电力市场环境下供电公司的购电风险分析
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摘要
全世界范围内进行的的电力工业结构重组和放松管制，在引导电力工业市场化运营、打破垄断、鼓励竞争、提高效率的同时，也使得各个市场主体面临着前所未有的风险。传统电力管制体制下，电价由监管者根据成本核定，在较长时期内电价具有稳定性和可预测性。然而电力市场化改革改变了这种规则，在竞争性电力市场中，电力作为商品的最大特点在于不能进行有效的存储，从而导致其需求弹性很小，致使电价易受到电力供求关系的影响，容易产生剧烈的价格波动。为了规避电价风险，远期、期货和期权等具有降低或转移风险功能的电力金融衍生工具相继出现。电力市场环境下，供电公司作为独立的市场参与者，拥有配电网并从发电公司和电力交易中心批发购电后向终端用户提供供电服务。研究和考察供电公司的交易策略，对供电公司优化分配各级市场的购电量、降低市场风险具有不可或缺的作用；对稳定电力市场具有重要意义。
本文首先介绍了电力金融衍生产品的特性及电力市场金融风险度量方法；其次将金融领域的投资组合理论应用到电力市场中，分析了供电公司、大用户在多市场中的购电组合问题；之后，考虑了电力资产间的相关性问题，采用了Copula函数来度量电力资产间的非线性相关性；最后，深入分析了供电公司的多期购电决策问题。本文的主要工作与创新如下：
①供电公司在多个市场上的购电组合问题，通常从预期收益和风险角度来进行分析。现有文献中，对多目标优化问题大多采用加权法处理为单目标优化问题进行求解，本文采用模糊多目标优化算法求解模型，将目标函数模糊化，设法求出与各目标尽可能接近的相对最优解。现有文献针对大用户的购电风险问题，多采用VaR、CVaR等风险度量方法，没有考虑大用户的主观风险厌恶程度。本文提出采用谱风险函数度量风险来构建大用户的最优购电组合模型，同VaR、ES相比，不用先验地选择置信水平，大用户可以根据自己的风险厌恶程度自由选择谱风险函数，基于谱的度量方法更加灵活。
②对于各市场电价序列的联合分布问题，现有文献通常假设电价收益序列服从多元正态分布，并用线性相关系数度量电力资产间的相关性。但是众多研究表明电价收益序列有明显的厚尾、异方差现象，电力资产之间表现出较强的非线性特征，线性相关系数不能全面地反映相关结构。本文结合Copula函数的特点和GARCH模型的优势，考虑了电力实时、期货两市场收益间的相关结构和两市场收益序列的统计特性，构造了两市场收益组合的Gumbel Copula-（GARCH-GED，GARCH-t）模型，在此基础上对供电公司的购电风险进行了分析。实证结果表明，与二元正态分布模型相比，基于Copula函数的风险度量值更加准确。
③针对供电公司的多阶段购电问题，现有文献多采用各个阶段单期风险的简单加总来度量多期组合的风险或者采用单阶段连续决策。事实上多阶段投资时，投资者所考虑的是投资期末的最小风险值，而不是使单个阶段的风险值尽可能的小。基于此，本文构建了以供电公司期末收益最大、风险最小的多期购电组合优化模型，其中期末收益的风险采用多期一致性风险度量函数测度。同时考虑到电力期货市场和实时市场电价序列的尖峰、厚尾特性和两市场电价序列间的相关结构，采用时变SJC Copula-（GARCH-T，GARCH-GED）模型拟合电价序列，并进行模拟。实证结果表明，多期投资组合决策优于连续决策，也优于整体购电决策。
The power industry restructuring and deregulation conduct in the world. They guidemarket operators in the electric power industry, break monopoly, encourage competition,improve efficiency, and also make market participants confront with hitherto unknownrisk. Under the traditional power control system, electricity price is determined byregulators according to the cost. The electricity price is stable and predictable in a longperiod. However, the power market reform changes the rules. In the competitiveelectricity market, the power cannot be effectively stored. It is the biggest feature of thepower as a commodity. This defect leads to the low elasticity of power demand, the highprice volatility influenced by the electric power supply and demand relationship. In orderto avoid price risk, the forward, futures and options of the power financial derivativesappear in succession which play a role in reducing or transferring price risk. In thecompetitive electricity market, the power supply company as an independent marketparticipant owns distribution network and provides power purchased from power tradecenter and power generation company to end-users. It is important to study andinvestigate the power company’s trading strategy because the strategy plays an importantrole in optimizing power distribution of different markets and reducing market risk. Inaddition, it has importment significance on stabilizing power market.
The paper first introduces the features of power financial derivatives and thefinancial risk measure methods in electricity market. Then, we apply the finance portfoliotheory to the electric power market and research the power supply company and the largeconsumer’s power purchase from different markets. Later, the correlation among powerassets is analysed. And the Copula function is adopted to measure the nonlinearcharacteristic. At last, we make deep analysis about the multi-phase power purchasestrategy of power supply company. The main work and innovation are as following:
①We usually analysis the power supply company’s power purchase problem fromthe perspective of expected return and risk. In the existing literatures, multi objectiveoptimization problem is mostly transformed to single-objective problem by the weightedmethod. The single-objective optimization problem can be solved easily. In this paper, wemake the objective function fuzzy and solve the model by fuzzy multi-objectiveoptimization algorithm in order to seek out the relatively optimum solution as close aspossible to each target. Some existing literatures on the large consumer’s power purchase problem risk problem mainly use VaR and CVaR measure methods without consideringthe large consumer’s subjective degree of risk aversion. In this paper, a new method is putforward. The spectral risk function measuring risk is used to extablish the largeconsumer’s power purchase model. Compared with VaR and ES, the spectral risk methodis more flexible and practical without setting the confidence level previously. The largeconsumers can choose spectral risk freely according to the degree of risk aversion.
②For the joint distribution of price sequences in different markets, some of theprevious literatures make the hypothesis that price sequence follows a normal distributionand measure the correlation among electric power assets by using linear correlationcoefficient. However, it is studyed that price return series have obvious thick tail andheteroscedasticity phenomenon. The strong nonlinear characteristic between power assetsmakes it hard for linear correlation coefficient to describe their relations properly.Considering the correlation of the profits between the electricity real-time market andfutures market and the statistical characteristics of the profit series, in this paper weestablish the dependence portfolio model-Gumbel Copula-（GARCH-GED，GARCH-t）based on the advantages of the copula function and the GARCH model. We analysis thepower supply company’s purchasing risk based on the model. The empirical results showthat the risk measure results under the copula model is more accurate than the resultsunder binary normal joint distribution.
③For the power supply company’s multi-phase power purchase problem, most ofprevious literatures mainly measure multi-phase portfolio risk by simple sum of singleperiod risk or use single-phase succession strategy. In fact, investors mainly consider theminimum risk at the end of the investment instead of making all the single-phase riskvalues as small as possible. Based on the above analysis, we establish a multi-phasepower purchasing model which takes the maximum expected accumulated return and themimimum risk measured by dynamic coherent risk function as objectives. Beside this,time-variation SJC Copula-（GARCH-T，GARCH-GED）model is adopted to fit andsimulate power price series, considering the correlation of the return between theelectricity real-time market and futures market and the statistical characteristics of theprices series. The empirical results show that multi-phase strategy is superior tosingle-phase succession strategy, also better than the entirety strategy.
本文首先介绍了电力金融衍生产品的特性及电力市场金融风险度量方法；其次将金融领域的投资组合理论应用到电力市场中，分析了供电公司、大用户在多市场中的购电组合问题；之后，考虑了电力资产间的相关性问题，采用了Copula函数来度量电力资产间的非线性相关性；最后，深入分析了供电公司的多期购电决策问题。本文的主要工作与创新如下：
①供电公司在多个市场上的购电组合问题，通常从预期收益和风险角度来进行分析。现有文献中，对多目标优化问题大多采用加权法处理为单目标优化问题进行求解，本文采用模糊多目标优化算法求解模型，将目标函数模糊化，设法求出与各目标尽可能接近的相对最优解。现有文献针对大用户的购电风险问题，多采用VaR、CVaR等风险度量方法，没有考虑大用户的主观风险厌恶程度。本文提出采用谱风险函数度量风险来构建大用户的最优购电组合模型，同VaR、ES相比，不用先验地选择置信水平，大用户可以根据自己的风险厌恶程度自由选择谱风险函数，基于谱的度量方法更加灵活。
②对于各市场电价序列的联合分布问题，现有文献通常假设电价收益序列服从多元正态分布，并用线性相关系数度量电力资产间的相关性。但是众多研究表明电价收益序列有明显的厚尾、异方差现象，电力资产之间表现出较强的非线性特征，线性相关系数不能全面地反映相关结构。本文结合Copula函数的特点和GARCH模型的优势，考虑了电力实时、期货两市场收益间的相关结构和两市场收益序列的统计特性，构造了两市场收益组合的Gumbel Copula-（GARCH-GED，GARCH-t）模型，在此基础上对供电公司的购电风险进行了分析。实证结果表明，与二元正态分布模型相比，基于Copula函数的风险度量值更加准确。
③针对供电公司的多阶段购电问题，现有文献多采用各个阶段单期风险的简单加总来度量多期组合的风险或者采用单阶段连续决策。事实上多阶段投资时，投资者所考虑的是投资期末的最小风险值，而不是使单个阶段的风险值尽可能的小。基于此，本文构建了以供电公司期末收益最大、风险最小的多期购电组合优化模型，其中期末收益的风险采用多期一致性风险度量函数测度。同时考虑到电力期货市场和实时市场电价序列的尖峰、厚尾特性和两市场电价序列间的相关结构，采用时变SJC Copula-（GARCH-T，GARCH-GED）模型拟合电价序列，并进行模拟。实证结果表明，多期投资组合决策优于连续决策，也优于整体购电决策。
The power industry restructuring and deregulation conduct in the world. They guidemarket operators in the electric power industry, break monopoly, encourage competition,improve efficiency, and also make market participants confront with hitherto unknownrisk. Under the traditional power control system, electricity price is determined byregulators according to the cost. The electricity price is stable and predictable in a longperiod. However, the power market reform changes the rules. In the competitiveelectricity market, the power cannot be effectively stored. It is the biggest feature of thepower as a commodity. This defect leads to the low elasticity of power demand, the highprice volatility influenced by the electric power supply and demand relationship. In orderto avoid price risk, the forward, futures and options of the power financial derivativesappear in succession which play a role in reducing or transferring price risk. In thecompetitive electricity market, the power supply company as an independent marketparticipant owns distribution network and provides power purchased from power tradecenter and power generation company to end-users. It is important to study andinvestigate the power company’s trading strategy because the strategy plays an importantrole in optimizing power distribution of different markets and reducing market risk. Inaddition, it has importment significance on stabilizing power market.
The paper first introduces the features of power financial derivatives and thefinancial risk measure methods in electricity market. Then, we apply the finance portfoliotheory to the electric power market and research the power supply company and the largeconsumer’s power purchase from different markets. Later, the correlation among powerassets is analysed. And the Copula function is adopted to measure the nonlinearcharacteristic. At last, we make deep analysis about the multi-phase power purchasestrategy of power supply company. The main work and innovation are as following:
①We usually analysis the power supply company’s power purchase problem fromthe perspective of expected return and risk. In the existing literatures, multi objectiveoptimization problem is mostly transformed to single-objective problem by the weightedmethod. The single-objective optimization problem can be solved easily. In this paper, wemake the objective function fuzzy and solve the model by fuzzy multi-objectiveoptimization algorithm in order to seek out the relatively optimum solution as close aspossible to each target. Some existing literatures on the large consumer’s power purchase problem risk problem mainly use VaR and CVaR measure methods without consideringthe large consumer’s subjective degree of risk aversion. In this paper, a new method is putforward. The spectral risk function measuring risk is used to extablish the largeconsumer’s power purchase model. Compared with VaR and ES, the spectral risk methodis more flexible and practical without setting the confidence level previously. The largeconsumers can choose spectral risk freely according to the degree of risk aversion.
②For the joint distribution of price sequences in different markets, some of theprevious literatures make the hypothesis that price sequence follows a normal distributionand measure the correlation among electric power assets by using linear correlationcoefficient. However, it is studyed that price return series have obvious thick tail andheteroscedasticity phenomenon. The strong nonlinear characteristic between power assetsmakes it hard for linear correlation coefficient to describe their relations properly.Considering the correlation of the profits between the electricity real-time market andfutures market and the statistical characteristics of the profit series, in this paper weestablish the dependence portfolio model-Gumbel Copula-（GARCH-GED，GARCH-t）based on the advantages of the copula function and the GARCH model. We analysis thepower supply company’s purchasing risk based on the model. The empirical results showthat the risk measure results under the copula model is more accurate than the resultsunder binary normal joint distribution.
③For the power supply company’s multi-phase power purchase problem, most ofprevious literatures mainly measure multi-phase portfolio risk by simple sum of singleperiod risk or use single-phase succession strategy. In fact, investors mainly consider theminimum risk at the end of the investment instead of making all the single-phase riskvalues as small as possible. Based on the above analysis, we establish a multi-phasepower purchasing model which takes the maximum expected accumulated return and themimimum risk measured by dynamic coherent risk function as objectives. Beside this,time-variation SJC Copula-（GARCH-T，GARCH-GED）model is adopted to fit andsimulate power price series, considering the correlation of the return between theelectricity real-time market and futures market and the statistical characteristics of theprices series. The empirical results show that multi-phase strategy is superior tosingle-phase succession strategy, also better than the entirety strategy.
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