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Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model

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  • Lim, Juin Yau
  • Safder, Usman
  • How, Bing Shen
  • Ifaei, Pouya
  • Yoo, Chang Kyoo

Abstract

The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Suggested Citation

  • Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316883
    DOI: 10.1016/j.apenergy.2020.116302
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