Security system architecture for data integrity based on a virtual smart meter overlay in a smart grid system

As an efficient and intelligent system for managing and monitoring electricity usage, the “smart grid” has many useful applications. It supports energy producers and energy consumers efficiently by supporting the estimation and provision of the proper amount of electricity at the proper time. The most serious threat to a smart grid system may be when information is altered or forged by attackers; the system could malfunction and could collapse. In some cases, the attackers could waste system resources on purpose and this could lead to disastrous results. In this paper, we propose security system architecture to provide the secure and reliable smart grid service. It includes a data authentication process to verify the aggregated data and virtual network management to detect a compromised area or node. It detects a false data injection attack and further prevents a denial of service attack efficiently with less overhead for the individual devices comprising the smart grid system.     

Energy efficient and reliable data gathering using internet of software-defined mobile sinks for WSNs-based smart grid applications

The smart grid is an emerging concept that introduces innovative ways to handle the power quality and reliability issues for both service provider and consumers. The key aims of the smart grid (SG) in smart cities (SCs) is to preserve a certain level of residents’ life quality and support the entire spectrum of their economic activities. In this paper, we present a novel Energy Efficient and Reliable Data Gathering Routing Protocol (ODGRP) for wireless sensor networks (WSNs)-based smart grid applications. The developed scheme employs a software-defined centralized controller and multiple mobile sinks for energy efficient and reliable data gathering from WSNs in the SG. The extensive simulation results conducted through the EstiNet 9.0 show that the designed scheme outperforms existing approaches and achieves its defined goals for event-driven applications in the SG.     

智能电网中基于MQTT协议的ABAC访问控制方案

在智能电网环境中,电力运营商和消费者通过智能电表进行大量高精度的用电数据的实时监测,用户机密数据持续暴露于未经授权的访问,在这种传统通信模式下,智能电表对家庭用户能源消耗的细粒度测量造成了严重的隐私安全问题,而现有的静态访问控制方法并不满足智能电网环境基于上下文的动态访问特性。针对此问题,提出一种基于物联网通信协议（MQTT协议）的访问控制方案,通过在MQTT协议中对树型结构的主题列表设计基于ABAC访问控制模型的动态上下文授权策略,并在WSO2系统使用XACML策略语言实现了提出的访问控制方案。性能评估结果表明,该方案能在较低的通信开销内支持动态的访问控制,以解决智能电网中用户的用电信息未经授权而泄露的隐私安全问题。     

电网转型背景下地市级智能电网示范区规划研究

在南方电网公司向智能电网运营商、能源产业价值链整合商、能源生态系统服务商转型的背景下,贵阳智能电网示范区面临着巨大的机遇和挑战。以南方电网公司安全、可靠、绿色、高效的智能电网发展总体要求为指引,结合贵阳市经济社会发展需求,研究贵阳智能电网示范区规划方案。提出了更加安全可靠、更加智能高效、更加规范统一、更加减量增益的贵阳智能电网示范区总体目标,明确了近期（2019-2020年）和远期（2021-2025年）两步走的智能电网示范区发展路线,提出了提高城市电网供电可靠性、提升农网供电能力、加强智能技术应用、推动大数据应用、提升智能用电水平、开展综合能源服务等6项重点任务、19项重点工作,以期全面建成贵阳智能电网示范区,进一步提升服务经济社会发展的能力。     

智能电网隐私保护工程分析与相关对策

保护电力消费者的数据和隐私对于智能电网来说是至关重要的。纵观全球,目前的智能电网往往趋于关注隐私安全的需要,隐私仅仅作为智能电网的一个特性存在。为了填补隐私保护在智能电网中的空白,同时帮助智能电网工程师分析隐私威胁、选择合适的策略即隐私保护技术最后达到解决智能电网系统开发阶段的隐私问题的目的,描述了相应的方法框架和指导规则,对现有的隐私保护技术进行了详细的总结,讨论了其应用的环境,并阐述了隐私实施过程中面对的潜在挑战。     

A New Roadmapping Technique for Creatively Managing the Emerging Smart Grid

The ‘Smart grid’ has been described as the ‘Energy Internet’, combining energy technology with information technology. The promise of the smart grid is to upgrade ageing energy infrastructure through the use of emerging technology. The resultant smart grid would improve efficiency, the ability to use alternative energy sources, responsiveness and security of the system. The need for creating a smarter grid is clear. Yet the traditional tool used by policy and other decision makers to rapidly advance technology product platforms, the technology roadmap, is proving ineffective due to the unique regulatory and market structure challenges of the smart grid. If the smart grid is important and its development constricted, then there is cause for concern. Today, most smart grid development teams are focusing their roadmapping efforts at the local and national levels. Yet regional roadmapping efforts can create higher quality and better integrated outcomes. A more advanced roadmapping technique is required for the effective and efficient management of the emerging smart grid technology product platform. Here, a case study approach utilizing the Pacific Northwest smart grid regional planning effort is utilized to generate an improved technology roadmapping technique, one that addresses the needs of emerging multi‐root technology‐based products like the smart grid. This roadmapping technique is designed to more effectively include the market structure and regulatory challenges inherent in the smart grid. The new roadmapping model integrates multiple technology, business and policy concerns.     

Power flow Petri Net modelling for building integrated multi-source power system with smart grid interaction

This paper presents an energy management modelling of a multi-source power system composed of photovoltaic (PV) array, storage and power grid connection, and taking into account messages from smart grid. The designed system can supply a tertiary building at the same time as PV may produce energy. The control strategy aims to manage the power flow through the load with respect to its power demand and public grid constraints. The proposed energy management modelling is based on interpreted Petri Nets formalism. The system is tested using a simulation Stateflow model and responds within certain limits. The results show that this approach is valid and can be a solution for the future smart grid communication between buildings and public grid and may contribute to a better balance between production and consumption and future energy management.     

A smart grid incorporated with ML and IoT for a secure management system

The economy, national safety, and health care are tremendously dependent on the faithful supply of power. The communication technology integration and sensors in power systems have been authorized as a smart grid (SG) that is revolutionizing the model of power generation, distribution, monitoring, and control. To know the Smart Grid compatibility, many problems are required to be directed. The safety of the smart grid is the most challenging function and very crucial difficulties. This paper proposed, a safe demand-side management machine deploying machine learning for the Internet of Things authorized phase is recommended. The propounded demand-side management (DSM) machine protects the effective energy use based on their preferences. A particular flexibility sample was proposed to manage incursion into the smart grid. Anelastic agent prognosticates swindling companies, the ML classifiers are utilized. Promoted power management and intermediate control companies are recommended for processing power data to improve energy usage. The proposed project's effective simulation is implemented to examine the efficiency. The outcome of the analysis discloses that the planned demand-side management (DSM) machine is less susceptible to the incursion and it is sufficient to decrease the smart grid's energy consumption.     

基于接纳控制的智能电网需求响应

采用效用函数刻画用户的用电满意度, 将需求响应问题建模为一类凸优化问题. 针对电力供应商的供电量不能满足用户最小用电需求的问题, 结合分布式用电量调度和实定价, 设计两类接纳控制算法. 仿真结果表明, 通过接纳控制, 满足了购电用户的最小用电需求, 保证了用户的用电质量, 能够实现电网的供需平衡.     

Privacy-aware electricity scheduling for home energy management system

In the context of smart grid, home energy management system (HEMS) needs to collect the fine-grained energy consumption data through smart meters. However, the fine-grained data contain the electricity consumption patterns of consumers, which can induce serious privacy issues. In order to protect the electric privacy of consumers, a privacy-aware electricity scheduling strategy for HEMS is proposed in this paper. Firstly, the basic scheduling model of HEMS is presented, and the basic scheduling objective is to minimize the electricity payment while satisfy the daily power demands of consumers. On this basis, a privacy-aware optimal scheduling model adopting rechargeable batteries is established, and the introduction of preference factor enables consumers to make a tradeoff between the total operation cost and privacy security. The electric privacy protection performance is measured by coefficient of determination and the number of features. Besides, the operation cost of batteries is also considered in the modeling process, and the influence battery capacity has on the performance of privacy protection is discussed. Simulation results show that the proposed method is effective and has strong practical application value.     

Simulation of the smart grid communications: Challenges,techniques, and future trends

This paper gives an overview of available simulation techniques to support the analysis of smart grid communications. Intelligent power grids, known as smart grids, are required as the demand for energy continues to grow and more emphasis is being placed on the supply of renewable energy. The main ingredient of smart grid is the integration of information and communication technology into the grids to monitor and regulate power generation and the demand. The impact of the communication networks on the performance of power system dynamics can only be understood through numerical simulations. This paper provides an overview of available simulation techniques for smart grid communications with a particular focus on the co-simulation frameworks and their enabling technologies. Moreover, a case study with VPNET for analyzing agent based shipboard smart grid protections considering communication impacts is provided.     

Competitive energy consumption under transmission constraints in a multi-supplier power grid system

Power grid architectures need to be revised in order to manage the increasing number of producers and, more generally, the decentralisation of energy production and distribution. In this work, we describe a multi-supplier multi-consumer congestion model of a power grid, where the costs of consumers depend on the congestion in nodes and arcs of the power supply network. The consumer goal is both to meet their energy demand and to minimise the costs. We show that the methods of non-atomic routing can be applied in this model in order to describe current distribution in the network. We formulate a consumer cost minimisation game for this setting, and discuss the challenges arising in equilibrium search for this game.     

A distributed control framework for smart grid development: Energy/water system optimal operation and electric grid integration

In this work, we propose a conceptual distributed control framework for electrical grid integrated with distributed renewable energy generation systems in order to enable the development of the so-called “smart electrical grid”. First, we introduce the key elements and their interactions in the proposed control architecture and discuss the design of the distributed control systems which are able to coordinate their actions to account for optimization considerations on the system operation. Subsequently, we focus on a specific wind/solar energy generation system connected to a reverse osmosis water desalination system and the electrical grid and design two supervisory predictive controllers via model predictive control to operate the integrated system taking into account short-term and long-term optimal maintenance and operation considerations, respectively. Simulations are carried out to illustrate the applicability and effectiveness of the proposed approach.     

The prediction of network efficiency in the smart grid

AMI is the core infrastructure of a smart grid and it is expected to be used for many industrial fields. The components of AMI generally include a smart meter, DCU, and MDMS. In such a system, the smart meter measures power consumption and DCU collects information from smart meters and send their data to MDMS. The MDMS is an end server to get information of the power usage and store its log and data. Since there are a lot of devices such as smart meters and DCUs in AMI, it is important to maintain the suitable number of them. In particular, it is necessary to calculate the proper number of DCUs for efficient management of AMI. In this paper, we suggest a way to predict the proper number of DCUs and this proposed method is useful to predict the total performance of heterogeneous AMI. The simulation results show that AMI is greatly influenced by the difference of performance between DCUs in the heterogeneous AMI.     

Predicting web site audience demographics using content and design cues

The size and dynamism of the Web pose challenges for all its stakeholders, which include producers and consumers of content as well as advertisers who want to place advertisements next to relevant content. A critical piece of information for producers/publishers of content as well as advertisers is the demographics of the consumers who are likely to visit a given web site. In this article we explore predictive models that attempt to deduce the demographics of the audience of a web site using cues embedded in the design or the content of its homepage. We find that it is possible to effectively predict different types of demographics of consumers of web sites on the basis of the suggested approach. Through a statistical analysis we observe that several design elements and the content differ significantly among web sites dominated by consumers of different demographic classes. We also suggest the use of an ensemble classifier that combines the content and design cues with the goal of further improving the prediction performance.     

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to consumer preferences while planning the operation of appliances. A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes. Furthermore, the allotment mechanism has been designed to manage the grid appliances through the edge node. The proposed model helps to improve the latency in the grid appliances scheduling process.     

A review on charging behavior of electric vehicles: data, model, and control

The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation, the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this work, based on which interested researchers can easily access these data for further investigation.     

A comparative study of collaboration-based reputation models for social recommender systems

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.