Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

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

doi:10.1109/TSG.2018.2878757

Title: Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

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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 work 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). Finally, we demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.

Authors:

Hassan, Ali ^[1]; Mieth, Robert ^[1]; Chertkov, Michael ^[2]; Deka, Deepjyoti ^[2]; Dvorkin, Yury ^[1]

New York Univ. (NYU), NY (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: Tue Oct 30 00:00:00 EDT 2018

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New York Univ. (NYU), NY (United States)

Sponsoring Org.:: USDOE; National Science Foundation (NSF)

OSTI Identifier:: 1483530

Report Number(s):: LA-UR-18-23536
Journal ID: ISSN 1949-3053

Grant/Contract Number:: 89233218CNA000001; CMMI-1825212

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 10; Journal Issue: 5; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; optimization; voltage control; computational modeling; heuristic algorithms; load modeling; uncertainty; reactive power

Citation Formats


                    Hassan, Ali, Mieth, Robert, Chertkov, Michael, Deka, Deepjyoti, and Dvorkin, Yury. Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow.  United States: N. p., 2018. 
Web.  doi:10.1109/TSG.2018.2878757.

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                    Hassan, Ali, Mieth, Robert, Chertkov, Michael, Deka, Deepjyoti, & Dvorkin, Yury. Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow.  United States.  https://doi.org/10.1109/TSG.2018.2878757

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                    Hassan, Ali, Mieth, Robert, Chertkov, Michael, Deka, Deepjyoti, and Dvorkin, Yury. Tue .  
"Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow".  United States.  https://doi.org/10.1109/TSG.2018.2878757.  https://www.osti.gov/servlets/purl/1483530.

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@article{osti_1483530,

  title        = {Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow},

  author       = {Hassan, Ali and Mieth, Robert and Chertkov, Michael and Deka, Deepjyoti and Dvorkin, Yury},

  abstractNote = {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 work 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). Finally, we demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.},

  doi          = {10.1109/TSG.2018.2878757},

  journal      = {IEEE Transactions on Smart Grid},

  number       = 5,

  volume       = 10,

  place        = {United States},

  year         = {Tue Oct 30 00:00:00 EDT 2018},

  month        = {Tue Oct 30 00:00:00 EDT 2018}

}

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