Published April 2019 | Version v1
Journal article

Model predictive control for load frequency of hybrid power system with wind power and thermal power

  • 1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206 (China)
  • 2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206 (China)

Description

Highlights: • Frequency response characteristics of wind turbine is studied. • Wind farm equivalent model is established by gap metric calculation. • Hybrid power system is modeled, containing multiple forms of energy. • Coordinated model predictive control is used to improve frequency response. -- Abstract: With the increase of wind power penetration in generation profile, the contribution of wind power to load frequency control (LFC) has become more significant. To improve the frequency characteristics of a wind-power-contained power system, this paper establishes an analytical linearized model for the frequency response characteristics of wind turbine generator (WTG) during LFC which is helpful to the design of frequency controller. Furthermore, the gap metric measure to the above models under different wind speed ranges is calculated. Under the per unit system, it can be extended as an equivalent model for the whole wind farm. Combining the derived wind farm model and a known thermal power model, an integrated model of hybrid power system can be built. To optimize the frequency-response performance to the power system, an improved LFC method based on model predictive control (MPC) is presented and applied to a multi-areas hybrid system. Then, the wind farms and thermal power plants in the same area can be controlled simultaneously and obtain their reference orders from the predictive controller. The simulation results show that the proposed method can effectively raise the frequency response level of the both power supplies and then improve the frequency performance of power system.

Additional details

Identifiers

DOI
10.1016/j.energy.2019.01.071;
PII
S0360544219300738;

Publishing Information

Journal Title
Energy (Oxford)
Journal Volume
172
Journal Page Range
p. 555-565
ISSN
0360-5442
CODEN
ENEYDS

Optional Information

Copyright
Copyright (c) 2019 Elsevier Ltd. All rights reserved.