An autonomous demand response program for electricity and natural gas networks in smart energy hubs

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

doi:10.1016/j.energy.2015.05.109

Cited by 218 publications

(93 citation statements)

References 27 publications

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“…However, these works only consider one LSE in their algorithm. In paper [14,15], a novel concept entitling smart energy hub, an integration of electricity and natural gas network, has been introduce and modeled. This paper adds an algorithm for each LSE on how to make continuous adjustments according to other participants' behaviors.…”

Section: Introductionmentioning

confidence: 99%

Game-based strategy for load service entity to level Duck Chart as well as gain EV consumers

Muqing

Song

Zheng

et al. 2016

Int. Trans. Electr. Energ. Syst.

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Summary With the increasing penetration of unregulated solar energy, a load service entity (LSE) can exert an externality cost to guide the charging behaviors of electric vehicle (EV) users and level the Duck Chart. This paper introduces a dynamic Stackelberg game model between LSE and EV users to analyze consumer behavior, and a complete information static game model to help LSEs increase profits by gaining share of the EV market. These dynamic and static game models are incorporated into a novel theoretic algorithm with mixed integer programming. A case study shows that the models and the proposed algorithm can level the Duck Chart and guarantee an LSE's share of the EV market. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Game-based strategy for load service entity to level Duck Chart as well as gain EV consumers

Muqing

Song

Zheng

et al. 2016

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…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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“…The optimal dispatch for a single energy hub with conversion, storage, and demand side management capabilities is studied in [15], but the coordinated operation of interconnected hubs is not addressed. Much research work has been done on optimal operation of energy hubs with demand side resources in the electricity market environment in [14,[16][17][18][19]. The interaction among smart energy hubs (SEHs), i.e.…”

Section: Introductionmentioning

confidence: 99%

“…energy hubs equipped with smart grid technologies, is formulated as a non-cooperative game and solved by a distributed algorithm in [16][17][18], but the end demand shifts are not taken into account. Based on [16][17][18], further studies are made in [14] and [19] to take the end demand shifts into consideration. To the best of our knowledge, the coordinated scheduling of conventional thermal generation units, renewable energy sources and SEHs, as well as the integrated applications of power to gas (P2G) devices, heat pumps, multiple kinds of energy storages, flexible loads has not yet been systematically addressed in existing publications.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the notion of demand response can be expanded to the mutual alternatives and conversions of different energy carriers at the energy demand side. The integrated demand response (IDR) program [14] can not only adjust the quantity of the end demand, but also switch the form of the consumed energy with economic incentives. The optimal dispatch for a single energy hub with conversion, storage, and demand side management capabilities is studied in [15], but the coordinated operation of interconnected hubs is not addressed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices

Liu

Wen

et al. 2018

J. Mod. Power Syst. Clean Energy

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In recent years, the increasing penetration level of renewable generation and combined heat and power (CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy (MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response (IDR) scheme is designed to coordinate the operation of power to gas (P2G) devices, heat pumps, diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity, natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy system and a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.

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An autonomous demand response program for electricity and natural gas networks in smart energy hubs

Cited by 218 publications

References 27 publications

Game-based strategy for load service entity to level Duck Chart as well as gain EV consumers

Game-based strategy for load service entity to level Duck Chart as well as gain EV consumers

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

Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices

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