Optimal real time cost-benefit based demand response with intermittent resources

Zareen, Naila; Mustafa, Mohd Wazir; Sultana, Umbrin; Nadia, R.; Khattak, Muhammad Adil

doi:10.1016/j.energy.2015.06.126

Cited by 20 publications

(3 citation statements)

References 45 publications

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“…The load curve, cost of electricity and the reduction of energy consumption are analyzed and compared with Time-of-Used based demand response programs. Simulation results show that almost reduction or even elimination of peak-hour energy consumption, and the rise of revenues with a 18% increase in the load reduction with system reliability [42]. In recent days, researches have been undergoing for optimal scheduling of loads using evolutionary algorithms.…”

Section: Demand Side Managementmentioning

confidence: 95%

Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand

Loganthurai

Rajasekaran

Gnanambal

2016

Energy

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Section: Demand Side Managementmentioning

confidence: 95%

Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand

Loganthurai

Rajasekaran

Gnanambal

2016

Energy

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“…By examining the costbenefit of different programs, the best plan can be selected for planning and implementation. So recognizing these benefits can help determine the optimal scheme more accurately [108][109]. These benefits are categorized into Market-driven and Saving-driven groups.…”

Section: B)mentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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The need to increase network efficiency, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants' economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select a proper plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.

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“…for Southern [66,67] and Central European markets [68,69]). Some studies take into account demand response and renewables but limit analysis to a local grid [70], apply a static market setting [71][72][73], or derive short-term equilibria [39][40][41]. The long-term interaction of renewable energies with demand flexibility is most thoroughly analysed in the energy system studies mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system

Katz

Andersen

Morthorst

2016

Energy

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Optimal real time cost-benefit based demand response with intermittent resources

Cited by 20 publications

References 45 publications

Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand

Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand

An Overview of Demand Response: From its Origins to the Smart Energy Community

Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system

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