Bees algorithm technique for loss minimization in power transmission network using Static Var Compensator

This paper presents an application of a flexible AC transmission system (FACTS) device as additional control in reactive power (VAR) optimization problem and analyzes the impact on system loss minimization. The adopted FACTS device is a new static VAR compensator (SVC). The general SVC consists of internal devices, and local devices, and the operation mode of the general SVC model is voltage control mode only. The new SVC model controls local, remote, internal and external devices simultaneously. It has two modes of operation: (1) Voltage control mode; and (2) Q control mode. This paper analyzes the functions of the new SVC, and implements them in the practical power systems. The impact of VAR optimization with new SVC model on loss minimization is also discussed by comparing it with VAR optimization with general SVC model as well as VAR optimization without SVC model.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

Flexible AC transmission systems (FACTS) technology is widely adopted and utilized to maintain the performance of power systems. However, the improvements of power system performance achieved by FACTS devices depend on the right sizing and allocation of such devices. For technical and economic considerations, a FACTS device's location and size should be selected very carefully in order to maximize its benefits to the power system. In this paper, the sizing and location of a static VAR compensator (SVC) are optimally determined using a new optimization technique called lightning attachment procedure optimization (LAPO). The optimal allocation of the SVC is determined regarding the improvement of voltage deviation index and the reduction of total active power losses. The system is optimized in two cases: once without SVC installation in order to find out the optimal settings of the system that achieve the objective functions, and another time with SVC installation to determine its optimum sizes and locations by which the required objective functions are achieved. Then the system performance is analyzed after optimization with and without SVC devices to show the impact of the optimum sizing and location of the SVC on the system. The study is validated using the standard IEEE 30-bus system, while the developed LAPO is performed by MATLAB M-Files and the system performance analysis in different cases is performed by NEPLAN software.

Robust SVC controller design and analysis for uncertain power systems

The demand of quality & reliable power by the consumers is increasing day by day in a tremendous manner. Everyone needs good quality of power with out any disturbances or interruptions. Also due to large no of present day consumers, the power system needs a boosting mechanism in order to capitalize the complexity of power system. So a reliable technology of FACTS(Flexible AC Transmission System) is introduced in the power system to enhance the flexibility of power grids. FACTS devices are power electronics based controllers & improve the system performance by shunt or series compensation. They injects or absorbs reactive power to/from the power system at the time of need. In this paper a shunt type of FACTS controller such as SVC(Static Var Compensator) is considered for stability enhancement & loss minimization in a nine bus power system. SVC consists of TCR(Thyristor Controlled Reactor), TSC(Thyristor Switched Capacitor), a fixed capacitor connected parallel to the line & filter elements for harmonic reduction. Here the enhancement of voltage stability & loss minimization is analyzed in terms of voltage magnitude variation & overall real & reactive power losses at each bus w.r.t the location of installation of SVC. Firstly Load flow study carried out for an uncompensated system & then for SVC compensated system by changing the location of installation of SVC from bus1 to bus9. And also thereby specifying the optimal location of SVC by analyzing the load flow data in terms of voltage magnitude variation & overall losses. All the performance analysis has been performed using MATLAB/SIMULINK software.

This paper primarily focuses on an investigative study on allocating Flexible AC transmission system (FACTS) devices on radial distribution systems. The FACTS device that is used in this paper is static var compensator (SVC). In this paper, SVC is placed on each bus except substation bus and study the results to find the best location for allocation. A design of SVC was done by fixed reactance method. The total power loss of network and voltage of nodes have been analyzed for each allocation and the sequences of each implementation have been compared to find the best location of SVC. The proposed algorithm has been executed on IEEE 33-bus and 69-bus standard test systems.

In the power system an unexpected increment of load demand has leads to the system to experience stressed condition. This phenomenon has led to voltage profile detract below the permissible limit. These Voltage profile in a stressed power system could be enhanced by having commensurate reactive power compensation. And this reactive power requirement is furnish through fast acting and self-commutated FACTS devices. This work presents an application of Artificial Bee Colony (ABC) in optimizing the rating of Static VAR compensator (SVC) for voltage stability augmentation in power system. The reactive power provided by SVC is randomly distributed and it is the control variable in ABC algorithm and produces near optimal solutions. The proposed approach has been evaluated with three different objective functions namely, loss minimization, voltage profile and stability enhancement. The IEEE 14-Bus test system is used as test systems to demonstrate the applicability and efficiency of the proposed method. All the above analysis is carried out in MATLAB software environment. Results for the proposed method are presented and interpreted.

Flexible alternating current transmission systems (FACTS) are power electronics-based static controllers which control the transmission network parameters to improve system performance. They are used to solve various power system problems which include; voltage stability, voltage deviation and power losses. However, these devices need to be optimized to achieve desired results. This paper presents the application of firefly algorithm (FA) to optimize Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) for voltage profile improvement and power loss minimization on IEEE 14 bus system. The results showed that the active and reactive power losses were minimized by 8.7 and 10.1%, respectively with FA optimized SVC and 10.7 and 14.7%, respectively with FA optimized STATCOM. Therefore, the optimal allocation of SVC and STATCOM improve the performance of the network which indicates the effectiveness of FA in optimizing SVC and STATCOM.KeywordsFACTSSTATCOMSVCFAVoltage magnitudePower loss

The surge in demand for electricity has driven the power system to operate at a lower efficiency level. With the many actions taken to improve the electric grid, voltage unbalance and line overload have become challenging problems to address. Voltage control can be determined in generation, transfer, and electricity usage. When the power system is under stress, reactive current unbalance significantly contributes to voltage instability. To maximize the efficiency of a power system, flexible transmission systems system (FACTS) devices must be used most effectively and feasible. FACTS devices like Static VAR Compensators (SVCs) and Static Synchronous Compensators (STATCOMs) can reduce instability in heavily burdened lines, resulting in lower system losses and more excellent network reliability. In this paper, FACTS controllers such as SVC and STATCOM are incorporated in the Kurdistan power system simulated by using PSAT software based on MATLAB for the improvement of voltage profile, minimization of the system losses, and cost of the system. Also, the graphical comparisons of the results are presented to investigate the effectiveness of the proposed methods.

The proposed work demonstrates the application of Bee Colony Optimization BCO technique for the tuning of Fractional Order Proportional-Integral-Derivative FOPID controller for Three Interacting Tank system. FOPID controller parameters are composed of proportionality constant, integral constant, integral order, derivative constant and derivative order. Grunwald-Letnikov definition is used for the defining the derivative controller and Oustaloup's filter technique is used for the approximation of the function. Tuning FOPID controller parameters is more complicated as it involves a five dimensional search. Tuning is effected using an evolutionary optimization technique, the bee colony optimization so as to minimize the Integral Time Absolute Error ITAE. The proposed technique is used to control three interacting tank process. The proposed FOPID controller tuned using Bee colony optimization technique may serve as an alternative for the tuning of the fractional order controllers.

This paper introduces a smart coordinated allocation of distributed generation (DG) units, shunt-connected capacitors (SC), and static VAR compensators (SVC) for power loss minimization. The problem is formulated as a mixed-integer non-linear programming and is solved using a BONMIN solver embedded in General Algebraic Modeling System (GAMS). The proposed method’s evaluation was performed on the IEEE 33-bus test system, where three cases were discussed. GAMS/BONMIN was used to find optimal locations and power outputs of DGs in the first case. Simulation results, in this case, showed that losses were reduced by 48.42% with one DG unit, 58.58% with two DG units, and 65.44% with three optimally allocated DG units. In the second case, the proposed method was used to coordinate DGs and SCs, while in the third case, coordinated allocation of DG and SVC was investigated. The results showed that SC/SVC coordination further reduces active power losses. With three SCs allocated, the reduction in the active power losses was 93.5% compared to the base case, and 82.18% compared to losses in the case where only 3 DGs are connected, while in the case of three SVC allocated, it was 93.85% compared to the base case, and 82.21% compared to the case where 3 DGs are connected. The second and third cases also showed that the coordination of SC/SVC leads to an improved voltage profile, where the bus voltages are close to the acceptable range, and voltage fluctuation is significantly reduced. The comparison between different non-linear programming solvers embedded in GAMS for solving optimal power flow was also presented.

Electric power systems are allowing higher penetration levels of renewable energy resources, mainly due to their environmental benefits. The majority of electrical energy generated by renewable energy resources is contributed by wind farms. However, the stochastic nature of these resources does not allow the installed generation capacities to be entirely utilized. In this context, this paper attempts to improve the performance of fixed-speed wind turbines. Turbines of this type have been already installed in some classical wind farms and it is not feasible to replace them with variable-speed ones before their lifetime ends. A fixed-speed turbine is typically connected to the electric grid with a Static VAR Compensator (SVC) across its terminal. For a better dynamic voltage response, the controller gains of a Proportional-Integral (PI) voltage regulator within the SVC will be tuned using a variety of optimization techniques to minimize the integrated square of error for the wind farm terminal voltage. Similarly, the controller gains of the turbine’s pitch angle may be tuned to enhance its dynamic output power performance. Simulation results, in this paper, show that the pitch angle controller causes a significant minimization in the integrated square of error for the wind farm output power. Finally, an advanced Proportional-Integral-Acceleration (PIA) voltage regulator controller has been proposed for the SVC. When the PIA control gains are optimized, they result in a better performance than the classical PI controller.

Many studies are performed by researchers to estimate energy demand but the bees algorithm (BA) technique has never been used for such a study. This paper presents application of the BA technique to estimate total energy demand in Iran, based on socioeconomic indicators. The BA demand estimation models (BA-DEM) are first developed in two forms (exponential and linear) in order to estimate energy demand based on population, gross domestic product, import and export data. Then, two scenarios are designed for forecasting each socioeconomic indicator in the years 2006–2030. Finally, these two scenarios are used to forecast energy demand up to year 2030 using demand estimation models. Energy consumption in Iran from 1981–2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). In order to show the accuracy of the BA, obtained results of BA-DEM models are compared with particle swarm optimization and genetic algorithm demand estimation models which are developed for the same problem.

Due to drastic changes in society, leads to enhance the electricity demand in the entire world and increase the gap between generation and demand. This is one of the significant problems for electrical engineers to reduce the imbalance between the load and demand. For this purpose, add new generating units or use automation technology for better utilization of generating units which increases the use of fast and efficient algorithms. In this paper, real power losses minimization is treated as objective to optimize the active power generation, and it is achieved with the application of Bat Algorithm (BA). Through this nature-inspired algorithm, the control parameters such as real power generation at the PV buses, & the rating of Static VAR Compensator (SVC) are controlled. The output is more effective than other optimization techniques present due to the intrinsic properties of BA. The proposed BA for the single objective optimization is applied to the IEEE 30bus system.

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.