City Research Online

Distributed Model Predictive Load Frequency Control of multi-area Power Grid: A Decoupling Approach

Vlahakis, E. E. ORCID: 0000-0002-7039-5314, Dritsas, L. and Halikias, G. ORCID: 0000-0003-1260-1383 (2019). Distributed Model Predictive Load Frequency Control of multi-area Power Grid: A Decoupling Approach. IFAC papers online, 52(20), pp. 205-210. doi: 10.1016/j.ifacol.2019.12.159

Abstract

A model-predictive scheme for load frequency control of a multi-area power system is proposed. The method depends on a decoupling technique which allows for a control design with a distributed architecture. Treating the total power inflows of each area as input variables, a decoupled linearized model for each area is derived. This allows for the formulation and solution of a model predictive control problem with a quadratic performance index and input saturating constraints on the individual tie-line power flows, along with an overall equality constraint to address the energy balance of the network. It is assumed that the interconnection topology (tie-lines) coincides with the communication topology of the network. The only information which needs to be shared between interconnected areas is the local frequency variables. The effectiveness of the method is illustrated via a simulation study of a three-area network. Future work will attempt to establish formally the stability of the control scheme and to enhance the versatility of the method by including constraints on the state variables.

Publication Type: Article
Additional Information: © 2019 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND
Publisher Keywords: distributed model predictive control, load frequency control, automatic generation control, interconnected power system
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date Deposited: 27 Apr 2020 15:01
URI: https://openaccess.city.ac.uk/id/eprint/24073
[img]
Preview
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (444kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login