Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources(RESs)are considered to be reliable and green electric power generation sources.Photovoltaics(PVs)and wind turbines(WTs)are used to provide electricity in remote areas.Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment.The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution.This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization(TLBO)named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost(TAC).The reliability of the system is considered by a maximum allowable loss of power supply probability(LPSPmax)concept.The results obtained from the JLBO algorithm are compared with the original Jaya,TLBO,and genetic algorithms.The JLBO results show superior performance in terms of TAC,and the PV–WT–battery hybrid system is found to be the most economical scenario.This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.

Evaluation of intermittent-distributed-generation hosting capability of a distribution system with integrated energy-storage systems

The penetration rate of distributed generation is gradually increasing in the distribution system concerned.This is creating new problems and challenges in the planning and operation of the system.The intermittency and variability of power outputs from numerous distributed renewable generators could significantly jeopardize the secure operation of the distribution system.Therefore,it is necessary to assess the hosting capability for intermittent distributed generation by a distribution system considering operational constraints.This is the subject of this study.An assessment model considering the uncertainty of generation outputs from distributed generators is presented for this purpose.It involves different types of regulation or control functions using on-load tap-changers(OLTCs),reactive power compensation devices,energy storage systems,and the reactive power support of the distributed generators employed.A robust optimization model is then attained It is solved by Bertsimas robust counterpart through GUROBI solver.Finally,the feasibility and efficiency of the proposed method are demonstrated by a modified IEEE 33-bus distribution system.In addition,the effects of the aforementioned regulation or control functions on the enhancement of the hosting capability for intermittent distributed generation are examined.

Battery Energy Storage System and Demand Response Based Optimal Virtual Power Plant Operation

With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.

Optimal Configuration for Design of Stand-Alone PV System

This paper presents a design for a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in rural area in Jordan. The complete design steps for the suggested household loads are carried out. Site radiation data and the electrical load data of a typical household in the considered site are taken into account during the design steps. The reliability of the system is quantified by the loss of load probability. A computer program is developed to simulate the PV system behavior and to numerically find an optimal combination of PV array and battery bank for the design of stand-alone photovoltaic systems in terms of reliability and costs. The program calculates life cycle cost and annualized unit electrical cost. Simulations results showed that a value of loss of load probability LLP can be met by several combinations of PV array and battery storage. The method developed here uniquely determines the optimum configuration that meets the load demand with the minimum cost. The difference between the costs of these combinations is very large. The optimal unit electrical cost of 1 kWh for LLP = 0.049 is $0.293;while for LLP 0.0027 it is $0.402. The results of the study encouraged the use of the PV systems to electrify the remote sites in Jordan.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System:Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid stability.Therefore,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid codes.As the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power fluctuations.However,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently exists.This paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid codes.In addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is proposed.Finally,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is developed.The BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined DS.The BESS sizing is directly related to the constant RR achieved using the proposed control.Finally,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

Multi-Objective Sizing of Solar-Wind-Hydro Hybrid Power System with Doubled Energy Storages Under Optimal Coordinated Operational Strategy

More and more attention has been paid to the high penetration of renewable energy in recent years.The randomness and intermittency of solar and wind energy make it an inevitable trend that renewables are coupled with energy storage technologies.Pumped hydro storage(PHS)is the most widelyused storage form in the power grid but the capacity is limited by geographic conditions.The concentrated solar power(CSP)plant with a thermal energy storage(TES)system can realize easier grid connections and effective peak shaving.Therefore,this paper proposes a solar-wind-hydro hybrid power system with PHS-TES double energy storages,and investigates the optimal coordinated operational strategy and multi-objective sizing.The optimal sizing problem which considers the minimum levelized cost of energy(LCOE)and loss of power supply probability(LPSP)as objectives is solved by multi-objective particle swarm optimization.Moreover,the seasonal uncertainties of renewables are considered by applying a scenario-based analysis using Kmeans clustering.Finally,a case study reveals the effectiveness of the coordinated operational strategy and double energy storages from the perspectives of economy and reliability.The comparisons of optimal sizing results show that the PV-WindCSP-PHS system decreases the LCOE by 19.1%compared to a PV-Wind-CSP system under the same LPSP,and reduces the LPSP compared to PV-Wind-PHS systems with limited reservoir capacity,which indicates that the proposed system with double energy storages has better economy and reliability performance compared to single storage.

Selecting and optimal sizing of hybridized energy storage systems for tidal energy integration into power grid

The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability.Therefore, energy storage resources are used to deal with the challenges imposed by power variability and demand-supply balance.The main focus of this paper is to investigate the appropriate storage technologies and the capacity needed for a successful tidal power integration.Therefore, a simplified sizing method, integrating an energy management strategy, is proposed.This method allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics.The optimal solutions given by the multi-objective evolutionary algorithm are presented and analyzed.

Analytical Hybrid Particle Swarm Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Smart Grid

In this paper,the hybridization of standard particle swarm optimisation(PSO)with the analytical method(2/3 rd rule)is proposed,which is called as analytical hybrid PSO(AHPSO)algorithm used for the optimal siting and sizing of distribution generation.The proposed AHPSO algorithm is implemented to cater for uniformly distributed,increasingly distributed,centrally distributed,and randomly distributed loads in conventional power systems.To demonstrate the effectiveness of the proposed algorithm,the convergence speed and optimization performances of standard PSO and the proposed AHPSO algorithms are compared for two cases.In the first case,the performances of both the algorithms are compared for four different load distributions via an IEEE 10-bus system.In the second case,the performances of both the algorithms are compared for IEEE 10-bus,IEEE 33-bus,IEEE 69-bus systems,and a real distribution system of Korea.Simulation results show that the proposed AHPSO algorithm converges significantly faster than the standard PSO.The results of the proposed algorithm are compared with those of an analytical algorithm,and the results of them are similar.

Sizing and Siting of Battery Energy Storage Systems:A Colombian Case

This paper presents a mixed-integer linear programming(MILP)formulation for sizing and siting of battery energy storage systems(BESSs).The problem formulation seeks to minimize both operation costs and BESS investment.The proposed model includes restrictions of the conventional securityconstrained unit commitment problem,a piece-wise linear approximation to model power losses,and a linear model of hydro generation units.The proposed model is tested in a 6-bus test system and a 15-bus system representing the Colombian power system.For the two studied systems,simulation results show that the reduction of operation costs due to the installation of BESSs compensates the investments,under some of the considered technical cost cases.Additionally,results show that adequate sizing and siting of BESSs reduce renewable energy curtailment in the Colombian power system with high penetration of fluctuating renewable generation.

基于多元储能的分布式能源系统优化调度方法研究

为进一步构建清洁低碳、经济节能的供能系统,研究建立了含风、光以及多元储能的分布式能源系统,针对北京某办公园区,采用DeST预测用户负荷以及当地风、光条件。以经济性、节能性和环保性三方面的综合效益最大为目标,提出一种自适应优化运行策略,分别采用穷举搜索法和遗传算法对系统优化调度方案进行优化。同时,采用以电定热运行策略作为对照,对比分析几种不同运行策略下系统的综合效益。结果表明:传统以电定热运行模式下的综合效益平均值为0.41;而在自适应优化运行策略下,使用遗传算法得到的调度方案,其综合效益平均值可达0.5,穷举搜索法得到的运行方案,其综合效益平均值可达0.51。

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

面向季节性短时高峰负荷的虚拟配电馈线功能架构及协同配置

传统线路增容方式面对季节性短时高峰负荷时存在线路整体利用率低、紧急功率支撑能力差等问题。因此,文中以功能虚拟化为核心,研究了传统配电馈线与储能元件融合的虚拟配电馈线功能架构,利用双端储能元件搭建虚拟电能传输通道,以应对季节性短时高峰负荷、延缓传统线路增容改造工程,并在非高峰时期提供紧急功率支撑和辅助服务功能,提高储能元件整体利用率。为充分挖掘虚拟配电馈线的灵活传输和紧急功率支撑潜力,提出了配电网线路增容优化配置模型,在保证经济性和满足高峰负荷需求的前提下,优化虚拟配电馈线储能配置。算例研究表明,虚拟配电馈线能够有效延缓传统线路增容改造工程、应对季节性短时高峰负荷;对于线路负荷增长较稳定、线路长度较长和设计延缓增容年限较短的配电区域,虚拟配电馈线的优势更加凸显。

含分布式电源及储能系统的配电网最优配置研究

针对大量分布式电源与储能系统接入电网,为有效对配电网系统进行配置、实现其优化经济运行,提出了配电系统中电池储能系统和分布式发电机的最优配置方法,建立混合整数非线性规划模型并采用规划-运行分解方法进行求解。采用一种定义邻域结构的模拟退火算法,提出分解方法来求解储能系统运行状态问题,以较低计算量来保证近全局最优解。最后,在一个包含230个节点的中低压配电系统上测试了所提出的规划-运行分解方法,利用规划-运行方法进行配置。结果表明,同时接入储能系统和分布式发电并进行合适的配置,其收益较高,虽成本也有所增加,但具有长期收益。同时验证了配置方法的有效性和可行性。

含共享储能的数据中心微网群分布式优化调度

多个数据中心微网可联合成微网群以实现不同数据中心微网的资源联动和能量互补。首先,建立了含共享储能的数据中心微网群运行模型,考虑了不同数据中心微网间的功率交互、数据负载交互、碳配额交互及共享储能容量使用权分配等耦合约束。其次,提出了考虑时变容量使用权和旋转备用服务的共享储能容量分配与运行策略,以进一步提高共享储能的效能。然后,采用高斯混合模型对数据中心微网内可再生发电的预测误差概率分布进行精确拟合,并结合机会约束处理预测误差的不确定性。最后,通过交替方向乘子法实现各个数据中心微网的独立求解,并引入动态乘子更新策略和预测-矫正因子以提高算法的收敛性。所提模型与方法的有效性在仿真中得到验证。

基于数字孪生技术的云储能点对点交易双层优化策略

为解决云储能(Cloud energy storage, CES)日前交易模型预测精度低、信息传输不及时、交易机制可靠性差等问题,提出一种基于数字孪生技术的云储能点对点交易双层优化策略。首先,针对环境因素的时变性,采用数字孪生(Digital Twins, DT)技术实现新能源电厂出力与负荷的超短期预测;其次,考虑到用户规模不断增大,构建以云储能供应商-社区-用户为体系的改进分布式点对点(Peer-to-peer, P2P)交易策略,实现交易的去中心化;再次,为同时满足CES供应商与用户两大主体的利益需求,建立基于日内滚动优化的双层优化配置模型,上层求解储能电站的优化配置,下层求解用户用电策略,并采用Karush-Kuhn-Tucher(KKT)条件与Big-M法将多目标双层非线性优化问题转化为单层线性优化问题;最后,通过两次使用shapley值计算社区与用户成本分摊。算例仿真结果表明,所提策略能够实现新能源电厂出力与负荷的高精度预测,降低用户群与CES供应商交易的复杂度,在用户购电成本最小的基础上最大化CES供应商的收益,保证交易市场的长期稳定运行。

基于净能力及二阶锥规划的分布式光储多场景协同优化策略

针对现有配电网中分布式光储调度模型存在资源协同不足、求解复杂等问题,提出了一种基于净能力及二阶锥规划的分布式光储多场景协同优化调度策略。通过引入储能接入配电网后的功率转移分布因子,提出一种基于系统净能力的储能最优选址计算方法;综合考虑储能的运行特性和分布式光伏的出力不确定性,建立以系统日综合成本和削峰填谷为目标的分布式光储多场景协同优化调度模型;利用二阶锥松弛和Big-M法对潮流约束、储能运行约束进行处理,将原规划模型转化为混合整数二阶锥规划问题。以IEEE 33节点系统和西北某实际系统为算例进行仿真分析,结果表明所提方法能在降低负荷峰谷差和日综合成本、平抑负荷波动的同时,显著提高对分布式光伏的消纳能力,验证了所提方法的有效性和可行性。

考虑需求响应和共享储能的微网联盟两阶段优化调度

随着共享储能的推广,能源信息的流动与共享得到进一步发展,为促进能源管理和提升利用效率,提出一种基于共享储能和需求响应的微网联盟两阶段优化调度策略。首先,建立多微网联盟-共享储能系统运行框架,并基于综合贡献度对微网利益分配过程进行建模;其次,建立两阶段优化调度模型,第一阶段以多微网联盟-共享储能系统净收益最大为目标,第二阶段考虑用户的需求响应,求解耗能用户的最低购能费用;再次,利用求解器对两阶段模型分别进行求解,得到多微网系统的分时能源价格、各微网设备的运行状态、需求响应后负荷和各微网利益;最后,设置4个方案作为算例,验证所提优化策略在提高各微网收益和激励用户需求响应的有效性。

考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化

提出考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化方法。首先建立计及储能四象限功率可调特性的有源配电网有功无功协同优化模型;其次给出储能约束条件和交流潮流方程的线性化表征方式;然后利用盒式集描述风电出力的不确定性,建立有源配电网有功无功协同鲁棒优化模型,并通过对偶变换得到混合整数线性规划形式的两阶段鲁棒主、子问题,采用列约束生成算法交互迭代求解;最后以IEEE33节点系统为例进行仿真验证。仿真结果表明,所提方法能够克服模型高度非凸非线性对其收敛精度和求解效率的影响,充分利用储能的无功调节能力,实现有源配电网有功无功协同鲁棒优化运行。

基于多目标沙猫群算法的含风光储配电网无功优化

针对现有智能优化算法在求解配电网无功优化时存在的收敛速度慢、易陷入局部最优解等问题,提出一种基于多目标沙猫群算法(MOSCSO)的含风光储配电网无功优化方法.MOSCSO融合了多目标算法中外部储存集的更新和选择机制,具有较好的全局寻优能力,而沙猫群算法(SCSO)特有的搜索和攻击的种群更新方式保证了其具有较快收敛速度和较好寻优能力.建立储能设施(ESS)作为控制变量的IEEE 33节点系统数学模型,应用MOSCSO进行仿真验证.结果表明,本文所提方法在平衡风光发电系统的同时能够降低网损和提高电网稳定性,通过与传统算法比较,验证了MOSCSO在无功优化模型上的有效性和稳定性.