scholarly journals A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

Energies ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 525 ◽  
Author(s):  
Jia Ning ◽  
Yi Tang ◽  
Qian Chen ◽  
Jianming Wang ◽  
Jianhua Zhou ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Strategy ◽  
Smart Appliances ◽  
Coordinated Optimization

scholarly journals Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

2018 ◽  
Vol 8 (3) ◽  
pp. 408 ◽  
Author(s):  
Radu Godina ◽  
Eduardo Rodrigues ◽  
Edris Pouresmaeil ◽  
João Matias ◽  
João Catalão
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Demand Response ◽  
Optimization Strategy ◽  
Home Energy Management

Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium

2015 ◽  
Vol 155 ◽  
pp. 79-90 ◽  
Author(s):  
R. D’hulst ◽  
W. Labeeuw ◽  
B. Beusen ◽  
S. Claessens ◽  
G. Deconinck ◽  
...  
Keyword(s):  
Demand Response ◽  
Pilot Test ◽  
Smart Appliances ◽  
Response Flexibility

scholarly journals An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5718
Author(s):  
Kalim Ullah ◽  
Sajjad Ali ◽  
Taimoor Ahmad Khan ◽  
Imran Khan ◽  
Sadaqat Jan ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Carbon Emission ◽  
Renewable Energy Sources ◽  
Operating Cost ◽  
Energy Sources ◽  
Optimization Strategy ◽  
Model Based ◽  
Demand Response Programs ◽  
Operation Cost

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.

scholarly journals Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1255 ◽  
Author(s):  
Huiru Zhao ◽  
Hao Lu ◽  
Bingkang Li ◽  
Xuejie Wang ◽  
Shiying Zhang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Renewable Energy ◽  
Demand Response ◽  
Storage System ◽  
Peak Load ◽  
High Price ◽  
Optimization Strategy ◽  
Response Cost ◽  
Load Curve ◽  
Operation Cost

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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