Research On Optimal Dispatchand Scheduling Rules For Hydropower Stations

With the accelerated pace of the development of hydroelectric energy, the optimal operation for large-scale cascade hydropower stations is becoming more and more complex and significant. The tranditonal systematic optimizing method and systematic analytical theory may possess some limitations because of the space-time contact of hydraulic power and electric power between upstream and downstream hydropower stations and uncertain factors of the runoff. Nowadays, although many literatures have given the basic rules of reservoir operation, only some general conclusions have been obtained, very few studies have been reported on the clearly different scheduling rules for the different varieties of runoff, and the feasibility of the methods is relatively poor. Therefore, the perfect optimizing scheduling method and theoretical studies for cascade reservoirs and feasible power generation scheduling rules is becoming an important topic. Based on the above, cascade hydropower stations in Jinsha River have been taken as the research object in this paper. Two improved particle swarm optimization algorithm for the optimal operation of the casacade hydropower stations have been proposed based on the two aspects of the theory and application research. Scientific theory foundation and metheod basis for the opearation and management of the hydropower stations have been complemented by the specific and practical scheduling operation rules presented in this paper. Specific results are as follows:(1) Two improved algorithm based on the particle swarm optimization algorithm have been proposed. Particle swarm optimization (PSO) has been modified from the macro level and micro level respectively to overcome the shortage which the PSO possesses when used in the optimizing scheduling of the hydropower stations. In the macro level, particle swarm optimization based on cultural algorithm (PSO-CA) which combines the cultural algorithm (CA) with the particle swarm optimization, and in the micro level, the virus particle swarm optimization algorithm (VPSO) which integrates the virus evolutionary mechanism into the particle swarm algorithm (PSO) is presented in this paper. For PSO-CA, the evolutionary mechanism of particle swarm optimization algorithm (PSO) is guided by cultural algorithm (CA). PSO-CA uses PSO in population space and guides the evolution by shape knowledge and standardization knowledge in belief space. Same examples are used to verify that the PSO-CA algorithm has a better applied prospect for its high reliability and fast operation speed in global optimization. VPSO integrates the virus evolutionary mechanism into the particle swarm algorithm (PSO) and host population and virus population are generated during the evolution. The former transmits genetic information between the different generations which is the same as PSO and the latter carries out the infection operation in the same generation through transcription and reverse transcription. VPSO can effectively inhibit the "premature" phenomenon of particle and accelerate the speed of convergence.(2) The applications of the improved particle swarm algorithm in optimizing operation of reservoir have been researched. Different optimizing scheduling problems of reservoir require the different optimization methods. Therefore, the suitable optimization method of cascade hydropower stations should be chosen according to the characteristics of hydropower system itself. The problems of the optimal flood dispatching of hydropower station and the short-term optimal operation of cascade hydropower stations haven been studied by using PSO-CA, and the problems of the load distribution of cascade hydropower stations and the economic operation of hydropower station have been studied by using VPSO in this paper. The effectiveness of the two improved particle swarm algorithm of cascade hydropower stations in the optimal operation problem also has been verified.(3) Short-term power generation scheduling rules for cascade hydropower stations (before Longpan station startup) of Jinsha River have been formulated. A great quantity of optimal scheduling processes were obtained by calculating the daily runoff process of Jinsha River within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were given, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling, and the effectiveness and practical applicability of the rules are testified by a case.(4) Long-term power generation scheduling rules for cascade hydropower stations (after Longpan station startup) of Jinsha River have been studied. The power generations of cascade hydropower stations in Jinsha River were calculated by using three conventional methods of conventional operation chart, energy storage chart and scheduling function, and then were compared with the results obtained by the maximum power generation model and the maximum guarantee output model. By comparing different simulations, the three conventional methods are objectively evaluated herein. The conclusions can provide an important reference for hydropower stations to discover scheduling rules and formulate operation guidelines, and also can provide a reliable basis for making the long-term power generation plan.