Real-time power system dispatch scheme using grid expert strategy-based imitation learning DOI Creative Commons
Siyang Xu, Jiebei Zhu,

Bingsen Li

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 161, P. 110148 - 110148

Published: July 29, 2024

Language: Английский

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles DOI Creative Commons
Reza Sepehrzad, Amir Saman Godazi Langeroudi, Amin Khodadadi

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105352 - 105352

Published: March 18, 2024

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable sources uncertainties and power fluctuations. Energy of active frequency control approach is based on probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The formulated with deep Markov decision process solved by soft actor-critic algorithm. NMG local controller (NMGLC) provides information such as frequency, power, generation data, status electric vehicle's battery storage system to central (NMGCC). Then NMGCC calculates support IPWFNN-DRLA sends results NMGLC. model developed a continuous problem-solving space two structures offline training decentralized distributed operation. For this purpose, each has agent (NMGCA) IPWFNN algorithm, NMGCA online back-propagation demonstrates computation accuracy exceeding 98%, along 7.82% reduction computational burden 61.1% time compared alternative methods.

Language: Английский

Citations

27
Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach DOI Creative Commons

Atefeh Hedayatnia,

Javid Ghafourian,

Reza Sepehrzad

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 160, P. 110142 - 110142

Published: July 18, 2024

Given the significant challenges posed by vast and diverse data in energy management, this study introduces a two-stage approach: optimal management system (OEMS) dynamic real-time operation (DRTOP). These stages employ multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating exchange costs through interactions networked microgrid (NMG) market. The primary objectives include minimizing distribution operator (DSO) cost optimizing exchanged power between DSO NMG, transmission losses secondary MG's cost, use of renewable resources (RER) storage systems (ESS), with main grid and, risk analysis. OEMS&DRTOP model is developed based on Stackelberg game theory DRL structure. two offline online distributed phases computational burden, time, process. This study's results show high efficiency presented approach price uncertainty, losses, RER ESSs participation. In addition, regarding load, proposed concept demonstrates 12.9% reduction compared dueling Q-network method 17% method. Also 17.13% 25.6%

Language: Английский

Citations

19
Demand Response-based Multi-Layer Peer-to-Peer Energy Trading Strategy for Renewable-powered Microgrids with Electric Vehicles DOI
Reza Sepehrzad, Amir Saman Godazi Langeroudi, Ahmed Al‐Durra

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135206 - 135206

Published: Feb. 1, 2025

Language: Английский

Citations

4
Reliable operation of reconfigurable smart distribution network with real-time pricing-based demand response DOI

Ramin Borjali Navesi,

Ahad Faraji Naghibi,

Hamidreza Zafarani

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 241, P. 111341 - 111341

Published: Dec. 12, 2024

Language: Английский

Citations

11
A Multi-Agent Deep Reinforcement Learning Paradigm to Improve the Robustness and Resilience of Grid Connected Electric Vehicle Charging Stations against the Destructive Effects of Cyber-attacks DOI
Reza Sepehrzad, Amin Khodadadi,

Sara Adinehpour

et al.

Energy, Journal Year: 2024, Volume and Issue: 307, P. 132669 - 132669

Published: July 30, 2024

Language: Английский

Citations

9
Game theory-based peer-to-peer energy storage sharing for multiple bus charging stations: A real-time distributed cooperative framework DOI
Jinkai Shi, Weige Zhang, Yan Bao

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 109, P. 115148 - 115148

Published: Jan. 1, 2025

Language: Английский

Citations

1
Research on regulation strategy of integrated energy system based on game theory and divide-and-conquer algorithm DOI
Yanjuan Wu,

Pengfei Jin,

Qing Li

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134860 - 134860

Published: Feb. 1, 2025

Language: Английский

Citations

1
Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids DOI
Jhon Montano,

Juan Pablo Guzmán-Rodríguez,

José Mena Palomeque

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 96, P. 112708 - 112708

Published: June 26, 2024

Language: Английский

Citations

4
Design optimization of community energy systems based on dual uncertainties of meteorology and load for robustness improvement DOI

Kai Xue,

Jinshi Wang, Shuo Zhang

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 232, P. 120956 - 120956

Published: July 18, 2024

Language: Английский

Citations

4
Optimal sizing and operation of microgrid considering renewable energy uncertainty based on scenario generation DOI
Dingding Hu,

Yinchao Fan,

Wenbin Shao

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 109, P. 115174 - 115174

Published: Dec. 30, 2024

Language: Английский

Citations

4