A Dynamic Game Model for Resource Allocation in Fog Computing for Ubiquitous Smart Grid

Li, Zhi; Liu, Yanzhu; Rui, Xiaobo; Gao, Lifang; Ding, Xueying; Hu, Yuxia

doi:10.1109/wocc.2019.8770573

Cited by 9 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 1, advanced sensing, measurement, control, decision-making, and other technical means, achieve the goals of reliability, safety, economy, high efficiency, and environmental friendliness of the grid [4]. Compared with the traditional grid, Grid 2.0 adopts a combination of distributed and centralized generation, which can effectively integrate various generation systems such as wind energy, solar energy, and fuel cells.…”

Section: A Background Presentationmentioning

confidence: 99%

“…It effectively improves the access success rate and greatly reduces the high-priority access delay. Aiming at the energy orchestration problem in smart grid fog computing, [4] proposes a dynamically optimized allocation framework and a dynamic differential game model. It well reduces transmission delay, improves resource utilization, and reduces resource overhead.…”

Section: Related Work a Development Of Smart Gridmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

et al. 2023

View full text Add to dashboard Cite

Smart Grid -Grid 2.0, is a promising grid scenario due to its low cost, high reliability, and high security. However, the high-intensity complex task interaction and the differentiated quality of service (QoS) requirements pose challenges to the energy orchestration of the dynamic and time-varying smart grid. In addition, Network Virtualization (NV) technology is widely utilized to decouple physical network resources. In this work, based on Virtual Network Embedding (VNE), we propose an energy orchestration strategy to better support the operation and maintenance of Grid 2.0. Specifically, a series of constraint indicators for energy orchestration is first formulated. Second, based on the advantages of deep reinforcement learning (DRL), a five-layer architecture energy orchestration model is proposed. It frequently extracts the grid energy matrix as the state input, adopts two-stage actions to reasonably orchestrate the grid energy, and combines the reward mechanism of DRL to effectively interact with the environment. Finally, we conduct experiments in a simulation environment and demonstrate the advantages of the proposed strategy through analysis and comparison.

show abstract

Section: A Background Presentationmentioning

confidence: 99%

Section: Related Work a Development Of Smart Gridmentioning

confidence: 99%

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

et al. 2023

View full text Add to dashboard Cite

show abstract

“…κ is the penalty factor of the constraint (12). It indicates whether the current policy meets the delay requirement of the service.…”

Section: ) Rewardmentioning

confidence: 99%

“…A multi-layer radio access network in the cloud is designed and a cooperative resource allocation algorithm is proposed to reduce service delays in edge networks and optimize throughput [9]. The heuristic algorithm [10], greedy algorithm [11] and game theory [12] are applied to optimize the allocation resource in smart grids.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Service-Oriented Resource Allocation in Smart Grids

Wang

Wang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Resource allocation has a direct and profound impact on the performance of resource-limited smart grids with diversified services that need to be timely processed. In this paper, we investigate a joint communication, computing, and caching resource allocation problem with distinct delay requirement of services in smart grids. This paper aims to optimize the long-term system utility based on reward and loss function. Considering the unknown dynamic environment as well as the huge state and action space in smart grids, a deep reinforcement learning algorithm based on the polling method is exploited to learn the policy by interacting with the environment. Specifically, the edge nodes (ENs) act as agents to enable the services to schedule resources appropriately. Then, the agents that are allocated based on the service requirements are queried according to the polling mechanism and the well-designed reward function is utilized to update the strategy. Extensive simulation results show that the proposed algorithm outperforms three known baseline schemes in terms of network performance with decision results. Besides, in the face of a large number of services in the smart grids, the proposed system still surpasses that of existing several baseline schemes, especially in the improvement of cache hit rate and the decrease of computing delay.INDEX TERMS smart grids, edge computing, deep reinforcement learning, resource allocation.

show abstract

“…The core idea of the model is to establish the hierarchical structure of cloud-fog computing and provide different types of computing services for smart grid resource management. Aiming at the resource allocation problem of fog computing in ubiquitous smart grid, a dynamic resource allocation framework is proposed by Li et al [51]. A dynamic differential game model is established, which can help service providers to make the optimal dynamic strategy when users change the strategy, to meet the needs of power grid users while maximizing resource utilization and efficiency.…”

Section: Cloud-edge-terminal Collaboration Technologymentioning

confidence: 99%

Thinking and Prospect of Power Chip Specificity

Xiang

et al. 2021

International Journal of Photoenergy

View full text Add to dashboard Cite

The contradiction between the demand of massive intelligent scene caused by the interconnection of things in power system and the bottleneck of the chip itself is becoming more and more serious. How to realize the self-controllable and domestic substitution of power chips has been widely concerned in the power field. Therefore, this paper analyzes the demand for power chips in power system and discusses the scene specificity of power chips. Then, it combs the design architecture of power chips and discusses the application scenarios and research focuses of power chips. Finally, the key scientific problems and major technical bottlenecks of power chips are prospected.

show abstract

A Dynamic Game Model for Resource Allocation in Fog Computing for Ubiquitous Smart Grid

Cited by 9 publications

References 11 publications

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

Deep Reinforcement Learning-Based Service-Oriented Resource Allocation in Smart Grids

Thinking and Prospect of Power Chip Specificity

Contact Info

Product

Resources

About