The large scale introduction of variable and limitedly predictable renewables requires flexible power system operation, enabled by, i.a., dynamic power plant operation, storage, demand response and enhanced interconnections. The fast start-up capabilities of combined-cycle gas turbines (CCGTs) are crucial in this regard. However, these non-standard operating conditions significantly reduce the lifetime of critical turbine components, as reflected in long-term service agreements (LTSAs). This should also be reflected in short-term scheduling models. In light of this challenge, we apply a unit commitment model that allows multiple start-up loading modes while accounting for the corresponding turbine maintenance costs based on LTSAs. Leveraging this model, we investigated the need for fast start-up capabilities of a set of CCGTs as part of a small scale test system considering various shares of renewables and dynamic reserve requirements. We have found that fast starts are often cost-optimal despite their greater turbine maintenance costs and a cost reduction of around 1 % is obtained when considering more costly fast start-up modes when scheduling. Furthermore, cost-optimal reserve sizing is a function of the planning frequency and is reduced by fast starting capabilities. We conclude that taking advantage of fast start-up capabilities benefits the electricity generation system and yields a significant cost reduction.

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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.

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