Joint Scheduling of DER under Demand Charges: Structure and Approximation

TL;DR

This paper develops a threshold-based control policy for scheduling distributed energy resources under demand charges, introduces an efficient approximation algorithm, and demonstrates its effectiveness through extensive simulations.

Contribution

It analytically characterizes the optimal control policy structure and proposes a scalable approximation algorithm for complex DER scheduling problems.

Findings

The optimal policy has a threshold-based structure.

The proposed algorithm scales linearly with the scheduling horizon.

It outperforms benchmarks in simulation, achieving small solution gaps.

Abstract

We study the joint scheduling of behind-the-meter distributed energy resources (DERs), including flexible loads, renewable generation, and battery energy storage systems, under net energy metering tariffs with demand charges. The problem is formulated as a stochastic dynamic program aimed at maximizing expected operational surplus while accounting for renewable generation uncertainty. We analytically characterize the optimal control policy and show that it admits a threshold-based structure. However, due to the strong temporal coupling of the storage and demand charge constraints, the number of conditional branches in the policy scales combinatorially with the scheduling horizon, as it requires a look-ahead over future states. To overcome the high computational complexity in the general formulation, an efficient approximation algorithm is proposed, which searches for the peak demand under a mildly relaxed problem. We show that the algorithm scales linearly with the scheduling horizon. Extensive simulations using two open-source datasets validate the proposed algorithm and compare its performance against different DER control strategies, including a reinforcement learning-based one. Under varying storage and tariff parameters, the results show that the proposed algorithm outperforms various benchmarks in achieving a relatively small solution gap compared to a theoretical upper bound.