Research On Optimal Hydroelectric Scheduling And Risk Management In Electricity Market

The electricity industry throughout the world, which has long been dominated by vertically integrated utilities, is underlying enormous changes. Restructuring has necessitated the decomposition of the three components of electric power industry: generation, transmission and distribution. In deregulated markets, hydropower producers are regarded as independent generating companies, and the management model of optimal scheduling has been greatly changed. The diversity of trading types provides hydropower producer more options to sell their generation products among multiple markets. With the sole objective of maximizing revenues, hydropower producers concern the power exchange in electricity market, and trading decisions are made with the center of market price. All these changes bring new field and challenge to hydroelectric scheduling. Furthermore, a major concern for hydropower producers in restructured market is the profit uncertainty caused by uncertainty in inflows and market prices, and introducing risk management to guarantee the market trade revenue has become an urgent need for hydropower producers.With the support of NSFC project"advanced theory and methodology of river basin cascade hydroelectric energy joint optimal operation and management in electricity market", this paper concerns hydroelectric scheduling problem and corresponding market trade strategy considering the uncertainty in inflows and market prices. Furthermore, risk management methods is incorporated, and risk-constrained optimal scheduling model is formulated to obtain the tradeoff between maximum of revenue and minimum of risk. The main research contents of this paper are listed as the following:(1) Stochastic linear programming model is introduced in detail, including the construction of model, solution method and the calculation of advantage measure. Scenario tree model is recognized as input of stochastic linear programming model, and its constructing techniques based on time series method and heuristic method are proposed and realized. Concerning the solution difficulty of stochastic linear programming model, a probability metric is used to control the goodness-of-fit of the approximations of the random data process. The design principle and technical realization of fast backward scenario reduction is further illustrated. The number of scenarios is endeavored to be reduced while still retaining the essential features of the scenario tree, which extends the application of stochastic linear programming.(2) A new model for medium term forward contracting determination is proposed based on stochastic linear programming, which considers the uncertainty in inflows and market prices simultaneously with scenario tree model based on different constructing methods. Forward contracting decisions and day-ahead market trading decisions are recognized as different decisions of different stages in a stochastic programming framework. Through the comparison with expected value model, the advantage of higher revenue is guaranteed for considering the influence of uncertainty; through the comparison with a different stochastic programming model, the similarity of influence on forward contract decisions and revenue further verifies the availability of the proposed stochastic linear programming model.(3) A new model for portfolio decisions is proposed based on stochastic linear programming, which considers hydropower scheduling and multi-market trading decisions under uncertainty in inflows and market prices. Portfolio decisions and recourse decisions of generation scheduling are recognized as different decisions of different stages in a stochastic programming framework, and uncertainty is modeled with different scenario tree model, which will result in different portfolio decisions, but would have no influence on entire stochastic linear programming model. The risk assessment on portfolio decisions shows that more revenue is guaranteed through the flexible portfolio decisions. However, portfolio decisions also face great risk, which demonstrates the necessary of risk management.(4) Based on the summary on methods and application of risk management in deregulated market, the deterministic models incorporating price risk into short-term hydropower scheduling problem are proposed, which are expected revenue-VaR risk utility model and integrating scenario risk penalty or risk constraint model. To solve the above models, IFEP-GA hybrid optimization algorithm is proposed, which integrates evolutionary mechanism and competition selection mechanism of IFEP algorithm in GA framework, and penalty mechanism and repair mechanism are combined in the evolutionary process. The considering of price risk on evolution algorithm is analyzed, which provide hydropower producer proper risk level and corresponding short-term optimal scheduling.(5) For the close relation between VaR risk measure and day-ahead spot market trade, a new expected revenue-VaR risk utility model based on stochastic programming is proposed. The solution of different risk factors provides the efficient frontier between the expected revenue and risk. Furthermore, risk will be different under different risk measures, which further provide the decision base for proper risk level.(6) For the close relation between risk measures, model of uncertainty and market trading strategy based on stochastic programming, Semi-Variance risk measure is proposed, which reflects the risk from the difference between price scenario and expected price in a scenario tree model, and a new risk-constrained portfolio management model is constructed, in which uncertainty model and risk model is combined. The influence of uncertainty is reflected and risk is effectively managed, and corresponding flexible portfolio decisions under different risk levels can be obtained.With the application of hydropower scheduling problem and optimization of multi-market trade strategy, the proposed optimization algorithm and mathematical models are verified effectively, which provide decision base for proper trade strategy in deregulated market and effective solution for tradeoff between maximum revenue and minimum risk.