English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many practical planning and operational applications in power systems requiresimultaneous consideration of a large number of operating conditions orMulti-Scenario AC-Optimal Power Flow (MS-AC-OPF) solution. However, when thenumber of exogenously prescribed conditions is large, solving the problem as acollection of single AC-OPFs may be time-consuming or simply intractablecomputationally. In this paper, we suggest a model reduction approach, coinedCloud-AC-OPF, which replaces a collection of samples by their compactrepresentation in terms of mean and standard deviation. Instead of determiningan optimal generation dispatch for each sample individually, we parametrize thegeneration dispatch as an affine function. The Cloud-AC-OPF is mathematicallysimilar to a generalized Chance-Constrained AC-OPF (CC-AC-OPF) of the typerecently discussed in the literature, but conceptually different as itdiscusses applications to long-term planning. We further propose a tractableformulation and implementation, and illustrate our construction on the exampleof 30-bus IEEE model.

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization