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A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

Author

Listed:
  • Ihsan Ullah

    (Department of Computer Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Muhammad Babar Rasheed

    (Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan)

  • Thamer Alquthami

    (Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Shahzadi Tayyaba

    (Department of Computer Engineering, The University of Lahore, Lahore 54000, Pakistan)

Abstract

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

Suggested Citation

  • Ihsan Ullah & Muhammad Babar Rasheed & Thamer Alquthami & Shahzadi Tayyaba, 2019. "A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid," Sustainability, MDPI, vol. 12(1), pages 1-36, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:184-:d:301774
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    References listed on IDEAS

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    Cited by:

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    2. Hang Liu & Yongcheng Wang & Shilin Nie & Yi Wang & Yu Chen, 2022. "Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation," Sustainability, MDPI, vol. 14(15), pages 1-29, July.
    3. Yeon-Ju Choi & Byeong-Chan Oh & Moses Amoasi Acquah & Dong-Min Kim & Sung-Yul Kim, 2021. "Optimal Operation of a Hybrid Power System as an Island Microgrid in South-Korea," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    4. Sławomir Bielecki & Tadeusz Skoczkowski & Lidia Sobczak & Janusz Buchoski & Łukasz Maciąg & Piotr Dukat, 2021. "Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users," Energies, MDPI, vol. 14(4), pages 1-32, February.

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