Soma Shekara Sreenadh Reddy Depuru

Smart meters for power grid — Challenges, issues, advantages and status

Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.

Support vector machine based data classification for detection of electricity theft

Most utility companies in developing countries are subjected to major financial losses because of non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to the poor infrastructure. Electricity theft and billing irregularities form the main portion of NTL. These losses affect quality of supply, electrical load on the generating station and tariffs imposed on electricity consumed by genuine customers. In light of these issues, this paper discusses the problems underlying detection of electricity theft, previously implemented ways for reducing theft. In addition, it presents the approximate energy consumption patterns of several customers involving theft. Energy consumption patterns of customers are compared with and without the presence of theft. A dataset of customer energy consumption pattern is developed based on the historical data. Then, support vector machines (SVMs) are trained with the data collected from smart meters, that represents all possible forms of theft and are tested on several customers. This data is classified based on rules and the suspicious energy consumption profiles are grouped. The classification results of electricity consumption data are also presented.

Measures and setbacks for controlling electricity theft

Most of the utility companies in developing countries incur huge losses because of the non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to their poor infrastructure. Electricity theft and billing irregularities form a major chunk of NTL. These losses affect quality of supply, electrical load on the generating station and tariff imposed on electricity consumed by genuine customers. This paper discusses various factors those influence the consumer to make an attempt to steal electricity In addition, some handy cases where electricity theft are detected will be illustrated. In view of these ill effects, some methods for detection and estimation of the theft will be discussed. This paper also illustrates several methods to quantify and control theft. In essence, setbacks for implementation of these measures and techniques will be illustrated in detail. Motivation of this work is to conserve the interest of utility companies in providing quality electricity to genuine customers at affordable tariff.

A conceptual design using harmonics to reduce pilfering of electricity

Electricity theft is a major problem in developing countries and it has been very difficult for the utility companies to detect and fight against the people responsible for theft. This paper proposes an architectural design of smart meter, external control station, harmonic generator, and filter circuit, which can detect and chastise the appliances of people responsible for electricity theft. The motivation of this work is to deject the illegal consumers, and conserve and effectively utilize energy. As well, smart meters are designed to provide data of various parameters related to instantaneous power consumption. Dynamic behavior of such meters can be managed and controlled by utility companies. Total loss in the distribution feeder is computed by the external control station from the values of total load consumption and technical losses in the distribution feeder. If considerable amount of non-technical losses are detected at any given feeder, harmonic generator is operated for introducing harmonics into the feeder for destroying the appliances of the illegal consumers. In addition, harmonic analysis of the distribution feeder and consumer appliances due to the presence of harmonics is carried out to estimate the effect of induced harmonics. For illustration, cost-benefit analysis for implementation/maintenance of the proposed system in India is presented.

Demand response simulation implementing heuristic optimization for home energy management

This paper introduces optimized operation of household appliances in a Demand-Side Management (DSM) based simulation tool. DSM can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. The principal purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customers cost. An optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for optimizing the DSM operation of the tool. The tool also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study for various household situations.

A hybrid neural network model and encoding technique for enhanced classification of energy consumption data

Total losses in transmission and distribution (T&D) of electrical energy including nontechnical losses (NTL) are huge and are affecting the good interest of utility company and its customers. In this context, importance of customer load profile evaluation for detection of illegal consumers is explained in this paper. Classification of the customers based on load profile evaluation using SVMLIB requires us to choose training function and related parameters. Selecting these parameters would consume a lot of time and is not suggestible evaluation of real time electricity consumption patterns, as, the suspicious profiles are to be predicted instantly. In light of this issue, this paper implements a neural network (NN) model and suggests a hierarchical model for enhanced estimation of the classification efficiency, if that data was classified using support vector machines (SVM). In addition, this paper proposes an encoding technique that can identify illegal consumers with better efficiency and faster classification of data.

A demand-side management simulation platform incorporating optimal management of distributed renewable resources

This paper introduces dynamic distributed resource management in a Demand-Side Management (DSM) based simulation tool. The principle purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customers cost. The tool simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization, which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study, and compared with the operating costs of the same system without the optimization algorithm.

Evaluating the impact of Plug-in Hybrid Electric Vehicles on composite power system reliability

Climate change is a matter of pressing importance for modern society. One method that has been suggested for mitigating the role that fossil fuel based transportation plays in this issue is the introduction of Plug-in Hybrid Electric Vehicles (PHEVs) in order to electrify the transportation sector. While these vehicles would have a significant impact in reducing greenhouse gases (GHGs), they may also place a larger strain on the current power grid and the coming Smart Grid. In order to address this issue much work has been completed in order to examine the impact of PHEVs on distribution systems in modern power grids while little effort has been made to examine the effect that the inclusion of this new load will have on generation and transmission systems. As such, this work extends the probabilistic reliability evaluation of power systems by developing a model for reliability evaluation using Monte Carlo Simulation (MCS) to examine the impact that PHEVs will have on composite power systems. The model is examined using the IEEE-RTS across multiple load and penetration levels.

Enhanced encoding technique for identifying abnormal energy usage pattern

Transmission and Distribution (T&D) of electricity from a power generation station involve substantial losses. T&D losses include technical as well as nontechnical losses (NTL). Most portion of the NTL constitutes of electricity theft. This paper explains the significance of the evaluation of customer energy consumption profiles for identification of illegal consumers. To reduce the complexity of the instantaneous energy consumption data for evaluation, this paper proposes and implements a data encoding technique. This encoding technique maps instantaneous energy consumption data into irregularities in consumption. In addition, exclusivity in each customers energy consumption has been preserved. After the encoding process, the data has been inputted to a support vector machine (SVM) classification model that classifies customers into three categories: genuine customers, illegal consumers or suspicious customers. Classification accuracy of the SVM model with the encoded data is 92%. The obtained results demonstrate that this encoding procedure is significantly quick and robust in identifying (classifying) illegal consumers.

An examination of artificial immune system optimization in intelligent state space pruning for LOLP estimation

The probabilistic reliability evaluation of composite power systems is a complicated and computation intensive task. Monte Carlo Simulation (MCS) is often used as the method of choice for tackling this difficult problem, though MCS may also suffer from issues regarding high dimensionality leading to an increased need for computational resources. In order to address this issue an algorithmic method known as state space pruning has been developed in two flavors: Analytical and Metaheuristic based. The state space pruning methodology reduces the size of a given state space by removing states where there is no loss-of-load. This allows the MCS algorithm to sample a state space with a higher density of failure states which, in turn, leads to faster convergence. This study applies the CLONALG algorithm to the metaheuristic based version of state space pruning, compares and contrasts the results with genetic algorithm (GA) and particle swarm optimization (PSO) implementations, and discusses its strengths and weaknesses as applied to test systems both with and without the consideration of transmission line outages. Simulations are completed using the IEEE reliability test system (RTS) and the modified RTS (MRTS).