Abstract
Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.
Highlights
The growing trend of Renewable Energy Resources (RES), and their rapid development in recent years, poses key challenges for power sys tem operators
Electricity markets are split between retail markets, in which elec tricity retailers contract the supply of electricity with the end-users, and wholesale markets, in which retailers, suppliers, producers, grid opera tors and third parties as aggregators interact to allow retailers to supply their customers while maintaining the integrity of the grid
artificial neural networks (ANNs) could fall under both categories of machine learning and nature-inspired Artificial Intelligence (AI) ap proaches, we present them in this review as a distinct category since they are heavily utilised in Demand Response (DR) applications
Summary
The growing trend of Renewable Energy Resources (RES), and their rapid development in recent years, poses key challenges for power sys tem operators To accommodate this new energy generation mix, energy systems are forced to undergo a rapid transformation. The majority of RES are characterised by variability and intermittency, making it diffi cult to predict their power output (i.e. they depend on solar irradiation or wind speed) These attributes make more challenging the operation and management of power systems because more flexibility is needed to safeguard their normal operation and stability [1]. Power systems operation is entering the digital era New technologies, such as Internet-of-Things (IoT), real-time monitoring and control, peer-to-peer energy and smart contracts [3], as well as cyber-security of energy assets can result in power systems which are more efficient, secure, reliable, resilient, and sustainable [4]. AI can alleviate the load on humans by assisting and partially automating the decision-making, as well as automating the scheduling and control of the multitude of devices used
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
