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• A new novel method is proposed for performance estimation of PV modules under varying operating conditions. • Effect of reference condition on performance estimation is eliminated and accuracy is improved. • The model parameters are calculated without setting a reference condition or using reference values. • The effectiveness and accuracy of output power estimation is validated by massive experimental data. 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. [ABSTRACT FROM AUTHOR]

• New algorithm particle swarm optimization with oscillatory inertia factor with exponential decay. • Simultaneous adjustment of the AVR and PSS parameters through the PSO-OED optimization technique. • Better adjustment of the AVR and PSS providing a better dynamic performance and transient system response. • New objective function capable of analyzing the performance of several generators simultaneously. This paper presents a new algorithm for simultaneous tuning of the optimal parameters of Automatic Voltage Regulator (AVR) and the Power System Stabilizer (PSS) using a new technique of Particle Swarm Optimization with Oscillating Exponential Decay (PSO-OED). The proposed algorithm was applied both the Single-Machine Infinite-Bus (SMIB) Power System and the 9-Bus Multi-Machine Power System. The results of the simulation show that the proposed method is efficient and guarantees an improvement in the dynamic stability and convergence rate of the simulations. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel approach to depict the practical constraints of generator units such as reserve constraints, prohibited operating zones and valve-point effects in the optimal dynamic dispatch problem. Determining the power generation output of units at minimum total fuel cost a dynamic environment with ramp rate limits to satisfy load demand and transmission losses is too complicated and has a lot of local optima in its search space. In addition, the proposed problem has a non-linear, non-convex, non-smooth, multi-modal, non-separable, and non-differentiable nature. In order to overcome above problems a new hybrid technique, based on bacterial foraging and simplified swarm optimization algorithms combined with a new mutation operator and opposition-based initialization is proposed to restraint the premature convergence of the solutions. Therefore, the bacteria in chemo-tactic procedure are moved in short or long steps as well as swimming movements. Furthermore, to increase the diversity of the solution of the search space a novel self-adaptive mutation strategy which profits from four mutation rules is implemented. For more validation the simulation results are applied on four small, medium and large scale systems with 5-unit, 10-unit, 30-unit and 100-unit and compared with those of other methods in the area. [Copyright &y& Elsevier]

Abstract: This paper presents artificial bee colony optimization for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system. [Copyright &y& Elsevier]

Abstract: In this paper, Benders Decomposition method improved by Bacterial Foraging oriented by Particle Swarm Optimization method (BDI-BFPSO) is used for solving AC constrained hydro-thermal generation scheduling problem. The objective function of the proposed generation scheduling problem is to minimize the generation cost and emission cost of the power system by considering the valve point effect and prohibited operating zones of thermal units. This problem is a large scale mixed integer nonlinear programming problem with a great number of equality and inequality constraints related to generation units, hydro system and power system. In order to solve this problem using BDI-BFPSO, the optimization problem is decomposed into a master problem and a sub-problem which they can be solved iteratively. The performance of the BDI-BFPSO method is tested and evaluated on IEEE 6-bus and 118-bus standard systems. Simulation results confirm the effectiveness of the proposed BDI-BFPSO method for solving the scheduling problem in comparison with other methods. [Copyright &y& Elsevier]

Abstract: In this paper coordination scheme to improve the stability of a power system by optimal design of power system stabilizer and static synchronous series compensator (SSSC)-based controller is presented. Time delays owing to sensor and signal transmission delays are included in the design. The coordinated design problem is formulated as an optimization problem and hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach. [Copyright &y& Elsevier]

Abstract: Smart grid is an upgrade of the existing electricity infrastructure in which integration of non conventional energy sources are an integral part. This leads to the introduction of harmonics and increased switching losses in the system. Thus there is a need of loss less switching techniques for smart grid applications. Switched mode power supplies (SMPSs) are being extensively used in most power processes [1]. Developments were carried out centered on hard switched converters, where switching frequency is limited to 10s of kHz [2]. The uses of soft switching techniques, [3–6] zero voltage switching (ZVS) or zero current switching (ZCS), is an attempt to substantially reduce the switching losses and hence attain high efficiency at increased switching frequency. The soft-switching topologies belong to families namely resonant load converters [3], resonant switch converters [2,4], resonant transition converters [5,6], and most recently active clamped PWM converters [7–9]. The active clamp topology adds an active clamp network, consisting of a small auxiliary switch in series with a capacitance plus the associated drive circuitry to the traditional hard switch converters. The proposed paper basically deals with the design, modeling and simulation of a ZVS–PWM active clamp/reset forward converter having features like zero switching power losses, constant frequency and PWM operation, Soft-switching for all devices and Low voltage stresses on active devices due to clamping action. [Copyright &y& Elsevier]

Abstract: This paper presents a θ-particle swarm optimization (θ-PSO) based algorithm to solve constrained economic load dispatch (ELD) problems of thermal plants. The proposed methodology easily takes care of practical constraints such as transmission losses, ramp rate limits, and prohibited operating zones, and also deals with non-smoothness of cost function arising due to the use of valve point effects. The main evaluation mechanism of classical particle swarm optimization (CPSO) is modified in the novel θ-PSO by replacing the velocity vector with a phase angle vector. So, the positions are decided by the mapping of phase angles in θ-PSO. The performance of proposed algorithm has been tested on systems possessing 6, 13, 15, and 40 generating units involving varying degree of complexity. Detailed comparisons under equitable conditions are provided with prevalent approaches to measure the efficiency of the proposed algorithm. The findings affirm that the method outperforms the existing techniques in terms of solution quality and computational efficiency and can be a promising alternative approach for solving the ELD problems in practical power system. [Copyright &y& Elsevier]

Abstract: With increasingly scarce of fossil fuel resources required for energy demand, hydropower has become one of the most important energy resources. As a renewable and sustainable energy, hydropower system has developed rapidly during recent decades. The unprecedented rate of expansion and development scale of hydropower has posed a challenge to the operation of multi-reservoir system (OMRS). The optimal operation of multi-reservoir system is a large-scaled, non-linear and multi-stage problem which subject to a series of hydraulic constraints. The main objective of OMRS is to find out the optimal hourly water discharge rate of each hydro station in the multi-reservoir system to minimize the power deficit and distribute the uniformly deficit if any. In order to solve OMRS problem effectively, in this paper, a multi-elite guide particle swarm optimization (MGPSO) is proposed by introducing archive set into standard particle swarm optimization. External archive set which can preserve elite solutions along the evolution process is employed to provide multi-elite flying directions for particles. Meanwhile, an effective constrain handling method is proposed to handle the operational constraints of OMRS problem. This proposed method is applied to a multi-reservoir system consisting of 10 cascaded hydro plants for case study. Compared with several previous methods, the simulation results of MGPSO can get better solutions with smaller energy deficit, which proves it is an alternative method to deal with OMRS problem. [Copyright &y& Elsevier]

Abstract: This paper describes a methodology for allocating repair times and failure rates to segments of a meshed distribution system using differential evolution (DE) technique. A cost function based on Duane’s reliability growth testing model has been proposed. Constraints on basic reliability indices i.e. failure rate, average interruption duration and average interruption duration per year have been accounted. The algorithm has been implemented on a meshed distribution system and results have been compared with those obtained using particle swarm optimization (PSO) and co-ordinated aggregation based PSO (CAPSO). [Copyright &y& Elsevier]

Abstract: The optimal power flow is an important problem of power systems in which certain control variables are adjusted to minimize an objective function such as the cost of active power generation or the losses, while satisfying physical and operating limits on various controls, dependent variables and function of variables. This paper presents an efficient and reliable evolutionary based approach to solve the optimal power flow (OPF) problems. The proposed approach employs the integration of Fuzzy Systems with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm for optimal setting of OPF problem control variables. The proposed approach has been tested on the modified IEEE 30-bus test system with objective function that reflects fuel cost minimization with different linear and non-linear constraints. The proposed approach results have been compared with the results those reported in the literature. The results of proposed approaches are promising and it shows the effectiveness and robustness of proposed methods. [Copyright &y& Elsevier]

Abstract: This work presents a comparison of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, when applied to the generation and demand dispatch problem. An optimization problem is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. Consumption restrictions associated with flexible demand are modeled by equality and inequality energy constraints. Power flow equality constraints and inequality constraints due to operational limits for each dispatch interval are represented. The paper discusses a methodology for evolutionary algorithms performance assessment and states the importance of using statistical tools. The comparison is initially conducted using the IEEE 30-bus test system. Problem dimension effect is addressed considering different number of dispatch intervals in the time horizon. Moreover, the algorithms are applied to the 192-bus system of a Brazilian distribution utility, in the particular context of a load management program for large consumers of the company. In this application, the quality of the near-optimal solution obtained with the stochastic algorithms is evaluated by comparing with an analytical optimization algorithm solution. [Copyright &y& Elsevier]

Abstract: This research discusses the multi-stage security-constrained transmission network expansion planning. In modern power systems, the problem is formulated as a large-scale, mixed-integer, non-linear programming problem, which for a real power systems is very difficult to solve. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. In this paper, a new constructive heuristic approach, based on a local controlled random search (simulated rebounding algorithm) is proposed to choose the decision variables. The model can produce better solutions than other references techniques such as particle swarm optimization, evolutionary particle swarm optimization, genetic algorithms, and simulated annealing algorithm, among other evolutionary methods. The methodology is applied to assess the capabilities of the proposed approach in the Ecuadorian and Chilean Power Systems as an example of application. Simulation results show that the proposed approach is accurate and very efficient, and it has the potential to be applied to real power system planning problems. The algorithm has been presented and applied to the multi-stage security-constrained transmission expansion planning. [Copyright &y& Elsevier]

Abstract: This paper presents a study on isolated hybrid distributed generation (DG) system for improving the frequency deviation profile. The hybrid DG system consists of wind turbine generator (WTG), diesel engine generator (DEG), aqua-electrolyzer (AE), fuel cell (FC) along with energy storage units. The frequency control problem is addressed for DG system connected with superconducting magnetic energy storage (SMES) or ultra-capacitor (UC). The particle swarm optimization (PSO) based loop shaping of H-infinity controller is used and compared with those obtained by genetic algorithm (GA) to minimize the frequency deviation. The frequency stabilizing performance is analyzed under different disturbances. Also, the controller robustness in terms of system parameter uncertainties is tested for changes in parameter up to ±30% from its nominal value. The results demonstrate minimum frequency deviation as achieved by proposed controller with use of UC in hybrid DG system. [Copyright &y& Elsevier]

Abstract: The power system needs to satisfy its customers demand with minimum cost and emission. Fuel cost has straight relationship with energy cost. This paper presents an advanced parallelized particle swarm optimization algorithm (PSPSO) for finding optimal combination of power generation units that minimizes the total fuel cost and emission. In this algorithm, time requirements for solving complex large-scale CEED problem can be substantially reduced using parallel computation and it performs the update of positions and velocities in the end of each iteration. The proposed approach is applied on four test systems and compared with other techniques. The results represent that PSPSO has better convergence than other techniques. [Copyright &y& Elsevier]

Abstract: This paper analyzes the alternative of a hybrid active power filter (HAPF) connected to the medium-voltage (10kV) distribution network to enhance the power quality. It proposes a multi-objective optimization algorithm based on improved particle swarm optimization (PSO) to design and optimize the main circuit of HAPF. On the basis of theoretical analysis, it removes engineering experience and redundant constraints from the optimization algorithm, and establishes a unified description of performance for HAPF. To avoid premature of PSO algorithm, an adaptive inertial weight based on sigmoid function is employed to improve the diversity of the solution space. The simulation and experimental results show that the proposed design and optimization of main circuit for HAPF is correct and effective. [Copyright &y& Elsevier]

Abstract: This paper presents a multi-objective evaluation approach for finding the optimal schemes for upgrading primary feeders with distributed generation resources (DGs) from normally closed loop to a mesh arrangement. To build a DG-friendly distribution system for the development of DGs, a genetic algorithm (GA) based approach has been proposed to solve this multi-objective optimization problem. The major objectives include improving the average voltage deviations, minimizing the total line losses and maximizing the possible installed capacity of DGs. Based on the given total installation capacity and location for DGs, two distinct planning strategies are discussed. The results show that the proper schemes for utilizing additional feeders are important to improve the voltage profile along feeders, to reduce the system power losses and maximize the possible installed capacity of DGs. The outcomes are of value to the upgrades and expansions of primary distribution systems and the implementation of sustainable development of renewable energy. [Copyright &y& Elsevier]

Abstract: Controlled islanding operation of distribution systems having significant penetration of distributed generation (DG) is becoming an important option for economical and technical reasons. Implementation of intentional islanding of DG improves the continuity of supply and reliability of power system. In this paper, comprehensive learning particle swarm optimization (CLPSO) is used to optimally partition the distribution system in case of main upstream loss. The objective is to find the optimal islanding scheme of distribution system to achieve minimum active power generation cost, minimum reactive generation cost and minimum cost of the un-served power while satisfying system operational constraints. The solution proceeds by splitting the system into islands after the loss of main upstream feeder. In each island, the power balanced is achieved through load shedding. An optimal dispatch of the generating units of each island is then carried out to achieve minimum active power generation cost. A power flow calculations are carried out to calculate the reactive power generated by each unit and to check the operational constraints of the system. Finally, the effects of controlled islanding with and without utility owned DGs on the system reliability indices is studied. The proposed algorithm is applied to two radial and meshed test systems. [Copyright &y& Elsevier]

Abstract: Determination of optimal location and size of Distributed Generations (DGs) is one of the major problems of distribution utilities. In this paper, a multi-objective methodology for optimal distributed generation allocation and sizing in distribution systems is proposed. The objective function is considered willing to minimize the costs of active and reactive losses, and to improve the voltage profile and reliability of the distribution systems. Besides, the effect of load models is considered in the optimal DG allocation. A hybrid method based on improved particle swarm optimization (IPSO) algorithm and Monte Carlo simulation is proposed for solving the problem. The proposed algorithm is applied to the practical 33-bus distribution system and results are compared with other versions of PSO and artificial bee colony (ABC) algorithm. Numerical studies are representing of the effectiveness and out-performance of the proposed method. [Copyright &y& Elsevier]

Abstract: Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so as to operate an electric power system most economically. This paper proposes a hybrid methodology integrating bee colony optimization with sequential quadratic programming for solving dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates bee colony optimization as a base level search which can give a good direction to the optimal region and sequential quadratic programming as a local search procedure which is used to fine tune that region for achieving the final solution. Numerical results of a ten-unit system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared with those obtained from hybrid of particle swarm optimization and sequential quadratic programming and hybrid of evolutionary programming and sequential quadratic programming. [Copyright &y& Elsevier]

Abstract: This paper develops a novel approach to model the Interline Power Flow Controller (IPFC) with the purpose of enhancing the power system dynamic stability. The dynamic behavior of the IPFC is modeled using a new and detailed current injection model. On the basis of designing a supplementary damping controller, the effectiveness of the proposed model in robust damping of the oscillations is evaluated. Thus, the problem of attaining the damping controller parameters transmitted into an optimization process which is solved using Particle Swarm Optimization algorithm (PSO). The PSO has a strong and reliable capability to find out the optimistic solution. The optimization procedure is performed in a multi-machine power system and under various operating conditions. Assessment the derived results from the nonlinear time domain simulation and through some performance indices with considering to a severe transient disturbance clearly indicates the major performance of the proposed model and the model based designed controller in improvement the system stability margins. Moreover, to identify the most suitable IPFC control signal, a precise evaluation of the employed indices is accomplished. Numerical results verify the superior stabilization effect of the m 1 (one of the IPFC control signals) in the wide range of operating conditions. [Copyright &y& Elsevier]

Abstract: Planning of fuel energy management of a CHP-based microgrid requires strategic deployment of DERs. Strategic deployment of DERs is meant to select their optimal locations, optimal sizes and optimal technologies. Optimal locations and sizes, which are independent of types of CHP-based DERs used, are selected, here, by loss sensitivity index (LSI) and by loss minimization using differential evolution (DE) algorithm respectively. In the context of planning of a 14-bus radial microgrid present paper incorporates originality in ideas in the analysis technique based on differential evolution (DE) algorithm to evaluate how different optimal output sets of a group of four DERs, while operating within their respective capacity limits as well as tracking an electrical demand without grid participation, could satisfy a range of heat demands, each representing a specific solution of optimal fuel consumption. For investment decision in the perspective of the owner of the microgrid the author performs the analysis with two separate groups of 4-DER each of various sizes – one group with all diesel generators (i.e. All-Dg) and other with mix of diesel generators (Dgs) and microturbines (Mts) (i.e. Mix-DER). As the present microgrid is intended to cater both heat and electric demands, as per profiles, to its all-commercial customers, a boiler is considered as need-based back-up source to meet the deficit of daily heat generation, if any, for balancing the daily heat demand. At the best result of optimal fuel cost of each group obtained from above analysis, an economic comparison is done between the two groups with the help of pay back period (PP), internal rate of return (IRR) and net present value (NPV) of the microgrid to decide better investment option of the two. Results of DE are confirmed and compared with particle swarm optimization (PSO) technique. [Copyright &y& Elsevier]

Abstract: This paper presents an artificial bee colony algorithm to enhance the fault section estimation performance in power systems. Through mimicking the foraging behaviors of honeybee swarms, the algorithm owns exploitation and exploration procedures to constitute an effective near-optimal search mechanism. This proposed method excels at saving decision on external parameters such as crossover and mutation rates, facilitating the improvement of computation performance. Meanwhile, the method has added a random selection scheme to look for a new source, by which the probability of being trapped into local minimum can be largely reduced, hence serving as beneficial aids of grasping the faulted section more effectively. Through this proposed approach, it benefits the engineers to find the accurate fault section among voluminous alarms, and reduces the possibility of inaccurate diagnosis. To validate the effectiveness of the method, it has been tested on practical power systems with comparisons to published techniques. [Copyright &y& Elsevier]

Abstract: This paper presents a novel and efficient method for the optimal tuning and placement of power system stabilizer (PSS). The proposed modified particle swarm optimization (MPSO) integrates the particle swarm optimization with passive congregation (PSOPC) and the chaotic sequence. Passive congregation helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. In addition, the chaotic sequence concept is introduced to improve the global searching capability and prevent the premature convergence due to local minima. The proposed optimization procedure handles the problem-specific constraints, in this case it is related to low frequency oscillations in power systems and PSS, using a penalty function. Based on this, tuning and placement of PSS over a wide range of system configurations is formulated as a multi-objective function where the main objective is the aggregation of the three objectives related to the damping ratio and damping factor, and the number of PSS. The robustness of the proposed PSS tuning technique is verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSO is also compared to the PSOPS and PSO, genetic algorithm through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Finally, the proposed method is applied to find out the best candidate machines to be equipped with PSSs. The obtained results showed that the new method can find the best placements and the optimum PSSs parameters simultaneously with an excellent global damping performance. [Copyright &y& Elsevier]

Abstract: This paper presents a fuzzy controlled and multi-population based binary clustered particle swarm optimization (BCPSO) algorithm to solve short term thermal generation scheduling problem. In order to incorporate the uncertainties regarding forecasted load demand, the spinning reserve requirement and the total production cost, the formulations are modified by employing fuzzy logics. Each of these uncertain entities are associated with fuzzy membership functions which determine the degree of acceptance. The aggregated membership function, which combines the individual membership functions of fuzzified variables, is incorporated with the fitness value to provide the acceptability measurement of a particular candidate schedule. Typically, generation scheduling is a highly non-linear, multi-peak combinatorial optimization problem. In this method, the potential candidate schedules (or individuals) are distributed among several clusters based on their acceptance values. Each individual of a particular cluster then flies through to its cluster-space towards the cluster best while improving its personal best position. Gradually, as the population grows, the cluster space is also increased to ensure the global convergence. Therefore, this algorithm explores a larger search space and thus reduces the probability of local trapping. A dynamic probabilistic mutation operator is applied on the individual solutions based on their associated fitness values. Simulation result is provided to show the effectiveness of BCPSO while considering two different power system configurations. [Copyright &y& Elsevier]

Abstract: This paper presents three intelligent evolutionary optimization techniques to investigate the optimal design parameters of a 3-phase furnace transformer. The transformer rating is derived from the operating conditions of a medium size direct arc furnace. Scatter Search (SS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques are employed on the developed nonlinear mathematical model of the transformer for constrained optimization minimizing the cost. The design and analysis programs of the furnace transformer are developed using codes written in C++/C language. The optimal design data results validated by an example show the efficacy of the three intelligent techniques. Among the three methods, the optimal results obtained by GA and PSO techniques show the potential for implementing as efficient search techniques for design optimization of furnace transformers. [Copyright &y& Elsevier]

Abstract: The unified power flow controller (UPFC) is one of the most promising flexible AC transmission systems (FACTS) devices for the load flow control. Simultaneous optimization of location and parameters for UPFCs is an important issue when a given number of UPFCs is applied to the power system with the purpose of increasing system loadability. This paper presents the application of hybrid immune algorithm (HIA) such as immune genetic algorithm (IGA) and immune particle swarm algorithm (IPSO) to find optimal location of UPFC to achieve optimal power flow (OPF). The overall cost function, the objective function in the OPF, includes the total active and reactive production cost function of the generators and installation cost of UPFCs and hence, should be minimized. The OPF constraints are generators, transmission lines and UPFCs limits. In power system, it may not always be possible to dispatch the contracted power transactions completely due to congestion of the corresponding transmission corridors. In this study simulations were performed on IEEE 14-bus and IEEE 30-bus test systems for different methods. Under all equality and inequality constraints, the HIA proposed approach minimized the objective function better than other methods such as GA, PSO, and IA; and as far as HIA methods were concerned, the IPSO algorithm gave better minimum cost than IGA method. Results of simulations are encouraging and could efficiently be employed for power system operations. [Copyright &y& Elsevier]

Abstract: In this paper, a Biogeography Based Optimization (BBO) technique is introduced to solve multi-constrained optimal reactive power flow (ORPF) problem in power system. ORPF is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. The feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over other well established population based optimization techniques like conventional particle swarm optimization (PSO), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA) and interior point based OPF (IP-OPF). [Copyright &y& Elsevier]

Abstract: This paper describes an analytical methodology for reliability evaluation and enhancement of distribution system having distributed generation (DG). Standby mode of operation of DG has been considered for this purpose. Reliability of the composite distribution system has been optimized with respect to failure rate and repair time of each section of the distribution system subject to constraints on (i) failure rates/ repair times and (ii) customer and energy based reliability indices i.e. SAFI, SAIDI, CAIDI and AFNS. The objective function includes (i) cost of modification for failure rates/repair times (ii) additional cost of expected energy supplied by DG. Differential evolution (DE), Particle swarm optimization (PSO) and co-ordinated aggregation based PSO (CAPSO) have been used to develop computational algorithms. Developed algorithms have been implemented on a sample distribution system. [Copyright &y& Elsevier]

Abstract: In this paper, a novel global harmony search algorithm (NGHS) is used to determine the optimal location and size of shunt reactive power compensators such as shunt capacitors, static Var compensators (SVCs), and static synchronous compensators (STATCOMs) in a transmission network. The problem is decomposed into two subproblems. The first deals with the optimal placement of shunt Var compensation devices using the modal analysis method. The second subproblem is the optimization of the load flow using the NGHS algorithm. A multi-criterion objective function is defined to enhance the voltage stability, improve the voltage profile, and minimize power loss while minimizing the total cost. The results from a 57-bus test system show that the NGHS algorithm causes lower power loss and has better voltage profile and greater voltage stability than the improved harmony search algorithm (IHS) and particle swarm optimization (PSO) techniques in solving the placement and sizing problem of shunt Var compensators. Finally, a comparison of the convergence characteristics of three optimization methods demonstrates the greater accuracy and higher speed of the proposed NGHS algorithm in finding better solutions compared with PSO and IHS. [Copyright &y& Elsevier]

Abstract: In order to improve the performance of traditional grey models for short-term electricity price prediction in competitive power markets, a novel grey model, named PGM(1,2,a,b), is proposed in this paper. In the proposed model, the reference sequence is determined with correlation method. Furthermore, considering the limitation of the least square method (LSM), particle swarm optimization algorithm (PSO) is adopted to identify the parameters instead of LSM. To demonstrate the superiority of the proposed model, public available data coming from Nordpool, California, and Ontario power markets were used for training and testing. Simulation results show that the proposed model is capable of forecasting short-term electricity price efficiently and accurately. [Copyright &y& Elsevier]

Abstract: The Optimal Power Flow (OPF) problem with transmission loss as an objective function is solved using Particle Swarm Optimization (PSO) technique. PSO is employed with and without fully informed characteristic. Transmission loss is a major concern in the prevailing electrical energy crisis. The test cases used in this paper are the Ward-Hale 6-bus system, the standard IEEE 30-bus system and the standard IEEE 118-bus system. Results obtained using PSO and Fully Informed Particle Swarm Optimization (FIPS) are compared with each other and with the previously reported results in the literature. The statistical measures for fair comparison are the average power loss and the standard deviation in power loss. It has been found that FIPS outperforms PSO resulting in much reliable results. [Copyright &y& Elsevier]

Abstract: Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This paper introduces dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation tool. The principal purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer’s cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the tool. The tool also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study for various household situations. [Copyright &y& Elsevier]

Abstract: Differential evolution (DE), a simple evolutionary algorithm which shows superior performance in global optimization. Since it utilizes the differential information to get the new candidate solution, sometimes it results in instability of performance. Particle swarm optimization (PSO) is widely used to solve the optimization problems as it can converge quickly. But PSO easily gets stuck in local optima. Hybridization of DE and PSO (DEPSO) eliminates the disadvantages of both. This paper presents the application of DEPSO algorithm to determine the maximum loadability limit of power system. It is tested on Matpower 30 bus and IEEE 118 bus systems. To compare the performance of this DEPSO algorithm with other evolutionary algorithms like DE and Multi Agent Hybrid PSO, statistical measures like best, mean, standard deviation of results and average computation time over 20 independent trials are considered here. The results show the better performance of DEPSO algorithm to solve the maximum loadability problem. DEPSO algorithm provides high maximum loading point in reduced time. [Copyright &y& Elsevier]

Abstract: This paper presents artificial immune system algorithm for solving the combined heat and power economic dispatch problem. Artificial immune system is based on the clonal selection principle which implements adaptive cloning, hypermutation, aging operator and tournament selection. The proposed algorithm is illustrated for a test system and the test results are compared with those obtained from particle swarm optimization and evolutionary programming. [Copyright &y& Elsevier]

Abstract: This paper presents a novel heuristic algorithm for solving economic dispatch (ED) problems, by employing iteration particle swarm optimization with time varying acceleration coefficients (IPSO-TVAC) method. Due to the effect of valve-points and prohibited operation zones (POZs) in the generating units’ cost functions, ED problem is a non-linear and non-convex optimization problem. The problem even may be more complicated if transmission losses are taken into account. The effectiveness of the proposed method is examined and validated by carrying out extensive tests on three different test systems. Valve-point effects, POZs, ramp-rate constraints and transmission losses are modeled. Numerical results show that the IPSO-TVAC method has a good convergence property. Furthermore, the generation costs of the IPSO-TVAC method are lower than other optimization algorithms reported in recent literature. [Copyright &y& Elsevier]

Abstract: Traditional economic dispatch focuses mainly on minimizing the total operation cost of the power system. With the appearance of energy crisis and environmental pollution becoming a public issue, environmental effect of generator should be taken into consideration through the dispatch process. In this paper a multi-objective dispatch problem considering the integration of wind power is solved whose objectives include the generation cost, the reserve capacity and the environmental emission. To compromise different objectives, a coordination degree combined with a satisfaction degree are introduced in order to transform the multi-objective dispatch problem into a single-objective one, i.e., the optimal generation dispatch (OGD) model. Then the OGD is solved by a particle swarm optimization algorithm on an IEEE 30-bus system, with wind power generation and coal-fired generation embedded. The simulation results show that better results in terms of all the three objectives can be obtained from the OGD model, in comparison with two other multi-objective dispatch models. The simulation results also show that the integration of wind power will cause the increase of both the generation cost and the reserve capacity but will decrease the environmental emission. [Copyright &y& Elsevier]

Abstract: Along with continuous global warming, the environmental problems, besides the economic objective, are expected to play more and more important role in the operation of hydrothermal power system. In this paper, the short-term multi-objective economic environmental hydrothermal scheduling (MEEHS) model is developed to analyze the operating approach of MEEHS problem, which simultaneously optimize energy cost as well as the pollutant emission effects. Meanwhile, transmission line losses among generation units, valve-point loading effects of thermal units and water transport delay between hydraulic connected reservoirs are taken into consideration in the problem formulation. In order to solve MEEHS problem, a new multi-objective cultural algorithm based on particle swarm optimization (MOCA-PSO) is presented in way of combining the cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Furthermore, an effective constrain handling method is proposed to handle the operational constraints of MEEHS problem. The proposed method is applied to a hydrothermal power system consisting of four hydro plants and three thermal units for the case studies. Compared with several previous methods, the simulation solutions of MOCA-PSO with smaller fuel cost and lower emission effects proves that it can be an alternative method to deal with MEEHS problems. The obtained results demonstrate that the change of optimization objective leads to the shift of optimal operation schedules. Finally, the scheduling results of MEEHS problem offer enough choices to the decision makers. Thus, the operation with better performance of environment is achieved by more energy system cost. [Copyright &y& Elsevier]

Abstract: This paper describes a technique for selection of buses in a sub transmission system for location of distributed generation (DG) and determination of their optimum capacities by minimizing transmission losses. The buses have been selected based on incremental voltage (dV/dP) sensitivities. Line flow constraints have been accounted. Type-3 DG i.e. wind turbine along with induction generator has been considered for the study. Differential evolution (DE) has been used to evaluate the optimum DG capacity and results have been compared with those obtained using bare bones particle swarm optimization (BBPSO) and multi-membered non-recombinative (μ + λ) evolution strategy (MMNRES). The developed algorithms have been implemented on standard 6-bus and 30-bus test systems. [Copyright &y& Elsevier]

Abstract: A Hybrid Mutation Particle Swarm Optimisation (HMPSO) technique for improved estimation of Available Transfer Capability (ATC) as a decision criterion is proposed in this paper. First, this is achieved by comparing a typical application of the Particle Swarm Optimisation (PSO) technique with conventional Genetic Algorithm (GA) methods. Next, a multi-objective optimisation problem concerning optimal installation and capacity allocation of Flexible AC Transmission Systems (FACTSs) devices is presented and demonstrated. Modern heuristic techniques such as PSO have been demonstrated to be suitable approaches in solving non-linear power system problems. The outcome of this research further demonstrates that with better utilisation of FACTS devices, it is possible to improve transmission capabilities. The motivation of this research is a direct consequence of the deregulation of electricity industries and power markets worldwide. The current deregulated environment provides transmission systems operators (TSOs) with more options when procuring transmission services. The effectiveness of the proposed algorithm is demonstrated across a range of case studies, and the results are validated through analyses conducted on IEEE 30-bus and 57-bus test systems. [Copyright &y& Elsevier]

Abstract: This paper presents Particle Swarm Optimization Algorithm, with dynamic weights, applied to reduce the real power loss in a system. Particle Swarm Optimization with detailed study on weights for particle movements is used. Generator bus voltages, transformer tap positions and switch-able shunt capacitor banks are used as variables to control the reactive power flow. Particle Swarm Optimization has been applied to IEEE 6 bus system to present the case. The proposed dynamic weights show better, fast and consistent results with higher rate of convergence. [Copyright &y& Elsevier]

Abstract: Rephasing strategy is one of the main methods used for phase balancing and neutral current reduction in electrical distribution networks and the reconfiguration technique is an effective method for network loss reduction. In this paper, a new method for the simultaneous implementation of reconfiguration and phase balancing strategies is presented as a combinational strategy. In order to solve the proposed optimization problem, Nelder Mead algorithm combined with a bacterial foraging algorithm (BF–NM) is used based on a fuzzy multi-objective function. The proposed method allows for the simultaneous execution of reconfiguration and phase balancing while minimizing the interruption cost of rephasing in addition to eliminating network unbalancing and reducing neutral current and network losses. To demonstrate the efficiency of the BF–NM algorithm, its performance is compared with bacterial foraging (BF), particle swarm optimization (PSO), genetic and immune algorithms (GA and IA). The proposed method is applied to the IEEE 123-bus test network for evaluation. The simulation results confirm the efficiency of the method in reducing the system costs and network phase balancing. [Copyright &y& Elsevier]

Abstract: Accurate monitoring and estimating the state of the distribution system poses an immense challenge to power engineering researchers because of bidirectional distribution system. This paper is executed in two stage methodology. The initial stage is to identify the optimal location for the installation of monitoring instrument with minimal investment cost through DE and PSO. The second stage is to estimate the bus voltage magnitude where real time measurement is measured through identified meter location which is more essential for decision making in DSCADA. The hybrid Intelligent technique is applied to execute the above two algorithms. The algorithms are tested with IEEE and TNEB benchmark systems. [Copyright &y& Elsevier]

Abstract: This paper presents a heuristic optimization methodology, namely, Bacterial foraging PSO-DE (BPSO-DE) algorithm by integrating Bacterial Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving non-smooth non-convex Dynamic Economic Dispatch (DED) problem. The DED problem exhibits non-smooth, non-convex nature due to valve-point loading effects, ramp rate limits, spinning reserve capacity, prohibited operating zones and security constraints. The proposed hybrid method eliminates the problem of stagnation of solution with the incorporated PSO and DE operators in original bacterial foraging algorithm. It achieves global cost by selecting the bacterium with good foraging strategies. The bacteria with good foraging strategies are obtained in the updating process of every chemo-tactic step by the PSO operator. The DE operator fine tunes the solution obtained through bacterial foraging and PSO operator. A 3- and 7-unit systems for static economic dispatch, a 26-bus, 6-generator test system and an IEEE 39-bus, 10-unit New England test systems are considered to show the effectiveness of the proposed method over other methods reported in the literature. [Copyright &y& Elsevier]

Abstract: In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. [Copyright &y& Elsevier]

Abstract: This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and thyristor controlled series capacitor (TCSC) in a multimachine power system. The coordinated design problem of PSS and TCSC over a wide range of loading conditions is formulated as an optimization problem. The bacterial swarm optimization (BSO) algorithm is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and TCSC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial swarm based coordinated controllers gives robust damping performance over wide range of operating conditions, and different disturbances in compare to optimized PSS controller based on BSO (BSOPSS) and optimized TCSC controller based on BSO (BSOTCSC). Moreover, the results are compared to the results obtained using the bacteria foraging (BF) and particle swarm optimization (PSO) to show the effectiveness of using BSO to attain a global optimal solution of the proposed coordinated design problem. [Copyright &y& Elsevier]

Abstract: When a system is restored after an extended outage, the demand is greater than that was before the outage. The increase in the demand is due to the cold load pickup (CLPU) condition. The attempt to restore such load simultaneously will cause an excessive loading on the network elements in addition to limits violation. For these reasons, step-by-step restoration was suggested by researchers to overcome such problem. However, this way of restoration requires a long time for the complete system to be back to service. In this paper, simultaneous single step restoration of transmission and distribution networks is introduced with the help of distributed generation (DG) units optimally sited and sized by particle swarm optimization (PSO) algorithm. The aim of the proposed DG allocation algorithm is to reduce the distribution substation transformer loss of life due to overloading and to reduce the lost load and hence the additional power demand caused by CLPU. The capacity of the DG required is determined on the basis of additional power demand and the load diversity preserved. The proposed algorithm is applied to the Egyptian 66kV transmission network in the city of Alexandria including a 33-bus and a 69-bus, 11kV primary distribution feeders. [Copyright &y& Elsevier]

Abstract: This paper developed a fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm for solving the economic dispatch (ED) problems of thermal generators in power systems. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψd and ri of chaotic swarm optimization (CASO). The proposed method was applied to two cases of power systems. The simulation results demonstrate the applicability and effectiveness of the proposed algorithm to the practical ED problem. [Copyright &y& Elsevier]

Abstract: Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology. [Copyright &y& Elsevier]

Abstract: In this paper, three particle swarm optimization (PSO) based power system stabilizers (PSSs) are developed for three power systems. The system under study here is a power pool consisting of 3 power systems. System I represents the Egyptian power system, system II represents the Jordan and Syrian power systems, and system III for the Libyan power system, which are originally self standing and completely independent systems. As a matter of fact each of them should equipped with its own PSS. For this reason this work is started by designing an optimum power stabilizer for each of them standing alone. After which, the developed PSSs are firstly installed one at a time. Then the three PSSs are installed together in the interconnected power system and their effect on its dynamic performance is studied. As a test for stabilization efficiency, the detailed power system model is subjected to a forced outage of a 600-MW generator, which is the biggest unit in the pool, when it is fully loaded. This outage results in loosing of about 3% of the spinning capacity of system I and about 2% of the spinning capacity of the whole interconnected system. The obtained results show an improvement in the power pool performance accompanied with an improvement in the inter-area oscillation. [Copyright &y& Elsevier]