Emission-aware Energy Storage Scheduling for a Greener Grid

TL;DR

This paper proposes an emission-aware scheduling method for distributed energy storage to reduce carbon emissions in the electric grid, utilizing robust optimization and neural network load forecasting.

Contribution

It introduces a novel optimization framework for emission-aware energy storage scheduling that accounts for load prediction uncertainties and renewable intermittency.

Findings

Reduced annual carbon emissions by over 0.5 million kg

Achieved a 23.3% reduction in grid emissions

Demonstrated effectiveness on real load data from 1,341 homes

Abstract

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.