Restructured electricity market strategies for the Indian utility system using support vector regression and energy valley optimizer

Abstract

Restructuring the electricity market has led to the establishment of a competitive open market environment. The electricity market has introduced uncertainty and risk in the economic sector conventionally owned by the state. The power generated in the generating station is transferred to the distribution side in the power markets, which share a common transmission network. The power transfer will be in bulk amounts and needed to operate the electricity market securely and economically. The bulk amount of power transfer relies on accurately estimating Available Transfer Capability (ATC), representing the maximum allowable power flow through the existing transmission network while maintaining system reliability. The estimation of the ATC using a proposed hybrid method. The hybrid method comprises Repeated Power Flow (RPF) and Support Vector Regression (SVR) methods. The electricity market participants such as sellers and buyers submit bids to maximize their profit with the help of ATC values. The linear bid function is proposed to formulate participant strategies. Each participant will submit the availability of power requirement and willing price in the linear bid function. The Energy Valley Optimizer (EVO) algorithm is proposed to maximize the profit of each participant. The EVO algorithm efficiently explores a vast solution space, considering complex constraints and uncertainties inherent in the market dynamics to enhance economic gains. The proposed work is tested on the practical UPSEB (Uttar Pradesh State Electricity Board) 75-bus Indian utility system.