Fuzziness of decision making and planning parameters for optimal generation mix

An efficient computational algorithm for selecting the optimal generation plan under uncertain circumstances has been proposed in this paper. The fuzziness of some planning parameters, such as load growth, fuel price can be integrated into a fuzzy decision-making based on fuzzy sets theory and approximate reasoning. Firstly, a set of scenarios differing in the growth rate of future demand is prepared for the generation planning. Secondly, the optimal generation plan for each scenario is determined by using the Dynamic Programming (DP) technique. In the DP approach, each type of generation plants is assigned to a stage and generation capacity to be constructed is assigned to a state, respectively. The proposed method can easily accommodate not only the fuzziness but also many constraints of generation expansion planning, such as integer solutions of unit capacities, composition of existing units, and so forth. By making use of the membership functions and approximate reasoning, the optimal generation plan for any possible future demand can be drew up effectively. The feasibility of the proposed method is demonstrated on a test power system, by comparing the optimal plan obtained from the proposed approximated approach with that solved by the dynamic programming.