On representative day selection for capacity expansion planning of power systems under extreme operating conditions

Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve pl...

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Vydáno v:International journal of electrical power & energy systems Ročník 137; s. 107697
Hlavní autoři: Li, Can, Conejo, Antonio J., Siirola, John D., Grossmann, Ignacio E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2022
Témata:
Capacity expansion planning
Clustering algorithm
Extreme operating conditions
Power systems
Renewable units
Representative day selection
Capacity expansion planning
Extreme operating conditions
Renewable units
Representative day selection
Power systems
Clustering algorithm
ISSN:0142-0615, 1879-3517
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Shrnutí:Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve planning horizons of several years, solving the fullspace models where the operating decisions corresponding to all the days is intractable. Therefore, some “representative days” are selected as a surrogate to the fullspace model. We present an input-based and a cost-based approach in combination with the k-means and the k-medoids clustering algorithms for representative day selection. The mathematical properties of the proposed algorithms are analyzed, including an approach to calculate the “optimality gap” of the investment decisions obtained from the representative day model to the fullspace model, and the relationship between the clustering error and the optimality gap. To capture the extreme operating conditions, two novel approaches, i.e., a “load shedding cost” approach and a “highest cost” approach, are proposed to identify the “extreme days”. We conclude with a case study based on the Electric Reliability Council of Texas (ERCOT) region, which compares the different approaches and the effects of adding the extreme days. •Representative days are used for capacity expansion planning.•We present an input-based and a cost-based methods for representative day selection.•The theoretical properties of both methods, including the optimality gap, are analyzed.•Computational studies are performed for the Texas region.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2021.107697