Model Predictive Control for Power System Frequency Control Taking into Account Imbalance Uncertainty

Ersdal, Anne Mai; Fabozzi, Davide; Imsland, Lars; Thornhill, Nina F.

doi:10.3182/20140824-6-za-1003.01631

Cited by 23 publications

(24 citation statements)

References 16 publications

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“…Even though the RNMPC showed to be more conservative when more realistic, fluctuating disturances were imposed on the system, it is still less conservative than the RNMPC in Ersdal et al (2014). The main reason for this difference is that in Ersdal et al (2014) there was only one input per area, whereas here there are several.…”

Section: Discussionmentioning

confidence: 84%

“…The main reason for this difference is that in Ersdal et al (2014) there was only one input per area, whereas here there are several. Several inputs per area makes it easier for the RNMPC to fulfill the system constraints, also in case of worst-case disturbance scenarios, and it does not need to be as conservative.…”

Section: Discussionmentioning

confidence: 92%

“…In Ersdal et al (2015) the disturbance ΔP L was kept constant over the prediction horizon. In this work however, the idea from Ersdal et al (2014) to make the NMPC more robust against fluctuations in produced wind power by using different disturbance scenarios in the predicted system behavior, is implemented.…”

Section: Disturbance Modelmentioning

confidence: 99%

“…This is something which is not easily done with a PID controller. Ersdal et al (2014) presented an NMPC for AGC which is robustified against variations in produced wind power. This robustified NMPC (RNMPC) was tested without any model-plant mismatch, and it showed how the RNMPC was able to plan production so that it could handle a worst-case wind-power production scenario without breaking any system constraints.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of the work presented in Ersdal et al (2014Ersdal et al ( , 2015.The RNMPC from Ersdal et al (2014) is implemented and tested with the proxy system and prediction model from Ersdal et al (2015), hence there is a more realistic test of the controller on a large, realistic simulator. Ersdal et al (2015) presented a complete solution to the LFC problem by including generator participation-factors in the optimization problem as well as state estimation for full state feedback.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Model predictive load–frequency control taking into account imbalance uncertainty

Ersdal

Imsland

Uhlen

et al. 2016

Control Engineering Practice

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Nonlinear model predictive control (NMPC) is investigated for load frequency control (LFC) of an interconnected power system which is exposed to increasing wind power penetration. The robustified NMPC (RNMPC) proposed here uses knowledge of the estimated worst-case deviation in wind-power production to make the NMPC more robust. The NMPC is based on a simplified system model that is updated using state-and parameter estimation by Kalman filters, and takes into account limitations on among others tie-line power flow. Tests on a proxy of the Nordic power system, shows that the RNMPC is able to fulfill system constraints under worst-case deviations in wind-power production in cases where the nominal NMPC is not. Keywords

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 92%

Section: Disturbance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model predictive load–frequency control taking into account imbalance uncertainty

Ersdal

Imsland

Uhlen

et al. 2016

Control Engineering Practice

Self Cite

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A multistage H‐infinity–based controller for adjusting voltage and frequency and improving power quality in islanded microgrids

Sedhom

El‐Saadawi

Hatata

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper proposes a novel methodology for controlling voltage and frequency of islanded microgrids and meanwhile improving the system power quality. The proposed methodology is based on applying a multi‐stage H‐Infinity (H∞) controller with different control loops to restore the nominal values of voltage and frequency after any load variation. These control loops include power control, current control, and voltage control loops. The H∞ is also applied with an LCL filter to enhance the system power quality. The proposed controller is applied to a test system under three different loading conditions and the obtained results are compared with that obtained by applying the model predictive control (MPC) method. The results are compared with that obtained by applying a proposed traditional control method in the same test system. A comparison between the droop controller and the proposed controller, MPC is performed based on fitness value, controller response time, and maximum frequency deviation. In addition, the system stability is analysed using root locus, system step response, singular value decomposition, and bode diagram. The results prove the effectiveness and the efficiency of the proposed method in restoring voltage and frequency and improving the system power quality.

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CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,585

976

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We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

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Model Predictive Control for Power System Frequency Control Taking into Account Imbalance Uncertainty

Cited by 23 publications

References 16 publications

Model predictive load–frequency control taking into account imbalance uncertainty

Model predictive load–frequency control taking into account imbalance uncertainty

A multistage H‐infinity–based controller for adjusting voltage and frequency and improving power quality in islanded microgrids

CasADi: a software framework for nonlinear optimization and optimal control

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