Co-Optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering

TL;DR

This paper presents a linear-complexity co-optimization algorithm for managing distributed energy resources in prosumers under net energy metering, significantly reducing computation costs while maintaining near-optimal performance.

Contribution

It introduces a novel relaxation-projection approach to solve a stochastic dynamic program with linear complexity, enabling efficient energy management in prosumer systems.

Findings

Orders of magnitude reduction in computation costs

Significant decrease in optimality gap

Effective management of renewable and storage resources

Abstract

The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system co-optimizes the consumptions and battery storage based on locally available stochastic renewables by solving a stochastic dynamic program that maximizes the expected operation surplus. To circumvent the exponential complexity of the dynamic program solution, we propose a closed-form and linear computation complexity co-optimization algorithm based on a relaxation-projection approach to a constrained stochastic dynamic program. Sufficient conditions for optimality for the proposed solution are obtained. Numerical studies demonstrate orders of magnitude reduction of computation costs and significantly reduced optimality gap.