{"type":"ScholarlyArticle","title":"Efficient Cost-Minimization Schemes for Electrical Energy Demand Satisfaction by Prosumers in Microgrids with Battery Storage Capabilities","authors":[{"name":"Laura Codazzi","slug":"laura-codazzi","given":"Laura","family":"Codazzi","affiliation":"Hamburg University of Technology , Institute for Algorithms and Complexity , Hamburg , Germany"},{"name":"Gergely Csáji","slug":"gergely-csaji","given":"Gergely","family":"Csáji","affiliation":"HUN-REN Centre for Economic and Regional Studies , Budapest , Hungary"},{"name":"Matthias Mnich","slug":"matthias-mnich","given":"Matthias","family":"Mnich","affiliation":"Hamburg University of Technology , Institute for Algorithms and Complexity , Hamburg , Germany"}],"abstract":"We introduce and study various models for satisfying electrical energy demands of prosumers in a microgrid, while optimizing their costs.\r\nEach prosumer has individual demands of electrical energy, which can vary day-by-day, and which they can satisfy by either generating electrical energy through a self-operated mini power plant like a solar panel, through buying from an external energy provider, such as the main grid or by trading with other prosumers.\r\nOur models take into account two key aspects motivated by real-life scenarios: first, we consider a daily volatility of prices for buying and selling the energy, and second, the possibility to store the self-generated energy in a battery of finite capacity to be either self-consumed or sold to other prosumers in the future.\r\nWe provide a thorough complexity analysis, as well as efficient algorithms, so that prosumers can minimize their overall cost over the entire time horizon. As a byproduct, we also solve a new, generalized version of the KNAPSACK problem which may be of independent interest. \r\nWe complement our theoretical findings by extensive experimental evaluations on realistic data sets.","doi":"10.24963/ijcai.2024/207","url":"https://ijcai.org/proceedings/2024/207","pdf":"https://ijcai.org/proceedings/2024/0207.pdf","markdown":"https://ijcai.org/proceedings/2024/207.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/207","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":207,"pages":{"first":1873,"last":1880},"keywords":["Planning and Scheduling: PS: Planning under uncertainty","Planning and Scheduling: PS: Planning algorithms","Constraint Satisfaction and Optimization: CSO: Constraint optimization problems","Planning and Scheduling: PS: Theoretical foundations of planning"],"videos":[],"references":[{"position":1,"raw":"[Angenendt et al., 2018] Georg Angenendt, Sebastian Zurmühlen, Hendrik Axelsen, and Dirk Uwe Sauer. 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