Optimal Ensemble Control of Loads in Distribution Grids with Network Constraints

TL;DR

This paper presents a stochastic optimal control framework for aggregating thermostatically controlled loads in distribution grids, ensuring system constraints are met while accounting for load uncertainty and enabling demand response participation.

Contribution

It introduces a network-constrained, open-loop stochastic control model using Markov Processes and a novel Spatio-Temporal Dual Decomposition algorithm for coordinated load management.

Findings

Effective aggregation of TCLs with Markov Processes demonstrated.

The ST-D2 algorithm efficiently solves the multi-period power flow optimization.

Results show improved load control while maintaining grid constraints.

Abstract

Flexible loads, e.g. thermostatically controlled loads (TCLs), are technically feasible to participate in demand response (DR) programs. On the other hand, there is a number of challenges that need to be resolved before it can be implemented in practice en masse. First, individual TCLs must be aggregated and operated in sync to scale DR benefits. Second, the uncertainty of TCLs needs to be accounted for. Third, exercising the flexibility of TCLs needs to be coordinated with distribution system operations to avoid unnecessary power losses and compliance with power flow and voltage limits. This paper addresses these challenges. We propose a network-constrained, open-loop, stochastic optimal control formulation. The first part of this formulation represents ensembles of collocated TCLs modelled by an aggregated Markov Process (MP), where each MP state is associated with a given power consumption or production level. The second part extends MPs to a multi-period distribution power flow optimization. In this optimization, the control of TCL ensembles is regulated by transition probability matrices and physically enabled by local active and reactive power controls at TCL locations. The optimization is solved with a Spatio-Temporal Dual Decomposition (ST-D2) algorithm. The performance of the proposed formulation and algorithm is demonstrated on the IEEE 33-bus distribution model.