A Modified Immune Particle Swarm Algorithm And Research On Cascaded Reservoir Optimal Scheduling

With the development of the hydropower project and the exploitation of large scale reservoirs, many large scale cascaded reservoirs are coming into being. How to use optimization theory and system engineering to establish and achieve the optimal scheduling of reservoirs, which is under the existing constraint conditions, is very significant to solve the problems of energy shortage in our country. Consequently it's academically and practically important to find a proper method to solve the cascaded reservoir optimal scheduling problem.The traditional methods are limited when solving the problems by the time, efficiency, storage capacity and local optimum etc. With the development of modern intelligent optimization techniques, some robust algorithms, such as artificial neutral network, particle swarm optimization algorithm, ant colony algorithm, generic algorithm were introduced, which provide new avenues to cascaded reservoir optimal scheduling. But these methods are limited more or less. Using any optimization algorithm will restrict the solution of the problem. Consequently several optimal techniques are usually integrated to solve the problem.In this paper, a modified immune particle swarm optimization algorithm is proposed to solve the optimal scheduling problem. The intersection operation and high frequency variation is introduced to keep the population's diversity and avoid early ripe of PSO. The algorithm's global searching ability is improved through Cauchy variation, and local searching ability is improved by Gauss variation. In addition, the vaccine operation is introduced to solve the potential degradation caused by the probability distributed intersection and variation. The test results of some classical testing functions show that the evolution speed and convergence precision of the proposed algorithm are improved.Finally, the modified immune particle swarm optimization algorithm was applied to the long-term optimal scheduling of a cascaded reservoir and obtained well optimal results, which provided an effective and feasible way to solve the complex cascaded optimization problem.