Economic Modeling and Policy Instruments for Solar Energy Deployment: A Comprehensive Framework for Sustainable Energy Transition
Downloads
The global imperative for decarbonization has positioned solar photovoltaic (PV) technology as a critical component in achieving sustainable energy systems. This study presents a comprehensive analysis of economic modeling frameworks and policy instruments essential for ex-ante evaluation of solar energy investments. Through systematic examination of three distinct modeling paradigms—least-cost generation expansion models, integrated energy system models, and macroeconomic assessment tools—we quantify the economic value proposition of solar technologies across diverse deployment scenarios.
Our empirical analysis reveals a paradigmatic shift in solar energy economics, characterized by a 99.0% cost reduction in PV modules between 1982 and 2023, fundamentally altering the investment landscape from subsidy-dependent to market-competitive deployment. We identify critical barriers including financing constraints (85% importance), technological limitations (75% importance), and regulatory uncertainties (70% importance) that continue to impede optimal deployment strategies. The research contributes a novel decision-analytical framework integrating technical performance metrics with economic indicators, providing actionable insights for policymakers and investors.
Results demonstrate that while solar energy exhibits substantial economic potential, successful integration requires sophisticated modeling approaches that balance theoretical rigor with practical applicability. The proposed framework addresses existing gaps in standardized evaluation methodologies, offering a robust foundation for evidence-based decision-making in solar energy development. Global solar investment reached $220 billion in 2023, representing a 175% increase from 2010 levels, with China maintaining 55% market share.
Breyer, C., & Gerlach, A. (2013). Global overview on grid-parity. Progress in Photovoltaics: Research and Applications, 21(1), 121-136.
https://doi.org/10.1002/pip.1254
Cole, W., Frazier, A. W., Donohoo-Vallett, P., Mai, T., & Das, P. (2017). 2016 Standard Scenarios Report: A U.S. Electricity Sector Outlook. National Renewable Energy Laboratory. NREL/TP-6A20-66939.
Couture, T., & Gagnon, Y. (2010). An analysis of feed-in tariff remuneration models: Implications for renewable energy investment. Energy Policy, 38(2), 955-965. https://doi.org/10.1016/j.enpol.2009.10.047
Abdussalam, S. O. (2023). Recognizing and charging generators for their contribution to network voltage support in a market environment. In First Libyan International Conference on Engineering Sciences & Applications (FLICESA_LA), Tripoli, Libya.
Denholm, P., Nunemaker, J., Gagnon, P., & Cole, W. (2021). The challenges of achieving a 100% renewable electricity system in the United States. Joule, 5(6), 1331-1352.
https://doi.org/10.1016/j.joule.2021.03.028
Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.
Fraunhofer ISE. (2023). Photovoltaics Report 2023. Fraunhofer Institute for Solar Energy Systems. Retrieved from
https://www.ise.fraunhofer.de/en/publications/studies-reports/photovoltaics-report.html
Green, M. A. (2019). How did solar cells get so cheap? Joule, 3(3), 631-633.
https://doi.org/10.1016/j.joule.2019.02.010
Hirth, L. (2013). The market value of variable renewables: The effect of solar wind power variability on their relative price. Energy Economics, 38, 218-236.
https://doi.org/10.1016/j.eneco.2013.02.004
Hirth, L., Ueckerdt, F., & Edenhofer, O. (2016). Why wind is not coal: On the economics of electricity generation. The Energy Journal, 37(3), 1-27. https://doi.org/10.5547/01956574.37.3.lhir
IEA. (2023). World Energy Outlook 2023. International Energy Agency. Retrieved from https://www.iea.org/reports/world-energy-outlook-2023
IRENA. (2023). Renewable Power Generation Costs in 2022. International Renewable Energy Agency. Retrieved from
https://www.irena.org/publications/2023/Aug/Renewable-Power-Generation-Costs-in-2022
Kumbaroğlu, G., Madlener, R., & Demirel, M. (2008). A real options evaluation model for the diffusion prospects of new renewable power generation technologies. Energy Economics, 30(4), 1882-1908. https://doi.org/10.1016/j.eneco.2007.06.004
Loulou, R., Goldstein, G., Kanudia, A., Lettila, A., & Remme, U. (2016). Documentation for the TIMES Model PART I. Energy Technology Systems Analysis Programme. Retrieved from https://iea-etsap.org/docs/Documentation_for_the_TIMES_Model-Part-I_July-2016.pdf
Nemet, G. F. (2006). Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy, 34(17), 3218-3232. https://doi.org/10.1016/j.enpol.2005.06.020
Ueckerdt, F., Hirth, L., Luderer, G., & Edenhofer, O. (2013). System LCOE: What are the costs of variable renewables? Energy, 63, 61-75. https://doi.org/10.1016/j.energy.2013.10.072
Wei, M., Patadia, S., & Kammen, D. M. (2010). Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US? Energy Policy, 38(2), 919-931. https://doi.org/10.1016/j.enpol.2009.10.044
Copyright (c) 2025 Salem Osman Abdussalam
This work is licensed under a Creative Commons Attribution 4.0 International License.
All Content should be original and unpublished.
