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

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

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

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.

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.

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.

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.

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.

This paper proposes a peer-to-peer energy trading system based on multi-objective game-theoretic optimisation for a clustered microgrid to find suitable sizes of distributed generations, including energy storage systems. The chosen architecture includes three different microgrids, and each microgrid can be a combination of solar panels, wind turbines and batteries, to meet the requirements of their residential loads. In the clustered microgrid, all microgrids are connected with one another, and also with the main grid, and they have the ability to perform both peer-to-peer and peer-to-grid energy trading based on load requirements and to reach the optimum value of the outlined objective function. To formulate a multi-objective function, two different criteria’ the annual profit and the loss of power supply probability are considered, and the Nash equilibrium-based game theory technique is modelled for clustered microgrids using a particle swarm optimisation algorithm to obtain suitable sizes of the players and payoff values. In the end, for each microgrid, suitable sizes of solar panels, wind turbines and batteries are proposed to meet the residential load requirements in the grid-connected mode. The outcomes of the multi-objective function are analysed and compared for two energy trading schemes, including peer-to-grid and peer-to-peer. The trends of objective functions and sizes of the players are analysed to verify the design of the proposed architecture.

Due to distinct differences in dynamic characteristics of large-scale photovoltaic plants (LS-PVPs) compared to conventional generation units, utilities face some new non-traditional operational problems, more specifically in terms of voltage stability. This paper analyzes the impact of various control methods of LS-PVPs on long-term (LT) voltage stability. A new corrective voltage control (CVC) scheme is proposed for the large-scale battery energy storage system (LS-BESS) to prevent LT voltage instability in a power system integrated with LS-PVPs. For this purpose, a new coupling between optimization and dynamic simulations is proposed to treat the problem as an objective function, which uses the particle swarm optimization (PSO) algorithm. This objective function is used to minimize investment costs and voltage drops of the system by determining the optimal number, location, and capacity of LS-BESSs. Furthermore, the effects of local voltage control modes and power priority settings of LS-BESSs are investigated. Besides, effectiveness of the proposed approach is validated through several scenarios using time-domain simulations of a more realistic system, namely the Nordic system. According to the results, upon using the proposed approach, negative effects of LS-PVPs on voltage stability and costs required for expanding and reinforcing the transmission network decreased. In addition, voltage collapse was prevented, investment costs of LS-BESSs were minimized, and costs required for reactive power compensation devices and communication between controllers decreased.

Hybrid distribution transformer (HDT) is a new kind of smart devices applied in active distribution network (ADN). It has the ability to provide reactive power compensation and ac voltage dynamic regulation. In the context of ADN, the allocation (placement and sizing) and control of HDT are, in fact, coupled issues. Considering this fact, this paper proposes a bi-level programming approach to achieve the optimal allocation of HDTs in a dynamic context. In the upper level, particle swarm optimization (PSO) is used to optimally determine the location and size of HDT installations, with the objective of minimizing HDT investment cost. While in the lower level, model predictive control (MPC) is utilized to design the multi-HDTs’ cooperative control scheme, with the objective of dynamical reactive power optimization. The key feature of this method is that it integrates the allocation and control problem, thus achieving the optimal allocation in a dynamic context. The proposed method is validated using the IEEE 33-bus and 123-bus test system. The results show that it considerably reduces investment cost and improve voltage profile.

As a key component of an alternating current (AC) multimicrogrid (MMG), an energy management system must gather the necessary information and coordinate the corresponding equipment to optimize the economic benefits. To achieve this goal, this paper proposes a bilevel energy optimization model. The lower level model considers the real-time electricity price of distribution networks (DNs) to optimize the operation cost of each grid-connected submicrogrid (SMG). The upper level constructs an MMG optimization model based on graph theory, which optimizes the energy transmission path among the SMGs by considering the transmission losses. In this manner, the upper level model can further reduce the economic cost of an MMG by energy trading among the SMGs. To solve this model, a nested Jaya–Dijkstra algorithm is customized. Moreover, a Jaya algorithm with nonlinear variation capability (NVCJA) is developed to avoid the premature convergence problem and enhance the solution precision. The performance of the bilevel optimization strategy is evaluated considering a typical AC MMG system. For the proposed NVCJA, the convergence time is 24.07%, 39.47%, 37.76%, and 48.77% less, and the standard deviation is 12.97%, 28.71%, 23.89% and 35.92% lower than those for the Jaya, genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms, respectively. The analysis results show that the proposed optimization algorithm exhibits a high operating speed and stability. The economic cost of the proposed bilevel optimization model is 19% and 29% lower than those of the hierarchical optimization and linear relaxation optimization models, respectively. The test results show that the proposed optimization model can effectively enhance the economic benefits of an MMG.

This paper presents an approach to automatically analyse events on electric power systems using data from phasor measurement units installed at low voltage level. The proposed approach is based on the application of multiresolution analysis through the wavelet transform, decomposing signals into functions of both time and frequency domains, aiming to detect and characterize systemic events. Detection and classification algorithms are designed to recognize systemic events according to the energy extracted from wavelet detail coefficients, taking into account decomposition levels, thresholds and decision variables which are set using a particle swarm optimization algorithm. Numerical results, validated with low voltage phasor measurements of the Brazilian and Chilean power systems, show that the approach can be used effectively for event detection and classification.

Made available in DSpace on 2021-06-25T15:04:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Instituto Nacional de Ciencia e Tecnologia de Energia Eletrica - Brazil (INCT-INERGE) ENEL The aggregator units are essential in implementing smart grids since they are responsible for connecting smart meters installed on the consumers and ensuring exchanging information with the utility control centers. This paper presents a methodology to allocate these aggregators in low voltage distribution systems with solar photovoltaic generation. The proposed method is composed of two stages. In the first one, the aggregators are allocated considering the minimization of the distance to the remote terminal unit and their installation costs using a method based on Particle Swarm Optimization. In the second stage, the smart meters are connected to each allocated aggregator using a graph method. These connections are performed, taking into account a function, which depends on the distance between the meters and the load density within the aggregator coverage area. The proposed methodology was tested using a 400 V low voltage distribution system with 45 residential loads and 12 photovoltaic generators. The results showed that the proposal allocates aggregators at points where the consumer groups? load curves into the aggregator coverage area are better characterized. The values of the average voltage profile and the system?s power losses in each aggregator coverage area presented an excellent match with values obtained with the test system without installing aggregators. From this characterization, it is possible to get useful information for operational planning and expansion planning of low voltage networks with photovoltaic generators by aggregation areas. Fed Univ ABC, Engn Modeling & Appl Social Sci Ctr, Santo Andre, SP, Brazil Sao Paulo State Univ UNESP, Dept Elect Engn, Ilha Solteira, SP, Brazil Sao Paulo State Univ UNESP, Dept Elect Engn, Ilha Solteira, SP, Brazil FAPESP: 2015/21972-6 FAPESP: 2019/00466-6 CAPES: 001 CNPq: 310299/2020-9 CNPq: 440088/2019-4 CNPq: 422044/2018-0 ENEL: PEE-00390-1062/2017 ENEL: P&D-00390-1083-2020_UFABC ENEL: ANEEL 001-2016

Accurate medium- and long-term power consumption forecast is the premise and foundation of secure scheduling and layout planning of power system, which are conducive to the coordinated development of various power generation channels so that renewable energy generation can be improved. In view of this problem, this paper analyzes power consumption intensity in different regions in China and proposes a modified grey model with novel initial condition to enhance its forecast accuracy. Influence of initial condition in grey model is analyzed synthetically by several commonly used models, and the Inverse Square Root Unit (ISRU) function with adjustable parameters is defined as the novel initial condition to better reveal the characteristics of data growth. Particle Swarm Optimization (PSO) algorithm is adopted to calculate the introduced coefficients. Using data available from National Bureau of Statistics of China from 2007 to 2014, consumption data of 31 provinces (municipalities) from 2015 to 2018 is forecasted to attest the efficiency and adaptability of ISRU-GM(1,1). Forecast results are compared with other improved prediction models as well. Potential power consumption ability in China in the next few years from provincial perspective is studied, which guides the layout planning of power system.

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.

In this paper, a Wave Energy Conversion (WEC) model consisting of a Savonius type wave turbine plus DC generator, Battery Energy Storage System (BESS), inverter, and Inductance-Capacitance-Inductance (LCL) filter is designed. In this study, the optimization techniques are used to maximize Savonius type wave turbine self-efficiency considering different factors such as the height, depth, length, and time period of the wave. Four meta‐heuristic optimizations are competing in this article for obtaining the optimum parameter design based on the output electrical power of the wave. These are Whale Optimization Algorithm (WOA), Artificial Immune System (AIS), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) algorithm. The comparative results verify the reliability of WOA in maximizing the overall electrical output power from the wave turbine while confining with the constraints. Moreover, an efficient control system is designed to operate in Islanded Mode (IM) and Grid-Connected (GC) operations according to the matching between the maximum estimated output power from the WEC system and the system load. A proposed control scheme is performed for adjusting the system frequency and voltage in islanded mode operation and ensuring active power-sharing in grid-connected mode. The islanded control scheme consists of three levels; droop control, inner voltage, and current control, outer control based on model predictive control (MPC) to maintain the system frequency and voltage to their nominal values. WOA is also proposed to improve the performance index by optimally evaluate the control signal and tracking error weighted parameters. The GC control scheme is based on the Active/Reactive Power (P/Q) controller to ensure active power-sharing. The effectiveness of the proposed controller has been verified by considering system parameters uncertainties.

Tripping time and optimal overcurrent coordination are nowadays one of the main power system protection concern, due to the high penetration of renewable energy sources in the electrical power network. In this article, optimal coordination scheme has been developed using nonstandard tripping characteristics for the Over Current Relays (OCRs) in a power network connected to a Photovoltaic System (PV). Considering the impact of Distribution Generation (DG) on fault currents and locations on the network, this paper presents and verifies an optimal coordination scheme based on two optimization methods, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The optimal coordination scheme aims to improve the sensitivity and reliability of the protection system by using a nonstandard tripping characteristic to reduce the operating time of OCRs. A specific case study, IEEE-9 bus connected to PV system is developed and presented using Industrial software (ETAP) and the results of the proposed optimal coordination scheme is compared to conventional characteristics in the literature. The proposed nonstandard OCR coordination scheme and optimization algorithms are evaluated using different fault and power network model scenarios. The results indicate that the proposed optimal nonstandard scheme successfully reduce the total operating time of OCRs and improve the relay sensitivity during minimum and maximum fault scenarios.

Achieving the maximum economic profitability is a priority for microgrid developers. However, although economic indicators usually dominate the business decision making, rarely numerical indicators are fully able to capture the entire sociopolitical, technical and geographical circumstances affecting the business environment, especially in rural areas of developing countries. Typical planning approaches achieve a single solution, or a set of solutions in multi-objective approaches, and near-optimal solutions are usually discarded even when they may better fit the specific multi-faceted circumstances of a project. In this paper, we propose a multi-objective approach that not only calculates the traditional Pareto-frontier but also compiles near-optimal solutions that enlarge the options portfolio for microgrid developers. The proposed iterative approach stores all the simulated solutions, and post-processes them to provide the developer with multiple design options (MDO). A modified version of Multiple-Objective Particle Swarm Optimization (MDO-MOPSO), with improved convergence criteria based on quadratic mean of the crowding distances and spread, is developed and used. A numerical case study of a Kenyan hybrid microgrid using real data confirms that near-optimal solutions can correspond to extremely different design solutions, even ± 100 % w.r.t. the Pareto-efficient ones, with only very limited disparities in the economic objective functions. The results, supported by a Key Performance Indicator (KPI) analysis, based on diversity measures, show that MDO methodology can successfully support the business decision making and help developers size microgrids considering several nearly-equivalent sizing options.

This paper proposes a procedure to determine the optimal railway power conditioner (RPC) size in an autotransformer-fed traction power supply system, using the current-based Newton-Raphson power flow calculation in combination with the particle swarm optimisation (PSO) method. The PSO is chosen because of searching capability for highly non-linear objective functions, and having better performance in this application comparing to the Genetic Algorithm (GA). The optimisation-based RPC steady-state model is also introduced in the power flow calculation. The optimal capacity is designed to accommodate an overall range of traction load, regardless of overload tolerance and a period of overload; moreover, it is designed on the basis of partial compensation. The East Corridor commuter rail line in Denver is used as a case study for the proposed procedure. The power supply of the line is modified to have more severe power quality problems than the original ones, and two different load levels are simulated for optimal size searching. The results show that the optimal power capacities were successfully obtained with the power factor and voltage unbalance factor within the permissible levels; the optimal values were obviously smaller in comparison with the full compensated ones.

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.

This paper aims to minimize the feeder power flow deviation from scheduled value by balancing generation and load demand within the distribution system under various local disturbances. The available distributed energy resources (DER) can be properly utilized to control the feeder flow which gains significant advantages (i) to support load frequency control (LFC) problem at transmission level (ii) reduces unscheduled interchange in feeder which is priced higher (iii) increased reliability of the grid. The deviation in feeder flow is reduced by properly coordinating the power output from DER such as fuel cell, battery storage system and responsive loads (demand response) through an adaptive fuzzy controller with optimally tuned gains using particle swarm optimization. The level of participation from each DER units are decided using fuzzy inference system based on the availability of resources and level of disturbance. The photovoltaic array and wind turbine are also considered in the system and always operated at the maximum power point. The proposed method is examined on IEEE 37 bus system implemented in the MATLAB based Power Analysis Toolbox (PAT) for performing time-domain dynamic analysis. It is shown that the proposed adaptive controller exhibits better performance for various disturbance conditions.

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.

Reactive power generation has been commonly used for power loss minimization and voltage profile improvement in power systems. However, the opportunity cost of reactive power generation should be considered since it affects the frequency control capability of the generator to some degree. This paper proposed a distributed nonlinear control based algorithm to achieve the optimal reactive power generation for multiple generators in a power grid. The reactive power control setting update for each generator only requires local measurement and information exchange with its neighboring buses. It is demonstrated that the proposed algorithm can reduce the non-convex objective function monotonically till convergence and achieve comparable solutions to the centralized technique: particle swarm optimization with faster convergence speed. The proposed algorithm has been tested on the IEEE 9-bus, 39-bus and 162-bus systems to validate its effectiveness and scalability.

To meet ever increasing load demand in a sustainable way, reinforcement of photovoltaic (PV) array, wind turbine (WT) and capacitor bank in distribution network is proposed in this paper. A comprehensive planning model is presented to determine location and required installation capacity of multiple PV array, WT and capacitor units in an electric power distribution network under heavy load growth situation. Intermittent power generation of renewable energy sources (RESs) are quantified with suitable probability distribution functions and incorporated in the planning model. The planning approach considers several welfare areas in the distribution systems, viz., increment of profit margin, reduction of carbon-di-oxide emission, minimization of distribution power losses, enhancement of voltage stability level and improvement of the network security considering power flow, voltage limit, line capacity, RES penetration, capacitor penetration and utility economy constraint. Non-dominated sorting based multi-objective particle swarm optimization algorithm along with fuzzy decision making criteria is used to find the best allocation alternative for mix RES and capacitor planning problem. The effectiveness of the proposed model has been tested on a typical 28-bus Indian rural distribution network. The results show that more efficient techno-eco-environmental optimization can be obtained from combined RES and capacitor planning model.

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.

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.

This paper proposes a practical formulation for the non-convex economic dispatch problem to consider multi-fuel options, ramp rate limits, valve loading effect, prohibited operating zones and spinning reserve. A new optimization algorithm based on the θ-bat algorithm (θ-BA) is suggested to solve the problem. The θ-BA converts the Cartesian search space into the polar coordinates such that more search ability would be achieved. According to the complex, nonlinear, and constrained nature of the problem, a new self-adaptive modification method is proposed. The proposed modified θ-BA (θ-MBA) is constructed based on the roulette wheel mechanism to effectively increase the convergence of the algorithm. The high ability and satisfying performance of the proposed optimization method is examined on IEEE 15-unit, 40-unit and 100-unit test systems.

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.

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.

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.

With the rapid growth of centralized utilization of large-scale renewable energy, to improve the stability of power grid and reduce operating costs, microgrid and renewable distributed generation (DG) has become the key direction of China. For a microgrid integrated with wind turbines, photovoltaic (PV) and micro-gas-turbine, the installed capacities need to be configured rationally to achieve economical and reliable power supply. In this paper, a structure of a microgrid based on multi-agent system (MAS) is established, and a game-theory-based optimization model is presented for the capacity configuration of these agents. The economic interests between the agents and their actions are analyzed by the game model. Besides, the interactions between microgrid and power grid, and the uncertainty of wind power and solar power are taken into account. The Nash Equilibrium of the game is worked out by particle swarm optimization, as the reference for the configuration of the agents. In the end, a wind/solar/gas microgrid model is used for the numerical tests to analyze and compare the effectiveness of the game optimization. The results show that under cooperative game, the investors’ profits of wind turbines, PV and micro-gas-turbine increase by 6%, 19%, 88% compared with the non-cooperative game, while the total configured capacities decrease by 10%.

The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.

Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The technical route is to employ the electricity data from honest users as the training samples, and the autoencoder can learn the effective features from the data and then reconstruct the inputs as much as possible. For the anomalous behavior, since it contributes little to the autoencoder, the detector returns to a comparatively higher reconstruction error; hence the theft users can be recognized by setting an appropriate error threshold. To improve the feature extraction ability and the robustness, the sparsity and noise are introduced into the autoencoder, and the particle swarm optimization algorithm is applied to optimize these hyper-parameters. Moreover, the receiver operating characteristic curve is put forward to estimate the optimal error threshold. Finally, the proposed approach is evaluated and verified using the electricity dataset in Fujian, China.

Unbalanced loads create unacceptable voltage unbalance (VU) in low voltage (LV) three-phase four-wire distribution networks with unbalanced rooftop photovoltaic (PV) systems. This paper proposes a mitigation strategy for restricting the VU depending on the distributed batteries that included in grid interfaced rooftop PV systems. Moreover, it introduces a dynamic control model based on two PI-controllers for determining the required batteries’ compensating currents and ensuring equality resultant stress current on each bus. It overcomes three existing challenges; the inequality stresses currents on the distributed batteries along the feeder, the insufficient size or non-existing of the distributed batteries, and satisfies the same stress among batteries on the same bus. For investigating the influence of equal phase magnitude batteries’ compensating current at the same bus on the whole distribution network (DN). Particle Swarm Optimization (PSO) and Bat optimization techniques are used to determine the optimum self and mutual sharing co-efficients of each battery. The limits of the distributed batteries' size are taken into consideration in the optimization process. The proposed dynamic model is implemented using MATLAB software. The simulation results show the effectiveness of the proposed control scheme for mitigating the VU on the distribution feeder’s buses at three scenarios; different loads, PVs, and states of charge of batteries. Moreover, the batteries’ compensating currents are presented to examine the implementation feasibility.

As distributed energy (DE) and storage devices being integrated into microgrids (MGs), demand side management (DSM) is getting more and more complicated. The real-time pricing (RTP) mechanism based on demand response (DR) is an ideal method for DSM, which can achieve supply–demand balance and maximize social welfare in the future. This paper proposes a hierarchical market framework to address RTP between the power supplier and multi-microgrids (MMGs). Firstly, an expectation bilevel model is proposed to adjust the energy scheduling of MMGs, including uncertain loads, multi-energy-supply and storage devices,etc. In the proposed bilevel model, the upper level aims to maximize the profit of the power supplier, while the lower level is formulated to maximize the expectation of total welfares for MMGs. Then, the lower level is transformed into a deterministic optimization problem by mathematical techniques. To solve the model, a hybrid algorithm-called distributed PSO-BBA, is put forward by combining the particle swarm optimization (PSO) and the branch and bound algorithm (BBA). In this algorithm, the PSO and BBA are employed to address the subproblems of upper and lower levels, respectively. Finally, simulations on several situations show that the proposed distributed RTP method is applicable and effective under uncertainties, and can reduce the computational complexity as well. The results show that the hierarchical energy dispatch framework is not only more reasonable but also can increase the profits of power suppliers and the welfare of MGs effectively.

Gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution problems have led to the use of renewable energy sources (RESs) such as wind turbines (WTs) and solar photovoltaic (PV) cells in distribution networks all around the world. The uncertain nature of these sources, along with network power demands, necessitates probabilistic evaluation to extract results with high applicability and efficiency. Distribution network flexible AC transmission system (D-FACTS) devices such as distribution static compensator (D-STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible. This paper presents a probabilistic technique for optimal allocation of the D-STATCOM, considering the correlation between uncertain variables. The proposed solution method helps to mitigate expected active power losses, improve expected voltage deviation index (VDI), and decrease D-STATCOM expected installation cost for radial/mesh distribution networks. The k-means based data clustering method (DCM) and Latin hypercube sampling (LHS) method are used for probabilistic evaluation of distribution networks. In addition, the particle swarm optimization (PSO) algorithm is employed as the optimization tool. The proposed algorithm is applied to the IEEE 69 node test network, and the results are compared with the Monte Carlo simulation (MCS) method. Also, the efficacy of the proposed study method has been investigated for a real meshed distribution network.

In recent years, nonlinear load and inverter-based distributed generation (like photovoltaic and fuel cells) have been increasing rapidly. This type of DGs can amplify and propagate the harmonic level of network. Shunt capacitor banks and DG units allocation without considering harmonic limits will increase harmonic pollution. In this paper the biogeography-based optimization (BBO) algorithm is applied in simultaneous optimal sitting and sizing of inverter-based DGs and capacitor banks regarding multi-level and nonlinear loads. The objective of problem are reduction of active and reactive power loss, reduction of purchased energy from transmission line and improvement of voltage profile considering equal and unequal constrains. Also, effect of total harmonic distortion (THD) constraint according to IEEE 519 standard has been investigated in the objective function which is one of the main advantages of the proposed method. The proposed method is tested on IEEE 33 bus and IEEE 69 bus radial distribution systems (RDS). Comparing BBO method results with particle swarm optimization (PSO) and genetic algorithm (GA) results will indicate the high capability of the proposed method in simultaneously optimal sitting and sizing of DGs and capacitors.

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.

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.

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.

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.

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.

Multi-area dynamic economic dispatch determines the optimal scheduling of online generator outputs and interchange power between areas with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators, tie line constraints, and transmission losses. This paper presents quasi-oppositional group search optimization for solving multi-area dynamic economic dispatch problem with multiple fuels and valve-point loading. Group search optimization (GSO) inspired by the animal searching behavior is a biologically realistic algorithm. Quasi-oppositional group search optimization (QOGSO) has been used here to improve the effectiveness and quality of the solution. The proposed QOGSO employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. The QOGSO is tested on two multi-area test systems having valve point loading and mult-fuel option. Results of the proposed QOGSO approach are compared with those obtained from group search optimization (GSO), biogeography-based optimization (BBO), gravitational search algorithm (GSA), differential evolution (DE) and particle swarm optimization (PSO). It is found that the proposed QOGSO based approach is able to provide better solution.

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.

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.

The present paper formulates the state space modelling of doubly fed induction generator (DFIG) based wind turbine system for the purpose of small-signal stability analysis. The objective of this study is to discuss the various modes of operation of the DFIG system under different operating conditions such as three phase fault and voltage sags with reference to variable wind speed and grid connection. In the present work, teaching learning based optimization (TLBO) algorithm optimized proportional–integral (PI) controllers are utilized to control the dynamic performance of the modelled DFIG system. For the comparative analysis, TLBO based simulated results are compared to those yielded by particle swarm optimization (PSO) method for the same DFIG model. The simulation results show that the proposed TLBO based PI controller effectively works in minimizing the damping phenomena, oscillation in rotor currents and fluctuation in electromagnetic torque for the studied DFIG model. It is also observed that TLBO is offering better results than the PSO for the dynamic performance analysis of the studied model.

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.

In this paper, the Fault Current Limiter (FCL) is used to restore the coordination between the protection devices in distribution systems with high-level of DG penetration. The FCL allocation may be described as an optimization problem involving multiple objective functions which are contradictory and of different dimensions. So, it is formulated as a multi-objective constrained nonlinear programming problem. The interaction among different objectives gives rise to a set of compromised solutions, largely known as the Pareto-optimal solutions. The objectives are to simultaneously minimize: the increase in fault current levels due to the penetration of DG, voltage sag, and the total cost (size) of required limiters. The optimization problem is solved using Particle Swarm Optimization (PSO). The method is applied to two distribution test systems. Effects of different operating factors are assessed. Comparative analysis of results is provided.

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.