Multi-objective capacity configuration optimization of an integrated energy system considering economy and environment with harvest heat

Energy Conversion and Management, Год журнала: 2022, Номер 269, С. 116116 - 116116

Опубликована: Авг. 18, 2022

Язык: Английский

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Multi-objective optimization of a solar-driven trigeneration system considering power-to-heat storage and carbon tax

Energy, Год журнала: 2022, Номер 250, С. 123756 - 123756

Опубликована: Март 23, 2022

Язык: Английский

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Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage

Energy, Год журнала: 2023, Номер 277, С. 127672 - 127672

Опубликована: Май 4, 2023

Язык: Английский

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Optimization and analysis of an integrated energy system based on wind power utilization and on-site hydrogen refueling station

International Journal of Hydrogen Energy, Год журнала: 2023, Номер 48(57), С. 21531 - 21543

Опубликована: Март 22, 2023

Язык: Английский

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Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

Expert Systems with Applications, Год журнала: 2023, Номер 240, С. 122539 - 122539

Опубликована: Ноя. 13, 2023

Язык: Английский

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Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy

Energy, Год журнала: 2023, Номер 285, С. 128673 - 128673

Опубликована: Авг. 9, 2023

Язык: Английский

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Coordinated energy management for integrated energy system incorporating multiple flexibility measures of supply and demand sides: A deep reinforcement learning approach

Energy Conversion and Management, Год журнала: 2023, Номер 297, С. 117728 - 117728

Опубликована: Окт. 6, 2023

Язык: Английский

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Design and analysis of a solar hybrid combined cooling, heating and power system: A bi-level optimization model

Energy, Год журнала: 2024, Номер unknown, С. 130362 - 130362

Опубликована: Янв. 1, 2024

Язык: Английский

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The impact and performance of carbon-supported platinum group metal electrocatalysts for fuel cells

International Journal of Electrochemical Science, Год журнала: 2024, Номер 19(4), С. 100524 - 100524

Опубликована: Март 2, 2024

Due to its high energy density and combustion temperature, hydrogen (H2) is being investigated as a potential carrier compete with replace fossil fuels, yet evolution requires the employment of efficient catalysts in liquid organics sources. These include methanol ethanol. The latter gains preference due low toxicity. To have an fuel cell system, carbon-supported platinum group metal (PGM)--based are usually employed these types exhibit superior catalytic properties. inclusion carbon supports important for reduction agglomeration PGM-based while also improving enhancing electron transfer processes. catalysts, turn, can be used fabricate conversion devices reactors such proton exchange membrane cells (PEMFCs) direct alcohol (DAFCs) name few. DAFCs, particular, more easily handled higher power output than better efficiency, lower emissions when compared other conventional technologies, simple design no moving parts, promising low-cost durability. However, system constrained by various challenges slow kinetics oxidation, crossover, anode poisoning strongly adsorbed intermediates. Thus, come into play systems. In this review, we discuss performance as-fabricated PEMFCs, DMFCs.

Язык: Английский

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Distributed energy management of multi-area integrated energy system based on multi-agent deep reinforcement learning

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 157, С. 109867 - 109867

Опубликована: Фев. 26, 2024

This paper addresses the problem of distributed energy management in multi-area integrated systems (MA-IES) using a multi-agent deep reinforcement learning approach. The MA-IES consists interconnected electric and thermal networks, incorporating renewable sources heat conversion systems. objective is to optimize operation system while minimizing operational costs maximizing utilization. We propose strategy that makes hierarchical decisions on intra-area inter-area energy. based framework, where each agent represents component or unit MA-IES. formulate as Markov Decision Process employ Q-learning with experience replay double networks train agents. proposed evaluated simulation four-area results demonstrate significant improvements compared traditional methods, higher utilization lower costs. Specifically, achieves 100% wind power, decreases by 5.53%. Furthermore, it leverages generalization capabilities respond real-time uncertainties demand power output. highlight advantages strategy, making promising solution for optimizing

Язык: Английский

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