Integrated Demand Side Management Game in Smart Energy Hubs

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

doi:10.1109/tsg.2014.2377020

Cited by 253 publications

(117 citation statements)

References 38 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. Other studies of this topic propose and integrate a demand response program for SEH in order to modify the consumption patterns on the customer side [28][29].…”

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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“…In other words, the program that enables loads to be cut or shifted to other hours is defined as DSM [48]. DSM programs are classified into two policies: based on price, and encouragement and penalty.…”

Section: Demand Side Management (Dsm) Modelmentioning

confidence: 99%

Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties

2017

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Abstract:A microgrid (MG) is a small-scale version of the power system which makes possible the integration of renewable resources as well as achieving maximum demand side management (DSM) utilization. The future power system will be faced with severe uncertainties owing to penetration of renewable resources. Consequently, the uncertainty assessment of system performance is essential. The conventional energy scheduling in an MG may not be suitable for active distribution networks. Hence, this study focuses on the probabilistic analysis of optimal power dispatch considering economic aspects in a multi-carrier networked microgrid. The aim is to study the impact of uncertain behavior of loads, renewable resources, and electricity market on the optimal management of a multi-carrier networked microgrid. Furthermore, a novel time-based demand side management is proposed in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. The optimization model is formulated as a mixed integer nonlinear program (MINLP) and is solved using MATLAB and GAMS software. Results show that the energy sharing capability between MCMGs and MCMGs and the main grids as well as utilization of demand side management can decrease operating costs for smart distribution grids.

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“…Responsive loads are enabled users that are encouraged or rarely forced by the DSM policy to curtail or shift their demands, often optionally, to other hours [49]. The DSM program is classified into two policies based on price or encouragement and penalty.…”

Section: Demand Side Management (Dsm) Modelmentioning

confidence: 99%

Optimal Design of a Multi-Carrier Microgrid (MCMG) Considering Net Zero Emission

2017

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Abstract:In this paper, a two-stage optimum planning and design method for a multi-carrier microgrid (MCMG) is presented in the targeted operation period considering energy purchasing and the component's maintenance costs. An MCMG is most likely owned by a community or small group of public and private sectors comprising loads and distributed energy resources (DERs) with the ability of self-supply to regulate the flows of various energies to local consumers. The operation cost is undoubtedly reduced by selecting the proper components. In the proposed model, the investment and operation and maintenance costs of MCMG are simultaneously carried out in order to choose the right component and its size in the given period. Moreover, in this innovative model, net zero emission (NZE) is regarded as an environmental constraint. The genetic algorithm of MATLAB and the mixed-integer nonlinear programming (MINLP) technique of GAMS (general algebraic modeling system) software are used to solve the optimization problem. Illustrative examples show the efficiency of the proposed model.

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Integrated Demand Side Management Game in Smart Energy Hubs

Cited by 253 publications

References 38 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

Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties

Optimal Design of a Multi-Carrier Microgrid (MCMG) Considering Net Zero Emission

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