|
|
||||||||||||
|
Abstract The study examines how high levels of renewable penetration shape the economic and operational performance of the Spanish power system. It develops a monthly multicriteria index using the TOPSIS–CRITIC framework and evaluates two viewpoints. The demand perspective values tariff moderation and price stability, while the generator perspective focuses on value capture and revenue recovery. Both perspectives rely on the same data and weights; they diverge only in how each criterion is classified as a benefit or a cost. This structure sharpens the contrast between affordability for consumers and economic viability for producers. The 2025 results reveal high renewable participation alongside persistent intraday volatility and marked swings in economic margins. Winter exhibits positive average margins and a modest share of energy priced below LCOE. From March to May, margins turn negative and nearly all renewable energy trades below LCOE, reflecting abundant supply and depressed prices. Price-level variables add limited explanatory power, whereas volatility, penalized energy, and hourly alignment (ICPG) drive most of the variation in index values. System performance, therefore, does not follow a simple seasonal pattern; it reflects specific interactions between market signals and the evolving mix of renewable technologies. Key words: Renewable Energy Markets, TOPSIS–CRITIC, Capture Price Dynamics, Solar-Wind Integration.
References [1] D. M. Newbery, M. G. Pollitt, R. A. Ritz, and W. Strielkowski, “Market design for a high-renewables European [2] L. Hirth, “The market value of variable renewables: The effect of solar and wind power variability on their relative price,” [3] E. Gelabert, X. Labandeira, and P. Linares, “An ex-post analysis of the effect of renewables on electricity prices,” Energy Economics, vol. 33, no. 1, pp. S59–S65, 2011. [4] K. Würzburg, X. Labandeira, and P. Linares, “Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria,” Energy Economics, vol. 62, pp. 230–236, 2017. [5] A. Mills and R. Wiser, “Strategies to mitigate declines in the economic value of wind and solar,” Energy Policy, vol. 80, pp. 197–203, 2015. [6] L. Hirth, F. Ueckerdt, and O. Edenhofer, “Integration costs revisited: An economic framework for wind and solar [7] T. Kaya and C. Kahraman, “Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology,” Expert Systems with Applications, vol. 38, no. 6, pp. 6577–6585, 2011. [8] I. Hassan, I. Alhamrouni, and N. H. Azhan, “A CRITIC–TOPSIS multi-criteria decision-making approach for optimum [9] J. Mathebula and N. Mbuli, “Application of TOPSIS for multi-criteria decision analysis (MCDA) in power systems: A [10] Y. Zhang et al., “Hourly electricity price prediction for electricity market with high proportion of wind and solar power,” [11] C. Flygare et al., “Correlation as a method to assess electricity users’ contributions to grid peak loads: A case study,” [12] Lazard Ltd., “Levelized Cost of Energy Analysis,” 2023. https://www.lazard.com/research-insights/2023-levelized-cost-ofenergyplus/?utm [13] International Renewable Energy Agency (IRENA), “Renewable Power Generation Costs in 2023,” 2023.
|
||||||||||||
![]() |
||||||||||||
![]() |
||||||||||||
|
||||||||||||