Spatio-Temporal Flexibility Management in Low-Carbon Power Systems
Abstract
The deepening penetration of renewable power generation is challenging how the minute balancing of supply and demand is carried out by power system operators. Several proposals to short-term operational planning rely on robust optimization to offer guarantees on the ability of the operator to meet a wide array of possible scenarios. The main downside of these approaches is their conservative results whose operating costs and/or carbon footprint may be sub-economical. Such results come by because these approaches immunize their solutions for the required level of security against realizations of potential events within their uncertainty set. Moreover, these approaches also often ignore the inherent time and spatial couplings of wind and solar generation variability. In this article, we seek to reduce the conservativeness of the robust solution by proposing the concept of spatio-temporal flexibility requirement envelopes. We show how it is able to efficiently capture and model the temporal trends and spatial correlation of multisite renewable generation and load. A mathematical program for energy scheduling is also developed using the projections of this envelope. We showcase the use and advantages of spatio-temporal flexibility requirement envelopes and their associated scheduling approach in a microgrid and on a modified IEEE Reliability Test System.
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Flexibility requirements are determined based on analysis of historical load fluctuation patterns and renewable generation forecast errors, representing the flexibility levels needed for system stability at different time scales.
“Flexibility requirements are determined based on analysis of historical load fluctuation patterns and renewable generation forecast errors, representing the flexibility levels needed for system stability at different time scales [36].”
From Flexibility Region Quantification and Optimal Scheduling of Fast Charging Stations Considering EV User Discharge Willingness (2025) · cites this paper as [36]
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Zhang, X., Zhao, Q., Guo, L., & Wang, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3594615 · Cited by 1 · see citing works
- Authors
- Huo, Y., Bouffard, F., & Joos, G.
- Affiliations
- Group for Research in Decision Analysis (CA)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2020-10
- Volume
- 11
- Issue
- 4
- Pages
- 2593-2605
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W3000723039
- Cited by
- 49
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 17 · 2023: 5 · 2022: 14 · 2021: 7 · 2020: 1
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- InnovEE
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- robust optimization, renewable energy integration, short-term operational planning, supply and demand balancing, energy scheduling, microgrids, low-carbon power systems, operational cost reduction, IEEE Reliability Test System
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