Smart Meters Research Articles

Power Grid Smart Meters: Current Status, Issues, Benefits, and Trials

Power Grid Smart Meters: Current Status, Issues, Benefits, and Trials

Comparing four machine learning algorithms for household non-intrusive load monitoring

The combination of Machine Learning (ML), smart energy meters, and availability of household appliance energy profile data has opened new opportunities for Non-Intrusive Load Monitoring (NILM). However, the number of options makes it challenging in selecting optimal combinations for different energy applications, which requires studies to examine their trade-offs.This paper contributes one such study that investigated four established ML approaches – K Nearest Neighbour (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) – and their performance in classifying appliance events from Alternating Current (AC) and Root Mean Square (RMS) energy data where the sampling frequency and training dataset set size was varied (10 Hz–1 kHz and 50–2000 examples per class, respectively). The computational expense during training, testing and storage was also assessed and evaluated with reference to real-world applications.The CNN classifier trained on AC data at 500 Hz and 11,000 examples gave the best F1-score 0.989 followed by the KNN classifier 0.940. The storage size required by the CNN models was 3̃MB, which is very close to fitting on cost-effective embedded system microcontrollers. This would prevent high-rate data needing to be sent to the cloud as analysis could be performed on edge computing Internet-of-Things (IoT) devices.

A deep learning approach for fairness-based time of use tariff design

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

Residential PV-battery scheduling with stochastic optimization and neural network-driven scenario generation

Residential end-users have increasingly been attracted to installing behind-the-meter (BTM) photovoltaic (PV) and battery energy storage systems (BESS). The inherent stochastic nature of PV introduces challenges to energy management for PV-BESS owners. This work presents a two-stage stochastic optimization (SO) framework for battery operation that takes into account uncertainties in PV outputs to minimize daily grid consumption and battery degradation. A combination of a feed-forward neural network (FFNN) and an error analysis statistical technique is proposed to generate a highly accurate scenario set for estimating PV behavior. Then, the scenario set is reduced using a backward scenario reduction technique to address the issue of dimensionality that SO suffers from. The reduced scenario set is then fed into the two-stage SO model to calculate expected electricity costs and battery degradation for the day-ahead PV-BESS problem. The suggested model's performance in controlling BTM batteries is evaluated using real-world data collected from smart meters installed in a house in Aran Islands, Ireland. The results show that the model can effectively withstand sudden changes in the PV output and generate results that are comparable to an ideal case with accurate PV data available.

Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.

Techno-Economic Assessment of Battery Systems for PV-Equipped Households with Dynamic Contracts: A Case Study of The Netherlands

Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESS’s profitability compared to the removal of NM.

Current State and Future Potential of Energy Efficiency and Demand Side Management in the South African Context

This paper explores the current state and future potential of Energy Efficiency and Demand Side Management (EEDSM) in South Africa. With a heavy reliance on coal for electricity generation, South Africa faces significant environmental and sustainability challenges. Integrating renewable energy sources like wind, water, and solar (WWS) is crucial for reducing greenhouse gas emissions and ensuring energy security. The study highlights the importance of demand-side management in optimising energy consumption and mitigating peak demand pressures. The South African government has implemented various policies and regulatory frameworks to promote EEDSM, focusing on reducing energy consumption through technological innovations and strategic planning. Models such as the Five-Stage Model for Computer Technology Integration, the E-Capacity Model, and the Systemic Planning Model for ICT Integration provide structured approaches to integrating advanced technologies and practices into energy systems. The research includes a comprehensive analysis of South Africa's current energy mix, predominantly coal and nuclear, and projects a shift towards a diversified portfolio dominated by renewable sources by 2050. This transition is expected to create substantial employment opportunities and enhance energy efficiency. Case studies and success stories from both the public and private sectors underscore the economic and environmental benefits of EEDSM initiatives. South Africa can significantly improve its energy efficiency and sustainability by leveraging innovative energy technologies, such as smart grids and smart metering. The findings emphasise the need for continued investment in renewable energy and demand- side management to achieve a more resilient and efficient energy sector.

Securing the green grid: A data anomaly detection method for mitigating cyberattacks on smart meter measurements

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid.

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data.

Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people's care.

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.

Home-personas meet energy narratives of demand response: Uncovering mismatches between Swedish stakeholder expectations and everyday life

Smart grids are proposed to enable the integration of renewables and facilitate the energy transition. Households have been pointed out as a significant resource for demand response, that is to adapt their electricity consumption based on the status in the grid. This article analyses narrative mismatches in the context of smart grid implementation in Sweden. We compare policy narratives on the role of homes in the future energy system with home personas, emerging from interviews with households. The policy narratives envision households to become either actively engaged in time-shifting motivated by information and incentives, or bypassed through automation. The home personas, although seemingly similar, show great diversity, being well informed about their electricity use, concerned regarding the safety of technology, preferring to manage flexibility themselves, and reluctant to give up control. Several dissonances are identified between narratives and the home personas regarding smart meter communication, energy awareness, trust, agency, and control, that need further attention for demand response to be realised. The analysis illustrates how policy visions of the home in the future grid would encounter severe challenges in living up to values and characteristics of real households. Policy thus needs to acknowledge households as a diverse group to ensure a sustainable and democratic energy transition. We encourage the use of home personas to substantiate this diversity.

Consortium blockchain based secure and efficient data aggregation and dynamic billing system in smart grid

In a smart grid, collected electricity consumption periodically from smart meters allow entities to bill the customers, power company to operate the grid successfully, and users to control the use of their appliances. However, energy consumptions of users should be protected since the data provides the user’s daily habit that an adversary uses the data to extract useful information about the users. Moreover, users’ identities should not be disclosed to untrusted entities since the untrusted entities map identities to their real identities. In this paper, we propose a system that protects users’ data privacy using multi-pseudorandom identities and a randomization technique. Moreover, the proposed work provides fast authentication for smart meters to send their readings to data aggregators. Furthermore, the proposed work is based on consortium blockchain to eliminate a single point of failure and provides transparency of messages and operations. In addition, we use dynamic billing and pricing mechanism for the users to see their bills.

Extraction of High-Resolution Air Conditioning Load Profiles From Low-Resolution Smart Meter: A Semi-supervised Nonintrusive Approach

Extraction of High-Resolution Air Conditioning Load Profiles From Low-Resolution Smart Meter: A Semi-supervised Nonintrusive Approach

Anomaly‐detection‐based learning for real‐time data processing in non‐intrusive load monitoring

AbstractA power system can be regarded as a cyber‐physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non‐intrusive load monitoring (NILM), an emerging smart‐grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection‐based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber‐attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection‐based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real‐time assessment of power‐ loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

An Online Home Energy Management System using Q-Learning and Deep Q-Learning

The users of home energy management systems schedule their real-time energy consumption thanks to advancements in communication technology and smart metering infrastructures. In this paper, a data-driven strategy is proposed, which is an Online Home Energy Management System (ON-HEM) that uses reinforcement learning algorithms (Q-Learning and Deep Q-Learning) to control the optimal energy consumption of a smart home system. The proposed system comprises power resources (grid, photovoltaic), communication networks, and appliances with their agents classified into four groups: deferrable, non-deferrable, power level controllable, and electric vehicle. The system reduces electricity costs and high peak demands while considering the cost of user dissatisfaction with real-life data. Simulations are performed on the proposed ON-HEM considering different pricing approaches (Real Time Pricing and Time of Use Pricing) with Q-Learning and Deep Q-Learning (DQL) algorithms using PyCharm Professional Edition software. The findings demonstrate both the superiority of DQL over Q-Learning and the efficiency of the proposed ON-HEM in decreasing high peak demand, electricity costs, and customer dissatisfaction costs. The efficiency and dependability of the proposed system were verified by utilizing simulation-based findings with real-life data using IBM SPSS Statistics software.

An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels

Smart metering in electricity power grid is an optimistic trend at global level. All smart devices and appliances based on Internet of Things (IoT) are now playing very significant role in household. These days’ electric power usage mining and optimization is possible down to meter level only. However, it is very challenging and significant to go down to different granularity levels such as appliances, various sensors and activities etc. The shifting of the electric power usage to low price electricity is also significant and possible by mining and optimizing electric power usage behaviour at low level. All smart appliances and activities are needs to be customized to when you use them. This paper proposes an adaptive methodology based on predictive deep learning and context aware clustering to discover new ways for mining and optimization of electric power usage at different granularity levels and make optimal decisions for shifting electric power usage to low cost. Here we have considered households and business meters approximately 2000 with unique id of each meter. The data of three months is used for user preference of starting appliance. The predictive accuracy of proposed methodology for usage mining and optimization is improved by average 4 %. Different input data features are used to form clusters of meters with similar power consumption behaviour for household occupancy. The clustering accuracy for household occupancy is improved from 0.68 to 0.91. The impact of accurate household occupancy detection and appliance usage mining and optimization is in reduction of electric power cost. The consumer can see how electric power efficiency and time-of-use shift makes a difference using experimental setup.

Smart Energy Meter by Using IOT

Abstract: It is an IOT based paper electricity is one of the fundamental necessities of human beings which is commonly Used for Domestic, Agriculture, Industrial purpose. Power theft and Meter billings are biggest problems in recent days which causes a lot of loss to electricity board. If we can prevent these thefts we can save a lot of power. This is done by Smart Energy Meter by using IOT, present an efficient and cost-effective way to transfer the information of energy consumed by consumer wirelessly as well as it provides facilities to detect illegal usage of electricity. Smart Energy meter by using IOT basically consists of three major components namely controller, Wi- Fi, theft detection device. Whenever there is any fault or theft, the theft detector sensor detects the error. The controller plays a crucial role in keeping all the components in the working state. In this system the energy meter is connected to internet. The proposed system is capable of continuously monitoring and being notified about the number of units consumed to energy provider and consumer. The energy consumption is calculated automatically, and the bill is updated on the internet by using a network of internet of things. This automation can eliminate the manual work in electricity maintenance. In this paper mainly focuses on automatic billing, theft detection, power optimization and providing the relevant energy consumption information to user.

IOT Based- Smart Metering and Charging System for EV

Abstract: Electric vehicles often face concerns regarding overcharging, leading to detrimental outcomes like pollution, reduced hardware lifespan, and safety hazards. Unlike conventional fuels, electric vehicles lack standardized systems to regulate charging parameters and costs. This absence of fail-safe mechanisms can lead to overcharging, causing negative consequences without intervention. To tackle this issue, the project proposes an Arduino-based solution coupled with a relay sensor and dedicated application. This system aims to prevent overcharging, minimizing both financial and electrical wastage. The project serves as a compact prototype, offering a viable resolution to the problem of unregulated charging in electric vehicles.

Smart meter-based outage detection method for power distribution systems

This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.

Analytic Hierarchy Process Assessment Framework for Blockchain in Renewable Energy

The urgency to reach net-zero emissions until 2050 has created a vast interest in innovative technologies for renewable energy sector. At the same time the need for trusty and unfailing solutions will rise considering the increasing amount of decentralized and decarbonized energy systems. It has previously been described in the literature that the aforementioned characteristics could be combined by blockchain technology. Therefore, this study focuses on the practical side – the opinion of industry experts about the use of blockchain technology in renewable energy. By using analytic hierarchy process analysis, the goal of this study is to develop a methodology for the selection of the most appropriate blockchain applications in renewable energy and to identify and evaluate the possible types of use taking into account socio-economic, political and legal, technological and environmental factors. The results of this study highlights the important influence of political, legal and technological factors like involvement of government institutions and possible attacks to the system, but considers the economic factors as the least significant for the introduction and use of blockchain technology in the renewable energy sector. The most promising use cases for blockchain would be associated with reliable and immutable certificates of origin for renewable energy and use in smart grid (including smart metering), smart homes and relevant Internet of Things applications. The use of crypto-assets and initial coin offerings for renewable energy development should be viewed with precaution and willingness to inform and educate considering social factors like public opinion and societies’ knowledge on crypto-assets.