基于节点电价的主动配电网日前-实时阻塞管理

在主动配电网中分布式资源(distributed energy resources，DERs)渗透率不断上升及电力市场改革不断推进的背景下，高比例DERs引起的线路过载和节点电压越限等网络阻塞现象不容忽视。针对主动配电网的阻塞问题，该文提出基于配电网节点电价(distribution location marginal price，DLMP)的日前-实时阻塞管理模型。在日前阶段，各负荷聚合商(aggregator，Agg)首先预测日前市场电价并收集相关DERs信息，然后配电网管理员在保证用户用电需求的同时使其用电支出最小，并兼顾网络约束制定DLMP发布给Agg，Agg得到日前交易计划；在实时阶段，各Agg更新DERs信息，并根据配电网管理员更新的DLMP重新调整日前交易产生的偏差。最后，通过IEEE33节点算例进行仿真验证，结果表明提出的阻塞管理模型可以有效解决主动配电网在日前和实时两阶段的阻塞问题，保证线路容量及节点电压在允许的安全范围内。     

基于主从博弈的含电动汽车虚拟电厂协调调度

电动汽车作为配电网中最为重要的主动负荷,其充电优化管理成为目前售电侧开放下的重点研究领域之一。而虚拟电厂作为电网分布式能源管理的重要解决方案,其传统定义侧重于在发电侧聚合小容量分布式能源以提高可再生能源在电网中的消纳程度。结合售电侧开放下的电力市场改革需求,文中提出以虚拟电厂作为售电实体参与电动汽车充电管理的协调调度优化模型。在能量市场交易模型中,虚拟电厂作为分布式能源与电动汽车的聚合代理商参与电力市场电能申报与交易;在主从博弈模型中,虚拟电厂通过主从博弈制定合理的售电价格引导电动汽车的有序充电入网,并通过协调调度集中整合优化分布式能源。算例分析表明,通过虚拟电厂的集中优化管理可以有效实现分布式能源与电动汽车的能源互补并提升整体运行经济性。     

电力现货市场环境下园区型综合能源系统多阶段联合优化运行

针对国内最新电力现货市场规则,设计了园区型综合能源系统参与电力市场的系统框架和运行模式,基于多时间尺度多步递进优化的思想,建立了包含日前、日内、实时三阶段联合优化经济运行模型:在日前和日内阶段,采用随机优化概率约束解决光伏、负荷的不确定性问题,通过线性规划算法求解现货日前市场最优申报用电量和日内24 h功率计划曲线;在实时阶段,采用模型预测控制算法,形成了对功率、电价信息的滚动跟踪预测和校正反馈,实现与实时市场的同步滚动优化.算例分析结果表明,该优化经济运行模型可以使园区用户积极跟踪电力现货市场价格波动,通过降低综合用能成本有效提升盈利空间.     

基于神经网络的短期电价预测

利用系统旋转备用、历史负荷和清算电价对未来时段电价的影响来进行电价预测。以澳大利亚昆士兰州电力市场为背景,采用神经网络的方法,建立了提前1天电力市场清算电价短期预测模型。模型为3层BP神经网络结构,并采用昆士兰州电力市场1998年的电价数据进行了训练和预测分析,结果表明该模型具有良好的预测效果。     

Experience with PJM market operation, system design, and implementation

This paper outlines the fundamental features of the PJM day-ahead energy market and real-time energy market. The Day-ahead market is based on a voluntary least-cost security constrained unit commitment and dispatch with several fundamental design features that ensure the market is robust and competitive. This market offers market participants the option to lock in energy and transportation charges at binding day-ahead prices. The flexibility of the day-ahead market rules provide all participants with equal access to the day-ahead market through consistent price signals and by providing all participants with the ability to submit virtual demand bids and virtual supply offers. These mechanisms promote liquidity in the markets. Economic incentives drive the convergence of the day-ahead and real-time market prices. The real-time energy market is based on security-constrained economic dispatch and is cleared based on the actual system operating conditions. The LMP-based markets support reliable grid operations through efficient price signals.     

可再生能源消纳责任权重制下电力市场优化决策模型

可再生能源消纳责任权重制与电力现货市场建设并行推进,实际消纳可再生能源电力将成为电力用户完成考核要求的主要完成方式,并且可再生能源电力也将逐步进入电力现货市场参与交易。基于以上框架建立了可再生能源消纳责任权重制下包含可再生能源日前市场、常规能源日前市场、实时市场的双层多主体优化决策模型,通过库恩-塔克(KKT)条件、强对偶定理、二进制拓展法等方法将其转化为混合整数线性规划模型来求解市场均衡点。算例分析探究了考核权重指标、绿色电力证书价格、可再生能源功率渗透率对市场均衡点的影响。分析结果表明,在市场均衡点日前市场中,可再生能源电力出清电价等于常规能源出清电价加上绿色电力证书价格,且随着可再生能源功率渗透率提高,电力现货市场出清电价呈现下降趋势。     

A Reinforcement Learning Model to Assess Market Power Under Auction-Based Energy Pricing

Auctions serve as a primary pricing mechanism in various market segments of a deregulated power industry. In day-ahead (DA) energy markets, strategies such as uniform price, discriminatory, and second-price uniform auctions result in different price settlements and thus offer different levels of market power. In this paper, we present a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework that allow assessment of market power in DA markets. Since there are no available methods to obtain exact analytical solutions of stochastic games, an RL-based approach is utilized, which offers a computationally viable tool to obtain approximate solutions. These solutions provide effective bidding strategies for the DA market participants. The market powers associated with the bidding strategies are calculated using well-known indexes like Herfindahl-Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue-based market power index (RMPI), which are developed in this paper. The proposed RL-based methodology is tested on a sample network     

基于均衡理论的虚拟电厂市场参与模式及方法

随着化石能源的枯竭与其对环境的污染，分布式发电这种经济、环保、灵活的新型发电方式逐渐得到广泛的应用。但分布式电源大量并入电网将带来电能质量问题，影响电网安全稳定运行。虚拟电厂的出现有效解决了这些问题，它通过对分布式电源进行协调优化，减小了分布式电源并网对电网产生的冲击，提高了电网运行稳定性。然而目前虚拟电厂与主网之间的协调研究仍有待深入，因此提出了虚拟电厂与主网之间交互迭代的市场均衡方法，对虚拟电厂的出力与主网的电价迭代求解从而实现收敛。首先，对虚拟电厂的定义与内部主体进行分析，进而将虚拟电厂与主网之间的协调优化表述为两个层次之间的市场均衡问题，提出虚拟电厂与主网交互的双层模型，分别构建以自身成本最小化为目标，计及分布式能源运行约束与电网安全约束的虚拟电厂与主网数学模型，并采用迭代算法对模型进行求解。算例分析表明提出的市场均衡方法在考虑虚拟电厂对主网影响的同时能够有效降低虚拟电厂与主网协调的复杂程度，同时减小运行总成本，提升经济效益。     

考虑电动汽车可调度潜力的充电站两阶段市场投标策略

在电力市场环境下,充电站优化投标策略能降低电力成本,甚至通过售电获取收益.文中考虑了电动汽车成为柔性储荷资源的潜力,提出了日前电力市场和实时电力市场下充电站的投标策略.首先,基于闵可夫斯基加法提出了充电站内电动汽车集群模型的压缩方法,并建立了日前可调度潜力预测模型和实时可调度潜力评估模型.同时,考虑充电站间的非合作博弈,建立了电力零售市场下充电站的策略投标模型,并基于驻点法将其转化为一个广义Nash均衡问题.然后,提出了基于日前报价和实时报量的两阶段市场交易模式,并与合作投标模式、价格接受模式和集中调度模式进行对比.最后,基于一个38节点配电系统进行了仿真.仿真结果表明所提出的可调度潜力计算方法能够将电动汽车集群封装为广义储能设备,从而降低了模型的维度.基于可调度潜力的策略投标模型能够挖掘电动汽车的储荷潜力,实现电动汽车与电网的有序互动.     

基于鲁棒后悔度的含风电日前和日内市场两阶段出清优化

由于风电的不确定性,含有风电较多的系统将引入日内市场,以减小实时市场的功率偏差,提高系统运行的经济性和稳定性。文中利用区间模型表示风电出力的不确定性,在日前市场中考虑日内市场风电偏差功率的不确定性,建立日前市场和模拟日内市场联合优化模型;在各个日内市场考虑对应实时市场风电偏差功率的不确定性,建立各日内市场和模拟实时市场联合优化模型。在日前市场和日内市场中考虑购电决策者的后悔心理,以鲁棒后悔度最小为目标,实现分阶段不同时间尺度购电出清优化,提高日前市场和日内市场购电方案的经济性和鲁棒性。最后,基于一个具体算例对优化模型和结果进行详细的分析,体现了两阶段优化出清方法的可行性和优越性。     

面向多级市场出清的负荷聚合商联合交易策略

在以新能源为主体的新型电力系统发展模式下,电网将面临灵活性资源严重不足的问题,亟需引导用户侧可调控资源主动参与系统的平衡调节服务。提出一种考虑日前主能量市场、日前备用市场及实时平衡市场的多级市场衔接框架,基于负荷聚合商引导终端用户参与至多级耦合市场的市场化调度中。在日前主能量市场与备用市场,提出负荷聚合商可调节负荷状态感知模型及日前电能量与备用联合市场的竞价模型,系统运营商实现日前主辅能量市场的阶段性出清。在实时平衡市场,提出负荷聚合商基于用户侧富余可调节资源的实时平衡市场竞标模型,系统运营商基于实际系统运行需求实现平衡资源的出清与备用资源的调度。最后基于 IEEE 30 节点系统验证了所提出市场框架可有效激励用户侧主动响应电网调控需求、降低系统供需不平衡风险。     

Auction design in day-ahead electricity markets

Competition in day-ahead electricity markets has been established through auctions where generators and loads bid prices and quantities. Different approaches have been discussed regarding the market auction design. Multi-round auctions, despite its implementation complexity, allow market participants to adapt their successive bids to market prices considering their operational and economic constraints. However, most of the day-ahead electricity market implementations use noniterative single-round auctions. This paper presents a market simulator to compare both auction models. Different auction alternatives, such as the Spanish single-round auction that takes into account special conditions included in the generator bids, and multi-round auctions with different stopping rules, are analyzed. The results and acquired experience in the simulation of the Spanish market, started in January 1998, are presented. Hourly market prices, average daily price, price/demand correlation and several economic efficiency indicators, such as generator surplus, consumer surplus and social welfare, are compared to derive conclusions regarding the performance of the auction alternatives     

Auction design in day-ahead electricity markets

Competition in day-ahead electricity markets has been established through auctions where generators and loads bid prices and quantities. Different approaches have been discussed regarding the market auction design. Multi-round auctions, despite its implementation complexity, allow market participants to adapt their successive bids to market prices considering their operational and economic constraints. However, most of the day-ahead electricity market implementations use noniterative single-round auctions. This paper presents a market simulator to compare both auction models. Different auction alternatives, such as the Spanish single-round auction that takes into account special conditions included in the generator bids, and multi-round auctions with different stopping rules, are analyzed. The results and acquired experience in the simulation of the Spanish market, started in January 1998, are presented. Hourly market prices, average daily price, price/demand correlation and several economic efficiency indicators, such as generator surplus, consumer surplus and social welfare, are compared to derive conclusions regarding the performance of the auction alternatives     

含风电和电动汽车的VPP现货市场投标鲁棒优化模型

当风力发电商(WPG)和电动汽车(EV)聚合商组成的虚拟电场(VPP)参与市场投标时,风电出力的不确定性、预测出力偏差以及市场价格的波动性,都是VPP在参与市场投标时需要考虑的因素。在计及上述因素的影响下,文中研究了由WPG和EV聚合商组成的VPP在日前市场和实时市场的联合竞价模型:假定VPP是价格的接受者,综合考虑日前和实时价格的不确定性,在日前市场中根据风电出力和市场价格的预测结果进行日前竞价,然后在实时市场上参与实时竞价。VPP不但可以通过EV充放电平抑WPG投标偏差,还可以根据价格信号进行充放电投标,实现削峰填谷。通过引入偏差考核机制,在日前和实时市场结束后进行统一结算。基于合作博弈理论,利用Shapley值法将总收益在WPG和EV之间根据各自的贡献进行合理分配。最后,通过算例验证了模型的可行性和有效性,结果表明VPP参与日前和实时市场可以增加收益,降低出力和价格不确定性带来的风险,为新能源参与现货市场的建设提供参考。     

Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method

Environmental concerns, improvements in renewable energy technologies, governmental incentives for the use of these resources, and increased T&D costs, are the main factors driving the energy sector into a new era, where considerable portions of electrical demand will be met through widespread installation of Distributed Energy Resources (DERs). The Virtual Power Plant (VPP) is a decentralized energy management system tasked to aggregate the capacity of some Distributed Generations (DGs), storage facilities, and Dispatchable Loads (DLs) for the purpose of energy trading and/or providing system support services. Due to the stochastic behavior of the prime sources of some DGs, such as wind speed and temperature, the steady state analysis of the systems with integration of such DG units requires a probabilistic approach. In this paper, a probabilistic Price Based Unit Commitment (PBUC) approach using Point Estimate Method (PEM) is employed to model the uncertainty in market price and generation sources, for optimal bidding of a VPP in a day-ahead electricity market. Also, the uncertainty of stochastic DGs generations is handled through increasing the amount of required reserve. The proposed model allows a VPP to decide on the unit commitment of its DERs, and the optimal sale/purchase bids to the day-ahead market. The proposed optimization algorithm is applied to an 18-buses system.     

基于智能电网调度控制系统基础平台的新能源优化调度

针对中国新能源大规模集中式开发的现状,分析了开展新能源调度的必要性以及新能源调度的特点。鉴于目前传统调度技术支持系统功能已无法应对大规模新能源并网给电网调度运行带来的挑战,介绍了一种基于多时间尺度的新能源协调优化调度方法,包括年/月度新能源电量计划制定、日前及日内滚动优化调度,并基于正在推广应用的智能电网调度控制系统基础平台设计开发了新能源调度技术支持系统。通过该系统可实现对新能源进行多个时间尺度的协调优化调度,逐步减少新能源预测误差带来的影响,保证了新能源的最大安全消纳。研究成果已在全国16个网/省级电网实现了应用。     

计及电价不确定性和损耗成本的储能竞价策略

储能是促进新能源消纳、提高电力系统稳定性和灵活性的有效措施。然而储能参与电力市场的策略极其复杂,现已成为实现储能商业化应用的关键问题之一。文中提出储能在日前和实时市场价格不确定环境下考虑循环损耗成本的最优竞价策略。为权衡储能多次循环增加的售电利润和损耗成本,在制定储能竞价策略时,将其循环损耗成本的影响计及在内,并充分考虑电池充、放电深度对循环损耗成本和利润的影响;在日前市场中建立电价-电量投标模型,对电价和电量同时进行投标以充分考虑电价不确定性;在实时市场中建立电量投标模型对日前市场投标进行弥补修正,使竞价策略更加合理与优化。算例验证了所提储能竞价策略的有效性,并说明所建模型可以确定最优电池充、放电深度。     

Realistic electricity market simulator for energy and economic studies

Electricity market simulators have become a useful tool to train engineers in the power industry. With the maturing of electricity markets throughout the world, there is a need for sophisticated software tools that can replicate the actual behavior of power markets. In most of these markets, power producers/consumers submit production/demand bids and the Market Operator clears the market producing a single price per hour. What makes markets different from each other are the bidding rules and the clearing algorithms to balance the market. This paper presents a realistic simulator of the day-ahead electricity market of mainland Spain. All the rules that govern this market are modeled. This simulator can be used either to train employees by power companies or to teach electricity markets courses in universities. To illustrate the tool, several realistic case studies are presented and discussed.     

考虑深度调峰的电力日前市场机制设计

中国电力系统缺乏灵活调峰资源,面临严重的弃风、弃光困局,迫切需要充分调动各种资源的调峰潜力。文中提出了考虑深度调峰的电力日前市场机制设计,将深度调峰、可再生能源消纳和日前市场相结合。设计了基于深度调峰报价和避免削减降价的市场机制,深度挖掘火电机组的调峰潜力,激励火电机组主动降低自身出力参与调峰,激励可再生能源出让部分发电上网的红利,从而以共赢的方式促进可再生能源的消纳。构建了考虑深度调峰的电力日前市场出清模型,实现火电与可再生能源在低谷时段的发电权交易,实现了火电深度调峰和日前市场出清的联合建模,解决了两个市场割裂的情况下难以考虑机组爬坡约束的问题。文中采用IEEE 118节点算例系统,验证了所提机制和模型的有效性。     

多级电力市场之间协调的模型与方法

在电力工业市场化运行过程中,年度/月度合约交易市场、日前市场、时前/实时平衡市场及辅助服务市场是并存的,而电力各个市场之间的衔接与协调一直是电力市场理论研究中的一个重要问题.目前国内外真正实现多级电力市场的情况并不多见,而对于经济性、安全性进行多级市场协调的研究也不够深入.为此,提出了多级市场之间协调的模型与方法,能够做到在保障电网安全稳定经济运行的情况下保证市场公平交易.