Maximizing On-Bill Savings through Battery Management Optimization

TL;DR

This paper presents an optimization framework for microgrid battery management to maximize on-bill savings by reducing peak demand charges, incorporating probabilistic peak load scenarios.

Contribution

It introduces a scenario-based optimization approach that accounts for peak load uncertainties to enhance microgrid cost savings strategies.

Findings

Optimized battery management reduces peak demand charges.

Scenario-based planning improves cost savings accuracy.

Microgrid configurations significantly impact savings potential.

Abstract

In many power grids, a large portion of the energy costs for commercial and industrial consumers are set with reference to the coincident peak load, the demand during the maximum system-wide peak, and their own maximum peak load, the non-coincident peak load. Coincident-peak based charges reflect the allocation of infrastructure updates to end-users for increased capacity, the amount the grid can handle, and for improvement of the transmission, the ability to transport energy across the network. Demand charges penalize the stress on the grid caused by each consumer's peak demand. Microgrids with a local generator, controllable loads, and/or a battery technology have the flexibility to cut their peak load contributions and thereby significantly reduce these charges. This paper investigates the optimal planning of microgrid technology for electricity bill reduction. The specificity of our approach is the leveraging of a scenario generator engine to incorporate probability estimates of coincident peaks and non-coincident peaks into the optimization problem.