The capacity market is a response to potential capacity scarcity in the system. The missing money problem may occur as a result of the dynamic development of renewable energy sources because their capacity factors are significantly lower in comparison to those of conventional generating units. The capacity market is a response to capacity scarcity in dynamic growth in renewable energy sources with lower capacity factors than thermal power plants. It is a support mechanism that provides additional funds in order for generation companies to be ready to produce electricity in system stress events. So far, seven capacity auctions have been held for 2021–2027 delivery periods. Since the vast majority of capacity market units are coal-fired public thermal power plants and combined heat and power plants, the analysis of capacity auction results provides valuable findings on coal consumption in the years to come. With this in mind, the objective of the study is to investigate the potential of coal consumption resulting from the long-term capacity contracts signed thus far. For this purpose, a comprehensive analysis of the capacity auctions’ results is conducted, including the analysis of the duration of the contracts, the structure of ownership, and fuels used in power units. The results show that the figures relating to the consumption of steam coal in units that have won capacity auctions are around 21,306 thousand Mg for 2023 and decreasing to 9,603 thousand Mg for 2035. Although European restrictions were introduced to limit remuneration for high-emission units, the long-term contracts ensure that these will remain in the system and will have an impact on the total consumption of steam coal in the medium- and long-term in the Polish power system.

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R ² = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.