Showing 7 results for Power Loss
M. Sharma, K. P. Vittal,
Volume 6, Issue 4 (12-2010)
Abstract
The recent trends in electrical power distribution system operation and management are aimed at improving system conditions in order to render good service to the customer. The reforms in distribution sector have given major scope for employment of distributed generation (DG) resources which will boost the system performance. This paper proposes a heuristic technique for allocation of distribution generation source in a distribution system. The allocation is determined based on overall improvement in network performance parameters like reduction in system losses, improvement in voltage stability, improvement in voltage profile. The proposed Network Performance Enhancement Index (NPEI) along with the heuristic rules facilitate determination of feasible location and corresponding capacity of DG source. The developed approach is tested with different test systems to ascertain its effectiveness.
O. Herbadji, L. Slimani, T. Bouktir,
Volume 15, Issue 1 (3-2019)
Abstract
In this study, a multiobjective optimization is applied to Optimal Power Flow Problem (OPF). To effectively achieve this goal, a Multiobjective Ant Lion algorithm (MOALO) is proposed to find the Pareto optimal front for the multiobjective OPF. The aim of this work is to reach good solutions of Active and Reactive OPF problem by optimizing 4-conflicting objective functions simultaneously. Here are generation cost, environmental pollution emission, active power losses, and voltage deviation. The performance of the proposed MOALO algorithm has been tested on various electrical power systems with different sizes such as IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus systems and on practical Algerian DZ114-bus system. The results of the tests proved the versatility of the algorithm when applied to large systems. The effectiveness of the proposed method has been confirmed by comparing the results obtained with those obtained by other algorithms given in the literature for the same test systems.
A. Boukaroura, L. Slimani, T. Bouktir,
Volume 16, Issue 3 (9-2020)
Abstract
The progression towards smart grids, integrating renewable energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. However, several economic and technical challenges can result from the unsuitable incorporation of DGs in existing distribution networks. Therefore, optimal placement and sizing of DGs are of paramount importance to improve the performance of distribution systems in terms of power loss reduction, voltage profile, and voltage stability enhancement. This paper proposes a methodology based on Dragonfly Optimization Algorithm (DA) for optimal allocation and sizing of DG units in distribution networks to minimize power losses considering variations of load demand profile. Load variations are represented as lower and upper bounds around base levels. Efficiency of the proposed method is demonstrated on IEEE 33-bus and IEEE 69-bus radial distribution test networks. The results show the performance of this method over other existing methods in the literature.
Nguyen Cong Chinh,
Volume 20, Issue 3 (9-2024)
Abstract
This paper presents an intelligent meta-heuristic algorithm, named improved equilibrium optimizer (IEO), for addressing the optimization problem of multi-objective simultaneous integration of distributed generators at unity and optimal power factor in a distribution system. The main objective of this research is to consider the multi-objective function for minimizing total power loss, improving voltage deviation, and reducing integrated system operating costs with strict technical constraints. An improved equilibrium optimizer is an enhanced version of the equilibrium optimizer that can provide better performance, stability, and convergence characteristics than the original algorithm. For evaluating the effectiveness of the suggested method, the IEEE 69-bus radial distribution system is chosen as a test system, and simulation results from this method are also compared fairly with many previously existing methods for the same targets and constraints. Thanks to its ability to intelligently expand the search space and avoid local traps, the suggested method has become a robust stochastic optimization method in tackling complex optimization tasks.
Nguyen Nhat Tung,
Volume 21, Issue 3 (8-2025)
Abstract
This paper presents an effective approach for determining optimal integration of renewable energy distributed generator (RE-DGs) of solar farms (SFs) and wind farms (WFs) in IEEE 69-node power distribution network (PDN) with target of minimizing (1) the single objective function of total active power loss and (2) multi-objective function including a) total active power loss, b) total reactive power loss, c) the voltage deviation and d) imported energy from the main power gird. Intelligent and adaptive meta-heuristic optimization algorithm called bonobo optimizer (BO) is introduced to address optimization problem considering the changing four seasons of winter, spring, summer and autumn from both generation and consumption. The obtained results from BO show its outstanding performance in determining the suitable installation of SFs and WFs compared with many published methods and implemented methods for two cases of single and multi-objective functions.
Rupika Gandotra, Kirti Pal,
Volume 22, Issue 1 (3-2026)
Abstract
The rising demand for electricity has led to the installation of renewable-based distributed generators in a power system network to meet the increasing load. The eco-friendly nature of these DGs is another compelling reason to incorporate them in a power system network but their installation process requires careful consideration such as determining the optimal quantity and location because these factors have a significant impact on various constraints and parameters of the power system network. The main objective of this paper is to determine the optimal siting and sizing of Type-1 and Type-2 DGs in a power system network such that network has minimum real and reactive power losses in the transmission lines, also fuel cost of convectional generators is reduced and voltage profile is improved. For this purpose, hybrid GA-PSO approach is developed and implemented on case 33 bus system and results were compared under different loading conditions such as 100%, 150%, 200% to show which type of DG is most effective. Further, the evaluated results have been compared with other algorithms including OCDE, WOA, SFSA, TGA and EJSA in order to ensure the validity of the suggested approach. The numerical results validate the performance of this proposed technique for DG unit placement.
Ola Badran,
Volume 22, Issue 2 (3-2026)
Abstract
Dynamic Network Reconfiguration (DNR) is a vital and effective technique for reducing energy loss. Due to its complexity, nonlinearity, and large-scale optimization challenge, DNR is still a very difficult problem. This paper presents a new strategy for improving the DNR's stability and reliability under Real-Time Operation Mode (RTOM). It addresses a simultaneous optimization technique within various limitations and constraints about network power flow, voltage limits, output generation of Renewable Energy Resources (RER), Distributed generation mode, and network load profile. In real-time operating mode, it optimizes Distributed Generations Sizing and Location (DG_SL) for Renewable Energy and Dynamic Network Reconfiguration (DNR). Reducing the overall daily active and reactive energy losses of the network, raising the Voltage Stability Index (VSI), distributing the load more evenly, and enhancing distribution efficiency in real-time operation mode are the primary goals. A Multi-Objective Decision-Making Approach (MODMA) based on the Analytic-Hierarchy Process (AHP) and Crow Search Algorithm (CSA). To evaluate the practicality of the proposed method, MATLAB simulations were conducted on the IEEE 33- and 69-bus networks. In the IEEE 33-bus case, the proposed AHP–CSA framework achieves up to 91.75% reduction in daily active losses and more than 90.70% reduction in daily reactive losses, with the Voltage Stability Index consistently improved toward unity. In the IEEE 69-bus case, the method delivers up to 81.78% reduction in daily active losses and 59.78% reduction in daily reactive losses, also enhancing the overall voltage stability profile. These outcomes confirm the effectiveness and robustness of the proposed approach for real-time distribution network operation with renewable DG integration.
