电力市场中的价格钉研究

电力市场中普遍存在价格钉现象。结合近年来的相关文献对价格钉进行了综述：首先分析自由竞争的电力市场中电价的基本特点,并从经济学角度分析价格钉产生的原因,影响价格钉的供给与需求方面的主要因素,然后重点对价格钉的预测方法和控制途径进行了评述,对比了各种预测方法的优缺点及适用面,以及各种可能的控制途径。文章最后探讨了今后的研究方向。     

电力市场中的价格钉研究

电力市场中普遍存在价格钉现象.结合近年来的相关文献对价格钉进行了综述:首先分析自由竞争的电力市场中电价的基本特点,并从经济学角度分析价格钉产生的原因,影响价格钉的供给与需求方面的主要因素,然后重点对价格钉的预测方法和控制途径进行了评述,对比了各种预测方法的优缺点及适用面,以及各种可能的控制途径.文章最后探讨了今后的研究方向.     

Bootstrap方法在基于ARMA-GARCH模型的电力市场价格区间预测中的应用(英文)

当今开放式电力市场是一个基于竟价拍卖形式的市场,市场清算价格(market clearing price-MCP)主要受变化的供需平衡、市场参与者的竞价策略以及电力企业已经签订的双边买卖合同等因素影响,现有的价格预测技术只集中在如何提高价格预测准确度上,而忽视了对预测本身不确定性的度量;其中ARMA-GARCH价格预测模型的前提是假设预测误差符合正态分布,而实际电力市场价格非线性波动使得正态分布的假设完全不成立.针对这一现象,将不依赖任何分布假设的Bootstrap技术应用于基于ARMA-GARCH模型的电力市场清算价格区间预测,并对澳大利亚电力市场的真实数据进行分析评测,精度高,可靠性好.     

我国电价时间序列混沌特性分析及电价预测

本文在分析电价时间序列形成机理的基础上,分析了我国电价时间序列的的混沌特性,通过国内外电价时间序列的比较,证明了传统的基于电价时间序列周期性的预测方法在我国电力市场不能取得较好结果的原因。并利用我国电力市场电价时间序列和负荷时间序列的混沌相关性建立模型,该模型可以很好的解决我国四川电网日前市场和实时市场的电价预测问题,该改进模型对“价格钉”具有一定的预测能力,并为“价格钉”预测提供一种思路。     

基于相似日的短期电价区间预测

准确的短期电价预测有助于电力市场各个参与者选择交易策略和估算效益,因此短期电价预测受到人们广泛关注。为了解决特殊样本带来的预测误差,应用模糊C-均值聚类算法进行相似日聚类,以与预测日相似的数据构建样本集。再采用高斯过程回归来建立短期电价预测模型,对短期实时电价进行预测,得到具有概率分布及对应置信水平的区间预测结果。最后,采用美国代顿电力市场的历史数据进行实例计算,证明了该方法可有效提高模型的预测精度,与BP神经网络相比预测效果更佳,可以向电力市场参与者提供更全面的信息。     

面向类别不平衡负荷序列模式识别的两阶段选择集成学习策略

为解决集成学习负荷模式识别中的类别不平衡及基分类器冗余等问题，提出一种计及类别平衡的两阶段选择集成电力负荷模式识别方法。首先，采用一种基于密度聚类的高斯人工合成少数类过采样技术，根据少数类负荷样本的密度分布特性合成新样本，以强化负荷分类模型对少数类负荷样本的学习。然后，设计出一种包括基分类器聚类剪枝和优化选择集成的两阶段选择集成策略，基于基分类器池的训练结果，遴选最优基分类器子集参与负荷分类任务。最后，通过UCI标准数据集算例验证了所提方法的有效性和优越性。     

浙江省电力市场清算价格预测系统实现

价格预测在新的竞争性电力市场中与生产者和消费者的关系越来越大。对于当时的电力市场和长期的电力契约来说,价格预测对发展出合理的价策略或谈判技术来说是必需的,以此来实现经济利益的最大化。提供两种方法来预测电价。这两种方法分别是线性回归预测方法和人工神经网络预测方法。这两种方法一直被广泛应用于分析预测领域。比较这两种预测方法,从而找到最佳预测模型。     

电力市场环境下价格突变的机理分析和风险防范

指出电力市场的价格存在不确定性,描述了电力市场中异常价格的类型.预防和规避市场的价格风险,是建立公平、有序、稳定的电力市场的关键.通过对加州电力市场价格的统计,分析异常价格出现的规律,剖析了影响电力市场价格的主要因素,介绍了价格突变的预测方法,提出了结合价格预测和风险防范的市场竞价流程.     

电力市场中预测问题的新内涵

传统的负荷预测是电力系统规划与运行的基础工作,电力市场化的改革对负荷预测提出了更高的要求,赋予其新的内涵。文中探讨了电力市场中的预测问题,分别针对发电侧竞价和用户侧开放的电力市场,提出了一些新的预测内容和预测思想,包括扩展短期负荷预测、连续多日负荷曲线预测、电力市场价格预测、市场风险预测与评估、考虑需求侧管理影响的电力需求预测、用电需求曲线预测、基于价格弹性的需求预测等,指出了这些预测问题的研究热点和难点,探讨了未来的主要研究方向。     

电力市场价格钉预测模型研究

为威胁电力系统稳定安全运行的一个重要方面。从电价价格钉的经济学分析着手,给出价格钉判别标准。最大Lyapunov指数为正数是一个时间序列具有混沌特性的必要条件。对具有混沌特性的电价序列,提出了一种基于混沌理论的价格钉预测模型——基于径向基函数的神经网络模型。运用新西兰NEMCO电力市场中昆士兰区域的电价时间序列为例,识别出电价时间序列的混沌特性,并通过数值仿真验证了模型的有效性。     

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes, which may be tens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far, most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply–demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour, electricity demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results.     

Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.     

市场力对发电公司竞价策略的影响

完全竞争电力市场中，发电公司按机组边际成本报价。而在实际的寡头电力市场．发电公司利用其市场力操纵市场，常常采用抬高报价和持留容量这两种策略。利用经济学利润最大化原理，对这两种策略进行定量分析。实例表明，都会使电价飞升，利润增大。文章的分析为发电公司确定最优竞价提供了理论指导，同时为电力监管部门平抑市场力的作用提供了理论依据。     

发电市场势力研究与交易方式对发电市场势力影响的分析

该文利用瓦尔拉斯假设对发电公司的市场行为进行了分析，并由此定义了一个度量发电市场势力的指标函数。该文利用该指标说明市场电价的飚升除了电力确实缺乏之外主要是由于发电的市场势力决定的，同时也研究了市场中存在双边合同交易时对发电市场势力的影响，表明由于双边交易的存在会削弱发电公司的市场势力，抑制电价抬升。实例也证明了该指标的合理性。     

抑制电价飞升的系统边际价格与报价价格混合拍卖机制

目前电力市场中的系统边际价格拍卖与报价价格拍卖都不能有效解决不正当市场竞争引起的电价飞升问题。文中给出了设计抑制电价飞升拍卖机制的原则，提出了一种能有效抑制物理持留和经济持留的系统边际价格与报价价格混合拍卖机制。在电价超过高价市场启动价格后，根据卖电商是否拥有系统价格权以及报价是否不利于缓解电力供需矛盾，执行系统边际价格或报价价格两种电价之一，从而加强对卖电商报低电价、减少持留的经济激励，可以有效地限制市场势力，抑制电价飞升。提出多价格限机制限制强市场势力，对不同类卖电商采用不同的价格限，使各类卖电商都能获取正当范围内的稀缺租金，改善市场结构，避免影响发电的长期发展。     

用利润空间法实现从价格上限到利润上限的转变

价格上限是电力市场化改革的简单产物。事实上价格上限不能有效地抑制电价飞升和规避市场风险。作者首次将最高限价同动态调整机制联系起来，提出了利润上限模型。该模型吸取了英国的零售价格指数法(RPI-X)和美国的回报率法(ROR)两种监管思想，克服了价格上限在吸引投资和规避风险方面的两难选择，矫正了边际成本定价造成的利润剧烈波动，为发电侧市场的最高限价监管提供了一套可行的解决方案。     

浅谈区域电力市场环境下的电价机制

电价是电力市场的杠杆和核心内容。计算交易电价(包括上网电价、输电电价、销售电价、辅助服务电价等)是电力市场的重要内容。将区域电力市场的理论研究与我国电力工业改革发展现状相结合,介绍了适合我国国情的上网电价、输配电电价和销售电价定价原则和定价方法。     

Gaming and price spikes in electric power markets

Many challenging issues arise under the newly deregulated competitive electric power markets. Instead of centralized decision-making in a vertically integrated environment as in the past, decision-making is now decentralized and driven by market forces. Gaming and price spikes have been observed in almost every electricity market but explicit analysis of these phenomena is rare. In this paper, the authors study historical bidding behavior to see how power suppliers and demand service providers were actually bidding in the California day-ahead energy market. Based on their observations, they formulate a Prisoner's dilemma matrix game and introduce the notion of “opportunistic tacit collision” to explain strategic bidding behaviors in which suppliers withhold generation capacity from the market to drive up prices. This explanation is applicable with or without market power, transmission constraints, and insufficient supply, and is only enhanced by these factors. Their analysis is generally applicable to any uniform price electricity market in which there is significant insensitivity to price on the demand side