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Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

Author

Listed:
  • Sheraz Aslam

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Nadeem Javaid

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Farman Ali Khan

    (COMSATS Institute of Information Technology, Attock 43730, Pakistan)

  • Atif Alamri

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Ahmad Almogren

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Wadood Abdul

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

Abstract

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

Suggested Citation

  • Sheraz Aslam & Nadeem Javaid & Farman Ali Khan & Atif Alamri & Ahmad Almogren & Wadood Abdul, 2018. "Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid," Sustainability, MDPI, vol. 10(4), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1245-:d:141883
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    References listed on IDEAS

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    2. Sheraz Aslam & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Khursheed Aurangzeb & Syed Irtaza Haider, 2017. "Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 10(12), pages 1-25, December.
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    4. van der Stelt, Sander & AlSkaif, Tarek & van Sark, Wilfried, 2018. "Techno-economic analysis of household and community energy storage for residential prosumers with smart appliances," Applied Energy, Elsevier, vol. 209(C), pages 266-276.
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    Cited by:

    1. Eric Galvan & Paras Mandal & Shantanu Chakraborty & Tomonobu Senjyu, 2019. "Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    2. Ciprian Sorandaru & Sorin Musuroi & Flaviu Mihai Frigura-Iliasa & Doru Vatau & Marian Dordescu, 2019. "Analysis of the Wind System Operation in the Optimal Energetic Area at Variable Wind Speed over Time," Sustainability, MDPI, vol. 11(5), pages 1-16, February.
    3. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Yang, Zhendong & Abbas, Qaiser & Hanif, Imran & Alharthi, Majed & Taghizadeh-Hesary, Farhad & Aziz, Babar & Mohsin, Muhammad, 2021. "Short- and long-run influence of energy utilization and economic growth on carbon discharge in emerging SREB economies," Renewable Energy, Elsevier, vol. 165(P1), pages 43-51.
    5. Jian Xiao & Wei Hou, 2022. "Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    6. Rasool Bukhsh & Nadeem Javaid & Zahoor Ali Khan & Farruh Ishmanov & Muhammad Khalil Afzal & Zahid Wadud, 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid," Energies, MDPI, vol. 11(12), pages 1-21, November.
    7. Syed Muhammad Mohsin & Tahir Maqsood & Sajjad Ahmed Madani, 2022. "Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
    8. Adia Khalid & Sheraz Aslam & Khursheed Aurangzeb & Syed Irtaza Haider & Mahmood Ashraf & Nadeem Javaid, 2018. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid," Energies, MDPI, vol. 11(12), pages 1-17, December.
    9. Amro M. Elshurafa & Mohammad H. Aldubyan, 2019. "State-of-Charge Effects on Standalone Solar-Storage Systems in Hot Climates: A Case Study in Saudi Arabia," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    10. Jingpeng Yue & Zhijian Hu & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2019. "A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    11. Faran Asghar & Adnan Zahid & Muhammad Imtiaz Hussain & Furqan Asghar & Waseem Amjad & Jun-Tae Kim, 2022. "A Novel Solution for Optimized Energy Management Systems Comprising an AC/DC Hybrid Microgrid System for Industries," Sustainability, MDPI, vol. 14(14), pages 1-19, July.

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