Amit Saraswat

A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA

This paper presents a development of a new hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving complex multi-objective optimization problems. In this proposed algorithm, two significant parameters such as crossover probability (P C) and mutation probability (P M) are dynamically varied during optimization based on the output of a fuzzy controller for improving its convergence performance by guiding the direction of stochastic search to reach near the true pareto-optimal solution effectively. The performance of HFMOEA is examined and compared with NSGA-II on three benchmark test problems such as ZDT1, ZDT2 and ZDT3.

Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.

Multi-objective congestion management based on generator's real & reactive power rescheduling bids in competitive electricity markets

In this paper, a competitive bidding based congestion management strategy is developed for a pool type electricity market model. The proposed congestion management strategy alleviates the transmission congestion by using rescheduling both the real and reactive power output of generators. The separate bids for real and reactive powers are invited from all participating generators in the congestion market. Further, the multi-objective optimization frameworks are developed which minimizes two contradictory objective functions such as Total Cost of Congestion Management and Congestion Severity Index (CSI) subjected to various system constraints. The proposed strategy is tested for different congestion scenarios on 39-bus New England power system.

Principal component analysis-based real coded genetic algorithm for optimal reactive power dispatch

This paper presents a new solution method for optimal reactive power dispatch (ORPD) problem based on principal component analysis (PCA). In heavily loaded power system, security, reliability and economy are some unavoidable concerns. Therefore, ORPD becomes an essential tool to achieve these goals. It is a highly non-linear constrained optimisation problem. A PCA-based real coded genetic algorithm (PCA-RCGA) is proposed in this paper to deal with such type of problem. In PCA-RCGA, PCA theory is applied in mutation operator to enhance convergence of conventional genetic algorithm by guiding the direction of stochastic search to reach near the global optimal solution effectively. The proposed PCA-RCGA is tested on standard IEEE-30 bus and IEEE-118 bus system for ORPD problem. The simulation results for both cases are compared with various optimisation techniques applied to ORPD available in literature.

Day-Ahead Zonal Reactive Power Market Clearing Model for Competitive Markets: A Multi-objective Approach

In this paper, a multi-zone/localized reactive power market clearing model namely MZ-RPMC model is proposed for day-ahead competitive markets. In proposed model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and total real transmission loss (TRTL) are optimized simultaneously while satisfying various system equality and inequality constraints. A well known elitist non-dominated sorting genetic algorithm called as NSGA-II is used to solve the multi-objective reactive power clearing models. The proposed multi-zone model is compared with single-zone model to explore its superiority.

Multi-Objective Zonal Reactive Power Market Clearing Model for Improving Voltage Stability in Electricity Markets Using HFMOEA

This paper presents a development of a new multi-objective zonal reactive power market clearing (ZRPMC-VS) model for improving voltage stability of power system. In proposed multi-objective ZRPMC-VS model, two objective functions such as total payment function (TPF) for rective power support from generators and syncronus condensers and voltage stability enhancement index (VSEI) are optimized symultanously by satisfying various power system constraints using hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA). The results obtained using HFMOEA are comapared with a well known NSGA-II solution technique. This analysis helps the independent system operators (ISO) to take better decisions in clearing the reactive power market in competetive market environment.