An inexact fractional multi-stage programming (IFMSP) method for planning renewable electric power system

Xiajing Lin, Guohe Huang, Xiong Zhou and Yuanyuan Zhai

Renewable and Sustainable Energy Reviews, 2023, vol. 187, issue C

Abstract: The growing levels of greenhouse gas (GHG) emissions have made renewable energy sources a crucial aspect of power generation, especially in developing countries. However, with more investment in additional renewable energy components and the mandatory decommissioning requirement of the thermal stations, the uncertainty of renewable energy availability and the changeable policy environment have brought great complexity to the electric power systems. Hence, an innovative approach, the inexact fractional multistage programming (IFMSP), has been proposed to address the challenges of electric power system planning in transitioning to clean energy, specifically in South Africa from 2021 to 2050. The IFMSP approach balances economic benefits and GHG emission mitigation, providing a more constrained optimum interval solution than binary integer programming. The results illustrated a decline in the expansion of conventional energy sources, with natural gas, wind energy and solar energy taking the place of thermal power generation. In terms of performance, solar power ranks second to wind. By assessing various expansion scenarios, it is demonstrated that zero nuclear energy growth would yield more competitive economic advantages and fewer GHG emissions than no geothermal expansion, whereas a 20% increase in gas expansion capacity would achieve the most optimal ratio at [304.2, 317.3] (Rand/t CO2 eq). By utilizing the proposed approach, decision-makers in developing countries can generate multiple expansion scenarios for Belt and Road Initiative (BRI) investments. This enables them to identify generation modes, capacity expansion schemes, financial planning, and carbon tax regulations, even under complex uncertainties.

Keywords: Developing country; Interval programming; Linear fractional programming; Stochastic multi-stage programming; Non-coal-fired technology; Greenhouse gas (GHG) emissions (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.rser.2023.113611

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