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.

Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.

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.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

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.

Optimal Offering of Energy Storage in UK Day-Ahead Energy and Frequency Response Markets

The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors(deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

From Viewpoint of Reserve Provider:A Day-ahead Multi-stage Robust Optimization Reserve Provision Method for Microgrid with Energy Storage

Energy storage(ES),as a fast response technology,creates an opportunity for microgrid(MG)to participate in the reserve market such that MG with ES can act as an independent reserve provider.However,the potential value of MG with ES in the reserve market has not been well realized.From the viewpoint of reserve provider,a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process,which differs from the model that MG with ES acts as a reserve consumer in most existing studies.Based on the proposed model,MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling.Considering that the proposed model is just in concept and cannot be directly solved,a multi-stage robust optimization reserve provision method is proposed,which leverages the structure of model constraints.Next,the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility.Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.

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 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 coordination of zero carbon building energy systems

Optimal scheduling of renewable energy sources and building energy systems serves as a pivotal strategy for achieving zero carbon emission.However,the coordination of zero carbon building energy systems(ZCBS)is still challenging due to the complicated interactions among multi-energy hybrid storage and the complex coordination between seasonal and daily scheduling.Therefore,this study develops a coordination scheduling approach for ZCBS.An operation model and a seasonal-daily scheduling approach are developed to optimize the operation of hydrogen,geothermal,and water storage devices.The performance of the developed method is demonstrated using numerical case studies.The results show that the ZCBS can be achieved by using renewable energy sources with the system flexibility provided by hydrogen,geothermal,and water storage devices.It is also found that the developed scheduling approach reduces operation costs by more than 43.4%under the same device capacity,compared with existing scheduling approaches.

考虑移动氢能存储的港口多能微网两阶段分布鲁棒优化调度

为有效应对海上风电固有的间歇及波动性给港口多能微网带来的不确定性风险,提出一种考虑移动氢能存储的港口多能微网两阶段分布鲁棒优化调度模型。首先,结合Wasserstein距离实现风电出力概率分布模糊集的精确刻画,并通过非参数核密度估计拟合海上风电预测误差概率分布,获得不同置信水平下风电出力区间及场景。其次,分析氢能船舶、汽车等移动氢能存储资源对间歇性风电出力的能源存储潜力,并结合用能心理、交通属性差异,将两者分别建模为激励型、价格型需求响应,实现港口移动氢能存储灵活性资源的高效聚合。再次,针对含移动氢能存储的港口多能微网,构建基于概率分布模糊集的日前-日内两阶段分布鲁棒优化调度模型,并运用线性决策规则与强对偶理论将其转换为混合整数线性规划模型求解。最后,基于海上风电实测数据进行仿真验证。结果证明,移动氢能存储可显著提升港口多能微网的低碳灵活性,所提模型在兼顾港口多能微网经济性的同时,可进一步保证风电不确定性风险下的鲁棒性。

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。

含整体煤气化燃料电池-碳捕集电厂的风火储系统分布鲁棒调度方法

整体煤气化燃料电池(integrated gasification fuel cell,IGFC)是与碳捕集技术高度适配的清洁、高效、稳定的绿色煤电技术,有望克服传统碳捕集技术在低浓度CO_(2)环境下产生的高成本、高能耗问题。该文构建含IGFC碳捕集电厂的风火储发电系统。研究IGFC碳捕集电厂的运行机理,对IGFC内部各环节建立数学模型,提出工况匹配时燃烧利用率和阴极空气利用率需要满足的运行条件,并针对该型碳捕集电厂的电碳特性进行分析。为计及风电出力的不确定性,构建风火储系统的两阶段分布鲁棒经济调度模型,引入k阶适应性理论,提出基于正交支撑子集策略的求解方法来获得鲁棒对等式。最后,在改进IEEE-30节点系统中进行算例分析,结果表明,该型碳捕集电厂可利用阳极碳富集的优势,实现系统低碳经济调度的目标,并验证所提优化方法能为可行解提供可解释性。

风电租赁储能参与电能-调频市场竞价策略

风电参与市场化竞价运营,可有效激发风电的市场力及其主动租赁储能改善调频性能的积极性,在提升风电运营效益的同时,助力电网的调频调峰,但需解决风储调频性能指标优化及其电能-调频双市场竞价策略协同优化等关键问题。为此,提出了风电参与电能-调频市场竞价双层优化模型:上层为双市场多主体竞价出清模型;下层为各主体竞价策略优化模型,响应上层出清结果,优化调整竞价策略,达到各主体效益最大化。下层模型嵌套了考虑风电不确定性的储能运营商与风电集群储能租赁价格/容量主从博弈优化模型。双层模型联合求解,得出最终风电集群租赁储能容量及其双市场竞价策略。算例结果表明,所提方法能够提升风电运营效益,助力电网调频调峰。

光——氢建筑能源系统园区容量配置鲁棒优化

碳中和目标促使全球建筑行业能源结构向低碳化、无碳化与清洁化的方向转型。为了开发更清洁、更高效的光伏耦合技术,零碳排燃料——氢能提供了一个新路径。同时,为了提高可再生能源消纳水平,促进能源系统综合发展,针对光——氢建筑电热不确定性,以年均综合成本最优为目标,考虑系统能量平衡条件、系统设备效力等约束条件,引入鲁棒性对偶变量,提出一种包含云储能(CES)的电-氢-热能源系统(EHT-ES)鲁棒优化方法,得到基于氢储能的微电网热电容量配置方案。结果表明,考虑光伏资源和电力负荷需求不确定性时,需增加10.05%光伏发电投建设备容量配置规模。

氢储能系统容量双层鲁棒随机优化配置方法

氢能作为一种清洁无污染、能量密度大的二次能源,是大规模消纳新能源的理想储能载体,耦合氢储能系统和可再生能源的电热氢综合能源系统为消纳新能源提供新的思路和方案。围绕电热氢综合能源系统中如何以经济合理的方式投入氢储能设备展开研究,解决氢储能设备容量的合理配置问题以及考虑源荷不确定性对电热氢综合能源系统运行的影响,提出了一种考虑季节性存储和源荷不确定性的电热氢综合能源系统中氢储能容量优化配置方法。首先针对风电功率预测误差较大,电热气负荷预测精度较高的特点,分别采用不确定集和抽样场景描述源和荷两侧的不确定性。然后建立了考虑源荷不确定性和季节性存储的氢储能容量配置双层鲁棒随机优化模型,其上层问题以年化投资成本和运行成本的总成本最小化为目标确定氢储能系统装置容量,下层问题采用两阶段鲁棒随机优化模型模拟电热氢综合能源系统典型日在风电出力最恶劣场景下的最优运行方案。由于该模型难以直接求解,提出基于粒子群优化算法和列与约束生成算法对该类复杂模型进行求解。最后,通过对某个电热氢综合能源系统算例进行分析,算例分析结果验证了所提方法的有效性,获得的氢储能系统容量优化配置方案能够促进风电消纳和提高系统运行的经济性。

电动汽车虚拟储能支撑的多元调峰辅助服务优化

可再生能源出力的波动性加剧了电网调峰需求,大规模电动汽车(electric vehicle,EV)聚合为虚拟储能可大幅提升系统调峰能力。然而,调峰需求方(风电､光伏等可再生能源运行商)与调峰供应方(火电站､储能系统等)之间的交易机制尚未完善,如何调动EV作为调峰供应方的积极性,在多元主体间合理分摊调峰辅助服务费用,成为亟需解决的问题。提出EV虚拟储能支撑的多元调峰优化调度及辅助服务费用分摊机制:首先,构建多元调峰辅助服务市场主体,包括风电、光伏、火电及EV虚拟储能等。其次,设计EV虚拟储能参与辅助服务费用分摊机制,风电、光伏等调峰需求方按各自发电容量比例,分摊火电及EV虚拟储能等调峰供应方所需的辅助服务费用。特别地,每辆EV根据其调峰的具体贡献给予补偿,充分体现EV车主作为储能自主参与电力市场的价值,并调动其参与调峰的积极性。然后,以系统运行总成本最低为目标构建日前调峰调度模型。最后,以湖北省某区域为例进行算例仿真,分析表明所提模型中EV虚拟储能的加入可有效降低系统运行总成本8.5%,同时提高系统调峰裕度,促进EV虚拟储能等灵活资源可持续发展。

燃料电池船复合储能容量优化与配置经济性分析

复合储能系统可改善由船舶负载功率波动引起的燃料电池寿命损耗问题,但配置成本限制了其在燃料电池船上的广泛应用。为合理配置储能容量,使船舶动力系统设计具备长期的可靠性,提出了一种计及复合储能系统全寿命周期的容量优化配置方法。在构建各电源系统模型的基础上,建立包括购置成本、维护成本、置换成本和能耗成本的复合储能系统全寿命周期成本模型,并采用雨流计数法来评估储能的置换成本。最后依据“Alsterwasser”号燃料电池船的典型功率需求数据,以储能设备的容量参数、燃料电池的输出功率和电源系统的运行参数为优化变量,采用灰狼优化算法进行求解。通过不同储能类型和优化目标下的配置方案对比,验证了所提方法的经济性。

基于PSTSO多目标优化储能系统功率分配

为提高电池储能系统的功率分配合理性,提出基于状态优先的金枪鱼群优化PSTSO(priority of status tuna swarm optimization)算法的储能系统功率分配策略。首先设定了3个储能系统功率分配的评价指标,其次建立储能系统的运行成本、储能单元的健康状态SOH(state-of-health)损失、储能系统的荷电状态SOC(state-of-charge)一致性的数学模型,最后在满足系统功率平衡和SOC上、下限约束条件下,采用PSTSO算法进行功率分配。算例分析结果表明,所提策略可以有效减少电池单元充放电次数,降低电池单元的容量损耗,且保证储能系统的SOC一致性好。

阶梯成本下考虑混合租建模式的云储能优化配置

为解决盲目投建云储能造成资源浪费、成本增加的问题,提出阶梯成本下“自建+租赁”混合模式的园区云储能优化配置方法。首先,分析云储能的特点,构建园区内有大量光伏用户参与的云储能服务模式。其次,建立不同时间尺度、双主体的双层优化模型,上层求解长时间尺度下云储能的规划问题,下层求解短时间尺度下用户群的运行问题。然后,通过KarushKuhn-Tucker(KKT)条件将双层模型转化为单层模型,再利用Big-M法对所得单层模型进行线性化处理。最后,在3个不同场景下进行算例分析,结果验证了所提云储能配置模型的有效性,在降低储能投资成本、提高储能资源利用率的同时可节省用户用电成本。