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Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

Author

Listed:
  • Arslan Ahmad Bashir

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Mahdi Pourakbari Kasmaei

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Amir Safdarian

    (Electrical Engineering Department, Sharif University of Technology, Tehran 11365-11155, Iran)

  • Matti Lehtonen

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

Abstract

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

Suggested Citation

  • Arslan Ahmad Bashir & Mahdi Pourakbari Kasmaei & Amir Safdarian & Matti Lehtonen, 2018. "Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building," Energies, MDPI, vol. 11(9), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2409-:d:169275
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    References listed on IDEAS

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    1. Zafar Iqbal & Nadeem Javaid & Saleem Iqbal & Sheraz Aslam & Zahoor Ali Khan & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2018. "A Domestic Microgrid with Optimized Home Energy Management System," Energies, MDPI, vol. 11(4), pages 1-39, April.
    2. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    3. Jaeyeong Yoo & Byungsung Park & Kyungsung An & Essam A. Al-Ammar & Yasin Khan & Kyeon Hur & Jong Hyun Kim, 2012. "Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs," Energies, MDPI, vol. 5(4), pages 1-19, April.
    4. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2018. "Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems," Energies, MDPI, vol. 11(5), pages 1-11, May.
    5. Salom, Jaume & Marszal, Anna Joanna & Widén, Joakim & Candanedo, José & Lindberg, Karen Byskov, 2014. "Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data," Applied Energy, Elsevier, vol. 136(C), pages 119-131.
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    Cited by:

    1. Javier Huertas Tato & Miguel Centeno Brito, 2018. "Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production," Energies, MDPI, vol. 12(1), pages 1-12, December.
    2. Madia Safdar & Ghulam Amjad Hussain & Matti Lehtonen, 2019. "Costs of Demand Response from Residential Customers’ Perspective," Energies, MDPI, vol. 12(9), pages 1-16, April.
    3. Andres Annuk & Wahiba Yaïci & Matti Lehtonen & Risto Ilves & Toivo Kabanen & Peep Miidla, 2021. "Simulation of Energy Exchange between Single Prosumer Residential Building and Utility Grid," Energies, MDPI, vol. 14(6), pages 1-13, March.
    4. Arslan Ahmad Bashir & Andreas Lund & Mahdi Pourakbari-Kasmaei & Matti Lehtonen, 2021. "Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities," Energies, MDPI, vol. 14(7), pages 1-20, March.
    5. Mattia Dallapiccola & Grazia Barchi & Jennifer Adami & David Moser, 2021. "The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector," Energies, MDPI, vol. 14(8), pages 1-18, April.
    6. Juha Koskela & Pertti Järventausta, 2023. "Demand Response with Electrical Heating in Detached Houses in Finland and Comparison with BESS for Increasing PV Self-Consumption," Energies, MDPI, vol. 16(1), pages 1-25, January.
    7. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    8. Masoumeh Javadi & Mousa Marzband & Mudathir Funsho Akorede & Radu Godina & Ameena Saad Al-Sumaiti & Edris Pouresmaeil, 2018. "A Centralized Smart Decision-Making Hierarchical Interactive Architecture for Multiple Home Microgrids in Retail Electricity Market," Energies, MDPI, vol. 11(11), pages 1-22, November.
    9. Arslan Ahmad Bashir & Matti Lehtonen, 2019. "Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland," Energies, MDPI, vol. 12(6), pages 1-16, March.

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