Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Faria, Pedro; Vale, Zita

doi:10.35833/mpce.2020.000317

Cited by 27 publications

(11 citation statements)

References 31 publications

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“…e computer itself lacks intelligence and has obvious advantages in computing speed, but it is often inefficient in decision-making and analysis. Insufficient intelligence of computer operations is a serious problem, and based on this situation, early artificial intelligence such as IBM's deep blue can only solve problems in specific environments [9]. To enable computers to control information in an open environment, researchers use knowledge bases to give computers access to artificially generated information.…”

Section: Tensorflow Deep Learning Frameworkmentioning

confidence: 99%

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

Zhu

Huang

2022

Mobile Information Systems

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Under the current trend of economic globalization and international competition, more and more production enterprises are introducing the project management model, that is, customer or order projection, and using the concept of project management to manage operations. In an enterprise, there are multiple customers at the same time, and the production line can also generate multiple orders from different customers at the same time. That is to say, multiple projects are running at the same time, resulting in continuous changes in management processes, heavy project coordination tasks, and a serious waste of corporate resources. The resources that can be used are limited. In the production business using project management, how to rationally utilize the limited business scheduling resources has become the focus of research. Aiming at the method of business management resource scheduling, this paper investigates the application of enhanced particle swarm algorithm on the cloud deep learning platform, and the mapping problem between virtual machines and physical machines. As a heuristic algorithm, particle swarm optimization is suitable for solving combinatorial optimization problems. By improving the diversity of particle hosts and setting parameters, it improves the integration speed and accuracy of the algorithm. Through the analysis of the current situation of multi-project business management, it examines the allocation of resources. In terms of business management, it has established a resource allocation model for multiple projects. The results show that the method improves the efficiency of the enterprise by 35% compared to the traditional method with a 20% reduction in personnel. A better configuration scheme is simulated by MATLAB, which verifies the scientificity and effectiveness of the method studied in this paper.

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Section: Tensorflow Deep Learning Frameworkmentioning

confidence: 99%

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

Zhu

Huang

2022

Mobile Information Systems

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“…In addition, in the process of allocating heterogeneous cloud resources, the system also needs to monitor the allocated heterogeneous cloud resources in real time to ensure the even distribution of all resources, calculate the remaining resources to be allocated according to the current allocation of heterogeneous cloud resources, and reserve the execution nodes for the allocated resources in advance. Currently, the collaborative scheduling of heterogeneous cloud resources can be macroscopically divided into static scheduling strategy and dynamic scheduling strategy [3]. The static scheduling strategy refers to the unified scheduling of all heterogeneous cloud resources with a single scheduling target, while the dynamic scheduling strategy refers to the scheduling of heterogeneous cloud resources in a hierarchical manner with different metrics as the scheduling target, and then combining the scheduling results of each layer to form the final comprehensive scheduling strategy.…”

Section: Introductionmentioning

confidence: 99%

Research on collaborative and integrated resource schedulingalgorithm in heterogeneous cloud environment

Fu,

Liao,

Meng

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The current conventional collaborative resource scheduling algorithms in heterogeneous cloud environments mainly process the allocation through the quantified results of data characteristics of heterogeneous cloud resources, which leads to low integrated scheduling efficiency due to the differences in the attributes of resources. In this regard, a collaborative and comprehensive resource scheduling algorithm in heterogeneous cloud environment is proposed. Firstly, the heterogeneous cloud resource information data is sampled and processed, and the resource quality is graded. The scheduling task model is constructed by constructing the mapping function of scheduling task assignment sub-nodes, and the hierarchical scheduling strategy is proposed by combining with ant colony algorithm. In the experiments, the designed collaborative integrated scheduling algorithm is tested for the scheduling efficiency. The final results can prove that the algorithm has a lower average delay and a more desirable integrated scheduling efficiency when the proposed method is used for scheduling heterogeneous cloud resources.

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“…Using Energy Management Systems (EMS) is something common in the context of smart grids, enabling the management of electric loads and resources using centralized or decentralized techniques [1]. Considering the context, it is possible to enable the efficient control of resources with these systems implemented in smart homes [2]. One of the great advantages of these systems is that they can include artificial intelligence models, the most common being learning models, which learn with the user and the context while being able to provide intelligent control [3].…”

Section: Introductionmentioning

confidence: 99%

A Trustworthy Building Energy Management System to Enable Direct IoT Devices’ Participation in Demand Response Programs

et al. 2022

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Nowadays, internet of things devices are becoming more prominent since they can integrate energy management systems. However, data sharing between end-users and other external entities is a concern that must be addressed to ensure data privacy. Given the above, this paper proposes a trustworthy energy management system for energy communities to enable direct internet of things devices’ participation in demand response programs at the community level. This solution is based on a building energy management system framework that considers signed tokens to maintain end-user data privacy and manage data access. Likewise, this solution can plan future demand response events to balance the consumption and generation, using the internet of things device’s flexibility. The results demonstrated that the planned demand response event for one hour efficiently balanced the energy community energy resources, enabling a reduction of approximately 2.8 kWh. Thus, the proposed solution allows safeguarding data ownership rights while efficiently managing energy resources.

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Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Cited by 27 publications

References 31 publications

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

Research on collaborative and integrated resource schedulingalgorithm in heterogeneous cloud environment

A Trustworthy Building Energy Management System to Enable Direct IoT Devices’ Participation in Demand Response Programs

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