Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

Ali Hassan, Robert Mieth, Michael Chertkov, Deepjyoti Deka, Yury Dvorkin

    Research output: Contribution to journalArticlepeer-review

    25 Citations (Scopus)

    Abstract

    Distribution system operators (DSOs) world-wide foresee a rapid roll-out of distributed energy resources. From the system perspective, their reliable and cost effective integration requires accounting for their physical properties in operating tools used by the DSO. This paper describes an decomposable approach to leverage the dispatch flexibility of thermostatically controlled loads (TCLs) for operating distribution systems with a high penetration level of photovoltaic resources. Each TCL ensemble is modeled using the Markov Decision Process (MDP). The MDP model is then integrated with a chance constrained optimal power flow that accounts for the uncertainty of PV resources. Since the integrated optimization model cannot be solved efficiently by existing dynamic programming methods or off-the-shelf solvers, this paper proposes an iterative Spatio-Temporal Dual Decomposition algorithm (ST-D2). We demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.

    Original languageEnglish
    Pages (from-to)5186-5195
    JournalIEEE Transactions on Smart Grid
    Volume10
    Issue number5
    DOIs
    Publication statusPublished - Sep 2019

    Keywords

    • Chance constraints
    • Markov decision process
    • TCL ensemble
    • dynamic programming
    • linearly solvable MDP
    • optimal power flow
    • spatio-temporal dual decomposition algorithm
    • thermostatically controlled loads
    • uncertainty

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