A Fuzzy-based Genetic Algorithm for Social Welfare Maximization by Placement and Sizing of Static Synchronous Series Compensator

Abstract

This article presents a fuzzy-based genetic algorithm to maximize total social welfare and alleviate congestion by placement and sizing of one static synchronous series compensator device, considering its investment cost in a double-sided auction market. The generating units cost curves are considered to be quadratic with sine components to show the impacts of valve point loading. By adding the valve point effect, the model presents non-differentiable and convex regions that challenge most gradientbased optimization algorithms. In addition, the impact of distribution companies on the social welfare maximization and congestion management is presented as a quadratic function. The proposed approach makes use of the fuzzy-based genetic algorithm to optimal schedule generating companies and distribution companies and setting the static synchronous series compensator location and its size while the NewtonRaphson algorithm minimizes the mismatch of the power flow equations. Simulation results on the modified IEEE 14-and 30-bus test systems (with/without line flow constraints, before/after compensation) are used to examine the impacts of the static synchronous series compensator on the total system social welfare improvement versus its cost. Several cases are considered to test and validate the consistency of detecting best solutions. Simulation results are compared to solutions obtained by the genetic algorithm and sequential quadratic programming approach, which has been used in MATPOWER (available on-line; see [1]). The aim of this article is the utilization of static synchronous series compensator for the social welfare maximization problem considering the impact of valve point loading effect on the operation point of the generating companies by inclusion of fuzzy rules in the genetic algorithm to guarantee fast convergence for locating/sizing the static synchronous series compensator. The proposed method shows the benefits of the static synchronous series compensator in a deregulated power market and demonstrates how it can be utilized by the independent system operator to improve the total social welfare and prevent congestion.

Transmission Congestion Management in Deregulated Electricity Market using Multi-Objective Grasshopper Optimization Algorithm

“…In addition, four types of FACTS controller are used where a genetic algorithm is performed for finding their optimal location in a power system [19]. A fuzzy-based genetic algorithm is use in [20]. The article in [21] shows the potential of Non-Sorting Genetic Algorithm-II for computing the congestion management problem which is to minimize the management cost and improving the voltage and transient stability margins in a pool-based electricity market.…”