Power Loss In Distribution System Research Articles

Power Quality Enhancement by Integrating Distributed Generation in Distribution System

This research is to enhance the quality of power by reduce real power loss in distribution system and enables voltage profile enhancement at each bus. This will achieve by integrating Distributed Generation (DG) in optimal place with suitable size. In order to overcome the disadvantage of sluggish convergence of conventional algorithms the BAT Algorithm (BA) is used. In this paper the week buses are finding by using Backward/Forward (BW/FW) sweep approach based on real power loss. Later by using BA approach determination of optimal capacity and location will be done. This optimal size and location will leads to great minimization in real power loss and improvement of voltage at each bus. In this research the wind energy and Photo Voltaic (PV) energies are consider as DGs. This research is to determine the advantage of the proposed analysis on IEEE-69 radial bus using MATLAB software. The results were evaluated with the GSA approach existing in literature. Finally simulation outcomes prove that the proposed approach performance is superior in enhancing the power quality by optimal placement of DG and capacity of the DG.

Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems

In the power system operation, the reduction of the power loss in distribution systems has significance in the reduction of operating cost. In this paper, a novel chaotic stochastic fractal search (CSFS) method is implemented for determining the optimal siting, sizing, and number of distributed generation (DG) units in distribution systems. The objective of the optimal DG placement problem is to minimize the power loss in distribution systems subject to the constraints such as power balance, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. The proposed CSFS method improves the performance of the original SFS by integrating chaotic maps into it. On the other hand, ten chaotic maps are utilized to replace the random scheme of the original SFS to enhance its performance in terms of accuracy of solution and convergence speed, corresponding to ten chaotic variants of the SFS where variant being chosen is the best chaotic variant regarding search performance. For solving the problem, the CSFS is implemented to simultaneously find the optimal siting and sizing of DG units and the optimal number of DG units will be obtained via comparing optimal results from different numbers of DG in the problem. The proposed method is tested on the IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the CSFS are verified by comparing to those from the original SFS and other methods in the literature. The result comparisons indicate that the proposed CSFS method can obtain higher quality solutions than the original SFS version and many other methods in the literature for the considered cases of the test systems. Moreover, the incorporation of chaos theory allows performing the search process at higher speeds. Therefore, the proposed CSFS method can be a very promising method for solving the problem of optimal placement of DG units in distribution systems.

An optimal reactive power control method for distribution network with soft normally-open points and controlled air-conditioning loads

The paper proposes an optimal reactive power control method for distribution systems with soft normally-open points (SNOP) and considering the direct load control (DLC) of thermostatically controlled air-conditioning loads. The starting voltage constraints of aggregate air-conditioning loads connected into a distribution network is studied according to the direct control mode and the starting characteristic of air-conditioning load. Using the flexible regulating capacity of power flow of SNOP, an optimal model of reactive power flow for distribution systems with SNOP is established to meet the voltage quality of sensitive loads and the starting requirements of aggregate air-conditioning loads. In the model, the load balancing commands of different DLC aggregators, the on/off status of thermostatically controlled air conditioner, the power regulating command of SNOP, the voltage adjusting and reactive power compensation devices are comprehensively controlled in the constraints of reduction balance and starting voltage of air-conditioning loads, as well as the conventional network and voltage constraints. And a multi-objective functions are developed for the objectives of solving the low-voltage problems, minimizing the comfort impacts on users of air-conditioning and minimizing the active power loss of distribution systems. Finally, the simulation results of a practical 53-bus 10 kV distribution system demonstrate the accuracy and effectiveness of the proposed method.

Power loss minimisation of rural feeder of Jaipur city by renewable-based DG technologies

ABSTRACTPower systems are becoming more complex day by day due to stressed situation in a distribution system, augmented growth in population and high demands of electrical power. In India, the distribution losses are around 20–25%. Distributed Generation (DG) units are broadly used to reduce the power losses in distribution system. To optimise the benefits of DGs, it is essential to determine its appropriate size and location. A new mathematical expression, power voltage sensitivity constant, has been formulated to determine its size and location. In this paper, two types of DG units (PV module and shunt capacitor) are employed for power loss reduction. The size of DG units (PV module) has been restricted to 50% of total system load in the paper. The proposed approach is tested on standard IEEE 69 bus distribution system and 130 bus real distribution system of Jamwa Ramgarh village, Jaipur, India. The results obtained from standard systems are compared with latest optimisation techniques to show the effectiveness and robustness of the proposed approach. The results of real systems are also encouraging.

Optimal Reconfiguration and Distributed Generation placement in Baghdad Distribution Sector

The power losses in distribution system are high, which form 70 – 80% of total transmission and distribution losses. High losses have severe impact on stability, reliability as well as economy. Therefore, minimization of these losses is very necessary. In this paper proposed various schemes to reduce the active power losses in distribution network, given as:- Optimum reconfiguration network,- Optimum Distributed Generation (DG) placement and- Optimum reconfiguration with optimum (DG) placement.Using Cymdist software to implement the optimal reconfiguration algorithm and proposed Genetic Algorithm (GA) to find the size and location, which programmed under MATLAB software package. Whereon the proposed methodology (GA) simplifies the problem by dividing it in two phases, namely Placement Planning Model (PPM) and Size Planning Model (SPM) thereby reducing the search space. It was the integration of the two methods were used after each method individually to obtain minimum real power losses with better bus voltage (better efficiency for network).To verify the proposed algorithms, IEEE 33-bus system and al – jihad neighborhood distribution system (Baghdad distribution sector) are tested. The simulation results are compared with proposed works in literature.

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

This paper introduces a stochastic second-order cone programming (SSCOP) approach to solve the distributed generation coordination problem considering the uncertainty of electric vehicle charging in distribution networks. To minimize the total power loss in the distribution system, the problem is formulated to coordinate the output power of distributed generations (DGs). Two stages are presented to solve the optimization problem: the first stage is to optimize the output power of distributed generators without electric vehicle charging in the distribution system, and the second stage is to optimize the output power of distributed generators according to the stochastic increased load due to the uncertainty of electric vehicle charging. The proposed approach is tested on 69-node and 118-node large-scale distribution systems. The simulated results demonstrate the feasibility and effectiveness of SSCOP.

Optimal Allocation of Multi-DG on Distribution System Reliability and Power Losses Using Differential Evolution Algorithm

The paper proposes the effects of multi-distributed generators (multi-DGs), which is integrated into the distribution system in terms of reliability indices and power losses. The distributed generators (DGs) under the study are connected to the distribution system at 22 kV of Thanyaburi substation on the Provincial Electricity Authority (PEA), which is a part of distribution system in Thailand. Multi-objective functions are considered the effects of multi-DG to improve system reliability and power losses. Moreover, this paper has proposed to determine the system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI) and the expected interruption cost (ECOST) are evaluate as reliability indices and determine active power loss (Ploss) and reactive power loss (Qloss) of power losses in distribution system. The optimal allocation of multi-distributed generators on distribution system reliability and power losses using Differential Evolution algorithm in MATLAB calculated and investigated in DIgSILENT Power Factory program. Aggregated results can be concluded by focuses on the placement and sizing of multi-DG, which are important factors that impacting the reliability indices and power losses.

CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs

A method based on chance constrained second-order cone programming (CCSOCP) is presented for the optimal risk value control of power loss in distribution systems with the distributed generation (DG) of renewable energy systems and electric vehicles (EVs). The charging power of the EV is seen as a random variable, and the risk value of the power loss – due to the uncertainties in the power output of distributed generation of renewable energy systems and charging power of electric vehicles – is studied. Moreover, a second-order cone programming based method is also presented to constrain the potential risk of power loss to an acceptable range by optimally coordinating the power output of DG and the EV charging power in a distribution system. A conditional value at risk (CVaR) model for the power loss of distribution systems is presented and CVaR is taken as a constraint to control the risk value of power loss due to uncertainties in DG and EV charging. The results of a test on a 69-node system are used to verify the validity of the risk control method proposed in this paper.

Managing Real Power Loss of Distribution System Connected with Distributed Generator Using Least Square Quadratic Approximation

Managing Real Power Loss of Distribution System Connected with Distributed Generator Using Least Square Quadratic Approximation

Minimizing Power Losses in Distribution Systems Using GA in Planning and Operation: A Case Study for DGs and SCs

Minimizing Power Losses in Distribution Systems Using GA in Planning and Operation: A Case Study for DGs and SCs

A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

This paper presents a multiobjective optimization methodology to optimally place a STATCOM in electric power distribution networks. The combination of multiobjective ant colony optimization (MACO) and the bacterial foraging optimization algorithm (BFOA) is proposed to minimize the power loss and total cost. The main intention of this analysis is to optimally place the STATCOM at multiple locations such as the transmission side, middle, and load side. Identifying the type and location of the STATCOM is a combinatorial optimization problem in power systems. In order to overcome this problem, the combination of hybrid MACO and BFOA algorithms is applied in this analysis to minimize the total cost and power loss. The total cost of the overall network is calculated by using system average interruption duration index and[ system average interruption frequency index metrics. Moreover, the BFOA is used in this paper to minimize the power loss during distribution, which is adequate in searching for the optimal solution. The problem of reducing power losses in distribution systems through the BFOA approach is described here for a 5-bus system. Test results of a 5-bus network show that the proposed MACO with BFOA method can efficiently ensure the power loss and total cost minimization.

Methods of reducing power losses in distribution systems

The object of research is plot of an urban electricity distribution network of 10/0.4 kV of Tomsk city. The purpose of work – research and development of scientific and technical solutions to optimize the plot operation modes of an urban power supply system on the basis of modern software and hardware. The study found ways to reduce the unbalance of currents and voltages. Applications: urban and rural distribution networks 10/0.4 kV. Economic efficiency: the measures proposed can reduce the power loss. In the course of the research calculation and analyzing of the operation mode in the programs were carried out.

A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms

ABSTRACTMeta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.

Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization

An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy.

Large Scale EVs’ Charging Scheduling Ensuring Secure and Efficient Operation of Traffic and Distribution

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

AN MINLP APPROACH FOR OPTIMAL DG UNIT'S ALLOCATION IN RADIAL/MESH DISTRIBUTION SYSTEMS TAKE INTO ACCOUNT VOLTAGE STABILITY INDEX

Application of distributed generation (DG) units in distribution networks has increased in recent years. Sizing and sitting of DG units are two important factors which should be considered, especially for reduction of power losses in distribution system. This paper presents a new approach for DG placement in distribution systems using the mixed integer nonlinear programming (MINLP). This approach determines the optimal number, sitting and sizing of DG units in both radial and meshed distribution networks with the objectives of reducing the power losses, as well as minimizing the investment and operation costs of DG units, and the monetary value of voltage stability index improvement. The performance of the proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO, and combined loss sensitivity factor and simulated annealing (LSFSA). Compared with the conventional methods, the proposed scheme can be applied in the meshed distribution networks as well. A 33-bus radial network and CIVANLAR meshed distribution system have been used for simulation studies. The obtained results approve the efficiency of proposed method for sitting and sizing of DG units in distribution networks.

English

In the power system network, radial distribution system plays main role to distribute power for the consumers. When the power is supplied to the consumers due to resistance nature of distribution system power losses occurs and due to inductive nature reactive power loss occur which in turn causes the voltage level decrement in the system. The power consumption depends mainly on power factor. The consumer has to pay for this power loss. This paper presents the analysis of energy loss cost and power factor evaluation for balanced radial distribution systems. The power factor is calculated by using given load data directly by using a simple equation. Energy loss cost depends on power loss of radial distribution. In this paper radial distribution systems without laterals (only main feeder) are considered. 10-bus and 12-bus radial distribution systems are used to evaluate (i) Power factor and (ii) Energy loss cost.

Gravitational Search Algorithm and Selection Approach for Optimal Distribution Network Configuration Based on Daily Photovoltaic and Loading Variation

Network reconfiguration is an effective approach to reduce the power losses in distribution system. Recent studies have shown that the reconfiguration problem considering load profiles can give a significant improvement on the distribution network performance. This work proposes a novel method to determine the optimal daily configuration based on variable photovoltaic (PV) generation output and the load profile data. A good combination and coordination between these two varying data may give the lowest power loss in the system. Gravitational Search Algorithm (GSA) is applied to determine the optimum tie switches positions for 33-Bus distribution system. GSA based proposed method is also compared with Evolutionary Programming (EP) to examine the effectiveness of GSA algorithm. Obtained results show that the proposed optimal daily configuration method is able to improve the distribution network performance in term of its power loss reduction, number of switching minimization and voltage profile improvement.

Minimization of Power Losses in Distribution System via Sequential Placement of Distributed Generation and Charging Station

Energy saving in a distribution system through minimization of power losses is a crucial task for cost-effective operation. One of the most effective approaches in minimizing losses is through the application of distributed generation (DG). To achieve minimum losses, the correct size of DG could be determined using various optimization techniques. Different from other approaches, this paper presents an alternative method to minimize losses through optimal size of DG and optimal location of charging station (CS), which overall reduces the power losses of the network. This paper also investigates the impact of constant voltage (PV) or constant power (PQ) mode of DG to the overall power losses with the aim of choosing the best mode. The suitable DG operation modes and proper sequential placement between DG and CS does not only reduce the power losses, it has also opened new possibilities to add new additional loads in the system. The algorithm's performance shows promising results in minimizing power losses when tested using 33 bus radial distribution networks.

A simpler and exact mathematical model for the computation of the minimal power losses tree

This paper presents a new model to compute the tree of minimum active power losses in distribution systems. The main idea consists of using a non-conventional group of variables instead of the classical bus complex voltages, in addition to algebraically formulating the topological and electrical constraints before the actual radial configuration is determined. The new variables allow to set out a simpler mathematical optimization problem where, additionally, the product of continuous and binary variable does not appear, so one of the main problems related to the mathematical reconfiguration problem is avoided. The final minimization problem is made up of linear and quadratic equality and inequality constraints, also being linear the objective function. The new proposed model has been tested on different systems used specifically in the literature. In all the cases the reached solution matches the better known and even in same cases it has been improved.