scholarly journals Optimization strategy of price‐based demand response considering the bidirectional feedback effect

Author(s):  
Hejun Yang ◽  
Lei Wang ◽  
Yinghao Ma ◽  
Dabo Zhang ◽  
Hongbin WU
2018 ◽  
Vol 8 (3) ◽  
pp. 408 ◽  
Author(s):  
Radu Godina ◽  
Eduardo Rodrigues ◽  
Edris Pouresmaeil ◽  
João Matias ◽  
João Catalão

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5718
Author(s):  
Kalim Ullah ◽  
Sajjad Ali ◽  
Taimoor Ahmad Khan ◽  
Imran Khan ◽  
Sadaqat Jan ◽  
...  

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.


Energies ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 525 ◽  
Author(s):  
Jia Ning ◽  
Yi Tang ◽  
Qian Chen ◽  
Jianming Wang ◽  
Jianhua Zhou ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1255 ◽  
Author(s):  
Huiru Zhao ◽  
Hao Lu ◽  
Bingkang Li ◽  
Xuejie Wang ◽  
Shiying Zhang ◽  
...  

More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.


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