Comparing scenario reduction methods for stochastic transmission planning

Park, SangWoo; Xu, Qingyu; Hobbs, Benjamin F.

doi:10.1049/iet-gtd.2018.6362

Cited by 30 publications

(11 citation statements)

References 34 publications

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“…Most existing methods use clustering [19,[22][23][24] or sampling methods to reduce the number of scenarios from a randomly generated initial set. In a recent review article, Park et al [25] compared four methods for scenario reduction using a two-stage stochastic transmission planning model, including random sampling, importance sampling [26], the distance-based method [17,27,28], iterative scenario reduction approaches, and stratified scenario sampling [25]. They used these methods to reduce a whole set of 20 scenarios to smaller subsets and compared their pros and cons.…”

Section: Stochastic Transmission Planningmentioning

confidence: 99%

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

2020

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Reliable transmission expansion planning is critical to power systems’ development. To make reliable and sustainable transmission expansion plans, numerous sources of uncertainty including demand, generation capacity, and fuel cost must be taken into consideration in both spatial and temporal dimensions. This paper presents a new approach to selecting a small number of high-quality scenarios for transmission expansion. The Kantorovich distance of social welfare distributions was used to assess the quality of the selected scenarios. A case study was conducted on a power system model that represents the U.S. Eastern and Western Interconnections, and ten high-quality scenarios out of a total of one million were selected for two transmission plans. Results suggested that scenarios selected using the proposed algorithm were able to provide a much more accurate estimation of the value of transmission plans than other scenario selection algorithms in the literature.

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Section: Stochastic Transmission Planningmentioning

confidence: 99%

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

2020

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“…Therefore, the application of scenario reduction is necessary to keep the size of the problem manageable. A detailed literature review on scenario reduction can be found in [46]. The fast forward selection algorithm [47] is used in this paper for scenario reduction.…”

Section: Energy Pfr and Inertia Scheduling Under Uncertaintiesmentioning

confidence: 99%

PFR ancillary service in low‐inertia power system

Liu¹,

Du²,

Liu

2020

IET Generation, Transmission & Distribution

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“…However, including a substantial number of scenarios into the critical lines identification model will result in significant challenge to computations. To reduce the computational burden and make the CSN optimisation and critical line identification models tractable, some typical and representative scenarios that capture most of the possible situations should be selected from the original scenario set [18]. The original scenarios can be attained from historical data or statistical models of the concerned power system, which is not focused in this work.…”

Section: Architecture and Typical Scenarios Of The Proposed Critical mentioning

confidence: 99%