确定性合约电量分解算法在浙江发电市场的应用

介绍了浙江发电侧电力市场中合约电量的制定、分解的过程,以澳大利亚推荐的合约电量分解算法为基础,提出适用于浙江电网的确定性合约电量分解算法、年度合约电量模型、负荷模型、计划检修处理模型和技术出力处理模型,最后得出分解算法有效平抑2000年1月份发电市场的低电价和2月份的高电价的结论.     

基于内嵌增广拉格朗日函数Q-learning方法的虚拟电厂市场博弈策略

聚合了多品类能源资源的虚拟电厂为多主体参与下的竞争电力市场增添了较多不确定性。为了刻画虚拟电厂在市场博弈行为中的特点,探寻更高效的电力市场交易机制,提出了一种含虚拟电厂的电力市场古诺博弈模型,并证明了其纳什均衡解的唯一存在性。由于传统优化算法难以准确模拟现实中发电商之间的竞争过程,而适用于不完全信息博弈的Q-learning算法又难以处理复杂的约束条件,提出了基于多智体框架下Q-learning算法内嵌拉格朗日函数的联合求解算法,对含虚拟电厂的电力市场博弈模型进行求解。算例分别采用遗传算法与所提算法对模型进行仿真测试,结果表明,在电能需求总量恒定且统一出清模式的电力市场中,虚拟电厂较传统电厂有着优越的资源整合与盈利能力,且所提算法能够获得更为稳定的收敛结果。     

基于判定树的电力市场中发电商竞标能力数据挖掘的新思路

提出通过信息熵构造判定树的数据挖掘算法对历史竞标样本进行分类的新思路。介绍了算法中如何处理高分枝属性、数值属性和缺失数据及剪枝等关键环节。一个考虑市场需求水平、机组报价水平、机组容量等多因素的算例说明了该算法的实现过程并得到机组的负荷率与这些因素一些潜在的规则性知识，从而得到不同特征机组在市场中的竞标能力。这些知识对支持发电商决策和电力中介机构PX和ISO安排最优交易计划等方面有重要现实意义。     

非限能系统中考虑电价波动因素的随机生产模拟

针对非限能发电系统,结合电力市场下机组竞价上网的运行特点,在随机生产模拟过程中．增加了对机组容量分段报价策略以及市场清算电价MCP(Market Clearing Price)随机波动的考虑,建立了基于预测电价概率分布的随机生产模拟模型,该模型为制定符合电力市场运行规则的发电计划,燃料采购、运输、存贮计划等提供了决策参考,并将成为不同负荷水平下MCP概率分布预测精度和合理性的重要验证手段。在给出的具体算例中采用传统Baleriaux-Booth卷积、反卷积算法进行求解．算例表明该算法具有一定的应用价值。     

欧洲统一电力市场演变和日前市场出清模型

本文首先梳理了欧洲一体化电力市场的融合过程,介绍了用于计算电价及跨境容量的区域价格耦合项目;其次研究了泛欧混合电力市场集成算法,研究了节点边际电价、可用传输容量和基于潮流的市场耦合数学模型;最后总结了各种市场耦合模型的特点,并针对南方区域电力现货市场建设提出了建议。     

节点电价理论评估与电力市场均衡

电力市场是电力系统运行规律同经济学原理相结合的产物。通过系统功率平衡方程的引入及严密的理论推导,对节点电价理论进行了分析,说明了其产生市场赢余的原因。通过报价曲线的修正以及市场利益交易前分配原则的采用,使电力市场重新达到均衡状态,改变了节点电价理论的局限性。等报价方法是基于均衡原则的一种电力市场交易算法,以IEEE-30节点系统对其进行验证,计算结果证明了该算法的可行性。     

发电竞价算法(五)——线性规划法

介绍了发电竞价问题的线性规划模型,并用2机组为例说明了计算过程 。着重对网络安全约束、下降报价特性及网损的处理进行了研究。线性规划适用于发电竞价 问题,可以比较方便地处理各种约束问题,是目前较为实用的算法。     

电力市场环境下梯级水库发电优化调度的协进化粒子群算法应用研究

本文针对传统优化算法在求解高维、复杂的梯级水库发电优化调度时多约束条件难以处理、计算机时长、易陷入局部最优解等缺陷,结合电力市场环境下梯级水库发电优化调度的特点,提出了基于协进化的粒子群优化算法,并建立了相应罚因子的评价机制,在此基础上对协进化粒子群优化算法进行了改进.通过实际算例验证了该方法的合理性和可靠性,从而为市场环境下高维、复杂梯级水库发电优化调度提供了一种新的求解途径.     

电力市场预测分析系统的开发及应用

提出并应用了一种多元、多层次的负荷预测分析系统。该系统在采用多种预测算法同时进行预测的前提下 ,能够根据误差信息自动选择并推荐好的预测算法 ,并具有大用户异动分析和智能误差补偿功能 ,及时对占市场比重较大的用户进行异动分析。采用该系统能够对各级电力市场进行预测和分析 ,并取得了较好的效果。     

基于遗传算法的电力市场环境下电源规划的研究

通过对电力市场条件下电力系统电源规划的研究,提出了市场环境下的电源规划模型和算法,该模型以传统电源规划为基础,在体现可靠性与经济性综合运筹的同时兼顾了可持续发展问题。该模型采用Grefenstette编码的遗传算法进行求解,在求解过程中对简单遗传算法的算子进行了部分改进,不仅能可靠获得全局最优解和次优解,而且加大了求解规模与速度。给出的算例和结果表明该文提出的模型和算法均合理有效。     

Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach

This paper addresses the self-scheduling problem faced by a price-maker to achieve maximum profit in a pool-based electricity market. An exact and computationally efficient mixed-integer linear programming (MILP) formulation of this problem is presented. This formulation models precisely the price-maker capability of altering market-clearing prices to its own benefits, through price quota curves. No assumptions are made on the characteristics of the pool and its agents. A realistic case study is presented and the results obtained are analyzed in detail.     

An optimal pricing scheme in electricity markets by parallelizing security constrained optimal power flow based market-clearing model

To improve economic efficiency of electricity markets, the market-clearing model must be designed to give transparent information for pricing system security and to quantify the correlation between the market operations and the power systems operations, which is an immensely provocative and challenging issue in electricity markets. This paper sets out to propose a novel approach to pricing the system security by parallelizing the security constrained optimal power flow (SCOPF) based market-clearing model, while providing market solutions as a function of complying with the required voltage security margin and N-1 contingency criteria. The proposed SCOPF based market-clearing framework also takes into consideration the bilateral transaction information and, at the same time, optimal pricing expressions through computing locational marginal prices (LMPs) and nodal congestion prices (NCPs) for ensuring voltage security are derived. The results from a 129-bus model of the Italian HV transmission system turn out to be the validity of the proposed market-clearing model for managing and pricing the system security.     

Simultaneous Optimal Auction and Unit Commitment for Deregulated Electricity Markets

Is the cost function minimized in the unit commitment problem comparable to the costs incurred in a market-clearing price auction mechanism? The authors present an alternative minimization problem: a simultaneous optimal auction that is consistent with deregulated electricity markets using a market-clearing price auction.     

Modeling of stochastic behavior of plug-in hybrid electric vehicle in a reactive power market

This paper presents a stochastic reactive power market which incorporates plug-in hybrid electric vehicles (PHEVs) in the reactive power market. The uncertainty of PHEVs as well as synchronous generators in the form of system contingencies are explicitly considered in the stochastic market-clearing scheme. The capability curve of PHEV is extracted and after that the expected payment function of PHEV is proposed based on its capability curve. In the proposed stochastic reactive power market, the Monte-Carlo simulation is used to generate random scenarios. The stochastic market-clearing procedure is then implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each scenario is total payment function of PHEVs and synchronous generators which refers to the payment paid to the generators and PHEV owners for their reactive power compensation. A typical 17-node microgrid is used in the case study.     

Pool Strategy of a Producer With Endogenous Formation of Locational Marginal Prices

This paper considers a strategic power producer that trades electric energy in an electricity pool. It provides a procedure to derive the optimal offering strategy of this producer. A multiperiod network-constrained market-clearing algorithm is considered. Uncertainty on demand bids and offering strategies of rival producers is also modeled. The proposed procedure to derive strategic offers relies on a bilevel programming model whose upper-level problem represents the profit maximization of the strategic producer while the lower-level one represents the market clearing and the corresponding price formation. This bilevel model is reduced to a mixed-integer linear programming problem using the duality theory and the Karush-Kuhn-Tucker optimality conditions. Results from an illustrative example and a case study are reported and discussed. Finally, some relevant conclusions are duly drawn.     

基于自组织映射神经网络的市场清算电价预测

市场清算电价预测是电力市场中交易决策的基础。人工神经网络是电价预测较为理想的方法,但依然存在一些问题,如样本训练有时需要很长时间,存在收敛问题,特别是当样本特征量不明显的时候,这种现象更为突出。针对这一问题,利用自组织映射的聚类特性将历史数据进行特征分类和筛选处理,处理后形成的新数据用于训练三层BP神经网络,仿真结果表明,经过这种数据处理后,网络的收敛速度得到了显著提高,且预测效果良好。     

Application of actor-critic learning algorithm for optimal bidding problem of a Genco

The optimal bidding for Genco in a deregulated power market is an involved task. The problem is formulated in the framework of Markov decision process (MDP), a discrete stochastic optimization method. When the time span considered is 24 h, the temporal difference method becomes attractive for application. The cumulative profit over the span is the objective function to be optimized. The temporal difference technique and actor-critic learning algorithm are employed. An optimal strategy is devised to maximize the profit. The market-clearing system is included in the formulation. Simulation cases of three, seven, and ten participants are considered and the obtained results are discussed.     

Transmission loss allocation: a comparison of different practical algorithms

A pool-operated electricity market based on hourly auctions usually neglects network constraints and network losses while applying its market-clearing mechanism. This mechanism determines the accepted and nonaccepted energy bids as well as the hourly market-clearing prices. As a result, ex post procedures are needed to resolve network congestions and to allocate transmission losses to generators and demands. This paper focuses on transmission loss allocation procedures and provides a detailed comparison of four alternative algorithms: (1) pro rata (PR); (2) marginal allocation; (3) unsubsidized marginal allocation; and (4) proportional sharing. A case study based on the IEEE RTS is provided. Different load scenarios covering a whole year are analyzed. Finally, conclusions and recommendations are stated.     

Quantifying the Effect of Demand Response on Electricity Markets

It is widely agreed that an increased participation of the demand side in the electricity markets would produce benefits not only for the individual consumers but also for the market as a whole. This paper proposes a method for quantifying rigorously the effect that such an increase would have on the various categories of market participants. A new centralized complex-bid market-clearing mechanism has been devised to take into consideration the load shifting behavior of consumers who do submit price-sensitive bids. The effects of the proportion of demand response on the market are illustrated using a test system with ten generating units scheduled over 24 periods.      

MCP预测技术在浙江电力市场中的应用

市场清算电价(MCP)预测是电力市场决策的基础。文中以浙江电力市场为背景,对一些预测技术作了介绍;用神经网络、时间序列以及基于小波分解的时间序列预测方法对浙江电力市场MCP作了预测。预测结果表明时间序列方法和基于小波分解的时序方法在一周的MCP预测过程中精度衰减较快,但是基于小波分解的时序方法在下一日的MCP预测中还是有较好的精度;神经网络方法预测精度衰减较慢,预测效果相对比较稳定。