Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow
- New York Univ. (NYU), NY (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New York Univ. (NYU), NY (United States)
- Sponsoring Organization:
- USDOE; National Science Foundation (NSF)
- Grant/Contract Number:
- 89233218CNA000001; CMMI-1825212
- OSTI ID:
- 1483530
- Report Number(s):
- LA-UR-18-23536
- Journal Information:
- IEEE Transactions on Smart Grid, Vol. 10, Issue 5; ISSN 1949-3053
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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