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Sajjad Ali
Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan

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Short Biography

SAJJAD ALI received the B.Sc. degree in computer information systems engineering from the University of Engineering and Technology at Peshawar, Pakistan, and the M.S. degree in computer systems engineering from the University of Engineering and Technology at Peshawar, where he is currently pursuing the Ph.D. degree. He is a lifetime Chartered Engineer of the Pakistan Engineering Council. He is working as a Lecturer in the Department of Telecommunication Engineering, University of Engineering and Technology, Mardan. He has authored or coauthored over 4 peer-reviewed research articles in reputed national, international journals and conferences. His research interests include optimization, planning, energy management, in smart/micro grids and cognitive networks.

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Journal article
Published: 15 April 2021 in Energies
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Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

ACS Style

Sajjad Ali; Imran Khan; Sadaqat Jan; Ghulam Hafeez. An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid. Energies 2021, 14, 2201 .

AMA Style

Sajjad Ali, Imran Khan, Sadaqat Jan, Ghulam Hafeez. An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid. Energies. 2021; 14 (8):2201.

Chicago/Turabian Style

Sajjad Ali; Imran Khan; Sadaqat Jan; Ghulam Hafeez. 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid." Energies 14, no. 8: 2201.

Journal article
Published: 02 November 2020 in Energies
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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.

ACS Style

Kalim Ullah; Sajjad Ali; Taimoor Khan; Imran Khan; Sadaqat Jan; Ibrar Shah; Ghulam Hafeez. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies 2020, 13, 5718 .

AMA Style

Kalim Ullah, Sajjad Ali, Taimoor Khan, Imran Khan, Sadaqat Jan, Ibrar Shah, Ghulam Hafeez. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies. 2020; 13 (21):5718.

Chicago/Turabian Style

Kalim Ullah; Sajjad Ali; Taimoor Khan; Imran Khan; Sadaqat Jan; Ibrar Shah; Ghulam Hafeez. 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs." Energies 13, no. 21: 5718.