On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid

Yaghmaee, Mohammad Hossein; Leon-Garcia, Alberto; Moghaddassian, Morteza

doi:10.1109/tsg.2017.2688486

Cited by 64 publications

(19 citation statements)

References 28 publications

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“…Additionally, there must be training for professionals who will carry out these activities [98,126] by taking into account the technological interdisciplinarity of the elements that make up ECM [107]. Thus, as a way to maintain the quality of services [127], an opportunity to be explored is the establishment of collaborative networks between service providers by sharing information and market space [92], but maintaining ethical aspects in the provision of these services [102].…”

Section: Third-party Servicesmentioning

confidence: 99%

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

et al. 2020

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The use of emerging technologies such as cloud computing, Internet of Things, and Big Data, is increasing as tools to assist the management of data and information related to energy systems grow. This allows for greater flexibility, scalability of solutions, optimization of energy use, and management of energy devices. In this sense, the objective of this research is to present the basic elements and requirements for the energy cloud and its management and discuss the main management challenges and opportunities for the development and diffusion of the energy cloud. This study was based on a systematic review carried out to identify the elements that compose the energy cloud and what is necessary for its management, and to list the challenges and opportunities that may be explored by researchers and practitioners. The results show that the layout for the energy cloud and its management can be structured in layers and management support blocks’ format. It was found that 70 basic elements make up the main layers and 36 basic elements make up the management support blocks. The findings of this article also provide insights into the technical, scientific, and management development necessary for the evolution of energy systems toward the cloud computing environment.

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Section: Third-party Servicesmentioning

confidence: 99%

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

et al. 2020

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“…Moreover, for a faster response time in large-scale deployments, cloud-based demand response architecture is presented in [31] by Moghaddam et al The studied demand responses are of two types: cloud-based demand response and distributed demand response. These are optimized by two models, including a demand response and a communication model.…”

Section: Sg With Cloud-based Architecturementioning

confidence: 99%

Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

Zahoor

Javaid

et al. 2018

Sustainability

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A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud-fog-based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud-fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user's requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.

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“…Real time pricing programs are automated in charging stations for electric vehicles considering the affectation of voltage profiles [113]. Referring to the communications between the DR actors, protocols have been proposed for information exchange and balancing the demand of users by optimizing the network [106,107].…”

Section: Automationmentioning

confidence: 99%

A Review and Analysis of Trends Related to Demand Response

et al. 2018

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This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.

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On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid

Cited by 64 publications

References 28 publications

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

A Review and Analysis of Trends Related to Demand Response

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