Designing optimal arbitrage policies for distributed energy systems in building clusters using reinforcement learning

Philip Odonkor, Kemper Lewis

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations

    Abstract

    In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.

    Original languageEnglish
    Title of host publication45th Design Automation Conference
    ISBN (Electronic)9780791859186
    DOIs
    StatePublished - 2019
    EventASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States
    Duration: 18 Aug 201921 Aug 2019

    Publication series

    NameProceedings of the ASME Design Engineering Technical Conference
    Volume2A-2019

    Conference

    ConferenceASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
    Country/TerritoryUnited States
    CityAnaheim
    Period18/08/1921/08/19

    Keywords

    • Battery storage
    • Building cluster
    • Operational strategy design
    • Reinforcement learning

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