Incorporation of generator maintenance scheduling with long-term power sector forecasting and planning studies

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Ilseven E., Göl M.

IET GENERATION TRANSMISSION & DISTRIBUTION, vol.14, pp.2581-2591, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14
  • Publication Date: 2020
  • Doi Number: 10.1049/iet-gtd.2019.1545
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2581-2591
  • Keywords: power markets, maintenance engineering, power generation economics, power generation scheduling, load forecasting, power generation planning, hydroelectric generators, GMS algorithm, storage hydropower plants, renewable electricity generation modelling, price-dependent parts, renewable generation expectation, storage hydropower plant utilisation, long-term price forecasting, maintenance effect, long-term power sector forecasting, dynamic generator maintenance scheduling algorithm, electricity price, merit-order dispatch, actual maintenance planning studies, Turkish system, reserve capacity calculation, DYNAMICS, SYSTEMS, OPTIMIZATION, MODEL, ALTERNATIVES, RELIABILITY, POLICY, UNITS


The objective of this study is to propose a dynamic generator maintenance scheduling (GMS) algorithm for long-term power sector forecasting and planning studies in which electricity price and the resulting supply composition are determined with merit-order dispatch. Compatible with the GMS algorithm, a reasonable strategy for the utilisation of storage hydropower plants along with clear definitions for each stage including must-run renewable electricity generation modelling, calculation of reserve capacity, derivation of scenarios for storage hydropower plants and problem formulation is presented. Generation from storage hydropower plants are modelled such as must-run and price-dependent parts, to better approximate reality. The proposed structure is tested with real data of the Turkish system, with a demand and capacity projection in the long term. The results are compared with the actual maintenance plan of the base year and the general profile is evaluated as satisfactory. The results show that the GMS plan and profile may significantly change based on hydro and renewable generation expectation, future capacity evolution, and storage hydropower plant utilisation. Therefore, the proposed GMS algorithm can be utilised especially in long-term price forecasting and supply modelling studies, instead of using a fixed factor to represent the maintenance effect on available generation capacity.