A Hierarchical Approach to Multi-Energy Demand Response: From
Electricity to Multi-Energy Applications
- URL: http://arxiv.org/abs/2005.02339v1
- Date: Tue, 5 May 2020 17:17:51 GMT
- Title: A Hierarchical Approach to Multi-Energy Demand Response: From
Electricity to Multi-Energy Applications
- Authors: Ali Hassan, Samrat Acharya, Michael Chertkov, Deepjyoti Deka and Yury
Dvorkin
- Abstract summary: This paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers.
This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.
- Score: 1.5084441395740482
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to proliferation of energy efficiency measures and availability of the
renewable energy resources, traditional energy infrastructure systems
(electricity, heat, gas) can no longer be operated in a centralized manner
under the assumption that consumer behavior is inflexible, i.e. cannot be
adjusted in return for an adequate incentive. To allow for a less centralized
operating paradigm, consumer-end perspective and abilities should be integrated
in current dispatch practices and accounted for in switching between different
energy sources not only at the system but also at the individual consumer
level. Since consumers are confined within different built environments, this
paper looks into an opportunity to control energy consumption of an aggregation
of many residential, commercial and industrial consumers, into an ensemble.
This ensemble control becomes a modern demand response contributor to the set
of modeling tools for multi-energy infrastructure systems.
Related papers
- MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids [0.0]
This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids.
It seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders.
arXiv Detail & Related papers (2023-03-15T08:42:48Z) - 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) - Renewable energy integration and microgrid energy trading using
multi-agent deep reinforcement learning [2.0427610089943387]
Multi-agent reinforcement learning is used to control a hybrid energy storage system.
Agents learn to control three different types of energy storage system suited for short, medium, and long-term storage.
Being able to trade with the other microgrids, rather than just selling back to the utility grid, was found to greatly increase the grid's savings.
arXiv Detail & Related papers (2021-11-21T21:11:00Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z) - Empowering Prosumer Communities in Smart Grid with Wireless
Communications and Federated Edge Learning [5.289693272967054]
The exponential growth of distributed energy resources is enabling the transformation of traditional consumers in the smart grid into prosumers.
We propose a multi-level pro-decision framework for prosumer communities to achieve collective goals.
In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources.
arXiv Detail & Related papers (2021-04-07T14:57:57Z) - Virtual Microgrid Management via Software-defined Energy Network for
Electricity Sharing [10.13696311830345]
This article proposes an approach to build a virtual microgrid operated as a software-defined energy network (SDEN)
The proposed cyber-physical system presumes that electrical energy is shared among its members and that the energy sharing is enabled in the cyber domain by handshakes inspired by resource allocation methods utilized in computer networks, wireless communications, and peer-to-peer Internet applications (e.g., BitTorrent)
This article concludes that the proposed solution generally complies with the existing regulations but has highly disruptive potential to organize a dominantly electrified energy system in the mid- to long-term.
arXiv Detail & Related papers (2021-02-01T06:09:40Z) - Continuous Multiagent Control using Collective Behavior Entropy for
Large-Scale Home Energy Management [36.82414045535202]
We propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid.
Our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.
arXiv Detail & Related papers (2020-05-14T16:07:55Z) - Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers.
We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid.
To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
arXiv Detail & Related papers (2020-03-02T20:32:10Z) - 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) - NeurOpt: Neural network based optimization for building energy
management and climate control [58.06411999767069]
We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification.
We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
arXiv Detail & Related papers (2020-01-22T00:51:03Z)
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.