Ahead of Print article · Journal article
A Control-based Method to Meet TSO and DSO Ancillary Services Needs by Flexible End-Users
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
University of Adelaide3
University of Málaga4
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark5
This paper presents a new methodology to exploit consumers’ flexibility for the provision of ancillary services (AS) in the smart grid era. The proposed framework offers a control-based approach that adopts price signals as the economic driver to modulate consumers’ response. In this framework, various system operators broadcast price signals independently to fulfil their AS requirements.
Appropriate flexibility estimators are developed from the transmission system operator (TSO) and distribution system operator (DSO) perspectives for price generation. An artificial neural network (ANN) controller is used for the TSO to infer the price-consumption reaction from pools of consumers in its territory.
A proportional-integral (PI) controller is preferred to represent the consumers’ price-response and generate time-varying electricity prices at the DSO level for voltage management. A multi-timescale simulation model is built in MATLAB to assess the proposed methodology in different operational conditions.
Numerical analyses show the applicability of the proposed method for the provision of AS from consumers at different levels of the grid and the interaction between TSO and DSOs through the proposed framework.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2020 |
| Pages: | 1868-1880 |
| ISSN: | 15580679 and 08858950 |
| Types: | Ahead of Print article and Journal article |
| DOI: | 10.1109/TPWRS.2019.2951623 |
| ORCIDs: | 0000-0002-3087-7108 , 0000-0003-1115-4200 , 0000-0002-9114-686X , Madsen, Henrik and Poulsen, Niels Kjølstad |
Ancillary services Artificial neural network Demand response Flexibility resources SDG 7 - Affordable and Clean Energy TSO-DSO interaction
ANN Aggregates DSO ancillary services Load modeling MATLAB Matlab Neural networks Numerical models PI control Real-time systems TSO Voltage control artificial neural network artificial neural network controller demand response distribution system operator economic driver flexibility resources multitimescale simulation model neural nets numerical analysis power distribution economics power engineering computing power markets power pools power transmission economics pricing proportional-integral controller smart grid smart power grids time-varying electricity prices transmission system operator
