Technical–economic analysis of including wind farms and HFC to solve hybrid TNEM–RPM problem in the deregulated environment

Abstract

This paper addresses a mathematical model for solving transmission network expansion management (TNEM) associated with reactive power management (RPM) in the presence of double-fed induction generator (DFIG) variable speed wind turbine and hybrid flow controller (HFC). According to this plan, the reactive power capability of DFIG wind turbine is gained and the constraints of deliverable power are derived for each operation point. Strong control capability of the HFC with controlling bus voltage and line power flow offers a great potential for solving many of the problems facing electric utilities. For a precise steady-state analysis of HFC, the injection model is considered which it is an adaptive model in steady-state analysis. The main goals of the proposed management strategy are to minimize investment cost, real power loss cost in transmission lines and voltage deviation and maximize voltage stability index and social benefit at the same time. A three-stage scheme has been developed for solving hybrid TNEM–RPM problem. In the beginning of the plan, it is assumed that all the reactive demands are acquired from local sources and an optimal transmission plan is specified. After transmission lines are constructed, in the second step, reactive power sources will be allocated to weak buses. In order to assess reliability of the system, EENS criterion is used. Due to multi-objective nature of the proposed management method, an improved non-dominated sorting genetic algorithm-II (INSGA-II) style is applied for an optimization procedure. Also, a decision making method based on fuzzy decision making (FDM) is used for finding the best compromise solution from the set of Pareto-solution obtained by INSGA-II technique. In a real power system, Azarbaijan regional power system of Iran, comparative analysis of the results obtained from previous management methods and the proposed approach is represented.