Strategic equilibrium of economic dispatch in smart grid with a bi‐level game approach

Yang, Jie; Guo, Wenbo; Ma, Kai; Tian, Zhenhua; Dou, Chunxia

doi:10.1049/iet-gtd.2019.1050

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…In this section, we present a brief review of some related work. The papers [16]- [21] adopt the idea of game theory for the SG-based energy management problem. In [16], the pricing strategy in the SG is analyzed by modelling the economic dispatch problem as a bi-level game in the electricity market.…”

Section: Related Workmentioning

confidence: 99%

“…The papers [16]- [21] adopt the idea of game theory for the SG-based energy management problem. In [16], the pricing strategy in the SG is analyzed by modelling the economic dispatch problem as a bi-level game in the electricity market. To guarantee the profit of the energy generation company and the social welfare of the utility companies simultaneously, the Nash bargaining solution is adopted to find an optimal wholesale price.…”

Section: Related Workmentioning

confidence: 99%

“…With the Nash, extended-egalitarian and proportional bargaining solutions in three control levels, the total energy of smart-grid system is hierarchically controlled, and the smart-grid utility is maximized adaptively to increase the system reliability. Even though research literatures in [16]- [22] introduce some interesting methods for the energy management and price decision problems, they mainly focus on the SG control issues without considering the SG-DC combined platform.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Kim

2021

IEEE Access

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Recently, data centers (DCs) have become an indispensable part of modern computing infrastructures. However, DCs often consume a significant amount of energy and lead to the workload unbalance with increasing service requests. Keeping focus on this point, in this paper, we propose a novel energy-aware DC management scheme. To design an efficient DC control algorithm, the main challenge is uncertainties such as uncertain energy price and unpredictable users' demands. In response to these uncertainties, we adopt the idea of cooperative game theory, and introduce a new two-phase bargaining model to get the mutual advantage. To decide the energy price, we formulate the Stackelberg bargaining game while adapting the current system situation. To balance the workloads among DCs, the migration bargaining game is developed. These two game models are tightly coupled to achieve greater and reciprocal advantages during dynamic DC operations. The main novelty of our proposed two-phase bargaining approach is to handle comprehensively contradictory requirements for the DC management. Finally, extensive experiment results validate the efficiency of our proposed algorithm by comparing with the existing state-of-the-art DC management protocols in terms of average payoff of all DCs, system throughput and fairness among DCs.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Kim

2021

IEEE Access

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“…Although traditional intelligent algorithms guarantee the overall goal of a model, they do not consider the interests of each agent, resulting in one‐sidedness. The Nash equilibrium theory in game theory can maximise the interests of various players [29]. By combining reinforcement learning, the Nash‐Q algorithm can solve multiagent game problems [30].…”

Section: Introductionmentioning

confidence: 99%

Collaborative optimization strategy of source‐grid‐load‐storage considering dynamic time series complementarity of multiple storages

Huang

Liu³

2023

IET Generation Trans & Dist

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The multi‐scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system. This paper first considers the interaction mechanism of multi‐type storage peak regulation time sequences based on the Euclidian distance, dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source‐grid‐load‐storage (SGLS) was constructed. To attain a low‐carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time‐series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K‐means clustering and dual‐structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

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“…The deployment of distributed energy resources (DERs) has been steadily increasing in recent years [1,2]. On the other hand, the electrical energy systems have been evolving toward the smart microgrids (SMGs) and smart grids' frameworks [3,4]. The SMG and smart grid concepts could effectively improve the energy systems' resiliency and reliability [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

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Strategic equilibrium of economic dispatch in smart grid with a bi‐level game approach

Cited by 8 publications

References 40 publications

Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Collaborative optimization strategy of source‐grid‐load‐storage considering dynamic time series complementarity of multiple storages

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

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