Deployment Of Distributed Generation Research Articles

Holistic regulatory framework for distributed generation based on multi-objective optimization

Regulatory changes associated with distributed generation have occurred in several countries (e.g., the USA, Germany, the UK, and Australia). However, there is a lack of robust and holistic analytical models that can be used to implement the best regulatory framework among possible options. In this context, the present paper proposes a cutting-edge regulatory framework for distributed generation based on multi-objective optimization, taking into account socioeconomic (socioeconomic welfare created by the regulated electricity market and electricity tariff affordability) and environmental (global warming potential) indicators. Such indicators are modeled primarily based on the optimized tariff model (socioeconomic regulated electricity market model), Bass diffusion model (forecasting model of distributed generation deployment), and life cycle assessment (environmental impact assessment method). The design variables are assumed to be the regulated electricity tariff and remuneration of the electricity injected into the grid over the years. First, the proposed methodology is applied to fifteen large-scale Brazilian concession areas with a significant deployment of distributed generation assuming two approaches, a multi-compensation scenario, where the compensation is set individually for each concession area, and a single-compensation scenario, where the compensation is set equally for all concession areas. Then, the optimal solutions are compared to Ordinary Law 14300, which is a recently implemented regulatory framework for distributed generation in Brazil. Results demonstrate that Ordinary Law 14300 is a dominated or non-optimal solution since it is not located on the optimal Pareto frontiers for any of the assessed concession areas. Assuming the Euclidian knee points, benefits averaging 33% and 15% were achieved in terms of electricity tariff affordability for the multi and single-compensation scenarios, respectively, with small losses of 8% and 3% in terms of socioeconomic welfare and global warming potential. Though the proposed methodology is applied in the Brazilian context, it can also be applied to other countries with regulated electricity markets; thus, it is expected to be valuable for researchers, government institutions, and regulatory agencies worldwide.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

Sharing the grid: The key to equitable access for small-scale energy generation

Small-scale distributed generation (DG) installations, like rooftop solar photovoltaics, are adopted earlier in high-income households than in other portions of society. This can limit grid electricity export capacity for latecomers, causing inequitable access and slower renewables deployment. In search for an equitable solution, this work challenges current DG permissioning practices: instead of applying rules of thumb to assign DG, we propose a policy that represents grid limitations at all hierarchies of the grid (i.e., customer, distribution, sub-transmission and transmission levels). A method to predict interest in household DG installations over time based on demographic and socioeconomic variables is presented and validated. This is leveraged to test a novel policy for small-scale DG deployment: sharing the grid; and to explore its potential to improve equity in grid access and speed of renewables deployment. Proposed, current and alternative policy paths on small-scale DG adoption are tested to identify benefits and trade-offs. Using Ireland’s national utility as case study, we perform a long-term simulation of solar photovoltaic adoption. Sharing electricity export capacities increases participation from 77.9 % to 100 %, unlocking access to the grid for 364,064 electricity customers compared to current policy—without the need for infrastructure upgrades. Furthermore, the proposed policy achieves the same overall level of installed capacity as current policy 44 % faster, accelerating small-scale DG rollout, benefiting households and decarbonisation targets. Ultimately, to determine equitability, we connect these results to demographic variables and find that grid limits are not biased towards any demographic group studied, suggesting that policy can be tailored to prevent DG access inequity.

Hosting Capacity Estimate Based on Photovoltaic Distributed Generation Deployment: A Case Study in a Campus of the University of São Paulo

Distributed generation, which is mainly deployed with PV systems that benefit economically prosumers, has soared in use in Brazil. Despite this, PV capacity in excess may cause technical issues which concern planning engineers who have adopted rules of thumb to screen interconnection requests without any detailed study. Recently, the hosting capacity concept has been employed to assess how much PV capacity a distribution grid can host without deteriorating grid parameters, reliability, or power quality. A steady-state and worst-case-based scenario was used to run deterministic power flow simulations to estimate the hosting capacity of a specific radial circuit at a campus of the University of São Paulo, referred to as “USP-105”. Although the result may be not completely accurate, it was found that USP-105 can accommodate 103% of its peak load or 4970.6 kW of PV power, which reduced the circuit’s annual peak load by 9%. Another finding was that hosting capacity increased when PV-DG deployment was dispersed along the circuit rather than concentrated on a single location (e.g., closest, or furthest to the substation). Utilities may therefore benefit from a simple and quick assessment to obtain an overview of how specific circuits behave on PV deployment and indicate which locations are technically more beneficial.

Contingency constrained TCSC and DG coordination in an integrated transmission and distribution network: A multi-objective approach

Transmission System Operators (TSO) deploy Flexible AC Transmission Systems (FACTS) for congestion management in order to meet technical and multilateral power supply transactions. These power commitments are however constrained by thermal, voltage and stability limitations. On the other hand, the campaign to de-carbonize the power supply framework as seen Distribution System Operators (DSO) accommodate an increased penetration of Distributed Generation (DG) within their distribution networks. However, prerogative of system operators for separate planning of FACTS and DG systems can worsen power system’s key performance indicators especially under huge load growth and severe contingencies. Therefore, this paper developed an approach for contingency constrained coordination of Thyristor Controlled Series Compensator (TCSC) and DG through a multi-level optimization, comprising of a hybrid real power flow index and particle swarm in the first level and a multi-objective variant of particle swarm optimization in the second level. The contingency constrained coordination aimed to improve Available Transfer Capability (ATC), power loss and voltage deviation. Two models of DG were coordinated with TCSC under normal and contingency cases. Results indicate that while ATC improvement for various transactions were achieved with TCSC, additional power losses incurred was further reduced with DG deployment in coordination with TCSC. Furthermore, the Pareto front, which establishes the correlation between objectives shows a diving parabola that is partly nonlinear. Also, the TCSC−DGPQ provide superior ATC and power losses compared with TCSC−DGPV. Again, under (N−1) contingencies, the TCSC−DGPQ provides improved ATC compared with other contingency cases under TCSC only.

Assessment of the status and trends of photovoltaic distributed generation in Brazil: An in-depth approach based on big data processing

The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset’s average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research.

Power-to-gas as an option for improving energy self-consumption in renewable energy communities

Renewable Energy Communities (RECs) have been introduced by the Renewable Energy European Directive (REDII) in order to allow their members to collectively produce, consume, store and sell renewable energy. With the distributed generation deployment, the electricity injection into power grids has to be limited. Thereby, the RES management has to maximise the local energy self-consumption (SC). The present work deals with Power-to-Gas (PtG) application for blending hydrogen in the local gas grid for maximising the energy-SC, comparing it with traditional electric batteries (PtP). Moreover, this study investigate how SC-based tariffs for RECs can represent an indirect incentive for hydrogen production. To do so, a case study, consisting of 200 dwellings, has been analysed. Four PV configuration have been considered for evaluating different RES excess conditions. PtP and PtG systems have been implemented and compared each other. The hydrogen production cost has been assessed exploiting the renewable electricity incentive scheme.

Integrated intelligent geoprocessing tool for screening candidate locations suitable for Distributed Generation Deployment

The pursuit of economic growth leads to an increase demand for energy that, in turn, may cause negative impacts on the environment due to consumption of non-renewable resources. Thus, it is necessary to search for sustainable strategies for the electric energy's conversion, standing out the distributed generation and the renewable energy sources. In this context, this work presents an intelligent computational tool that combines the concepts of sustainable development and distributed generation to detect locations that are suitable for the installation of renewable energy sources such as wind turbines and solar energy systems. This tool incorporates an intelligent system based on fuzzy logic integrated within a geoprocessing software package, providing an indicative methodology to assist the stakeholders of the energy industry in their studies of energetic planning aiming a sustainable development.

A metaphor-less based AI technique for optimal deployment of DG and DSTATCOM considering reconfiguration in the RDS for Techno-Economic benefits

The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial Intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy.

Exploration of DG Placement Strategy of Microgrids via FMFO Algorithm: Considering Increasing Power Demand and Diverse DG Combinations

Distributed energy resource (DER) has been widely deployed, and distributed generation (DG) can complement the distribution system. Favorable DG deployment provides the grid-connected microgrid (MG) with stable voltage and reduces emission and power generation costs. DGs are considered distributed feeders, and MG is required to be operated under the optimal state. Reconfiguration is a practical approach to optimizing resource allocation. The optimal global solution is obtained via optimization algorithms. In this paper, three objectives are defined, namely, minimization of economic cost (ECC), emission cost (EMC), and voltage deviation (VD). Consequently, a fuzzy moth-flame optimization (FMFO) algorithm is proposed to coordinate the interests of multiple objectives. Moreover, the simulation is conducted based on the standard IEEE 33-bus radial distribution system (RDS), under which the impact of deployment of various DG type and quantity on the MG is explored. In particular, diverse DG combinations are tried under the increasing power demand, and a high-stable voltage strategy is proposed to meet the specific demands. The simulation results reveal that: (1) the DG type has a significant impact on ECC and EMC; (2) penetration level of DG shows a positive-like relationship with the MG stability; and (3) the proposed FMFO algorithm exhibits an efficient performance in convergence.

Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS’s are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optimal and game theory in obtaining better results to the problem of optimal deployment of DG in RDS to improve technical as well as economic benefits.

Assessment of Deployment of DGs and D-STATCOMs in Distribution Network using Gravitational Search Algorithm

The assessment of distribution network (DN) for reduce the losses and improve power quality, obtaining precise allocation of distributed generation (DG) and distribution static synchronous compensator (D-STATCOM) is mandatory in DN. This paper shows DN analysis with allocation of DG and D-STATCOM for various cases. The nature inspired metaheuristic method, the Gravitational Search Algorithm (GSA) is proposed to obtain the precise allocation of DG and D-STATCOM individually and simultaneously in DN for power losses reduction and total annual energy saving for distribution network operator. To check the performance of algorithm, it is tested on IEEE 33 and IEEE 69 buses DN for various positions of DG and D-STATCOM in DN. The proposed algorithm's performance is compared by some existing algorithms. It is found that proposed technique is able to reduce losses and annual energy saving in distribution network.

Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm

In recent years, the substantial upsurge of electricity demand has directly impacted the performance of the distribution networks concerning the active power losses and voltage drops. In such circumstances, the distributed generators (DGs) could uphold these concerns if they are optimally deployed in terms of sizing and placement. For this reason, in current research, the optimal deployment of DGs has been proposed with the plant propagation algorithm (PPA) to simultaneously maximize the total active power loss reduction and to upgrade the magnitude of the minimum bus voltage. Alongside, the authors have examined four rounds of DGs. In that context, the optimal deployment of one DG is investigated in the first round. In each succeeding round, the number of DGs is increased: in the second round, this investigation is carried out for two DGs, for three DGs in the third round, and finally, for four DGs in the fourth round of the investigation. The effectiveness of the proposed PPA has been tested on IEEE 33 and 69-bus test networks in the load flow analysis, and results are compared with the standard optimization algorithms. Thereafter, a post deployment economic assessment based on loss calculation has been undertaken out as well. The ANOVA test has also been performed for statistical evaluation of standard algorithms. The simulation results exhibit that the proposed algorithm outdo other algorithms both technically and economically. It has been seen that as the deployment of DGs is increased, the total active power losses and voltage drops are also reduced. In terms of economic assessments, the total cost decreases with the increased deployment of DGs in IEEE 33-bus test network, whereas, the total cost increases with the increased deployment of DGs in IEEE 69-bus test network.

Optimal DG Deployment Based on Technical and Economic Considerations with Daily Load Variation

The concept of distributed generation (DG) has been developed as a hopeful solution to meet the demand increase in distribution systems. The optimal DG deployment in the distribution system can improve the technical benefits. This study presents a framework to find the optimum location and size for different types of DG units to enhance the voltage stability index (VSI), improve the voltage regulation and reduce the daily energy losses. The emission effect and cost function (environment and economic benefits) are augmented in the multi-objective function. Combinations of DG types are introduced to obtain the best benefits from each one. The daily load variations and dynamic output of each DG type are considered in the problem formulation. The genetic algorithm (GA) technique is used to find the optimal allocation for different cases. The typical IEEE 33-bus system is utilized to evaluate the proposed framework.

Optimal siting and sizing of distributed generators to retain the existing protection setup along with their technical and economic aspects

Now-a-days, most studies strive towards high penetration of distributed generators (DGs) in distribution systems. Interestingly, the deployment of DGs has the potential to question the selectivity between the relays and also the interrupting capacity of circuit breakers (CBs) upon increased penetration. Furthermore, the integration of DGs leads to sympathetic tripping during faults, which mandates the DGs to be disconnected from the grid, which in turn conflicts the reliability of the distribution systems. Gradually, this facilitates the need for fault ride through of DGs without affecting the security and technical benefits of the system. Hence, to get the best out of DGs and to pull through their challenges, this work optimally sites and sizes the DGs to retain the existing relay settings and CBs and to avoid mal-operations during faults due to reverse power flow along with their potential benefits and their economic impacts. Besides minimising the CB replacements and sympathetic tripping of relays during optimisation of DGs, few fault current limiters are incorporated to avoid these issues completely. Furthermore, this work also explores optimal penetration using a fuzzy logic tool. The proposed methodology is validated for the standard 33 bus radial distribution system using particle swarm optimisation algorithm.

Decentralized Total Transfer Capability Evaluation Using Domain Decomposition Methods

In order to ensure the reliable operation of the electric power grid, infrastructure with increasing levels of distributed generation deployment, various entities and subsystems (e.g., ISOs, distribution utilities, micro-grids) needs a higher level coordination. Such coordination requires a formal decentralized control and management architecture that creates mechanisms for the operation and planning of numerous entities. A key function in power system operations and planning is the determination of total transfer capability (TTC), or the maximum amount of power that can be transferred from a set of source points to a set of sink points. This paper develops an algorithm for the decentralized computation of TTC through the application of the domain decomposition method (DDM). DDM decouples the correlation among interconnected control areas by taking advantage of the Schur complement matrix to form the power transfer distribution factor matrix. The decentralized TTC evaluation requires sparse information exchange among control areas. It is efficient and fully parallelizable. We demonstrate the decentralized TTC evaluation method on the IEEE 118-bus test case.

Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems

Introducing distributed generation (DG) in a distribution network has considerable advantages such as reducing power loss and cost, environmental friendliness, voltage improvement, postponing system upgrades and enhancing system reliability and continuity of service. Practical application of the DG, however, proves difficult. Social, economic and political factors affect the final optimal solution. Solution techniques for DG deployment rely on optimisation methods. The technique proposed here finds the optimal location and size of the DG to minimise the total system power loss for radial distribution feeder systems by solving two independent sub-problems: (i) location and (ii) size. A sufficient sensitivity test for the first problem is suggested. Determining the optimal DG size is done using a new heuristic curve-fitted technique that reduces the search-space by selecting fewer DG-tests. Four DG sizes, which are carefully selected based on the system's total load demand percentages, are used to determine the optimal solution. To validate the proposed technique, the 33-bus and 69-bus feeder systems are examined and the results obtained by the presented technique are compared with those obtained using other competing methods.

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

Multicriteria Analysis Of Impacts Of Distributed Generation Sources On Operational Network Characteristics For Distribution System Planning Concerning Steady-state And Transient Operations

Recent advances in power generation technology and new directions in the Brazilian electricity regulation allowed wide spreading of the distributed generation (DG), with some consequent impacts on operational characteristics of the distribution networks. So, new methods for identifying such impacts become necessary in research and development of new tools and resources to maintain and ease the continued expansion towards DG deployment. Initially, this paper presents a study aiming at determination of the appropriate DG locations within distribution systems. This analysis presents a multicriteria analysis to define some qualitative and quantitative parameters using fuzzy logic to determine the appropriate DG sites. In this direction, the paper analyzes transient effects on the feeder power flow from which it is possible to determine the minimal conditions for guaranteeing DG reliability and high power quality. The considerations and results described in this paper make part of the Research & Development Program developed by the State Company for Electric Energy (CEEE) and the Federal University of Santa Maria (UFSM), Brazil.