Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning

2023-01-31 · Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar

This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RES) and flexible customers within a certain power system zone, endowed with an agent to match. The key idea is to pick the IHR zones so that the power loss effects within the IHRs can be neglected. This simplifies the overall matching problem into independent IHR-level matching problems and an upper-level optimal power flow problem to meet the IHR-level upstream flow requirements while respecting the grid constraints. Within each IHR, the agent employs a scalable Deep Reinforcement Learning algorithm to identify matching solutions such that the customer's service constraints are met. The central agent then solves an optimal power flow problem with the IHRs as the nodes, with their active power flow and reactive power {capacities}, and grid constraints to scalably determine the final flows such that matched power can be delivered to the extent the grid constraints are satisfied. The proposed framework is implemented on a test power distribution system, and multiple case studies are presented to substantiate the welfare efficiency of the proposed solution and the satisfaction of the grid and customers' servicing constraints.

📄 PDF Abstract BibTeX arXiv:2301.13796

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Golden Queue Managers 설명 없음

Test 설명 없음

AbideGym: Turning Static RL Worlds into Adaptive Challenges

2025-09-25 · Abi Aryan, Zac Liu, Aaron Childress arxiv

Agents trained with reinforcement learning often develop brittle policies that fail when dynamics shift, a problem amplified by static benchmarks. AbideGym, a dynamic MiniGrid wrapper, introduces agent-aware perturbation…

Reinforcement Learning Continual Learning

Scalable Variational Inference for Dynamical Systems

2017-05-19 · NeurIPS 2017 12 · Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann

Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach, which, for fully observable systems is at times competitive with …

Numerical Integration Variational Inference

GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments

2025-10-13 · Ahmed Alanazi, Duy Ho, Yugyung Lee arxiv

Robots navigating dynamic, cluttered, and semantically complex environments must integrate perception, symbolic reasoning, and spatial planning to generalize across diverse layouts and object categories. Existing methods…

RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations

2025-03-29 · Enrico Marchesini, Benjamin Donnot, Constance Crozier, Ian Dytham 외

Reinforcement learning (RL) can transform power grid operations by providing adaptive and scalable controllers essential for grid decarbonization. However, existing methods struggle with the complex dynamics, aleatoric u…

Benchmarking reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control

2023-12-07 · Han Xu, Jialin Zheng, Guannan Qu

This paper addresses the challenges associated with decentralized voltage control in power grids due to an increase in distributed generations (DGs). Traditional model-based voltage control methods struggle with the rapi…

Multi-agent Reinforcement Learning