Advances on Application of Modern Energy Storage Technologies

Published: July 19, 2023

To combat the escalating environmental concerns and particularly increased CO 2 emission of last century, international society is remarkably accelerating adoption renewable energy sources (RES) their integration into existing grid. Because they almost do not emit any carbon dioxide or other pollutants, RES have a huge potential to reduce emissions. On hand, are dependent on generating from solar, wind, hydropower, geothermal. These typically time year, weather conditions. The obtained can be stored with help variety methods, then used consistently under control as needed effect sporadic nature sources. total capacity storage systems currently around 118 000 GW/h. most important characteristics, subcategories, applications, research prospective major types discussed.

Language: Английский

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Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

IET Generation Transmission & Distribution, Journal Year: 2023, Volume and Issue: 17(21), P. 4763 - 4780

Published: Sept. 27, 2023

Abstract System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges control. Therefore, load control (LFC) strategy multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive (MPC) is proposed. First, controller‐interconnected multimicrogrid topology proposed; thus, consisting microturbines (MTs), distributed power sources, EVs their random constraints established. Second, parameter adaptive algorithm based MPC designed. The real‐time offset EV station output boundary are used as state set, adjustable parameters controller action reward function set deviation so that adjustment weight realised. Additionally, designed in this paper transform process into an optimization problem, well adapted various process. In addition, deep deterministic policy gradient (DDPG)‐MPC double‐layer prevent machine learning failure. Finally, simulation results show that, compared traditional algorithms, applied interconnection structure exchange information between submicrogrids. Moreover, experience accumulated prelearning process, updated according environmental real time, thereby improving robustness rapidity Meanwhile, DDPG controller, proposed coupling better ensure safe system when agent fails cannot actions normally.

Language: Английский

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A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4281 - 4281

Published: Aug. 27, 2024

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling strategies microgrids, based on expert system rules. The second stage problem, framework similar to that the stage, then established networks. Then, optimization model uncertainties demand, price, power generation renewable sources, demand electric vehicles, accessibility zone agents. Further, grey wolf algorithm solve formulated problem achieve an optimal reliable solution. proposal validated 69-bus network consisting three results validate minimizes microgrids’ utilization indices, such as costs, losses, voltage drops, while simultaneously managing flexible network. It also verified proposed design provides high-speed optimized solution

Language: Английский

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PV Sizing Evaluation for Low-Voltage Distribution Power Systems

Published: Oct. 16, 2024

Language: Английский

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Investigation on the stability of networked-control integrated energy systems for frequency regulations

IFAC Journal of Systems and Control, Journal Year: 2023, Volume and Issue: 25, P. 100219 - 100219

Published: June 7, 2023

Language: Английский

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