Showing 5 results for Power Distribution
A. Fereidunian, H. Lesani, C. Lucas, M. Lehtonen, M. M. Nordman,
Volume 2, Issue 3 (7-2006)
Abstract
Almost all of electric utility companies are planning to improve their
management automation system, in order to meet the changing requirements of new
liberalized energy market and to benefit from the innovations in information and
communication technology (ICT or IT). Architectural design of the utility management
automation (UMA) systems for their IT-enabling requires proper selection of IT choices for
UMA system, which leads to multi-criteria decision-makings (MCDM). In response to this
need, this paper presents a model-based architectural design-decision methodology. The
system design problem is formulated first then, the proposed design method is introduced,
and implemented to one of the UMA functions–feeder reconfiguration function (FRF)– for
a test distribution system. The results of the implementation are depicted, and
comparatively discussed. The paper is concluded by going beyond the results and fair
generalization of the discussed results finally, the future under-study or under-review
works are declared.
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.
S. Rajamand,
Volume 18, Issue 2 (6-2022)
Abstract
Fair distribution of generated power has a significant impact on the performance of the power system. Many methods have been proposed for the safe and secure operation of power systems under the uncertainties of distributed generators and system load. In this paper, we present an optimal power distribution algorithm for distributed generators against uncertainties and load changes of direct-current and alternating-current transmission systems. In this optimal algorithm, considering the stable-state constraints for all uncertainties is performed. In order to establish these constraints at the lowest cost, the adaptive droop coefficients are employed to optimize the power sharing, reloading and modifying the power coefficient of each distributed generator in the power system. Simulation results show the efficiency of the proposed method to improve the performance of the system and reduce the total cost. The voltage/power deviation from reference value in the proposed method is about 1-1.5% where in the conventional droop control, it is more than 2-3%. In addition, in the same uncertainty of the load/distributed generator power in the test system, proposed method requires 20% less power redistribution compared to the conventional droop method. Also, total cost increasing (due to uncertainty increasing) in the conventional droop method is higher than the proposed method (about 10-15%) which shows the robustness of the suggested method against uncertainty changes.
Akanksha Jain, S.c. Gupta,
Volume 20, Issue 3 (9-2024)
Abstract
Due to the anticipated increase in loads, the power grid will encounter the issue of system peak loads in the future, which is typically addressed through grid reinforcement. However, implementing a flexibility service option can prevent the need for grid development. As the overall load continues to rise, the distribution transformer becomes overloaded. The presented work focuses on enhancing one of the parameters that define the insulation life of the transformer, known as the Loss-of-Life (LOL). Transactive approach involves the rescheduling of the battery and photovoltaic generation. Dominated Group Search Optimization (DGSO) algorithm is utilized to optimize the objective function of reducing the peak transformer load under the power flow and voltage constraints of the network. Experimental validation of the proposed method is conducted using MATLAB 2018 software. Modified IEEE 34-bus system is used to implement the proposed methodology. Numerical results obtained from various cases elucidate that the proposed model reduces the LOL of the transformer from 0.0103 to 0.0017 p.u.Comparative analysis of the proposed method with the already used methods of voltage-control and Volt-Var control have been presented.
Syazwan Ahmad Sabri, Siti Rafidah Abdul Rahim, Azralmukmin Azmi, Syahrul Ashikin Azmi, Muhamad Hatta Hussain, Ismail Musirin,
Volume 21, Issue 2 (6-2025)
Abstract
The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.
