Review on deep learning applications in frequency analysis and control of modern power system

Y Zhang, X Shi, H Zhang, Y Cao, V Terzija - International Journal of …, 2022 - Elsevier
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …

Optimal operation of integrated electricity and heat system: A review of modeling and solution methods

M Zhang, Q Wu, J Wen, Z Lin, F Fang… - … and Sustainable Energy …, 2021 - Elsevier
The optimal operation of the integrated electricity and heat systems (IEHS) can bring
environmental benefits, reduce the operational cost, and achieve high penetration levels of …

Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning …

Y Li, F Bu, Y Li, C Long - Applied Energy, 2023 - Elsevier
Multi-uncertainties from power sources and loads have brought significant challenges to the
stable demand supply of various resources at islands. To address these challenges, a …

A hybrid grasshopper optimization algorithm and harris hawks optimizer for combined heat and power economic dispatch problem

M Ramachandran, S Mirjalili, M Nazari-Heris… - … Applications of Artificial …, 2022 - Elsevier
Abstract The Combined Heat and Power Economic Dispatch (CHPED) is a real-world
optimization problem with several complex constraints that has been a topic of studies …

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

Z Yi, Y Luo, T Westover, S Katikaneni, B Ponkiya… - Applied Energy, 2022 - Elsevier
New ways to integrate energy systems to maximize efficiency are being sought to meet
carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts …

[HTML][HTML] AI agents envisioning the future: Forecast-based operation of renewable energy storage systems using hydrogen with Deep Reinforcement Learning

A Dreher, T Bexten, T Sieker, M Lehna, J Schütt… - Energy Conversion and …, 2022 - Elsevier
Hydrogen-based energy storage has the potential to compensate for the volatility of
renewable power generation in energy systems with a high renewable penetration. The …

Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects

Y Li, C Yu, M Shahidehpour, T Yang… - Proceedings of the …, 2023 - ieeexplore.ieee.org
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …

Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method

P Xu, J Zhang, T Gao, S Chen, X Wang, H Jiang… - International Journal of …, 2022 - Elsevier
With the increasing penetration rate of electric vehicles, the fast charging demands of
electric vehicles will have a significant influence on the operation of coupled power …

Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation

Q Yang, P Liu, J Zhang, N Dong - Applied Energy, 2022 - Elsevier
In power system operation, the combined heat and power economic dispatch (CHPED) is an
attractive and momentous optimization problem where the major objective is to find an …

A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

M Ramachandran, S Mirjalili, MM Ramalingam… - Expert Systems with …, 2022 - Elsevier
This paper attempts to obtain optimal generation scheduling for Combined Heat and Power
Economic Dispatch (CHPED) problems and seeking a possible solution for the global …