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Optimization-based provincial hybrid renewable and non-renewable energy planning – A case study of Shanxi, China

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  • Liu, Yuan
  • He, Li
  • Shen, Jing

Abstract

Energy planning is crucial to regional sustainable development, it contributes to dealing with electricity demand and supply effectively and tackling air-pollution control in a long-term view. However, the planning is complicated with various factor interrelationships and uncertainties. In this paper, an inexact Bi-level optimization method based on provincial scale hybrid renewable and non-renewable energy planning is developed. This method incorporates Analytic Hierarchy Process based on induced ordered weighted averaging operator and demand side management policies (IOWA-AHP-DSM), interval linear programming (ILP), and bi-level programming method (BLP) into electric power system (EPS) to optimize energy planning and air pollution control. A case study with both environmental and economic objects in Shanxi Province, China, are involved to demonstrate the availability of this method. Seven renewable energy proportion scenarios (0%, 5%, 10%, 15%, 20%, 25% and 30%) are set in this study. Results show that as the proportion increases, the amount of power generation and capacity expansion from natural gas and renewable energy resources increases, while the amount of power from coal and oil, the pollutants emissions and the trading volume of SO2 decreases. According to the satisfaction degrees of these solutions, results show that it meets both goals when the proportion is 20%.

Suggested Citation

  • Liu, Yuan & He, Li & Shen, Jing, 2017. "Optimization-based provincial hybrid renewable and non-renewable energy planning – A case study of Shanxi, China," Energy, Elsevier, vol. 128(C), pages 839-856.
    • Handle: RePEc:eee:energy:v:128:y:2017:i:c:p:839-856
    DOI: 10.1016/j.energy.2017.03.092
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    2. Huang, Xiaojian & Luo, Xianglong & Chen, Jianyong & Yang, Zhi & Chen, Ying & María Ponce-Ortega, José & El-Halwagi, Mahmoud M., 2018. "Synthesis and dual-objective optimization of industrial combined heat and power plants compromising the water–energy nexus," Applied Energy, Elsevier, vol. 224(C), pages 448-468.
    3. Xu, Jiuping & Wang, Fengjuan & Lv, Chengwei & Huang, Qian & Xie, Heping, 2018. "Economic-environmental equilibrium based optimal scheduling strategy towards wind-solar-thermal power generation system under limited resources," Applied Energy, Elsevier, vol. 231(C), pages 355-371.
    4. Ma, Haoran, 2022. "Prediction of industrial power consumption in Jiangsu Province by regression model of time variable," Energy, Elsevier, vol. 239(PB).
    5. Yuan Liu & Qinliang Tan & Jian Han & Mingxin Guo, 2021. "Energy–Water–CO 2 Synergetic Optimization Based on a Mixed-Integer Linear Resource Planning Model Concerning the Demand Side Management in Beijing’s Power Structure Transformation," Energies, MDPI, vol. 14(11), pages 1-17, June.
    6. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    7. Li, Chong & Zhou, Dequn & Zheng, Yuan, 2018. "Techno-economic comparative study of grid-connected PV power systems in five climate zones, China," Energy, Elsevier, vol. 165(PB), pages 1352-1369.

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