Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Abstract

Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.