Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

An approach to Iocational marginal price based zonal congestion management in deregulated electricity market

Congestion of transmission line is a vital issue and its management pose a technical challenge in power system deregulation. Congestion occurs in deregulated electricity market when transmission capacity is not sufficient to simultaneously accommodate all constraints of power transmission through a line. Therefore, to manage congestion, a locational marginal price （LMP） based zonal congestion management approach in a deregulated elec- tricity market has been proposed in this paper. As LMP is an economic indicator and its difference between two buses across a transmission line provides the measure of the degree of congestion, therefore, it is efficiently and reliably used in deregulated electricity market for conges- tion management. This paper utilizes the difference of LMP across a transmission line to categorize various congestion zones in the system. After the identification of congestion zones, distributed generation is optimally placed in most congestion sensitive zones using LMP difference in order to manage congestion. The performance of the proposed methodology has been tested on the IEEE 14-bus system and IEEE 57-bus system.

边际定价与经济调度深度融合的边际成本比较型竞价模式

随着新能源快速发展、新型市场主体不断涌现,以成本最小化为目标的经济调度机制和以边际定价为基础的策略型竞价模式,难以在电力供应充裕性不足时兼顾公平和效率。为此,在考虑中国电力工业基本国情的基础上,提出了一种深度融合边际定价与经济调度的边际成本比较型竞价模式,并从保障电力供应、提升市场经济效率、降低市场风险和促进市场公平四方面,探讨了该竞价模式在中国式电力市场建设中的潜在应用价值。最后,基于电力市场仿真平台开展算例分析,对边际成本比较型竞价模式的有效性进行了验证。

基于藤Copula理论的海上风电建模及电力市场运行分析

海上风电因具有更高的发电利用小时数,将成为中国实现“碳中和·碳达峰”的重要技术选项。随着海上风电装机容量的不断提升,其出力的间歇性、波动性将对电力系统的安全稳定和电力市场的经济运行造成显著影响。为刻画不同站点间风速的相关性,基于藤Copula理论对海上风电的出力进行建模,并利用C藤Copula函数构建多风电场出力的联合分布模型。在此基础上,提出基于共识聚类的电力市场典型阻塞场景构建方法,并采用简化的中国广东省电网架构数据开展算例仿真计算,对大规模海上风电并网前后电力系统的发电结构、碳排放量、市场电价等关键指标的变化进行实证分析。

基于边际成本比较型竞价模式的电力现货市场机制设计

在建设新型电力系统、极端天气频发、一次能源价格飙涨、保供电常态化等背景下,文中提出了基于边际成本比较型竞价模式的电力现货市场机制,为统筹多市场主体利益、协调省间与省内交易提供了可行思路。首先,深度解析了现有电力现货市场机制在统筹多市场主体利益上面临的挑战,在采用边际成本比较型竞价模式的基础上,深入考虑不同类型发电机组的成本特性,提出了一种两阶段分层边际定价的电力现货市场机制,可有效统筹发电侧成本的可疏导性、用电侧电费的可负担性以及新能源机组的激励性。然后,提出了基于边际成本比较型竞价模式的省间-省内两级现货市场机制,并对其应用价值进行了探讨。最后,基于电力现货市场仿真平台开展算例分析,对所提市场机制的有效性和可行性进行了验证。

计及复合碳泄漏效应的电力系统双层规划

“双碳”目标下,不对称碳监管政策在电力行业低碳转型路径中引发的强、弱复合碳泄漏效应需要引起关注。针对单边碳减排政策在电力系统中的影响,结合边际电价理论构建了考虑输电网级能源网架扩展与配电网级能源枢纽负荷行为的电力系统双层规划模型。首先,在上层能源网架规划中分析线路扩展带来的弱碳泄漏效应,通过拉格朗日乘子求解电网边际电价,在此基础上计及强碳泄漏效应影响下的负荷转移行为,建立下层能源枢纽经济规划模型;其次,分析碳泄漏的成因并提出缓和手段,建立对应模型;然后,提出电力系统双层低碳规划的求解流程,并设计提升双层规划收敛性的二分启发式方法;最后,在改进的IEEE 12节点系统与改进的IEEE 236节点系统中进行仿真,分析碳价格与碳泄漏缓和手段对碳泄漏效应的影响,算例结果验证了碳泄漏的形成渠道与影响。分析免费碳配额或碳排放关税对碳泄漏效应的缓和作用与风险,同时,研究双层规划模型收敛性,可为电力系统低碳转型路径中开展合理碳监管提供参考。

含储能参与的日前市场价值公平分配机制

现有的节点边际电价机制中,由于传统发电商具有市场操控力,当储能独立参与市场出清时,各发电商采取策略性报价,打压并挤占储能电站的市场份额,阻碍了储能电站参与市场出清,间接导致市场出清总成本增大。为此,文中提出一种包含传统机组以及储能电站参与的市场竞争机制。首先,分析现有市场结算机制的弊端以及阻碍储能参与市场出清的原因;其次,建立含储能参与的市场出清模型,采用样本均值近似法求解二阶段随机规划模型;然后,基于Vickrey-Clarke-Groves(VCG)结算机制,提出适应储能参与的日前市场价值分配机制;最后,提出解决激励相容而造成的系统收支不平衡问题的策略。文中采用IEEE 30节点系统为例,证明该机制满足激励相容、收支平衡以及削弱传统发电商的市场操控力等要求,同时储能的参与将会减小系统出清总成本,降低市场价格剧烈波动的风险。

Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization

Accurately forecasting short-term electricity prices is of great significance to electricity market participants.Compared with the time series forecasting methods,machine learning forecasting methods can consider more external factors.The forecasting accuracy of machine learning models is greatly affected by the parameters,meanwhile,the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting.Therefore,this paper proposes a random forest(RF)electricity price forecasting model based on the grey wolf optimizer(GWO)to improve the accuracy of forecasting.Among them,RF has a good ability to deal with the problem of non-linear and unstable electricity prices.The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters.On this basis,the short-term electricity prices of the PJM power market in four seasons are separately predicted.Experimental results show that the RF algorithm can better predict the short-term electricity price,and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and Long Short-Term Memory

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.

中国电力中长期市场分时段交易价格形成机制及模型

电力中长期市场分时段交易是中国电力市场改革的重要举措之一。针对当前电力中长期市场分时段交易价格形成机制不合理、不完善的问题,提出了中国电力中长期市场分时段交易价格形成机制及其模型。首先,分析影响中国电力中长期分时段交易价格形成的因素,提出电力中长期交易的4种典型场景;然后,综合考虑生产成本和用户效用,探索性地提出适用于中长期分时段交易的系统平均成本定价、用户失负荷价值定价、系统边际成本定价和发电企业失负荷价值定价4种成本定价机制,构建了基于成本定价的分时段交易定价模型;最后,以某省实际数据为例进行测算,验证了模型的有效性。

考虑两阶段鲁棒优化配置的多微网合作博弈

随着分布式可再生能源在配电网中的广泛部署,微网作为其有效载体具备产销者特性。该特性使微网如何优化配置以及互利共赢面临挑战。因此,文中提出了一种耦合两阶段鲁棒配置与合作博弈的模型,研究多微网能源精细化管理。首先,在两阶段鲁棒优化下,将长期设备规划与短期运营相结合。配电网运营商通过配电节点边际电价发布市场出清电价,受到价格信号引导,为微网制定最优电能交易决策。然后,根据决策信息,进一步采用纳什议价模型进行利润分配,实现多主体合作博弈。最后,通过IEEE 33节点配电系统验证所提模型的有效性。

基于不动点映射的多微网与配电网主从博弈定价策略及均衡求解方法

随着配电侧微电网接入数量的逐年提升和市场化改革的推进,开展多微网与配电网的协同优化定价问题具有重要意义。针对该问题,文中提出基于不动点映射的主从博弈定价策略,并分析系统博弈均衡状态的求解方法。建立了含多微网接入的配电网二阶锥最优潮流模型,并推导其等价的对偶模型以获得准确的节点边际电价。考虑储能、分布式光伏和弹性负荷的协调,建立基于价格响应的微网群运行优化模型。基于不动点理论描述含多微网的配电系统定价策略,利用基于外推改进的最佳响应算法进行求解实现博弈均衡,并基于压缩映射定理证明所提算法的收敛性。基于改进IEEE-33节点配电系统案例的仿真结果表明,所提方法能够有效求解多主体博弈问题并获得均衡解,且具有更高的求解效率。

基于图神经网络多智能体强化学习的电力-交通融合网协同优化运行

针对多重不确定性因素下配电系统、交通系统和电动汽车动态交互的电力−交通融合网序贯协同优化问题,提出一种基于图神经网络多智能体强化学习的电力−交通融合网协同优化方法。首先,基于图理论方法将电动汽车间的相互影响关系转换为一种动态网络图模型,采用一种基于注意力机制的图神经网络多智能体强化学习算法求解电动汽车充电引导策略,探讨电动汽车多智能体间的相互影响作用。然后,在含可再生能源出力的主动配电网中采用二阶锥优化及对偶优化理论对配电网最优潮流进行求解,得到配电网节点边际电价,研究电力和交通系统的动态交互特性。最后,在某区域108节点交通网络和IEEE 33节点电力系统上验证所提方法有效性。

考虑线路边际价值和用户时空信息的输电定价方法

输配电价改革是实现“管住中间、放开两头”电力体制改革的重要任务。科学合理的输配电价形成机制,对营造公平有序的电力市场环境以及促进电力资源的优化配置具有重要意义。为此,提出考虑线路边际价值和用户时空信息的输电定价方法。首先,将线路成本拆分为反映线路边际价值的扩容成本和剩余成本两部分进行回收;其次,扩容成本不含主观假设而是基于经济学原理客观得到,通过计算节点边际电价机制下电力现货市场出清阻塞盈余求解;然后,剩余成本通过分布因子法和邮票法以各节点对输电线路的使用程度进行分摊;最后,基于3节点系统和IEEE 30节点系统对所提出的输电定价方法进行有效性验证。

显式计及新能源不确定性多统计矩特征的电力市场定价方法

电力市场是推动我国能源转型的关键。高比例新能源接入显著改变了电力市场资源配置特性。区别于传统火电机组,新能源强不确定性使得市场出清边界模糊,现有确定性市场出清模型难以计及为平抑新能源不确定性所需的灵活性资源成本。在市场定价方法中显式计及新能源不确定性是解决上述问题的关键。为此,基于机会约束随机优化方法,提出显式计及新能源不确定性多统计矩的电力市场定价方法。通过拉格朗日对偶问题转化推导,提出了考虑新能源不确定性多统计矩特征的市场定价方法。针对任意分布形式下新能源不确定性含大量统计参数易造成市场价格分量多、各分量物理意义不清晰的问题,通过引入不确定性度量方法,实现了对新能源不确定进行单维表征;通过灵敏度分摊方法,提出基于不确定性表征降维的新能源不确定性定价方法。在PJM5节点、IEEE118节点系统以及某省实际661节点系统的仿真结果表明,所提方法能够提供明确的价格信号引导市场资源配置,计算效率满足市场出清时间窗要求。

电力现货价格预测及其应用——以西班牙市场为例

电力现货市场的重要特征是价格随时间波动,且可再生能源占比大的市场,现货价格波动幅度更为剧烈。对于开拓海外市场的中国新能源投资企业来说,电力现货价格的波动性和不确定性是要面对的一项重要挑战。以具有代表性的西班牙电力市场为例,剖析现货电价预测的影响因素、方法论和应用方式。电力现货市场价格是电力需求和供给共同作用的结果,影响电价预测的关键因素包括电力需求、不同发电类型的发电成本、装机和发电比例以及燃料和碳价格等。现货电价预测的本质是将这些影响因素汇总到一起作为底层数据,依靠模型推演出未来特定时间的电力供需关系,进而模拟形成市场边际价格。由于全发电类型的现货预测电价并不能直接用于计算具体项目的销售收入,因此将电价预测应用到具体项目收入预测时,还应研究、预测具体发电类型捕获率的未来走势。投资商在决策过程中要理性看待电价预测的准确性和局限性,因为不同咨询机构的电价预测结果会存在差异,而且也无法预测黑天鹅事件的发生。电力投资商应充分尊重现货市场价格预测的专业性,并做好咨询机构的选聘和指导工作。

基于金融储能权的市场风险规避方法

随着可再生能源在配电网中的渗透率日益提高,其发电的不确定性和波动性日渐加剧,进而引发线路阻塞等一系列问题。由于线路阻塞引起的电力市场节点边际电价波动,为市场参与者带来了额外的不确定性风险。为此,针对我国电力市场的特点,提出了一种基于金融储能权的阻塞风险规避机制。该机制允许市场参与者通过金融储能权交易来获取以节点边际电价结算的储能使用权,从而有效规避因节点边际电价波动而产生的阻塞风险。然后,设计了基于金融储能权的阻塞风险规避模型,该模型结合节点边际电价和阻塞价格来优化储能的充放电策略及金融储能权的交易策略,旨在实现金融储能权利润的最大化,帮助市场参与者有效规避因线路阻塞引起的风险。以扩展的IEEE 33节点配电网作为仿真案例,仿真结果展示了所提出的阻塞风险规避机制和模型的有效性。

Why Marginal Pricing?

Under perfect competition,marginal pricing results in short-term efficiency and the subsequent right short-term price signals.However,the main reason for the adoption of marginal pricing is not the above,but investment cost recovery.That is,the fact that the profits obtained by infra-marginal technologies(technologies whose production cost is below the marginal price)allow them just to recover their investment costs.On the other hand,if the perfect competition assumption is removed,investment over-recovery or under-recovery generally occurs for infra-marginal technologies.