Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow
For distribution system operators, this provides a method to leverage load flexibility for reliable and cost-effective integration of distributed energy resources.
This paper integrates Markov Decision Process models of thermostatically controlled loads with chance-constrained optimal power flow to manage distribution systems with high PV penetration, and proposes an iterative Spatio-Temporal Dual Decomposition algorithm to solve the resulting optimization problem efficiently. The method is demonstrated on the IEEE 33-bus test system.
Distribution system operators (DSO) 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 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 the 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 usefulness of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.