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Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance. [ABSTRACT FROM AUTHOR]

The present work focuses on the power oscillation damping of an interconnected multi-area multi-source power system depicting the load fluctuations issue in reference frame of automatic generation control (AGC). In real-time, the load profile characteristic is un-deterministic and uncertain in nature. Therefore, a diverse perspective of area load profiles such as step load disturbance, pulse load perturbation, sinusoidal load pattern, random load pattern and uniformly distributed random load are taken into study. The investigated power system model is a two-area system, classified by reheat thermal, hydro and gas generating units, lumped together in a control area with the impacts of governor deadband and generation rate constraint. AGC performance work is propagated by proportional-integral-derivative (PID) controller with filter and, then, fractional order PID (FPID) controller. Also, a fast-acting thyristor controlled series compensator (TCSC) device modified by the Taylor theorem is incorporated as a damping controller to pursuit its significances in AGC tool. In an additional work, a lead-lag compensator block is also incorporated to the basic TCSC controller to show its impact on AGC. Sensitivity analysis of both the basic and the advanced TCSC damping controller is investigated under loading and model parameter variations. The design problem i.e., the constrained optimization problem is tuned by employing the quasi-oppositional harmony search (QOHS) algorithm. From the mathematical point of view, calculations eigenvalues, performance indices and transient parameters, and sensitivity analysis are presented in support of the designed TCSC-QOHS controller. Graphically, the simulation results are presented. The obtained simulation results showed that the realization of TCSC in series with the tie-line with the FPID controller can be an effective approach for the AGC tool. [ABSTRACT FROM AUTHOR]

This paper proposes a new combined controller, the proportional integral derivative-second derivative with a proportional derivative (PIDD2-PD), to improve the frequency response of a multi-area interconnected power system with multiple generating units linked to it. The optimum gains of the presented controller are well-tuned using a wild horse optimizer (WHO), a modern metaheuristic optimization approach. The main study is a two-area-linked power system with varied conventional and renewable generating units. The physical constraints of the speed turbines and governors are considered. The WHO optimization algorithm is proven to outperform various other optimization approaches, such as the whale optimization algorithms (WOA) and chimp optimization algorithms (ChOA). The efficacy of the proposed WHO-based PIDD2-PD controller is evaluated by comparing its performance to other controllers in the literature (cascaded proportional integral derivative-tilted integral derivative (PID-TID), integral derivative-tilted (ID-T) controller). Multiple and varied scenarios are applied in this work to test the proposed controller's sturdiness to various load perturbations (step, random, and multi-step), renewable energy source penetration, and system parameter variations. The results are provided as time-domain simulations run using MATLAB/SIMULINK. The simulation results reveal that the suggested controller outperforms other structural controllers in the dynamic response of the system in terms of settling time, maximum overshoot, and undershoot values, with an improvement percentage of 70%, 73%, and 67%, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, the valid development and full utilization of renewable energy sources such as wind power and solar energy are considered to be one of the most significant approaches to get rid of the energy dilemma and realize environmental governance. However, although there are more and more renewable power sources connected to power systems, the renewable energy has not been fully used yet. In this context, an optimization model for the regional power system with the pump storage power, wind, hydro, and thermal power is proposed to enhance the economy, environmental protection, energy-saving and stability of the power system. Besides, the fuzzy chance constraint programming and the improved analytic hierarchy process are used to deal with the uncertainty of the wind power and the load demand and the multi-objectives function, respectively. The case study not only verify the effectiveness of the proposed model but also shows that better comprehensive performance of the power system can be achieved through the proposed model when considering the effect of the pump storage power.

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance.

In this study, load frequency control (LFC) of two-area non-reheat thermal power system and multi-source power systems is addressed. A simple PID-structured controller is used as a secondary controller in these systems. To raise the performance of PID controller, a new multi-objective function is designed and PID controller parameters are acquired by minimizing the value of this function with symbiotic organisms search (SOS) algorithm. All electrical power systems simulated are modeled in MATLAB/Simulink environment and the optimizer is coded in MATLAB/M-file platform. In order to affirm the contribution of the work, results collected from each power system are compared with popular results published in prestigious journals. As per the comparative results, despite its simplicity, SOS:PID controller tuned via the proposed objective function is observed to result in better performance than other approaches in terms of oscillations, settling time, maximum overshoot and maximum undershoot time domain indicators of the frequency and tie-line power change curves. [ABSTRACT FROM AUTHOR]

This paper introduces a fractional order tilt-integral-derivative (FOTID) controller which is structurally analogous to fractional order proportional-integral-derivative controller in a power system for solving automatic generation control (AGC) problem. It is optimized by a recent metaheuristic optimizer called pathfinder algorithm (PFA). An interconnected two-area power system model comprising of multi-sources like thermal, hydro and gas generating units including physical constraints namely, governor dead band (GDB) and generation rate constraint (GRC) are taken into consideration for the study. The efficiency of the proposed controller for AGC is shown by comparing it with PFA optimized tilt-integral-derivative (TID) and proportional-integral-derivative (PID) controllers with integral of time multiplied absolute error (ITAE) taken as the objective function. Simulation study supports the claim that the proposed controller provides better dynamic responses as compared to the others. Sensitivity and robust analyses are done to demonstrate the effectiveness of the proposed PFA optimized FOTID controller to a wide variation in system parameters, at different step load and random load disturbances. [ABSTRACT FROM AUTHOR]

This article discusses a Whale Optimization (WO) controller for design analysis, stability management, application, and performance analysis of a deregulated two-area multi-source energy system. Within the power grid, the Automatic Generation Control (AGC) maintains the power balance and strengthens the sudden frequency disturbance of the grid. Various researchers are targeted many kinds of fast-acting energy-storing devices to dampen space frequency oscillations and diversion of the tie line because of unexpected load variation. To regulate the power flow between multi-areas, load frequency control (LFC) is used by keeping frequency constant. But, to design a controller for minimizing the issues of LFC remains a challenge within the deregulated atmosphere. The main objective of the proposed methodology is to design a controller for minimizing the issues in LFC within the deregulated atmosphere. A two-area deregulated hydrothermal power system is employed to analyze many LFC problems in the deregulated power grid. During this modeling, the Thyristor-Controlled Phase Shifters (TCPS) and Capacitive Energy Storage unit (CES) are placed in the tie line and its control space. To mitigate the various issues of LFC in a two-area deregulated hydrothermal power system, the CES–TCPS combination is placed between the two control areas and the transient performance of constant is evaluated. The WO controller will increase the system power size to focus on the better performance of low-frequency management tasks. In addition, the effectiveness of the WO algorithm in tuning the proportional integral controller is also compared in a different contract (unilateral, bilateral and contract violation cases) scenarios of the proposed deregulated power system. Finally, the simulation is carried out in MATLAB/Simulink platform and the results obtained for the WO controller is compared with the normal and fuzzy PI controllers. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.

Typhoons and other natural disasters affect the normal operation of power systems thus it is an important goal for strong and intelligent power grid construction to improve the ability of power systems to resist typhoons and other natural disasters. Especially, an effective coordinated and optimized dispatching strategy for a multi-source power system is greatly helpful to cope with the impact of typhoons and other natural disasters on power system operation. Given this background, a typhoon wind circle model considering the temporal and spatial distribution of typhoons is established to obtain the input wind speed of the wind farm at first. Second, based on the initial input wind speed of wind farms, a typical scenario set of wind power output is constructed to reflect its fluctuation and uncertainty. Next, an optimal coordinated dispatching model of a multi-source power system with wind, hydro and thermal power based on the conditional value at risk (CVaR) is established with the target of minimizing the total cost of system dispatching, in which a 72 h pre-dispatching mode is studied to optimize the system operation for 72 h on the day before, on and after the typhoon. Finally, a revised 24-node transmission network system in a coastal area with typhoon is served as a case for demonstrating the effectiveness of the proposed model, and the simulation result shows that the proposed model could take the advantages of the coordination and complementarity of multi-sources power system and decrease the total cost of system dispatching and improve the renewable energy consumption level.

In this paper a fuzzy logic (FL) based load frequency controller (LFC) called discrete FuzzyP+FuzzyI+FuzzyD (FP+FI+FD) is proposed to ensure the stability of a multi-source power system in restructured environment. The whale optimization algorithm (WOA) is used for optimum designing the proposed control strategy to reduce fuzzy system effort and achieve the best performance of LFC task. Further, to improve the system performance, an interline power flow controller (IPFC) and superconducting magnetic energy system (SMES) is included in the system. Governor dead band, generation rate constraint, and time delay are considered as important physical constraints to get an accurate understanding of LFC task. The performance of the optimized FP+FI+FD controller is evaluated on a two area six-unit hydro-thermal power system under different operating conditions which take place in a deregulated power market and varying system parameters in comparison with the classical fuzzy PID controller. Simulation results shows that WOA based tuned FP+FI+FD based LFC controller are relatively robust and achieve good performance for a wide change in system parameters considering system physical constraints.

In the multi-source power system, automatic regulation of load frequency is a demanding mission for power system (PS) engineers. Difficulties involved in these tasks are further exacerbated by increasing power generation and integrating numerous energy sources. The presented paper intends to develop a bull–lion optimization method that depends on the heuristic method for obtaining the purpose of regulating the frequency of the PS. The designed optimization approach utilizes the key structures of three distinct optimization methods: gravitational search algorithm (GSA), Firefly algorithm (FA), and Particle swarm optimization (PSO). In this research, tuning of PID controller metrics by the proposed Bull–Lion optimization algorithm is involved, in order to ensure frequency regulation with multiple sources in a two-area power system (TAPS). In addition, by adjusting the parameters of the controller, the tie-line power is maintained within the prescribed range. The proposed bull–lion optimization-based PID controller attains better performance in regulating the load frequency as well as the tie line power in the suggested PS. Taking into account the settling time, rise time and overshoot of frequency and tie line power response, the performance of the proposed controller is demonstrated. • The presented paper intends to develop a latest hybridized optimization method which depends on heuristic method concerning to regulate the frequency of proposed two area power system. • The developed optimization approach utilizing the key features of three distinctive optimization methods: Gravitational Search Algorithm, Firefly Algorithm and the Particle Swarm Optimization. • To obtain the regulation frequency of power system, the metrics related to proportional integrated derivative (PID) controller be adjusted by the developed algorithm. The integrated time absolute error is considered as an objective function. [ABSTRACT FROM AUTHOR]

In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA-PSO-based PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA-PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA hydrothermal power system. Finally, sensitivity analysis is performed to ascertain the robustness of the controller designed for the systems under study. It is observed that the suggested FPI/FPID controller optimized for nominal conditions is able to handle generation rate constraints and wide variations in nominal loading condition as well as system parameters. [ABSTRACT FROM AUTHOR]

In this study, an attempt is made to present the application and comparative performance analysis of optimal control approach for automatic generation control (AGC) of electric power generating systems. Optimal controller is designed utilizing performance index minimization criterion. To conduct the study, various single and multi-area models with/without system nonlinearities from the literature are simulated under sudden load perturbation. In this comparative study, to corroborate the worth of optimal controller, the performance of optimal AGC controller is compared with that of I/PI controller optimized adopting recently published the best established techniques such as teacher learning based optimization (TLBO), differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), hybrid bacteria foraging optimization algorithm-PSO (hBFOA-PSO), craziness based PSO (CBPSO), firefly algorithm (FA), krill herd algorithm (KHA), moth-flame optimization (MFO), glow swarm optimization (GSO), simulated annealing (SA), bat algorithm (BA), stochastic fractal search (SFS) and hybrid SFSlocal unimodal sampling (hSFS-LUS) technique. The simulated results are compared in terms of settling time (ST), peak undershoot (PU)/overshoot (PO), various performance indices (PIs), minimum damping ratio (ξmin) and system eigenvalues. A sensitivity study is conducted to certify the robustness of optimal controller. [ABSTRACT FROM PUBLISHER]

In this paper, a novel Teaching Learning Based Optimization (TLBO) algorithm with 2-Degree Freedom of Proportional–Integral–Derivative (2-DOF PID) controller is proposed for Automatic Generation Control (AGC). Initially a widely used two area thermal system is considered and the gains of the PI/PID/2-DOF PID controller are optimized employing a TLBO algorithm. The supremacy of the proposed 2-DOF PID controller has been shown by comparing the results with recently published technique such as conventional ZN, GA, BFOA, DE and hBFOA-PSO based PI controllers for the same system. Additionally, the proposed approach is further extended to multi source power system such as thermal, hydro and gas power plant. The advantage of the proposed approach is demonstrated by comparing the results with some recently published approaches. Sensitivity analysis is performed which demonstrates the ability of the proposed approach to wide changes in system parameters. Finally the proposed approach is investigated under random load variation. [ABSTRACT FROM AUTHOR]

In this study, fuzzy gain scheduling controllers are proposed for automatic generation control of interconnected electrical power systems. Primarily, the study is done for automatic generation control of a two-area non-reheat thermal power system, and the parameters of fuzzy gain scheduling controllers are optimized by a genetic algorithm. Simulation results show the performance of fuzzy gain scheduling controllers is superior compared to the optimal and controllers based upon the gravitational search, the bacteria foraging optimization, and the hybrid bacteria foraging optimization–particle swarm optimization algorithms for an identical power system. The proposed approach is further protracted to a two-area reheat thermal system; the benefits of the fuzzy gain scheduling approach are demonstrated over optimal, conventional proportional-integral, and genetic algorithm-based integral controllers. Next, a multi-source multi-area hydro thermal system is considered, and the superiority of fuzzy gain scheduling controllers is established by comparing the results to the genetic algorithm and best claimed hybrid firefly algorithm–pattern search technique-based controllers. Finally, the effectiveness of the proposed approach is established for a two-area restructured reheat thermal power system. The simulation results indicate that the proposed fuzzy gain scheduling controllers work efficiently and provide better dynamic performance without being redesigned for separate systems. [ABSTRACT FROM PUBLISHER]

This paper deals with the load frequency control (LFC) study of single-area and interconnected two-area power system having diversified power sources. The two areas considered in the present study are identical. Each area is having thermal, hydro and gas based power plants. Split-shaft model of gas turbine is used in the present work as one of the diversified generating unit for the purpose of LFC study. Optimal gains of the classical controllers (like integral controller, proportional–integral controller and proportional–integral–derivative (PID) controller, one installed at a time in the studied models) are obtained by using a novel music-inspired metaheuristic harmony search algorithm (HSA) which incorporates quasi opposition based learning technique for memory initialization and also for generation jumping. Single-area power system with diverse power sources is considered and its optimal transient performances are obtained and compared for step load perturbation. The same approach is further extended to two-area interconnected power system consisting of diverse power sources with nominal values of area input parameters. The performance of PID controller is found to be the best one for the studied power system models. It is also revealed that the performance of the interconnected two-area power system with AC–DC tie line is better in comparison to AC tie line. [ABSTRACT FROM AUTHOR]

Highlights: [•] DE parameters tuning and its application to LFC of a multi-source power system is presented. [•] Both single-area and multi-area system with/without HVDC are considered. [•] Robustness analysis is performed by changing loading condition and system parameters. [•] Proposed approach exhibits better performance than optimal output feedback controller. [ABSTRACT FROM AUTHOR]

This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA's proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Typhoons and other natural disasters affect the normal operation of power systems thus it is an important goal for strong and intelligent power grid construction to improve the ability of power systems to resist typhoons and other natural disasters. Especially, an effective coordinated and optimized dispatching strategy for a multi-source power system is greatly helpful to cope with the impact of typhoons and other natural disasters on power system operation. Given this background, a typhoon wind circle model considering the temporal and spatial distribution of typhoons is established to obtain the input wind speed of the wind farm at first. Second, based on the initial input wind speed of wind farms, a typical scenario set of wind power output is constructed to reflect its fluctuation and uncertainty. Next, an optimal coordinated dispatching model of a multi-source power system with wind, hydro and thermal power based on the conditional value at risk (CVaR) is established with the target of minimizing the total cost of system dispatching, in which a 72 h pre-dispatching mode is studied to optimize the system operation for 72 h on the day before, on and after the typhoon. Finally, a revised 24-node transmission network system in a coastal area with typhoon is served as a case for demonstrating the effectiveness of the proposed model, and the simulation result shows that the proposed model could take the advantages of the coordination and complementarity of multi-sources power system and decrease the total cost of system dispatching and improve the renewable energy consumption level. [ABSTRACT FROM AUTHOR]