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A stochastic-robust optimization model for inter-regional power system planning

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  • Kang, Jidong
  • Wu, Zhuochun
  • Ng, Tsan Sheng
  • Su, Bin

Abstract

This study develops a stochastic-robust optimization model for inter-regional power system planning. The model is formulated as Mathematical Programs with Equilibrium Constraints (MPEC) with two levels of decision makers. In the bottom-level, various individual regions separately make generator investment, operating, and trading plans for the minimization of their own costs in a perfectly competitive market. In the top-level, a central system operator makes the investment plan of cross-border transmission lines for the minimization of total system cost, anticipating how generators in various regions respond to those investments. Two different levels of data uncertainties are considered in the problem, namely scenario and local uncertainties. The uncertainties are handled by combining the advantages of stochastic programming and robust optimization methods. The proposed model is applied for power system planning in the Association of Southeast Asian Nations (ASEAN) between 2020 and 2040. The modelling results show that the integration of a cross-border power grid results in a substantial shift in the generation portfolio, and can potentially reduce total costs by up to 6.0%. The cost savings primarily result from the increased utilization of renewable energy resources, facilitated by cross-border electricity transmission. We assessed the performance of our proposed approach in handling data uncertainties by comparing it with existing practices. The comparison results demonstrate that our stochastic-robust approach not only enhances the capacity expansion plan’s robustness to meet power demand and CO2 emissions targets, but also significantly reduces investment and operational costs.

Suggested Citation

  • Kang, Jidong & Wu, Zhuochun & Ng, Tsan Sheng & Su, Bin, 2023. "A stochastic-robust optimization model for inter-regional power system planning," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1234-1248.
    • Handle: RePEc:eee:ejores:v:310:y:2023:i:3:p:1234-1248
    DOI: 10.1016/j.ejor.2023.03.024
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