The Evolving Challenges of Load Frequency Control
In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective.
To address these new challenges, this article proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO.
The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios.
Interconnected Power Systems and the Need for Load Frequency Control
An interconnected power system comprises several control areas connected by tie lines to exchange power among them. The load in power systems is never steady, it continually changes with rising and falling trends. Failing to match any small sudden load change in any control area in an interconnected power system will change the system frequency and power flows in the tie lines. Large deviations in frequency can lead to power system instability and large fluctuations in tie-line power flows.
Therefore, it is important in an interconnected power system to maintain an active power balance in all control areas to keep the system frequency and the tie-line power flows as close as possible to their scheduled values. In a large power interconnected system, the load is matched at the control area level by regulating the active power produced by generators in the control area using load frequency control (LFC).
The balance in a control area is reached when the scheduled power exchange with neighbouring control areas equals the actual power exchange. In the presence of continually varying load, balancing active power between generation and load is a very challenging task, requiring superior controllers for the load frequency control.
Emerging Trends and Limitations in LFC Strategies
Widely used controllers are the Proportional-Integral (PI), and Proportional-Integral-Derivative (PID) controllers because of their simple structure. There are several traditional and modern methods for tuning these controllers to get the best performance out of them.
The integration of renewable energy sources (RES) into conventional power systems has become a defining imperative in the quest for sustainable and environmentally responsible energy generation. However, integrating renewable energy sources into the power grid presents significant challenges. Due to their intermittency and randomness, renewable energy sources complicate the balancing of active power between generation and load. This increased complexity necessitates the use of more advanced controllers for load frequency control.
Several different methods have been proposed in the literature to achieve more efficient LFC strategies capable of maintaining active power balance between generation and load in the presence of severe power system disturbances. These methods include state estimation techniques like Kalman filtering, data-driven modeling and system identification approaches, reinforcement learning-based control, fuzzy logic control, and signal processing methods such as the wavelet transform.
However, these proposed methods in the literature to increase the control quality of RES-integrated power systems exhibit various limitations. Kalman filtering can be computationally intensive. Advanced state estimation techniques, such as the Extended Kalman Filter and Unscented Kalman Filter, can be computationally demanding, making real-time implementation challenging. Fuzzy logic control relies on expert knowledge and rule-based systems, potentially making it less adaptable to unforeseen changes. H-Infinity control is generally complex to implement and necessitates a clear understanding of system uncertainties and performance specifications. Model Predictive Control comes with computational intensity, potential latency, complex implementation, and sensitivity to modelling errors.
In response to the challenges and computational demands posed by these advanced control strategies in load frequency control, researchers are actively focusing on the use of fixed gain controllers. The design method of this type of controller is a two-step procedure consisting of determining the controller structure and finding a suitable method to calculate its parameters. In this controller design approach, researchers are actively exploring the use of metaheuristic optimization techniques to obtain the optimal gain parameters for the fixed gain controllers.
The Proposed FOPI-FOPIDN Controller
To address the above challenges more effectively, this article includes an optimized derivative filter in the FOPI-FOPID structure to obtain FOPI-FOPIDN for a control area, which contains hydro and thermal plants, as well as renewable energy sources (RES). The gain parameters of the proposed controller consisting of fractional order parameters, and the filter coefficient “N” are then optimized using the ADIWACO PSO variant.
The structural representation of the cascaded FOPI-FOPIDN controller is as follows:
The FOPI serves as the master control, addressing tie-line power and frequency deviations using fractional-order calculus. The output of the FOPI controller is sent to the FOPIDN controller for further fine-tuning and enhanced disturbance rejection. The main contribution of this methodology is the inclusion of the derivative filter to alleviate the impact of high-frequency noise.
The primary objective of the load frequency control is to maintain a zero Area Control Error (ACE), which is given by the following equations for the two-area and three-area power systems, respectively:
ACE = B1Δf1 + ΔPtie,12
ACE = B1Δf1 + B2Δf2 + B3Δf3 + ΔPtie,12 + ΔPtie,13 + ΔPtie,23
To achieve this primary objective, the optimal parameters of the FOPI-FOPIDN controller are determined using the ADIWACO PSO variant. The integral time-absolute error (ITAE) is used as the fitness function for the optimization.
Improved PSO (ADIWACO) for Optimal Tuning
The standard PSO is a swarm-based optimization technique inspired by the collective behavior of bird flocks or fish schools. In the PSO, a population of potential solutions, represented as particles, explores the solution space by adjusting their positions based on their own best-known solutions and the globally best solution found by the entire population.
The improved PSO in this study, called ADIWACO, enhances the performance of the standard PSO by employing adaptive dynamic inertia weight and acceleration coefficients. The ADIWACO PSO is used to obtain the optimal values of Kp1, Kp2, KI1, KI2, KD, N, λ1, λ2, and μ for each of the cascaded fractional order controllers (FOPI-FOPIDN).
Performance Evaluation and Benchmarking
The performance of the proposed LFC strategy is evaluated on the two-area and three-area test systems using MATLAB/Simulink. The assessment is done using step load perturbation, combined random load, PV and wind perturbations, and system parameters variation. The resulting ITAE values, frequency, and tie-line power responses are compared with those of various strategies from the literature to establish its superiority.
Step Load Perturbation
On the two-area test system, the proposed FOPI-FOPIDN controllers outperform widely used PID controllers and the cascaded fuzzy PID-fractional-order PID with double derivative filters (FPIDN-FOPIDN) in terms of deviations in all the responses, yielding the best overshoot, undershoot and settling time. The proposed strategy shows a 10.7826% improvement on ICA-tuned FPIDN-FOPIDN, 30.96% on ADIWACO tuned PID, 96.29% on hFA-PS tuned PID, 96.3797% on GWO tuned PID and 99.99% on MBO tuned PID controllers in terms of ITAE values.
On the three-area test system, the proposed LFC strategy gives the least settling time, overshoot and undershoot in the responses of the tie-line power flows and frequencies. It shows 92.6864% improvement on the ICA tuned FFOPI-FOPID, 99.0593% over the ICA tuned FPIDN-FOPIDN and 99.7628% on ADIWACO tuned PID. The highest improvement, representing 99.9986%, is obtained over that of a recent CSA-tuned FFOPI-FOIDN.
Combined Random Load, PV and Wind Perturbations
The proposed LFC strategy demonstrates the least deviations in all responses and the lowest ITAE value when subjected to severe power disturbances, reaffirming its superior performance and highlighting its potential application to larger interconnected power systems.
System Parameters Variation
The controller is also found to be robust enough to perform just as predicted by the simulation results even if the parameters used for the system model are within ± 50% of the actual power system parameters.
Incorporating Physical Constraints
To test the efficacy of the proposed LFC strategy on a more practical system, the following physical constraints are considered in the models of the test systems:
- Communication Time Delay (CTD): A delay of 10 ms is introduced.
- Generation Rate Constraints (GRC): A value of 10% pu/min is used.
- Governor Dead Band (GDB): A value of 100 mHz is set.
The simulation results show that the proposed LFC strategy clearly outperforms the LFC strategy based on the fuzzy FOPI-FOPID when the controllers are tuned with the constraints included in the test system model. The fuzzy FOPI-FOPID exhibits unstable behavior in both frequency and tie-line power flow responses.
Conclusion
This article presents an LFC strategy based on a cascaded fractional-order proportional integral-fractional-order proportional integral derivative (FOPI-FOPIDN) with a derivative filter. The controller is optimally tuned using the ADIWACO PSO variant.
The robustness and scalability of the proposed LFC strategy are rigorously tested on a two-area test system and a three-area test system using step load perturbation, combined random load, PV, and wind perturbations, as well as system parameter variations. The experimental results obtained are compared with those of recent LFC strategies in the literature, clearly showing the superiority of the proposed LFC strategy.
Furthermore, the proposed control strategy is subjected to more stringent testing by incorporating key physical constraints, namely generator rate constraints, governor dead band, and communication time delays in the test system. The simulation results confirm the robust performance of the proposed LFC strategy under these practical conditions.
Overall, the study shows that the proposed LFC strategy is more effective and robust than the recent comparison strategies and has enormous potential for load frequency control in real-world RES-integrated power systems. The limitation of the proposed method is its reliance on fixed gains, which means it is not an adaptive strategy. Future work should explore ways to enhance the adaptiveness of the proposed method.
