Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch

Zhang, Xiaoshun; Yu, Tao; Zhang, Zhiyi; Tang, Jianlin

doi:10.1109/access.2018.2853263

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Energy internet A fully distributed algorithm based on neural networks, applicable for nonsmooth and general convex objective functions [139] Networked microgrids A distributed algorithm for energy management based on online alternating direction method of multipliers and machine learning [140] Microgrid A fully distributed algorithm based on neural networks, capable of solving convex optimization where objective function is not necessarily strict convex or smooth [141] A cooperative RL algorithm [142] Smart grid PI frequency controller and neural network-based frequency controllers are used to implement distributed economic dispatch control [143] Multiple energy carrier systems A novel multiagent bargaining learning algorithm [144] Demand response Smart grid A GA-based solution [145] A novel deep transfer Q-learning method associated with a virtual leader-follower pattern [146] Stand-alone microgrid Multiagent cooperation system based on fuzzy Q-learning [147] Microgrid Distributed energy and load management approach based on RL [148] Abbreviations: GA, genetic algorithm; RL, reinforcement learning.…”

Section: Economic Dispatchmentioning

confidence: 99%

“…For the distributed energy hub economic dispatch of the multiple energy carrier systems, the use of the multiagent bargaining learning method can significantly reduce energy loss while ensuring the minimum total cost [144]. In order to avoid the shortcomings of slow convergence, curse of dimensionality and weak disposal ability to deal with continuously controllable variables in previous research [159][160][161], Q-learning with associative memory is adopted for the learning process of each agent.…”

Section: Economic Dispatchmentioning

confidence: 99%

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Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Wei

Qin

et al. 2021

IET cyber-phys. syst.

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Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI-based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems. | INTRODUCTION | Energy internetNowadays, the rapid development of human society has led to the massive consumption of fossil energy, forcing mankind to face many challenges such as energy crisis, environmental pollution and global warming. Therefore, people began to pay attention to the production and utilization of renewable energy [1,2]. According to statistics in [3], the annual growth of the world's total wind and solar power generation since 2000 is 22% and 40%, respectively. It is estimated that by 2050, renewable energy will account for 80% of the total power generation in the United States [4]. However, the traditional power grid cannot adapt to the large-scale access of renewable energy due to their disadvantages of intermittence and randomicity [5], which limit the use of clean energy. Taking wind power as an example, China's wind power curtailment in 2016 was as high as 4.97 � 10 10 kWÂůh, accounting for 17% of China's total wind power generation [6].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Economic Dispatchmentioning

confidence: 99%

Section: Economic Dispatchmentioning

confidence: 99%

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Wei

Qin

et al. 2021

IET cyber-phys. syst.

View full text Add to dashboard Cite

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“…Jianzhu took CHP as the central part of the model and optimized the RIES in the distributed double layer, which took the delay of the bottom thermal ring network into account. In the study by Zhang et al, (2018), ADMM was adopted to optimize the pricing strategy of the multi-EH system with the underlying heating network. In the studies by Chen et al (2018) and Chen et al (2021), the static and dynamic characteristics of the EHs were taken into consideration and optimized, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In the studies by Chen et al (2018) and Chen et al (2021), the static and dynamic characteristics of the EHs were taken into consideration and optimized, respectively. Zhang et al (2018) proposed a multi-agent bargaining learning method, which optimized the large-scale IES in a distributed way, while the EH worked as the agent. Based on C-ADMM, Xu et al (2019) improved the algorithm and analyzed the MRIES consisting of electrical, gas, and thermal three-ring networks with the four EHs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

et al. 2022

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The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

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Developing a Framework for Distributed and Multi-agent Management of Future Sustainable Energy Systems

Mohammadi

2019

Advances in Intelligent Systems and Computing

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Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch

Cited by 11 publications

References 37 publications

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

Developing a Framework for Distributed and Multi-agent Management of Future Sustainable Energy Systems

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