Smart Energy and Transport Solutions VTT Technical Research Centre of Finland Espoo Finland
Power Systems Engineering Center National Renewable Energy Laboratory Golden Colorado, Energy and Resources Group University of California Berkeley California
Power Systems Engineering Center National Renewable Energy Laboratory Golden Colorado, Department of Electrical Computer and Energy Engineering, University of Colorado Boulder Boulder Colorado
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.
Helistö, Niina, et al. "Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches." Wiley Interdisciplinary Reviews. Energy and Environment, vol. 8, no. 5, Mar. 2019. https://doi.org/10.1002/wene.341
Helistö, Niina, Kiviluoma, Juha, Holttinen, Hannele, Lara, Jose Daniel, & Hodge, Bri‐Mathias (2019). Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches. Wiley Interdisciplinary Reviews. Energy and Environment, 8(5). https://doi.org/10.1002/wene.341
Helistö, Niina, Kiviluoma, Juha, Holttinen, Hannele, et al., "Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches," Wiley Interdisciplinary Reviews. Energy and Environment 8, no. 5 (2019), https://doi.org/10.1002/wene.341
@article{osti_1498566,
author = {Helistö, Niina and Kiviluoma, Juha and Holttinen, Hannele and Lara, Jose Daniel and Hodge, Bri‐Mathias},
title = {Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches},
annote = {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 },
doi = {10.1002/wene.341},
url = {https://www.osti.gov/biblio/1498566},
journal = {Wiley Interdisciplinary Reviews. Energy and Environment},
issn = {ISSN 2041-8396},
number = {5},
volume = {8},
place = {United States},
publisher = {Wiley Blackwell (John Wiley & Sons)},
year = {2019},
month = {03}}
2012 IEEE International Conference on Power System Technology (POWERCON 2012), 2012 IEEE International Conference on Power System Technology (POWERCON)https://doi.org/10.1109/PowerCon.2012.6401421