Flexibility aggregation of local energy systems—interconnecting, forecasting, and scheduling
Mashlakov, Aleksei (2021-12-13)
Väitöskirja
Mashlakov, Aleksei
13.12.2021
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Energiatekniikka
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-749-5
https://urn.fi/URN:ISBN:978-952-335-749-5
Tiivistelmä
The socio-technical transformation of the energy sector in pursuit of its decarbonization and innovations in digital and energy technologies are shifting the present electric power systems with a centralized governance to a more decentralized structure consisting of local energy systems with self-motivated energy governance. In this context, establishing a co-organized operation management between the existing central system governance and emerging decentralized systems for a common value, which is the efficient and reliable operation of a low-carbon power system at the lowest costs, is a challenging problem. This doctoral dissertation addresses the problem with a flexibility aggregation solution that integrates the operational flexibility of local energy systems into centralized power system management through the market-based provision of flexibility services for system operators of distribution and transmission grids.
First, the methodology of model-driven architecture development is adopted to design a smart grid architecture enabling technological interoperability of a flexibility management environment. The study describes the interoperability profile of local energy management platforms consisting of functional, information, and communication requirements empowering them to provide grid-related flexibility services. The results show that the technical interoperability among the platforms can be achieved with message-oriented middleware following the Web design principles. Then, backtesting methodology is applied to quantitatively evaluate the predictive uncertainty of the data-driven models in the probabilistic energy forecasting of data generating processes assisting in the decisionmaking of flexibility management. Several practical criteria are recommended to leverage the performance of these models using quality-driven loss functions, multidistribution testing, and a cross-learning technique. Finally, mathematical modeling is employed to formulate the decentralized cooperative flexibility scheduling of a local energy system. It is shown quantitatively that the value of prosumer flexibility can be effectively distributed among the prosumer’s individual techno-socio-economic motivations, the reliability of the shared power grid, and the provision of system-level services but impaired by the forecast uncertainty of the flexibility management parameters.
First, the methodology of model-driven architecture development is adopted to design a smart grid architecture enabling technological interoperability of a flexibility management environment. The study describes the interoperability profile of local energy management platforms consisting of functional, information, and communication requirements empowering them to provide grid-related flexibility services. The results show that the technical interoperability among the platforms can be achieved with message-oriented middleware following the Web design principles. Then, backtesting methodology is applied to quantitatively evaluate the predictive uncertainty of the data-driven models in the probabilistic energy forecasting of data generating processes assisting in the decisionmaking of flexibility management. Several practical criteria are recommended to leverage the performance of these models using quality-driven loss functions, multidistribution testing, and a cross-learning technique. Finally, mathematical modeling is employed to formulate the decentralized cooperative flexibility scheduling of a local energy system. It is shown quantitatively that the value of prosumer flexibility can be effectively distributed among the prosumer’s individual techno-socio-economic motivations, the reliability of the shared power grid, and the provision of system-level services but impaired by the forecast uncertainty of the flexibility management parameters.
Kokoelmat
- Väitöskirjat [1075]