Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

TL;DR

This paper presents a novel method for integrating thermostatically controlled loads into distribution system operation with high photovoltaic penetration, using a Markov Decision Process and a new iterative algorithm for efficient optimization.

Contribution

It introduces an integrated optimization model combining TCL flexibility with chance-constrained power flow and proposes the ST-D2 algorithm for solving it efficiently.

Findings

Effective modeling of TCL ensembles with MDPs.

Successful application of the method on IEEE 33-bus system.

Enhanced integration of distributed energy resources.

Abstract

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.