Global Demand Response Status: Potentials, Barriers, and Solutions

Abstract

Aging infrastructures, growing demand, climate changes, and limited budgets for reinforcements force electric energy industry to utilize the existing system more efficiently and wisely. To do so, electric energy systems across the world are becoming smart, decarbonized, and digitalized. This is truly the era of smart energy systems. In smart energy systems, there is a two-way interaction between energy and service suppliers and different groups of consumers. This interaction transforms passive consumers into active players in the electric energy systems. The programs activating consumers are generally known as demand response (DR) programs. In DR programs, voluntary changes in electricity usage in response to signals from the supply side are encouraged. DR provides electric energy systems with an opportunity to modify the normal consumption pattern when electricity procurement prices are higher, or service reliability is jeopardized. It also provides consumers with the power to better manage their electricity bills. The planning and operation of smart energy system incorporating DR is a complex task as it involves several decision variables, constraints, and nonlinear objective functions. In recent years, it has been seen that the use of artificial intelligence (AI)-based and machine learning (ML)-assisted methods for mitigating the complexity is trending. This chapter will delve into AI-based and ML-based methods for solving the planning and operation of smart energy systems incorporating DR. This chapter firstly provides valuable explanations on the background needed for readers to better understand the concept. Then, the global status of DR programs is followed by a review of AI-based and ML-based methods for solving planning and operation of smart power systems incorporating DR. Finally, two case studies showcasing sample applications of AI-assisted methods in enabling DR potentials in electric energy systems are presented.