From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems
Abstract As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of fle...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-10-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-023-00286-z |
| _version_ | 1797653563341537280 |
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| author | Tim Fürmann Michael Lechl Hermann de Meer Anke Weidlich |
| author_facet | Tim Fürmann Michael Lechl Hermann de Meer Anke Weidlich |
| author_sort | Tim Fürmann |
| collection | DOAJ |
| description | Abstract As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of flexibility options, considering factors such as timing and location, shares similarities with scheduling problems encountered in computer networks. In both cases, the objective is to coordinate various distributed units and manage the flow of either data or power. Among the methods for scheduling and resource allocation in computer networks, stochastic network calculus (SNC) is a promising approach that estimates worst-case guarantees for Quality of Service (QoS) indicators of computer networks, such as delay and backlog. Promising QoS indicators in the power system are given by the amount of stored energy, the serviced demand, and the demand elasticity. In this work, we investigate SNC for its capabilities and limitations to quantify flexibility service guarantees in power systems. We generate and aggregate stochastic envelopes for random processes, which was found useful for modeling flexibility in power systems at multiple time scales. In a case study on the reliability of a solar-powered car charging station, we obtain similar results as from a mixed-integer linear programming problem, which provides confidence that the chosen SNC approach is suitable for modeling power system flexibility. |
| first_indexed | 2024-03-11T16:46:24Z |
| format | Article |
| id | doaj.art-8d60def5c5954e96b3a78b13e9f83d67 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-03-11T16:46:24Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-8d60def5c5954e96b3a78b13e9f83d672023-10-22T11:28:55ZengSpringerOpenEnergy Informatics2520-89422023-10-016S112010.1186/s42162-023-00286-zFrom computer systems to power systems: using stochastic network calculus for flexibility analysis in power systemsTim Fürmann0Michael Lechl1Hermann de Meer2Anke Weidlich3Department of Sustainable Systems Engineering (INATECH), University of FreiburgChair of Computer Networks and Communications, University of PassauChair of Computer Networks and Communications, University of PassauDepartment of Sustainable Systems Engineering (INATECH), University of FreiburgAbstract As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of flexibility options, considering factors such as timing and location, shares similarities with scheduling problems encountered in computer networks. In both cases, the objective is to coordinate various distributed units and manage the flow of either data or power. Among the methods for scheduling and resource allocation in computer networks, stochastic network calculus (SNC) is a promising approach that estimates worst-case guarantees for Quality of Service (QoS) indicators of computer networks, such as delay and backlog. Promising QoS indicators in the power system are given by the amount of stored energy, the serviced demand, and the demand elasticity. In this work, we investigate SNC for its capabilities and limitations to quantify flexibility service guarantees in power systems. We generate and aggregate stochastic envelopes for random processes, which was found useful for modeling flexibility in power systems at multiple time scales. In a case study on the reliability of a solar-powered car charging station, we obtain similar results as from a mixed-integer linear programming problem, which provides confidence that the chosen SNC approach is suitable for modeling power system flexibility.https://doi.org/10.1186/s42162-023-00286-zPower system flexibilityStochastic network calculusQuality of Service indicatorsNetwork engineeringFlexibility service guarantees |
| spellingShingle | Tim Fürmann Michael Lechl Hermann de Meer Anke Weidlich From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems Energy Informatics Power system flexibility Stochastic network calculus Quality of Service indicators Network engineering Flexibility service guarantees |
| title | From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems |
| title_full | From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems |
| title_fullStr | From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems |
| title_full_unstemmed | From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems |
| title_short | From computer systems to power systems: using stochastic network calculus for flexibility analysis in power systems |
| title_sort | from computer systems to power systems using stochastic network calculus for flexibility analysis in power systems |
| topic | Power system flexibility Stochastic network calculus Quality of Service indicators Network engineering Flexibility service guarantees |
| url | https://doi.org/10.1186/s42162-023-00286-z |
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