Power Losses In Distribution Network Research Articles

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Decomposition of unbalanced operation incremented active power loss in distribution network

Decomposition of unbalanced operation incremented active power loss in distribution network

Reconfiguration of distribution network for improving power quality indexes with flexible lexicography method

Reconfiguration is a low-cost method to reduce power loss in distribution network (DN). Harmonics and unbalancing cause additional losses in DNs. Therefore, it is necessary to consider these two factors in distribution network reconfiguration (DNR). In this paper, the effect of reconfiguration in two harmonic polluted medium and large scale DN is evaluated. In addition, the effect of harmonic injection of distributed generation (DG) is studied. Moreover, the effect of reconfiguration in unbalanced DN in the presence of single-phase DGs is investigated, and it is concluded that distribution system operator (DSO) should consider network unbalance in DNR to more reduction of power losses. Furthermore, a new flexible lexicography method is introduced to solve multi-objective problems, especially in DNR. In the proposed method, the DSO determines the priority of each goal in comparison to the other goals, instead of using weighing factors of them. This method is effective for power quality goals which cannot be illustrated as cost function. In this paper, validation of the proposed method in IEEE 69 bus and 95 bus practical harmonic polluted DN and 25 bus unbalanced DN in presence of three-phase and single-phase PVs are investigated. Results show the effectiveness of the method in increasing the flexibility of DSO in DNR to improve power quality goals such as harmonics and unbalance, as well as reduce power losses.

Enhancing efficiency in electrical distribution systems: A novel approach via modified genetic optimization algorithm for loss reduction in optimal network distribution

The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method’s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation.

Co-simulation-based optimal reactive power control in smart distribution network

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity

One of the most famous methods for minimizing power loss is distribution network reconfiguration (DNR). Accordingly, many researchers have focused their work on finding a network’s optimal configuration in planning mode. However, few address the switching sequence process during operation mode. This paper introduces an innovative approach to minimize power loss in distribution networks. It addresses the often-overlooked real-time switching sequence order (SSO) during network operation, ensuring a smooth transition to the optimal configuration within operational constraints. Simultaneously, it optimizes distribution network reconfiguration (DNR) and distributed generation location and sizing (DG-LS) to maximize load capacity. The primary goal is to reduce power losses, improve voltage profiles, and enhance network efficiency. Utilizing multi-objective decision methods based on AHP, particle swarm optimization (PSO), and firefly algorithm (FA), this study achieves efficient results for SSO, DNR, and DG-LS optimization.

Dynamic Prediction Algorithm for Low-Voltage Distribution Network Power Loss in a Smart City Based on Classification Decision Tree and Marketing Data

Abstract When the current algorithm is used to predict the power loss of the low-voltage distribution network, the missing marketing data cannot be processed, which leads to the problem of relatively large root mean square error in the algorithm. To this end, this paper proposes a dynamic prediction algorithm for low-voltage distribution network power loss that combines classification decision trees and marketing data. First, use the classification decision tree to classify the marketing data, and select the missing marketing data. Second, the combined threshold filling method is used to fill the missing data. Finally, the process state characterization method is used to realize the dynamic prediction of the power loss of the low-voltage distribution network based on the complete marketing data. The experimental results show that the data missing ratio of the proposed algorithm is less than 0.2, the root mean square relative error is less than 0.02, and the fitness is higher than 0.08 on average, as with the comparison with the three methods of comparison. The results prove the future prediction to be implemented in a smart city.

Optimization of Distributed Energy Resources in Distribution Networks: Applications of Convex Optimal Power Flow Formulations in Distribution Networks

The increasing penetration of distributed energy resources (DER) has imposed several challenges in the analysis and operation distribution networks. In the last decade, the implementation of battery energy storage systems (BESS) in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. However, a complex formulation of the optimization problem regarding DER implementations can easly become nonconvex, thus limiting the scope of the results (quality) and affecting the computational efficiency. In this paper, convex formulations of the optimal power flow (OPF) problem for DER (PV and BESS) installations in San Andres distribution network were implemented to minimize power losses. Four main simulation cases were stablished covering convex power flow analysis, convex optimal power flow to locate and size dispatchable and nondispatchable DER (PV) with demand and irradiation profiles, and the inclusion of aggregated and distributed PV and BESS systems. The computational realization of those formulations was made with three different software packages: CVX in python, CPLEX, and MATLAB (MATPOWER/PSO). The results show that installing DER capacity (both PV and/or BESS) can substantially improve the power losses in distribution networks, especially if considered distributed instead of aggregated, achieving reductions up to 75% for power losses and up to 57% for conventional generation utilization. Besides power losses, it was observed that optimal DER (PV and BESS) implementations behaved similarly as demand response signals do, potentially increasing economic benefits for DISCOs. In terms of computational efficiency, convex formulations help substantially to reduce computation times, especially if scalability is to be considered when distributed DER installations were studied.

Planning of fast charging stations with consideration of EV user, distribution network and station operation

Despite the benefits of electric vehicles (EV) as a sustainable alternative to fossil fuel-powered vehicles, the lack of suitable fast charging stations (FCS) continues to be a barrier to widespread EV adoption. Different factors affecting EV user, distribution network and CS operation must be taken into account when selecting CS locations and capacity. The purpose of this study is to design a FCS location and capacity planning framework that considers EV user travel time and waiting time, distribution network power loss, and station utilization aspects. Besides, a prior utilization rate-based queuing algorithm is proposed to determine the CS capacity, which simultaneously reduce user waiting time and affirms adequate station utilization. Then binary atom search optimization algorithm is employed to solve the FCS location and capacity problem. The proposed model is tested on an integrated transportation network and distribution network and the numerical results are provided and analysed from the perspectives of the distribution network, EV user, and CS operation.

Algorithmic foundations of automated monitoring of commercial and technical power losses in distribution networks

It is known that significant power losses in power distribution networks (PDNs) caused by unauthorized power withdrawals (UPWs) lead to a decrease in their technical and economic performance. There are no digital technologies designed for separate assessment and monitoring of technical and commercial power losses in modern automatic system for commercial metering of power consumption (ASCMPC), which are currently widely used for automation and computerization of processes in distribution networks. The article proposes a new algorithm for solving the above problem on the basis of data obtained from electricity meters included in the ASCMPC structure. In order to ensure the solvability of the identification problem, the concept of a virtual network model characterizing the desired state of the PDN in the absence of UPW is introduced. On its basis, algebraic equations are obtained, the solution of which allows to identify technical and commercial power losses in three-phase networks. The obtained results are oriented on further improvement of modern ASCMPC and increase of their efficiency and reliability.

Multi-Objective Optimization for Distribution Network Reconfiguration With Reactive Power Optimization of New Energy and EVs

The rise of new energy and the wide application of electric vehicles (EVs) have led to the substantial expansion of distribution network in recent years. The problems such as the decline of transmission reliability and the rise of power loss in distribution network are becoming increasingly serious. Therefore, distribution network can greatly improve its reliability and quality of power supply voltage through changing topology. This work built a distribution network reconfiguration (DNR) model with new energy and EVs firstly. Then, the position of bus tie switches and the reactive power regulation range of new energy and EVs were proposed as decision variables. A multi-objective evolutionary algorithm would be applied to this DNR model and the optimization results will be obtained when considering the line loss and voltage deviation as the objective function. In order to get different optimal compromise solutions with the changes of actual environment, this work employed a new decision-making method named Prevalence Effect Method (PEM). Finally, a high-quality strategy of DNR and reactive power regulation will be obtained.

Proposing a distributed generation curtailment optimization model to minimize total power losses of distribution network

In recent years, Distributed Generation has grown explosively, especially solar power, and the impact of the COVID-19 which has lowed the load, caused the unbalance between electricity supply and demand. In order to solve this problem, the Ministry of Industry and Trade has taken measures to direct Vietnam Electricity to manually reduce this energy source. At present, the member Electricity companies only curtail equally for all investors, this curtailment model is not optimal in terms of power losses. In this study, an optimization model is developed to help power companies obtain an optimal curtailment result in terms of minimizing the power losses on the distributed grid when needed. The proposed model is validated with the real data provided by Thue Thien Hue Power company and commercial software.

Robust Mixed-Integer Programing Model for Reconfiguration of Distribution Feeders Under Uncertain and Variable Loads Considering Capacitor Banks, Voltage Regulators, and Protective Relays

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers’ power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependence on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be robust enough against load uncertainty and variations. Thus, this article presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is robust enough and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches.

Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study

Improving the performance of distribution systems is one of the main objectives of power system operators. This can be done in several ways, such as network reconfiguration, system reinforcement, and the addition of different types of equipment, such as distributed generation (DG) units, shunt capacitor banks (CBs), and voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs. This article applies a new optimization technique called RUN to increase hosting capacity (HC) for a rural Egyptian radial feeder system called the Egyptian Talla system (ETS). RUN relies on mathematical concepts and principles of the widely known Runge–Kutta (RK) method to get optimal locations and sizes of DGs, CBs, and VRs. Furthermore, this paper presents a cost-benefit analysis that includes fixed and operating costs of the compensators (DGs, CBs, and VRs), the benefits obtained by reducing the power purchased from the utility, and the active power loss. The current requirements of Egyptian electricity distribution companies are met in the formulated optimization problem to improve the HC of this rural system. Uncertain loading conditions are taken into account in this study. The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area. The introduced RUN optimization algorithm is used to solve the optimal coordination problem between DGs, CBs, and VRs. Excellent outcomes are obtained with a noteworthy reduction in the distribution network power losses, improvement in the system’s minimum voltage, and improvement of the loading capacity. Several case studies are investigated, and the results prove the efficiency of the introduced RUN-based methodology, in which the probabilistic HC of the system reaches 100% when allowing reverse power flow to the utility. In comparison, this becomes 49% when allowing reverse power to flow back to the utility.

Effect of Capacitor Switching on Reactive Power Control in Electrical Distribution Network

In electrical distribution network, load varies over the day, with very low load from midnight to early morning and peak values occurring in the evening due to the load demand by the consumer. This variation in load demand leads to variation in reactive power because most loads are inductive especially in industries where three phase industrial motors are used. In addition, Furthermore, the variation in reactive power affects current flow through the lines, transformers, generators and cause power losses in distribution network. Hence, there is the need to ensure reliable energy distribution even in the presence of load and reactive power variations. This research paper applied shunt switching capacitor for reactive power control of electrical distribution network using 11 kV distribution network located at Monatan, Ibadan, Oyo State Nigeria as a case study. Hourly load data of the network were collected to determine the reactive power of the network for stability evaluation. Capacitor Switching Compensation was incorporated into distribution network using KCL algorithm as power flow to form Capacitor Switching Compensation Model (CSCM) to improve the reactive power of the distribution network and simulation was carried out in MATLAB environment. The results showed that reactive power of the distribution network was 3.1. The CSCM improved the reactive power to 52 %. The study showed that incorporating capacitor switching as a compensation technique enhances the stability of the distribution network. Keywords: Electrical Distribution Network, Reactive Power, Capacitor Switching Compensation Model, Power Loss, Load Data, MATLAB. DOI: 10.7176/CEIS/13-4-03 Publication date: August 31 st 2022

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks.

The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

Impact of small hydro generation on voltage profile and losses in 20 kV distribution network

Planning in the development of electricity availability focuses on Renewable Energy power plants in support of the Government’s strong determination to suppress fossil-based energy and increase in New Renewable Energy whose mix is targeted to reach 23% in 2025. Distributed generation (DG) is one of the new approach in the installation of power plants which the plant location is no longer centralized, but it was spread on the distribution of electricity networks. Nowadays, the application of distributed generation has increased because it is a renewable energy especially small hydro of distributed power plants can affect the conditions of the distribution network. Therefore, it is necessary to know the impact of the installation of distributed power plants. This study was conducted to analyze the effects of DG connected to network at two conditions First, the distributed generation connect when under load condition in network and second, distributed generation connect when peak load condition in network. Simulation in this study was conducted using the ETAP software and The Newton-Raphson method has been used in order to calculate power losses and voltages across the network with real data of rural distribution network in Tanah Jawa, North Sumatera, Indonesia. The 20kV distribution network was used in this feeder, and it was divided into one main feeder. The results of this study are expected to provide information about the impact of the DG on voltage and power losses in distribution network. The results of the study show that the installation of DG enhances the voltage profile and ready for installation Small Hydro as DG.

Multi-Objective Optimization for Optimal Allocation and Coordination of Wind and Solar DGs, BESSs and Capacitors in Presence of Demand Response

The renewable energy sources (RES) based distributed generations (DGs) have been proven to be of great technical and economic benefits if optimally allocated in distribution networks. Their proper deployment is usually made in conjunction with demand response (DR) programs and renewables curtailment to match the demand load with the available generated power. Battery energy storage systems (BESSs) and capacitor banks (CBs) are two other tools that can compensate for the shortcomings of the RES. BESSs could complement the renewables generation intermittency and improve the reliability of the system while CBs could indemnify the limited reactive power support of the RES and improve the power quality of the system. Thus, the simultaneous integration of RES-based DGs, DR, BESSs, CBs, and curtailment could have great benefits if optimally planned and coordinated. In this paper, a multi-objective optimization planning model is formulated to determine the optimal locations and capacities of different RES-based DGs, BESSs, and CBs in presence of DR and renewables curtailment to maximize the economic index and the average voltage stability factor, and to minimize the average power losses in distribution networks. The modelling of RES-generated power is considered. The proposed model is implemented and tested on standard IEEE 33-bus radial distribution system, the model is formulated and solved in GAMS environment. Different renewable configurations and test cases are investigated. The obtained results validate the effectiveness of the simultaneous integration of the used tools in optimizing the techno-economic benefits.

Analysis of Distributed Generation Integration Effect on Active Power Losses in Distribution Networks

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

Employing load and irradiance profiles for the allocation of PV arrays with inverter reactive power and battery storage in distribution networks–A fast comprehensive QSTS technique

The paper presents a novel computationally efficient Quasi-Static-Time-Series (QSTS) approach for the sizing and sitting of photovoltaic arrays in a distribution network, which uses historical hourly demand and irradiance data. Additionally, the proposed approach combines a battery energy storage system (BESS) with the PV array for smoothing the array output and integrates inverter reactive power control for voltage support. An optimized search method based on the combined Non-dominated Sorting Genetic Algorithm-II and Fuzzy Decision-Making Tool (NSGAII/FDMT) is employed for the sizing and sitting problem along with three objectives: (a) minimization of total cost; (b) minimization of distribution network power losses; and (c) maximization of system security. The distribution network-PV array-BESS system along with its control functions is solved in hourly steps for a yearly 8760-h horizon obtaining an accurate estimate of the system performance. In order to decrease execution time, a robust formulation of the direct power flow method is developed in the paper yielding a fast 8760-solution algorithm. The performance of the proposed method is tested through different case studies on a modified IEEE 123-bus and 33-bus radial distribution systems. The results demonstrate that detailed system analysis using historical data produces more efficient options than conventional methods based on system analysis at peak load only.