Stochastic Search and Rescue Method for Day Ahead Economic Emission Load Dispatch Under Wind Uncertainty

Abstract

This article contributes to solve all day Economic Emission Load Dispatch (EELD) problem of a wind integrated power system specifically based on field data of Jaisalmer, Rajasthan, India using Search and Rescue method in stochastic approach. The proposed S-SAR method utilizes a stochastic technique to account for the inherent uncertainty of wind. It employs the weibull probability density function (pdf) to represent wind speed and conducts 1000 Monte Carlo simulations to capture the uncertainty in the frequency distribution model of wind power. The SAR method has also been employed to solve the EELD problem using a conventional/deterministic methodology to facilitate a comparative analysis of its performance in comparison to a stochastic approach. In addition, the performance of S-SAR is compared to four established algorithms, namely Whale Optimization Algorithm, Grey Wolf Optimization, Moth Flame Optimization, and Particle Swarm Optimization, in order to demonstrate its superiority. The proposed approach is tested using criteria such as optimal generation cost, emission, power deviation factor, simulation time, convergence curves, etc. Results are statistically validated by analyzing the statistical metrics of optimum cost from 1000 monte carlo simulations. The efficacy of the proposed approach to tackle EELD at different levels of wind penetration is further evaluated through a supplementary case study conducted on the system. The simulation findings demonstrate the effectiveness of the suggested S-SAR technique in addressing wind uncertainty, with a 2.46% decrease in cost and a 4.08% decrease in emissions compared to the next competitive option being studied.

Keywords

• Economic emission load dispatch
• search and rescue algorithm
• wind energy
• uncertainty
• probability density distribution
• Monte Carlo simulation