Electric End-User Consumer Profit Maximization: An Online Approach

TL;DR

This paper introduces an online convex optimization framework for end-user electricity profit maximization in smart grids, enabling real-time decision-making with limited data and no reliance on extensive historical information.

Contribution

It proposes a novel online decision-making model for responsive consumers, accommodating scenarios with or without predictive data, and demonstrates its effectiveness through simulations.

Findings

Achieves significant profits in simulations

Effective with limited or no predictive data

Outperforms existing models in numerical tests

Abstract

The fast growth of communication technology within the concept of smart grids can provide data and control signals from/to all consumers in an online fashion. This could foster more participation for end-user customers. These types of customers do not necessarily have powerful prediction tools or capability of storing a large amount of historical data. Besides, the relevant information is not always known a priori, while decisions need to be made fast within a very limited time. These limitations and also the novel structure of decision making, which comes from the necessities to make the decision very fast with a limited amount of information, implies a requirement for investigating a novel framework: online decision-making. In this study, we propose an online constrained convex optimization framework for operating responsive end-user electrical customers in real-time. Within this online-decision-making framework, algorithms are proposed for two cases: no prediction data is available at the moment of decision-making, and a limited number of forward time periods predictions of uncertain parameters are available. The simulation results exhibit the capability of the model to achieve considerable profits in an easy-to-implement procedure. Comprehensive numerical test cases are performed for comparison with existent alternative models.