Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Conference Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems |
| Untertitel | ECOS 2024 |
| Seiten | 2307-2318 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 9798331307660 |
| Publikationsstatus | Veröffentlicht - 30 Juni 2024 |
| Veranstaltung | 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024 - Rhodes, Griechenland Dauer: 30 Juni 2024 → 5 Juli 2024 |
Abstract
Decentralized energy systems, pivotal in transitioning towards a sustainable energy future, require intelligent dispatch strategies for the operation of flexible components in order to integrate inflexible renewable energy sources economically. Conventional rule-based dispatch strategies often fail to optimally exploit the capabilities of flexible system components, while optimal dispatch models, based on the assumption of perfect forecasting, tend to overestimate their performance. This paper investigates the effectiveness of artificial neural networks (ANNs) as a dynamic dispatch strategy for distributed energy systems (DES), evaluating their performance in operational scenarios with a predefined system layout and during the system design optimization phase. Our analysis shows that ANN-based dispatch strategies outperform conventional rule-based methods by up to 8.19% in operational efficiency according to training datasets and by 3.19% in validation datasets. However, they fall short of optimal dispatch strategies by 4.52% and 1.59% in training and validation datasets, respectively. When applied to system design optimization, ANN-based strategies outperform rule-based approaches by 5.80-9.19% but underperform against optimal dispatch designs by 10.63%. Crucially, the study highlights that dispatch strategies not only influence overall system costs but also significantly impact the sizing and configuration of individual system components. This underlines the importance of incorporating intelligent dispatch strategies like ANNs early in the design process to ensure a balanced and cost-effective system architecture.
ASJC Scopus Sachgebiete
- Energie (insg.)
- Allgemeine Energie
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
- Umweltwissenschaften (insg.)
- Allgemeine Umweltwissenschaft
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Conference Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems: ECOS 2024. 2024. S. 2307-2318.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Design and Dispatch of Decentralized Energy Systems using Artificial Neural Networks
AU - Koenemann, Lukas
AU - Bensmann, Astrid
AU - Gerster, Johannes
AU - Hanke-Rauschenbach, Richard
N1 - Publisher Copyright: © 2024 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024. All rights reserved.
PY - 2024/6/30
Y1 - 2024/6/30
N2 - Decentralized energy systems, pivotal in transitioning towards a sustainable energy future, require intelligent dispatch strategies for the operation of flexible components in order to integrate inflexible renewable energy sources economically. Conventional rule-based dispatch strategies often fail to optimally exploit the capabilities of flexible system components, while optimal dispatch models, based on the assumption of perfect forecasting, tend to overestimate their performance. This paper investigates the effectiveness of artificial neural networks (ANNs) as a dynamic dispatch strategy for distributed energy systems (DES), evaluating their performance in operational scenarios with a predefined system layout and during the system design optimization phase. Our analysis shows that ANN-based dispatch strategies outperform conventional rule-based methods by up to 8.19% in operational efficiency according to training datasets and by 3.19% in validation datasets. However, they fall short of optimal dispatch strategies by 4.52% and 1.59% in training and validation datasets, respectively. When applied to system design optimization, ANN-based strategies outperform rule-based approaches by 5.80-9.19% but underperform against optimal dispatch designs by 10.63%. Crucially, the study highlights that dispatch strategies not only influence overall system costs but also significantly impact the sizing and configuration of individual system components. This underlines the importance of incorporating intelligent dispatch strategies like ANNs early in the design process to ensure a balanced and cost-effective system architecture.
AB - Decentralized energy systems, pivotal in transitioning towards a sustainable energy future, require intelligent dispatch strategies for the operation of flexible components in order to integrate inflexible renewable energy sources economically. Conventional rule-based dispatch strategies often fail to optimally exploit the capabilities of flexible system components, while optimal dispatch models, based on the assumption of perfect forecasting, tend to overestimate their performance. This paper investigates the effectiveness of artificial neural networks (ANNs) as a dynamic dispatch strategy for distributed energy systems (DES), evaluating their performance in operational scenarios with a predefined system layout and during the system design optimization phase. Our analysis shows that ANN-based dispatch strategies outperform conventional rule-based methods by up to 8.19% in operational efficiency according to training datasets and by 3.19% in validation datasets. However, they fall short of optimal dispatch strategies by 4.52% and 1.59% in training and validation datasets, respectively. When applied to system design optimization, ANN-based strategies outperform rule-based approaches by 5.80-9.19% but underperform against optimal dispatch designs by 10.63%. Crucially, the study highlights that dispatch strategies not only influence overall system costs but also significantly impact the sizing and configuration of individual system components. This underlines the importance of incorporating intelligent dispatch strategies like ANNs early in the design process to ensure a balanced and cost-effective system architecture.
UR - http://www.scopus.com/inward/record.url?scp=85216918890&partnerID=8YFLogxK
U2 - 10.52202/077185-0198
DO - 10.52202/077185-0198
M3 - Conference contribution
AN - SCOPUS:85216918890
SP - 2307
EP - 2318
BT - Conference Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
T2 - 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024
Y2 - 30 June 2024 through 5 July 2024
ER -