Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

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

Research output: Contribution to journalArticle

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 (US)
JournalIEEE Transactions on Smart Grid
DOIs
StateAccepted/In press - Jan 1 2018

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Keywords

  • Computational modeling
  • Heuristic algorithms
  • Load modeling
  • Optimization
  • Reactive power
  • Uncertainty
  • Voltage control

ASJC Scopus subject areas

  • Computer Science(all)

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