Related papers: Stochastic Long-Term Joint Decarbonization Plannin…
Optimizing car sharing systems under demand uncertainty is an emerging problem for ensuring profitable and sustainable operations of these services while taking into account quality of service concerns. With the increasing adoption of…
We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation. Under a cost-minimizing framework, the EMS of renewable-colocated data center (RCDC) co-optimizes AI workload…
Data centers are the building blocks of IT business organizations providing the capabilities of centralized repository for storage, management, networking and dissemination of data. With the rapid increase in the capacity and size of data…
The power sector is responsible for 32 percent of global greenhouse gas emissions. Data centers and cryptocurrencies use significant amounts of electricity and contribute to these emissions. Demand-side flexibility of data centers is one…
This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and…
Carbon matching aims to improve corporate carbon accounting by tracking emissions rather than energy consumption and production. We present a mathematical derivation of carbon matching using marginal emission rates, where the unit of…
Large scale grid expansion planning studies are essential to rapidly and efficiently decarbonizing the electricity sector. These studies help policy makers and grid participants understand which renewable generation, storage, and…
This study investigates the transformation of energy models to align with machine learning requirements as a promising tool for optimizing the operation of combined cycle power plants (CCPPs). By modeling energy production as a function of…
Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as…
Large data centers are being deployed in the U.S. at an unprecedented rate, introducing significant flexible load potential. A portion of data center workloads - best-effort (BE) jobs - can be scheduled flexibly to reduce power system…
Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both…
The increasing demand for Artificial Intelligence (AI) computing poses significant environmental challenges, with both operational and embodied carbon emissions becoming major contributors. This paper presents a carbon-aware holistic…
Energy infrastructure planning under uncertainty has become increasingly complex as electrification, interdependence between energy carriers, decarbonization, and extreme weather events reshape long-term investment decisions. This paper…
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands,…
We consider joint energy storage management and load scheduling at a residential site with integrated renewable generation. Assuming unknown arbitrary dynamics of renewable source, loads, and electricity price, we aim at optimizing the load…
The increasing number of gas-fired units has significantly intensified the coupling between power and gas networks. Traditionally, the nonlinearity and nonconvexity in gas flow equations, together with renewable-induced stochasticity,…
Decentralized energy management is of paramount importance in smart microgrids with renewables for various reasons including environmental friendliness, reduced communication overhead, and resilience to failures. In this context, the…
Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy…
In this study, a stochastic power management strategy for in-wheel motor electric vehicles (IWM-EV) is proposed to reduce the energy consumption and increase the driving range by considering the unpredictable nature of the driving power…
Multi-energy systems can provide a constant level of service to end-use energy demands, while deriving delivered energy from a variety of primary/secondary energy sources. This fuel-switching capability can be used to reduce operating…