Wolf pack hunting strategy for automatic generation control of an islanding smart distribution network

Xi, Lei; Zhang, Ze-Yu; Yang, Bo; Huang, Linni; Yu, Tao

doi:10.1016/j.enconman.2016.05.039

Cited by 42 publications

(24 citation statements)

References 39 publications

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“…Therefore, future Smart EEPS (represented by SG and EI) will inevitably form large‐scale complex systems that deeply integrate physical networks, cyber networks, and social networks—ie, CPSS . The rapid development of the new generation of AI technologies, including big data, ML, situational awareness, and DL, will provide a powerful impetus for the study of such complex giant systems, especially in the following aspects: Applications supporting coordinated and optimized operation of energy systems containing various heterogeneous energy networks such as electric power networks, thermal networks, and gas networks. Applications that support coordinated planning and optimized operation of multienergy complementary systems that may include wind power, photovoltaic, hydropower, thermal power, energy storage, or combinations thereof. Applications that support efficient integration and timely consumption of high‐penetration renewables and EVs. Applications for distributed multienergy complementary integrated i‐parks/microgrids Applications in SG and EI energy management …”

Section: Prospect and Conclusionmentioning

confidence: 99%

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A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng

2019

Int J Energy Res

Self Cite

210

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Summary The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.

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Section: Prospect and Conclusionmentioning

confidence: 99%

“…• Applications for distributed multienergy complementary integrated i-parks/microgrids. 146 • Applications in SG and EI energy management. 147 • Applications that support energy and EM trading 148,149 and ancillary services markets for SG and EI.…”

Section: Bottlenecks For ML Development In Smart Eepsmentioning

confidence: 99%

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng

2019

Int J Energy Res

Self Cite

210

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“…It can be found that when a fault occurred, both of its upstream LPs and downstream LPs will be affected to some degree conditionally. The degree relies on the operations state of the involving protective devices and AS [24,25]. In other words, considering a particular component, the effect of its downstream failures on its upstream LPs will more or less depend on its own operation state.…”

Section: General Model For Distribution Systemmentioning

confidence: 99%

Recursive Method for Distribution System Reliability Evaluation

Wang

Zhang

Li³

et al. 2018

Energies

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This paper proposes a novel hybrid recursive method for distribution system reliability evaluation to deal with the computational limit and low-efficiency problem which exist in previously developed techniques as the system becomes larger. This method includes a bottom-up process and a top-down process, which are developed on the basis of a recursive principle, and the synthesis of both processes yield the reliability performance of each bus of the system. The bottom-up process considers the effects of downstream failures on upstream customers, and the top-down process considers the effects of upstream failures on downstream customers. In addition, a novel switch zone concept is defined and introduced into the bottom-up recursive process to save the computation cost. Besides, section technique (ST) and shortest path method (SPM) are employed to effectively simplify the recursive path and thus, the computation efficiency can be improved. The most significant feature of the proposed method over ST, SPM, failure mode and effect analysis (FMEA) is that it provides a more generalized equivalent approach to maximally simplify the network for reliable evaluation irrespective of the network topology. The effectiveness of the proposed method has been validated through comprehensive tests on Roy Billinton test system (RBTS) bus 6 and a practical-sized distribution system in China.

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“…Therefore, this paper attempts to explore an AGC method with a hierarchical and distributed control (HDC) structure to solve the above problem. Based on authors' previous work [23][24][25], a novel multiagent system stochastic consensus game (MAS-SCG) framework was designed through the combination of MAS-SG and MAS-CC frameworks to solve the basic problem of "homogeneous/heterogeneous multiagent mixed stochastic game." Based on this framework, an ecological population cooperative control (EPCC) strategy is proposed, which can realize the total cooperative control and optimization of a distributed HDC system, to resolve the multisolution problem and stochastic disturbance problems arising from distributed energy access.…”

Section: Introductionmentioning

confidence: 99%

A Novel Automatic Generation Control Method Based on the Ecological Population Cooperative Control for the Islanded Smart Grid

Huang

et al. 2018

Complexity

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To achieve automatic generation control coordination in the islanded smart grid environment resulted from the increasing penetration of renewable energy, a novel ecological population cooperative control (EPCC) strategy is proposed in this paper. The proposed EPCC, based on the new win-loss criterion and the time tunnel idea, can compute the win-loss criterion accurately and converge to Nash equilibrium rapidly. Moreover, based on a multiagent system stochastic consensus game (MAS-SCG) framework, a frequent information exchange between agents (AGC units) is implemented to rapidly calculate optimal power command, which achieves the optimal cooperative control of the islanded smart grid. The PDWoLF-PHC(λ), WPH strategy (wolf pack hunting), DWoLF-PHC(λ), Q(λ)-learning, and Q-learning are implemented into the islanded smart grid model for the control performance analysis. Two case studies have been done, including the modified IEEE standard two-area load frequency control power system model and the islanded smart grid model with distributed energy and microgrids. The effectiveness, stronger robustness, and better adaptability in the islanded smart grid of the proposed method are verified. Compared with five other smart ones, EPCC can improve convergence speed than that of others by nearly 33.9%–50.1% and the qualification rate of frequency assessment effectively by 2%–64% and can reduce power generation cost.

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Wolf pack hunting strategy for automatic generation control of an islanding smart distribution network

Cited by 42 publications

References 39 publications

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Recursive Method for Distribution System Reliability Evaluation

A Novel Automatic Generation Control Method Based on the Ecological Population Cooperative Control for the Islanded Smart Grid

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