Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer - Jain, Kavita; Saxena, Akash (Journal of Computational and Cognitive Engineering, 2022)
In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes. Received: 16 January 2022 | Revised: 12 March 2022 | Accepted: 17 March 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.