{"type":"ScholarlyArticle","title":"A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC","authors":[{"name":"Sanjay Chandlekar","slug":"sanjay-chandlekar","given":"Sanjay","family":"Chandlekar","affiliation":"International Institute of Information Technology (IIIT) , Hyderabad , India"},{"name":"Shweta Jain","slug":"shweta-jain","given":"Shweta","family":"Jain","affiliation":"Indian Institute of Technology Ropar , India"},{"name":"Sujit Gujar","slug":"sujit-gujar","given":"Sujit","family":"Gujar","affiliation":"International Institute of Information Technology (IIIT) , Hyderabad , India"}],"abstract":"One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers' (agents') usage patterns in response to the signal from the distribution company. Often, these signals are in the form of incentives offered to agents. This work studies the effect of incentives on the probabilities of accepting such offers in a real-world smart grid simulator, PowerTAC. We first show that there exists a function that depicts the probability of an agent reducing its load as a function of the discounts offered to them. We call it reduction probability (RP). RP  function is further parametrized by the rate of reduction (RR), which can differ for each agent. We provide an optimal algorithm, MJS--ExpResponse, that outputs the discounts to each agent by maximizing the expected reduction under a budget constraint. When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algorithm, namely MJSUCB--ExpResponse, to learn RRs. Experimentally we show that it exhibits sublinear regret. Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the PowerTAC simulator as a test bed.","doi":"10.24963/ijcai.2023/389","url":"https://ijcai.org/proceedings/2023/389","pdf":"https://ijcai.org/proceedings/2023/0389.pdf","markdown":"https://ijcai.org/proceedings/2023/389.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/389","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":389,"pages":{"first":3497,"last":3504},"keywords":["Machine Learning: ML: Applications","Multidisciplinary Topics and Applications: MDA: Energy, environment and sustainability"],"videos":[],"references":[{"position":1,"raw":"[Chandlekar et al., 2022] Sanjay Chandlekar, Bala Suraj Pedasingu, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, and Sujit Gujar. 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