Study Of The Optimization Reservoir Optimal Operation Based On Multi-objective

Integrated scheduling of the reservoir is based on the multi-objective reservoir opera-tion, a number of tasks, including flood control, power generation, irrigation, watersupply, shipping, sediment, and reservoir operation should be a library with more than awater use, should try to reconcile all the target scheduling between the primary andsecondary.Based on the scheduling period reservoir operation cycle is divided into the floodseason and non-flood season study. According to the characteristics of different periods,the establishment of a different target. According to the theory of multi-objective floodseason using weighting method to determine the weight of each target, multi-objective sin-gle-objective dynamic programming dynamic control of the flood season the reservoir wa-ter level, and get dynamic reservoir water level diagram will be its reservoir oftraditional scheduling Figure conduct contrast, the results of the analysis. Schedulingof non-flood season, the first non-flood season, the establishment of multi-objective non-flood season, use the main goal of law, change these goals into a main goal, a numberof secondary objectives. The main objective of the establishment of non-floodseason scheduling model using a genetic algorithm to solve the model, results wereanalyzed, and eventually the water level of the non-flood season, and the dischargedvolume of the control chart.Main contents are:(1) Discusses the reservoir operation method, the reservoir parameters andmulti-goal theory and solution, a good theoretical basis for the following;(2) Flood season reservoir operation stage research, and multi-goal theory approach tomulti-target conversion using dynamic programming to control the flood limit level,and comparative analysis with traditional scheduling methods;(3) Multi-objective non-flood season, the main target, and convert it to one of the maingoals and secondary objectives. Limited constraints to model the main object of the use ofintelligent optimization algorithms for model solution, and then analyzed the results.