Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment
Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy manage...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/1190103 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551099065696256 |
|---|---|
| author | Razieh Darshi Saeed Shamaghdari Aliakbar Jalali Hamidreza Arasteh |
| author_facet | Razieh Darshi Saeed Shamaghdari Aliakbar Jalali Hamidreza Arasteh |
| author_sort | Razieh Darshi |
| collection | DOAJ |
| description | Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. |
| format | Article |
| id | doaj-art-2caec9c5432a4dbabef892d07294a9e9 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-2caec9c5432a4dbabef892d07294a9e92025-02-03T06:04:51ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/1190103Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic EnvironmentRazieh Darshi0Saeed Shamaghdari1Aliakbar Jalali2Hamidreza Arasteh3School of Electrical EngineeringSchool of Electrical EngineeringSchool of Electrical EngineeringPower Systems Operation and Planning Research DepartmentMicrogrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids.http://dx.doi.org/10.1155/2023/1190103 |
| spellingShingle | Razieh Darshi Saeed Shamaghdari Aliakbar Jalali Hamidreza Arasteh Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment International Transactions on Electrical Energy Systems |
| title | Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment |
| title_full | Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment |
| title_fullStr | Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment |
| title_full_unstemmed | Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment |
| title_short | Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment |
| title_sort | decentralized reinforcement learning approach for microgrid energy management in stochastic environment |
| url | http://dx.doi.org/10.1155/2023/1190103 |
| work_keys_str_mv | AT raziehdarshi decentralizedreinforcementlearningapproachformicrogridenergymanagementinstochasticenvironment AT saeedshamaghdari decentralizedreinforcementlearningapproachformicrogridenergymanagementinstochasticenvironment AT aliakbarjalali decentralizedreinforcementlearningapproachformicrogridenergymanagementinstochasticenvironment AT hamidrezaarasteh decentralizedreinforcementlearningapproachformicrogridenergymanagementinstochasticenvironment |