Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Optimal Multi-Timescale Scheduling of Integrated Energy Systems with Hybrid Energy Storage System Based on Lyapunov Optimization

The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

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.

Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

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.

Optimal Sizing of Solar/Wind Hybrid Off-Grid Microgrids Using an Enhanced Genetic Algorithm

This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.

Modelling and Optimal Design of Hybrid Power System Photovoltaic/Solid Oxide Fuel Cell for a Mediterranean City

This work presents a hybrid power system consisting of photovoltaic and solid oxide fuel cell(PV-SOFC)for electricity production and hydrogen production.The simulation of this hybrid system is adjusted for Bou-Zedjar city in north Algeria.Homer software was used for this simulation to calculate the power output and the total net present cost.The method used depends on the annual average monthly values of clearness index and radiation for which the energy contributions are determined for each component of PV/SOFC hybrid system.The economic study is more important criterion in the proposed hybrid system,and the results show that the cost is very suitable for the use of this hybrid system,which ensures that the area is fed continuously with the sufficient energy for the load which assumed to be 500 kW in the peak season.The optimized results of the present study show that the photovoltaic is capable of generating 8733 kW electricity while the SOFC produces 500 kW electricity.The electrolyzer is capable of producing 238750 kg of hydrogen which is used as fuel in the SOFC to compensate the energy lack in nights and during peak season.

基于EnergyPlus和Jeplus+EA联合模拟的建筑围护结构及光储系统协同优化研究

建筑本体和可再生能源系统的协同作用对于降低建筑碳排放具有重要意义.以北京某办公建筑为例,基于NSGA-Ⅱ的优化算法,以运行阶段碳排放和生命周期成本为目标函数,以热舒适为约束条件,利用EnergyPlus和Jeplus+EA软件进行联合仿真,开展建筑围护结构和光储系统的协同优化研究.结果表明:配置光储系统可较大程度降低建筑电网购电量及运行阶段碳排放,虽然生命周期成本会略有增加,但其减碳效果,大于单纯依靠围护结构性能提升的传统做法;尽可能利用建筑空间配置光伏对减少碳排放及生命周期成本都是有利的;通过软件联合模拟可实现建筑围护结构和光储系统的协同优化,且较于各自独立优化,能取得更好的设计方案.

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.

Optimal allocation method of energy storage for integrated renewable generation plants based on power market simulation

This study designs and proposes a method for evaluating the configuration of energy storage for integrated re-newable generation plants in the power spot market,which adopts a two-level optimization model of“system simulation+plant optimization”.The first step is“system simulation”which is using the power market simu-lation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant.The second step is“plant optimization”which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage.In the third step,“sys-tem simulation”is conducted again,and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage.In the case analysis of the provincial power spot market,an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted.The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large.In the case that the investment benefit of energy storage only considers the in-come of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services,the income of energy storage cannot fulfill the economic requirements of energy storage investment.

Optimal allocation of hybrid energy storage for microgrids based on multi-attribute utility theory

To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.

Smart optimization in battery energy storage systems:An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization

A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.

Method of Electric Powertrain Matching for Battery-powered Electric Cars

The current match method of electric powertrain still makes use of longitudinal dynamics, which can’t realize maximum capacity for on-board energy storage unit and can’t reach lowest equivalent fuel consumption as well. Another match method focuses on improving available space considering reasonable layout of vehicle to enlarge rated energy capacity for on-board energy storage unit, which can keep the longitudinal dynamics performance almost unchanged but can’t reach lowest fuel consumption. Considering the characteristics of driving motor, method of electric powertrain matching utilizing conventional longitudinal dynamics for driving system and cut-and-try method for energy storage system is proposed for passenger cars converted from traditional ones. Through combining the utilization of vehicle space which contributes to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement together, a comprehensive optimal match method of electric powertrain for battery-powered electric vehicle is raised. In simulation, the vehicle model and match method is built in Matlab/simulink, and the Environmental Protection Agency (EPA) Urban Dynamometer Driving Schedule (UDDS) is chosen as a test condition. The simulation results show that 2.62% of regenerative energy and 2% of energy storage efficiency are increased relative to the traditional method. The research conclusions provide theoretical and practical solutions for electric powertrain matching for modern battery-powered electric vehicles especially for those converted from traditional ones, and further enhance dynamics of electric vehicles.

A multi agent-based optimal control method for combined cooling and power systems with thermal energy storage

Combined cooling,heating and power(CCHP)systems have been considered as a potential energy saving technology for buildings due to their high energy efficiency and low carbon emission.Thermal energy storage(TES)can improve the energy efficiency of CCHP systems,since they reduce the mismatch between the energy supply and demand.However,it also increases the complexity of operation optimization of CCHP systems.In this study,a multi-agent system(MAS)-based optimal control method is proposed to minimize the operation cost of CCHP systems combined with TES.Four types of agents,i.e.,coordinator agents,building agents,energy management agents and optimization agents,are implemented in the MAS to cooperate with each other.The operation optimization problem is solved by the genetic algorithm.A simulated system is utilized to validate the performance of the proposed method.Results show that the operation cost reductions of 10.0%on a typical summer day and 7.7%on a typical spring day are achieved compared with a rule-based control method.A sensitivity analysis is further performed and results show that the optimal operation cost does not change obviously when the rated capacity of TES exceeds a threshold.

Supercharging of Diesel Engine with Compressed Air: Experimental Investigation on Greenhouse Gases and Performance for a Hybrid Wind-Diesel System

Supercharging is the process of supplying air for combustion at a pressure greater than that achieved by natural or atmospheric induction, as applied to internal combustion engines. As a consequence of demonstrated technological, economical and energetic advantages in multiple literature evaluations concerning the large scale wind-compressed air hybrid storage system with gas turbines, the utilization of a hybrid wind-diesel system with compressed air storage (HWDCAS) has been frequently explored. These will mainly have average or small scale application such as the powering of isolated sites. It has been proven in numerous studies that the HWDCAS combined with an additional supercharging of the diesel engines will contribute to the increase of the power and efficiency of the diesel engine, the reduction of both fuel consumption and the emission of greenhouse gases (GHG). This article presents the obtained results from experimental validation of the selected design with an aim to valorize this innovative solution and become trustworthy.

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。

改进MOGOA及其在风储容量优化配置中的应用

针对传统方法在风储容量优化配置过程中求解精度低、效率低等问题,提出一种改进多目标蝗虫优化算法(improved multi-objective grasshopper optimization algorithm,IMOGOA),采用Fuch混沌映射、余弦自适应参数和莱维飞行三种策略进行改进,使算法的初始解分布更均匀、全局探索和局部开发更协调,同时增强了算法跳出局部最优的能力。对改进算法和多目标粒子群等多个算法进行性能测试对比,实验结果表明改进算法具有更好的寻优精度和稳定性。将该算法应用于风电场混合储能系统容量优化配置,对比其他算法,改进算法能够快速找出Pareto最优解集,在满足系统要求的同时,最大限度降低混合储能系统成本,可以验证算法改进策略的有效性和应用于实际优化问题的适用性。