Development of Smart Energy Meter for Energy Cost Analysis of Conventional Grid and Solar Energy

In view of the low measurement accuracy of traditional mechanical energy meters, the inability to charge by time, and the inability to display relevant parameters, a smart energy meter was designed. The smart energy meter takes the single chip microcomputer as the main control chip, adopts the ADE7755 module to realize the collection and calculation of the electric energy, and combines the 12864 display module, the independent button module, the clock DS1302 module, the storage 24C02 module, etc. to improve the electric energy measurement accuracy, realize time-sharing billing and Parameter display function. Completed the hardware design, software design and physical development of the smart energy meter. The physical test results show that the smart energy meter can realize dual-rate charging, reduce resource waste, and the rate range can be adjusted according to the actual situation, and can display data such as electric energy, electricity cost, time, and date.

In order to study the quality analysis method of key links in smart energy meters, the author proposes a data fusion based quality analysis and prediction method for smart energy meters. This method is based on the relevant data of key links in the electric energy meter, and selects the data of the electric energy meter in research and development design, material procurement, production and manufacturing, acceptance testing, installation and operation, dismantling and scrapping as the sample data for model construction. The XGBoost algorithm classification method is used to establish an intelligent electric energy meter quality analysis model. Taking the dismantled electricity meter data of a certain power company as an example, this paper conducts modeling analysis and prediction of various quality issues of smart electricity meters, and conducts on-site verification. Based on the verification results, the model is continuously optimized. The results indicate that: The model was optimized using cross validation and grid search methods, and the final model achieved an accuracy rate of 0.74 and a recall rate of 0.82 on the validation set. This method can meet the actual needs of power grid business and objectively reflect the quality situation of key links in smart energy meters.

In recent years, with the State Grid Corporation's vigorous construction of smart grids, smart meters have been rapidly promoted in the power system. After several years of online operation, the operating reliability of smart meters has also stabilized. However, in the process of widespread use of smart electric energy meters, the quality problems of clock chips, batteries and other components have also been exposed. This article takes the component failures encountered in the detection and operation of the smart electric energy meter as an example, analyzes the causes of the failures based on the four typical abnormal phenomena, and then conducts the opening detection for the above reasons and determines the nature of the failure based on the test conclusions. Finally, a corresponding fault management platform is established through the collection of electric energy meter fault data, which realizes all-round management and control of the fault handling process and selection of the qualifications of smart meter suppliers, thereby ensuring the quality of components and improving the operation of smart electric meters reliability.

In order to solve the problems of heavy workload, weak planning, and repetitive maintenance in the periodic rotation of smart energy meters, the author proposes a verification cycle optimization method based on the evaluation results of energy meter status. This method first obtains data on six indicators of smart energy meters: regional factors, reliability, full event, abnormal metering events, battery overload, and clock battery undervoltage; Subsequently, on the one hand, the coefficient of variation assignment method is used to obtain the status score of each electricity meter, and on the other hand, these six indicator data are used as input data, and the K means clustering algorithm is used to classify and obtain the corresponding categories. Finally, the two algorithms are combined to obtain a new method for evaluating the status of smart energy meters, and the final evaluation result is output. The experimental results indicate that: The number of electricity meters scored below 80 points obtained by this method accounts for 22.08% of the total number of electricity meters, while electricity meters scored above 80 points account for 77.93% of the total number of electricity meters. This indicates that this method is in line with the actual situation and objective laws. Constructing a state evaluation model for electric energy meters, using historical data and on-site calibration data as state variables, analyzing the annual operational quality of electric energy meters, and providing reference basis for adjusting the calibration cycle of electric energy meters.

In recent years, smart electric energy meters are demand at 70 million to 90 million with the strong smart grid construction every year in China. However, there are some issues in smart electric energy meters data collection such as the interference of environment, low collection efficiency and inability to work when the power is off. In order to solve these issues above, it uses the RFID communication technology to collect the numbers and electric energy information of smart electric energy meters on the basis of the existing smart electric energy meters, and the related data collection communication experiments were made. The experimental result shows that the electric information and other data batch collection of RFID smart electric energy meters are realized in power and power off. It improves the efficiency and the overall success rate is 99.2% within 2 meters. It provides a new method for smart electric energy meters data collection.

With China’s rapid smart grid development, smart energy meters have been widely applied in the power system. To solve large measurement errors and poor stability in traditional electric energy meters, an online electricity metering method based on heuristic Q-learning algorithm is designed. Based on prior knowledge, a heuristic action learning module is designed to guide the action output. A parallel hybrid deep neural network model is constructed to explore the relationship between electric energy data, line energy loss values, and line loss sequences at different time periods from multiple dimensions. The results showed that the accuracy in analyzing abnormal data was higher than 95%. The accuracy of the online error estimation algorithm was higher than 90%, which was significantly improved when compared with previous algorithms. When the line loss rate was less than 7.5%, the accuracy of the online error estimation algorithm was higher than 90%, greatly reducing the interference of line energy loss on online error estimation. This method can effectively solve the problems such as large error, poor stability, and insufficient adaptability of traditional electric meters, especially in the complex cases such as changes in line energy loss and abnormal data types. The proposed method can effectively improve the online measurement accuracy of smart energy meters, accurately estimating the errors of cluster smart energy meters in real-time.

Based on the technical specifications of smart meters, the electrical performance evaluation methods of single-phase smart meters are studied, and the accuracy, stability and safety of smart meters are analyzed and studied through specific data verification. Analysis the basic characteristics of the electrical performance of the smart energy meter, and evaluate the electrical performance based on the basic characteristics. Collect the specific working characteristic data of the intelligent electric energy meter, accurately measure the parameters of the intelligent electric energy meter, and analysis the electrical performance. Analysis the influence of the harmonic components of the power system on the measurement of the electric energy meter, study the power consumption of the smart electric energy meter, and the accuracy and stability of the operation.

The increasing demand for efficient energy management and the rapid growth of Internet of Things (IoT) technology have led to the development of smart energy metering systems. This abstract presents a conceptual design of a smart energy meter that leverages IoT capabilities for enhanced monitoring, control, and optimization of energy consumption in residential and commercial settings. The proposed smart energy meter integrates advanced metering infrastructure (AMI) with IoT sensors, connectivity, and data analytics to enable real-time energy monitoring, intelligent load management, and energy efficiency improvements. The meter collects data on energy consumption, voltage levels, power quality, and other relevant parameters, which are transmitted wirelessly to a central monitoring system. By utilizing IoT connectivity, the smart energy meter facilitates two-way communication, allowing consumers to access their energy usage data remotely via mobile applications or web portals. This empowers users to monitor their energy consumption patterns, set energy-saving goals, and receive personalized recommendations for optimizing their energy usage. Furthermore, the smart energy meter employs data analytics algorithms to analyze the collected energy data and generate actionable insights. These insights can be used to identify energy wastage, detect anomalies, and suggest energy-saving strategies. Additionally, the meter can automatically adjust energy loads based on peak demand periods, optimizing energy distribution and reducing overall energy costs. The proposed smart energy meter offers several benefits, including real-time monitoring, increased energy efficiency, cost savings, and reduced environmental impact. It enables consumers to make informed decisions about their energy usage, promotes sustainable practices, and contributes to the development of smarter and greener communities.

In this paper, the method of parameters measurement and identification of non linear load, using a Smart Energy Meter (SEM), is proposed. Currently, most loads are non-linear containing harmonics when connected to the electrical network. Most modern electricity meter algorithms take these harmonics into account, but with flaws. The proposed system in this paper replaces the traditional meter. The reading method and the communication module are based on the Node MCU ESP8266 WiFi module, a PIC18F4550 microcontroller and the results (active power P, deformation factor D and the active energy We) are displayed and illustrated by graphs on LabView. An LCD displays the numerical values of power and energy.

Environmental sustainability and life cycle cost analysis of smart versus conventional energy meters in developing countries

The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer’s instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.

In the application process of smart electric energy meter, not all components are in working state all the time. According to the working stress to predict its reliability index will lead to the conservatism of the predicted results. Therefore, it is necessary to consider the non-stress state of the intermittent mode unit, so as to make a more reasonable and accurate prediction. In this paper, a reliability prediction method considering non-stress state of components in intermittent operation mode is proposed, and a practical reliability prediction model of electric energy meter is established, which realizes the accurate reliability prediction of intelligent electric energy meter in intermittent mode. Based on the theory of reliability prediction and life cycle cost of electric energy meter, a comprehensive evaluation model of reliability and economy of smart electric energy meter is established, which provides a theoretical basis for the formulation of reliability index and related management policies of smart electric energy meter.

Digitalization is very important in all sector of a developing country. A digital smart energy meter can reduce the harassment of a consumer and effort of vendors over the traditional physical energy meter reading and billing process. This paper presents a very effective solution for this problem with a smart energy meter and digital billing system. This energy meter can measure the real-time consumed energy and store this data into an SD card. For determining the consumed energy different electrical parameters such as AC voltage, the current, phase angle is measured against real-time. It can be implemented in both the prepaid and postpaid billing system. The proposed energy meter has a 20*4 LCD for understating the consumed energy or remaining energy to consumers. A GSM modem is integrated with the system that allows the vendor to inquire about consumed energy and making digital billing without errors. The main power passes through a relay to the consumer. The vendor can cut off or on the consumer's power supply by triggering relay via SMS. By implementing this smart energy meter, the vendor can ensure the proper use of energy. That will be a huge improvement on the national power grid. This smart energy meter is developed by using low-cost components that are available at the local market.

The performance of the smart electric energy meter deteriorates during the operation, which will affects the accuracy of energy metering. In order to estimate the smart meter’s error during the operation, a method based on parameter degradation model is proposed. The meter’s degradation parameters and degradation acting parameters are determined, aiming at building parameter degradation model and putting forward error estimation constraint. Big data analysis methods are adopted in the process of solving degradation network. As the data are of multiple categories and data changing rates are variable, pre-processing method of differential normalized data is employed. Additionally, feed-forward neural network is adopted to approximate degradation characteristics, because elementary function is incapable of describing degradation network. Therefore, the smart meter’s error can be estimated according to the error estimation constraint, assuming degradation acting parameters pre-determined. Case analysis results show that estimation results using the proposed method is in accordance with operating state in short time, and absolute error is less than 0.1%, demonstrating that the error of smart meters can be estimated with this method effectively and dynamically.

The advancement in emerging technologies to make life easier has brought a boon to the world of innovations. In the rural parts of the country, still it has been seen in door-to-door electricity bill collection where a person from the electricity department needs to visit each home to take a household electric meter reading. This manual process of reading bills is a rigid and time taking task. Errors in the reading bill such as extra amount and crime committed in name of reading bills have been recorded in the last few years. In times of pandemic, it became more difficult to read bills, and consumers were pressurized to pay bills of more than 9- 10 months at a single time. To overcome this, there is a need for a smart energy meter that could automate the system of bill reading. This paper introduces the idea of the smart energy meter using LoRa technology. LoRa module which can work with a microprocessor can be very useful to implement this idea. LoRa can transmit a huge amount of data accurately up to a large distance without any significant loss. In big cities, smart energy meter is being used which can read the units and send bills. But since these devices need high-speed network connectivity, hence it is not possible to implement the same technology in rural parts of the country. This paper tends to implement a smart energy meter that could make the existing meter smart, rather than installing a new meter. The implemented idea uses very low energy and works without internet connectivity. The stored data can be accessed by the department globally as it is stored in the cloud.