Real-Time Demand Response Management for Controlling Load Using Deep Reinforcement Learning

With the rapid economic growth and improved living standards,electricity has become an indispensable energy source in our lives.Therefore,the stability of the grid power supply and the conservation of electricity is critical.The following are some of the problems facing now:1)During the peak power consumption period,it will pose a threat to the power grid.Enhancing and improving the power distribution infrastructure requires high maintenance costs.2)The user’s electricity schedule is unreasonable due to personal behavior,which will cause a waste of electricity.Controlling load as a vital part of incentive demand response(DR)can achieve rapid response and improve demand-side resilience.Maintaining load by manually formulating rules,some devices are selective to be adjusted during peak power consumption.However,it is challenging to optimize methods based on manual rules.This paper uses SoftActor-Critic(SAC)as a control algorithm to optimize the control strategy.The results show that through the coordination of the SAC to control load in CityLearn,realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.

Design and Development of a New Control System for Improving Energy Efficiency and Demand Response

One of the best strategies for improving energy efficiency in any system is using the energy resources in the facilities properly.Using energy systems only when they are absolutely necessary is one of the best cost-benefit ratio strategies,i.e.the best energy saving strategy is,not using it.The aim of this paper resides on introducing a new Energy Management and Control System(EMCS),developed by the authors,which has been installed at the Universitat Politècnica de València.Alongside the paper,the architecture,the components and the installation cost analysis of the EMCS,as well as management actions implemented in the university and the obtained results are presented.Furthermore,this innovative system has been designed to improve demand response in energy systems by providing consumers with a tool for responding actively to energy demands,and also to provide all the different electrical market agents with a communication and business platform for exchanging information.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

An Interactive Operating Demand Response Approach for Hybrid Power Systems Integrating Renewable Energy Sources

The increased deployment of renewable energy in existing power networks has jeopardized rotational inertia,resulting in system degradation and insta-bility.To address the issue,this paper proposes a demand response strategy for ensuring the future reliability of the electrical power system.In addition,a modified fuzzy logic control topology-based two-degree-of-freedom(fractional order proportional integral)-tilt derivative controller is designed to regulate the frequency within a demand response framework of a hybrid two-area deregulated power system.The test system includes thermal power plants,renewable energy sources(such as wind,parabolic trough solar thermal plant,biogas),and electric vehicle assets.To adaptively tune the controller’s coefficients,a quasi-opposition-based harris hawks optimization(QOHHO)algorithm is developed.The effectiveness of this algorithm is compared to other optimization algorithms,and the stability of the system is evaluated.The results demonstrate that the designed control algorithm significantly enhances system frequency stability in various scenarios,including uncertainties,physical constraints,and high penetration of renewables,compared to existing work.Additionally,an experimental assessment through OPAL-RT is conducted to verify the practicality of the proposed strategy,considering source and load intermittencies.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.

计及需求响应的光热电站参与深度调峰的分层优化调度策略

从源、荷两侧挖掘系统调峰潜力,建立计及需求响应的光热电站参与深度调峰的分层优化调度模型。上层从负荷侧出发,提出一种基于负荷分类的价格需求响应模型,可有效缓解系统调峰压力;中层从电源侧出发,利用光热电站灵活的调节特性在深度调峰时段协调火电机组参与辅助调峰,构建以运行总成本最小为目标函数的日前调度模型;下层提出一种基于模型预测控制的日内动态调整模型,在滚动优化的同时,通过状态反馈环节实时调整光热电站储热装置充放热修正日前调度计划。仿真结果表明,所提调度策略在降低系统调峰成本的同时能有效抑制风光以及负荷的短时功率波动,在保证系统安全稳定运行的前提下提升风光消纳率。

考虑空气源热泵负荷聚合参与的需求响应

基于“电网-聚合商-负荷”三级架构,提出空气源热泵负荷聚合参与需求响应的控制策略。供暖运营商作为热泵负荷的聚合商,在保证用户热舒适度的基础上,利用建筑本身的蓄能能力,结合分时电价最小化供热成本,并对负荷可调节潜力进行评估。当电网调度部门下发调控指令后,考虑用户舒适度和电网调节需求,基于多目标遗传算法分配各负荷调节量,在满足调控目标的同时可改善调控带来的聚合功率振荡、反弹负荷大等问题。最后,仿真验证所提策略的有效性。

基于负荷准线的5G基站虚拟电厂优化控制方法

在新型电力系统中,亟待深度挖掘需求侧资源以提升系统灵活性和新能源消纳能力。在“新基建”背景下,5G基站作为一种新型需求侧资源正迅速发展。研究如何在保证基站备用需求的前提下,由铁塔公司组建含大规模5G基站的虚拟电厂(virtual power plant,VPP)并常态化参与需求响应。首先,提出了考虑储能动态备用容量的5G基站运行可行域构建方法,建立了5G基站VPP的聚合模型。然后,建立了5G基站VPP响应负荷准线的日前优化模型,提出了适合对大规模5G基站进行协调控制的日内解聚合方法。最后,建立了含高比例新能源的区域电网仿真算例。仿真结果表明,聚合大规模基站参与准线型需求响应,可以显著降低5G基站的运行成本,同时提高电网的新能源消纳能力。

考虑需求侧响应的配电网柔性软开关优化控制方法

由于当前配电网技术使配电网系统的有功损耗增加,出现网损数值增加、渗透率较低等问题,提出考虑需求侧响应的配电网柔性软开关优化控制方法。首先,建立需求侧响应模型与柔性软开关优化控制模型;然后,利用粒子群算法对模型进行求解,完成配电网负荷转移的设计;最后,通过设定柔性软开关运行、配电网系统潮流及可再生能源发电出力等约束条件完成配电网柔性软开关控制优化。实验证明,应用本文方法可有效转移配电网系统的负荷,大幅度降低配电网系统的有功损耗电量及整体网损量,提升配电网系统的渗透率与消纳可再生能源发电的能力,实现对配电网系统的运行优化,为配电网系统的安全经济运行提供保障。

基于共享储能的台区多元负荷协同控制策略

大规模不确定可再生能源的接入,对台区电网负荷的灵活性调整提出了更高的要求。基于共享储能的商业模式,考虑各类电器设备的用电及功能特性,提出了台区多元负荷协同控制策略。提出了基于共享储能的台区多元负荷协同优化框架,并详细阐述了多元负荷的协同控制过程;根据负荷的可调特性,对用户负荷进行分类,建立负荷功率模型和舒适度成本模型;建立了以用电综合成本最低为目标的用户层优化模型和以调峰为目标的台区层优化模型。基于实际数据进行仿真验证,结果表明所提控制策略可以有效保证新能源的消纳,降低峰值负荷,并协助用户参与电力辅助服务市场。

基于需求的停车场电动车集群充放电管理策略

电动车作为电网的分布式储能装置可以有效提高电能的使用效率.当前的车网互联(V2G)方案主要采用集中式策略实现电动车充放电管理与用电负荷的调整.然而,当大规模电动车接入电网,由于车辆用户出行行为的不确定性,集中式策略无法满足个体电动车的实时需求以及电网负荷的有效调整.本文以停车场内的电动车集群作为研究对象,并将停车场作为电动车的电力供应站,进而,提出了一个实现用电负荷调整和分布式实时控制的充放电控制方案.首先,依据车群出行统计数据和实时电价信息,基于动态规划算法提出了车群一天内各个时刻最优能源需求规划策略,从而达到了高效利用能源的目的.在车群最优能源需求的约束下,基于实时优化方案提出了针对个体车辆的分布式充放电控制策略.最后,通过不同情况的仿真实验验证了提出方案的可行性.

大规模变频空调的可信聚合优化与自适应分散协调方法

变频空调(IAC)可观的调节潜力使其成为电力系统的重要灵活性资源,但其海量、异质和隐私敏感的特点使得集中式调控实施性不佳。现有的弱中心化方法较为关注计算效率的提高,对结果的可行性和解聚合的自适应性考虑不足,极易导致实际负荷分配越界且收敛困难。因此,首先基于IAC物理模型,通过构建保守能量场景得到个体可信边界,以保证集群调度曲线始终处于可调范围内。然后,为了敏捷地分配负荷,提出基于分层分散式架构的自适应解聚合策略,使集群在各时段精确跟踪调度目标并最小化效用损失。其控制过程轻量、无参数化,且仅需极少迭代即可快速得到可行次优解;数据经分层汇总后实现降维,避免了个体隐私泄露,更具实施性。最后,为进一步挖掘实时调节潜力,利用IAC集群进行三相负载均衡,开创了应用场景,并通过“迭代即调整”方式平抑负荷与光伏波动,实现能量优化、分配、平衡的完整统一。算例结果验证了所提方法的高效性,为海量IAC的灵活性挖掘提供了新思路。

考虑源网荷储协调互动的配电网无功优化

高比例新能源发电是新型电力系统的主要特征,挖掘新能源发电、储能以及柔性负荷等柔性可控资源的无功电压潜力能有效提高新型电力系统运行的稳定性和安全性。文中提出了一种考虑源网荷储互动的配电网无功电压控制方法。该方法从负荷需求侧响应出发,基于市场实时电价信息,以负荷转移成本和用户购电成本为基础,提出基于价格驱动需求响应的负荷平移策略;利用储能系统协调新能源发电出力和负荷平移规则,构建综合考虑源网荷储互动的配电网无功优化模型。通过调节无功补偿设备、储能充放电策略协调新能源出力,在满足负荷需求侧响应需求的基础上达到系统无功电压的最优控制。通过改进的IEEE33节点系统仿真分析,实验结果表明通过源网荷储的充分互动能够有效提高系统无功电压调节能力,实现配电网安全、经济运行。

基于模型预测控制的源荷储低碳经济调度

为提高电力系统风电消纳的经济性以及实现低碳排放的目标,基于模型预测控制方法,构建了多时间尺度下含碳捕集电厂的源荷储低碳经济调度模型。源侧分析碳捕集电厂的运行机理及能量时移特性;荷侧根据需求响应时间尺度的差异性,调用不同响应速度的价格型、动态激励型需求响应资源分别参与日前-日内调度;储能快速调整充放电功率提高运行经济性,日前调度以运行成本和碳排放量最小为目标,通过协调调度源荷储资源优化系统各单元出力计划。日内调度在滚动优化平滑功率波动的基础上,以跟踪和修正日前调度计划为目标,实时应对风光和负荷的功率波动,通过算例验证所提方法可降低运行成本和碳排放量,实现对经济与低碳协同优化。

城市配电网数智化电动汽车需求响应控制措施及机制研究

新能源电动汽车快速发展对城市配电网乃至全网供需平衡运行的影响日益突出。笔者通过分析新能源发电特性、典型配电网台区用电特性,提出电动汽车需求响应技术手段、市场及政策机制、车桩行政规范建设等控制措施,设计了移峰填谷引导、填谷响应充电、谷用峰发双向三种响应模式,并探索配电网电动汽车需求响应步骤及控制流程。电动汽车电力需求响应可最大程度平抑大量新能源的随机性引发的低谷消纳困难和高峰电力供应紧缺交替运行的阶段性波动,减少大量电动汽车随机充电对城市配电网用电高峰的冲击及重过载影响,增加低谷富余电力消纳能力,降低电动汽车用电成本,并提高发输变配电力设备利用效率,减少电力固定建设投资。

基于改进蜣螂算法的多区域空调系统需求响应DMPC供冷策略

针对夏季电网高峰期间办公建筑空调系统的峰值负荷造成电网短缺等问题,提出一种基于需求响应的多区域空调系统分布式模型预测控制(DMPC)供冷策略;以西安市某办公建筑的5个区域为研究对象,分别建立该办公建筑的物理模型及空调系统能耗数学模型,并验证模型的准确性;构建多区域空调系统仿真模型,优化目标为最小化空调系统运行能耗和室温与设定值的误差最小;选取蜣螂算法作为优化工具,并针对该算法存在全局搜索速度慢、易早收敛和陷入局部最优等缺点;采取混沌映射策略优化种群初始化,生成更加均匀的种群以提升种群个体质量;利用螺旋搜索策略对蜣螂的觅食和繁殖行为进行更新,进一步扩展算法的全局搜索性;同时引入随机扰动和自适应因子改进蜣螂的偷窃行为,改善算法易陷入局部最优等问题;运用改进后的蜣螂算法对DMPC的滚动优化进行优化求解,并与PID温度反馈控制进行对比,验证DMPC的控制性能;实验结果表明,在所研究的5个区域中,DMPC比PID控制方法的响应速度分别提升了8.91、8.65、12.04、5.79和1.79%;结合需求响应策略利用分时电价进行调控,提出温度与启停优化调控策略对空调系统的峰值负荷进行削峰转移;结果表明两种预冷启停优化策略的峰时负荷转移率分别为27.29%和29.16%,可以有效地将系统高峰时段的冷负荷转移到其他时段,降低电网运行压力。

基于时间触发机制的温控负荷集群综合惯性控制研究

面对新型电力系统的低惯量趋势,规模庞大而位置分散的温控负荷在参与电力系统频率响应的同时,能够为电网提供虚拟惯量和一次调频支撑将是提升电网频率安全的新思路。为此,以集中-分布式调控框架下的温控负荷集群为研究对象,首先在详细分解控制层调控中心、协调层聚合商、响应层温控负荷终端三者在辅助调频过程中的采样、计算、控制、执行一系列实时任务的基础上,设计了事件触发与时间触发相结合的温控负荷集群终端异步响应机制,实现温控负荷集群功率遵从综合惯性控制律的效果。随后,对所提综合惯性控制下温控负荷集群参与调频的电力系统进行频率响应建模,并进一步利用改进劳斯近似实现系统降阶,从而获得系统频率响应关键特征的时域解析表达式。最后,通过仿真算例验证了所提基于时间触发机制的温控负荷集群综合惯性控制的有效性,并分析了采用改进劳斯近似对系统模型降阶的可行性。

考虑碳排放流与需求响应的电力系统两阶段优化调度

风电接入从发电侧降低了电力系统碳排放,而引入需求响应消纳弃风为负荷侧降碳提供了新思路。文中综合考虑风电和需求响应,基于电力系统碳排放流理论提出日前、日内两阶段电网低碳优化调度方法。首先,分析电力系统碳排放流理论,建立负荷侧的节点碳势模型;其次,将柔性负荷分为可转移负荷和可削减负荷,基于负荷节点碳势模型设计调用这两类负荷降碳的响应机制;然后,建立考虑低碳性和经济性的源荷协调日前优化调度模型,基于模型预测控制求解日前优化调度模型,并通过反馈校正调整日内调度结果。最后,分别通过改进PJM-5节点和IEEE 300系统进行仿真验证,证明了文中所提优化调度方法能有效促进柔性负荷消纳风电,减少弃风,同时实现负荷侧降碳。