Meeting greenhouse gas (GHG) emissions reduction targets will require a multi-pronged
approach to decarbonizing all GHG-contributing sectors, including intersectional strategies …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Variable renewable energy (VRE) and energy storage systems (ESS) are essential pillars of
any strategy to decarbonize power systems. However, there are still questions about the …

Technological advancement and supportive government policy have accelerated
investments in distributed energy resources (DERs) in buildings, demand-side management …

In recent years, analytical tools and approaches to model the costs and benefits of energy
storage have proliferated in parallel with the rapid growth in the energy storage market …

Energy storage reduces total operational costs and greenhouse gas emissions on the grid,
while enhancing resilience and renewables integration. This makes energy storage a …

While energy arbitrage from energy storage can lower power system operating costs, it can
also increase greenhouse gas emissions. If power system operations are conducted with the …

Energy storage will play a key role in the unfolding energy transition, but current market
design and the modeling efforts that inform discussions surrounding its role broadly assume …

Battery energy storage systems (BESSs) are often used for demand charge reduction
through monthly peak shaving. However, during economic analysis in the feasibility stage …

Rising peak demand is a major cause for high emissions from the electricity sector. In this
study, we investigate how different combinations of distributed energy technologies affect …