Studying emergency demand response programs based on exponential modeling in smart grids

Nikzad, Mehdi; Farahani, Shoorangiz Shams Shamsabad

doi:10.1109/redec.2012.6416701

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…As each type of consumer has distinct characteristics, it is essential that the VPP provides different models to adequately remunerate the respective consumers. This way, the proposed methodology takes into account this aspect, differing from the other underlying literature [8]- [10].…”

Section: Proposed Dr Programs Definition Methodologymentioning

confidence: 99%

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Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

Pereira

Soares

Faria

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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The development of demand response programs has been allowing to improve power system performance in several ways, both in terms of the management of electricity markets, as well as regarding benefits in its operation. In order to model the remuneration for the participation of consumers in the scheduling of resources, this paper proposes a methodology based on the use of four incentive-based tariffs for the remuneration of demand response participation. It considers steps, quadratic, constant and linear remuneration. The optimization model enables Virtual Power Players to minimize operation costs, considering different critical situations of management and operation. The optimization problem has been solved by Quantum Particle Swarm Optimization. The case study concerns 168 consumers, classified into 5 consumer types, 118 distributed generation resources and 4 external suppliers.

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Section: Proposed Dr Programs Definition Methodologymentioning

confidence: 99%

“…Several studies have distinct models approaches to remunerate consumers, such as logarithmic or exponential models, as well as compensation based in elasticity models. These distinct approaches do not distinguish the several consumers, being applied the same remuneration model for all types of consumers [8]- [10].…”

mentioning

confidence: 99%

Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

Pereira

Soares

Faria

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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“…During peak hours, incentive based DR programs are offered to the consumers to reduce the load [81,82]. In [83], modeling and analysis of exponential emergency demand response programs (EDRP) have been presented for the SG systems. In [84] simulation of the distributed agent-based intelligent system for DR program has been presented in the SG environment.…”

Section: Numerical Analysis and Simulationmentioning

confidence: 99%

HEMSs and enabled demand response in electricity market: An overview

Khan

Razzaq

Khan

et al. 2015

Renewable and Sustainable Energy Reviews

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“…Many research papers have pointed out the benefits of EDR in spectra of power system application for reducing system cost and peak demand to establish reliability during emergency event. In papers [12]- [13], technical and economic advantages of EDR program on electrical load in Iranian power grid is presented. In [14], assessment of EDR in distribution system to pacify the overloading of specific overloaded node is studied.…”

Section: Introductionmentioning

confidence: 99%

Optimal decision making model of battery energy storage-assisted electric vehicle charging station considering incentive demand response

Upadhaya

Feng

Zhou

et al. 2019

8th Renewable Power Generation Conference (RPG 2019)

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Considering large scale implementation of electric vehicles (EVs), public EV charging stations are served as fuel tanks for EVs to meet the need of longer travelling distance and overcome the shortage of private charging piles. The allocation of local battery energy storage (BES) can enhance the flexibility of the EV charging station. This paper proposes an optimal decision making model of the BES-assisted EV charging station considering the incentive demand response. Firstly, the detailed models of the BES-assisted EV charging station are presented. Secondly, as a representative incentive demand response, the emergency demand response (EDR) model is introduced. Thirdly, based on the charging load forecast data, an optimal decision making model of the BESassisted EV charging station considering the EDR to maximize the charging station's operating profit is established. Finally, the feasibility of the proposed method is verified through case studies. The conclusions of this paper are as follows: 1) Through the optimal decision making model, correct and profitable EDR participation decision can be determined for the BES-assisted EV charging station effectively. 2) Local BES in the EV charging station can improve the charging station's ability to participate in the EDR.

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Studying emergency demand response programs based on exponential modeling in smart grids

Cited by 11 publications

References 7 publications

Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

HEMSs and enabled demand response in electricity market: An overview

Optimal decision making model of battery energy storage-assisted electric vehicle charging station considering incentive demand response

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