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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.18325765 |
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| _version_ | 1866901684668596224 |
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| author | Gregor Golja, Janez Peter Kosovinc, Klemen Omrčen, Damir |
| author_facet | Gregor Golja, Janez Peter Kosovinc, Klemen Omrčen, Damir |
| contents | <p>This deliverable presents the second phase of the innovation activity “<strong>Optimal Selection of Available Flexibility</strong>”, developed within Task 4.4 of the OPENTUNITY project. It builds on the foundations established in the first phase, where data from electric vehicle (EV) charging stations and Home Energy Management Systems (HEMS) were analyzed, integrated into the KOL aggregation platform, and used to develop advanced baseline and flexibility forecasting models. These outputs now serve as key inputs for the work presented here.</p> <p>The main goal of this task is to support <strong>aggregators and flexibility service providers (FSPs)</strong> in making optimal decisions on where to offer flexibility and which assets to activate for delivery. Accordingly, the deliverable covers two complementary algorithms:</p> <ul> <li><strong>Market Selection Algorithm</strong>, which identifies the most profitable flexibility markets (day-ahead, intraday, TSO ancillary, or DSO local) considering technical and regulatory constraintsand using forecasted consumption, flexibility potential, and price signals. It compares different participation strategies and demonstrates that combining day-ahead optimization with ancillary services participation can significantly increase revenues while maintaining operational feasibility. It also includes a brief module on implicit flexibility: a household-level tariff optimization case that shows how end-users can manage loads under the new Slovenian network tariff system.</li> <li><strong>Optimal Selection Algorithm</strong>, which determines the least-cost feasible combination of assets to activate in response to flexibility requests, given available resources and constraints. Implemented via a genetic-algorithm framework, it selects the most cost-efficient asset set while satisfying technical and operational requirements. The framework is modular and adaptable to evolving market settings or new asset types.</li> </ul> <p>Together, these components form a <strong>comprehensive flexibility-management framework</strong> — spanning the identification of profitable market opportunities, activation of distributed resources in a cost-efficient manner, and enabling household-level optimization under evolving tariff schemes.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18325765 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | D4.4 Optimal selection of available flexibility v2 Gregor Golja, Janez Peter Kosovinc, Klemen Omrčen, Damir <p>This deliverable presents the second phase of the innovation activity “<strong>Optimal Selection of Available Flexibility</strong>”, developed within Task 4.4 of the OPENTUNITY project. It builds on the foundations established in the first phase, where data from electric vehicle (EV) charging stations and Home Energy Management Systems (HEMS) were analyzed, integrated into the KOL aggregation platform, and used to develop advanced baseline and flexibility forecasting models. These outputs now serve as key inputs for the work presented here.</p> <p>The main goal of this task is to support <strong>aggregators and flexibility service providers (FSPs)</strong> in making optimal decisions on where to offer flexibility and which assets to activate for delivery. Accordingly, the deliverable covers two complementary algorithms:</p> <ul> <li><strong>Market Selection Algorithm</strong>, which identifies the most profitable flexibility markets (day-ahead, intraday, TSO ancillary, or DSO local) considering technical and regulatory constraintsand using forecasted consumption, flexibility potential, and price signals. It compares different participation strategies and demonstrates that combining day-ahead optimization with ancillary services participation can significantly increase revenues while maintaining operational feasibility. It also includes a brief module on implicit flexibility: a household-level tariff optimization case that shows how end-users can manage loads under the new Slovenian network tariff system.</li> <li><strong>Optimal Selection Algorithm</strong>, which determines the least-cost feasible combination of assets to activate in response to flexibility requests, given available resources and constraints. Implemented via a genetic-algorithm framework, it selects the most cost-efficient asset set while satisfying technical and operational requirements. The framework is modular and adaptable to evolving market settings or new asset types.</li> </ul> <p>Together, these components form a <strong>comprehensive flexibility-management framework</strong> — spanning the identification of profitable market opportunities, activation of distributed resources in a cost-efficient manner, and enabling household-level optimization under evolving tariff schemes.</p> |
| title | D4.4 Optimal selection of available flexibility v2 |
| url | https://doi.org/10.5281/zenodo.18325765 |