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Optimization modeling to support renewables integration in power systems

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  • Pereira, Sérgio
  • Ferreira, Paula
  • Vaz, A.I.F.

Abstract

This study focuses on the problem of generation expansion planning and the integration of an increasing share of renewable energy sources (RES) technologies in the power grid. A survey of papers addressing the use of optimization models for electricity generation planning is presented. From this, an electricity planning model able to integrate thermal and RES power plants was proposed. An analysis of different electricity scenarios for a mixed hydro–thermal–wind power system is presented using the proposed mixed integer optimization model. The results show the importance of these tools to support the strategic energy policy decision making under different regulatory or political scenarios. The expected impacts in terms of costs and CO2 emissions are evaluated for a 10 year planning period, and a set of optimal scenarios is analyzed. The use of the model to obtain and characterize close to optimal scenarios is shown to be strategically useful. In particular the impact of different wind power scenarios is addressed, demonstrating the relevance of assessing other possible strategies that, despite not being original Pareto solutions, may be worth considering by the decision makers.

Suggested Citation

  • Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2016. "Optimization modeling to support renewables integration in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 316-325.
    • Handle: RePEc:eee:rensus:v:55:y:2016:i:c:p:316-325
    DOI: 10.1016/j.rser.2015.10.116
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    7. Zhang, Yi & Sun, Hexu & Tan, Jianxin & Li, Zheng & Hou, Weimin & Guo, Yingjun, 2022. "Capacity configuration optimization of multi-energy system integrating wind turbine/photovoltaic/hydrogen/battery," Energy, Elsevier, vol. 252(C).
    8. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
    9. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    10. Wu, C.B. & Guan, P.B. & Zhong, L.N. & Lv, J. & Hu, X.F. & Huang, G.H. & Li, C.C., 2020. "An optimized low-carbon production planning model for power industry in coal-dependent regions - A case study of Shandong, China," Energy, Elsevier, vol. 192(C).
    11. Xiaowen Ding & Lin Liu & Guohe Huang & Ye Xu & Junhong Guo, 2019. "A Multi-Objective Optimization Model for a Non-Traditional Energy System in Beijing under Climate Change Conditions," Energies, MDPI, vol. 12(9), pages 1-21, May.
    12. Xiangyu Kong & Jingtao Yao & Zhijun E & Xin Wang, 2019. "Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources," Energies, MDPI, vol. 12(13), pages 1-20, June.
    13. Ávila, Leandro & Mine, Miriam R.M & Kaviski, Eloy & Detzel, Daniel H.M., 2021. "Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study," Renewable Energy, Elsevier, vol. 167(C), pages 685-699.
    14. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2017. "Generation expansion planning with high share of renewables of variable output," Applied Energy, Elsevier, vol. 190(C), pages 1275-1288.
    15. Rodgers, Mark D. & Coit, David W. & Felder, Frank A. & Carlton, Annmarie, 2018. "Generation expansion planning considering health and societal damages – A simulation-based optimization approach," Energy, Elsevier, vol. 164(C), pages 951-963.
    16. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    17. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
    18. Guo, Zheng & Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian & Sun, Yong, 2017. "A multi-region load dispatch model for the long-term optimum planning of China’s electricity sector," Applied Energy, Elsevier, vol. 185(P1), pages 556-572.
    19. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    20. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Georgiadis, Michael C. & Papaioannou, George & Dikaiakos, Christos, 2016. "A mid-term, market-based power systems planning model," Applied Energy, Elsevier, vol. 179(C), pages 17-35.
    21. Sinem Yapar Saçık & Nihal Yokuş & Mehmet Alagöz & Turgut Yokuş, 2020. "Optimum Renewable Energy Investment Planning in Terms of Current Deficit: Turkey Model," Energies, MDPI, vol. 13(6), pages 1-21, March.
    22. Wooyoung Jeon & Chul-Yong Lee, 2019. "Estimating the Cost of Solar Generation Uncertainty and the Impact of Collocated Energy Storage: The Case of Korea," Sustainability, MDPI, vol. 11(5), pages 1-18, March.

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