Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Research on Operation of Electrothermal Integrated Energy System Including Heat Pump and Thermal Storage Units Based on Capacity Planning

In view of the Three North areas existing wind power absorption and environment pollution problems,the previous scholars have improved the wind abandon problem by adding electrothermal coupling equipment or optimizing power grid operation.In this paper,an electrothermal integrated energy system including heat pump and thermal storage units was proposed.The scheduling model was based on the load data and the output characteristics of power units,each power unit capacity was programmed without constraints,and the proposed scheduling model was compared with the traditional combined heat and power scheduling model.Results showed that the investment and pollutant discharge of the system was reduced respectively.Wind power was fully absorbed.Compared with the traditional thermal power unit,the proportion of the output was significantly decreased by the proposed model.The proposed system could provide a new prospect for wind power absorption and environment protection.

Enhancing Autonomy Capability in Regional Power Grids:A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Analysis of Energy Storage Operation Configuration of Power System Based on Multi-Objective Optimization

Driven by the goal of“carbon neutrality,”the increase in use of renewable energy power systems will be inevitable in the future.Uncontrolled output power and random volatility make it difficult to balance power in real time during system operation.Therefore,energy storage is considered to be an effective way to ensure the real-time balance of system power.However,cost of energy storage is relatively expensive.As a solution,energy storage can be used to balance the system power in order to reduce system operating costs.Taking the high proportion of wind power systems as an example,the impact of the“supply side”low-carbon transformation on the economics and reliability of power system operation is explored.In order to solve the problem of power system operation configuration optimization under the background of“carbon neutrality,”this paper establishes a multi-objective programming model.

Energy-efficient Approach to Minimizing the Energy Consumption in An Extended Job-shop Scheduling Problem

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

计及灾后恢复全过程储能应急响应能力的配电网韧性规划

储能因响应快、灵活性高的特点,已成为可再生能源发展必不可少的支撑技术,不仅具有削峰填谷、改善电能质量的作用,在灾后恢复过程中的应急响应能力同样不可忽视。提出了一种计及灾后恢复全过程储能应急响应能力的配电网韧性规划方法,构建了同时考虑网架增强和储能配置策略的配电网韧性增强双层规划模型,系统地刻画了极端灾害发生后配电网网架重构、设备抢修及复电等灾后恢复全过程的源-网-荷-储互动模式场景。最后,通过IEEE33节点系统的算例分析表明,所提方法能更充分地挖掘储能的应急响应潜力,有效提高增强性规划决策的精准度,获得更佳的投资回报。

面向新型电力系统的储能规划方法研究进展及展望

储能是新型电力系统非常重要的灵活性调节资源,在源网荷各端均可发挥重要的作用,在中国,储能的发展已经进入规模化发展期,如何合理、经济地利用好储能的关键是规划配置方法。在对储能规划方法研究现状的基础上,分析了储能投资运营主体多元化、应用场景和运行模式复杂化、储能技术多样化等新变化对储能规划方法的影响;提出了新能源聚集区考虑时空互补和共享机制的储能规划方法、电网侧充分市场竞争下的储能与输配电系统协同规划方法,以及用户侧电、热、氢多元储能合作/竞争共存下的规划方法。面向新型电力系统的储能规划,未来应注重电力系统不同发展阶段的整体协调性、储能投资运营主体多元化内生博弈机制的影响,以及规划方案在多目标多场景下的迁移学习方法。

新型储能技术进展与挑战Ⅲ:储能集成技术、安全技术和系统规划调度

新型储能日益成为中国建设新型能源体系和新型电力系统的关键技术,已成为中国经济发展的新动能。与之相应,新型储能技术的相关研究也在快速发展。开展了该领域的系列评价性综述工作,共分为电化学储能技术、物理储能与储热技术、储能集成技术、安全技术和系统规划调度3个部分,对各类新型储能技术的应用领域、最新研究进展及局限性等问题进行了全面系统的对比分析,并进一步探讨了储能集成技术、安全技术、系统规划调度等储能系统相关领域面临的挑战及发展趋势。第3部分为新型储能技术进展与挑战,重点对储能集成技术、安全技术和系统规划调度中的高压级联技术、构网型储能技术、数智化和数字孪生技术、分布式温度监测技术、浸没式液冷技术、高效的优化算法等技术进展进行综合分析与讨论。

计及功能性衰减的独立运营模式下电化学储能容量配置研究

电化学储能(electrochemical energy storage,EES)内部的多种功能性老化行为是影响EES规划决策以及经济性评估的重要因素。针对独立运营模式下EES同时参与电能量和调频市场的容量配置问题,首先提出计及功能性衰减的EES两阶段容量优化配置决策模型。第一阶段模型以EES期望寿命周期内收益最大化为目标进行EES容量优化配置;第二阶段模型考虑EES寿命和功能性老化行为约束,以日运行利润最大化为目标优化各运行年内EES运营决策。然后采用多参数规划理论方法对两阶段模型进行联合优化求解,以获得独立运营模式下EES的配置容量及其期望寿命。最后通过算例仿真分析了EES的功能性老化行为对其经济寿命及容量配置的影响,验证了所提模型和方法的有效性。

含P2G和复合储能的高速公路服务区综合能源系统日前优化调度

为了提高高速公路服务区综合能源系统可再生能源消纳能力、降低经济成本,本文面向高速公路服务区中电、气、冷、热等多态能源,基于能源枢纽矩阵建模方法,建立能源供给侧、能量转换侧和负荷需求侧各模块的能量转换与供需耦合特性模型;在充分消纳可再生能源的情况下,以系统运行总成本最小为目标函数,建立系统优化调度模型;针对同一负荷不同运行场景下的调度结果表明,所提模型能最大程度使用风光发电,降低系统总运行成本;供给侧引入电气耦合与负荷侧引入复合储能的调度结果表明,所提模型能达到系统运行费用最优,同时可实现系统周期内运行费用的“削峰填谷”。

基于IGDT的含广义储能的独立直流微网日前优化调度

微电网的能量管理与优化调度作为构建新型电力系统的重要环节,提高其可再生能源的消纳水平、降低源荷不确定性风险以及优化系统运行成本具有重要意义。因此,文中提出一种基于信息间隙决策理论(information gap decision theory,IGDT)的含广义储能的独立直流微电网日前优化调度模型。首先,构建含超级电容的混合储能系统,以降低蓄电池运行成本,将具备虚拟储能特性的柔性负荷与混合储能相结合,形成广义储能,充分发挥微电网系统内灵活性资源特性;其次,考虑系统风光荷不确定性,引入IGDT模型,在确定性模型基础上建立风险规避策略下的鲁棒模型和风险投机策略下的机会模型,从2种决策角度追求降低风险与最大化收益;最后,基于算例仿真分析,证明该调度策略在降低微电网运行成本的基础上可量化不确定性因素对系统调度决策的影响,验证了模型的有效性和可参考性。

基于储能和水电调节的跨区风光水联合优化调度策略

针对大规模新能源经特高压直流通道跨区并网消纳,在送端采用储能调节满足直流通道直线化功率传输等约束的同时,如何减小风光入网扰动度问题;在受端采用水电调节满足风光水跟踪电网负荷变化趋势,如何解决水电调节的利益受损问题,构建了风光水跨区域联合运行的三阶段调度模型.首先,在送端基于储能调节,构建了考虑受端电网净负荷变化趋势的风光功率直线化分段调节多目标优化模型;其次,在受端基于梯级水电调节,实现风光水联合功率跟踪电网负荷变化,构建受端电网与风光水联盟主从博弈模型,优化电网电价和联盟售电计划;最后,构建了基于纳什谈判的联盟利润分配模型,实现合作后各成员利润较合作前得到提升,维系联盟合作机制的稳定.仿真算例结果表明,采用本文方法可有效提升调节资源利用效率,保证水电调节利益,促进新能源消纳.

面向高比例分布式能源接入的输配网两阶段风险调度研究

随着大量高比例可再生能源接入配电网,潮流呈现双向化,输电网和配电网互联性增加,分散独立的调度影响电网可靠稳定运行水平。面对输网与配网间互济需求增强的现状,提出了面向高比例分布式能源接入的输配电网两阶段风险调度研究。首先以最小运行成本为目标,构建考虑输配电网不同运行风险的日前经济调度模型,在风险限值约束情况下协同优化系统源网荷储多种可调资源;其次构建目标函数为综合风险值最小的日内风险调度模型,计算三类运行风险指标值,对日前可调控资源进行出力计划调整;最后通过改进的粒子群算例求解模型,证明了该模型可使输配电网间交互协调,协同优化运行成本与运行风险,验证了所提两阶段风险调度模型的有效性和优越性。

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。

基于变时段设计改进多目标差分进化算法的风/光/火/储日前优化调度

在高比例新能源馈入的新型电力系统中,新能源出力的不确定性导致火电难以满足调度计划的精度需求,风/光/火/储系统的经济调度求解算法面临严峻挑战。为此,提出一种基于变时段设计的多目标差分进化算法。首先按各时段负荷特征构建风/光/火/储系统的变时段日前调度规则。进而以系统运行经济成本与污染排放量为目标,基于多目标差分进化算法求解变时段系统日前调度模型的Pareto解集。最后,用IEEE 39节点系统进行测试。结果表明在风、光、储与火电的约束条件均符合校验的情形下,相较于其他算法,该方法使计算结果更加优化,火电机组出力跟踪调度计划效果显著提高,验证了所提方法的有效性。

考虑电-热储能协同的综合能源系统规划优化研究

针对传统电化学储能造价高导致我国可再生能源消纳困难问题,提出一种考虑电-热储能协同的综合能源系统最优化规划方法。分析铅酸电池和蓄热罐的物理特性,建立其状态表征模型;设计电-热储能系统融合平抑可再生能源出力波动、保障供需平衡、峰谷套利三种功能场景的协同运行策略,建立考虑电-热协同的规划优化策略;基于最优化理论,以年总成本最小为优化目标,提出一种考虑电-热储能协同的综合能源系统规划优化模型,并以改进粒子群算法对模型进行求解。算例结果表明,所提出的模型能够有效提升能源系统的可再生能源消费占比。

共享储能模式下电动汽车充电站双层优化运行策略

在电动汽车持有量快速增长的背景下,电动汽车充电站的建设需求日益强烈。为了提高充电站运行经济性,文中提出接入共享储能的电动汽车充电站优化调度方法。首先,分析分布式储能和共享储能的运行模式,以充电站年投资成本和年运行成本最低为上层目标函数,以典型日运行成本最小为下层目标函数,构建考虑储能不同运行模式的电动汽车充电站双层优化调度模型。其次,对3个具有差异性的电动汽车充电站进行仿真,并将多个场景下的不同储能运行模式进行对比。然后,在此基础上,针对共享储能服务费以及电动汽车放电损耗进行灵敏度分析。最后,算例表明,共享储能的接入对降低电动汽车充电站规划和运行成本具有积极作用,合理的储能服务费定价和电动汽车放电损耗成本能减少充电站运行成本。