Study On Long-term Reservoir Optimal Scheduling And Risk Analysis

There exist many unpredictable factors during the hydro scheduling process, which cause many kinds of risk for the actual operation such as: inflow risk, decision-making risk, power generation revenue risk. The reservoir inflow risk is the potential one and plays a decisive role. Risk analysis for the decision-making is to take related methods to analyze and assess various possible errors and identify the most suitable decision-making with risk-benefit or risk preference matching. The inflow risk and generation revenue risk are studied.For the facing period of the whole regulation horizon, the main scheduling risk comes from the runoff uncertainty, which causes the uncertainty of the decision-making results and expectations. For the general adept hydrological forecasting model in applications, they are treated as certainty ones. The output of the forecast variable is always released to the users as a determinant value. However, the occurrence and development of the hydrological phenomenon is a complex dynamic process depending on the meteorological and geographical factors. Hydrological forecasting model accepts hydrological, meteorological and other inputs, using overviewed parameters and those are only the simulations of the objective hydrological process. All those complex factors lead the hydrological (runoff forecasts) forecasting to be uncertainty.For the reservoir generation operation based on the Markov process, the extensively applied optimization criterion is the optimal expectations revenue. However, the expectations criterion is not sensitive to the risk. and is not suitable for the optimal problems which need to reflect or limit the risk directly. For the optimal operation of hydropower, in order to guarantee a certain level of generation revenue, the operation strategy adopted should ensure the probability of the generation revenue beyond a given one to be lager than some given value while pursuing the expectations revenue to be more lager. Limited only to the expectation revenue as the optimization objective is not enough, it is necessary to choose other optimal goal to reflect the actual risk or the policy-makers'will or risk preferences.Based on reading and studying large number of literatures about reservoir scheduling, the long-term optimization scheduling problem and the scheduling risk assessment are studied. The main thesis content and innovative achievements include the following parts:A long-term compensation optimization scheduling method is introduced in chapter 3. The principles and characteristics and solving method of the model are introduced in detail. A detailed analysis on the results of the example is given too.One risk analysis method for the period scheduling in introduced in chapter 4. Bayesian statistic forecasting theory is adopted to formulate the reservoir long-term runoff forecasting model, which describes the uncertainty of hydrological forecast by distribution function. Gray correlation prediction model of meteorological factors is presented to count the uncertainty of the input factor. Real-time meteorological information and history hydrological data are coupled effectively, which breakthrough the limitations on information utilization and samples learning of the determined forecast method and improve the accuracy of hydrological forecast. The established model is tested on the runoff forecast of the Feng-man reservoir.A minimum risk control model for the reservoir long-term generation optimization is presented in chapter 5. The optimal scheduling criterian is the probability that the expected generation of the whole period not exceeding the pre-set generation target to be smallest. Compared with the generally used guidance of the largest expectation power generation model, this model is fitted for the decision-making in which the risk is needed to be limited to reflect the risk preference of the policy makers.Finally, a summarization of the dissertation is given in the last part (Chapter 6), and also proposes problems that need to be settled in the future and some suggestions for further study.