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標(biāo)題: Titlebook: Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives; Jiahu Qin,Yanni Wan,Weiming Fu Book 2023 The Editor [打印本頁]

作者: 一再    時間: 2025-3-21 17:54
書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives影響因子(影響力)




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives影響因子(影響力)學(xué)科排名




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives網(wǎng)絡(luò)公開度




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives被引頻次




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives被引頻次學(xué)科排名




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives年度引用




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives年度引用學(xué)科排名




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives讀者反饋




書目名稱Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives讀者反饋學(xué)科排名





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作者: OATH    時間: 2025-3-22 18:36
2198-4182 complicated multi-objective PEVs scheduling problem.PresentsThis book aims to work out the distributed economic operation in smart grids in a systematic way, which ranges from model-based to model-free perspectives. The main contributions of this book can be summarized into three folds. First, we in
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作者: Ganglion    時間: 2025-3-23 04:52
,Extensions to?PEVs Charging/Discharging Scheduling, needs of PEVs and the capacity constraints of charging station. The key challenge here is that the coordinated charging/discharging scheduling involves multiple objectives at the same time and the behavior of PEVs and the electricity price is random.
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作者: reflection    時間: 2025-3-23 12:51
Model-Free Distributed Optimization,ge-scale renewable energies and the flexibility of the load units. That is to say, these fundamental problems cannot be directly modeled as deterministic optimization problems. So we design learning-based approaches to address the uncertainty and randomness.
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Studies in Systems, Decision and Controlhttp://image.papertrans.cn/e/image/281885.jpg
作者: Repetitions    時間: 2025-3-24 05:04
Model-Free Distributed Optimization,ge-scale renewable energies and the flexibility of the load units. That is to say, these fundamental problems cannot be directly modeled as deterministic optimization problems. So we design learning-based approaches to address the uncertainty and randomness.
作者: 影響    時間: 2025-3-24 06:34
Great Britain and Northern IrelandThe electric power system has become the backbone of the modern society since the first complete electric power system built by Thomas Alva Edison in 1882.
作者: contrast-medium    時間: 2025-3-24 12:58
https://doi.org/10.1057/9780230270527In this chapter, some basic concepts, definitions, and lemmas are introduced, including graph theory, multi-agent system consensus algorithms, related optimization algorithms, reinforcement learning, and game theory.
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Preliminaries,In this chapter, some basic concepts, definitions, and lemmas are introduced, including graph theory, multi-agent system consensus algorithms, related optimization algorithms, reinforcement learning, and game theory.
作者: flaggy    時間: 2025-3-25 01:52
Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives978-981-19-8594-2Series ISSN 2198-4182 Series E-ISSN 2198-4190
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Great Britain and Northern IrelandF. Specifically, the optimization problems can be modeled as the deterministic models and we propose some distributed optimization algorithms by combining the multi-agent consensus theory and optimization techniques to achieve the distributed coordination of various generation units and loads in EDP
作者: 著名    時間: 2025-3-25 20:33
https://doi.org/10.1057/9780230270527ge-scale renewable energies and the flexibility of the load units. That is to say, these fundamental problems cannot be directly modeled as deterministic optimization problems. So we design learning-based approaches to address the uncertainty and randomness.
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