燃气电厂天然气短期订购优化决策

由于天然气日前市场早于电力日前市场关闭,大部分发电商需要为所属燃气电厂在未知实际发电量的情况下制定次日的燃料订购决策。这是一个市场机制下的新问题。文中提出一种用于日前市场天然气订购的分步模拟优化方法。首先通过蒙特卡罗模拟方法得到机组最优发电量和所有可行的天然气订购决策下的利润分布情况;由此构建机组利润的有效前沿,然后利用效用最大化理论得到最优的天然气订购决策。在决策过程中综合考虑了燃料市场和电力市场的双重不确定因素。最后用算例说明了该模拟优化方法的有效性。     

容量电价机制对发电商竞价行为的激励

容量电价是调控电源投资和建设的一种宏观手段，还会对市场成员的竞价行为及电力市场交易产生影响。该文从激励的角度分析容量电价对市场成员竞价行为的影响，并利用基于报价中标概率信念函数的电力市场模拟方法，通过建立考虑容量电价因素的发电商个体新决策模型，定量研究和比较了国内两种不同容量电价补偿机制对发电商竞价行为和市场均衡状态的影响。理论分析和算例表明，按照中标容量支付容量电价，会抑制发电商的报价投机行为，使市场均衡价格较低。     

考虑省间经济差异的区域电力市场调节分析

我国区域电力市场中,普遍存在各省经济发展不平衡和省间电价差异较大的问题,这是区域电力市场建设需要有效解决的一个重要问题。运用庇古定理,采取“税收-补贴”的市场调节措施,向低成本省的发电商征税,建立区域电力平等基金,并利用基金向低购买力省的供电公司提供补贴,以省为节点,建立区域电力市场单时段市场交易模型。算例分析表明,通过市场调节能够提高高成本省的发电商出力和市场占有率,提高低购买力省的供电公司购电功率和市场占有率,在一定程度上保护了高成本省的发电商和低购买力省的供电公司的市场竞争力,解决了存在省间经济差异的区域电力市场失灵问题。     

缺电情况下能开放电力市场吗?——兼论当量电价的应用

大多数发展中国家，特别是中国，电力需求还处在快速增长阶段，而且装机容量本来就不富裕，在投资体制还未理顺甚至还时有短缺的情况下，如何开放和建立电力市场是必须面对的问题。文中讨论了在容量短缺情况下开放和建立电力市场的基本要求：①必须在保持市场效率前提下，给予投资者充分合理的回报以鼓励投资；②必须同时建立容量市场风险制约的投资机制，加以制衡，以形成良性循环；②必须建立能够自动抑制市场电价不合理飞升的交易机制；④政府要有比设置价格上限更为有效合理的监管手段。指出：在规模经济性没有完全消失的条件下，传统边际成本定价方法无法给予容量充分的回报，而且也不能在寡头垄断市场条件下，有效、合理地抑制哄抬电价和持留容量行为，因此一般说来，在容量短缺情况下不适合使用该方法启动电力市场。认为：当量电价在保持市场效率前提下能给予投资者充分合理的回报，鼓励投资；同时引入容量市场风险制约的投资机制，加以制衡；还能自动抑制发电厂商哄抬电价和持留容量的不良行为，并为电力监管提供了清晰的思路，因此，即使在容量短缺情况下开放和建立电力市场，当量电价仍不失为一种可操作的、简单易行的方法。     

适应高水电占比的电力现货市场机制设计与实践

不同于中国其他水电规模占比较小的电力体制改革综合试点电网,中国四川电网存在水电装机占比高、丰枯水期发电原则差异大、网架复杂约束多、发电调度策略分散等独有特点,故设计丰枯双期的市场机制以适应四川丰枯水期电力供需形势及电网运行特性差异.首先,从市场设计思路、市场总体模式、丰枯水期电力现货市场等角度说明四川电力现货市场机制;然后在此基础上,提出适应四川现货市场机制的功能架构,以及分析四川电力现货市场在丰枯双期市场模式、市场运营物理空间、水电耦合约束、优化出清流程等方面的特色之处;最后,结合四川丰枯双期电力现货市场的运行实践情况提出若干建议.     

电转气设备与燃气机组的联合竞价策略

电转气(P2G)设备和燃气机组作为联系电力系统与天然气系统的能源转换单元,是未来多能源互联系统的重要枢纽。在竞争性能源市场环境下,P2G设备和燃气机组如何构造竞价策略是相关各方关注的重要问题。对于P2G设备和燃气机组而言,在构造其参与竞争性电力市场和天然气市场的竞价策略时,这两个市场的出清价格未知,可以采用概率分布或其他方法进行模拟。在此背景下,发展了基于场景分析的P2G设备和燃气机组的最优竞价策略模型。首先,采用拉丁超立方抽样生成含概率信息的场景集合,用于处理采用概率分布模拟的市场出清价格,并对场景进行消减。之后,以收益最大化为目标,分别构建了P2G设备同时参与日前能量市场和调频市场的单独竞价模型及其与燃气机组的联合竞价模型,确定次日各交易时段的竞价策略。然后,针对具体的市场出清结果,采用夏普利值法对P2G设备与燃气机组的收益进行合理分配。最后,用算例对所构建的模型和采用的求解方法加以说明,并分析了P2G设备和燃气机组竞价策略的特征。     

Energy bidding strategies for restructured electricity market

Many countries around the world have changed the economics of their electricity markets from monopolies to oligopolies in an effort to increase competition. The restructured electricity markets have introduced risk and uncertainty into a sector of the economy that was traditionally state-owned. Suppliers and customers use financial methods to manage the risk from market prices. The maximization of profit for power companies is highly associated with the bidding strategies. In order to maximize the profit, participants need suitable bidding models. In an open access electricity market, the bidding problem is a complicated task because of producer’s uncertain behaviors and demand fluctuation. Therefore, developing bidding strategy is extremely important for electricity market participants to achieve the maximum profit. This paper analyses a comprehensive literature on the state of the art research of bidding strategies in restructured electric power market.     

Preventive analysis and solution of overloads in the Spanish electricity market

The technical feasibility of a power generation dispatch in competitive electricity markets consists of not having any overloaded equipment, not only in case of normal operating condition, but also when any contingency established in the security criteria occurs. In addition, bus voltages should be within their limits. This paper describes an optimization method to analyze and solve the transmission overloads that arise in each hourly scenario of the Spanish power system, after the electricity market has been cleared. Overloads are solved in the Spanish market by increasing and decreasing generation of connected units, and by connecting off-line ones. The proposed method comprises three steps: (a) contingency analysis, (b) preventive active dispatch and (c) classification of generation re-dispatch. The performance of the method is illustrated using an actual example of the Spanish electricity market.     

适用于不同电力市场行为的电力市场仿真器

设计并开发了一个基于网络结构,能够模拟电力运行方式的电力市场仿真器,并在.NET Frame- work上开发了此电力市场仿真系统。该仿真器采用了ASP.NET 2.0开发用户界面,C#作为编程语言以及采用SOL Server 2000作为后台数据库,这样的结构可以灵活地仿真及图形化显示电力库架构下的日前及实时市场运行行为,计算出市场结清价及结清容量。该电力市场仿真器利用了内嵌于.NET Framework的浏览器/服务器结构的卓越特性,这种结构可以实现基于开放标准和异构平台的分布式网络服务请求。并且,这种基于.NET平台构架的结构很适合用于地理位置相隔甚远的市场参与者(诸如市场运营商、发电商、用户等)进行信息传递。仿真实例显示,该仿真器具有很好的可扩展性和维护性。     

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.