scholarly journals Robust management and optimization strategy of energy hub based on uncertainties probability modelling in the presence of demand response programs

2021 ◽  
Author(s):  
Saied Iranpour Mobarakeh ◽  
Ramtin Sadeghi ◽  
Hadi Saghafi ◽  
Majid Delshad
Keyword(s):  
Demand Response ◽  
Optimization Strategy ◽  
Energy Hub ◽  
Demand Response Programs ◽  
Probability Modelling

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.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

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