Privacy-optimal strategies for smart metering systems with a rechargeable battery

Intelligent buildings are not imaginable anymore without smart metering devices. Smart metering systems are used not only for the provisioning of instantaneous metering information on commodities, such as electricity, water and gas, to the service providers but also to make this information available to customers. This helps the customers in dynamically adapting their energy consumption behavior. The smart metering devices also help in balancing the power generation and distribution in a smart grid by tailoring the power generation according to the demand. However, the liberalization of the metering market requires few strong security and privacy requirements for the metering data. Smart metering raises many security and privacy concerns. There are worries that the personal information of consumers could be disclosed. There are also concerns about frauds exploiting security vulnerabilities in smart metering systems on a large scale, e.g., making smart meters provide false metering data to the service providers. From a macro perspective, the smart grids, including the smart metering systems and devices can be attacked to bring down the whole grid or at least some parts of the grid, which is a concern of national security. This paper focuses on the security and privacy aspects of the smart metering systems. Potential attackers, security threats and attacks on smart metering systems are listed and the security approaches to address the security issues are presented. A security by design approach for secure smart metering is discussed in the paper. The major results of a security by design approach for smart metering systems developed in the project, entitled “Trusted Computing Engineering for Resource Constraint Embedded Systems Applications”, funded by the European commission, are summarized in the end.

Smart-metering systems report electricity usage of a user to the utility provider on almost real-time basis. This could leak private information about the user to the utility provider. In this paper, we investigate the use of a rechargeable battery in order to provide privacy to the user. We assume that the user load sequence is a first-order Markov process, the battery satisfies ideal charge conservation, and that privacy is measured using normalized mutual information (leakage rate) between the user load and the battery output. We study the optimal battery charging policy that minimizes the leakage rate among the class of battery policies that satisfy causality and charge conservation. We propose a series reduction on the original problem and ultimately recast it as a Markov Decision Process (MDP) that can be solved using a dynamic program. In the special case of i.i.d. demand, we explicitly characterize the optimal policy and show that the associated leakage rate can be expressed as a single-letter mutual information expression. In this case, we show that the optimal charging policy admits an intuitive interpretation of preserving a certain invariance property of the state. Interestingly an alternative proof of optimality can be provided that does not rely on the MDP approach, but is based on purely information theoretic reductions.

In this study, we tackle the challenge of integrating volatile wind generation into the bulk power systems, by lever-aging multi-timescale scheduling and pricing with two classes of energy users - traditional energy users and opportunistic energy users (e.g., electric vehicles or smart appliances). In day-ahead scheduling, with the distributional information of wind generation and energy demands, decisions on the optimal procurement of conventional energy supply and the day-ahead retail price are made; in real-time scheduling, with the realization of wind generation, the load of traditional energy users, the real-time prices are announced to manage the demand of opportunistic energy users so as to achieve system-wide reliability. Focusing on the case when the opportunistic energy users are persistent, i.e., they stay in the system until a real-time retail price is acceptable, we formulate the scheduling problem as a multi-timescale Markov decision process with special characteristics. We then show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques.

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.

Smart Charging of Electric Vehicles (EV) is a method of optimizing the EV charging schedule. As a result, it can provide more profit for EV charging stations. This profit usually comes from reducing the peak demand charge cost while still satisfying the EV user demand. Most charging stations let EV users input their demand. However, users can intentionally or unintentionally poorly estimate their demand, leading to lower profit for EV charging stations. In this paper, we propose an end-to-end framework of Smart Charging that aims to maximize the profit of charging stations while satisfying EV user demand. Our framework consists of two main modules. First, the demand forecasting module focuses on predicting the EV user's energy demand and session duration using various machine learning techniques, e.g., XGBoost, Random Forest (RF), and TabNet. Second, the EV charging schedule optimization based on model predictive control (MPC) has been employed to optimize the charging schedule. The optimization module has been further improved by using the feedback from causal information and behavior of EV charging profiles called Constant Current Constant Voltage (CC-CV). The experiment was conducted on simulation with real EV charging data. The results show that our framework outperforms the baseline with higher profits of $616.28 a month, a 27.18% improvement. In addition, our forecasting module can avoid user biases from user inputs and predicts more accurately-the winning model is XGBoost with a symmetric mean absolute percentage error (SMAPE) of 10.72% and 11.85% for session duration and energy demand, respectively.

The global economy and sustainability issues are driving suppliers to new operating modes. Smart grids and their smart metering systems can yield sustainable and profitable operating modes. Thus, smart grids are important enablers of economic development. However, along with benefits, smart grids bring drawbacks. Similar to other interconnected technologies, security and privacy are crucial to smart grids. Neglecting security concerns might eventually compromise, for instance, the supply of electricity, water, or gas. Neglecting privacy concerns might cause the violation of the right to privacy of customers, enable surveillance, and permit manipulation of all customers. Indeed, smart meters are becoming ubiquitous, and smart grids face unprecedented threats. Public infrastructures might be jeopardized, and citizens might be manipulated. Luckily, Privacy-Preserving Protocols (PPPs) can solve this impasse. This work advances state-of-the-art PPPs with the development of several protocols that preserve customers’ privacy secure in smart grid scenarios. Four of them are revisited and improved in this thesis. Such development culminated in the concept of Asymmetric DC-Nets (ADC-Nets)—from “Dining Cryptographers”—, which are generalizations of additive homomorphic encryption primitives. In addition, we can use such primitives to construct ADC-Nets, which are cryptographic primitives for encryption, aggregation, and decryption of aggregated data. ADC-Nets underlie secure, verifiable, efficient, and scalable protocols with low communication overhead, which are independent of trusted parties, and resistant to collusion. Furthermore, smart meters can send the minimum number of required messages directly to their supplier. Thus, they can sign their messages, and as consequence, the protocols can ensure non-repudiation and fault tolerance. The former ensures that customers cannot deny the messages of their smart meters were transmitted. The latter ensures that their supplier can detect smart meters with failure—in themselves or in the communication channel—and can run the protocols without the compromised smart meters. Moreover, ADC-Nets can enforce customers’ privacy. Besides the concept and results of ADC-Nets, this thesis presents other contributions listed as follows. • This thesis contextualizes smart metering systems in smart grids around the world and points out the needed models to have security and privacy in smart grids scenarios. Furthermore, it reviews the state of the art of privacy-enhancing technologies for smart metering systems. • This thesis presents three scenarios that require remote and frequent measurements. In addition, it assesses the minimum requirements for PPPs. Moreover, it is shown how computations can be done over encrypted measurements. • An algebraic and a probabilistic analysis show that PPPs cannot keep customers’ privacy secure using data aggregation with a small number of customers. Counterintuitively, when the number of measurements increases, the effectiveness of PPPs also increases. The optimal effectiveness is achieved when the sum of measurements and the number of smart meters are equal. These results are independent of PPPs. • The four selected PPPs have different interesting properties. The first protocol leads to the conjecture that it has the fastest encryption algorithm, because it requires only a “one-way function”. The second is based on elliptic curves, and further, the encryption algorithm uses only two scalar multiplications that lead to a fast protocol. The third uses an ADC-Net and inherits its benefits. When the level of security is increased, the second and the third protocol become increasingly faster than typical solutions. The fourth follows the laws of quantum mechanics, which surprisingly implies that the smart meters do not need to store a key, but they can send messages directly to their supplier without compromising privacy. • To compare the protocols’ performance, this thesis presents simulations with millions of real-world measurements that validate the theoretical results. It is shown that the raw dataset has inconsistencies that reinforce the necessity to verify the truthfulness of the transactions. Encrypted measurements are necessary and sufficient to determine whether the computations and the measurements are correct. Besides smart grids, several application areas can use the results of this thesis, for instance, electronic voting, reputation systems, sensor networks, electronic money, mobile sensing, multi-party computation, image processing. ADC-Nets can be used to create several protocols provided with security, privacy, verifiability, scalability, reliability, efficiency, etc. More important than efficiency, PPPs should enforce the security of customers’ privacy by means of cryptography. Considering smart grids, PPPs are paramount for suppliers, for customers, and for the proper development of society.

The main objective of this research paper is to investigating the impacts on house hold electricity consumption after installing smart meters than the conventional energy meters. The installation of smart meters in the home not only gives real-time data on power use, but also improves customer services. Consumers become more aware of their energy usages as a result of the smart metering system’s ability to track it frequently. The smart meter has the capability of communicating data on power use between customers and providers. It also aids in the tracking of daily power use and the understanding of consumption trends in order to save excess consumption for the consumer’s advantage. Intelligent Energy Networks are made up of devices that can do their jobs while using less energy. These devices can also connect to each other and be controlled from a distance. Because of this, some of these devices, like smart energy meters, are becoming more appealing for use in the power generation and distribution industries. This brings the Smart Grids idea to life. But there are other problems that need to be solved before we have a smart grid that works well and is safe. Smart meters will have trouble measuring, controlling, communicating, getting power, showing information, and staying in sync. This research goes into more detail about a thorough definition of what smart meters must have and also looks at how smart meters are used in smart grids right now. By the end of this research paper, the reader should have a full understanding of what smart meters can do now and what they will be able to do in the future to help smart grids deal with their problems.

A rechargeable battery may alleviate the issue of privacy loss in a smart metering system by distorting a household's load profile. However, existing studies involve a single rechargeable battery, whereas in a network scenario, there could be multiple batteries connected together. In this letter, we study the extension where a user's electricity load is input into a network of two rechargeable batteries, connected in series, and operating individually. This battery network attempts to mask the user load from the utility provider. We focus on the case of independent identically distributed load profile and a system of ideal batteries with no conversion loss, and use normalized mutual information (leakage rate) as the privacy metric. We derive upper and lower bounds on the leakage rate in terms of (single-letter) mutual information expressions. On the achievability side, our information-theoretic upper bound captures the novel tension between minimizing the leakage across each individual battery and the effect of their joint interaction. For the lower bound, we show that a system with a single battery, whose storage capacity is the sum of the two individual batteries, can achieve a leakage rate at least as small as our proposed setup. Furthermore, we use simulations to compare achievable leakage of our proposed scheme with several baseline schemes. The achievable leakage rates obtained in this study could help us to elucidate the privacy performance of a network of batteries.

Clarifying the user's energy demand is the first step to develop integrated energy services. This paper collects multi-dimensional energy consumption data of users and builds an energy demand model for user. Firstly, users' energy consumption data is collected by the monitoring devices, authors identify and correct the abnormal data, and fill in the missing data. Secondly, authors unify the dimensions of different energy consumption data, construct consumption curves of different energies. The demands of different energies for user can be calculated by the areas enclosed by the energy consumption curve. Thirdly, based on different energy demands, the radar map can be obtained. The demand of integrated energy can be calculated by the areas of radar map. Finally, taking 6 users in central China as an example to verify the rationality of the method proposed in this paper.

The objective of the work considers the development of a sustainable envelope for the thermo-energetic adaptation of Evolutionary Social Housing in Arid Zones. Due to the current growth of neighborhood constructions executed by the Sanjuanino Provincial Housing Institute (VSE-IPV) in Argentina, they show that they are designed to increase consumption, and they do not prove to solve the user's energy demand from the project stage. Naturally, nor contribute to energy poverty and environmental sustainability. The methodology used considers theoretical and practical studies at Urban, Housing and Construction Scales. Theoretical studies included analyzes of sectorial bioclimatic urban and architectural design, of the location, growth and climate, proposals for Passive Strategies according to bioclimatic zones. Those who guided the research to technological-constructive studies of the envelope, thermal behavior, and energy efficiency (EE), with proposals for improvements to the base and expanded prototypes considering International and National Level standards (ISO, CTE, IRAM). For the Proposed Optimized Expanded, with the incorporation of complementary external sustainable thermal insulating layers, a high EE is obtained, class B, weighted average transmittance K' m=0.41W/m2°K, with a weighted mean thermal variation τm=1.44°C. It is concluded that when the thermal insulation improvement integrates the entire envelope.

The objective of the work considers the development of a sustainable envelope for the thermo-energetic adaptation of Evolutionary Social Housing in Arid Zones. Due to the current growth of neighborhood constructions executed by the Sanjuanino Provincial Housing Institute (VSE-IPV) in Argentina, they show that they are designed to increase consumption, and they do not prove to solve the user's energy demand from the project stage. Naturally, nor contribute to energy poverty and environmental sustainability. The methodology used considers theoretical and practical studies at Urban, Housing and Construction Scales. Theoretical studies included analyzes of sectorial bioclimatic urban and architectural design, of the location, growth and climate, proposals for Passive Strategies according to bioclimatic zones. Those who guided the research to technological-constructive studies of the envelope, thermal behavior, and energy efficiency (EE), with proposals for improvements to the base and expanded prototypes considering International and National Level standards (ISO, CTE, IRAM). For the Proposed Optimized Expanded, with the incorporation of complementary external sustainable thermal insulating layers, a high EE is obtained, class B, weighted average transmittance K' m=0.41W/m2°K, with a weighted m

This report presents a method for evaluating relative environmental impacts of coal transportation modes (e.g., unit trains, trucks). Impacts of each mode are evaluated (rated) for a number of categories of environmental effects (e.g., air pollution, water pollution). The overall environmental impact of each mode is determined for the coal origin (mine-mouth area), the coal or coal-energy product destination (demand point), and the line-haul route. These origin, destination, and en route impact rankings are then combined into a systemwide ranking. Thus the method accounts for the many combinations of transport modes, routes, and energy products that can satisfy a user's energy demand from a particular coal source. Impact ratings and system rankings are not highly detailed (narrowly defined). Instead, environmental impacts are given low, medium, and high ratings that are developed using environmental effects data compiled in a recent Argonne National Laboratory report entitled Data for Intermodal Comparison of Environmental Impacts of Inland Transportation Alternatives for Coal Energy (ANL/EES-TM-206). The ratings and rankings developed for this report are generic. Using the method presented, policy makers can apply these generic data and the analytical framework given to particular cases by adding their own site specific data and making some informed judgements. Separate tables of generic ratings and rankings are developed for transportation systems serving coal power plants, coal liquefaction plants, and coal gasification plants. The final chapter presents an hypothetical example of a site-specific application and adjustment of generic evaluations. 44 references, 2 figures, 14 tables.

As more and more new energy sources are integrated into integrated energy systems (IES), it is important to consider energy storage technologies in integrated energy planning systems so as to reduce energy costs. In this paper, combined heating and power (CHP), gas boiler (GB) and thermal storage (TS) are contained in this IES. According to the user's energy demand, an optimization model was established along with the minimum cost of the system as the objective function through the coordination and cooperation of various energy conversion equipment and energy storage equipment in the energy hub. This paper fully considers the constraint conditions and power balance conditions of various energy conversion equipment and energy storage equipment, and uses improved particle swarm optimization (PSO) algorithms to finally obtain the optimal economy. Finally, an example of a comprehensive energy system is used to verify the accuracy of the algorithm.

In recent years significant change in the power grid for the distribution of electrical energy have been observed. Thanks to liberalization of the electrical market and the incentives offered to the production of energy from renewable sources, the number of medium and small producers has increased. Moreover, with the development of communication technology, the transmission grid has been increasingly equipped with automated devices capable of monitoring and transmitting some information about the grid and, in some cases, to control the actuation devices connected to the grid itself. In this scenario, the term smart grid was born, used to describe energy networks that can automatically monitor energy flows and adjust to changes in energy supply and demand accordingly. When coupled with smart metering systems, smart grids reach consumers and suppliers by providing information on real-time consumption. As an example, consumers can adapt their energy usage to different energy prices throughout the day with personal smart meters, saving money on their energy bills by consuming more energy in lower price periods. As it can be expected, the wider the grid, the larger the number of smart meters required for its monitoring. However, deploying thousands of such devices turns rapidly to be too expensive. Consequently, the realization of the distributed measurement system could not be economically sustainable. To overcome the considered limitations, the research activities have been focused on the possibility to move towards a different approach for monitoring grid deployed on wide geographical areas, exploiting the advantages of an innovative acquisition paradigm: the Compressed Sampling (CS) . CS is a signal processing technique for efficiently acquiring and reconstructing a signal from far fewer samples than those required by the Shannon-Nyquist sampling theorem. In particular, the proposed architecture consists of low cost nodes mandated only to sample and digitize a limited number of input signal samples and transmit them to a central measurement unit, thus saving the costs related to large memory supports and expensive digital processing units. Once the samples are received, the central unit recovers the signal spectrum thanks to CS-based algorithm and carries out the desired measurements. Thanks to the CS-based approach, it is possible to design and realize a measurement node, characterized by reduced memory depth and only one ADC, suitable for poly-phase system with neutral wire that allows meeting the requirements of a distributed measurement system. Numerical and experimental test verified highlight the capability of CS-based acquisition approach of correctly measuring the root-mean-square amplitude of voltage waveforms and assuring simultaneous multi-channel acquisitions. Finally, the compliance of the measurement node based on CS approach with the current Power Quality standards is assessed and discussed.

At present, buildings account for a great share of energy consumption. It is well known that building automation control systems allow for increasing opportunities of improvement in the performance of buildings, with respect to e.g. energy performance and indoor comfort. As system within a building become more and more complex, buildings can be regarded not merely as a load but as a smart micro grid, with the possibility of actively interacting with a smart grid. In the depicted context, metering is essential for assessing the performance of management and detecting improvement opportunities. The scope of the present work is to propose a best practice for the implementation of smart metering systems in buildings and a practical methodology to classify the systems. In the present work, a novel classification protocol is devised; an existing metering system is then evaluated and an improved metering system is proposed. The proposed protocol rates the system performance via a set of weighted indicators -according to positioning of meters, measured data, system architecture, data visualization and monitored loads -, then calculates an overall grade. The protocol is tested on an existing metering system in an educational building. The metering system returns a poor rating and a number of flaws are detected thanks to the benchmark protocol. An improved metering system is then proposed which fixes existing flaws and returns a much better grade. In conclusion, the designed classification protocol allowed diagnosing an existing metering system and pinpointing improvement opportunities and it can be a useful practice in diagnostics or design of smart metering systems.