【作者】 庄晓丹；

【导师】 甘德强； John N. Jiang；

【作者基本信息】 浙江大学 ， 电力系统及其自动化， 2010， 博士

【摘要】 随着全球性的电力工业结构重组和解除管制的市场化改革,发电商和供电公司都将作为独立的市场主体参与竞争。电能因为难以大规模长期储存且缺乏短期价格弹性,其价格的波动远远大于普通商品,给参与市场的电力企业带来了巨大的金融风险。加州电力市场在2000-2001年的危机使得电力企业在市场竞争中越来越意识到金融风险管理的重要性。电力企业进行金融风险管理的主流方法通常有采用组合交易,构造竞价策略和应用电力金融衍生工具,同时还需要对有关企业运行经济性的影响因子进行识别、定量计算和分析。因为这些影响因子会对企业的经济运行起作用,其不同取值有可能改变企业的最优组合和风险收益的平衡点,也会影响到企业对于最优风险决策的选取。本论文针对电力企业在市场环境下所遇到的实际问题,就电力市场金融风险管理及其相关问题进行了研究。具体而言,本文主要做了如下工作:(1)在华东区域电力市场中,电力公司在月度市场上报价,日前市场只申报负荷需求,以管制价格对用户售电。各市场的电量分配以及报价方案直接影响电力公司收益和市场稳定。基于华东市场的实际运行过程,借鉴现代投资组合理论,对电力公司考虑风险的月度购电策略进行了研究。对3种风险计量指标(方差,半方差和条件风险价值)的购电决策模型从实际应用角度进行了分析,其中基于半方差的模型为首次提出。并且针对华东市场的购电侧分段报价规则,提出了利用分段降低风险的月度分段报价方案。目前已在浙江省试用,具有一定的实用性。(2)在节点电价体系下,网络约束导致的阻塞费用波动将使得供电公司的最优购电组合策略更加复杂。针对此问题,提出采用金融输电权对冲阻塞风险,建立了供电公司在远期合同市场和日前现货市场上的购电组合决策模型。该模型引入条件风险价值作为风险测量因子,综合考虑了现货市场的电价风险和远期合同市场的阻塞风险。计算结果表明:购电组合模型中引入输电权将有效降低购电风险,并且输电权价格对购电组合损失和相应市场分配也有较明显的影响。(3)结合目前金融输电权拍卖市场的实际情况,提出了将输电权交易和电能交易相结合的供电公司双层最优购电组合模型,以输电权对冲阻塞风险。该模型上层优化以供电公司效用最大化为目标,下层以输电权拍卖收益最大化为目标。在决策过程中综合考虑了电能市场和输电权拍卖市场的双重不确定因素。并针对该双层优化模型的随机性特点设计了基于蒙特卡罗和微分进化的算法BDE进行模型求解。该双层优化模型通用性较强,并且对电力市场和FTR拍卖市场上的不确定性因子的分布状况没有严格要求。(4)针对燃气电厂的实际运行情况,研究了其在天然气日前市场上的最优订购问题。由于受天然气市场运行和输送技术方面的限制,天然气日前市场早于电力日前市场关闭,大部分发电商需要为所属燃气电厂在未知实际发电量的情况下做出次日的燃料订购的决策。这是一个市场机制下的新问题。提出了一个用于日前市场天然气订购的分步模拟优化方法。首先通过蒙特卡洛模拟的方法得到机组的最优发电量和所有可行的天然气订购决策下的利润分布情况;由此构建机组利润的有效前沿,然后利用效用最大化理论得到最优的天然气订购决策。在决策过程中综合考虑了燃料市场和电力市场的双重不确定因素。(5)针对某大型风电场的实际运行情况,研究了风电场的风能损失问题。大型风电场由于风机数量众多,各台运转的风机之间的相互干扰作用会影响到风机的有功输出,从而造成风电场的风能损失。由于风能损失将直接影响到风电场的发电量,首先就风能损失对风电场的经济运行进行了讨论,说明准确计算风能损失值将对改善风电场的运行经济性至关重要,并有可能影响到风电场的收益风险平衡点。然后提出了一种新的迭代回归的计算方法。该计算方法分离了风机的正常输出值和非正常输出值。在获得正常输出值的基础上,应用统计学中的回归分析方法正确估算风机的理想输出曲线。该方法基于实际数据,可以作为相似风况及机组类型的近似,从而定量计算风电场的风能损失。可服务于已并网的大型风电场,为风电场的风险管理最优决策提供参考。更多还原

【Abstract】 As the worldwide restructuring and deregulation of electric power industry proceeds, generation companies (Gencos) and load serving entities (LSEs) will participate in competition as main individual players. Because it is difficult to store large amount of electricity power for a long time, the volatility of electricity price is more severe than other common commercial products, which means huge financial risk for electric power corporations. For instance, the crisis of California electricity market in 2000 and 2001 arouse great attention to the problem of financial risk management for electric power corporations. In term of financial risk management, the mainstream approaches consist of portfolio strategies, bidding strategies and electricity financial derivatives implementation, in the meanwhile it is necessary for an ulitity company to identify, quantitatively analyze the factors related to its economic operations. These factors will affect the economic of corporations operations, whose different values possibly change the optimal portfolio strategy and the trade-off between benefit and risk, while affect the optimal decision-making process.For several concrete problems, this paper focuses on the financial risk management in electricity market and relevant problem research. The main contributions are summarized as follows:(1) In East China region market, the transmission company acts as a LSE, which quotes bidding price in monthly market and power quantity in day-ahead market, and has the mandate to supply power with regulated price. The purchasing energy allocation among different market and bidding scheme will affect the LSE’s benefit. Based on the practical operation process in and modern portfolio theory, the optimal monthly purchase allocation strategy with risk is studied for LSE. The monthly purchase models with different risk measurement indices, which are variance, semi-variance and conditional value at risk (CVaR), are proposed and compared. Furthermore, a systematic framework for monthly purchasing decision is developed employing step-wise bidding rules. The data of East China region market is used to illustrate the proposed method.(2) In the locational marginal price system, the optimal purchasing portfolio strategy for LSE is more complicated because of the volatility of congestion expenses caused by network constraint. In terms of a risk index based on conditional value at risk (CVaR) as the measuring index for market risk, a purchasing allocation model between forward contract market and day-ahead market is presented, which consists of the power price risk in day-ahead market and the congestion risk in forward contract market, and the financial transmission rights (FTR) is adopted to hedge the congestion risk. The examples illustrate that the FTR can effectively lower portfolio loss, and the price of FTR have explicit effects on the portfolio loss and allocation.(3) According to the practical conditions in FTR auction market, a bi-level purchasing energy allocation model with congestion risk is presented, in which the energy transaction markets are combined with the FTR auction market, and the FTR is adopted to hedge the congestion risk. The upper optimization of the proposed model is to maximize the utilities of LSEs, and the lower optimization is to maximize the FTR auction benefit for ISO. The major uncertain factors between the electricity markets and FTR auction market are considered as well. An intelligent algorithm based on Monte-Carlo and differential evolution algorithm named BDE is designed to solve the proposed model.(4) The short-term natural gas markets usually close earlier than the electric power markets. Most electric utility companies have to make fuel purchase decisions for natural gas fired power plants without knowing the actual generation levels, which depend on many uncertain factors such as, new market driven unit commitments, load variations, dispatch instructions and/or other service requests next day. This paper proposes a novel two-step simulation-optimization framework for valuation of day-ahead natural gas purchases. In Step I, the optimal generation levels and profit distributions of feasible gas purchase decisions are simulated, and gas purchase decision is made based on Utility Maximization Theory in Step II. The impacts of all major factors such as variable load, price volatilities and long-term fuel contract/storage are considered. An application of the proposed framework is demonstrated.(5) In a large-scale wind farm, the interactions between every running wind turbines will affect the wind turbine power output, and generate the wind energy loss. Because the wind energy loss will directly influence the wind farm power generation and operation economic, firstly, the effects of wind energy loss to wind farm economic operation are discussed, then a novel iterative regression method to eliminate the observation under the abnormal conditions to estimate the power curve and calculate the wind energy loss is presented. The method could serve for the operated large-scale wind farm, and provide reference for optimal risk decision-making.更多还原