Intelligent Optimization of Strategic Bidding in Deregulated Electricity Markets Using An Efficient and Robust Grey Wolf Algorithm

Abstract

The rapid transition in energy sector is encouraging competition across various segments of the industry by dismantling monopolies. To maximize profits of GENCOs in such a dynamic environment necessitates an advanced model of competitor behavior and market uncertainty. This paper introduces a bidding approach for GENCOs which consists of their bids structured into three price-quantity blocks. Stochastic probability functions are utilized to model the Market Clearing Price (MCP) under uniform market clearing price mechanism. Using this mathematical framework, an objective function is established for each GENCO, aiming to maximize profits in relation to its competitors. To solve this optimization problem, a highly efficient and robust Grey Wolf Optimizer (ERGWO) is applied. The findings are derived from 10,000 simulations. To highlight the computational efficiency of ERGWO, its outcomes are contrasted with those generated by other meta-heuristic optimization techniques, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Grey Wolf Optimization (GWO) algorithm. ERGWO algorithm exhibits superior performance as the profit calculated through the dispatch estimation is more than any other algorithm.