Research On Financial Risk Management And Relative Issues For Electricity Companies

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.