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An electricity generation system adequacy assessment aims to generate statistically significant adequacy indicators given projected developments in, i.a., renewable and conventional generation, demand, demand response and energy storage availability. Deterministic unit commitment (DUC) models with exogenous reserve requirements, as often used in today's adequacy studies to represent day-today power system operations, do not account for the contribution of operating reserves to the adequacy of the system. Hence, the adequacy metrics obtained from such an analysis represent a worst-case estimate and should be interpreted with care. In this paper, we propose to use a DUC model with a set of state-of-the-art probabilistic reserve constraints (DUC-PR). The performance of the DUC-PR model in the context of adequacy assessments is studied in a numerical case study. The Expected Energy Not Served (EENS) volume obtained with the DUC model is shown to be a poor estimate of the true EENS volume. In contrast, the DUC-PR methodology yields an accurate estimate of the EENS volume without significantly increasing the computational burden. Policy makers should encourage adopting novel operational power system models, such as the DUC-PR model, to accurately estimate the contribution of operating reserves to system adequacy. Highlights  We leverage a state-of-the-art generation adequacy assessment power system model  The model incorporates probabilistic operating reserve requirements  The contribution of operating reserves to system adequacy is accurately captured  The methodology yields more accurate adequacy indices than deterministic studies  Policy makers should adopt these novel power system models in adequacy assessments Highlights We acknowledge research funding from the European Commission Joint Research Centre (JRC), Ispra, Directorate CC .3 Energy system, distribution and markets Unit. Declarations of interest: none.

This study presents an analytical framework supporting coal regions in a strategy toward the clean energy transition. The proposed approach uses a combination of value chain analysis and energy sector analysis that enables a comprehensive assessment considering local specificities. Its application to a case study of the Slovakian region Upper Nitra demonstrates practical examples of opportunities and challenges. The value chain analysis evaluates the coal mining industry, from coal extraction to electricity generation, in terms of jobs and business that are at risk by the closure of the coal mines. The complementary energy system analysis focuses on diversification of the energy mix, environmental impacts, and feasibility assessment of alternative energy technologies to the coal combusting sources. The results show a net positive cost benefit for all developed scenarios of replacing the local existing coal power plant. Although the installation of a new geothermal plant is estimated to be the most expensive option from our portfolio of scenarios, it presents the highest CO2 reduction in the electricity generation in Slovakia—34% less compare to the system employing the existing power plant. In addition, the development of a new industrial polo around deep geothermal technology can boost the economic activity in the region by attracting investments in companies providing geological exploration services, transferring the local knowledge from the coal mining industry into an emerging sector.

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