Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

Data Analytics for Privacy in Smart Grids

The emergence of smart grids has allowed for integrating new technologies in the power grid, with information flowing across the system allowing for more efficient power delivery and event response. Demand response is a new technology enabled by smart grids, which is a program aiming to reduce or shift peak demand by varying the price of electricity or offering incentives for changing consumption habits.Despite demand response benefits, privacy advocates have raised concerns with information leakages allowed by the type of high-resolution data collected by smart meters, as it can reveal customer usage patterns and different parties can take advantage of that data. In this thesis, a utility vs. privacy framework is developed to maximize the utility of using smart meter data while also minimizing the privacy leakages from the smart meter.Two frameworks are developed, the first, a fault localization technique for radial distribution systems by using alarm processing through binary integer linear programming. The second, a power scheduling tool that uses renewables, a battery, and appliance scheduling to disguise the customer usage patterns by matching it to an average and the resulting collected data is not revealing of any characteristics the customer wants to hide.Fault localization was tested on two radial distribution systems, and locates the fault every time, with the variation in time till detection depending on system size, how the system is branched, fault location, and sampling rate. Power scheduling was tested using simulated home data, different scenarios are run by varying battery, solar, appliance, and privacy parameters, and results are compared for various sampling rates. Both frameworks were successful in hiding privacy leakages based their respective privacy metric.Future research on the fault localization could expand to find two faults simultaneously, along with implementing an emergency mode to find faults quicker in a sampling cycle. The power scheduling framework could expand to include thermostatically controlled load scheduling, by implementing deep learning algorithms on each home and factoring in variables such as historic data of weather, time of day, and day of week to determine how thermostatically controlled loads could fit into the scheduling problem

Privacy and Security for Smart Metering System with Fault Controlling Technique

Smart Metering (SM) is an important and essential element of the upcoming energy network. The importance of smart metering is that it interconnects smart grid elements and functions among a two way communication network. The target is to support an economically efficient sustainable power system with high quality and security. To achieve this objective, advanced smart metering functions might include automated meter readings, distributed energy storage, distributed energy resource management, as well as further energy efficiency mechanisms such as real time optimizations for load shifting and scheduling. In existing system the information transmission security, privacy, meter reading observation between network and client then data transmission monitoring system which can be controlled through Wireless Area Network (WAN). However the information transmission observation system has not economical for fault dominant technique. In proposed system, whole system will be monitored, controlled, data have been secured and also effective load scheduling can be provided for this system too. EB line faults such as short circuit, over voltage and under voltage are monitored and controlled through WAN. It improves the protection and privacy of information transmission between network and client. DOI: 10.17762/ijritcc2321-8169.15037

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Privacy In The Smart Grid: An Information Flow Analysis

Project Final Report prepared for CIEE and California Energy Commissio

A novel smart energy management as a service over a cloud computing platform for nanogrid appliances

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day