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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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

• A comprehensive metaheuristic electrical layout optimization model is proposed. • Algorithm is adapted to the specific requirements of floating offshore wind. • The optimized layout achieves a reduction in both total costs and energy losses. • The use of different dynamic power cable configurations is studied. 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. [ABSTRACT FROM AUTHOR]

• A strategy is proposed to increase the PFC's reliability in co-phase TPSS. • Switching frequency conversion and modulation changes is combined in this paper. • The reliability assessment process is established to calculate failure rate of PFC. • This strategy can reduce the failure rate of IGBT module in PFC by 39.01%. Co-phase traction power supply system (TPSS) is proposed to solve the problems of neutral section and power quality in electrical railways. As the key component of co-phase TPSS, the lifetime of the power flow controller (PFC) largely relies on the power module (PM)'s reliability. The PMs' lifetime is affected by junction temperature, including average temperature and temperature fluctuation. In this paper, a lifetime extension strategy is presented for junction temperature management, which combines switching frequency conversion with modulation changes. Firstly, in the low-load area, switching frequency conversion is utilized to minimize temperature fluctuation; in the high-load area, an optimized single-phase space vector pulse width modulation (SVPWM) is introduced to reduce the average temperature. Secondly, the particle swarm optimization with constriction factor is applied to solve the load partition point, as well as optimize switching frequency for PFC. Finally, the validity of the proposed lifetime extension strategy is verified by case studies, the result is shown that the failure rate of IGBT module can be reduced by 39.01% and the mean time to failure (MTTF) of the PFC is prolonged by 16.28%. The optimization algorithm can effectively improve the reliability of PFC. [ABSTRACT FROM AUTHOR]

• In this paper, an incident wave inversion model based on substation components is proposed, which realizes the accurate inversion of incident wave signal in substation. • A denoising and extraction method of mixed wave based on improved EMD and FFT was proposed to filter noise out and obtain the power frequency signal under the noise with a signal-to-noise ratio above 10 dB. The proposed method is capable of extracting mixed waves; • Using the equivalent circuit of multiple outgoing lines, bus-to-ground stray capacitances, transformers, and line traps in the substation, we established an inversion model of incident wave based on substation component parameters. The inversion calculation of the incident wave in the complex frequency domain effectively avoids the difficulty of solving high-order differential equations. • We then solved the parameter optimization model based on the PSO to ensure that the population is always distributed in the feasible region where we modified the particle swarm position update formula. This method accurately identifies the parameters of the substation's equivalent circuit components. In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the Particle Swarm Optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy. [ABSTRACT FROM AUTHOR]

In interconnected microgrids, facade thermal photovoltaics (TPVs) systems have to be efficiently scaled and allocated for cost-effective building energy consumption and network operation. This paper aims at defining pertinent innovative solutions for reducing the undesired severe voltage dips and minimizing the relevant total costs of the PVs allocation within interconnected microgrids. To optimally place and size the TPVs, different meta-heuristic optimization tools are considered. Dealing with several scenarios of loads and solar energy output uncertainties, the ability of the novel modified meta-heuristic optimizer based on coronavirus herd immunity optimizer (CHIO) to capture a global optimal solution is evaluated. Using Matlab T M numerical simulations, fair comparison with grey wolf optimization, particle swarm optimization, arithmetic optimization algorithm, and chimp optimization algorithm is presented. The coronavirus herd immunity optimizer tool surpasses the other algorithms in terms of fulfilling the objective function, convergence, and the execution time for the large-scale 295–bus system, which is established of the interconnected IEEE 141–bus, IEEE 85–bus, and IEEE 69–bus subsystems. With the flexible penetration of the building facade TPVs, the voltage profile at all buses is significantly improved. Regarding the overall operational expenses, the CHIO is deemed applicable, replicable and efficient. When compared to the grey wolf optimizer, the CHIO reports expenses of 18.8M$ with savings of 59.67%. The operational voltage level of the studied distributed network is maintained properly by a resilient cluster of 491 clean energy buildings with each having facade area of 200m 2. [Display omitted] • Uniqueness in studying a novel on grid 295–bus interlinked facade TPVs system. • The varying-scales IEEE 141–bus, IEEE 85–bus, and IEEE 69–bus are being studied. • Applying the CHIO to address the sizing and placement of facade TPVs in microgrids. • The obtained results are compared to other recent meta-heuristics. • The near-optimal estimated installation of facade TPVs at a single load point. • Assuring global progress across 24h day-ahead to attain enhanced voltage profile. [ABSTRACT FROM AUTHOR]

• This paper presents an effective hybrid supercapacitor-battery energy storage (SC-BES) system. • It uses a fuzzy type distributed control system (DCS) to optimally regulate the system transient. • The fuzzy type DCS gains are optimally adjusted by an heuristic algorithm to reduce the design effort. • This mechanism combines the features of fuzzy theory and distributed control, which has a flexible structure. • The high performance of the suggested methodology is represented on a typical wind-diesel test system. This paper presents an effective hybrid supercapacitor-battery energy storage system (SC-BESS) for the active power management in a wind-diesel system using a fuzzy type distributed control system (DCS) to optimally regulate the system transient. It addresses a new online intelligent approach by using a combination of the fuzzy logic and DCS based on the particle swarm optimization techniques for optimal tuning and reduce the design effort of the control system. This mechanism combines the features of online fuzzy theory and distributed control system (DOFCS), which has a flexible structure. The proposed energy management algorithm for the hybrid SC-BESS is well able to repel the peak-impact of the battery storage system during the wind speed and load changes. The high performance of the suggested methodology is represented on a typical wind-diesel test system. [ABSTRACT FROM AUTHOR]

The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation. [ABSTRACT FROM AUTHOR]

Power System Stabilizers are controllers installed on synchronous generators to damp power system oscillations through the excitation control. These controllers can have either a conventional fixed structure composed by stages of gain and phase compensation or a flexible modern structure composed by three bands that correspond to a specific frequency range (low, intermediate and high frequency) in which each band is composed by two branches that are based on differential filters (with a gain, lead-lag blocks and a hybrid block). Power system stabilizers design is a hard and time consuming task and an alternative for tuning controllers is by using optimization methods. This paper presents three hybrid metaheuristics for the robust and coordinated design of power system stabilizers. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients in closed-loop operation. Robustness requirement is met by using multiple operating scenarios in the design stage. For solving the optimization problem, three metaheuristics (Gravitational Search Algorithm, Bat Algorithm and Particle Swarm Optimization) are combined with the Steepest Descent Method for local search capability enhancement. The proposed hybrid algorithms are applied to benchmark systems for validation. [ABSTRACT FROM AUTHOR]

With the continuous development of distributed generation technology, the permeability of wind, solar and other renewable energy continues to increase. As a new kind of voltage control device, electric spring can suppress the voltage fluctuation caused by the power change of distributed generation effectively. In this paper, based on the analysis of the principle of voltage stability control for the electric spring, the optimization of control effect and non-critical load change are taken into consideration. Combined with advanced particle swarm optimization algorithm and fuzzy control algorithm, a method of voltage control for electric spring based on adaptive PI control is proposed, and the digital simulation is carried out. The simulation results show that the adaptive PI control has better voltage regulation effect than the traditional PI control, and can solve the adverse effect of the load variation on the electric spring. [ABSTRACT FROM AUTHOR]

This paper presents a new dynamic model and control approach for static synchronous series compensator (SSSC) to participate effectively in automatic generation control (AGC) of an interconnected deregulated power system. In doing so, a new mathematical formulation is extracted to present the participation of the SSSC in the tie-line power flow exchange. Besides, fractional order controllers (FOCs) are employed to design an effective SSSC damping controller. The effectiveness of the proposed SSSC-based damping controller in preparing an efficient AGC ancillary service is compared with its earlier model. Two heuristic algorithms of improved particle swarm optimization (IPSO) and modified group search optimization (MGSO) are compared to optimize the controller parameters. To achieve realistic results under a competitive scenario, a diverse-GENCOs multi-DISCOs power system with the physical nonlinear constraints, bilateral contracts, and pool-co transactions are taken into consideration, simultaneously. Dynamic simulation results reveal that the proposed FOC-based SSSC damping controller is superior to the earlier one to improve the restructured AGC performance. Comprehensive examinations are carried out under the un-contracted step, higher degree step, and random load demands which act as contract violation scenarios to validate the damping performance of the proposed controller. To demonstrate the robustness of the proposed control approach, sensitivity analysis is accomplished in a wide range of loading condition and system parameters. [ABSTRACT FROM AUTHOR]

This paper introduces a survey on the adaptive and intelligent methods that have been applied to microgrids systems. Interestingly, the adaptive technique is effectively exercised in various control issues including stability, tracking error, and parameter uncertainties. Adaptive control has been extremely developed by using intelligent algorithms to automatically tune the control parameters namely fuzzy logic, particle swarm optimization, bacterial search algorithm, and etc. The objective is to evaluate and classify the design control methods and evaluation algorithms for the microgrid systems to maintain stability, reliability, and load variations by adjusting the controller parameters especially in standalone operation mode. The stability of islanded microgrids are constantly impacted by the related loads. A significant part of the research on an islanded microgrid involves droop control technique. In normal operation, distributed generation units and storage units provide power quality control. Once a shutdown is occurred, microgrid can be isolated from the main grid and operate in a local grid to support the local loads. Thus, distributed generations co-operate storage units to sustain the stability of the islanded microgrid. [ABSTRACT FROM AUTHOR]

• A two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle. • The semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. • The optimization frame applies to different lithium-ion batteries. • The optimal configuration and operation varied from the types of lithium-ion batteries. Battery energy storage systems (BESSs) have been widely employed on the user-side such as buildings, residential communities, and industrial sites due to their scalability, quick response, and design flexibility. However, cell degradation is caused by the charging and discharging of batteries, which reduces the economy of BESSs. For the optimal configuration and operation of BESSs, the battery degradation process should be integrated into the optimal problem by considering the equivalent full cycles over the optimization time horizon. This paper proposes a two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle, the outer layer optimizes the rated capacity and power of BESSs, and the inner layer optimizes the daily operation strategy of BESSs. To this end, the semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. Particle swarm optimization (PSO) algorithm and fmincon toolbox of MATLAB are adopted to solve the two-layer frame to maximize the net profit of BESSs. Simulation results of the BESS for a typical industrial user in China demonstrate that the proposed frame can effectively improve the net profit of BESSs. [ABSTRACT FROM AUTHOR]

• This paper proposes a collaborative optimization allocation strategy for voltage-detection active power filters (VDAPFs) with multiple functions and static var generators (SVGs). • The proposed strategy is built around the partitioning idea. • It develops two methods to determine dominant nodes as candidates of VDAPFs and SVGs in each region. • The performance of two Allocations of the proposed collaborative allocation strategy is evaluated using an integrated evaluation index. • It is more effective to cooperatively allocate multi-types equipment to mitigate VH and VD than to mitigate VH or VD separately by allocating single-type equipment. This paper proposes a collaborative optimization allocation strategy for voltage-detection active power filters (VDAPFs) with multiple functions and static var generators (SVGs) to improve voltage harmonic (VH) and voltage deviation (VD) of distribution networks (DNs). The integrated voltage sensitivity is defined considering VH and VD simultaneously. According to the local characteristic of power quality (PQ), a partition algorithm based on integrated voltage sensitivity is developed to divide a network into several regions. Two methods based on single and integrated sensitivity are presented to determine dominant nodes as candidates of VDAPFs and SVGs in each region. The corresponding allocations of VDAPFs and SVGs are respectively provided considering the two methods. The sites and sizes of VDAPFs and SVGs are optimized through minimizing the total cost including investment and operation costs by improved particle swarm optimization (PSO) algorithm. An integrated evaluation index based on economy and PQ disturbance mitigation effect is defined to evaluate the performance of the two allocations, thereby determining an optimal allocation. The feasibility and effectiveness of the proposed strategy are verified implemented on IEEE 33-bus system and IEEE 69-bus system by comparing the two allocations with each other and with the conventional allocation strategy. [ABSTRACT FROM AUTHOR]

• An adaptive protection method to maintain the coordination of OCRs in different NST. • Determining the proper SG to activate, in each NST and for each relay particularly. • SG utilization to protect the network with higher speed and lower number of SGs. 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. [ABSTRACT FROM AUTHOR]

• Investigation of the cascaded impact of time delay on MG stability and impact of stability on power management. • Including the stability criteria in the MG power management optimisation. • Providing the solution to maintain the stability while optimising the MG cost. • Improving the communication time delay to improve stability. • Adding inter-operability to MG control elements to reduce engineering time and communication delay. 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. [ABSTRACT FROM AUTHOR]

This paper aims at adopting the Particle Swarm Optimization (PSO) technique to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function. The proper allocation and the optimized number of capacitors have led to adequate power losses reduction and voltage profile enhancement. Because of the wind power generation variations due to the nature of wind speed intermittency and the lack of reactive power compensation, the problem under study have been presented involving a nonlinear fitness function. In order to solve it, the corresponding mathematical tools have to be used. The formulated fitness cost function has consisted of four terms: cost of real power loss, capacitor installation cost, voltage constraint penalty, and capacitor constraint penalty. PSO technique has been used to obtain the near-optimum solution to the proposed problem. Simulation results demonstrate the efficiency of the proposed fitness cost function when applied to the system under study. Furthermore, the application of PSO to the modified IEEE 16-bus system has shown better results in terms of power losses cost and voltage profile enhancement compared to Genetic Algorithm (GA). In order to verify the successful adaptation of PSO toward attaining adequate near-optimal capacitor allocations in distribution systems, this metaheuristic technique has been employed to the large-scale IEEE 30-bus system. The proposed PSO technique has provided adequate results while modifying the objective function and constraints to include the power factor and transmission line capacities for normal and contingency (N-1) operating conditions. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of large-scale photovoltaic plant (LS-PVP) controllers on the voltage stability (VS) of a power system. Besides, a new control strategy is proposed for the large-scale battery energy storage system (LS-BESS) to enhance a power system integrated with LS-PVPs, especially VS. To this end, a probabilistic technique, based on the stochastic nature of LS-PVPs and loads, is employed to accurately evaluate the impact of LS-PVPs on the system. Next, a new coupling between the optimization algorithm and the probabilistic technique is proposed to determine the optimal number, location, and capacity of the LS-BESSs by the probabilistic particle swarm optimization (PPSO) algorithm. The objective function is then used to simultaneously maximize the total VS and minimize the total power losses, line loading, as well as investment costs of LS-BESSs. Furthermore, technical performance indices (TPIs) are used for a more comprehensive investigation to compare the proposed solution to the synchronous generators. Finally, several disturbances occurring in the Nordic system are used to validate the effectiveness of the proposed approach, using the Monte Carlo simulation (MCS) method. According to the results, when a fault occurs, a system integrated with LS-PVPs in the stressed area does not operate better than synchronous generators. Therefore, an advanced control method is required to reduce undesirable effects of LS-PVPs. Using the proposed solution, voltage instability was prevented, LS-BESS investment costs were minimized, and negative effects of LS-PVPs on the system were reduced. Besides, costs required for expanding the transmission network as well as communication between controllers and reactive power compensation devices were reduced. • A new advanced control scheme is presented to enhance static voltage stability. • Technical performance indices are applied to analyze the proposed solution. • A new coupling between optimization and probabilistic technique is proposed. • Calculating the optimal number, location, and capacity of the large-scale battery. • Minimizing the undesired effects of PV plants and investment costs simultaneously. [ABSTRACT FROM AUTHOR]

Huge numbers of residential energy prosumers (EPs) will be widely deployed in the next generation power system which is dominated by renewable energies. The intermittent and non-dispatchable natures of the renewable energies will inevitably bring sporadic and unpredictable disturbances to the power system, making "grid friendliness" an essential requirement for the EPs. To tackle this problem, this paper focuses on the hardware implementation of the energy router (ER) and corresponsive coordinated energy management strategy for grid friendly EPs. The coordinated energy management strategy, combined with a fuzzy logic controller (FLC) for energy storage (ES) unit control and an improved discrete binary particle swarm optimization (DBPSO) algorithm for load scheduling, is elaborated for embedded control of the ER by maintaining balance of the accuracy, efficiency and cost considerations. Built as the energy interface between the EP and the power system, the ER is facilitated with plug-and-play source/load/ES connections, intelligent monitor and control interfaces as well as a load switching module, enabling the functionalities of the coordinated energy management strategy to be implemented. Experimental verification shows that, the developed ER enhances the energy management flexibilities for the EP, and the presented FLC + DBPSO algorithm enables coordinated operation of the EP and potential benefits in renewable DG utilization by improving the performances of PV self-use ratio, load rate and energy cost of the EP system by about 20 %, 50 % and 20 %, respectively, while at the same time maintaining the EP system reliability and consumer comfort, as well as meeting the grid friendly requirements. [ABSTRACT FROM AUTHOR]

Over the past decades, significant revolutions have occurred on electricity market to reduce the electricity cost and increase profits. In particular, the novel structures facilitate the electricity manufacturers to participate in the market and earn more profit by cooperate with other producers. This paper presents a three-level gameplay-based intelligent structure to evaluate individual and collaborative strategies of electricity manufacturers, considering network and physical constraints. At the Level I, the particle swarm optimization (PSO) algorithm is implemented to determine the optimum power of distributed energy resources (DERs) in the power grid, to maximize the profits. Further, the fuzzy logic algorithm is applied to model the intermittent nature of the renewable sources and implement load demand in the power grid. At the Level II, DERs are classified into two different fuzzy logic groups to secure the fairness between every participant. Finally, at the Level III, the DERs in each group are combined each other by cooperative game theory-based algorithms to increase the coalition profits. Thereafter, Shapley, Nucleolus, and merge/split methods are applied to allocate a fair profit allocation by coalition formation. Ultimately, the results verify the proposed model influence electric players to find effective collaborative strategies under different conditions and environments. • Proposing an intelligent three-level structure based on cooperative game theory. • Achieving the maximum benefit from cooperation with other resources. • Lifting the restriction on the number of participants in the grand coalition. • Feasibility of proposed scheme with engaging a large number of buyers and producers. [ABSTRACT FROM AUTHOR]

Management of reactive power resources is essential for secure and stable operation of power systems in the standpoint of voltage stability. In power systems, the purpose of optimal reactive power dispatch (ORPD) problem is to identify optimal values of control variables to minimize the objective function considering the constraints. The most popular objective functions in ORPD problem are the total transmission line loss and total voltage deviation (TVD). This paper proposes a hybrid approach based on imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) to find the solution of optimal reactive power dispatch (ORPD) of power systems. The proposed hybrid method is implemented on standard IEEE 57-bus and IEEE 118-bus test systems. The obtained results show that the proposed hybrid approach is more effective and has higher capability in finding better solutions in comparison to ICA and PSO methods. [ABSTRACT FROM AUTHOR]

This paper presents a new and comprehensive Objective Function (OF) for capacitor placement in distribution networks. In this study, distribution network management’s viewpoint toward identifying comprehensive OF to maximize the benefit of a distribution company is considered. In addition to considering active power loss cost and capacitor cost, two other important terms, i.e. cost of buying reactive power and voltage drop penalty for maximizing the benefit of distribution companies are considered in the OF. All actual conditions including time varying nature of load, annual load growth, time varying price of active and reactive power, and switchable and fixed capacitor are taken into account based on the reality. The profit derived from the proposed OF is compared with two other common OFs, and it is shown that the benefit achieved from the proposed OF is more than the two other OFs. The proposed OF is validated and tested on radial distribution systems with differing topologies and varying sizes and complexities. [ABSTRACT FROM AUTHOR]

The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment. [ABSTRACT FROM AUTHOR]

The main challenge in integrating a Battery Energy Storage System (BESS) into a microgrid is to evaluate an optimum size of BESS to prevent the microgrid from instability and system collapse. The installation of BESS at a random size or non-optimum size can increase in cost, system losses and larger BESS capacity. Thus, this paper proposes the new method to evaluate an optimum size of BESS at minimal total BESS cost by using particle swarm optimization (PSO)-based frequency control of the stand-alone microgrid. The research target is to propose an optimum size of BESS by using the PSO method-based frequency control in order to prevent the microgrid from instability and system collapse after the loss of the utility grid (e.g., blackout or disasters) and minimize the total cost of BESS for 15 years installation in the microgrid. Then, the economical performance of BESS with modern different storage technologies is investigated and compared in the typical microgrid. Results show that the optimum size of BESS-based PSO method can achieve higher dynamic performance of the system than the optimum size of BESS-based analytic method and the conventional size of BESS. In terms of BESS economical performance with modern storage technologies, the installation of the polysulfide–bromine BESS is likely to be more cost-effective than the installation of the vanadium redox BESS for 15 years installation in the typical microgrid. It is concluded that the proposed PSO method-based frequency control can improve significantly power system stability, grid security, and planning flexibility for the microgrid system. At the same time, it can fulfill the frequency control requirements with a high economic profitability. [ABSTRACT FROM AUTHOR]

Hydro–wind–thermal scheduling is one of the most important optimization problems in power system. An aim of the short term hydrothermal scheduling of power systems is to determine the optimal hydro, wind and thermal generations in order to meet the load demands over a scheduled horizon of time while satisfying the various constraints on the hydraulic, wind and thermal power system network. In this paper we present optimal hourly schedule of power generation in a hydro–wind–thermal power system applying PSO technique. The simulation results inform that the proposed PSO approach appears to be the powerful to minimize fuel cost and it has better solution quality and good convergence characteristics than other techniques. [ABSTRACT FROM AUTHOR]

This paper presents novel power system stabilizers (PSS) tuning approach with particles swarm optimization (PSO) driven knowledge domain states mapping for multi area power system. Tuning of PSS parameters has been done by PSO technique offline at different operating conditions as load dynamics change. The objective function for PSS parameters tuning is integral time multiplied by absolute error (ITAE). The sets of tuned parameters at different operating conditions thus obtained are termed as knowledge domain. The process is viewed as knowledge domain mapping. The dynamical system response has been linked with states, and if there is any violation from desired limits, control action is initiated and thus retuned control parameters obtained straight away from the respective knowledge domain helps to provide quick response and precise damping of signals of interest. Proposed concept also demonstrates that if one controller (PSS) fails to stabilize the system at certain operating condition then another controller (UPFC) connected in the system acts as supplementary controller in automation which assists PSS functioning and thus entire system operation improves by way of modulating the signal dynamics at interface points which quickly damps oscillations. The system study has been performed on a sample six area power systems comprising of UPFC connected between area two and three and PSS to all areas. The proposed concept demonstrates auto tuning of PSS for quick oscillation damping as the operating conditions change and also under situations of PSS failure due to relatively larger perturbation, UPFC acts as supplementary controller by assisting the entire system PSS to recover ensuring stability. [ABSTRACT FROM AUTHOR]

An important objective of the Optimal Power Flow (OPF) problem is to minimize the generation cost and keep the power outputs of generators, bus voltages, bus shunt reactors/capacitors and transformer tap settings in their secure limits. Solving this OPF problem using classical methods suffer from the disadvantages of highly limited capability to solve the practical large scale power system problems. To overcome the inherent limitations of conventional optimization techniques, Swarm Intelligence (SI) methods have been developed. However, the environmental concern, dictate the minimization of emissions of the thermal plants. Individually, if one objective is optimized, other objective is compromised. Hence, Multi-Objective Optimal Power Flow (MO-OPF) problem has been formulated in this paper. Swarm Intelligence methods, such as Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO) have been used to solve the OPF problem with generation cost and emission minimizations as objective functions. The effectiveness of the proposed algorithms are tested on IEEE 30 bus and practical Indian 75 bus systems for cost minimization as objective function, and IEEE 30 bus test system for minimization of cost and emission as objectives. The results obtained from both the networks, the PSO and GSO are compared with each other based on different parameters. [ABSTRACT FROM AUTHOR]

• Analyzing the impact of driving several PV plants, connected to the same transmission grid, as PV-STATCOM. • Simulating one-day scenario with MATLAB platform and using MATPOWER 30-bus grid model. • Comparting two different optimization algorithms (particle swarm optimization and differential evolution) to prove that both techniques drive to the same target. • Suggesting an Internet of Things hardware architecture for method implementation. This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions. To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time. The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent. [ABSTRACT FROM AUTHOR]

• The active and reactive power coordinated dispatching is decoupled to obtain the dispatching scheme rapidly. • The reactive power optimization model is established to adjust transformer tap and change capacitor output capacity. • Active power dispatching model of active distribution network is set considering network reconfiguration. • The DMA is used to solve the model, among which PSO is embedded to deal with the sequential coupling problem. • The voltage quality can be improved and DTC grid loss can be reduced effectively just by several equipment adjustments. The operation and dispatching pressure of active distribution network is gradually increasing with the integration of distributed generators into urban distribution network unevenly. It is necessary to carry out research on active and reactive power coordinated dispatching. In order to obtain the coordinated dispatching scheme rapidly, the active and reactive power coordinated dispatching is decoupled in this paper. Reactive power optimization and active power dispatching are carried out respectively. The final coordination dispatching scheme is formulated through the cross iteration of active power dispatching and reactive power optimization. Firstly, the reactive power optimization model is set by mainly considering adjusting transformer tap and changing capacitor output capacity. The model is solved by a two-stage method to obtain the dynamic dispatching scheme. Secondly, active power dispatching model of active distribution network is set considering network reconfiguration. To obtain the solution quickly, the discrete monkey algorithm is used to solve the model, among which particle swarm optimization algorithm is used in the climb process to solve the sequential coupling problem of switch state variables. Finally, the node voltage quality can be effectively improved and grid loss of distribution transformer cluster can be significantly reduced by a few of adjustments of transformer taps, shunt capacitors and switch. The practicability of dynamic coordination dispatching method is proved. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

• Providing evaluation and comparison among eleven improved swarm intelligence-based algorithms. • Proposing a powerful optimization approach to solve the benchmarks as well as the TEP problems. • Considering three terms such as operating cost, investment cost, and the cost of power losses. • Considering multi-level wind power penetration into the TEP problems. • Considering short-term and long-term models of uncertainties into the TEP problems. 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. [ABSTRACT FROM AUTHOR]

• A novel operation of combined P2G-GfG for frequency regulation utilizing curtailed wind power is proposed. • A sizing model is proposed to determine optimal capacity of P2G-GfG for participating in frequency regulation. • The objective function is achieving the tradeoff between total cost and the revenue gained from frequency regulation. The increase in penetration of wind turbines in the power system has caused curtailment of wind power due to the stochastic nature of wind, which results in loss of revenue. Consequently, power to gas (P2G) has emerged as a device which can utilize curtailed wind power through the electrolysis of water to hydrogen or further processing to obtain methane. In addition, due to technical, economic and environmental benefits, the share of gas fired generating (GfG) units in power systems has been rising by replacing fossil fuel fired generating units. This paper proposes a coordinated operation and novel optimal sizing of a combined P2G-GfG system (CPGS) with gas storage tank (GST) for participating in frequency regulation. Firstly, the coordination operation of CPGS is presented in which P2G utilizes the curtailed wind power to facilitate the methanation process to produce methane. The methane is stored in a gas storage tank, then a GfG unit is supplied with the gas to participate in frequency regulation. Secondly, a novel optimization model using particle swarm optimization is presented considering the operating strategy and operation constraints to maximize net benefit when CPGS participates in frequency regulation market. Finally, case studies were conducted to verify the proposed coordinated operation and optimal sizing strategy considering actual AGC commands and curtailed wind power data. [ABSTRACT FROM AUTHOR]

Excessive load demand with reliability in power availability, demands for interconnection of large number of generating units over existing tie lines. Due to sudden change in demand, the power transfer over existing tie lines working close to their thermal limits results in low frequency power oscillations. Thus, in modern power systems the study of mitigation of these frequency oscillations is more involved and formulates the area of Load Frequency Control (LFC). Many conventional and heuristic control techniques have been recently applied to address the issue of LFC. This paper investigates load frequency control of large interconnected power system consisting of conventional and renewable energy sources, using hybrid heuristic approach. The proposed strategy is shown to result in improved system damping resulting in faster mitigation of low frequency oscillations. [ABSTRACT FROM AUTHOR]

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed. [ABSTRACT FROM AUTHOR]

Planning of reactive power sources is a serious issue for secure and economic operation of power systems. In this paper, a bi-level strategy is proposed to optimize the Reactive Power Planning (RPP) problem. In the first level, the weakest buses are selected to be the optimal placements to install the additional VAR sources and its corresponding suitable sizes are determined using a proposed Refined Heuristic Process (RHP). In the second level, two modified versions of Differential Evolution Algorithm (DEA) are proposed for optimizing the RPP control variables which able to minimize both the allocation costs of additional VAR sources throughout the system, and the system operational costs of real power losses. To validate the effectiveness of proposed strategy, several applications are carried out on three power systems networks namely IEEE 14-bus, IEEE 30-bus test systems and the West Delta region system as a part of the Egyptian Unified network. The proposed strategy is evaluated compared with other optimization methods as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and the commonly used Differential Evolution (DE) version (DE/rand/1). The robustness of the proposed versions of DEA is proven compared to other optimization techniques. Added to that, the control parameters of the proposed DEA are optimally identified. Numerical results show that the proposed version of DEA achieves highest reduction in the operation and investment costs compared to other optimizing algorithms in the literature which denotes that the proposed version of DEA can be efficiently applied to the RPP problem. [ABSTRACT FROM AUTHOR]

In order to satisfy the high accuracy and high security requirements of time synchronization in smart grid, this paper has conducted a research on high-precision time synchronization control algorithm based on IEEE 1588 protocol. A slave clock model is built considering both phase offset and frequency drift. A novel approach using proportional–integral–derivative (PID) controller in cooperation with the wolf colony algorithm (WCA) is proposed to eliminate the offset of the slave clock with respect to the master clock. As a new swarm-intelligence algorithm, WCA is introduced for self-tuning the optimal parameters of the PID controller, which simulates the intelligent predatory behaviors and prey distribution rule of the wolf colony. The WCA-based PID control approach is compared with the method based on standard Particle Swarm Optimization (PSO) and Back-Propagation (BP) neural network. The experimental results show that the proposed WCA-based PID control approach in time synchronization for smart grid has higher precision, can adaptively adjust compensation and keep the master–slave clock synchronization effectively. [ABSTRACT FROM AUTHOR]

Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN. [ABSTRACT FROM AUTHOR]

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO–ABC–HS combines the framework of Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm Optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm’s performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses. [ABSTRACT FROM AUTHOR]

Thyristor controlled series compensator (TCSC) as a promising series flexible AC transmission system (FACTS) device is generally used for controlling the real power flow in transmission lines. It can increase the system stability as the complementary functionality by minimizing the power oscillations. The effectiveness of TCSC in its primary and supplementary applications depends on the selection of its optimal location and defining a proper input signal. In this paper, a new method based on the active power sensitivity approach is applied to find the optimal location of TCSC. In addition, Hankel singular values (HSVs) and right half plane-zeros (RHP-zeros) analyses have been proposed to find the most appropriate stabilizing input signal for the supplementary functionality of TCSC to damp out the interarea modes of oscillation. Finally, the optimal design of power oscillation damper (POD) and simultaneous coordinated design of power system stabilizer (PSS) and POD are implemented separately in a large-scale power system. The tuning problem of POD-TCSC parameters as well as the coordinated POD-TCSC & PSS are converted to a multi-objective optimization problem and solved using particle swarm optimization (PSO) algorithm. The performance of the proposed method has been validated through eigenvalue analysis and nonlinear time domain simulation in a 16-machine 68-bus test system. The simulation results show a satisfactory robust performance with an excellent capability in damping of local and interarea power oscillations. [ABSTRACT FROM AUTHOR]

Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system. [ABSTRACT FROM AUTHOR]

In this paper, using particle swarm optimization (PSO) based method is developed to determine the optimal allocation of distributed generators (DGS) on a multi phased unbalanced distribution network. PSO algorithm has been programmed in MATLAB using open source software called OpenDSS in a co-simulation environment to solve the unbalanced three-phase optimal power flow (TOPF) and to find the optimal location and sizing of different types of distributed generators. Using the IEEE 123 node distribution feeder as a test bed, results from the proposed method is compared to those from the repeated load flow (RLF) method. For a realistic study, mixes of all type of DGs are considered. Results indicate that integrating optimally sized DGs at the optimal locations not only reduces the total power loss in the distributed system but improves the voltage profile as well. [ABSTRACT FROM AUTHOR]

This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system. [ABSTRACT FROM AUTHOR]

A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT), battery, Micro-Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid combination of Neuro and Fuzzy is proposed as a controller to solve the Automatic Generation Control (AGC) problem in a restructured power system that operates under deregulation pedestal on the bilateral policy. In each control area, the effects of the possible contracts are treated as a set of new input signal in a modified traditional dynamical model. The prominent advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter discrepancy and system nonlinearities. This newly developed strategy leads to a flexible controller with a simple structure that is easy to implement and consequently it can be constructive for the real world power system. The proposed method is tested on a three-area hydro-thermal power system in consideration with Generation Rate Constraint (GRC) for different contracted scenarios under diverse operating conditions. The results of the proposed controller are evaluated with the Hybrid Particle Swarm Optimisation (HCPSO), Real Coded Genetic Algorithm (RCGA) and Artificial Neural Network (ANN) controllers to illustrate its robust performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A new optimization technique called Cuckoo Search (CS) algorithm for optimum tuning of PI controllers for Load Frequency Control (LFC) is suggested in this paper. A time domain based-objective function is established to robustly tune the parameters of PI-based LFC which is solved by the CS algorithm to attain the most optimistic results. A three-area interconnected system is investigated as a test system under various loading conditions where system nonlinearities are taken into account to confirm the effectiveness of the suggested algorithm. Simulation results are introduced to show the enhanced performance of the developed CS based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and conventional integral controller. These results denote that the proposed controllers offer better performance over others in terms of settling times and various indices. [ABSTRACT FROM AUTHOR]