Electricity Market Risk Management Theory And Application

From 1990 until present day, the electric power industry has experienced dramatic changes worldwide. With the deregulation procedure, the generators, load serving entities, and the end user become the main player of the power market but they exposed to great risk at the same time. Under the increasingly competitive condition, facing the exercising of market power risk , price volatility, volumetric risk, fuel prices risk, default risk, et al, the electricity market now experience the impending need to perform risk assessment, quantifying and management and it is a very important and valuable task for the future power market. The goal of this dissertation is to provide appropriate risk measurement techniques such as Value-at-Risk and Conditional Value-at-Risk to quantify the trade risk for the generators and purchasers and provide some risk management methods such as bidding strategy, portfolio selection, electricity derivatives to mitigate the potential risk for the market players.In Chapter 1, the significance of the study and the main content of this dissertation are introduced. The broad area of risk management in the power industry is explored through a State-of-the-Art. Based on the literatures home and abroad, the risk measurement and quantifying techniques and methods is summarized and compared.Chapter 2, the behavior of suppliers with different level of rationality is reviewed. The problem of how to bring electricity market a potential behavior risk is investigated using behavior game theory. First, a behavior automata network model is developed on the condition that the rationality is taken as a variable. Then, a class of learning rules, such as Cournot and extended Cournot adjustment is discussed. The results of evolutionary experiments indicate that the variables may bring a great diversity of prediction to the market. As an improvement of price cap strategy, the artificial elasticity to the oligopolistic competition market is proposed. The results of evolutionary experiments show that the method and model are effective.In Chapter 3, the conception of Portfolio Selection and Efficient Frontier is introduced, and the illustration of risk techniques such as VaR and CVaR is presented. The shortage of the VaR applied for the portfolio optimization is given out and the three optimal Mean-CVaR models are discussed.In Chapter 4, using the conditional value at risk (CVaR) as risk measurement index, a novel Mean-CVaR portfolio optimal model is built by considering the risk and expected revenue rate synthetically and the objection is to obtain the maximum annual profits and the minimum risk value for the power generators. The calculation results showed that the proposed model can guarantee the generators to obtain the certain profits at the minimum CVaR risk level. So it provides a new way for power generators to make bidding decision-making and risk valuation.In Chapter 5, an optimal power purchasing portfolio model in multiple markets is presented for a load serving entity (LSE) to manage and hedge the market trading risk. The loss of LSE's profits is quantified as risk term with CVaR. The mean-CVaR model is proposed with maximizing the LSE's profit with the consideration of certain CVaR risk constraint. Three markets examples are given out; and the linear programming technique is used as solution methodology. Numerical testing results show that the model effectively provides purchase decision aids for LSE.In Chapter 6, for the first time, the credit risk model is introduced to electric power industry. The well established firm value model and the conception of Vulnerable Option in financial field are adapted to solve pricing problem for power option contract. Numerical example showed that the vulnerable option price is less than the Black-Scholes option price under the same condition, and that is mean the utilities can gain some loss protection in advance. This Chapter makes some pioneering work for managing the credit risk in power forward contract market.Chapter 7 makes a conclusion of this thesis, and describes the prospect of risk management in power market.