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Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment,…
Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Centralized storage can help but is costly. An alternative is to shift demand. Cooling and heating demands…
Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if…
This thesis demonstrate the efficacy of designing and developing machine learning (ML) algorithms to selected use cases that encompass many of the outstanding challenges in the field of experimental high energy physics. Although simple…
This work considers energy management in a grid-connected microgrid which consists of multiple conventional generators (CGs), renewable generators (RGs) and energy storage systems (ESSs). A two-stage optimization approach is presented to…
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising…
The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent…
One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full…
The integration of renewable energy into the power grid is often hindered by its fragmented infrastructure, leading to inefficient utilization due to the variability of energy production and its reliance on weather conditions. Battery…
Energy storage in data centers has mainly been used as devices to backup generators during power outages. Recently, there has been a growing interest in using energy storage devices to actively shape power consumption in data centers to…
While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs,…
Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of…
The pursuit of energy transition necessitates the coordination of several technologies, including more efficient and cost-effective distributed energy resources (DERs), smart grids, carbon capture, utilization, and storage (CCUS),…
An extended use of renewable energies and a trend towards increasing energy consumption lead to challenges such as temporal and spatial decoupling of energy generation and consumption. This work evaluates the possible applications and…
Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from…
Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery storage to minimize their energy bills and maximize renewable energy usage. This has spurred the development of advanced control algorithms that maximally achieve…
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping…
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…
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or…
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets.…