6533b7cffe1ef96bd1258484
RESEARCH PRODUCT
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
Atieh R. KhamesiSimone SilvestriAshutosh TimilsinaVincenzo Agatesubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniMathematical optimizationCorrectnessComputer Networks and CommunicationsRenewable Energy Sustainability and the EnvironmentComputer scienceHeuristicUser modelingRegretBounded rationalityReinforcement learningCoal Energy exchange Energy Sharing Systems Green products Power generation Production Reinforcement Learning Renewable energy sources User Preference Virtual Power PlantsEnergy marketHeuristicsdescription
Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}$ , with guaranteed termination and correctness. A comprehensive experimental analysis is conducted against state-of-the-art solutions using realistic datasets. Results show that including user modeling and learning provides significant performance improvements compared to state-of-the-art approaches. Specifically, the proposed algorithms result in 25% higher efficiency and 27% more transferred energy. Furthermore, the learning algorithms converge to a value less than 5% of the optimal solution in less than 3 months of learning.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-09-01 |