储能聚合商自营共享模式下电能交易方法

针对用户侧分布式储能成本较高、利用率低、运营模式单一等问题,提出了一种兼具电能自营和储能共享模式的储能聚合商(ESA)自营共享电能交易方法,ESA分别从与电网公司、用户的电能交易中获益。基于分时电价和用户负荷预测,建立ESA控制储能充放电的日前优化模型,以ESA与电网之间的交易成本最小化为目标,获得ESA日前各时段的充放电电量,并将其作为ESA实时优化和定价的储能控制策略。以最大化ESA效益为主目标、最大化用户日效益为从目标,以内部电价为控制变量、用户用电量为状态变量,以内部电价限价、用电刚性需求等为约束,建立一种基于主从博弈的电能交易实时优化模型。算例仿真表明：ESA对日前储能充放电的决策获得了最低购电成本;主从博弈优化的电能交易决策了内部电价,提升了ESA和用户的经济效益。ESA自营共享电能交易方法是电力系统在共享经济模式下的有效尝试。     

实时电价下计及转移成本的负荷优化

为准确反映用户的需求侧能力,提高用户需求侧响应的积极性,在实时电价体系下,计及转移成本,建立以用户购电费用最小为目标函数的负荷优化模型。利用Matlab对所建模型进行仿真,验证了基于实时电价的需求侧响应模型能够对负荷曲线削峰填谷。比较了不同的需求侧响应对负荷优化的效果,结果表明在一定范围内,峰谷时段负荷比与需求侧响应呈负相关性,削峰填谷效果与需求侧响应呈正相关性。对工业用户3种不同用电模式进行了总购电费用分析,结果表明三班制工作模式能够减少总购电费用。     

基于自回归积分滑动平均模型的可转移负荷竞价策略

研究了在较成熟的电力市场环境下,可转移负荷在日前市场和实时市场的最优竞价策略。在最优报价计算过程中,必须满足可转移负荷在截止时间内完成运行一定的电能消耗的约束条件。考虑到传统平均值短期负荷预测结果存在偏差,提出了一种基于自回归积分滑动平均模型的可转移负荷最优竞价策略,通过历史电价的变化特征估计模型的参数,从而预测第二天的日前电价和实时电价曲线,并以此为依据优化竞价策略。在此基础上,考虑功率限制,提出了基于贪心算法思想的电能竞价自动调整算法。通过实际电价数据进行计算,验证了此策略比单纯利用历史数据的期望值作为模型参数为购电者节省了更多购电费用,并能够有效完成竞价调整,以满足各个周期的功率限制。     

考虑电源调峰深度的外购电年度交易规划

我国新电改提出要完善跨省区电力交易机制政策,制定合理高效的跨省区外购电中长期交易规划。考虑省间电力传输的约束性,引入了外购电源的调峰深度概念,结合外购电价、省内负荷和发电出力等因素,通过对每月典型日的电力电量平衡分析,确定省内电力供应缺口,建立了外购电年度交易规划模型,从而优化不同电源的购电顺序,实现年度外购电费用最小的目标。模拟计算表明：1）通过增加调峰深度较好而电价较高的外购电量,可以减少调峰深度较差而电价较低的外购电量,从而降低购电费用;2）不同月份的电力需求情况对该月外购电交易的优化效果有较大影响,新方法在电力需求较少的月份能取得很好的优化结果。     

计及实时电量交易的售电公司日偏差电量考核优化模型

启动现货市场的目的是建立电力中长期交易和现货交易相结合的市场化电力电量平衡机制,而在市场运营机制不完善的建设初期,以日为结算期的短期考核机制有利于市场公平交易,稳定现货市场中的电力电量波动。为了更直观地体现短期偏差电量考核对售电公司收益及经营策略的影响,构建了计及日偏差电量考核机制的售电公司日前市场收益模型。基于实时市场购电和与储能电站电量交易,建立了以售电公司收益最大化为目标的日偏差电量考核优化模型。基于广东省2017年交易数据模拟典型日现货交易和实际用电情况,对比分析模型优化效果,并对统一出清价和单位考核成本及与储能电站的交易电价、正、负偏差量进行了敏感性分析。结果表明：该优化策略可明显提高售电公司收益;统一出清价、单位考核成本和向储能电站购电电价对额外成本影响最大,向储能电站售电电价对额外收益影响最大,正、负偏差量对售电公司整体收益影响最大。     

基于主从博弈的多微电网系统经济优化方法

随着售电侧电力市场改革的深入,研究多微电网系统的经济优化问题具有重要意义。针对多微电网系统,提出了一种基于主从博弈的经济优化方法。该方法主要由日前优化阶段与实时优化阶段组成。在日前优化阶段,微电网运营商根据日前预测信息和电网电价信息,优化储能单元的日前调度计划;在实时优化阶段,系统运营商根据实时预测信息,通过构建主从博弈模型求解内部最优电价,鼓励微电网运营商参与多微电网系统的电能交易,提升微电网之间的能量共享水平。算例结果表明,本文所提的两阶段经济优化方法可以有效地提升微电网运营商与系统运营商的经济效益,同时降低了多微电网系统的净负荷峰均比。     

基于鲁棒后悔度的日前市场购电联合优化

随着风电并网规模的不断增大,风电输出功率的不可预测性对电力市场交易的影响增大,弃风比例增加。为了优化日前市场的交易计划并合理利用风资源,该文联合日前能量市场、备用容量市场以及实时平衡市场优化日前购电计划,用区间数表示风电出力的不确定性和实时市场电价的不确定性。在确定日前市场最优购电策略时,由于风电出力和实时市场的能量平衡费用无法准确预知,该文考虑购电决策者的后悔心理,以鲁棒后悔度最小为目标得到日前市场最优购电策略。具体算例分析表明,该文购电方案可以有效降低购电决策者的后悔心理,合理利用风资源,降低购电成本。     

含电能直接交易的风电消纳机组组合模型设计

作为电力市场改革的关键环节,电能直接交易可以增大多方利益,却压缩了电力系统的调度空间,同时大规模风电的加入更加剧了电力系统调峰的难度.针对电能直接交易和风电对系统运行的影响,建立了含电能直接交易的风电消纳机组组合模型.该模型以社会总效益最大化为目标,在传统模型的基础上引入了直购电合同约束和风电出力约束,并加入了过网费、弃风惩罚与削减合同惩罚.通过理论推导与算例中对实际系统的日前调度,逐一分析了直购电价、直购电量与直购机组对社会总效益的影响;然后通过惩罚因子的动态变化,表明系统会根据惩罚因子的大小关系来决定放弃何种效益.所得结论验证了该模型的实用性与有效性,为中国电能直接交易的发展提供了参考.     

计及环保与需求响应的多目标中短期交易计划修正

考虑需求侧对电力市场交易的影响,提出了一种考虑环保和需求响应的多目标中短期交易计划修正模型.采用修正β参数的灰色模型分口径预测需求侧月峰、平、谷电量,结合考虑购电费用、环保和网损最小确定计划发电量.当电价变化后,利用电量电价弹性矩阵(E矩阵)计算出新的决策月峰、平、谷口径电量,得出考虑需求相应的修正月交易计划.提出奖惩费用,使得当修正电量波动到某一程度时购电价格随之变动,从而通过影响全网购电费用进一步影响交易计划.所提模型对交易中心具有辅助决策意义.     

计及需求侧参与的日前市场出清机制设计

随着我国电力现货市场的稳步推进以及电力零售商的出现,传统电网交易模式将产生极大变革.电力市场中需求侧响应资源可以有效抑制市场电价的波动,减少电力现货零售商购电费用,为此,设计计及需求侧参与的日前市场出清机制,通过考虑需求侧响应对节点电价的反馈作用,揭示日前市场中零售商需求侧响应的博弈机理,建立零售商间的博弈模型.采用逐...     

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.     

电力日前交易市场的运作机制及模型

日前交易市场是电力市场的重要组成部分，其运作方式有多种。文中提出了一种适合中国国情的日前交易市场运作模式，给出了相应的组织流程和运行规则。对于网络拥塞或线路、断面潮流越限的问题，提出了分区电价机制予以解决。     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and fexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum ofer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model diferent kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fuctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in diferent markets, reserve regulations, and risk preferences.     

电力现货市场下激励型需求响应交易机制及出清模型

为了进一步推动中国电力现货市场的建设,考虑发电侧和负荷侧共同参与、公平竞争的市场化交易模式将是电力现货市场未来发展的趋势。鉴于国内现货市场需求响应的研究尚处于起步阶段,需求响应产品如何参与市场是亟待解决的问题,对此设计了一种适用于目前国内电力现货市场的计及激励型需求响应的交易机制和出清模型。首先分析了批发市场下激励型需求响应的参与方式,探讨相关的交易机制和响应效益评估方法;然后,根据参与用户的市场投标信息构造激励型需求响应的现货市场竞价模型,在能量与备用市场联合优化模式下提出计及激励型需求响应的市场出清模型。最后,通过IEEE-30节点算例对激励型需求响应的市场效益进行定量化分析,验证了所提方法的实用性和模型的有效性。     

水火电力系统短期优化调度的不确定性模型

分析了电力市场中日前交易与实时交易的关系,考虑了水火电力系统的水力联系和电力联系,针对各种不确定性因素的影响,基于机会约束规划提出了一种新的水火电力系统短期优化调度的不确定性模型。允许所形成的调度方案在某些比较极端的情况下不满足约束条件,但这种情况发生的概率必须小于某一置信水平。兼顾日前交易和在此计划下可能存在的实时交易的费用,实现系统的火电机组和用以实时平衡的功率调整的费用最小化,并针对该模型给出了基于粒子群算法和蒙特卡罗仿真的求解方法。算例仿真说明了该方法的有效性。     

数据驱动下风电–抽蓄联合参与日前–实时市场随机鲁棒竞价策略

风电出力不确定性和波动性导致其市场竞争力较弱,且电价的预测偏差可能进一步加大其市场风险。为减少不确定性因素对风电-抽蓄联合系统收益的不良影响,由对抗变分贝叶斯神经网络生成以风电为代表的可再生能源出力场景,基于数据驱动方法对实时电价进行模糊不确定性建模。通过随机鲁棒优化建立风电-抽蓄联合参与日前和实时电力市场的三阶段模型,相较于常规的两阶段随机优化,增加了第三阶段鲁棒改进过程,保证了竞价方案能够既有经济性又能够有效应对极端场景、既有鲁棒性又不过分保守。结果表明,所提方法比传统不确定性分析方法具有明显优势,能够在避免人为假设的前提下,高效且真实反映可再生能源出力场景以及表征电价不确定性,可有效减少风电实时出力波动和电价预测偏差带来的较高不平衡惩罚。     

联营电力市场中需求侧的直接参与

在现有的允许需求侧竞标的日前联营市场中，每当某个时段的市场清算电价高于投标报价时，需求侧的兆瓦级购电竞标就被拒绝。由于用户不参与通过削减供电量来牟利的商业运作，因此未得到满足的用户需求只能从平衡市场中在更接近于预期用电的时段中获得。这就使用户遭受更大的不能以所希望的价格来满足其对电能的需要的风险。文章介绍了一种新的市场概念，它能在日前市场对需求侧的竞标者关闭后，以合并拍卖规则中需求移动机制的方法来降低其需求不平衡的风险。     

Optimizing demand-side bids in day-ahead electricity markets

We present a model of a purchaser of electricity in Norway, bidding into a wholesale electricity pool market that operates a day ahead of dispatch. The purchaser must arrange purchase for an uncertain demand that occurs the following day. Deviations from the day-ahead purchase are bought in a secondary market at a price that differs from the day-ahead price by virtue of regulating offers submitted by generators. Under an assumption that arbitrageurs are absent in these markets, we study conditions under which the purchaser should bid their expected demand and examine the two-period game played between a single generator and purchaser in the presence of a competitive fringe. In all our models, it is found that purchasers have an incentive to underbid their expected demand, and so the day-ahead prices will be below expected real-time prices. We also derive conditions on the optimal demand curve that purchasers should bid if the behavior of the other participants is unknown but can be modeled by a market distribution function.     

Multi-market energy procurement for a large consumer using a risk-aversion procedure

This paper provides a technique to derive the bidding strategy in the day-ahead market of a large consumer that procures its electricity demand in both the day-ahead market and a subsequent adjustment market. Price uncertainty is modeled using concepts derived from information gap decision theory, which allows deriving robust decisions with respect to price volatility. Risk aversion is built implicitly within the proposed model. Correlations among prices in the day-ahead and the adjustment markets are properly modeled. The proposed technique is illustrated through a realistic case study.