基于相对鲁棒CVaR的电网投资项目组合优化模型研究

在新一轮电改的背景下,电网投资将面临更多的不确定性风险,亟需落实精准投资以降低投资风险.将相对鲁棒CVaR风险度量模型应用于电网投资项目组合优化中,构建了基于相对鲁棒CVaR的电网投资项目组合优化模型,并通过蒙特卡洛仿真和K-means聚类方法进行随机样本的生成与削减.算例结果表明,相对鲁棒CVaR模型具有极好的鲁棒性,能够在相对最坏情景下保证电网投资风险的最小化;同时,相对于绝对鲁棒CVaR模型减小了决策结果的保守性.     

CVaR鲁棒均值-CVaR投资组合模型与求解

传统的均值-风险(包括方差、VaR、CVaR等)组合选择模型在计算最优投资组合时,常假定均值是已知的常值,但在实际资产配置中,收益的均值估计会有偏差,即存在着估计风险.在利用CVaR测度估计风险的基础上,研究了CVaR鲁棒均值-CVaR投资组合选择模型,给出了另外两种不同的求解方法,即对偶法和光滑优化方法,并探讨了它们的相关性质及特征,数值实验表明在求解大样本或者大规模投资组合选择问题上,对偶法和光滑优化方法在计算上是可行且有效的.     

鲁棒投资组合选择优化问题的研究进展

对近年来投资组合研究优化研究的热点问题——鲁棒投资组合优化研究的现状和发展趋势作了综述性研究．在投资组合选择优化的均值-方差模型的基础上,回顾了鲁棒投资组合选择优化问题的发展历史;详细地介绍了鲁棒投资组合选择优化的研究热点及国内外研究现状,就鲁棒投资组合选择优化问题的未来发展方向和主要研究内容,提出了新的观点,以期为相关领域的研究工作提供参考依据．     

直购电环境下电网公司风险控制优化模型及算法研究

随着直购电的出现,电网公司(PGC)在发电侧电力市场"单一购买者"的垄断局面被打破,直购电所引起的风险接踵而至.通过分析直购电所导致电网公司可能面临的潜在风险,建立了直购电环境下电网公司效用.函数最大化和风险最小的多目标风险控制组合优化模型,针对CVaR,风险函数在数值计算上的困难,提出了基于罚函数的光滑化样本平均值算法,并用Matlab编程实现了具体问题的求解,数值结果能够有效的反映出在直购电过程中电网公司所面临的市场风险本质,从而验证了模型的有效性.     

不同风险度量方法的投资组合比较研究

为了验证投资组合理论在中国证券市场的有效性,在不允许卖空情况,针对不同风险度量方法,文章运用旋转算法或结合序列二次规划法分别求解均值-方差、均值-下半方差投资组合模型、均值-半绝对偏差、均值-平均绝对偏差和均值-VaR.文章选取三年沪市六只业绩比较好的股票,依据前两年的数据作为样本数据,分别求出五个模型在不同期望收益率下的最优投资策略,将得出的最优投资策略应用到最后一年,进行模拟投资,从而计算出各模型的总收益率.以等比例投资为标准,比较五个模型的绩效.最后,证明了两个模型对于中国证券市场是适用.     

最小化破产概率的保险人鲁棒投资再保险策略研究

在模型不确定条件下,研究以破产概率最小化为目标的模糊厌恶型保险公司的最优投资再保险问题. 假设保险公司可投资于一种风险资产,也可购买比例再保险. 分别考虑风险资产的价格过程服从随机波动率模型和非随机波动率模型的两种情况,根据动态规划原理建立相应的HJB方程,得到保险公司的最优鲁棒投资再保险策略和价值函数的解析解. 最后,通过数值模拟分析了各模型参数对最优策略和价值函数的影响.     

鲁棒均值-CVaR投资组合模型及实证:基于安全准则的视角

考虑证券市场的不确定性,将资产的收益率看成区间随机变量。利用鲁棒优化方法,构建鲁棒均值-CVaR投资组合模型。采用对偶理论,将鲁棒均值-CVaR投资组合模型转换为线性规划问题,降低了模型的求解难度,有助于计算大规模的资产组合。进一步地,考虑投资者的安全性需求,在模型中引入最大违反概率,控制模型的保守程度,并直观反映投资者的安全性要求。采用实证的方法,研究模型的有效性。结果表明:鲁棒均值-CVaR投资组合模型具有较好的稳健性,且满足投资者的安全性要求,在实际的投资决策中具有可行性。     

智能电网下考虑风险的供电企业购电优化模型

针对智能电网带给供电企业购电决策的影响,提出了一种考虑风险的购电优化决策方法。智能电网建设并开展运营,发电侧考虑接纳更多的可再生能源发电,用电侧智能用电设备的使用导致主动负荷的出现等,这一系列变化给智能电网环境下供电企业购电决策带来一定程度的风险。首先,考虑了智能电网下负荷与风电出力不确定性给供电企业经营带来的风险,采用风险元传递理论与多目标规划理论,建立智能电网购电优化模型。然后,提出采用约束多目标粒子群优化算法(CMOPSO)对模型进行求解思路;最后,算例说明该模型的可行性,研究成果为我国智能电网运营风险管理提供新方法、新思路。     

计及输配电距离的大工业用户与发电企业购电优化模型

随着输配电改革试点的展开,电网企业的独立性逐步体现,大工业用户参与直购电交易也越来越多.对输配电改革试点环境下的大工业用户购电行为进行了研究,构建了不同情形下的购电优化模型,探讨了大工业用户的最佳购电策略.案例仿真分析表明:在不考虑输配电距离的输配电价会造成供电侧的不公平竞争;在考虑输配电距离情形下,通过调整可变电度电价,可使各发电企业处于公平的竞争环境下.     

凸概率密度分布簇下的多损失鲁棒优化等价模型及在直营连锁企业中的应用

首先,证明了凸概率密度分布簇的单周期期望均值下单损失鲁棒优化等价模型定理,以及凸概率密度分布簇的单周期期望均值下多损失鲁棒优化等价模型。然后,提出了直营连锁企业的产品在凸概率密度分布簇下的期望均值的单周期生产分配供应问题,建立了直营连锁企业的单周期生产分配供应期望均值鲁棒模型,在获得近似周期概率分布簇情形下给出了单周期生产分配供应鲁棒模型,这种近似鲁棒模型等价于一个线性规划问题。最后,通过已知一个产品的4个周期构成的混合分布簇进行了数值实验,数值结果表明了期望均值准则下的生产分配供应鲁棒模型的生产分配供应策略更加稳健。     

The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term

Within a competitive electric power market, electricity price is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of electricity price using chaotic sequences for forecasting of short term electricity price in the Australian power market. One modified model is applies seasonal adjustment and another modified model is employed seasonal adjustment and adaptive particle swarm optimization (APSO) that determines the parameters for the chaotic system. The experimental results show that the proposed methods performs noticeably better than the traditional chaotic algorithm.     

基于用户报价信息划分的可控负荷菜单定价模型

设计合理的需求侧电价是引导和促进用户实施需求响应的重要因素.基于可控负荷参与市场交易时的报价信息,借助机制设计中的激励相容理论,提出了一种可控负荷菜单定价模型,该模型以系统供电成本最小为目标,且用户类型是离散的.通过节点系统IEEE-30仿真实验,设计出适用于5种不同用户类型的菜单电价,并通过对比分析,表明所提出的菜单定价模型节约了系统供电成本,也为需求侧电价的设计提供了理论参考.     

基于两时段的WCVaR风险分析及其应用

针对随机变量的分布信息不完全的情况下,提出了两时段的Worst-Case Conditional Valueat-Risk(WCVaR)指标,并建立了两时段的风险-利润投资组合优化模型,该模型是一高维问题,具有复杂的优化结构.在损失函数为线性以及随机变量为离散界约束分布的假设下,运用最优化对偶理论将具有多层min-ma...     

Day-ahead market bidding for a Nordic hydropower producer: taking the Elbas market into account

In many power markets around the world the energy generation decisions result from two-sided auctions in which producing and consuming agents submit their price-quantity bids. The determination of optimal bids in power markets is a complicated task that has to be undertaken every day. In the present work, we propose an optimization model for a price-taker hydropower producer in Nord Pool that takes into account the uncertainty in market prices and both production and physical trading aspects. The day-ahead bidding takes place a day before the actual operation and energy delivery. After this round of bidding, but before actual operation, some adjustments in the dispatched power (accepted bids) have to be done, due to uncertainty in prices, inflow and load. Such adjustments can be done in the Elbas market, which allows for trading physical electricity up to one hour before the operation hour. This paper uses stochastic programming to determine the optimal bidding strategy and the impact of the possibility to participate in the Elbas. ARMAX and GARCH techniques are used to generate realistic market price scenarios taking into account both day-ahead price and Elbas price uncertainty. The results show that considering Elbas when bidding in the day-ahead market does not significantly impact neither the profit nor the recommended bids of a typical hydro producer.     

Study on probability distribution of prices in electricity market: A case study of zhejiang province,china

The study on probability density function and distribution function of electricity prices contributes to the power suppliers and purchasers to estimate their own management accurately, and helps the regulator monitor the periods deviating from normal distribution. Based on the assumption of normal distribution load and non-linear characteristic of the aggregate supply curve, this paper has derived the distribution of electricity prices as the function of random variable of load. The conclusion has been validated with the electricity price data of Zhejiang market. The results show that electricity prices obey normal distribution approximately only when supply-demand relationship is loose, whereas the prices deviate from normal distribution and present strong right-skewness characteristic. Finally, the real electricity markets also display the narrow-peak characteristic when undersupply occurs.     

基于多层博弈的智能电网住宅电力实时需求响应机制

通过建立多层博弈模型刻画智能电网系统中电力公司、家庭电力管理中心和家庭内各个电器设备间的电力实时需求响应过程。在此模型中,每个时段电力公司将电价通知各个家庭,每个家庭的电力管理中心接收到实时电价信息后为家庭内各个电器设备分配虚拟电价,各个设备则确定各自最优用电量并将其反馈给家庭电力管理中心,由其确定该家庭总用电量并发送至电力公司,电力公司再计算得到最优电价。证明了所建多层博弈模型的均衡存在唯一性,并得到均衡解。仿真结果验证了模型的有效性,并给出其实际应用价值。     

Weekly self-scheduling,forward contracting,and pool involvement for an electricity producer. An adaptive robust optimization approach

This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.     

一般工商业零售电价套餐适应性评估与优化方法

在电力体制改革的大背景下,合理评估零售电价套餐适应性,对控制电网经营风险和推进售电侧改革有重要意义。针对我国电力市场以及一般工商业的特点,首先从竞争、用户以及市场环境角度出发建立了一般工商业零售电价套餐评估指标体系;其次将层次分析法和改进的灰色白化权函数相结合,对电价套餐进行适应性评估;最后针对该评估方法建立了基于蚁群算法的优化模型,以最小成本得到提高电价套餐适应性等级的优化方案,并验证了该方法具有良好的鲁棒性,具有一定的参考意义。     

基于双曲型谱风险度量的大用户购电策略

直购电模式正在推行,大用户与电网公司的风险偏好却各不相同。本文将风险偏好纳入结算策略,建立了基于双曲型谱风险的购电优化模型,并用PJM日前市场的数据进行了实证分析。探讨了风险厌恶因子的敏感范围,将大用户划分为积极、稳健和保守三种类型,分别讨论了其购电策略。结果表明:无论风险偏好如何,大用户总愿意为获得高收益而承担更高的风险;风险偏好是购电策略的重要影响因素;当风险偏好既定时,大用户在远期合同市场和日前市场的购电比例可由谱风险值确定。随着谱风险值的增加,大用户会减少远期合同市场的购电量,更倾向于在日前市场购电。     

Evaluating the impact of average cost based contracts on the industrial sector in the European emission trading scheme

The inception of the emission trading scheme in Europe has contributed to power price increases. Energy intensive industries have reacted by arguing that this may affect their competitiveness and will induce them to leave Europe. Taking up a proposal of these industrial sectors, we explore the possible application of special contracts, where electricity is sold at average generation cost to mitigate the impact of CO₂ cost on power prices. The model supposes fixed generation capacities. We first consider a reference model representing a perfectly competitive market where all consumers (industries and the rest of the market) are price-takers and buy electricity at short-run marginal cost. We then change the market design by assuming that energy intensive industries pay power either at a regional or at a zonal average cost price. The analysis is conducted with simulation models applied to the Central Western European power market. The models are implemented in GAMS/PATH. This work has been financially supported by the Chair Lhoist Berghmans in Environmental Economics and Management and by the Italian project PRIN 2006, Generalized monotonicity: models and applications, whose national responsible is Prof. Elisabetta Allevi.