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Energy Storage in the Emerging Era of Smart Grids Part 4

Tham khảo tài liệu 'energy storage in the emerging era of smart grids part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 78 Energy Storage in the Emerging Era of Smart Grids system which implies a chromosome with 91 genes due to the fact that each chromosome stores information from all seven plants of the hydroelectric system. The values of the genes are real numbers ranging between 0 and 1 and the population is composed of 80 individuals. After defining the chromosome representation the design of GA focuses on the specification of an evaluation function. The evaluation function assigns a numerical value fitness ability index that reflects how well the parameters represented in the chromosome adapt and thus it is the way used to determine the quality of an individual as a solution to the problem. As the availability of water in a given interval depends on the degree of its former use this study used as evaluation function the difference between the maximum stored energy that can be achieved in the system ESSMAX and the energy stored in the system regarding the last interval of the planning horizon ESSge . Since the decisions taken at interval of the planning depends on the decisions taken in the past and determine the future development of the hydroelectric system the use of stored energy in the last interval of the horizon is feasible because it takes the link between operational decisions in time into account commonly known as temporal coupling problem coupled in time . Numerically the evaluation function is represented by 12 where 60 indicates the index of the last interval of the planning horizon Evaluation Function ESSMAX ESS60 12 Therefore there is a minimization problem whose goal is to find a value ESS60 so as to minimize the difference from ESSMAX. After calculating the evaluation function for every individual of the chromosomes population the selection process chooses a subset of individuals of the current population to compose an intermediate population in order to apply the genetic operators. The selection method adopted in this study was the method of the tournament .