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Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

Author

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  • Keon Baek

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

  • Woong Ko

    (Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

  • Jinho Kim

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

Abstract

This study proposes optimal day-ahead demand response (DR) participation strategies and distributed energy resource (DER) management in a residential building under an individual DR contract with a grid-system operator. First, this study introduces a DER management system in the residential building for participation to the day-ahead DR market. The distributed photovoltaic generation system (PV) and energy-storage system (ESS) are applied to reduce the electricity demand in the building and sell surplus energy on the grid. Among loads in the building, lighting (LTG) and heating, ventilation, and air conditioning (HVAC) loads are included in the DR program. In addition, it is assumed that a power management system of an electric vehicle (EV) charging station is integrated the DER management system. In order to describe stochastic behavior of EV owners, the uncertainty of EV is formulated based on their arrival and departure scenarios. For measuring the economic efficiency of the proposed model, we compare it with the DER self-consuming operation model without DR participation. The problem is solved using mixed integer linear programming to minimize the operating cost. The results in summer and winter are analyzed to evaluate the proposed algorithm’s validity. From these results, the proposed model can be confirmed as reducing operation cost compared to the reference model through optimal day-ahead DR capacity bidding and implementation.

Suggested Citation

  • Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2810-:d:250487
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    References listed on IDEAS

    as
    1. Toledo, Olga Moraes & Oliveira Filho, Delly & Diniz, Antônia Sônia Alves Cardoso, 2010. "Distributed photovoltaic generation and energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 506-511, January.
    2. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    3. Arteconi, Alessia & Patteeuw, Dieter & Bruninx, Kenneth & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2016. "Active demand response with electric heating systems: Impact of market penetration," Applied Energy, Elsevier, vol. 177(C), pages 636-648.
    4. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    5. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    6. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    7. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2015. "Assessment of direct normal irradiance and cloud connections using satellite data over Australia," Applied Energy, Elsevier, vol. 143(C), pages 301-311.
    8. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    9. Hagerman, Shelly & Jaramillo, Paulina & Morgan, M. Granger, 2016. "Is rooftop solar PV at socket parity without subsidies?," Energy Policy, Elsevier, vol. 89(C), pages 84-94.
    10. Bradley, Peter & Leach, Matthew & Torriti, Jacopo, 2013. "A review of the costs and benefits of demand response for electricity in the UK," Energy Policy, Elsevier, vol. 52(C), pages 312-327.
    11. Pearre, Nathaniel S. & Swan, Lukas G., 2015. "Technoeconomic feasibility of grid storage: Mapping electrical services and energy storage technologies," Applied Energy, Elsevier, vol. 137(C), pages 501-510.
    12. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    13. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    14. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    15. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    16. Varkani, Ali Karimi & Daraeepour, Ali & Monsef, Hassan, 2011. "A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets," Applied Energy, Elsevier, vol. 88(12), pages 5002-5012.
    17. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    18. Alimohammadisagvand, Behrang & Jokisalo, Juha & Sirén, Kai, 2018. "Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building," Applied Energy, Elsevier, vol. 209(C), pages 167-179.
    19. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    20. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    21. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
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