Real Power Loss Minimization Research Articles

Optimal Allocation of Capacitor Banks using Genetic Algorithm and Sensitivity Analysis

This paper presents a methodology for allocation of Bank of Capacitor (BC), based on the genetic algorithm (GA) and in the use of Sensitivity Analysis (SA). The AS is used to evaluate the candidate solutions directly instead of using a Optimal Power Flow (OPF), which is an iterative method. The proposed methodology was applied on three distribution systems, containing 34, 70 bus, respectively. Scenarios were analyzed for the insertion of two units of BC in each system. The proposed methodology AG - AS was compared with exhaustive methodology, solved by only OPF method and the gain in computational processing time is showed. As a main result, it was obtained the best locations for placement of BC units considering the minimization of real power losses, and consequently an improvement in the voltage levels in the buses of the system.

Solution of Optimal Reactive Power Dispatch by a Hybrid GSA-SQP Algorithm

The optimal reactive power dispatch (ORPD) is achieved by optimal coordination of the equipment which control the reactive power flows in the power system. The (ORPD) can be mathematically formulated as a nonlinear, static and large-scale optimization problem with constraints. This paper deals with solving the ORPD problem using a new hybrid algorithm consisting of gravitational search algorithm (GSA) and sequential quadratic programming (SQP). The performance of this hybrid algorithm for the ORPD problem is studied and evaluated on the standard IEEE 30-bus test system with two different objective functions, namely minimization of real power loss and voltage profile improvement. DOI: http://dx.doi.org/10.5755/j01.eie.22.3.15306

Genetic algorithm optimized impact assessment of optimally placed DGs and FACTS controller with different load models from minimum total real power loss viewpoint

This paper presents the impact assessment of optimally placed different distributed generations (DGs) with different load models (DLMs) (such as DG-1, DG-2, DG-3 and DG-4) and flexible alternating current transmission system (FACTS) controller like static VAR compensator (SVC) by employing genetic algorithm (GA) in a distribution power systems (DPSs) from minimum total real power loss viewpoint. Different DPS performance indices such as minimization of real power loss, minimization of the reactive power loss, improvement of the voltage profile, reduction of the short circuit current or MVA line capacity and reduction of the environmental greenhouse gases like carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen oxide (NOx) and particulate matters in an emergency e.g. under fault, sudden change in field excitation of alternators or load increase in DPSs are considered. A comparison among different DGs with DLMs (such as DG-1, DG-2, DG-3 and DG-4) and FACTS controller like SVC is presented in this paper by employing GA. The effectiveness of the proposed methodology is tested on IEEE 37-bus distribution test system (38-node system). This paper clarifies the fact that, among the four types of DGs considered, DG-2 and DG-4 types of DGs at different operating power factors and FACTS controller like SVC offer better DPS performance indices when power factors varies from 0.80 to 0.99 leading and lagging, respectively. It is revealed that DG-2 type DG and SVC gives better DPS performance indices as compared to DG-1, DG-3 and DG-4 types DGs with DLMs and SVC. It is observed that DG-2 with SVC is more reliable and efficient as compared to the rest types of DG like DG-1, DG-3and DG-4 with SVC.

Distribution Network Power Loss Minimization by Optimal DG Allocation

This paper presents a methodology based on analytical approach to optimally allocate (site and size) the Distributed Generation (DG) units(s) in radial power distribution networks for minimizing the real power losses. The proposed method requires only the results of base case load flow to determine the optimal size of DG unit(s) required at each bus. For this, suitable analytical expressions have been proposed to determine the optimal size of DG unit(s) to cause total minimum real power loss in a given distribution network with their corresponding optimal location(s). Two cases with two scenarios comprising of DG type and number of DG units (single and multiple), respectively, are considered. The proposed method is applied to an IEEE 33-bus radial distribution test system. Results obtained by this proposed method validate the suitability and importance of appropriate DG allocation and also the number of DG units in power distribution networks.

Optimal Location of Unified Power Quality Conditioner in Distribution System for Power Quality Improvement

The increased use of nonlinear loads in distribution system is increasing the distortion in the voltage and current waveforms. Moreover, the distribution systems are inherently unbalanced. This paper presents Cuckoo Optimization Algorithm (COA) based Unified Power Quality Conditioner (UPQC) allocation in three phase unbalanced distribution network. The performance of UPQC is studied in terms of minimization of load disturbance during fault condition in the test systems, % reduction of total harmonic distortion and individual harmonics, minimization of real power loss, decrease in voltage unbalance and increase in cost savings during normal operating condition. The performance of COA is compared with other soft computing techniques to get the better results, i.e., better voltage profile, the optimal location and optimal number of UPQCs.

Chaos embedded krill herd algorithm for optimal VAR dispatch problem of power system

Chaotic krill herd algorithm (KHA) (CKHA) is proposed in this paper to solve the optimal VAR dispatch problem of power system considering either minimization of real power loss or that of absolute value of total voltage deviation or improvements of voltage profile as an objective while satisfying all the equality and the inequality constraints of the power system network. Detailed studies of different chaotic maps are illustrated. Among these, Logistic map is considered in the proposed technique to improve the performance of the basic KHA. The performance of the proposed CKHA is implemented, successfully, on standard IEEE 14- and IEEE 118-bus test power systems in which the control of bus voltages, tap position of transformers and reactive power sources are involved. The results offered by the proposed CKHA are compared to other evolutionary optimization based techniques surfaced in the recent state-of-the-art literature. Simulation results indicate that the proposed CKHA approach yields better optimization efficacy over some other recent popular techniques in terms of results offered, effectiveness, quality of solution and convergence speed.

A teaching learning based optimization technique for optimal location and size of DG in distribution network

DGs are placed for the purpose of real power loss minimization and voltage improvement in distribution network system. This paper presents a recent optimization technique, i.e. teaching learning based optimization (TLBO) technique for finding the optimal size and location of Distributed generation (DG) in radial distribution system (RDS). The optimal location and size of DG is analyzed considering voltage stability index as an objective function. The superiority of the proposed approach has been shown by comparing the results with GA and PSO methods in RDS. The comparison is done using system performances such as the real power loss and voltage profile of RDS. In this paper, performance analysis is carried out considering IEEE 33 bus and 69 buses as the test system.

Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework

Abstract Network reconfiguration and capacitor placement are useful options applied to reduce power losses and to keep voltage profiles within permissible limits in distribution systems. This study presents an efficient algorithm for optimization of balanced and unbalanced radial distribution systems by a network reconfiguration and capacitor placement. An important property of the proposed approach is solving the multi-objective reconfiguration and capacitor placement in fuzzy framework and its high accuracy and fast convergence. The considered objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems). Numerical results obtained by simulation show that the performance of the Hybrid Big Bang Big Crunch (HBB–BC) algorithm is slightly higher than or similar to other meta-heuristic algorithms.

Unified power flow controller based reactive power dispatch using oppositional krill herd algorithm

In power system, minimizing the power loss in the transmission lines and/or minimizing the voltage deviation at the load buses by controlling the reactive power is referred as optimal reactive power dispatch (ORPD). This paper presents an improved evolutionary algorithm based on oppositional krill herd algorithm (OKHA) for obtaining optimal steady-state performance of power systems. This article also proposes the effect of UPFC location in steady-state analysis and to demonstrate the capabilities of UPFC in controlling active and reactive power flow within any electrical network. To verify the effectiveness of KHA and OKHA, two different single objective functions such as minimization of real power losses and improvement of voltage profile and a multi-objective function that simultaneously minimizes transmission loss and voltage deviation have been studied through standard IEEE 57-bus and 118-bus test systems and their results have been reported. The study results show that the proposed KHA and OKHA approaches are feasible and efficient.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

Economic Analysis for different Types of DG Placement in Radial Distribution Network

The installation of distributed generation (DG) in the distribution network produces the benefits such as the reinforcement of grid, power quality improvement, voltage regulation, line loss reductions, enhanced utility structure reliability, congestion management, reduction in fuel costs and reserve requirements. To achieve these benefits, the DG should be properly sized and placed in the distribution networks. In this work the performance of various types of DG systems in the radial distribution network (RDN) for loss reduction and voltage profile improvement is compared by inserting them at the optimal location. In the restructured power system environment, the choice about the type of DG for connection is taken by their investors or GENCOS depending on the location, availability of main fuel, transport facilities and climatic situation. In developing countries the investors are more concern about the investment cost and profit obtained. Hence in this paper the cost profit factor analysis for the optimal placement of various types of DG is projected. Here, a new Distributed Generation Sitting Index (DGSI) ranking method is projected to find the optimum DG Size and location for real power loss minimization in radial distribution network. The proposed DGSI method is compared with the existing loss minimization method and Bus Voltage Sensitivity Index (BVSI) ranking method based on real power loss minimization, voltage profile improvement and cost profit factor analysis. To validate the effectiveness of the proposed DGSI ranking method it is tested on 15 bus RDN and IEEE 33 bus RDN. The results show the efficacy of the proposed method.

Технологическое решение проблемы регулирования напряжения в распределенной энергетике

The development of the distributed generation is a world tendency. Consumers of the distributed generation act as “active” participants at producing electric energy and regulating grid modes when achieving their own aims. This system requires development and introduction of technologies promoting the interests of all process participants. This research offers a solution to the task of voltage regulation within the distributed electric grids under condition of inconsistent interests of power consumers and distribution companies. This solution includes a system of decentralized multi-agent voltage regulation, which provides trade-off network voltage based on a uniform practice code for each regulator. This system may be implemented as a special device controlling power demand – DSM (Demand Side Management). Experiments were carried out in the Surgut’s small-sized distributed generation. A specially developed software enabled simulation aimed at minimum real-power losses in adjacent grid regions. Voltage regulation methods were compared based on results of simulation of typical grid modes with due regard to voltage regulation peculiarities dependent on the specified load curves. An optimal voltage mode determined with the developed software is close to global optimization. It increases grid power capacity without the need for a total grid observability.

A FIS-GA combined approach for transmission congestion management in restructured power system

A power system under goes frequent changes due to disturbances. If the power system survives after the disturbance it will be operating in a new steady state, in which one or more transmission lines may be over loaded. The over loading of the transmission line can be eliminated by generator rescheduling and/or load shedding. This paper proposes fuzzy inference system (FIS)-based algorithm for overload indication in transmission line. The system parameters such as line overload factor (OF) and transmission congestion distribution factors (TCDFs) are given as the FIS input. The output from the FIS indicates whether the line is overloaded or not. Then generator rescheduling/load shedding is done to relief the congested line. The effectiveness of the congestion clusters method for CM is discussed by formulating two objective functions. Rescheduling of generation is formulated as an optimisation problem with the objective of obtaining minimum cost and proposed objective of achieving minimum real power loss. Attractive features of genetic algorithm (GA) are used for solving the formulated problem with the objectives considered separately. The effectiveness of the proposed approach has been tested for 5 bus system, 30 bus systems in MATLAB environment.

Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

This paper proposes a Firefly algorithm based technique to optimize the control variables for simultaneous optimization of real power loss and voltage stability limit of the transmission system. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained optimization problem with an objective function integrating both real power loss and voltage stability limit. Transformers taps, unified power flow controller and its parameters have been included as control variables in the problem formulation. The effectiveness of the proposed algorithm has been tested on New England 39-bus system. Simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm for single objective of real power loss minimization and multi-objective of real power loss minimization and voltage stability limit maximization. Also, a classical optimization method known as interior point successive linear programming technique is considered here to compare the results of firefly algorithm for single objective of real power loss minimization. Simulation results confirm the potentiality of the proposed algorithm in solving optimization problems.

Reconfiguration with Simultaneous DG installation to Improve the Voltage Profile in Distribution Network using Harmony Search Algorithm

Due to dynamic nature of loads, total system load is more than its generation capacity that makes relieving of load on the feeders not possible and hence voltage profile of the system will not be improved to the required level. In order to meet required level of load demand, Reconfiguration DG units are integrated in distribution network to improve voltage profile, to provide reliable and uninterrupted power supply and also to achieve economic benefits such as minimum power loss, energy efficiency and load leveling. This work proposes minimization of real power losses and improvement of voltage profile using network reconfiguration in the presence of distributed generation. Generally distributed generations (DG) are preferred with objective of minimizing real power loss and improving voltage profile in distribution system. In this work A meta heuristic Harmony Search Algorithm (HSA) is used to simultaneously reconfigure and identify the optimal locations for installation of DG units in a distribution network. Sensitivity analysis is used to identify optimal locations for installation of DG units. The proposed method has tested in MATLAB for 33-bus and 69- Bus radial distribution systems at three different load levels and the analysis is presented for loss minimization.

Multi-Objective Optimal Power Flow using BAT Search Algorithm with Unified Power Flow Controller for Minimization of Real Power Losses

In this paper a multi objective optimal power flow (OPF) is obtained by using BAT search algorithm (BAT) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. UPFC along with BAT algorithm is used to minimize the total real power generation cost, real power losses in OPF control. The BAT algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm are compared with Differential Evaluation (DE).

Self-adapting hybrid strategy particle swarm optimization algorithm

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms

Abstract In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.

Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller

Abstract Optimal power flow with generation reallocation is a suitable method for better utilization of the existing system. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Out of the FACTS devices unified power flow controller (UPFC) is a versatile device, capable of controlling the power system parameters like voltage magnitude, phase angle and line impedance individually or simultaneously. The main aim of this paper is to minimize real power losses in a power system using BAT search algorithm without and with the presence of UPFC. Minimization of real power losses is done by considering the power generated by generator buses, voltage magnitudes at generator buses and reactive power injection from reactive power compensators. The proposed BAT algorithm based Optimal Power Flow (OPF) has been tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results of the system with and without UPFC are compared in terms of active power losses in the transmission line using BAT algorithm. The obtained results are also compared with Genetic algorithm (GA).

Hybrid intelligent algorithm to solve multi objective reactive power control problem

Reactive power and voltage control is a minimization problem. In this paper the multi objective function consist of minimization of real power losses, minimization of voltage deviation and minimization of voltage stability index subjected to equality and inequality constraints are considered. This can be achieved by proper adjustment of control variables such as the generator voltages, the transformer tap settings, and the switchable VAR sources that would minimize the real power loss, the voltage deviation and the voltage stability index. In this work, the hybrid intelligent algorithm Genetic Algorithm - Artificial Bee Colony (GA- ABC) are used. The hybrid algorithms used are the combination of best features of two intelligent algorithms. GA has good crossover operator and hence inspires hybridization with ABC to identify the source effectively. The population is evolving iteration by iteration to find global optimal solution. To evaluate the performance of the developed algorithm standard IEEE 30 bus system is used. This hybrid algorithm gives better optimal solution as compared to individual intelligent algorithm.