Energy Reports, Год журнала: 2024, Номер 13, С. 914 - 928
Опубликована: Дек. 30, 2024
Язык: Английский
Energy, Год журнала: 2024, Номер 300, С. 131557 - 131557
Опубликована: Май 5, 2024
Язык: Английский
Процитировано
9
Energy and Buildings, Год журнала: 2025, Номер 331, С. 115408 - 115408
Опубликована: Фев. 2, 2025
Язык: Английский
Процитировано
0
International Journal of Electrical Power & Energy Systems, Год журнала: 2025, Номер 165, С. 110509 - 110509
Опубликована: Фев. 4, 2025
Процитировано
0
Energies, Год журнала: 2025, Номер 18(3), С. 749 - 749
Опубликована: Фев. 6, 2025
This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, heating networks. The need for a holistic approach manage these interconnected is driven by increasing complexity energy demands imperative adhere stringent environmental standards. proposed methodology leverages LSTM dynamic state estimation, enabling real-time accurate predictions operational states across different allows flows adapting fluctuations in demand supply with high precision, traditional static models are unable do. By comprehensively modeling unique characteristics interdependencies networks, ensures system operates efficiently, remains stable under varying loads, meets regulatory compliance emissions. A synthesized case study simulating operation MES—including IEEE 123-bus modeled Belgian high-caloric gas network, Danish district system—illustrates effectiveness model. results indicate significant improvements efficiency, reductions emissions, enhanced stability. Key contributions this development multi-layered addresses dynamics MESs, integration within strategy, robust validation LSTM-based model against simulated anomalies real-world scenarios.
Язык: Английский
Процитировано
0
Energies, Год журнала: 2025, Номер 18(9), С. 2188 - 2188
Опубликована: Апрель 25, 2025
The rapid integration of renewable energy sources and the increasing complexity demands necessitate advanced strategies for optimizing multi-region systems. This study investigates coordinated management interconnected parks by incorporating wind power, demand response (DR) mechanisms, storage A comprehensive optimization framework is developed to enhance sharing among parks, leveraging demand-side flexibility integration. Simulation results demonstrate that proposed approach significantly improves system efficiency balancing supply-demand mismatches reducing reliance on external power sources. Compared conventional methods, DR capabilities industrial commercial loads have increased 8.08% 6.69%, respectively, which primarily due enhanced utilization optimized deployment. inclusion contributed improved flexibility, enabling a more resilient exchange framework. highlights potential collaborative in multi-area systems provides pathway future research explore control algorithms additional
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Апрель 28, 2025
Addressing the challenges of integrating photovoltaic (PV) systems into power grids, this research develops a dual-phase optimization model incorporating deep learning techniques. Given fluctuating nature solar energy, study employs Generative Adversarial Networks (GANs) to simulate diverse and high-resolution energy generation-consumption patterns. These synthetic scenarios are subsequently utilized within real-time adaptive control framework, allowing for dynamic adjustments in operational strategies that enhance both efficiency grid stability. By leveraging approach, has demonstrated substantial improvements economic environmental performance, achieving up 96% while reducing expenses by 20%, lowering carbon emissions 30%, cutting annual downtime half (from 120 60 h). Through scenario-driven predictive analysis, framework provides data-driven systems, strengthening their resilience against renewable intermittency. Furthermore, integration AI-enhanced forecasting techniques ensures proactive decision-making, supporting sustainable transition toward greener solutions.
Язык: Английский
Процитировано
0
Journal of Renewable and Sustainable Energy, Год журнала: 2025, Номер 17(3)
Опубликована: Май 1, 2025
The integration of solar electric vehicles (SEVs) into microgrids, particularly those enriched with photovoltaic (PV) systems, presents unique challenges due to the inherent variability in energy and dynamic consumption patterns SEVs. This study aims address these complexities by developing an advanced operational framework that enhances management flows within leveraging capabilities modern artificial intelligence. Utilizing a deep double Q-network (DDQN), this research introduces sophisticated method dynamically adapt fluctuations generation SEV demands, ensuring efficiency, sustainability, grid stability. methodology encompasses detailed mathematical modeling generation, consumption, storage dynamics, integrated environmental economic constraints simulate realistic microgrid scenarios. DDQN is employed optimize distribution strategies real-time, based on predictive analytics responsive control mechanisms. approach not only copes stochastic nature renewable sources usage but also capitalizes aspects improve overall performance. paper contributes novel management, for systems incorporating SEVs PV generation. By optimizing interplay between power availability charging requirements, provides strategic insights can guide infrastructure investments tactics, promoting more efficient economically viable systems. proposed models are expected significantly advance field paving way development smarter, resilient urban environments.
Язык: Английский
Процитировано
0
Energies, Год журнала: 2024, Номер 17(23), С. 6184 - 6184
Опубликована: Дек. 8, 2024
To address the challenges of multi-energy coupling decision-making caused by complex interactions and significant conflicts interest among multiple entities in integrated energy systems, an management strategy for systems with electricity, heat, hydrogen storage is proposed. First, based on relationship flows, architecture system designed, mathematical model main components established. Second, evaluation indexes three dimensions, including device life, load satisfaction rate, new utilization are designed to fully characterize economy, stability, environmental protection during operation. Then, improved radar chart considering multi-evaluation index comprehensive optimization established, adaptability function constructed sector area perimeter. Combined operation requirements electric–thermal–hydrogen system, constraint conditions determined. Finally, effectiveness verified examples. The proposed can obtain optimal decision instructions under different objectives changing weight indexes, while avoiding huge space secondary problems non-inferior solutions conventional optimization, has multi-scenario adaptability.
Язык: Английский
Процитировано
3
Frontiers in Energy Research, Год журнала: 2024, Номер 12
Опубликована: Окт. 7, 2024
Introduction The increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability unpredictability. Traditional fossil-fuel-based systems are ill-equipped maintain stability cost-effectiveness in this evolving landscape. Methods This study presents a novel framework that integrates robust optimization with online learning dynamically manage uncertainties generation. Robust ensures system resilience under worst-case scenarios, while the component continuously updates operational strategies based on real-time data. was tested using an IEEE 30-bus test varying levels integration. Results Simulation results show proposed reduces costs by up 12% enhances reliability 1.4% increases from 10% 50%. Additionally, need for reserve is significantly reduced, particularly conditions high outputs. Discussion provides dynamic adaptive solution sustainable management transmission systems. approach not only improves economic environmental outcomes but also grid stability, making it promising strategy addressing posed reliance energy.
Язык: Английский
Процитировано
2
Frontiers in Energy Research, Год журнала: 2024, Номер 12
Опубликована: Июль 19, 2024
With the growing concern for environment, sustainable development centred on a low-carbon economy has become unifying pursuit energy industry. Integrated systems (IES) that combine multiple sources such as electricity, heat and gas are essential to facilitate consumption of renewable reduction carbon emission. In this paper, turbine (GT), capture storage (CCS) power-to-gas (P2G) device introduced construct new coupling model, GT-CCS-P2G (GCCP), which is applied integrated electrical system (IEGS). Multi-agent soft actor critic (MASAC) applies historical trajectory representations, parameter spatial techniques deep densification frameworks reinforcement learning reducing detrimental effects time-series data decisional procedure. The scheduling problem IEGS redefined Markov game, addressed by adopting low economic control framework based MASAC with minimum operating cost emission optimization objectives. To validate rationality effectiveness proposed model MASAC, paper simulates analyses in PJM-5 node seven nodes natural system.
Язык: Английский
Процитировано
0