Electric vehicle integrated MPC scheme for concurrent control of voltage and frequency in power system network

2021 ◽  
pp. 0309524X2110364
Author(s):  
Vineet Kumar ◽  
Veena Sharma
Keyword(s):  
Power Plant ◽  
Power System ◽  
Electric Vehicle ◽  
Power Plants ◽  
Frequency Control ◽  
Thermal Power ◽  
Load Frequency Control ◽  
Parameter Uncertainties ◽  
Concurrent Control ◽  
Simultaneous Control

The growing integration of renewable sources with grid poses challenges in the field of voltage and frequency control. This study features combined automatic voltage regulator and load frequency control model of a three-area power system network with each area containing thermal and diesel power plants along with the introduction of solar thermal power plant, wind, and geothermal power plants. The Harris Hawks Optimization (HHO) based centralized Model Predictive Controller (MPC) has been discussed and implemented for the effective and simultaneous control of voltage and frequency in all three areas of the wind integrated power plant. To further deal with increased oscillations in frequency profile of the system caused due to renewable integration, the electric vehicle (EV) participation has been considered in each area to provide an auxiliary control against frequency deviations. Effectiveness of the proposed EV based MPC-HHO algorithm has been successfully verified after adding different nonlinearities, parameter uncertainties, disturbances, and time-delays in the wind integrated power system.

scholarly journals Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1581
Author(s):  
Deepak Kumar Gupta ◽  
Amitkumar V. Jha ◽  
Bhargav Appasani ◽  
Avireni Srinivasulu ◽  
Nicu Bizon ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Source Power ◽  
Load Frequency ◽  
Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

scholarly journals Designing a GA-Based Robust Controller For Load Frequency Control (LFC)

2018 ◽  
Vol 8 (2) ◽  
pp. 2633-2639 ◽  
Author(s):  
K. Soleimani ◽  
J. Mazloum
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Plants ◽  
Frequency Control ◽  
Load Frequency Control ◽  
System Stability ◽  
Robust Controller ◽  
Power Flux ◽  
Pi Controllers ◽  
Multiple Units

Power systems include multiple units linked together to produce constantly moving electric power flux. Stability is very important in power systems, so controller systems should be implemented in power plants to ensure power system stability either in normal conditions or after the events of unwanted inputs and disorder. Frequency and active power control are more important regarding stability. Our effort focused on designing and implementing robust PID and PI controllers based on genetic algorithm by changing the reference of generating units for faster damping of frequency oscillations. Implementation results are examined on two-area power system in the ideally state and in the case of parameter deviation. According to the results, the proposed controllers are resistant to deviation of power system parameters and governor uncertainties.

Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System

2016 ◽  
Vol 5 (4) ◽  
pp. 62-83 ◽  
Author(s):  
Dipayan Guha ◽  
Provas Kumar Roy ◽  
Subrata Banerjee
Keyword(s):  
Power System ◽  
Pid Controller ◽  
Frequency Control ◽  
Thermal Power ◽  
Load Frequency Control ◽  
Fine Tuning ◽  
Average Error ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Load Frequency

In this article, a novel optimization algorithm called grey wolf optimization (GWO) with the theory of quasi-oppositional based learning (Q-OBL) is proposed for the first time to solve load frequency control (LFC) problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.

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