Journal of Energy Storage, Год журнала: 2024, Номер 109, С. 115220 - 115220
Опубликована: Дек. 31, 2024
Язык: Английский
Journal of Energy Storage, Год журнала: 2024, Номер 109, С. 115220 - 115220
Опубликована: Дек. 31, 2024
Язык: Английский
Journal of Cleaner Production, Год журнала: 2023, Номер 428, С. 139366 - 139366
Опубликована: Окт. 17, 2023
Язык: Английский
Процитировано
32Electric Power Systems Research, Год журнала: 2024, Номер 232, С. 110374 - 110374
Опубликована: Апрель 8, 2024
Язык: Английский
Процитировано
12Energy Conversion and Management, Год журнала: 2024, Номер 315, С. 118768 - 118768
Опубликована: Июль 5, 2024
Язык: Английский
Процитировано
11Journal of Energy Storage, Год журнала: 2024, Номер 84, С. 110940 - 110940
Опубликована: Фев. 21, 2024
Язык: Английский
Процитировано
10Journal of Renewable and Sustainable Energy, Год журнала: 2025, Номер 17(1)
Опубликована: Янв. 1, 2025
The diverse load profile formation and utility preferences of multitype electricity users challenge real-time pricing (RTP) welfare equilibrium. This paper designs an RTP strategy for smart grids. On the demand side, it constructs functions reflecting user characteristics uses multi-agents different interests. Considering industrial users, small-scale microgrids, distributed generation, battery energy storage systems are included. Based on supply interest, a online multi-agent reinforcement learning (RL) algorithm is proposed. A bi-level stochastic model in Markov decision process framework optimizes strategy. Through information exchange, adaptive scheme balances interest achieves optimal strategies. Simulation results confirm effectiveness proposed method peak shaving valley filling. Three fluctuation scenarios compared, showing algorithm's adaptability. findings reveal potential RL-based resource allocation benefits Innovations modeling, construction, application have theoretical practical significance market research.
Язык: Английский
Процитировано
2Journal of Energy Storage, Год журнала: 2023, Номер 75, С. 109747 - 109747
Опубликована: Ноя. 16, 2023
Язык: Английский
Процитировано
24Applied Energy, Год журнала: 2023, Номер 351, С. 121890 - 121890
Опубликована: Сен. 12, 2023
Язык: Английский
Процитировано
21Computers & Electrical Engineering, Год журнала: 2024, Номер 117, С. 109275 - 109275
Опубликована: Май 8, 2024
Язык: Английский
Процитировано
8Deleted Journal, Год журнала: 2025, Номер 2(1), С. 100073 - 100073
Опубликована: Янв. 1, 2025
<p>The development of a national energy base and modern system in the Beibu Gulf Guangxi requires an innovative system. General only consists single marine resource, this work introduces "Offshore Wind Energy—Multi-Marine Resources" integration system, which distinctively centers on offshore wind power while incorporating seawater hydrogen production, pumped storage, desalination, aquaculture, other resource utilization complexes. Its potential challenges during its future construction solutions for global optimization that need to be addressed are as follows: 1) creating high-precision speed prediction model across multiple scales; 2) developing under uncertainties; 3) proposing resilience assessment method systems subjected unconventional external shocks. This can contribute comprehensive resources establishment Province around world.</p>
Язык: Английский
Процитировано
1Energies, Год журнала: 2023, Номер 16(4), С. 1802 - 1802
Опубликована: Фев. 11, 2023
Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker complete information regarding Pareto front, as well a clear overview various trade-offs are involved in problem. The selection such representative set is, and itself, problem must take into consideration number choices to show uniformity representation and/or coverage order ensure quality solution. In this study, day-ahead scheduling been transformed optimization due inclusion objectives, operating cost multi-energy multi-microgrids (MMGs) profit Distribution Company (DISCO). purpose proposed system determine best operation combined heat power (CHP) unit, gas boiler, energy storage, demand response program, transaction electricity natural (NG). Electricity traded by MGs DISCO at prices dynamic fixed, respectively. Through scenario generation probability density functions, uncertainties wind speed, solar irradiation, electrical, demands have considered. By using mixed-integer linear programming (MILP) for reduction, high generated scenarios significantly reduced. ɛ-constraint approach was used solved nonlinear (MINLP) obtain solution meets needs both these objective functions.
Язык: Английский
Процитировано
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