Real-time Power Consumption Data Research Articles

Performance analysis of a blockchain enabled domestic power consumption monitoring and forecasting system

Purpose The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting. Design/methodology/approach The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms. Findings The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively. Originality/value A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

An Energy Consumption Monitoring and Control System in Buildings using Internet of Things

In this paper a system to monitor and control the electricity consumption by means of Internet of Things (IoT) technology in buildings is presented. Wi-Fi smart plugs act as sensors to provide real-time power consumption data on a per device basis. This information is passed to a cloud-based platform via MQTT for further analysis. This mobile app is used to access the building's energy usage data in both real-time and historical, as well allowing control over connected devices from anywhere. The architecture of the system, i.e. hardware components, software infrastructure as well data flow are explained in details. The results show that the IoT-based smart plugs are providing accurate power consumption data with a low deviation. By providing electricity consumers with a greater level of detail on how and when they consume their power, this system can enable them to consciously exercise control over their energy consumption; which may result in reductions in cost as well as levels of inefficiency from the amount used.

Antiquantum Privacy Protection Scheme in Advanced Metering Infrastructure of Smart Grid Based on Consortium Blockchain and RLWE

Smart meters in advanced metering infrastructure (AMI) collect fine-grained, real-time power consumption data may result in user privacy leakage. Aiming at the problem that existing privacy protection schemes cannot resist quantum attacks, an antiquantum privacy protection scheme in AMI of smart grid based on consortium blockchain and ring learning with errors (RLWE) is proposed. The scheme has the following contributions. First, the RLWE-based secure communication scheme is designed in this article, which ensures the resistance of the scheme to quantum attacks. Second, all smart meters in AMI use anonymous identity to communicate with the control center, and the fine-grained power consumption data is also encrypted and digitally signed to prevent user privacy leakage and tampering of power consumption data. And the scheme does not use bilinear pairings and exponential operations and has efficient computational performance. Third, a two-tier consortium blockchain storage system consisted of regional gateways and data aggregators are designed to eliminate the single point of failure problems of the data aggregators. The practical byzantine fault tolerance consensus mechanism is improved to reduce the communication cost in the consensus process. The scheme's security is proven theoretically, and the experimental results show that the scheme has a low computational cost.

Multiauthority Traceable Ring Signature Scheme for Smart Grid Based on Blockchain

As the next‐generation power grid system, the smart grid can realize the balance of supply and demand and help in communication security and privacy protection. However, real‐time power consumption data collection might expose the users’ privacy information, such as their living habits and economic conditions. In addition, during the process of data transmission, it may lead to data inconsistency between the user side and the storage side. Blockchain provides tamper‐resistant and traceable characteristics for solving these problems, and ring signature schemes provide an anonymous authentication mechanism. Therefore, in this work, we consider the applications of ring signature scheme in smart grid based on blockchain. We introduce the notion of multi‐authority traceable ring signature (MA‐TRS) scheme for distributed setting. In our scheme, there is an auditing node that can distinguish the identity of the real signer from the ring without any secret information. Last but not least, we prove that the proposed scheme is unforgeable, anonymous, and traceable.

A privacy protection scheme of smart meter for decentralized smart home environment based on consortium blockchain

In order to ensure the reasonable dispatch and utilization of power in smart homes, decentralized smart homes need to send power information to the power control center, and the power control center need to send control instructions to smart homes. However, the bi-directional interaction between smart homes and the power control center may lead to a series of security problems, such as smart meters of smart homes collect fine-grained real-time power consumption data and upload it to the control center, which will lead to the leakage of users’ privacy. The existing schemes protect users’ privacy by signcryption and data aggregation usually, and elliptic curve bilinear pairing or exponential operation are used in most of them, whose computation costs is high. In addition, the existing schemes for centralized storage of power consumption data may result in some security problems, such as single point failure, malicious tampering of data, etc. Aiming at the problems above, a privacy protection scheme of smart meter for decentralized smart home environment based on consortium blockchain is proposed, elliptic curve point multiplication operation is used in the certificateless aggregated ring signcryption algorithm, which reduces the computing and communication costs; distributed storage of users’ data with consortium blockchain is used to solve the problem of single point failure and data tampering. Theoretical analysis proves that the scheme not only guarantees users’ privacy, but also has characteristics of confidentiality and unforgeability, and experiments show that the scheme has lower computing and communication costs.

PIDS: An Intelligent Electric Power Management Platform

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

Economic and environmental aspects of Smart Grid technologies implementation in Ukraine

Interruptions in electricity supply may have a series of failures that can affect banking, telecommunications, traffic, and safety sectors. Due to the two-way interactive abilities, Smart Grid allows consumers to automatically redirect on failure, or shut down of the equipment. Smart Grid technologies are the costly ones; however, due to the mitigation of possible problems, they are economically sound. Smart grids can't operate without smart meters, which may easily transmit real-time power consumption data to energy data centers, helping the consumer to make effective decisions about how much energy to use and at what time of day. Smart Grid meters do allow the consumer to track and reduce energy consumption bills during peak hours and increase the corresponding consumption during minimum hours. At a higher level of management (e.g., on the level of separate region or country), the Smart Grid distribution system operators have the opportunity to increase the reliability of power supply primarily by detecting or preventing emergencies. Ukraine's energy system is currently outdated and cannot withstand current loads. High levels of wear of the main and auxiliary equipment of the power system and uneven load distribution in the network often lead to emergencies and power outages. The Smart Grid achievements and energy sustainability are also related to the energy trilemma, which consists of key core dimensions– Energy Security, Energy Equity, and Environmental Sustainability. To be competitive in the world energy market, the country has to organize efficiently the cooperation of public/private actors, governments, economic and social agents, environmental issues, and individual consumer behaviors. Ukraine gained 61 positions out of 128 countries in a list in 2019 on the energy trilemma index. In general, Ukraine has a higher than average energy security position and lower than average energy equity, and environmental sustainability positions. Given the fact that the number of renewable energy sources is measured in hundreds and thousands, network management is complicated and requires a Smart Grid rapid response. Keywords: economic development, Smart Grid, electricity supply, economic and environmental efficiency.

Real-time remote energy consumption location for power management application

ABSTRACT Electricity generation continue to increase to meet the ever-growing demand of the built environment. Building’s miscellaneous plug loads are targeted for energy savings potentials. However, to achieve these savings, monitoring their energy consumption and providing comprehensive real-time energy usage information to the end-user is paramount. Real-time energy monitoring devices are significant tools for this purpose. However, deploying these devices for each load and for entire building, is cost-prohibitive. An alternative approach is to deploy tools to remotely identify the location of active-loads in real-time. This research proposes the development of the Energy Node Locating Method (ENLM) platform that remotely locates and measures power consuming loads at every electrical node, in the building, in real-time based on Sequence Time Domain Reflectometry (STDR). The proposed ENLM utilizes the measured time-delay between an injected and reflected signal at a branch circuit from any connected load to calculate the length of the physical wire to identify the location of energy usage. This information with real-time power consumption data are correlated with occupant’s entry data to identify where and how much energy is used. Various tests are conducted to validate the proposed platform, and the results confirm the validity of the platform.

A privacy-preserving aggregation scheme based on immunological negative surveys for smart meters

The smart meter is a basic device of the smart grid, which improves the efficiency of the power grid and brings a lot of convenience for the industry and people’s daily life. However, real-time power consumption data contain some sensitive information, and could disclose the privacy of users. As an immunological technique, the negative survey is proposed to preserve the privacy of static data. In this paper, firstly, we demonstrate that traditional negative survey might disclose the privacy of users when it is used to collect time-series data. Secondly, we propose an improved negative survey method for collecting the time-series data. Thirdly, for the first time, we apply the negative survey to preserving the privacy of the power consumption data aggregated from smart meters. Theoretical analysis and experimental results demonstrate that the method proposed in this paper could aggregate the power consumption data while preserving the privacy of users. Compared with existing techniques, our method is simple and efficient, and does not need a trusted third party. Moreover, it could tolerate the failure of some users and resist differential attack.

Implemented IoT-Based Self-Learning Home Management System (SHMS) for Singapore

Internet of Things makes deployment of smart home concept easy and real. Smart home concept ensures residents to control, monitor, and manage their energy consumption without any wastage. This paper presents a self-learning home management system. In the proposed system, a home energy management system, demand side management system, and supply side management system were developed and integrated for real time operation of a smart home. This integrated system has some capabilities such as price forecasting, price clustering, and power alert system to enhance its functions. These enhancing capabilities were developed and implemented using computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data was collected from a Singapore smart home and a realistic experimental case study was carried out. The case study has shown that the developed system has performed well and created energy awareness to the residents. This proposed system also displays its ability to customize the model for different types of environments compared to traditional smart home models.

Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach

Energy theft is a notorious problem in electric power systems, which causes great economic losses and threatens the reliability of the power grid. Recently, the Smart Grid has been proposed as the next-generation power system to modernize the current grid and improve its efficiency, sustainability, and security. Key technologies of the Smart Grid include smart meters, which allow system operators to collect real-time power consumption data from users, and microgrids, which allow users to own and control renewable resources. However, the Smart Grid is vulnerable to cyber attacks, thus making stealing energy much easier in it. Most existing energy theft detection schemes require the collection of real-time power consumption data from users, i.e., users’ load profiles, which violates their privacy. In this paper, we first propose a centralized energy theft detection algorithm utilizing the Kalman filter, called SEK. It can efficiently identify the energy thieves but cannot protect users’ privacy. Then, based on SEK, we develop a privacy-preserving energy theft detection algorithm called PPBE, which privately finds the energy thieves by decomposing the Kalman filter into two parallel and loosely coupled filters. Finally, we conduct thorough privacy analysis and extensive simulations to validate our proposed algorithms.