A Control-Based Method to Meet TSO and DSO Ancillary Services Needs by Flexible End-Users

被引:29
作者
De Zotti, Giulia [1 ]
Pourmousavi, S. Ali [2 ]
Morales, Juan M. [3 ]
Madsen, Henrik [4 ]
Poulsen, Niels Kjolstad [4 ]
机构
[1] Tech Univ Denmark, Lyngby 2800, Denmark
[2] Univ Adelaide, Sch Elect & Elect Engn, Fac Engn Comp & Math Sci, Adelaide, SA 5005, Australia
[3] Univ Malaga, Escuela Tecn Super Ingn Ind, Dept Appl Math, Malaga 29071, Spain
[4] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby 2800, Denmark
关键词
Load modeling; Numerical models; Aggregates; Voltage control; Real-time systems; Neural networks; Ancillary services; TSO-DSO interaction; flexibility resources; demand response; artificial neural network; FLEXIBILITY; IMPACTS;
D O I
10.1109/TPWRS.2019.2951623
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 signalsas 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.
引用
收藏
页码:1868 / 1880
页数:13
相关论文
共 40 条
[1]   Evaluation of nonlinear models for time-based rates demand response programs [J].
Aalami, H. A. ;
Moghaddam, M. Parsa ;
Yousefi, G. R. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2015, 65 :282-290
[2]   Flexibility Scheduling for Large Customers [J].
Angizeh, Farhad ;
Parvania, Masood ;
Fotuhi-Firuzabad, Mahmud ;
Rajabi-Ghahnavieh, Abbas .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (01) :371-379
[3]  
[Anonymous], LOAD RESOURCE PROVID
[4]  
[Anonymous], 2017, MATLAB R2017B
[5]  
[Anonymous], HDB CLEAN ENERGY SYS
[6]  
[Anonymous], 2017, GUROBI OPTIMIZER REF
[7]  
[Anonymous], 1994, POWER SYSTEM STABILI
[8]  
[Anonymous], RENEWABLE SUSTAIN EN
[9]  
[Anonymous], 2016, MATPOWER VERSION 6 0
[10]  
[Anonymous], GAMS VERS 24 9 1