Dylan Wald
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View article: Adaptive Computing for Scale-Up Problems
Adaptive Computing for Scale-Up Problems Open
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocat…
View article: Multi-Fidelity Modeling and Control for Building Temperature Control
Multi-Fidelity Modeling and Control for Building Temperature Control Open
The ability to control energy loads such as a building's heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control …
View article: Adaptive Computing for Scale-up Problems
Adaptive Computing for Scale-up Problems Open
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocat…
View article: Grid-Interactive Electric Vehicle and Building Coordination Using Coupled Distributed Control: Preprint
Grid-Interactive Electric Vehicle and Building Coordination Using Coupled Distributed Control: Preprint Open
As an increasing number of controllable devices are introduced onto the grid, they can individually provide ancillary services in support of grid stability. However, the goals of each device differ due to their type and individual objectiv…
View article: PowerGridworld
PowerGridworld Open
We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training framewo…
View article: PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems Open
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training framewor…
View article: PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07]
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07] Open
NREL's PowerGridworld provides a modular simulation environment for training heterogenous, grid-aware, multi-agent reinforcement learning (RL) policies at scale. The package enables the user to create component gym environments that can be…