A cloud computing framework on demand side management game in smart energy hubs

Sheikhi, Aras; Rayati, Mohammad; Bahrami, Shahab; Ranjbar, Ali Mohammad; Sattari, Sourena

doi:10.1016/j.ijepes.2014.08.020

Cited by 124 publications

(53 citation statements)

References 39 publications

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“…In this topic, a cloud-computing framework is presented in [26] that aims to resolve the problem of coordinating several SEH, in order to optimize the performance of the network. Furthermore, an integrated DSM technique is shown in [27], which models the interactions between different SEH as a non-cooperative game.…”

Section: B Literature Review and Contributionsmentioning

confidence: 99%

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos

Andrade

Sala

et al. 2018

IEEE Trans. Smart Grid

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Abstract-This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.

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Section: B Literature Review and Contributionsmentioning

confidence: 99%

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos

Andrade

Sala

et al. 2018

IEEE Trans. Smart Grid

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“…However, this model doesn't consider the use of small power generation facilities and is not as broad as our model. [13] presents a demand response program considering with Distribution Locational Marginal Price (D-LMP) energy market. It is supposed that customers can receive D-LMP price signal through the home gateway.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud Based Demand Side Management Approach with Microgrid

2018

IJRTER

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Abstract-To developand increase the expediency of living, Internet of things (IoT) these dayshas become a good research topic. The enhancement in the quality of human living can be increased by using varying home appliances, which can be quick and fast. Different home appliances provide diverse functions and services. Hence, in this research, the IOT based Demand Side Management is asignificant application of the upcoming Smart Grid. DSM programs let consumers to joinand participate in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. This research focuses on cloud-based demand side management to control the residential load of customers equipped with local power generation and storage facilities as auxiliary sources of energy. A power system consisting of multiple regions and equipped with a number of microgrids can be considered. In each region an edge cloud is utilized to find the optimal power consumption schedule for customer appliances in that region. At the edge cloud, the power consumption level of local storage and the amount of power being demanded from both local storage facilities and power grid are scheduled using a bi-level optimization approach. The core cloud then gathers information of the total demand from consumers in different regions and finds the optimal power consumption schedule for each microgrid in the power system. The paper provides a study on the literature research, objectives of the cloud based model, methodology and basic requirements to be considered for the microgrid. The proposed model can reduce power consumption cost for the customers and improves the power grid in terms of peak load and peak-to-average load ratio.

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“…On the one hand, based on the Cloud Platform, they realized the analysis, prediction and monitoring of the big data gathered by intelligent ammeter, which elevated the precision of consumer's demand response to price signals and load curtailment [7]. On the other hand, power demand side management has been conducting based on energy intelligence center [8].…”

Section: Differentiation Strategymentioning

confidence: 99%

The Exploration of Competitive State in Electricity Retail Company under the Background of New Power Market Reformation

Zhang¹,

Yang²

2017

Proceedings of the 2017 2nd International Conference on Humanities and Social Science (HSS 2017)

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Abstract. With the reformation of power sale side moving forward constantly, an increasing number of electricity retail companies are involved in the competition of selling market, which is contributed by the release of retail side. Aimed at this background, the present paper explored the competition situation of the whole market, based on the Potter 's five force model from five aspects: the current rivals, the threats of entry, the threats of substitutes, the bargaining power of suppliers and the bargaining power of buyers. Finally, the relevant competitive strategies and suggestions were put forward for the current electricity retail companies.

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A cloud computing framework on demand side management game in smart energy hubs

Cited by 124 publications

References 39 publications

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Cloud Based Demand Side Management Approach with Microgrid

The Exploration of Competitive State in Electricity Retail Company under the Background of New Power Market Reformation

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