A review on recent advances in scenario aggregation methods for power system analysis
Aiusha Sangadiev, Alvaro Gonzalez-Castellanos, David Pozo

TL;DR
This paper reviews recent scenario aggregation methods in power system analysis, focusing on reducing computational complexity while maintaining accuracy in stochastic optimization models for renewable energy integration.
Contribution
It provides a comprehensive classification, analysis, and comparison of various scenario aggregation techniques specifically applied to power system optimization problems.
Findings
Identifies 16 aggregation methods for transmission expansion planning
Highlights tradeoffs between scenario detail and computational efficiency
Offers recommendations for future research in scenario aggregation
Abstract
Worldwide commitments to net zero greenhouse emissions have accelerated investments in renewable energy resources. The requirements for operating and planning power systems are becoming stringent because of the need to take into account the uncertainty associated with renewable generation. Several modeling frameworks that consider the inherent uncertainty in the operation and planning of the power system have been extensively studied. Stochastic optimization has been the most popular approach among these frameworks due to its intuitive representation, especially when formulated using discrete probabilistic scenarios to represent the random variables. Although many scenarios representing all possible uncertain operating conditions would be needed to accurate evaluate stochastic operation and planning models, the size of the scenario set impacts computational complexity, posing a…
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Taxonomy
TopicsElectric Power System Optimization · Energy Load and Power Forecasting · Integrated Energy Systems Optimization
