An annual framework for clustering-based pricing for an electricity retailer

In the competitive environment, it is necessary for a retailer to increase his/her profit as much as possible. There are few researches focused on the subjects related to the retailer and the retail market. In addition, those researches have mostly focused on the participation of the retailer in the wholesale market. In order to determine the optimal selling price, the knowledge of how and when consumers use electricity is essential to the retailer. This type of information can be found in load profiles of customers. In this paper, an annual framework for optimal price offering by a retailer is proposed which is based on clustering technique. For this purpose, load profiles of customers are used as their consumption patterns. Also, a profit function is defined as the objective of optimization problem based on the load profile considering conditional value at risk (CVaR) for risk modeling. Also, a new acceptance function is proposed to overcome drawbacks of the traditional ones. The objective function is a mixed-integer nonlinear problem which is solved by GAMS software.     

Risk-constrained optimal strategy for retailer forward contract portfolio

In the medium term planning, the objective of an electricity retailer is to configure its forward contract portfolio and to determine the selling price offered to its clients. To procure the electricity energy to be sold to the clients, a retailer has to face by two major challenges. Firstly, at buying electricity energy, it must cope with uncertain pool prices and sign forward contracts at higher average prices. Secondly, at selling electricity, it should handle the demand uncertainty and consider this fact that customers might choose a different retailer if the selling price is not competitive enough. In this paper the financial risk associated with the market price uncertainty is modeled using expected downside risk, which is incorporated explicitly as a constraint in the mixed-integer stochastic optimization problem. Roulette wheel mechanism and Lattice Monte Carlo Simulation (LMCS) are employed for random scenario generation wherein the stochastic optimization problem is converted into its respective deterministic equivalents. The proposed optimization problem is solved by a decomposition technique using Benders decomposition algorithm. A realistic case study is implemented to demonstrate the capability of the proposed method.     

现货市场环境下售电商激励型需求响应优化策略

需求响应是售电商应对现货市场风险的重要工具,但已有研究中的售电商均采取电价型需求响应策略,无法根据变化的实际情况灵活实施需求响应,缺乏关于激励型需求响应的深入研究。针对此,构建了单一售电商与多个用户之间的激励型需求响应主从博弈模型,其中售电商在现货市场电价高于售电价格的时段,制定需求响应补贴策略以减少其售电损失,而用户根据售电商制定的补贴价格决定相应时段的响应量以获取额外收益。通过分析给出了博弈模型的求解方法,算例表明,售电商及用户均可通过需求响应而获益。此外,文中分析了现货市场价格波动对售电商的补贴价格制定、用户响应量和各自需求响应收益的影响以及不同类型的用户加入需求响应项目时对售电商需求响应收益的影响。     

计及负荷规模效应的售电公司决策研究

负荷规模对售电公司的市场参与和盈利起着重要作用。从规模经济学的角度分析了售电公司负荷规模对其经营成本和盈利能力的意义;然后考虑一定规模的售电公司与发电市场的相互作用,构建详细的购电价格模型,并基于心理学模型建立售电价格与负荷规模的关系。在此基础上,考虑市场价格与需求的不确定性,应用随机规划理论构建售电公司决策模型,以实现单位负荷的利润最大和风险最小化。模型构成的EPEC(Equilibrium Problem With Equilibrium Constraints)问题,采用敏感度函数法迭代求解。算例结果表明,该算法可以有效收敛,且售电公司的规模对降低单位购电成本具有经济效益。     

计及风险规避的电力零售商平衡市场交易模型

由于新能源出力以及终端负荷需求的不确定性,电力零售商在日前市场的竞标电量与实时市场的购买电量之间存在不平衡而产生惩罚成本风险。引入用户侧可控负荷作为平衡资源参与市场交易,提出了一种风险规避程度指标,以此来度量交易前后电量偏差程度,以信息熵度量残差序列离散程度计算风险规避程度指标。以电力零售商运行收益、用户需求响应满意度以及风险规避程度最大为目标建立多目标风险规避模型,采用自适应权重粒子群算法进行模型求解。通过算例表明,所提出的模型从电网-电力零售商-用户多个角度去考虑电力零售商参与平衡市场交易策略,能够有效提高电力零售商的运行效益以及用户满意度,同时可以提高电力市场管理的可靠性与安全性。     

Quantifying price risk of electricity retailer based on CAPM and RAROC methodology

In restructured electricity markets, electricity retailers set up contracts with generation companies (GENCOs) and with end users to meet their load requirements at agreed upon tariff. The retailers invest consumer payments as capital in the volatile competitive market.

In this paper, a model for quantifying price risk of electricity retailer is proposed. An IEEE 30 Bus test system is used to demonstrate the model. The Capital Asset Pricing Model (CAPM) is demonstrated to determine the retail electricity price for the end users. The factor Risk Adjusted Recovery on Capital (RAROC) is used to quantify the price risk involved. The methodology proposed in this paper can be used by retailer while submitting proposal for electricity tariff to the regulatory authority.     

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。     

An aggregate Weibull approach for modeling short-term system generating capacity

Deregulation of electricity markets is occurring all over the world. This trend introduces new risks and uncertainties into the electricity industry, the most significant being price risk. The spot price of electricity is highly volatile, and the ability to price risk management contracts on this commodity is contingent on a robust and realistic model of the underlying price process. One key driver of electricity spot price is the forced outages of generating plants in the system. The current paper describes a system aggregate model of short-term generating capacity that can be adapted to any generating system of interest. After describing the model, we test it using the IEEE Reliability Test System (RTS).     

电力现货市场环境下结算权转让在阻塞管理中的应用

采用节点电价的电力现货市场通过节点电价中的阻塞分量反映线路或断面阻塞产生的成本,引导市场主体参与阻塞管理。除了形成合理准确的价格信号外,建立有效的阻塞风险管理机制也是开展阻塞管理的重要环节。考虑当前中国电力现货市场的实际特点及需求,提出了应用于阻塞风险管理的结算权转让交易机制。发电厂通过与其他发电厂或用户对特定电量进行结算权转让交易,获得对方以特定价格结算的权利,进而规避以自身所在节点价格结算时面临的阻塞风险。相对于国外成熟电力现货市场从阻塞盈余返还金额不确定性的风险管理角度建立的金融输电权等机制,结算权转让交易机制站在阻塞费用缴纳金额不确定性的风险管理角度,实现阻塞风险在交易双方间的重新分配,更适用于当前中国电力现货市场中长期合同交易与现货交易顺次结算、中长期合同交割点唯一设置在用户侧统一结算点、发电侧承担较大阻塞风险等特征。算例分析进一步验证了结算权转让交易的实用性和有效性。     

A DSM Approach for Distribution Systems with High Wind Power Penetration

Abstract

The basic objectives of demand side management (DSM) are shifting load from peak hours to off-peak hours and reducing consumption during peak hours. The DSM operation is cleared when deregulated electricity market is considered where the retailer purchases electricity from the electricity market to cover the end users requirements of energy. In this paper, DSM techniques (load shifting and peak clipping) are used to maximize the profit for retailer company by reducing total demand at peak hours and achieve an optimal daily load schedule using linear programing (LP) and genetic algorithm (GA). These techniques are implemented on the 33-bus radial network included wind generation penetration. A short term artificial neural network technique (ANN) is used to get forecasted wind speed and forecasted users load for date 25-March-2018. The ANN uses an actual hourly load data and an actual hourly wind speed data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for retailer company. Finally, comparison is carried out between profit using LP and GA. The optimized DSM succeeded to increase the profits of the company by around 4.5 times its old profit using LP and around 2.5 times using GA.     

各类紧急减负荷控制的在线风险评估和协调决策方案

通过对各类紧急减负荷控制措施的应用现状和国务院599号令对紧急减负荷的影响进行分析总结,研究了各类紧急减负荷控制措施的有序配合和多区域多级调度优化协调原则,提出了以在线监视为基础的综合考虑事故等级的紧急减负荷风险评估和协调决策框架。该框架包含第二道防线安全稳定控制系统在线监视及预警、第三道防线低频低压减载装置在线监视及预警、人工事故拉路在线监视管理和风险评估与协调决策4个部分。通过构建综合考虑事故等级的紧急减负荷控制代价协调模型,实现了控制措施的协调决策。     

计及供电方收益的分时电价优化

峰谷分时电价是电力需求侧管理的一个重要手段。基于利益驱动,用户对变化的电价作出响应,自动地调整用电时间和用电量,从而实现负荷曲线的削峰填谷,达到延缓投资、提高电网运行稳定和经济性的目的。以峰谷差最小为目标、用户利益和供电方合理利润等为约束,以负荷转移率模拟电价响应行为,建立了分时电价优化模型。模型求解采用遗传算法。最后,通过算例分析分时电价下的用户响应效果,验证了模型及方法的有效性,并分析了不同的时段划分对用户响应的影响。     

基于需求侧管理的城市电网风险评估

高受电比例城市电网中电源严重缺额,电网故障和失负荷风险极高,而用户负荷和系统风险关系密切。需求侧管理(demand side management,DSM)采取分时电价等方式来指引用户调整用电习惯,实现削峰填谷。研究了DSM对城市电网风险的影响,通过能效负荷优化和可中断负荷调度建立了需求侧综合管理模型,在此基础上,建立风险评估模型及指标体系,采用蒙特卡洛法对元件状态抽样,进而计算风险评估指标。结合某典型高受电比例城市电网实际进行风险评估,将考虑DSM风险指标与传统风险指标进行对比,结果表明DSM手段可以有效降低城市电网的失负荷风险,明显提升城市电网运行的安全水平。     

考虑售电公司最优经营策略的曲线交易机制设计

约定电力曲线的交易方式能为交易双方提供更多选择,避免中长期电量合同分解问题,细化电力交易的时间粒度,有利于中长期市场与现货市场的衔接。为此,提出一种面向需求侧的约定曲线形状及价格的曲线交易机制。在此基础上,考虑电力交易中心和售电公司两个利益主体,构建曲线交易机制设计双层优化模型。其中上层以电力交易中心平衡账户在结算周期内余额方差之和最小为目标,构建曲线交易机制关键参数优化设计模型。下层考虑了售电公司的购电策略、可中断负荷购买策略和储能租赁策略,构建以单个售电公司利润最大化为目标的经营决策模型。算例结果表明所提出的曲线交易机制设计双层优化模型能有效反映电力交易中心参数设置对售电公司经营策略的影响,为电力市场机制设计提供定量分析的依据。     

考虑可调负荷集群响应不确定性的联合调度模型

售电侧放开是新电改的一项重点任务,分析需求响应策略的经济性和安全性会为售电市场开放后的需求响应业务实施提供支持。需求侧管理将大量的小时响应负荷建模成多个负荷集群,通过电价信号调整集群的用电量,实现用电高峰时段减小电力缺口的目的。但负荷集群对电价响应的不确定性会造成潜在风险损失,在经济调度模型中考虑这种响应不确定性以达到降低风险值得研究。首先基于蒙特卡洛法得到负荷集群的响应偏差率,然后使用风险损失(VaR)度量负荷集群响应不确定性的风险损失,最后在满足各种约束条件的基础上,对引入风险损失的优化模型进行求解。算例结果表明该联合调度模型保证调度经济性的同时,减少需求侧因负荷响应不确定带来的风险损失。     

基于深度强化学习的激励型需求响应决策优化模型

随着中国电力市场化改革的推进,售电侧市场逐步开放,售电商可以聚合大量的分散负荷参与电力市场环境下的需求响应.文中提出以售电商和用户综合收益最大化为目标的基于深度强化学习的激励型需求响应建模和求解方法.首先,建立售电商和用户的需求响应模型,通过引入时间-价格弹性,改进现有的用户响应模型,考虑用户对相邻时段补贴价格差的反应.然后,基于马尔可夫决策过程框架构建补贴价格决策优化模型,并设计基于深度Q学习网络的求解算法.最后,以1个售电商和3个不同类型的用户为例进行仿真计算,通过分析算法收敛性和对比不同模型及参数下的优化结果,验证了改进模型的合理性和生成策略的有效性,并分析了激励型需求响应对售电商以及用户的影响.     

现货市场中考虑中长期交易限制的售电公司决策

不断深化的电力现货市场改革给售电公司的购售电业务带来了新的机遇和挑战,现货市场下售电公司的决策研究具有重要意义。通过价格-份额函数反映售电公司在零售市场中面临的竞争和用户选择行为,建立售电公司购售电的成本与收益的数学模型,基于对数正态分布产生一系列现货电价的不同场景,用条件在险价值（conditional value at risk, CVaR)模型量化给定置信度水平下所有场景中的购售电风险,最后建立收益-风险目标函数。通过算例分析比较不同风险偏好下,有中长期交易限制和无中长期交易限制下售电公司的售电价格、市场份额、中长期购电比例、风险和收益。算例分析表明：当中长期购电不存在限制时,售电公司会制定较高的零售电价并签订大量的中长期合同;当存在中长期购电限制时,售电公司必须降低零售电价、提高市场份额,以获取更多的中长期交易资格来实现收益最大化,此时售电公司的风险和收益均低于不存在中长期限制时的水平,用户侧福利上升。     

考虑负荷发展和用户行为的分时电价优化研究

分时电价作为需求侧管理的一种重要经济手段,其在国内的全面实施势在必行,但电力需求的快速增长导致分时电价对用户的激励效果缺乏时效性。针对此问题,提出一种考虑负荷发展的分时电价优化方法,利用BP神经网络预测和灰色预测法预测出未来2年的典型日负荷曲线,将未来负荷曲线代入分时电价优化模型的结果作为电价约束,再对当年的典型日负荷曲线进行优化计算,得到合理的分时电价。算例将仅考虑当年典型日负荷曲线的优化结果与考虑负荷发展的优化结果进行对比,验证该优化方法延长分时电价时效的有效性。     

考虑电力衍生产品的风险管理和资产组合优化模型

在竞争的电力市场中,不确定的电价和电量使市场参与者面临电力市场风险.文中根据国外成熟竞争性电力市场实际情况,对电力市场风险的来源、测度指标和管理工具进行了分析,建议采用条件在险现金流作为测度电力市场风险的指标.提出了竞争性电力市场下的电力市场风险计算模型,通过典型电力企业实例分析了电力市场风险的特点、电力衍生产品交易对电力市场风险管理的作用以及电力实物资产和衍生产品投资组合优化等问题,计算了不同市场条件下样本企业的电力资产组合有效前沿,并对竞争性电力市场中的电力市场风险管理和资产组合优化策略进行了总结.     

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.