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An Optimization Model for Large–Scale Wind Power Grid Connection Considering Demand Response and Energy Storage Systems

Author

Listed:
  • Zhongfu Tan

    (Institute of Energy Economics and Environment, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Huanhuan Li

    (Institute of Energy Economics and Environment, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Liwei Ju

    (Institute of Energy Economics and Environment, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Yihang Song

    (Electric Power Research Institute, China South Power Grid, Guangzhou 510080, China
    These authors contributed equally to this work.)

Abstract

To reduce the influence of wind power output uncertainty on power system stability, demand response (DRPs) and energy storage systems (ESSs) are introduced while solving scheduling optimization problems. To simulate wind power scenarios, this paper uses Latin Hypercube Sampling (LHS) to generate the initial scenario set and constructs a scenario reduction strategy based on Kantorovich distance. Since DRPs and ESSs can influence the distribution of demand load, this paper constructs a joint scheduling optimization model for wind power, ESSs and DRPs under the objective of minimizing total coal cost, and constraints of power demand and supply balance, users’ demand elasticity, thermal units’ startup-shutdown, thermal units’ output power climbing and wind power backup service. To analyze the influences of ESSs and DRPs on system wind power consumption capacity, example simulation is made in a 10 thermal units system with a 1000 MW wind farm and 400 MW energy storage systems under four simulation scenarios. The simulation results show that the introduction of DRPs and ESSs could promote system wind power consumption capacity with significantly economic and environment benefits, which include less coal consumption and less pollutant emission; and the optimization effect reaches the optimum when DRPs and ESSs are both introduced.

Suggested Citation

  • Zhongfu Tan & Huanhuan Li & Liwei Ju & Yihang Song, 2014. "An Optimization Model for Large–Scale Wind Power Grid Connection Considering Demand Response and Energy Storage Systems," Energies, MDPI, vol. 7(11), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7282-7304:d:42280
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    Citations

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    Cited by:

    1. Zezhong Li & Xiangang Peng & Yilin Xu & Fucheng Zhong & Sheng Ouyang & Kaiguo Xuan, 2023. "A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response," Mathematics, MDPI, vol. 12(1), pages 1-21, December.
    2. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    3. Kim, H.Y. & Kim, M.K., 2017. "Optimal generation rescheduling for meshed AC/HIS grids with multi-terminal voltage source converter high voltage direct current and battery energy storage system," Energy, Elsevier, vol. 119(C), pages 309-321.
    4. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    5. Rongxiang Yuan & Jun Ye & Jiazhi Lei & Timing Li, 2016. "Integrated Combined Heat and Power System Dispatch Considering Electrical and Thermal Energy Storage," Energies, MDPI, vol. 9(6), pages 1-17, June.
    6. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    7. Mohamed Nabil Fathy Ibrahim & Peter Sergeant & Essam Rashad, 2016. "Simple Design Approach for Low Torque Ripple and High Output Torque Synchronous Reluctance Motors," Energies, MDPI, vol. 9(11), pages 1-14, November.
    8. Muhammad Saeed Uz Zaman & Syed Basit Ali Bukhari & Khalid Mousa Hazazi & Zunaib Maqsood Haider & Raza Haider & Chul-Hwan Kim, 2018. "Frequency Response Analysis of a Single-Area Power System with a Modified LFC Model Considering Demand Response and Virtual Inertia," Energies, MDPI, vol. 11(4), pages 1-20, March.
    9. Peng Cheng & Ning Liang & Ruiye Li & Hai Lan & Qian Cheng, 2019. "Analysis of Influence of Ship Roll on Ship Power System with Renewable Energy," Energies, MDPI, vol. 13(1), pages 1-20, December.
    10. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    11. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    12. Ho-Young Kim & Mun-Kyeom Kim & San Kim, 2017. "Multi-Objective Scheduling Optimization Based on a Modified Non-Dominated Sorting Genetic Algorithm-II in Voltage Source Converter−Multi-Terminal High Voltage DC Grid-Connected Offshore Wind Farms wit," Energies, MDPI, vol. 10(7), pages 1-21, July.

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