Bargaining Game-Based Profit Allocation of Virtual Power Plant in Frequency Regulation Market Considering Battery Cycle Life

Chen, Wen; Qiu, Jing; Zhao, Junhua; Chai, Qingmian; Zhang, Dong

doi:10.1109/tsg.2021.3053000

Cited by 81 publications

(12 citation statements)

References 26 publications

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“…Because power system operation models are often linear, efforts have been made to come up with a linearized battery cost model [69]. Further battery cost models can be found in [70]- [72].…”

Section: Modeling a Battery System For Economic Dispatchmentioning

confidence: 99%

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Rouholamini

Wang

Nehrir

et al. 2022

IEEE Trans. Smart Grid

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Section: Modeling a Battery System For Economic Dispatchmentioning

confidence: 99%

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Rouholamini

Wang

Nehrir

et al. 2022

IEEE Trans. Smart Grid

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“…The concept of SBS is to highlight the features and individual contributions of players with reasonable shares. In the cooperation, the SBS can characterize the different attributes of game players by structuring the bargaining powers to quantify their preferences and contribution [8], [11]- [12].…”

Section: A Technical Conceptsmentioning

confidence: 99%

“…where 𝛤 is an adjustment factor for the 𝔘 ℳ ℬ 𝑖 (•), and 𝜂, 𝜃, 𝜚 are adjustment factors for the 𝔘 𝔓 (•). According to (11), each individual MEC nodes sequentially bargains with the 𝔓, and split its 𝜑 ℳ ℬ 𝔓 based on the current C-MEC system condition.…”

Section: The Proposed C-mec Resource Allocation Algorithmmentioning

confidence: 99%

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Collaborative Resource Sharing Game Based Cloud-Edge Offload Computing Orchestration Scheme

Kim

2022

IEEE Access

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In the foreseeable future, the rapid growth of mobile communications and cloud systems has substantially promoted edge computing paradigm. Although edge computing technology has been attracting much interest, most current research is application-specific without considering a control perspective of cloud providers that provides general-purpose edge services. In this study, we present a new cloud-edge computing orchestration scheme that integrates the different resource sharing problems to maximize benefits for offloading computing services. Specifically, we focus on two cooperative game solutions -Sim bargaining solution and interval Banzhaf Value -to effectively share the computing, communication and cache resources in the cloud-edge system platform. In the proposed scheme, we formalize two different cooperative games, and they work together and act cooperatively to satisfy contradictory requirements. Under widely different service request situations, we aim at accelerating an efficient resource orchestration through dynamic workload balancing. The primary goal of our cooperative game approach is to find optimal control decisions toward the appropriate system operation. Finally, we evaluate the proposed scheme in terms of several performance criteria from the numerical simulations. Experimental results show that we can achieve a mutually desirable solution with a good system performance balance while enhancing the resource efficiency better than the existing cloud-edge control protocols. INDEX TERMSCloud edge computing; Internet of Thing; Resource sharing problems; Sim bargaining solution; interval Banzhaf Value.

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“…The bidding strategy of battery storage systems in the secondary control reserve market is investigated in the work of Merten et al (2020). Chen et al (2021) studied the optimal bidding strategy of a PV-BESS VPP in frequency control ancillary services markets. A two-stage bidding strategy of households PV-BESSs is proposed in peer-to-peer market (Zhang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

Gao

Chan

et al. 2021

Front. Energy Res.

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Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

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Bargaining Game-Based Profit Allocation of Virtual Power Plant in Frequency Regulation Market Considering Battery Cycle Life

Cited by 81 publications

References 26 publications

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Collaborative Resource Sharing Game Based Cloud-Edge Offload Computing Orchestration Scheme

A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

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