This article presents an approach which optimizes the total investment cost of new transmission substations and transmission lines supplying the forecasted emerging distribution substations. By considering a set of system constraints, an Adaptive Tabu search algorithm is proposed to find the optimal solution from the set of candidate substations and transmission lines which are calculated from the mathematical clustering and power flow tracing techniques respectively. The proposed algorithm is tested in a modified IEEE Reliability Test System 79. The results show that the proposed method is efficient and promising.
Keywords-Adaptivetabu search, mathematical clustering technique, power flow tracing technique, substation expansion planning, transmission line expansion planning I. INTRODUCTION Due to the electric power demand growth, substation and transmission line expansions are needed to resolve the electricity inadequacy problem by the least construction cost, in which system operating constraints are not violated. The key factors are the determination of transmission substation (TS) and transmission line (TL) locations and capacities for the future power demand. From the viewpoint of effective long-term planning, this is very significant.Based on various solution methods, the substation expansion planning (SEP) and transmission line expansion planning (TEP) can be classified into three types: mathematical optimization, heuristic optimization, and metaheuristic optimization. In addition, the planning horizon should be considered. Therefore, SEP and TEP based on the planning horizon can be classified into two types: static and dynamic planning [1]. The dynamic planning considers the time of constructing in the planning process, while the static planning ignores.In the past, SEP and TEP are interested in many literatures. Reference [1] proposed the simultaneous SEP and TEP algorithm by using DC optimal power flow, while Mixed integer programming is presented in [2]. Reference [3] proposed the SEP algorithm by using genetic algorithm method. Reference [4] presented TEP based on genetic algorithm with reliability consideration. In references [1], [2], [3] and [4], the solutions are found from the list of candidates. References [2] and [3] have an algorithm to find the candidate TSs. However, all the past researches do not have an algorithm to find the candidate TL.