Study On Long-term Reservoir Optimal Scheduling And Risk

With the development of China electric power industry, the electricity market is being gradually formed; plants and power grids carry out selling energy with completive bidding; the objective of plants'optimal scheduling is changing. Objective becomes maximizing the generation benefit with the independent of the hydropower system, which leads to the rapidly progressing of optimal scheduling engineering and the application and development of the advanced optimal software for the power grids. The long-term optimal scheduling is a main part of the software and is very important to the reservoir optimal operation. Based on"the research and the development of the combination optimal scheduling for the plants belonging to Huazhong Power Grid","the exploitation engineering for the cascade reservoir optimal scheduling system on Yuan valley of Hunan"and"Design and service of the economical operation software for the Jialing river cascade operation centre and plants of Qinju and Dongxiguan", the research on the long-term reservoir dynamic probability scheduling is carried out. The method of long-term reservoir dynamic probability scheduling is theoretically improved and applied for engineering application. For the first time, Mean First Order Second Moment (MFOSM) is applied to the trial research on the stage risk analyses in order to provide the auxiliary reference for the operation decision, and some useful conclusion is achieved from the research.Firstly, the research field is established by presenting the indispensability and significance of long-term optimal scheduling and risk analyses. And then, the research field is established after evaluating long-term reservoir optimal algorithms. The theory of long-term reservoir dynamic probability scheduling is particularly proposed. The approach is brought forward by summarizing the engineering experiences, which is served as the auxiliary reference for long-term reservoir optimal scheduling originally. After completing the research, we find that favorable effect can be achieved from applying the approach to the optimal scheduling. The objective and restrictions are particularly introduced and the P-decomposition linear programming is used to solve the problem of long sequence simulation optimal scheduling. Through the probability and statistic analysis method, the dynamic probability scheduling chart, which can be real-time updated according to the reservoir observable values, is achieved. Then the long-term optimal scheduling is carried out using the head trial iterative algorithm and the computation flow chart is presented. Moreover, the function operation method is introduced briefly; and the evaluation of the approach is given. Finally, the engineering experiment results of long-term reservoir dynamic probability scheduling in Danjiangkou plant are presented, and the dynamic probability chart which combines of charts on pool level-probability, on power-probability and on outflow-probability is obtained. The long-term dynamic probability scheduling is achieved and compared with the routine method on the annual power energy. The result illustrates that the approach is better than the routine method and can be served as the assistance and applied for the optimal scheduling.The definitions of some kinds of risks are presented, and the risk definition should be chosen according to the actual circumstance of relative field. At the same time the risk character, classification and research in hydrology are introduced. And then, some familiar methods for risk analysis are evaluated. Some predictable risks in optimal scheduling are presented and the discharge risk is tentatively analyzed and calculated by MFOSM algorithm, which is proposed herein. And the de-correlation for random variables and the normalization of the non-normal variables are analyzed. The computation procedure and flowchart of the stage risk and the risk transformation are presented which provide a new perspective for further research. Further more, stage risk and horizon total risk are computed and the results satisfy the requirements of auxiliary reference for optimal scheduling.At last, the research achievements are summarized and directions for further research are pointed out.