The effect of time-based demand response program on LDC and reliability of power system

Samadi, Mahdi; Javidi, Mohammad Hossein; Ghazizadeh, Mohammad Sadegh

doi:10.1109/iraniancee.2013.6599801

Cited by 9 publications

(14 citation statements)

References 26 publications

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“…Several types of demand response programs have been proposed by different systems operators in order to try to develop adequate programs for different situations. According to [11], the load curtailment in the demand response programs can vary between 15 min and more than 5 h.…”

Section: Introductionmentioning

confidence: 99%

Dynamic load management in a smart home to participate in demand response events

Fernandes

Morais

Vale

et al. 2014

Energy and Buildings

123

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In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heat-ing systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources' management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.

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Section: Introductionmentioning

confidence: 99%

Dynamic load management in a smart home to participate in demand response events

Fernandes

Morais

Vale

et al. 2014

Energy and Buildings

123

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“…In recent years, many references have studied the effects of DSM/DR programs on system reliability . The authors in have examined the effect of several load management models on system and nodal reliability indices of a bulk electric power system.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in have studied the effects of demand‐side load shift on system and nodal reliability indices and on nodal price volatility of restructured power systems. The authors in have investigated the effects of time‐based demand response operation on load curve characteristics and the reliability index of Expected Energy Not Supplied (EENS). The authors in have evaluated the effects of price‐based and incentive‐based DR programs on system and nodal reliability indices.…”

Section: Introductionmentioning

confidence: 99%

“…efficiency and flexibility. Features like advanced metering infrastructure, which will be enabled by smart grids, can enhance the potential of employing the demand as an effective tool to manage power system operation [6,7].In recent years, many references have studied the effects of DSM/DR programs on system reliability [2,[8][9][10][11][12][13][14]. The authors in [2,14] have examined the effect of several load management models on system and nodal reliability indices of a bulk electric power system.…”

mentioning

confidence: 99%

“…Generally, these references investigate the effects of prespecified DSM/DR programs on system reliability, and they do not consider the planning issues such as the combination of DR resources and the strategy of employing them. They have not also considered the short-term effects of DSM/DR programs [2,9,14] or have not addressed the special issues regarding their short-term reliability evaluation [8,[10][11][12][13]. On the other hand, references that have addressed the planning of DR programs usually employ SCUC to determine the optimal demandside participation through maximizing the social welfare in power system [11,[15][16][17][18][19][20][21][22].…”

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confidence: 99%

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Reliability-based nodal evaluation and prioritization of demand response programs

Moshari

Ebrahimi

2014

Int. Trans. Electr. Energ. Syst.

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Summary Demand response (DR) programs are effective tools to relieve power systems operational risks and will play key role in future smart grids because of emerging advanced communication and metering technologies. Enhancing power system reliability is one of the main goals of employing DR programs. This paper presents a nodal viewpoint for reliability‐based planning of DR programs. It aims to demonstrate the superiority of nodal evaluation and prioritization of DR programs to improve reliability over the global approach. Here, a sequential state‐enumeration‐based method is presented for composite system operational reliability assessment, and a multi‐segment optimal power flow approach is developed to evaluate the effects of DR programs. In addition, analytic hierarchy process is employed for global and nodal prioritizing of different DR programs. The results show that employing DR programs based on nodal prioritization can lead to higher reliability, lower costs, and greater assurance on programs implementation. Copyright © 2014 John Wiley & Sons, Ltd.

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A demand response-based optimal scheduling framework considering renewable sources and energy storage: a deterministic approach

Ramesh,

Khedkar,

Kotha

et al. 2024

Electr Eng

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The effect of time-based demand response program on LDC and reliability of power system

Cited by 9 publications

References 26 publications

Dynamic load management in a smart home to participate in demand response events

Dynamic load management in a smart home to participate in demand response events

Reliability-based nodal evaluation and prioritization of demand response programs

A demand response-based optimal scheduling framework considering renewable sources and energy storage: a deterministic approach

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