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The paper describes two main approaches of periodic control under variable frequency conditions: frequency adaptive multioscillatory control (FAMOSC) and fractional delay repetitive control (FDRC). The methods are applied in the control system of a grid-tie converter to increase the quality of phase currents in case of distorted grid voltages. The paper compares structures, performance and implementation of FAMOSC, FDRC and the conventional approach of repetitive and multioscillatory control. Performances of the control systems are illustrated with experiments on a laboratory test bench. The article describes tuning process of FDRC and FAMOSC with the usage of Particle Swarm Optimization algorithm. A detailed analyse and research of the techniques in terms of computational complexity is presented. Practical hints for implementation and resource optimization of both techniques on DSP are included. For FAMOSC a fast implementation of prewarping is proposed.

Units within a frequency control area, participating in the automatic generation control (AGC) are usually spinning generating units already connected to the network and operating outside their range of optimal performance. This paper deals with an alternative method of providing secondary frequency control called Rapid-Start (RS) operation, consisting in assigning a regulation band to several off-line units (RS units) which are capable of being started and connected rapidly. As RS operation may have economic benefits (the on-line units operate closer to their optimal power level), an appropriate algorithm to start up and shut down RS units needs to be developed. This paper proposes a Threshold-Based Algorithm (TBA) that signals the start-up and shut-down decisions of RS-units based on two threshold parameters that measure the allowed distance of the regulating units of the control area to their operating limits. Total RS units cost and time of non-compliance of the control area are proposed as key performance indexes (KPIs) to measure the behavior of the algorithm. The two threshold parameters of the algorithm are tuned with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm that optimizes the KPIs. An AGC simulation model is developed to implement the MOPSO procedure and test the behaviour of the different parameter configurations. The performance of the whole methodology is illustrated for an actual secondary frequency control area within the Spanish power system.

In this paper, the effects of three FACTS controllers, static synchronous series compensator (SSSC), thyristor-controlled series compensator (TCSC), and static synchronous compensator (STATCOM), on the transient stability of a multi-machine power system in the presence of two 200 MW wind farms based on a doubly fed induction generator (DFIG) and a 120 MW photovoltaic (PV) solar plant are studied. Also, power system stabilizers (PSSs) are installed on four synchronous machines. It is generally accepted that there is a threat to the stability of power systems with the penetration of wind farm and PV plant. Therefore, in order to improve transient stability in the power system, this paper proposes the application of PI controllers in PV and wind farm controllers and a combination of PSS and FACTS controllers, whose coordination is very important. The design problems of the PSS, FACTS, and PI controllers are formulated as an optimization problem, and the adaptive velocity update relaxation particle swarm optimization (AVURPSO) algorithm, gravitational search algorithm (GSA), and genetic algorithm (GA) are employed to search for the optimal controller parameters. These three approaches identify the solution to a given objective function, but they employ different strategies and computational efforts. Therefore, a comparison of their performance is required.

The ultra-short-term prediction of traction load is a key aspect of electric power control in electrified railroads. In order to provide more accurate traction load prediction data for the control process, this paper designs a combined prediction method combining Discrete Wavelet Transform(DWT), Temporal Convolutional Network (TCN) and Support Vector Regression based on Particle Swarm Optimization(PSO_SVR) for the characteristics of traction load with strong random fluctuation, large jump amplitude and frequent no-load. This method will use DWT model to decompose the traction load with strong random fluctuation and difficult to predict into sub-series with single fluctuation frequency and easy to predict; then, according to the frequency difference of different series, TCN model is selected to predict medium and low frequency series. The SVR model is selected to predict the high frequency sequences. Finally, the prediction results of each sub-sequence are summed to obtain the final prediction. The combined prediction model designed in this paper is used to predict the traction load of traction power supply stations. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the existing prediction methods in the current prediction field.

Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods.

Intermittence from solar photovoltaic (PV) source can deteriorate overcurrent relays (OCRs) response to fault. This paper proposes a method for fault analysis with OCRs consideration to discuss relays faults currents fluctuations in time against PV fluctuations. The simulated PV fluctuations are short time dynamic high and low irradiations fluctuations and PV connection and disconnection based PV-dominated feeders. Two distribution systems (DSs) based accurate PV systems are modeled separately with and without Battery Energy Storage System (BESS) for the purpose of proposed method. Series and parallels PV modules are arranged to model accurate 6.5 kV PV farm power. Two level three-phase voltage source inverter (VSI), DC-DC boost converter and LCL filter are applied for each PV system. Precise detailed control strategies are implemented in PSCAD software. This paper proposed method complies with congestion which is frequently avoided in literature. The results discussions based proposed method clarified PV fluctuations scenarios impacts on OCRs dynamic fault currents profiles in presence and absence of BESS and clearly revealed and argued related important findings. In addition, OCRs performance is shortly analyzed based particle swarm optimization (PSO) algorithm efficiently coded in Matlab. The obtained results could be very profitable to power system relaying operators to handle protection challenges with PV power fluctuations.

This paper presents an efficient methodology of optimising the structure of a multicarrier energy microgrid. The term ‘microgrid structure’ denotes the type and parameters of energy microsources and storage devices with which a microgrid might be equipped. In order to represent a generation and storage structure of a microgrid, the use of a modified energy hub model has been proposed. An energy hub may be defined as an interface between energy demand/ generation and power supply infrastructure or available local energy resources. The process of planning a technically permissible and economically efficient microgrid structure entails the necessity of providing an adequate optimisation solution. This paper proposes for the first time a solution to the optimal planning problem with a novel energy hub model recently developed by the author. The main objectives for a given problem are set, and then a detailed mathematical model of a stated optimisation problem is described. Both the criterion function and a set of constraints has been presented in detail. Then, two independent metaheuristics - the evolutionary algorithm and particle swarm optimisation - have been proposed and substantiated as exemplary methods of solving the formulated optimisation problem. In the next part of the paper, a calculation example has been presented for the given optimisation problem. The obtained results have been widely analysed and discussed, and areas and topics of future research about the microgrid planning process have also been further presented.

This paper proposes a combined model of Multi-Objective Dynamic Economic and Emission Dispatch (MODEED) with Demand Side Management (DSM) to investigate the benefits of DSM on generation side. This model considers a day ahead based load shifting DSM approach. In order to analyse the effect of DSM on the generation side, the objectives of dynamic economic and emission dispatch problem were minimized individually and simultaneously with and without DSM. A test system with six thermal generating units was considered for the validation of the proposed method. In this paper, authors used Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to minimize the objectives of MODEED problem simultaneously. The simulation results of the MOPSO algorithm have also been compared with the non-dominated sorting genetic algorithm (NSGA-II). It is clear from the results that the proposed combined model is able to give benefits to both utilities and generating companies.

This paper presents an adapted particle swarm optimization model for the electrical layout planning of floating offshore wind farms (FOWFs). A comprehensive model is considered by taking into account the entire wind turbine connection possibilities as well as stochastic wind speed and wind direction and the computation of the wake effect within the wind farm. Furthermore, dynamic power cables used for the connection of floating offshore wind turbines are considered as well as their respective acquisition and installation costs. The reliability assessment of the electrical components and the influence on the energy generation are also taken into account. The developed optimization model is validated in this paper at first against a reference model developed by Banzo et al. Then the model is applied on a 500 MW FOWF case. The optimized collection grid results in a decrease of 4.5% of the total costs and a reduction of the energy losses by 6.4% compared to the existing layout of the FOWF. Finally, the use of either solely dynamic power cables or a combination of static and dynamic cables is studied. The findings show that for this particular case the use of solely dynamic power cables is favorable due to the avoidance of cost-intensive submarine joints and additional installation activities.

This paper proposes the application of fictitious reference iterative tuning (FRIT) method to optimize the gains of dc voltage controller of grid connected photo-voltaic (PV) system. It may be difficult to achieve good control of dc voltage using conventional PI controller having only one-degree-of-freedom (1-DOF) due to the trade-off between overshoot (in step response) and disturbance response. In this paper, the optimal control of dc voltage is proposed with improved disturbance response by implementing 2-DOF PI controller structure. FRIT method has been programmed in MATLAB based upon the particle swarm optimization (PSO) algorithm. The fundamental idea related to FRIT method is the extraction of input and output data, reference model setting and range of controller gains. The performance of dc voltage control for the optimized 2-DOF PI controller is also compared with the fuzzy logic controller (FLC) response.

The objective of this paper is to propose a simple transmission congestion pricing scheme based on tracing principle by considering generator fixed cost, cost for incurring loss and transmission congestion cost. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated type. Such a competitive market has paved way for innumerable participants. This concept of restructuring has led to overloading of transmission lines. In this paper, power flow tracing has been employed by using suitable optimization algorithm, where the real power generation has been maximized. Congestion in the transmission line has been produced in a new fashion by maximizing the real power demand. The power flow under normal operating condition and congestion is determined and hence the difference in power flow is estimated. Based on the estimated power flow difference, the transmission line congestion cost is computed. Pool model and bilateral model has been considered in simulation study to introduce the concept of deregulation. The proposed method is tested and validated on Modified IEEE 30 bus test system and Indian utility 69 bus test system.

There are different approaches to provide high power quality for sensitive loads in fault conditions. In this research, “Dynamic Voltage Restorer (DVR)” is used to compensate the harmful effects of disturbances on voltage. In order to improve the performance of DVR’s controller from point of view of power quality indices, voltage sag and THD are considered as first and second object respectively so a new structure is suggested for this controller. The proposed controller is based on human brain learning which is a self-tuning PI controller that is called emotional controller. In that paper a bi-objective structure emotional learning is recommended. Also using this controller, system had better performance during fault conditions in term of both these power quality’s indices. Furthermore, considering voltage THD as second goal influence on major goal considerably which is voltage sag. Power systems sometimes have complicated dynamic behavior especially during faults. Most controllers have difficulty doing their best performance in these situations. Therefore, in order to modify the performance of this controller from point of view of mentioned power quality indices, we make a decision to regulate this controller’s parameters with an optimization algorithm. Self Adaptive Modified Bat Algorithm (SAMBA) is considered as a powerful optimization algorithm in this paper. This optimization algorithm is a modified version of Bat algorithm. The optimization algorithm can overcome to some problems which are common in other optimization algorithms. The algorithm has had a considerable performance rather than standard Bat and PSO algorithm. Also according to simulation results, this proposed method works significantly better than classic PI controller, bi-objective emotional controller and some intelligent controllers that have introduced in other researches already.

Maintenance scheduling of power generating units is very essential for the economical and reliable operation of a power system. The objective of Generator Maintenance Scheduling (GMS) problem is to find the exact time interval for preventive maintenance of power generating units in order to minimize the operating cost, maximize the system reliability and to extend the life time of the generating units. In this paper, the problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem by considering minimizing the operating cost. Since generator maintenance scheduling is a mixed integer problem, differential evolution algorithm is suitably modified to handle the integer variables. The control variables in differential evolution algorithm are integers which denote the starting period of each generating unit for carrying out maintenance work. The lambda iteration method is used to determine the optimal generation schedule of committed generating units. This paper presents a mathematical approach assisted differential evolution (MADE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering two test systems. The result obtained by the proposed MADE method is compared with mathematical approach assisted particle swarm optimization. The test results reveal the capability of the proposed MADE algorithm in finding optimal maintenance schedule for the GMS problem.

In this paper, an optimal power dispatch formulation is proposed in the operating horizon using real time measurements to enhance the performance of online energy scheduling in microgrid considering renewable energy, grid power trade and demand response. A stochastic weight tradeoff particle swarm optimization (SWT-PSO) based backward–forward sweep (BFS) optimal power flow (OPF) method is used by the microgrid central controller (MGCC) to obtain the online optimal schedules of distributed energy resources (DERs). The effectiveness of the suggested approach is investigated on the residential feeder of the CIGRE LV benchmark microgrid in terms of reduction in fuel & emission costs and voltage deviation. In addition, settling time in overall implementation is drastically reduced. Also, the paper analytically justifies the use of (1) renewable energy sources, grid trade and prioritized load curtailment, (2) SWT-PSO over priority listing method and (3) online OPF over online economic dispatch (ED).

In this paper a novel approach regarding the optimal penetration of Distributed Generation (DG) in Distribution Networks (DNs) towards loss minimization is proposed. More specific, a Local Particle Swarm Optimization (PSO) variant algorithm is developed in order to define the optimal active and reactive power generation and/or consumption requirements for the optimal number and location of nodes that yield loss minimization. Thus, the proposed approach provides the optimal number, siting and sizing of DGs altogether. In addition, based on the optimal power requirements of the resulted nodes, a combination of potential DG types to be installed is recommended. The proposed objective function in this paper is also innovative since it embeds the constraint of reverse power flow to the slack bus by the formation of a new penalty term. The proposed methodology is applied to 30 and 33 bus systems. The results indicate the optimal number, locations, and capacity of DG units, which were calculated simultaneously. Finally, the impact of the predefined amount of permissible reverse power flow to the optimal solution is also examined through two scenarios: the first considers zero reverse power flow and the second unlimited reverse power flow.

The capability of network configuration change and high penetration of distributed generations (DGs) are two important characteristics of modern distribution networks. The configuration change and connection/disconnection of DGs result in the variation of fault currents’ magnitude and direction besides changing the role of protective devices as primary or back up protection. This fact can make the protection coordination complicated or even impossible in some cases. One of the solutions to this problem is to use a setting group (SG)-based adaptive protection schemes. SG-based protective schemes protect various network states (different configurations and connection/disconnection of DGs) using a limited number of SGs. Therefore, it is needed to propose an algorithm to specify the settings of each SG and the determination of SG selection in different network states (NSTs). In this paper, previous attempts in the utilization of SG capability of Overcurrent relays (OCRs) are examined and a novel approach is proposed which is capable of protecting the same network with lower delay in relays' operation times although it requires a fewer number of SGs. In this paper, a new method based on integer linear programming (ILP) and particle swarm optimization (PSO) is utilized to specify the proper SG to activate for each relay in each NST, in order to minimize the total operating time of OCRs, considering coordination constraints. The efficiency of the proposed method in comparison to the previous methods is demonstrated by applying on a sample distribution network.

Power management, system stability and communication structure are three key aspects of microgrids (MGs) that have been explored in many research studies. However, the cascaded effect of communication structure on system stability followed by the impact of stability on the power management has not been fully explored in the literature yet and needs more attention. This paper not only explores this cascaded impact, but also provides a comprehensive platform to optimally consider three layers of MG design and operation from this perspective. For generation cost minimization and stability assessment, the proposed platform uses an adaptive particle swarm optimization (PSO) while a new class of data exchange scheme based on IEC 61850 protocol is proposed to reduce the communication time delays among the inverters of distributed generations and the MG control center. This paper also considers the system stability using small-signal model of a MG in a real-time manner as an embedded function in the PSO. In this context investigations have been conducted by modeling an isolated MG with solar farm, fuel cell generator and micro-turbine in MATLAB Simulink. Detailed simulation results indicate the proposed power and stability management method effectively reduces the MG generation cost through maximizing the utilization of the available renewable generations while considering system stability.

This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.

This paper presents modified particle swarm optimization to solve economic dispatch problems with non-smooth/non-convex cost functions. Particle swarm optimization performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes Gaussian random variables in velocity term which improves search efficiency and guarantees a high probability of obtaining the global optimum without considerably worsening the speed of convergence and the simplicity of the structure of particle swarm optimization. The efficacy of the proposed method has been demonstrated on four test problems and four different non-convex economic dispatch problems with valve-point effects, prohibited operating zones with transmission losses, multiple fuels with valve point effects and the large-scale Korean power system with valve-point effects and prohibited operating zones. The results of the proposed approach are compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed modified particle swarm optimization based approach is able to provide better solution.

This paper addresses the problem of reactive power control of distributed generation (DG) units in the medium voltage (MV) distribution systems to maintain the system voltages within the predefined limits. An efficient approach for the load flow calculation is used here which is based on the topological structure of the network. It has been formulated for the radial distribution systems. A direct voltage sensitivity analysis method is developed in this paper which is also based on the topological structure of the network and independent of the network operating points. Thus, the sensitivity matrix is calculated once with the load flow program and it is used in all the system working conditions. The problem of DGs reactive power control is formulated as an optimization problem which uses the sensitivity analysis for linearizing the system around its operating points. The objective of the optimization problem is to return the system voltages inside the permitted range by using the reactive power of DGs in an optimal way. The optimal solutions are obtained by implementing particle swarm optimization (PSO) algorithm. Then, the results are verified by running a load flow considering new values of DGs reactive power. The procedure is repeated as long as a voltage violation is observed. Simulation results reveal that the proposed algorithm is capable of keeping the system voltages within the permitted limits.

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization

This paper presents a novel Primal–Dual Interior Point Method (PDIPM) based sensitivity approach for efficient assessment of the impact of uncertainties in Multi-objective Optimization (MO). This shall aid in robust decision making. The MO problem considered, in this paper, is the Environmental–Economic dispatch (EED) problem. The two objectives, i.e. the emission and economic cost, are continuous convex functions. The uncertainties in the system parameters such as loads (or injections) and limits on line flows and voltage magnitudes, are assumed to be of fuzzy type, more specifically in an interval. Results for the IEEE 30 bus system have been obtained using the proposed approach and compared with those obtained by Monte Carlo Simulations (MCS) and Particle Swarm Optimization (PSO) based on Harmony Search (HSPSO). The results obtained provide interesting insights on how uncertainties in input data can affect decision making in MO.

Optimal power flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In this paper, particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power system equipped with flexible ac transmission systems (FACTS). The two FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter are considered for this study. This study is implemented on modified IEEE 30-bus and IEEE 57-bus test power systems with four different objectives. The results presented in this paper demonstrate the potential of ALC-PSO algorithm and show its effectiveness for solving the OPF problem with FACTS devices over the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature.

It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations.

Proper placement of Distributed Generation (DG) in distribution system is still very challenging issue for obtaining their maximum potential benefits. This paper proposes a new constrained multi-objective Particle Swarm Optimization (PSO) based Wind Turbine Generation Unit (WTGU) and photovoltaic (PV) array placement approach for power loss reduction and voltage stability improvement of radial distribution system. The paper reflects the effectiveness of WTGU and PV array performance models in DG placement problem formulation. Wind and solar based DGs are operated in different active and reactive power mode and tested on 12-bus, 15-bus, 33-bus and 69-bus radial distribution system. Obtained results are compared with other DG placement technique and proposed method is found to be more effective in terms of voltage stability enhancement and power loss minimization. A novel Voltage Stability Factor (VSF) has been proposed in this paper which can quantify voltage stability levels of buses in the system. Comparing with other voltage stability index and power stability index, developed VSF has emerged as more simple and efficient tool.

This paper presents an optimal power control strategy, for an inverter based Distributed Generation (DG) unit, in an autonomous microgrid operation based on real-time self-tuning method. This research seeks to improve the quality of power supplied by DG units connected to the grid. Voltage and frequency regulation, dynamic response, steady-state response, and harmonic distortion are the main performance parameters considered, particularly when the microgrid is islanded or under the load change condition. The controller scheme comprises an inner current control loop and an outer power control loop based on a synchronous reference frame and conventional PI regulators. The power controller is designed for voltage-frequency (Vf) power control mode. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. In this paper, the proposed strategy is that when the microgrid is islanded or under load change condition, the DG unit adopts the Vf control mode in order to regulate the system voltage and frequency. The simulation results show that the proposed controller provided an excellent response to satisfy the power quality requirements and proved the validity of the proposed strategy.

In this paper, design and performance analysis of Differential Evolution (DE) algorithm based parallel 2-Degree Freedom of Proportional-Integral-Derivative (2-DOF PID) controller for Load Frequency Control (LFC) of interconnected power system is presented. A two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. The design problem is formulated as an optimization problem and DE is employed to search for optimal controller parameters. Conventional and modified objective functions are used for the design purpose. Conventional objective functions employed in the paper are Integral of Time multiplied by Squared Error (ITSE) and Integral of Squared Error (ISE). In order to further increase the performance of the controller, a modified objective function is derived using Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues, settling times of frequency and peak overshoots with appropriate weight coefficients. The superiority of the proposed approach has been demonstrated by comparing the results with a recently published technique, i.e. Craziness based Particle Swarm Optimization (CPSO) for the same interconnected power system. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the proposed controllers are quite robust for a wide range of the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to a more realistic power system model by considering the physical constraints such as time delay, reheat turbine, Generation Rate Constraint (GRC) and governor dead band.

In this paper a new, an Improved Particle Swarm Optimization (IPSO) is proposed for optimizing the power system performance. Recently, the Particle Swarm Optimization (PSO) technique has been applied to solve power engineering optimization problems giving better results than classical methods. Due to slow convergence and local minima, particle swarm optimization fails to give global results. To overcome these drawbacks, in this paper presents the application of improved particle swarm optimization for optimal sizing and allocation of a Static Compensator (STATCOM) and minimize the voltage deviations at all the buses in a power system. This algorithm finds an optimal settings for present infrastructure as well as optimal locations, sizes and control settings for Static Compensator (STATCOM) units. A 30 bus system is used as an example to illustrate the technique. Results show that the Improved Particle Swarm Optimization (IPSO) is able to find the best solution with statistical significance and a high degree of convergence. The simulation results are presented to show a significant improvement of the power system reliability and feasibility and potential of this new approach.

This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA_MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results.

This paper aims to select the optimal location and setting parameters of SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Compensator) controllers using PSO (Particle Swarm Optimization) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) associated with generators are mandatory requirements for damping of oscillations in the power system, its performance still gets affected by changes in network configurations, load variations, etc. Hence installations of FACTS devices have been suggested in this paper to achieve appreciable damping of system oscillations. However the performance of FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper the PSO based technique is used to investigate this problem in order to improve the small signal stability. An attempt has also been made to compare the performance of the TCSC controller with SVC in mitigating the small signal stability problem. To show the validity of the proposed techniques, simulations are carried out in a multimachine system for two common contingencies, e.g., load increase and transmission line outage. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response. It has been observed that the TCSC controller is more effective than SVC even during higher loading in mitigating the small signal stability problem.

This paper presents an optimal power control strategy for an autonomous microgrid operation based on a real-time self-tuning method. The purpose of this work is to improve the quality of power supply of the microgrid where some Distributed Generation (DG) units are connected to the grid. Voltage and frequency regulation, and power sharing are the main performance parameters which are considered in this work, particularly during the transition from grid-connected to islanding operation mode and also during load change. In this work, two typical DG units are connected in parallel to configure the microgrid. The controller scheme is composed of an inner current control loop and an outer power control loop based on a synchronous reference frame and the conventional PI regulators. The power controller employs two typical strategies: active–reactive power (PQ) control strategy and voltage–frequency (Vf) control strategy. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. The proposed strategy in this paper is that both DG units adopt the Vf control mode once the microgrid is islanded in order to regulate the microgrid voltage and frequency, whereas during the load change, only the second DG unit invokes the PQ control mode to ensure maximum power exportation. The results show that the proposed controller offers an excellent response to satisfy the power quality requirements and proves the validity of the proposed strategy.

In this paper a new algorithm is proposed to determine the optimal sizes of fixed and switched capacitors together with their optimal locations in a radial distribution system so that net savings are maximized and improvement in the voltage profile is achieved. The algorithm searches for all possible locations in the system for a particular size of capacitor and places the capacitor at the bus which gives maximum reduction in active power loss. The optimal sizes are chosen to be standard sizes that are available in the market i.e., discrete sizes of capacitors are considered. The algorithm is tested on standard 69 bus system, 85 bus system and practical 22 bus systems. And the results are compared with results of other methods like particle swarm optimization and genetic algorithm, as available in the literature. The loss reduction obtained in this paper for the two standard test systems is highest compared to the other technique as reported in the literature. Cost analysis is also presented with and without capacitor placement on the three bus systems considering three different loading levels.

This paper proposes a new method that uses the combination of particle swarm optimization (PSO) and simulated annealing (SA) to predict the bidding strategy of Generating Companies (Gencos) in an electricity market where they have incomplete information about their opponents and market mechanism of payment is pay as bid. In the proposed methodology, Gencos prepare their strategic bids according to Supply Function Equilibrium (SFE) model and they change their bidding strategies until Nash equilibrium points are obtained. Nash equilibrium points constitute a central solution concept in game theory and they are computed with solving a global optimization problem. In this paper a new computational intelligence technique is introduced that can be used to solve the Nash optimization problem. This new procedure, is based on the PSO algorithm, which uses SA method to avoid becoming trapped in local minima or maxima and improve the velocity’s function of particles. The performance of this procedure is compared with results of other computational intelligence techniques such as PSO, Genetic Algorithm (GA), and a mathematical method (GAMS/DICOPT). The IEEE 39-bus test system is employed to illustrate and verify the results of the proposed method.

This paper presents the estimation of harmonics in a voltage source converter based HVDC (VSC-HVDC) system for designing AC side filters. The extended Kalman filter (EKF) is well known for estimating amplitude, phase, frequency, and harmonic content of a signal corrupted with noise. However, the EKF algorithm suffers from instability due to linearization and costly calculation of Jacobian matrices, and its performance deteriorates when the signal model is highly nonlinear. This paper, therefore, proposes an unscented Kalman filter (UKF) to overcome these difficulties of linearization and derivative calculations for robust tracking of harmonics in VSC-HVDC system. The model and measurement error covariance matrices Q and R along with the UKF parameters are selected using a modified particle swarm optimization (PSO) algorithm. To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity and better accuracy of the UKF parameters. Various simulation results for harmonic signals corrupted with noise obtained from VSC-HVDC system reveal significant improvement in noise rejection and speed of convergence and accuracy.

Optimal Power Flow (OPF) is used for developing corrective strategies and to perform least cost dispatches. In order to guide the decision making of power system operators a more robust and faster OPF algorithm is needed. OPF can be solved for minimum generation cost, that satisfies the power balance equations and system constraints. But, cost based OPF solutions usually result in unattractive system losses and voltage profiles. In the present paper the OPF problem is formulated as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and loss, loss and voltage stability index, fuel cost and voltage stability index and finally fuel cost, loss and voltage stability index are obtained. The present paper combines a new Decoupled Quadratic Load Flow (DQLF) solution with Enhanced Genetic Algorithm (EGA) to solve the OPF problem. A Strength Pareto Evolutionary Algorithm (SPEA) based approach with strongly dominated set of solutions is used to form the pareto-optimal set. A hierarchical clustering technique is employed to limit the set of trade-off solutions. Finally a fuzzy based approach is used to obtain the optimal solution from the tradeoff curve. The proposed multi-objective evolutionary algorithm with EGA–DQLF model for OPF solution determines diverse pareto optimal front in just 50 generations. IEEE 30 bus system is used to demonstrate the behavior of the proposed approach. The obtained final optimal solution is compared with that obtained using Particle Swarm Optimization (PSO) and Fuzzy satisfaction maximization approach. The results using EGA–DQLF with SPEA approach show their superiority over PSO–Fuzzy approach.

A comparative study has been made on the solutions obtained using combined economic emission dispatch (CEED) problem considering line flow constraints using different intelligent techniques for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of the paper is to minimize the total production cost of the power generation. Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost of generating units. Combined economic emission dispatch (CEED) is obtained by considering both the economic and emission objectives. This bi-objective CEED problem is converted into single objective function using price penalty factor approach. In this paper, intelligent techniques such as genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE) are applied to obtain CEED solutions for the IEEE 30-bus system and 15-unit system. This proposed algorithm introduces an efficient CEED approach that obtains the minimum operating cost satisfying unit, emission and network constraints. The proposed algorithm has been tested on two sample systems viz the IEEE 30-bus system and a 15-unit system. The results obtained by the various artificial intelligent techniques are compared with respect to the solution time, total production cost and convergence criteria. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried out using Matlab software.

The objective of this paper is to evolve simple and effective methods for the economic load dispatch (ELD) problem with security constraints in thermal units, which are capable of obtaining economic scheduling for utility system. In the proposed improved particle swarm optimization (IPSO) method, a new velocity strategy equation is formulated suitable for a large scale system and the features of constriction factor approach (CFA) are also incorporated into the proposed approach. The CFA generates higher quality solutions than the conventional PSO approach. The proposed approach takes security constraints such as line flow constraints and bus voltage limits into account. In this paper, two different systems IEEE-14 bus and 66-bus Indian utility system have been considered for investigations and the results clearly show that the proposed IPSO method is very competent in solving ELD problem in comparison with other existing methods.

This paper attempts to investigate the performance of intelligent fuzzy based coordinated control of the Automatic Generation Control (AGC) loop and the excitation loop equipped with Proportional Integral Derivative (PID) controlled Automatic Voltage Regulator (AVR) system and Power System Stabilizer (PSS) controlled AVR system. The work establishes that PSS controlled AVR system is much more robust in dynamic performance of the system over a wide range of system operating configurations. Thus, it is revealed that PSS equipped AVR is much more superior than PID equipped AVR in damping the oscillation resulting in improved transient response. The paper utilizes a novel class of Particle Swarm Optimization (PSO) termed as Craziness based Particle Swarm Optimization (CRPSO) as optimizing tool to get optimal tuning of PSS parameters as well as the gains of PID controllers. For on-line, off-nominal operating conditions Takagi Sugeno Fuzzy Logic (TSFL) has been applied to obtain the off-nominal optimal gains of PID controllers and parameters of PSS. Implementation of TSFL helps to achieve very fast dynamic response. Fourth order model of generator with AVR and high gain thyristor excitation system is considered for PSS controlled system while normal gain exciter is considered for PID controlled system. Simulation study also reveals that with high gain exciter, PID control is not at all effective. Transient responses are achieved by using modal analysis.

In this paper, a practical planning method to allocate dynamic VAR sources (DVSs) using the concept of voltage control areas (VCAs) is proposed to enhance short-term voltage stability of power systems. Firstly, based on the defined transient distance index, a spectral clustering method is employed to partition a system into VCAs. Then, candidate DVS sites in each zone are determined by the proposed voltage supporting index. In each VCA, the capacity of candidate sites is preliminarily estimated by use of the dichotomy method to decide the most effective control bus and the initial capacity guess, based on which further system-wide optimization is implemented using particle swarm optimization (PSO) to obtain the final allocation scheme. Case study on the New England 39-bus and 68-bus test systems along with the comparisons with the existing methods confirm the effectiveness and advantages of the proposed methodology.

Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.

In power grids with large wind power integration, long-term wind turbine generator (WTG) de-loading to provide frequency reserves will greatly constrain the economic benefits of a wind farm (WF). Although energy storage (ES) often cooperates with the WF as a combined system to improve the frequency regulation performance, the coordination control inside the combined system and the trade-offs between the expensive investment costs of the ES, the long-term de-loading costs of the WF and the frequency regulation requirements should be further studied. Therefore, in this paper, a fuzzy-based coordinated control and sizing strategy is proposed to achieve dynamic cooperation inside the combined WTG-ES system and optimize the economic performance of the combined system for providing frequency support power. The fuzzy controller is designed to allocate the frequency regulation demand with considering the operation state and the frequency support capability of the WTG-ES system and the type of frequency incidents. Under the proposed control strategy, the WTG can operate at a rate closer to the maximum power point instead of having to maintain a large power reserve level for frequency support; SOC of the ES is separated into several sections with different features and operational rules to make full use of the ES energy capacity and constrain the SOC limit violation problems effectively. Subsequently, based on the proposed coordinated control strategy, an optimization model is presented that determines the optimal WTG de-loading level in different time intervals, ES rating powers and energy capacity, with the objective of minimizing the operation and frequency regulation costs of the combined WTG-ES system. The optimization model is solved by an improved PSO algorithm. Finally, case studies using actual wind power generation and frequency data were conducted to validate the performance of the proposed control and sizing strategy.

This paper develops an optimal simultaneous hourly reconfiguration and day-ahead scheduling framework in smart distribution systems considering the operation of the protection devices. The objective function of the model is defined as the minimization of system’s costs in terms of the costs associated with the purchased power from the wholesale market as well as the distributed generation (DG) owners, cost of switching actions, power losses cost and the cost for implementation of demand response (DR) programs. Moreover, a novel switching index along with the maximum number of switching actions based on the switch ages and critical branches in the network is presented. Due to the nonlinearity and non-convexity nature of the problem, the proposed optimization problem is then solved using a metaheuristic approach based on particle swarm optimization (PSO). As the result of the optimization process, the optimal set-points of DGs and responsive loads together with the optimal radial configuration of the distribution system for each hour of the scheduling time horizon are determined. To investigate the effect of DR programs and hourly reconfiguration on the load profile of the system, different price-based DR actions combined with interruptible load programs are also considered. Moreover, to demonstrate the satisfactory performance of the proposed model, the IEEE 33-bus distribution test system is thoroughly interrogated.

The variability and intermittency of renewable energy and power load bring great pressure to the dispatch of Micro-Grid, especially intra-day dispatch. In order to reduce the intra-day dispatch pressure, this paper proposes a data-driven two-stage day-ahead dispatch model for islanded Micro-Grid. The first stage dispatch model considers multiple demand responses, and applies phase space reconstruction and machine learning to predict renewable energy output and power load. Multi-objective particle swarm optimization algorithm is used to solve the first stage dispatch model. For the obtained Pareto Front, weight multiple objectives by entropy weight method to get the optimal solution. Since the deviation between the predicted value and the actual value can lead to renewable energy curtailment or load loss, the role of the second stage is to predict the renewable energy curtailment and load loss after the first stage dispatch. Firstly, extreme gradient boosting is used to predict when renewable energy curtailment and load loss occur. Secondly, extreme learning machine is used to predict the amount of renewable energy curtailment and load loss at the corresponding time points. Finally, linear programming and mixed integer linear programming are used to solve the second stage dispatch model. By comparative cases analysis, simulation results show that the enhancement of demand response to system efficiency is at the cost of increasing dispatch cost and reducing reliability. In contrast, the proposed two-stage dispatch method using regulation reserve capacity not only reduces dispatch cost, but also improves system efficiency and reliability.

With the introduction of active devices such as inverters in the microgrid the system stability has been jeopardized. A primary controller fails to maintain the system frequency and hence an additional secondary controller is needed. In this paper, a control technique for an isolated smart microgrid in a multi-agent based infrastructure is studied. The agents are software abstractions that utilize advanced algorithms such as Particle Swarm Optimization (PSO) and Adaptive Dynamic Programming (ADP) to tune the control parameters. The proposed ADP based control approach is applied in different multi-agent structures in the inverter based microgrid which is tested against oscillations in the system. The results show improvement in control performance of the microgrid system using the proposed multi-agent based control method.

Unbalanced distribution of single-phase customers among the three phases is an important factor in causing unbalance in low-voltage (LV) distribution systems. This unbalance phenomenon has some important consequences as higher losses in the distribution system, increasing voltage unbalance, and higher neutral wire current. The key challenge is that these networks only have the monthly reading of customers’ meters which is called data-scarce distribution systems due to the lack of enough information. In this paper, a method is proposed in order to have a balanced distribution of single-phase customers between the phases based on particle swarm optimization (PSO) algorithm to reduce the average-load losses (which is calculated losses in the average load of all of the customers and is an attainable data in real-world data-scarce distribution systems). First, a comparative study is performed between two load balancing methods. In the first method, the transformer neutral wire current is reduced. This method is used generally in electricity distribution companies. In the second method, the balancing is performed in order to reduce the losses in the average-load. Then, a probabilistic approach is proposed in order to manage the uncertainty of single-phase customer's consumption, which is an important issue in low-voltage distribution systems balancing. Numerical studies are performed on a practical system and the results show that the proposed method has higher performance comparing to the first method used by distribution companies. Also, it is observed that by rephasing of some single-phase customers, losses are reduced significantly.

In traditional methods, the process of performance estimation of a photovoltaic (PV) module is achieved through two steps. First, the model parameters are determined under some reference condition, and then, the model parameters are determined under varying operating conditions based on the reference values and the dependence of the model parameters on environmental conditions. This paper presents a novel method for the performance estimation of PV modules under varying operating conditions without setting a reference condition. In the proposed method, the dependence of the model parameters on environmental conditions is modeled and modified without setting a reference condition or using reference values. The model parameters are determined from experimental data using guaranteed convergence particle swarm optimization technology, and are suitable for all operating conditions. Thus, the performance of PV modules can be directly determined under varying operating conditions, which simplifies the process and improves the accuracy. The effectiveness and accuracy of the proposed method are validated by large amounts of experimental data for different types of PV modules at different locations. In comparison with the traditional methods, the proposed method exhibits better accuracy in terms of the I-V and P-V curve and maximum power point estimation under different irradiance and temperature conditions. It can be further used to estimate the output power of PV system under varying operating conditions.

The unbundling of the electricity sector in several activities, some of them provided in a regulated way and some others under competition, poses a number of challenging problems namely because in several areas there are conflicting objectives associated to different stakeholders. These different views and objectives paved the way to the development of new multiobjective tools able to represent this new paradigm. In this scope, this paper presents a multiobjective (MO) formulation for the Transmission Expansion Planning (TEP) problem using a new solution approach that combines concepts of evolutionary computation and multi agent population algorithms. The new proposed tool is termed as Multi-Population and Multiobjective Evolutionary Particle Swarm Optimization - MEPSO-II. The TEP problem is handled in a realistic way preserving the holistic view over the entire planning horizon and the true grid behavior because it considers the multi-stage nature of the problem and we use an AC Optimal Power Flow (AC-OPF) model to gain insight on the operation conditions of the network. The multi objective formulation considers the total system cost, on one side, and the Expected Power Not Supplied (EPNS), on the other. The total system cost comprises the investment cost in new equipment and the operation costs while the EPNS takes into account the uncertainties related to the non– ideal behavior of system components using a non-chronological Monte Carlo simulation. Numerical simulations are conducted using the IEEE 24 and the 118 Bus Test Systems in order to compare the proposed MO tool against other algorithms through performance evaluation indices. Although being a higher time-consuming tool, the MEPSO-II enables improving the Pareto-Front and therefore it gives more insight to transmission network planners when compared with other consolidated algorithms described in the literature.

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the ‘fat-tail’ effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed.

Effective energy management of microgrid is essential due to significant growth of renewable energy resources like photovoltaic (PV) systems. Reactive power compensation capability provided by grid-tie PV inverters when PV power is unavailable increases the efficiency of the PVs utilization in microgrids. In this paper, a robust model combining particle swarm optimization (PSO) algorithm and primal–dual interior point (PDIP) method is proposed for optimal energy management of the microgrid, considering VAR compensation mode of the PV inverters. Besides, both the uncertainty of insolation forecast and forced outages of system components (diesel generators and branches) are taken into account. A bi-objective method is employed to detect the worst-case probable 24-h scenario with the severest effects on the system security. Finally, the energy management problem of the microgrid is solved under the obtained worst-case scenario, minimizing the microgrid operation cost, as well as satisfying the physical constraints of the microgrid and insolation limitation of the PVs. Simulation studies have been carried out on a modified version of IEEE 33-bus standard distribution system operating as a microgrid with variable PV generation. The results demonstrate the effectiveness of the proposed robust model for the microgrid energy management.