An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework

• URL: http://arxiv.org/abs/2402.01688v1
• Date: Mon, 22 Jan 2024 15:29:54 GMT
• Title: An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework
• Authors: Antonino Capillo, Enrico De Santis, Fabio Massimo Frattale Mascioli, Antonello Rizzi
• Abstract summary: In 2018, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. In this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach.
• Score: 4.4269011841945085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework.

Related papers

• 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)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Optimal Economic Gas Turbine Dispatch with Deep Reinforcement Learning [4.059196561157555]
  Three popular DRL algorithms are implemented for an economic GT dispatch problem on a case study in Alberta, Canada. Among the tested algorithms and baseline methods, Deep Q-Networks (DQN) obtained the highest rewards. We propose and implement a method to assign GT operation and maintenance cost dynamically based on operating hours and cycles.
  arXiv  Detail & Related papers  (2023-08-28T22:42:51Z)
• 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)
• A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study [1.1580136767197162]
  PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results.
  arXiv  Detail & Related papers  (2023-03-31T18:25:03Z)
• Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting [2.774390661064003]
  unpredictability of renewable energy generation poses challenges for electricity providers and utility companies. We propose a novel framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory (LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning using Deep Deterministic Policy Gradient (DDPG) algorithm.
  arXiv  Detail & Related papers  (2023-02-27T19:12:50Z)
• 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)
• A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storage [3.0839245814393728]
  We implement an online Supervised Actor-Critic (SAC) algorithm supervised with a model-based controller -- Model Predictive Control (MPC) The energy storage agent is trained with this algorithm to optimally bid while learning and adjusting to its impact on the market clearing prices. Our contribution, thus, is an online and safe SAC algorithm that outperforms the current model-based state-of-the-art.
  arXiv  Detail & Related papers  (2021-06-04T10:22:58Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• 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.