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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| Published in: | IEEE transactions on power systems Vol. 36; no. 4; pp. 2815 - 2828 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0885-8950, 1558-0679 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Bruninx, Kenneth De Greeve, Zacharie Vallee, Francois Bottieau, Jeremie Toubeau, Jean-Francois |
| Author_xml | – sequence: 1 givenname: Jean-Francois orcidid: 0000-0001-9853-2694 surname: Toubeau fullname: Toubeau, Jean-Francois email: Jean-Francois.TOUBEAU@umons.ac.be organization: Power Electrical Engineering, Faculté Polytechnique de l'Université de Mons, Mons, Belgium – sequence: 2 givenname: Jeremie orcidid: 0000-0003-1268-5142 surname: Bottieau fullname: Bottieau, Jeremie email: Jeremie.bottieau@umons.ac.be organization: Power Electrical Engineering, Faculté Polytechnique de l'Université de Mons, Mons, Belgium – sequence: 3 givenname: Zacharie surname: De Greeve fullname: De Greeve, Zacharie email: zacharie.DEGREVE@umons.ac.be organization: Power Electrical Engineering, Faculté Polytechnique de l'Université de Mons, Mons, Belgium – sequence: 4 givenname: Francois surname: Vallee fullname: Vallee, Francois email: Francois.VALLEE@umons.ac.be organization: Power Electrical Engineering, Faculté Polytechnique de l'Université de Mons, Mons, Belgium – sequence: 5 givenname: Kenneth orcidid: 0000-0003-0838-5139 surname: Bruninx fullname: Bruninx, Kenneth email: kenneth.bruninx@kuleuven.be organization: Energy Institute, KU Leuven, Leuven, Belgium |
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| SubjectTerms | 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 |
| Title | Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery |
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