An efficient algorithm for optimal generation mix taking into account fuzziness of decision-making and planning parameters

This paper presents an efficient computational algorithm for selecting the optimal generation mix under uncertain circumstances. Subjective, experiential or linguistic uncertainties are selected from among various uncertainties, i.e. we treat fuzziness in generation expansion planning. The fuzziness can be divided into: (1) the fuzziness of decision making; and (2) the fuzziness of some planning parameters, such as load growth, fuel price, and so on. Both classes of fuzziness are integrated into a fuzzy decision based on fuzzy sets theory, and then the optimal generation mix can be determined by the Fuzzy Dynamic Programming technique. The proposed method, which is based on the dynamic programming technique, is extended by using the Bellman–Zadeh maximizing decision. In the method, each generation technology and generation capacity are selected as a stage and 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, condition of existing units, and so on. Furthermore, the arbitrary shape of membership function can be used. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.