Effective Initial Population Based GA For Mid-long Term Hydrothermal Generation Scheduling

With the problem of energy shortage and environment pollution, it is necessary to promote clean energy and develop hydropower to improve power efficiency. When mid-long term hydrothermal generation scheduling is made, the energy consumption in a relatively long planning period is basically unchangeable. Thus there is a huge potential to save energy by searching for a more reasonable mid-long term generation scheduling model.This paper presents a deterministic model for optimal mid-long term hydrothermal generation scheduling, which takes into account water head effects and unit characteristics. And an improved genetic algorithm proved to have shorter computation time and better solution is proposed which could generate initial population effectively. Normally initial population is randomly generated between the upper and lower limits which leads to many infeasible solutions and low search efficiency. In view of this, an algorithm to produce feasible water volume is derived which combines reservoir water relationships and linear programming method together. The solution process could be divided into primarily three steps. First, calculate water outflows and then allocate them among hydro units to acquire hydropower output. Second, compute thermal output by deducting hydro output from power load. And then allocate the total thermal output among thermal units to acquire the corresponding amount of coal consumption. Third, calculate individual fitness value considering final water storage, coal consumption and constraint violations. Then turn to stepl to start the next computation to find the lowest coal consumption. The simulation results prove that comparing with GA the proposed algorithm is an effective method with shorter computation time and higher precision.