Critical Review of Different Methods for Siting and Sizing Distributed-generators

Due to several benefits attached to distributed generators such as reduction in line losses, improved voltage profile, reliable system etc., the study on how to optimally site and size distributed generators has been on the increase for more than two decades. This has propelled several researchers to explore various scientific and engineering powerful simulation tools, valid and reliable scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size distributed generator(s) for optimal benefits. This study gives a critical review of different methods used in siting and sizing distributed generators alongside their results, test systems and gaps in literature

Analysis of the Effect of Distributed Generation on Loss Reduction in Electrical Distribution Network

Distribution network is said to be the most visual part of the electric production and the most observed by the utilities for investment, maintenance and operation. The system have been operated under stressed conditions due to limited structure and increasing day to day requirement of power consumption, which have a significant economic and social impact on the system. Due to the system high resistance to impendence ratio, large amount of power loss occur in the network. This loss is the most severity factors affecting the power quality delivered to the end users and depend on power network expansion and load complexity. Among the support methods available for power loss minimization in distribution network, strategic allocation of Distributed Generation (DG) in distribution system is widely considered a viable option. DGs are electrical sources connected to the power network located to consumer’s side but very small when compared with the centralized power plant. They can be in form of wind, mini-hydro, photovoltaic and fuel-based system such as fuel cells and micro-turbines. Therefore, in this study, different approaches for power loss minimization in electrical distribution system with the incorporation of DG by various researchers were reviewed. These approaches have become powerful tools to overcome the problem of power loss minimization in distribution system. Keywords: Distribution System, Power Loss. Distributed Generation, Power Consumption, Photovoltaic System, Centralized Power Plant. DOI: 10.7176/JETP/11-6-02 Publication date: November 30th 202

Modified analytical approach for PV-DGs integration into radial distribution network considering loss sensitivity and voltage stability

Abstract: Achieving the goals of distribution systems operation often involves taking vital decisions with adequate consideration for several but often contradictory technical and economic criteria. Hence, this paper presents a modified analytical approach for optimal location and sizing of solar PV-based DG units into radial distribution network (RDN) considering strategic combination of important power system planning criteria. The considered criteria are total planning cost, active power loss and voltage stability, under credible distribution network operation constraints. The optimal DG placement approach is derived from the modification of the analytical approach for DG placement using line-loss sensitivity factor and the multiobjective constriction factor-based particle swarm optimization is adopted for optimal sizing. The effectiveness of the proposed procedure is tested on the IEEE 33-bus system modeled using Matlab considering three scenarios. The results are compared with existing reports presented in the literature and the results obtained from the proposed approach shows credible improvement in the RDN steady-state operation performance for line-loss reduction, voltage profile improvement and voltage stability improvement

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Optimal Siting of Distributed Generators in a Distribution Network using Artificial Immune System

Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function

Stochastic Modeling and Planning of Wind-Based Distributed Generators in Distribution System

The increasing strain on the Earth resulting from pollution, climate change, and finite resources has established the development of renewable energy sourcing methods, such as wind, solar and geothermal energy. By reorganizing the power system structures, and the growth in customer demand, the development of Distributed Generation (DG) play a vital role in the power system planning. Furthermore, because of the inexhaustibility and cleanliness of the renewable DG units, they are inevitably the key to a sustainable energy supply infrastructure. Nevertheless, the random nature associated with the renewable DG units produces specific challenges that have to be addressed to accelerate the expansion of the renewable DG units in the distribution system. Firstly, a new method for the determination of the wind speed distribution based on hourly wind speed data is proposed. Thus, instead of using only the well-known unimodal distributions such as Weibull and Rayleigh, a combination of probability density functions (PDFs) is taken into account, considering four sets of parameters in which each set represents a distribution. Furthermore, this model enhances the likelihood of the estimated wind speed probabilities. The maximum likelihood estimation (MLE) method for finite mixture models through the expectation-maximization (EM) algorithm is used to estimate the optimal parameters of the mixture distribution. Then two types of error measurements assessed the performance of each unimodal and multimodal distribution. As a result, the mixture of Gamma (MoG) distribution returned the most accurate results. Secondly, the results of wind speed modeling will be used in the siting and sizing wind-based DG units. The methodology addresses a probabilistic generation load model that combines all possible operating conditions of the wind-based DG units and load levels with their probabilities. The objective of siting and sizing formulation is to minimize the annual energy losses of the system as well as keeping the system constraints such as voltage limits at different buses (slack and load buses) of the system, feeder capacity, discrete size of the DG units, maximum investment on each bus, and maximum penetration limit of DG units in an acceptable limit