Retailer profit maximization with the assistance of price and load forecasting processes

Abstract

The liberalization of electricity markets from structured monopolies to competitive forms provided the motives for more market participants to get involved and operate in day-ahead markets. Among them, the retailers act as the intermediates between generation companies and consumers. The retailer procures electricity mainly from pool market and forward contracts. The scope is to maximize its profits through the solution of a profit maximization problem. The system marginal prices are assumed a stochastic variable. A poor prediction would eventually lead to decreases in revenues. Instead of treating the system price as a stochastic variable where a set of scenarios are formulated, a computational intelligence based forecasting system can be implemented in order to decrease the optimization problem complexity. Also, another source of stochasticity are the consumers load patterns. In this case, a short-term load forecasting system can aid on the strategic decision of the retailer such as the amount of the electricity procurement and tariff structure. The present study presents a profit maximization method for retailers in deregulated markets. Two crucial variables, i.e., price and load, are not simulated via stochastic programming; instead of this, accurate forecasting algorithms are implemented to provide better predictions. © 2021 Elsevier Inc. All rights reserved.