Modern Heuristic Optimization Techniques Research Articles

Optimal Trading in Local Electricity Market Based on an Enhanced Evolutionary Algorithm with Distribution Estimation

The evolution from passive to active distribution network, the ability to actively control distributed energy resources and to perform bilateral energy exchange has significantly complicated the market transactions in recent years due to activities among consumers and prosumers. Local electricity market (LEM) is challenged to optimize welfare as well as reduction of emissions by involving participants in energy transactions. In this paper, the trading strategy is modeled as a single-objective optimization problem to determine each agent's optimal energy trading. Modern heuristic optimization techniques have proven their efficiency and robustness in large optimization problems and thus, an enhanced artificial bee colony (EABC) incorporating a distribution estimation algorithm is proposed, which guides the search for optimum by building and sampling explicit probabilistic models of promising candidate solutions. With the help of a distribution estimation algorithm, the EABC eliminates many tuning parameters in the original ABC and increases its exploitation to obtain more robust and competitive performance. A comparative study is conducted with the basic ABC, differential evolution, and particle swarm optimization, and the EABC demonstrates its efficiency on the case study where nine agents are involved in trading energy in the day-ahead LEM.

Optimization of Proportional Integral Derivative Parameters of Brushless Direct Current Motor Using Genetic Algorithm

Optimal performance of the Brushless Direct Current (BLDC) motor is to be realized using an efficient Proportional Integral Derivative (PID) controller. However, conventional tuning technique fails to perform satisfactorily under parameter variations, nonlinear conditions and time delay. Also using conventional technique to tune the parameters gain of the PID controller is a difficult task. To overcome these difficulties, modern heuristic optimization technique are required to optimally tune the Proportional, Integral, Derivative of the controller for optimal speed control of three phase BLDC motor. Thus, genetic algorithm (GA) based PID controller was used to achieve a high dynamic control performance. The Brushless DC Motor mathematical equation which describes the voltage and corresponding rotational angular speed and torque of the brushless DC motor was employed using electrical DC Machines theorem. The Genetic algorithm was further analyzed by adopting the three common performance indices i.e. Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE) in order to capture and compare the most suitable BLDC Motor speed and torque control characteristics. All simulations were done using MATLAB (R2018a). The simulation result showed that the system with GA-PID controller had the better system response when compared with the existing technique of ZN-PID controller.

Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050

Particle swarm optimization (PSO) and genetic algorithm (GA) are the most important optimization techniques among various modern heuristic optimization techniques. The study aims to forecast the energy consumption in Turkey until the year 2050 using PSO and GA models. The annual data provided by the Ministry of Energy and Natural Resources, International Energy Agency (IEA), OECD, Turkish Statistical Institute were used in the study. PSO and GA energy demand forecasting models are developed using population, import, export and gross domestic product (GDP). All models are proposed in linear and quadratic forms. Turkey's energy consumption is projected according to four different scenarios. According the analysis results, the study found for the PSO analysis theR^2 values in the linear model was 91.72%, in the quadratic model was 94.06% at the same time for the GA analysis R^2 values in the linear model was 91.71%, in the quadratic model was 93.97%. Additionally, the mean absolute percent error rates were 11.58% for PSO and 11.69% for GA in the quadratic model. According to Lewis, these values showed that models could be used for energy consumption estimation purposes. The study determined that the statistical performance criteria of PSO models were more successful than the statistical performance criteria of GA models.

Distribution power system reconfiguration using whale optimization algorithm

<p>This study discusses how to enhance the power distribution system and one of the most important ways to do that is by reconfiguration of the power system. Reconfiguration means changing the topology of the radial distribution network by changing the status of switches. The objective is to minimize the total power loss and enhance the voltage profile. Many optimization techniques were used to solve this problem such as classical optimization which is proven to be time consuming method and heuristic methods which are more efficient in our problem here. In this paper, the whale optimization algorithm (WOA) which is one of the modern heuristic optimization techniques and it has high efficiency to solve discrete optimization problems, is used to get the optimum case in reconfiguration problem. WOA is applied to (33 bus system, 69 bus system, and 118 bus system) and results are compared to other heuristic methods.</p>

AGC of two-area electric power systems using optimized fuzzy PID with filter plus double integral controller

The incessant swell in size, complexity, nonlinearity and structural variations in modern electric power systems, as well as rise in power demand has entailed the use of intelligent control strategies for the real-time satisfactory operation of power system. Hence, in this paper, a novel fuzzy PID with filter plus double integral (FPIDF-II) controller is proposed for automatic generation control (AGC) of two-area interconnected power systems. Initially, a well accepted two-area non-reheat thermal system is considered and the output scaling factors (SF) of FPIDF-II controller are optimized using imperialist competitive algorithm (ICA) employing an integral squared error (ISE) criterion. The supremacy of the proposed approach is demonstrated by contrasting the results with recently published optimal and various modern heuristic optimization techniques based controllers. To demonstrate the efficacy and scalability of the approach over other prevalent intelligent control techniques, the study is further extended to two-area non-reheat thermal system with governor deadband nonlinearity, two-area reheat thermal system, recently appeared two-area photovoltaic (PV)-reheat thermal system and two-area multi-source hydrothermal system. Finally, a sensitivity analysis is carried out to demonstrate the robustness of the proposed controller under broad variations in the system parameters from their nominal values.

Effect of TCPS, SMES, and DFIG on load frequency control of a multi-area multi-source power system using multi-verse optimized fuzzy-PID controller with derivative filter

In this paper, a novel multi-verse optimized fuzzy-PID (proportional–integral–derivative) controller with derivative filter (fuzzy-PIDF) is proposed for load frequency control of a two-area multi-source power system, with each area consisting of a reheat thermal and a hydro unit. The superiority of the Multi-Verse Optimizer algorithm is demonstrated against some recently published modern heuristic optimization techniques. For comparative analysis of the proposed control scheme, the conventional integral, proportional–integral, and PIDF controllers are also implemented. The effect of the thyristor-controlled phase shifter, superconducting magnetic energy storage, and doubly fed induction generators, considered individually and in various combinations, is investigated. Further, with an integral controller optimized using Multi-Verse Optimizer, the value of the integral of time multiplied absolute error, one of the performance indicators, is reduced by 85.61%, 83.91%, 70.42%, 67.69%, and 73.15% compared with optimization of the integral controller using Ant Lion Optimizer, Grey Wolf Optimizer, Differential Evolution, Bacterial Foraging, and Particle Swarm Optimization, respectively. Also, to quote a representative case, with Multi-Verse Optimizer, the settling times in respect of frequency deviations of area-1, area-2, and tie-line power are improved by 94.36%, 98.28%, and 95.44%, respectively, as compared with the results obtained using Ant Lion Optimizer. Further, robustness of the proposed control scheme is also investigated against variation of system parameters within ±10% besides random step load disturbances. Of all the controllers implemented here, the proposed optimized fuzzy-PIDF controller exhibits the best performance. Modeling and simulations were carried out using MATLAB/Simulink.

An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.

Optimal control strategy–based AGC of electrical power systems: A comparative performance analysis

SummaryThis study extensively addresses the application of optimal control approach to the automatic generation control (AGC) of electrical power systems. Proportional‐integral structured optimal controllers are designed using full‐state feedback control strategy employing performance index minimization criterion. Some traditional single/multiarea and restructured multiarea power system models from the literature are explored deliberately in the present study. The dynamic performance of optimal controllers is observed superior in comparison to integral/proportional‐integral controllers tuned using some recently published modern heuristic optimization techniques. It is observed that optimal controllers show better system results in terms of minimum value of settling time, peak overshoot/undershoot, various performance indices, and oscillations corresponding to change in area frequencies and tie‐line powers along with maximum value of minimum damping ratio in comparison to other controllers. The results are displayed in the form of tables for ease of comparison. Sensitivity analysis affirms the robustness of the optimal feedback controller gains to wide variations in some system parameters from their nominal values.

Hybrid GA–PSO for optimal placement of static VAR compensators in power system

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.

A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems

In this paper, a novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed for Load Frequency Control (LFC) of multi-area power system. Initially a two-area non-reheat thermal system is considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS (hDEPS) optimization technique. The superiority of the proposed controller is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as DE, Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI controllers for the same interconnected power system. Furthermore, robustness analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to multi-area multi-source power system with/without HVDC link and the gains of fuzzy PID controllers are optimized using hDEPS algorithm. The superiority of the proposed approach is shown by comparing the results with recently published DE optimized PID controller and conventional optimal output feedback controller for the same power systems. Finally, Reheat turbine, Generation Rate Constraint (GRC) and time delay are included in the system model to demonstrate the ability of the proposed approach to handle nonlinearity and physical constraints in the system model.

A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (hFA–PS) technique is proposed for Automatic Generation Control (AGC) of multi-area power systems with the consideration of Generation Rate Constraint (GRC). Initially a two area non-reheat thermal system with Proportional Integral Derivative (PID) controller is considered and the parameters of PID controllers are optimized by Firefly Algorithm (FA) employing an Integral Time multiply Absolute Error (ITAE) objective function. Pattern Search (PS) is then employed to fine tune the best solution provided by FA. The superiority of the proposed hFA–PS based PID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI/PID controllers for the same interconnected power system. Furthermore, sensitivity analysis is performed to show the robustness of the optimized controller parameters by varying the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to multi area multi source hydro thermal power system with/without considering the effect of physical constraints such as time delay, reheat turbine, GRC, and Governor Dead Band (GDB) nonlinearity. The controller parameters of each area are optimized under normal and varied conditions using proposed hFA–PS technique. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.

Tuning and Assessment of Proportional–Integral–Derivative Controller for an Automatic Voltage Regulator System Employing Local Unimodal Sampling Algorithm

—This article presents an approach for obtaining proportional–integral–derivative controller parameters for an automatic voltage regulator system based on a local unimodal sampling optimization algorithm. A conventional integral time of squared error objective function and modified objective functions in terms of integral time of absolute error, integral of absolute error, integral of squared error, peak overshoot, and settling time with appropriate weighting factors are employed to tune the controller parameters. Different objective functions are employed to obtain optimized proportional–integral–derivative controller gains. Superiority of proposed technique over some recently published modern heuristic optimization techniques, such as artificial bee colony algorithm, particle swarm optimization algorithm, and differential evolution algorithm, for the same automatic voltage regulator system is demonstrated. Simulation results reveal that the proposed proportional–integral–derivative controlled automatic voltage regulator system tuned by the local unimodal sampling algorithm with modified objective function exhibits better performance in terms of settling time, peak overshoot, and stability. The robustness of the system tuned by the proposed algorithm is also studied satisfactorily by varying the time constants of the automatic voltage regulator system in the range of –50% to +50% in steps of 25%.

Comparison of Honey Bee Mating Optimization and Genetic Algorithm for Coordinated Design of Pss and Statcom Based on Damping of Power System Oscillation

Recently, honey bee mating optimization (HBMO) technique and genetic algorithms (GA) have attracted considerable attention among various modern heuristic optimization techniques. This paper presents the application and performance comparison of HBMO and GA optimization techniques, for coordinated design of STATCOM and PSS. The design objective is to enhance damping of the low frequency oscillations. The design problem of the controller is formulated as an optimization problem and both HBMO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques for damping low frequency oscillations are tested and demonstrated through nonlinear time-domain simulation and some performance indices studies to different disturbances over a wide range of loading conditions. The results show that the designed controller by HBMO performs better than GA in finding the solution. Moreover, the system performance analysis under different operating conditions show that the φ based controller is superior to the C based controller.

Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system

This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional-Integral (PI) controller for Automatic Generation Control (AGC) of an interconnected power system. A two area non-reheat thermal system equipped with PI controllers which is widely used in literature is considered for the design and analysis purpose. The design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions using Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients are derived in order to increase the performance of the controller. The superiority of the proposed DE optimized PI controller has been shown by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA) and Genetic Algorithm (GA) based PI controller for the same interconnected power system.

Design of prestressed concrete flat slab using modern heuristic optimization techniques

The main objective of the current study is to utilize the huge capabilities of modern heuristic search algorithms for structural design optimization of pre-stressed concrete slab; providing a general, flexible, and relatively easy to use tool for practicing engineers. A robust numerical tool integrating design, analysis, and optimization techniques is developed for this purpose. The tool utilizes the Finite Element method for the structural analysis of the system. Optimum values for the slab thickness, number and size of tendons, and tendon profile are found subject to design constraints imposed by the relevant code of practice. The objective function incorporates the cost of both concrete and prestressing tendons. Although the objective function is simple and monotonic, the optimization problem is quiet challenging due to the complexity and nonlinearity of the constraints in addition to the discreteness of some of the variables. As a demonstration problem, the optimum design of a square prestressed flat slab is sought. Direct search methods, heuristic optimization techniques such as Genetic Algorithms, and multi-objective optimization techniques are considered. Results indicated that search should be conducted along a constraint boundary. It is suggested to divide the design variables into two groups and to define a second objective function representing the distance away from the constraint. Optimization should be carried out for both objective functions (cost and distance) simultaneously. Using the suggested procedure, optimum design is found more efficiently.

Comparative Analysis of Genetic Algorithms and Particle Swarm Optimization Algorithms for Optimal Reservoir Operation

Apart from traditional optimization techniques, modern heuristic optimization techniques, like genetic algorithms (GA), particle swarm optimization algorithm (PSO) have been widely used to solve optimization problems. This paper deals with comparative analysis of GA and PSO and their applications in a reservoir operation problem. Extensive component analysis, parameter sensitivity analysis of GA and PSO show that both GA and PSO can be used for optimal reservoir operation, but they display different features. GA can obtain very high approximate global optimal solutions of the problem with a high stability and a high computing efficiency, but it can’t obtain the problem’s accurate global optimal solutions. For GA, population size and mutation rate are two main parameters affect its solution qualities. Comparative to GA, PSO can obtain the accurate global optimal solutions of the problem with a higher computing efficiency, but with a less stability. For PSO, population size and velocity parameter are two main parameters affect its solution qualities.

Parameters Optimization of a Counter-Current Cascade Based on Using a Real Coded Genetic Algorithm

Analysis of economical aspects of centrifuge-based separation shows that the bulk of the cost is proportional to the number of centrifuges in a cascade. In this paper a program which is called MAKNO is used to obtain the velocity field in a centrifuge by solving popular purely axial flow in a gas centrifuge. Through using MAKNO and solving concentration equation in a single gas centrifuge a realistic function for α, separation factor, in relation to θ, cut, and f, feed flow rate is achieved. To minimize the number of centrifuges in a cascade one needs to determine optimal parameters, α, θ, and f. Finding the optimum solution requires a huge amount of calculation in classical methods. Recently, a genetic algorithm (GA) technique has attracted considerable attention among various modern heuristic optimization techniques. To optimize the parameters and the number of centrifuges for a given cascade a Real Coded Genetic Algorithm program, RCGA, is implemented and developed. It has been shown that the application of RCGA to this problem not only reduces the amount of calculation but also guarantees finding the best solution. It is found that, from an evolutionary point of view, the performance of the GA is excellent.

Robust FACTS Controller Design Employing Modern Heuristic Optimization Techniques

Recently, Genetic Algorithms (GA) and Differential Evolution (DE) algorithm technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of DE and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques has been compared. Further, the optimized controllers are tested on a weekly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.

Differential evolution algorithm for SSSC-based damping controller design considering time delay

Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this paper. Both local and remote signals with associated time delays are considered in the present study. The design problem of the proposed controller is formulated as an optimization problem, and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite-bus power system and multi-machine power system. The performance of the proposed controllers with variations in the signal transmission delays has also been investigated. Simulation results are presented and compared with a recently published modern heuristic optimization technique under various disturbances to show the effectiveness and robustness of the proposed approach. The performances of the proposed controllers are also evaluated under N−2 contingency situation.