Model predictive load–frequency control taking into account imbalance uncertainty

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

doi:10.1016/j.conengprac.2015.12.001

Cited by 34 publications

(12 citation statements)

References 36 publications

(53 reference statements)

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“…For example, as the most popular control technique, proportional-integral-derivative (PID) controller and its various variations have been widely applied to the LFC issue [3][4][5][6][7][8]. Moreover, some researchers have paid more attention to the advanced control theories based LFC methods recently, such as robust control theories [9], model predictive control [10][11][12][13][14], sliding mode control [15,16], neural network control [17], internal model control [18], and differential games [19]. It should be noted that there are different evolutionary algorithms based PID or proportional-integral (PI) control methods for the LFC issue of multi-area power systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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Abstract:As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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“…where ρ is the air density, R is the blade length, V m is the wind speed, w r is the speed of the rotor, C p is the power coefficient which is a function of blades pitch angle θ and tip-speed ratio λ. In addition, the first-order derivative of w r is given by (10), and the relationship of the speed between rotor and generator of the wind turbine can be obtained as (11).…”

Section: Agc Model With Wind Farm Participationmentioning

confidence: 99%

“…Therefore, some research, such as 2-degree-of-freedom proportional integral derivative (2-DOF PID) [7], fractional order PID [8], and proportional integral derivative plus second order derivative [9], demonstrated a commitment to adjust the structure of the controller to improve control performance. When considering the penetration of renewable energy, the adjustment of the controller structure is inadequately to compensate the disadvantage caused by the uncertainty of renewable generation [10]. Thus, some optimal control methods are used to deal with the problem.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

Zhao

Lin

et al. 2018

Energies

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High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

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“…The presented model was based on the simplified system model that was updated using the Kalman algorithm for estimation of state and parameter considering the tie-line flow limit. The second model incorporating tie-line power flow limit, the capacity of generation units, and their change rate was proposed in [10]. According to this research, considering certain problems of LFC the MPC is a more realistic solution to the issues that power systems are dealing with nowadays.…”

Section: Introductionmentioning

confidence: 99%

Predictive Active Power Control of Two Interconnected Microgrids

Tungadio

Bansal

Siti

et al. 2018

Technol Econ Smart Grids Sustain Energy

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When dealing with interconnected power systems, any sudden load changes leads to the deviation of active power and frequency in the tie-line. The daily management of the power system is also conditioned by the control of the active power and the frequency. It is a very important task in the conduct of the any electrical network to supply sufficiently and with high reliability the active power to be transmitted to the customers. Maintaining the frequency of each area and tie-line active power flow variation within prescribed value by adjusting the generator active outputs remain one of the main tasks of the network operators. This paper presents a model predictive regulator in controlling the power flow in tie-lines and frequency deviations in the microgrid, which will lead to power balance between the total active power generated and active power demand of the system. The system being studied consists of two microgrids, each made up of a wind farm, conventional thermal and hydro plants generator, photovoltaic (PV) system, storage system and active power demand. Predictive control algorithm is applied to control the power flow between two microgrids.

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Model predictive load–frequency control taking into account imbalance uncertainty

Cited by 34 publications

References 36 publications

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

Predictive Active Power Control of Two Interconnected Microgrids

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