An Object-Oriented Framework for Analysis of MV/LV Distribution Systems

This work was conducted as part of the AMEN (Agentbased Modelling of Electricity Networks) project, funded by the Engineering and Physical Sciences Research Council in the UK under Grant Reference EP/K033492/1.The interest of this work is in designing and developing a simulation package for comprehensive analysis of a distribution system based on object-oriented principles. Towards such an objective, we propose a design of a framework based on an object-oriented approach that is capable of handling such features and yet is simple and flexible enough for further extension. Here, we present a novel derivation of an object oriented methodology to firstly model the various components of a present day distribution system, and then to solve for the power flows across the system. Innovative aspects of the implementation of the derived system are how the various components are represented for power flow calculations using a standard backward/forward algorithm. An IEEE test feeder is used to demonstrate the framework. This work is of interest to model developers, distribution network planners, software designers and most importantly to users in the academia and industry

An NSGA-II based Multi-Objective Approach for Distribution System Voltage Control

The aim of this work is to offer a voltage control strategy for distribution networks that experience voltage unbalance due to single phase and unbalanced loads and voltage rise due to high penetration of Distributed Generation units. The objectives are minimization of voltage imbalance on each node and total power losses on the entire network. The control of node voltages by Distributed Generation units has potential to clash with the more traditional method of voltage control adopted by Distribution Network Operators namely, tap changing voltage regulators and shunt capacitors. We look at a coordinated method of voltage control that solves the multi-objective optimization problem of voltage profile improvement and power loss reduction using a Pareto optimal and elitist evolutionary optimization algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II). The study system is the IEEE 123 bus distribution test feeder which is highly unbalanced and includes most of the elements of a real network

Design and Analysis of Electrical Distribution Networks and Balancing Markets in the UK: A New Framework with Applications

We present a framework for the design and simulation of electrical distribution systems and short term electricity markets specific to the UK. The modelling comprises packages relating to the technical and economic features of the electrical grid. The first package models the medium/low distribution networks with elements such as transformers, voltage regulators, distributed generators, composite loads, distribution lines and cables. This model forms the basis for elementary analysis such as load flow and short circuit calculations and also enables the investigation of effects of integrating distributed resources, voltage regulation, resource scheduling and the like. The second part of the modelling exercise relates to the UK short term electricity market with specific features such as balancing mechanism and bid-offer strategies. The framework is used for investigating methods of voltage regulation using multiple control technologies, to demonstrate the effects of high penetration of wind power on balancing prices and finally use these prices towards achieving demand response through aggregated prosumers

Balancing electricity supply with distribution network constraints

The need for electricity demand to adapt to distribution network constraints and intermittent supply from renewable generators is widely recognised. However addressing both these goals simultaneously with a single demand side management scheme is difficult within a deregulated industry framework. This paper describes a scalable method by which electricity suppliers can shape demand from domestic and SME consumers, particularly that arising from adoption of heat pumps and electric vehicles. A process is proposed by which Distribution Network Operators notify a maximum power profile for each connection to the supplier, who then uses it to shape demand from each consumer so that DNO limits are satisfied as a priority, and demand responds to supply availability as far as possible

Estimation of multi-pattern-to-single-pattern functions by combining feedforward neural networks and support vector machines

Department of Electrical Engineering, Indian Institute of Science, Bangalore, India.In many fields there are situations encountered where a function has to be estimated to determine its output under new conditions. Some functions have one output corresponding to differing input patterns. Such types of function are difficult to map using a function approximation technique such as that employed by the multilayer perceptron networks. Hence to reduce this functional mapping to single pattern-to-single pattern type of condition, and then effectively estimate the function, we employ classification techniques such as the support vector machines. This paper describes in detail such a combined technique, which shows excellent results for a practical application in the field of power distribution systems

Loss Sensitivity and Voltage Deviation Index Based Intelligent Technique for Optimal Placement and Operation of Distributed Generators

The Publisher's final version can be found by following the DOI link.Distribution system is the crucial part of the power system which needs to be monitored for quality since it is the last stage in delivering electricity to consumers. The main aim of suppliers is therefore to maintain excellent quality of supply with minimal support at the network level. This includes dealing with issues around voltage regulation and minimization of real power losses on the network which are reflected in a uniform voltage profile through various load points on the feeders. However, with increase in decentralization, Distributed Generation (DG) becomes the focal point of distribution networks and can be used to achieve the above mentioned objectives. DGs are used in this paper to minimize the losses along with an improvement in the voltage profile. The location of these DG units on the network is addressed as an optimization problem using the loss sensitivity index and voltage deviation index. The size of power injected by DG units is obtained with using an intelligent approach that combines Genetic Algorithm (GA) with neural networks. Different cases of DG in terms of real and reactive power injected are considered resulting in an improvement in voltage profile. The loss sensitivity and voltage deviation indices show the best result for the location of DG needed to minimize the power loss along with improvement in voltage regulation. This approach is tested on a 13-bus distribution network with different types of loads placed throughout the feeder

Artificial neural network and support vector machine approach for locating faults in radial distribution systems

This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis

Voltage Stability Index and Voltage Deviation Improvements using Intelligent Algorithms for Capacitor Sizing and Placement

The Publisher's final version can be found by following the DOI link.Voltage stability of each bus of an electrical distribution system along with the deviation of voltage magnitudes from the tolerance limits are two of the most common but significant issues in the present day distribution networks. This work is carried out with the objective of identifying the optimal location and ideal sizes of capacitors to be used in a radial distribution network for alleviating the above problems. An intelligent two-stage methodology is used that employs genetic algorithm and neural networks in order to achieve this objective. By analyzing the load flow study of the base case of load profile, voltage deviation and voltage stability indices are calculated for each node of the system. From these indices, the candidate buses are identified for the capacitor allocation in stage one of the methodology. Then, the combination of two artificial intelligent algorithms based on modern learning methods are employed to identify the ideal sizes of the capacitors for operation throughout the day. This methodology is implemented on a 33 bus radial distribution network. The results of these intelligent algorithms predict the ideal sizes of capacitors with optimal locations, providing a stable system with a smooth voltage profile across the entire duration of the day