**5. Conclusions**

Driven by the idea of transitioning to a green electricity grid, an hourly power flow analysis was conducted to understand the potential, limitations, and implications of using solar energy as a driver for decommissioning coal power plants. The weather-driven scenario analysis ensured the robustness of the results and recommendations. The analysis revealed that solar power could reduce about half of Kyushu's coal capacity with the aid of LNG. Beyond 12 GW, solar power could not reduce the minimum coal capacity necessary to ensure the system's reliability, but it could still reduce the coal generation and the overall CO2 emissions. The reduction in coal capacity comes at a cost, since solar power is still relatively more expensive in Japan. By installing 20 GW of solar PV systems and having 28 TWh of available LNG, the levelized CO2 emissions could be reduced by 37%, but this would increase the levelized cost of generation by 5.6%. Most of the price increase is owed to the price of solar electricity generation, which remains high in Japan. In Kyushu's case, this change could be achieved without constructing additional power plants, since the LNG plants are operated at a low LF. However, additional planning is necessary to acquire more LNG. Countries that use LNG plants as peak-load generators share the same potential, and the results show that a minor change in the system could have a significant impact on emission goals.

The results emphasized that solar power with the aid of LNG could partially replace coal capacity, but it alone could not phase-out coal. For energy planners who are only starting to increase their solar capacity, insights from this work could help with understanding the interactions between coal, solar, and LNG electricity generation. For planners in countries with a considerable amount of solar power (>8%), the results from this study could serve as a precaution by highlighting the risks of further increasing the solar power

penetration. Although solar power helped solve midday peak power, the problem remains because it simply shifted to periods where there is no solar energy. Summer and winter are challenging periods due to the increase in peak demand. Although it is counterintuitive, solar energy is not enough during summer, or, to be more precise, misaligned since the problem occurs in the late afternoon. Diurnal storage can address the misalignment in summer, but winter presents a more intricate problem, since the solar energy is insufficient. Thus, exploring other technologies that could further complement solar energy is necessary.

The weather-driven approach revealed the importance of weather in the analysis, as it affected the results to varying degrees. In addition, 400–600 MW of standby coal capacity is necessary due to the yearly fluctuations. Coal generation, coal load factor, curtailment rate, and CO2 emissions vary by 7–18%, 8–27%, 0–5%, and 6–8%, respectively. Identifying the representative year is crucial since it should cover the worst case, best case, and the cases in between. Energy planners and policymakers should consider the weather when analyzing energy plans, as it could provide a range of values that can guide them in making the correct decisions. Since the approach can generate scenarios based on weather data, it could also be used for storage assessment and capacity planning. The approach could also be used for grid expansion planning by increasing the number of buses and modeling multiple demands. These energy planning topics could also benefit from the range of insights generated through the weather-driven approach.

**Author Contributions:** Conceptualization, S.M.G.D. and K.N.I.; methodology, S.M.G.D.; software, S.M.G.D.; validation, S.M.G.D. and K.N.I.; formal analysis, S.M.G.D.; investigation, S.M.G.D.; resources, K.N.I.; data curation, S.M.G.D.; writing—original draft preparation, S.M.G.D.; writing review and editing, K.N.I.; visualization, S.M.G.D.; supervision, K.N.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data and code presented in this study are openly available on GitHub at https://github.com/smdumlao/demandfingerprint/tree/main/papers/coaldecommissioning (accessed on 16 April 2021).

**Acknowledgments:** The authors are grateful for the financial support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan for the doctoral studies of Samuel Matthew Dumlao at Kyoto University. The authors are also grateful to the Ambitious Intelligence Dynamic Acceleration (AIDA) Program of Kyoto University for the financial support.

**Conflicts of Interest:** The authors declare no conflict of interest.
