Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO

• URL: http://arxiv.org/abs/2412.20946v1
• Date: Mon, 30 Dec 2024 13:38:31 GMT
• Title: Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO
• Authors: Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč,
• Abstract summary: This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources efficiently.
• Score: 5.195669033269619
• License:
• Abstract: This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate designed net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that FedTRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tunning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids.

Related papers

• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems [3.716849174391564]
  We study the energy-sharing problem in a system consisting of several DERs. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric.
  arXiv  Detail & Related papers  (2024-05-29T11:54:11Z)
• EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management [0.0]
  This paper introduces EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management framework. It enables user-centric and multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. The efficacy of EnergAIze was evaluated through case studies employing the CityLearn simulation framework.
  arXiv  Detail & Related papers  (2024-04-02T23:16:17Z)
• Generalized Policy Learning for Smart Grids: FL TRPO Approach [6.058785372434129]
  Federated Learning (FL) can train models on heterogeneous datasets while maintaining data privacy. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs.
  arXiv  Detail & Related papers  (2024-03-27T10:47:06Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning [6.519522573636577]
  Multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization. This paper studies a multi-agent deep reinforcement learning (MADRL) control method for energy management of the multi-mode PHEV. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL system can save up to 4% energy compared to the single-agent learning system and up to 23.54% energy compared to the conventional rule-based system.
  arXiv  Detail & Related papers  (2023-03-16T21:31:55Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem [4.338938227238059]
  Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO$. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer.
  arXiv  Detail & Related papers  (2021-03-10T23:01:22Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.