Substation and transmission line expansion planning using Adaptive Tabu search algorithm

Abstract

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.

Simultaneous generation and network expansion planning in large‐scale power systems under exact AC power flow equations

“…In available researches for NEP and G-NEP such as [47][48][49][50][51][52][53][54][55][56], making an expansion decision relies on a list of candidate substations and transmission lines using cost-effective analysis. However, multiple transformers with different capacities and voltage ratios can be installed in new and existing substations according to geographical limitations.…”

“…and NEP [45][46][47][48][49][50][51][52][53][54][55]. The authors of [2][3][4] pay considerable attention to the prediction of uncertainties and the efficiency of the obtained generation expansion plans in a DC power flow-based framework.…”

“…Therefore, DC and LAC network representations tend to create a gap in comparison with the solutions obtained from the exact AC ones. This is why [9–29, 38–40, 55] aim to solve these large‐scale mixed‐integer nonlinear and non‐convex optimization problems. The taxonomy of the literature is tabulated in Table 1, which can clarify that there exist the following major research gaps in the field of power system planning.…”

“…On the other hand, the NEP problem identifies the optimal number of circuits, voltage levels, timing, location, size, and technology of new network facilities, including transmission lines and power transformers within existing/candidate power stations, to supply the future load demand. It is noteworthy to mention that the inherent complexity of the expansion planning problems has led researchers to solve the GEP and NEP problems separately as GEP [2–4], transmission expansion planning (TEP) [5–35], coordinated GEP and TEP (G‐TEP) [36–44], and NEP [45–55]. The authors of [2–4] pay considerable attention to the prediction of uncertainties and the efficiency of the obtained generation expansion plans in a DC power flow‐based framework.…”

A Less-Effort Method for Spatial Network Planning

“…An interaction between these two problems is modeled in the studies dealing with simultaneous TEP and substation placing [19]. In this case, the route of new lines connected to unconstructed substations should be evaluated during the optimization process and cannot be 70 determined before solving TEP.…”