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Found 14 papers, 0 papers with code
Federated Learning Assisted Distributed Energy Optimization
The increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents).
Equitable Coordination in Multi-agent Power Systems: Impacts of Computation Granularity
The growing integration of distributed energy resources drives the centralized power system towards a decentralized multi-agent network.
Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping
The evolution towards a more distributed and interconnected grid necessitates large-scale decision-making within strict temporal constraints.
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets
Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in wholesale energy markets.
Electric Vehicle Aggregation Review: Benefits and Vulnerabilities of Managing a Growing EV Fleet
Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption.
EVs and ERCOT: Future Adoption Scenarios and Grid Implications
The results showcase the fact that the achievement of ambitious EV market share goals will be manageable on a statewide level regarding electricity supply into 2030, but will eventually necessitate ambitious charging management strategies in order to limit the EV fleet's potentially heavy impact on peak demand looking forward into 2050 and beyond.
Learning to Solve Optimization Problems with Hard Linear Constraints
We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.
Mapping Disruption Sources in the Power Grid and Implications for Resilience
Developing models and metrics that can address resilience against disruptions is vital to ensure power grid reliability and that adequate recovery and adaptation mechanisms are in place.
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time
Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems.
A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading
Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges.
Teaching Networks to Solve Optimization Problems
In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP.
Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilities
Federated computation is an approach well suited to addressing these issues that are increasingly important for multi-agent energy systems.
Distributed, Parallel, and Cluster Computing
Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time
Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF.
Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks
This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires.
