Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively r...Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.展开更多
Digital networked communications are the key to all Internet-of-things applications, but especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users...Digital networked communications are the key to all Internet-of-things applications, but especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users, the transport layer security （TLS） protocol, a mature and well standardized solution for secure communications, may be used. We implemented the TLS protocol in its latest version in a way suitable for embedded and resource-constrained systems. This paper outlines the challenges and opportunities of deploying TLS in smart metering and smart grid applications and presents performance results of our TLS implementation. Our analysis shows that given an appropriate implementation and configuration, deploying TLS in constrained smart metering systems is possible with acceptable overhead.展开更多
To implement the access and backhaul networks for Smart Metering (SM) systems various technologies are combined with the existing communications infrastructure. This paper deals with data transmission in SM systems, f...To implement the access and backhaul networks for Smart Metering (SM) systems various technologies are combined with the existing communications infrastructure. This paper deals with data transmission in SM systems, focusing on how the existing cellular networks infrastructure is employed to implement SM access communication networks. The analysis aims at analyzing the role of the cellular communications infrastructure taking into account the spatial distribution and installation points of the smart meters, the urban and topological characteristics of the SM deployment areas and the common practice so far followed by the utilities. It is demonstrated that cellular communications, either exclusively or combined with power line communications, enable immediate and scalable deployment of SM access communication networks at low installation cost, thus constituting the basic option for the implementation of smart metering.展开更多
This article introduces the current situation of the smart then describes the relationship of meter reliability characteristics meter＇s reliability and the failure mechanisms at first, and combined with its Bathtub C...This article introduces the current situation of the smart then describes the relationship of meter reliability characteristics meter＇s reliability and the failure mechanisms at first, and combined with its Bathtub Curve. It also introduces both the feasible failure tree model for meter lifecycle prediction based on actual experiences and meter reliability prediction methodology by SN 29500 norms based on this model. This article also brings forward that it is necessary that the ＂Learning Factor＂ shall be adopted in meter reliability prediction for new materials, new process, and customized parts by referring to GJB/Z299C. Thereafter, this article also tries to apply IEC 62059 and JB/T 50070 to introduce the feasible method for the lifecycle prediction result verification by accelerated lifecycle test. Furthermore, the article also explores ways to increase the firmware reliability in smart meter.展开更多
Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on d...Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.展开更多
In some countries, there exists a risk of power deficit in the EPS （electrical power system）. This is a very serious problem and there are various solutions to deal with it. A power deficit in the EPS leads to frequ...In some countries, there exists a risk of power deficit in the EPS （electrical power system）. This is a very serious problem and there are various solutions to deal with it. A power deficit in the EPS leads to frequency decrease in the power system. A dedicated automation to load shedding is used to maintain proper EPS operation. For some time, it has applied a mechanism called demand-side response, which in case of an emergency situation allows for a ＂more civilized＂ rationing of electricity to customers, with their consent. Such programs require that the utilities pay the customers for their agreement. The author proposes a new solution, intermediate between strict ALS （acting relieving automation） and demand-side response programs, where the companies have to send information about the price of energy or control signals to households.展开更多
Advanced intelligent or ＂smart＂ meters are being deployed in Asia. A result of deployment of smart meters, with associated equipment, is the electric power industry faced with new and changing threats, vulnerabiliti...Advanced intelligent or ＂smart＂ meters are being deployed in Asia. A result of deployment of smart meters, with associated equipment, is the electric power industry faced with new and changing threats, vulnerabilities and re-evaluate traditional approaches to cyber security. Protection against emerging cyber-security threats targeting smart meter infrastructures will increase risk to both the utility and customer if not addressed within initial rollouts. This paper will discuss the issues in SMI （smart meter infrastructures） deployments that pertain to cyber security. It will cover topics such as the threats to operations, infrastructure, network and people and organization and their associated risks. SMI deployments include not only the smart meter, but also the interfaces for home energy management systems as well as communication interfaces back to the utility. Utilities must recognize and anticipate the new threat landscape that can attack and compromise the meter and the associated field network collectors. They must also include threats to the WAN （wide-area-network） backhaul networks, smart meter headends, MDMS （meter data management systems） and their interfaces to CIS （customer information systems） and billing and OMS （outage management systems）. Lessons learned from SMI implementations from North America, Europe and recently, Japan, will be discussed. How white-box and black-box testing techniques are applied to determine the threat impact to the SMI. Finally, organizational change risk will be discussed and how utilities have responded to re-organizing and developing a security governance structure for the SMI and other smart grid applications.展开更多
Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction...Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction incentive） scheme. This paper discusses these two programs and evaluates their respective performances. We develop an efficient approach based on marginal cost pricing to redesign the TOU rate scheme. In our finding, the TOU price levels could be revised to encourage more customers to participate by enlarging the price gap. Moreover, the DRI scheme can be further improved in order to reach an efficient win-win solution among customers, the utility and society. This can be achieved via a careful design of incentive tariff discounts to take account of the time-of-use or location-specific features of the power supply/demand condition.展开更多
Prepaid energy meters have been widely adopted by utilities in different countries across the world as an innovative solution to the problem of affordability and consumption management. However, the present smart card...Prepaid energy meters have been widely adopted by utilities in different countries across the world as an innovative solution to the problem of affordability and consumption management. However, the present smart card based systems have some inherent problems like added cost, low availability and lack of security. In the future Smart Grid paradigm, use of smart meters can completely overhaul these prepaid systems by introducing centralized accounting, monitoring and credit-control functions using state-of-the-art telecommunication technologies like WiMAX. In this paper we pro-pose a prepaid smart metering scheme for Smart Grid application based on centralized authentication and charging using the WiMAX prepaid accounting model. We then discuss its specific application to Demand Response and Roam-ing of Electrical Vehicles.展开更多
Smart meter networks are the backbone for smart electrical distribution grid. Smart meter network requires the bidirectional communications medium and interoperability capability. As thousands of meters are interconne...Smart meter networks are the backbone for smart electrical distribution grid. Smart meter network requires the bidirectional communications medium and interoperability capability. As thousands of meters are interconnected in the smart meter network, it is vital to select an appropriate communication bandwidth to facilitate real-time two-way information flows and this will also allow further uptake of greenhouse-friendly technology options and enhance energy security. Optimized Network Engineering Tools (OPNET) Modeler is one of most powerful simulation tools for the analysis of communication networks. In this paper, several models of different structured smart meter networks were developed with network parameters which were connected with different communication links such as 10 BaseT and 100 BaseT in order to measure propagation delay, throughput, and utilization of the network. It was found that the propagation delay decreases with higher bandwidth. The other network parameters, namely network utilization and network throughput were also analysed. Based on the investigation, it is recommended that the 100 BaseT communication link is suitable for the smart meter network. The outcome of this paper provided a guideline to the future smart meter network developer so as to avoid catastrophic challenges faced by some of the distribution companies.展开更多
Smart meters provide a lot of convenience for both power supply and consumption. Due to the frequent transmission of information, it brings great challenges to the privacy preservation of the user’s household power c...Smart meters provide a lot of convenience for both power supply and consumption. Due to the frequent transmission of information, it brings great challenges to the privacy preservation of the user’s household power consumption data in the smart grid. In order to achieve the anonymity of smart meters. A smart meter privacy preservation scheme based on identity authentication is proposed. The third-party certification authority is introduced in this scheme;it issues pseudonym certificates to realize the identity privacy preservation of smart meters. The masking technology with the Advanced Encryption Standard algorithm is used for data aggregation. The results show that our scheme reduces the computational cost and the communication overhead.展开更多
Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. ...Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.展开更多
At present, DL/T 645-2007 communication protocol is used to collect data for smart meters. However, in the beginning, this protocol is not designed to be a secure protocol and only the function and reliability were ta...At present, DL/T 645-2007 communication protocol is used to collect data for smart meters. However, in the beginning, this protocol is not designed to be a secure protocol and only the function and reliability were taken into account. Plaintext is used in the protocol for data transmission, as a result, attackers can easily sniff the information and cause information leakage. In this paper, man-in-the-middle attack was used to verify that the smart meter data acquisition process was vulnerable when facing third-party attacks, and this can result in data eavesdropping. In order to resist such risks and prevent information being eavesdropped, a real ammeter communication experimental environment was built, it realized two-way identity authentication between data acquisition center and ammeter data center. At the same time, RSA (Rivest-Shamir-Adleman) was used to encrypt the meter data, which encrypted the collection, storage process of meter data and ensured the confidentiality and integrity of the meter data transmission. Compared with other methods, this method had obvious advantages. The analysis showed that this method can effectively prevent the data of smart meters from being eavesdropped.展开更多
With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distri...With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.展开更多
The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of...The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.展开更多
It has been widely recognized that the efficiency of a thermal power system can be improved by technological advancement of electricity generation and manipulation of electricity consumption. The smart meter enables t...It has been widely recognized that the efficiency of a thermal power system can be improved by technological advancement of electricity generation and manipulation of electricity consumption. The smart meter enables two-way communication between the customers and the electricity generation system. The electricity generation system uses price incentive （i.e. a higher price in the peak period and a lower price in the off-peak period） to shift part of demands from peak to off-peak period under the smart grid environment. Given the fact that fuel consumption in each period is a strictly increasing convex function of power output, we propose two-period and multi-period pricing strategies, and study the effect of different pricing strategies on reducing fuel consumption.展开更多
The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classificati...The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.展开更多
The massive development of internet of things(IoT)technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth,smart city,agriculture or waste management.This o...The massive development of internet of things(IoT)technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth,smart city,agriculture or waste management.This ongoing development is further pushed forward by the gradual deployment of 5G networks.With 5G capable smart devices,it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT.Massive-IoT(low-power wide area network-LPWAN)enables improved network coverage,long device operational lifetime and a high density of connections.Despite all the advantages of massive-IoT technology,there are certain cases where the original concept cannot be used.Among them are dangerous explosive environments or issues caused by subsurface deployment(operation during winter months or dense greenery).This article presents the concept of a hybrid solution of IoT LoRaWAN(long range wide area network)/IRC-VLC(infrared communication,visible light communication)technology,which combines advantages of both technologies according to the deployment scenario.展开更多
Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters hav...Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.展开更多
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
文摘Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.
基金supported in part by the Federal Ministry of Economics and Energy as a cooperative ZIM-KF project under Grant No.KF2471305ED2the good cooperation with the project partner SSV Software Systems GmbH
文摘Digital networked communications are the key to all Internet-of-things applications, but especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users, the transport layer security （TLS） protocol, a mature and well standardized solution for secure communications, may be used. We implemented the TLS protocol in its latest version in a way suitable for embedded and resource-constrained systems. This paper outlines the challenges and opportunities of deploying TLS in smart metering and smart grid applications and presents performance results of our TLS implementation. Our analysis shows that given an appropriate implementation and configuration, deploying TLS in constrained smart metering systems is possible with acceptable overhead.
文摘To implement the access and backhaul networks for Smart Metering (SM) systems various technologies are combined with the existing communications infrastructure. This paper deals with data transmission in SM systems, focusing on how the existing cellular networks infrastructure is employed to implement SM access communication networks. The analysis aims at analyzing the role of the cellular communications infrastructure taking into account the spatial distribution and installation points of the smart meters, the urban and topological characteristics of the SM deployment areas and the common practice so far followed by the utilities. It is demonstrated that cellular communications, either exclusively or combined with power line communications, enable immediate and scalable deployment of SM access communication networks at low installation cost, thus constituting the basic option for the implementation of smart metering.
文摘This article introduces the current situation of the smart then describes the relationship of meter reliability characteristics meter＇s reliability and the failure mechanisms at first, and combined with its Bathtub Curve. It also introduces both the feasible failure tree model for meter lifecycle prediction based on actual experiences and meter reliability prediction methodology by SN 29500 norms based on this model. This article also brings forward that it is necessary that the ＂Learning Factor＂ shall be adopted in meter reliability prediction for new materials, new process, and customized parts by referring to GJB/Z299C. Thereafter, this article also tries to apply IEC 62059 and JB/T 50070 to introduce the feasible method for the lifecycle prediction result verification by accelerated lifecycle test. Furthermore, the article also explores ways to increase the firmware reliability in smart meter.
文摘Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.
文摘In some countries, there exists a risk of power deficit in the EPS （electrical power system）. This is a very serious problem and there are various solutions to deal with it. A power deficit in the EPS leads to frequency decrease in the power system. A dedicated automation to load shedding is used to maintain proper EPS operation. For some time, it has applied a mechanism called demand-side response, which in case of an emergency situation allows for a ＂more civilized＂ rationing of electricity to customers, with their consent. Such programs require that the utilities pay the customers for their agreement. The author proposes a new solution, intermediate between strict ALS （acting relieving automation） and demand-side response programs, where the companies have to send information about the price of energy or control signals to households.
文摘Advanced intelligent or ＂smart＂ meters are being deployed in Asia. A result of deployment of smart meters, with associated equipment, is the electric power industry faced with new and changing threats, vulnerabilities and re-evaluate traditional approaches to cyber security. Protection against emerging cyber-security threats targeting smart meter infrastructures will increase risk to both the utility and customer if not addressed within initial rollouts. This paper will discuss the issues in SMI （smart meter infrastructures） deployments that pertain to cyber security. It will cover topics such as the threats to operations, infrastructure, network and people and organization and their associated risks. SMI deployments include not only the smart meter, but also the interfaces for home energy management systems as well as communication interfaces back to the utility. Utilities must recognize and anticipate the new threat landscape that can attack and compromise the meter and the associated field network collectors. They must also include threats to the WAN （wide-area-network） backhaul networks, smart meter headends, MDMS （meter data management systems） and their interfaces to CIS （customer information systems） and billing and OMS （outage management systems）. Lessons learned from SMI implementations from North America, Europe and recently, Japan, will be discussed. How white-box and black-box testing techniques are applied to determine the threat impact to the SMI. Finally, organizational change risk will be discussed and how utilities have responded to re-organizing and developing a security governance structure for the SMI and other smart grid applications.
文摘Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction incentive） scheme. This paper discusses these two programs and evaluates their respective performances. We develop an efficient approach based on marginal cost pricing to redesign the TOU rate scheme. In our finding, the TOU price levels could be revised to encourage more customers to participate by enlarging the price gap. Moreover, the DRI scheme can be further improved in order to reach an efficient win-win solution among customers, the utility and society. This can be achieved via a careful design of incentive tariff discounts to take account of the time-of-use or location-specific features of the power supply/demand condition.
文摘Prepaid energy meters have been widely adopted by utilities in different countries across the world as an innovative solution to the problem of affordability and consumption management. However, the present smart card based systems have some inherent problems like added cost, low availability and lack of security. In the future Smart Grid paradigm, use of smart meters can completely overhaul these prepaid systems by introducing centralized accounting, monitoring and credit-control functions using state-of-the-art telecommunication technologies like WiMAX. In this paper we pro-pose a prepaid smart metering scheme for Smart Grid application based on centralized authentication and charging using the WiMAX prepaid accounting model. We then discuss its specific application to Demand Response and Roam-ing of Electrical Vehicles.
文摘Smart meter networks are the backbone for smart electrical distribution grid. Smart meter network requires the bidirectional communications medium and interoperability capability. As thousands of meters are interconnected in the smart meter network, it is vital to select an appropriate communication bandwidth to facilitate real-time two-way information flows and this will also allow further uptake of greenhouse-friendly technology options and enhance energy security. Optimized Network Engineering Tools (OPNET) Modeler is one of most powerful simulation tools for the analysis of communication networks. In this paper, several models of different structured smart meter networks were developed with network parameters which were connected with different communication links such as 10 BaseT and 100 BaseT in order to measure propagation delay, throughput, and utilization of the network. It was found that the propagation delay decreases with higher bandwidth. The other network parameters, namely network utilization and network throughput were also analysed. Based on the investigation, it is recommended that the 100 BaseT communication link is suitable for the smart meter network. The outcome of this paper provided a guideline to the future smart meter network developer so as to avoid catastrophic challenges faced by some of the distribution companies.
文摘Smart meters provide a lot of convenience for both power supply and consumption. Due to the frequent transmission of information, it brings great challenges to the privacy preservation of the user’s household power consumption data in the smart grid. In order to achieve the anonymity of smart meters. A smart meter privacy preservation scheme based on identity authentication is proposed. The third-party certification authority is introduced in this scheme;it issues pseudonym certificates to realize the identity privacy preservation of smart meters. The masking technology with the Advanced Encryption Standard algorithm is used for data aggregation. The results show that our scheme reduces the computational cost and the communication overhead.
文摘Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.
文摘At present, DL/T 645-2007 communication protocol is used to collect data for smart meters. However, in the beginning, this protocol is not designed to be a secure protocol and only the function and reliability were taken into account. Plaintext is used in the protocol for data transmission, as a result, attackers can easily sniff the information and cause information leakage. In this paper, man-in-the-middle attack was used to verify that the smart meter data acquisition process was vulnerable when facing third-party attacks, and this can result in data eavesdropping. In order to resist such risks and prevent information being eavesdropped, a real ammeter communication experimental environment was built, it realized two-way identity authentication between data acquisition center and ammeter data center. At the same time, RSA (Rivest-Shamir-Adleman) was used to encrypt the meter data, which encrypted the collection, storage process of meter data and ensured the confidentiality and integrity of the meter data transmission. Compared with other methods, this method had obvious advantages. The analysis showed that this method can effectively prevent the data of smart meters from being eavesdropped.
文摘With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.
基金This work was performed as part of the Network Constraints Early Warning System(NCEWS)projectThe authors acknowledge the support of Innovate UK(project no.B16N12241)and the UK OFGEM(Network Innovation Allowance NIA_SPEN0016 and NIA_SPEN034)+1 种基金Robu and Flynn also acknowledge the support of UKRI projects Centre for Energy Systems Integration(CESI)[EP/P001173/1]and Community Energy Demand Reduction in India(ReFlex)[EP/R008655/1]Finally,the authors are grateful for the recognition of our work by UK’s Institute of Engineering and Technology’s(IET),through the award of the IET and E&T 2019 Innovation of the Year Award[43].
文摘The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.
基金supported by NSFC projects under Grant Nos.71090401/ 71090400, 71320107004 and 71371176
文摘It has been widely recognized that the efficiency of a thermal power system can be improved by technological advancement of electricity generation and manipulation of electricity consumption. The smart meter enables two-way communication between the customers and the electricity generation system. The electricity generation system uses price incentive （i.e. a higher price in the peak period and a lower price in the off-peak period） to shift part of demands from peak to off-peak period under the smart grid environment. Given the fact that fuel consumption in each period is a strictly increasing convex function of power output, we propose two-period and multi-period pricing strategies, and study the effect of different pricing strategies on reducing fuel consumption.
基金supported by the National Key R&D Program of China(No.2022YFB2403800)the National Natural Science Foundation of China(No.52277118)+1 种基金the Natural Science Foundation of Tianjin(No.22JCZDJC00660)the Open Fund in the State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources(No.LAPS23018).
文摘The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.
基金This work was supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project,Project Number CZ.02.1.01/0.0/0.0/16_-019/0000867 within the Operational Programme Research,Development and Education,and in part by the Ministry of Education of the Czech Republic under Project SP2021/32.
文摘The massive development of internet of things(IoT)technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth,smart city,agriculture or waste management.This ongoing development is further pushed forward by the gradual deployment of 5G networks.With 5G capable smart devices,it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT.Massive-IoT(low-power wide area network-LPWAN)enables improved network coverage,long device operational lifetime and a high density of connections.Despite all the advantages of massive-IoT technology,there are certain cases where the original concept cannot be used.Among them are dangerous explosive environments or issues caused by subsurface deployment(operation during winter months or dense greenery).This article presents the concept of a hybrid solution of IoT LoRaWAN(long range wide area network)/IRC-VLC(infrared communication,visible light communication)technology,which combines advantages of both technologies according to the deployment scenario.
基金supported by Sustainable Smart Campus as a Living Lab of Hong Kong University of Science and Technology and the Strategic Topics Grant from Hong Kong Research Grants Council(STG2/E-605/23-N).
文摘Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.
