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Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches
Author(s) -
Helistö Niina,
Kiviluoma Juha,
Holttinen Hannele,
Lara Jose Daniel,
Hodge BriMathias
Publication year - 2019
Publication title -
wiley interdisciplinary reviews: energy and environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 35
eISSN - 2041-840X
pISSN - 2041-8396
DOI - 10.1002/wene.341
Subject(s) - electric power system , operational planning , computer science , variable (mathematics) , operations research , procurement , portfolio , reliability engineering , variable renewable energy , investment (military) , industrial engineering , mathematical optimization , power (physics) , engineering , economics , mathematics , mathematical analysis , physics , management , quantum mechanics , politics , political science , law , financial economics
In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four‐level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft‐linked investment and operational models, (3a) bidirectionally soft‐linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co‐optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics. This article is categorized under: Wind Power > Systems and Infrastructure Energy Systems Analysis > Economics and Policy Energy Policy and Planning > Economics and Policy