of Karnataka state in India. The objective of the study is to minimize the total cost of generation and cost of energy using genetic algorithm (GA) based approach. Considering optimization power factor (OPF) and expected energy not supplied (EENS), optimum system feasibility has been investigated. Based on the study, it has been found that IRES is able to provide a feasible solution between 1.0 and 0.8 OPF values. However, power deficit occurs at OPF values less than 0.8 and the proposed model becomes infeasible under such conditions. Customer interruption cost (CIC) and deficit energy (DE) for all zones were also computed to quantify the reliability of the systems.     

A cost-effective approach for optimal energy management of a hybrid CCHP microgrid with different hydrogen production considering load growth analysis

An optimum design and energy management of various distributed energy resources is investigated in a hybrid microgrid system with the examination of electrical, heating, and cooling demand. This paper suggested an optimal approach to design and operate a microgrid incorporating with battery energy storage, thermal energy storage, photovoltaic arrays, fuel cell, and boiler with minimization of the total operational cost of the hybrid microgrid. Two different hydrogen production methods are considered to assure the advantage of the developed proposed methodology. Furthermore, besides natural gas, residential and municipal wastes are collected and are utilized to produce electricity in fuel cell units. Load growth for different type of loads is also considered. The new number of households are added to the proposed system in different years and the proposed program is determined the optimum size of each employed resources to add each year for satisfying the total demand. To find out the optimum energy management and the optimum capacity of each employed distributed energy resources, a meta-heuristic Particle Swarm Optimization Algorithm is utilized. It is concluded from the results that by utilizing residential waste, the amount of natural gas consumption by fuel cells is reduced about 6.2%, and by utilizing residential plus municipal waste, the reduction is about 26.7%. It is also observed that the amount of CO₂ emission is reduced significantly (46.8%) in the case of utilization of produced heat by fuel cells. Finally, the results confirmed the efficacy of the suggested optimal energy management of the hybrid microgrid.