Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery

Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since the real-time deployment of these services is uncertain, ensuring their availability requires merchant...

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Veröffentlicht in:IEEE transactions on power systems Jg. 36; H. 4; S. 2815 - 2828
Hauptverfasser: Toubeau, Jean-Francois, Bottieau, Jeremie, De Greeve, Zacharie, Vallee, Francois, Bruninx, Kenneth
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Availability
Balancing
Balancing markets
Capacity planning
chance-constrained programming
Competition
data-driven optimization
Energy storage
energy-operating reserve markets
Markets
Mixed integer
Optimization
Probabilistic logic
Real time
Real-time systems
Reserve capacity
Schedules
Scheduling
Storage systems
System effectiveness
ISSN:0885-8950, 1558-0679
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Zusammenfassung:Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since the real-time deployment of these services is uncertain, ensuring their availability requires merchant ESS to fully reserve the associated energy capacity in their day-ahead schedule. To improve such conservative policies, we propose a data-driven probabilistic characterization of the real-time balancing stage to inform the day-ahead scheduling problem of an ESS owner. This distributional information is used to enforce a tailored probabilistic guarantee on the availability of the scheduled reserve capacity via chance constrained programming, which allows a profit-maximizing participation in energy, reserve and balancing markets. The merit order-based competition with rival resources in reserve capacity and balancing markets is captured via a bi-level model, which is reformulated as a computationally efficient mixed-integer linear problem. Results show that a merchant ESS owner may leverage the competition effect to avoid violations of its energy capacity limits, and that the proposed risk-aware method allows sourcing more reserve capacity, and thus more value, from storage, without jeopardizing the real-time reliability of the power system.
Bibliographie:ObjectType-Article-1
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3046710