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Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the fl...

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Autores principales: Sang, Jinsong, Sun, Hongbin, Kou, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950638/
https://www.ncbi.nlm.nih.gov/pubmed/35336427
http://dx.doi.org/10.3390/s22062256
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author Sang, Jinsong
Sun, Hongbin
Kou, Lei
author_facet Sang, Jinsong
Sun, Hongbin
Kou, Lei
author_sort Sang, Jinsong
collection PubMed
description As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.
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spelling pubmed-89506382022-03-26 Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side Sang, Jinsong Sun, Hongbin Kou, Lei Sensors (Basel) Article As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics. MDPI 2022-03-14 /pmc/articles/PMC8950638/ /pubmed/35336427 http://dx.doi.org/10.3390/s22062256 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sang, Jinsong
Sun, Hongbin
Kou, Lei
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title_full Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title_fullStr Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title_full_unstemmed Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title_short Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
title_sort deep reinforcement learning microgrid optimization strategy considering priority flexible demand side
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950638/
https://www.ncbi.nlm.nih.gov/pubmed/35336427
http://dx.doi.org/10.3390/s22062256
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