Optimal Ensemble Control of Loads in Distribution Grids with Network Constraints
For power system operators, this work provides a method to integrate flexible loads into demand response while respecting distribution network constraints, addressing a practical bottleneck in real-world implementation.
This paper proposes a network-constrained stochastic optimal control formulation for aggregating thermostatically controlled loads (TCLs) in demand response programs, addressing challenges of aggregation, uncertainty, and distribution network constraints. The method is demonstrated on the IEEE 33-bus system, showing effective coordination of TCL flexibility with distribution operations.
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.