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This book constitutes the post-conference proceedings of the First International Conference on Smart Grid Inspired Future Technologies, SmartGIFT 2016, held in May 2016 in Liverpool, UK. Smart grid is the next generation electric grid that enables efficient, intelligent, and economical power generation, transmission, and distribution. The 25 revised full papers presented were reviewed and selected from 36 submissions. The papers cover technical topics such as high-level ideology and methodology, concrete smart grid inspired data sensing, processing, and networking technologies, smart grid system architecture, Quality of Service (QoS), energy efficiency, security in smart grid systems, management of smart grid systems, service engineering and algorithm design, and real-world deployment experiences.

Inhaltsverzeichnis

Frontmatter

Main Track

Frontmatter

Mobility Incorporated Vehicle-to-Grid (V2G) Optimization for Uniform Utilization in Smart Grid Based Power Distribution Network

V2G technology in smart grid architecture enables the bidirectional flow of electric energy where a Plug-in Electric Vehicle (PEV) can also discharge energy to the grid from its battery. Thus when a good number of PEVs are available, for instance, in a big parking lot enabled with Electric Vehicle Supply Equipment (EVSE), they form a large distributed energy storage system. A controller which can communicate with such EVSE enabled parking lots can optimally control charging/discharging schedules of each PEV to minimize the peak load demand of the distribution grid. While minimizing the peak demand, our optimization strategy also considers improved distribution of PEV loads throughout the distribution network to minimize the impact in any particular feeder or transformer utilizing mobility information by Vehicular Ad hoc Network (VANET) communications. This novel algorithm of uniform utilization can substantially reduce the need of expensive infrastructure upgrade of power distribution network.
Muhammad A. Hussain, Myung J. Lee

A Cost Function Based Prioritization Method for Smart Grid Communication Network

In Smart Grids (SG) scenarios, the different nodes composing the system have to communicate to the Control Stations several type of information with different requirements. There are many communication technologies (CTs), with different Quality of Service characteristics, able to support the SG communication requirements. By focusing on wireless communications, it is possible to notice that spectrum is becoming a rare source due to its exponential increasing demand. Thus, resource allocation to support different types of SG nodes should be performed in order to maximize the resource efficiency and respect the SG requirements. Defining a cost function (CF) helps to accomplish this goal. To this aim, it is also needed to prioritize the different SG nodes based on their goals. By using the SG nodes prioritization and the CF, a priority table is defined in which the nodes and the CTs are put in order, based on their weights. The numerical results show that the proposed method allows selecting the best CT for each type of SG nodes.
Vahid Kouhdaragh, Daniele Tarchi, Alessandro Vanelli-Coralli, Giovanni E. Corazza

Clustering Power Consumption Data in Smart Grid

For power distributors it is very important to have detailed information about the power consumption characteristics of their customers. These information is essential to plan correctly the required amount of energy from power-plants in order to minimize the difference between the demand and supply and to optimize the load of transportation grid as well. For industrial power consumer customers, on the market the actual rate of electric power may depend on their power consumption characteristics. By using intelligent meters and analyzing their behavior, relevant information can be obtained and the consumers can be classified in order to find the best rates for the supplier as well as for the consumer. In this paper, we introduce new results on clustering the consumers. The clustering method is based on forecasting the consumption time series. The numerical results prove that the method is capable of clustering consumers with different consumption patterns with good performance as a result the forecast based method proved to be the a promising tool in real applications.
Kálmán Tornai, András Oláh

Using a Cost Function to Choose the Best Communication Technology for Fulfilling the Smart Meters Communication Requirements

The conventional power grids are not efficient today so their ineffective functions need to be managed in a more effective way. This goal is at the base of the Smart Grid (SG) concept, to present intelligence in the energy grid. Among several SG nodes, smart meters (SMs) work in the demand side of the power grids and their number is much increasing over time. A SM is a SG device which records electric energy consumption in certain time intervals for communicating the information to the SG Control Station (CS) through the collectors and aggregators. The wireless communications have a significant role in SG functions. Hence, the spectrum scarcity due to the growing number of users is becoming a significant problem. Thus introducing an algorithm to avoid facing with spectrum scarcity by defining a Cost Function (CF) is helpful in sense of two issues. First, all SMs meet their communication requirements in the sense of delay sensitivity and data rate. The second goal is to avoid as much as possible the unnecessary allocation of the specific low delay Communication Technologies (CTs) spectrum to the SMs that are not delay sensitive. It is more preferable to support the less delay sensitive users with satellite or the CTs (with certain communication configurations such as high latency specification) and keep the low delay communication spectrum such as LTE for the users that are more delay sensitive. This paper is focused to introduce a method to achieve these goals.
Vahid Kouhdaragh, Alessandro Vanelli-Coralli, Daniele Tarchi

Assessing Loss Event Frequencies of Smart Grid Cyber Threats: Encoding Flexibility into FAIR Using Bayesian Network Approach

Assessing loss event frequencies (LEF) of smart grid cyber threats is essential for planning cost-effective countermeasures. Factor Analysis of Information Risk (FAIR) is a well-known framework that can be applied to consider threats in a structured manner by using look-up tables related to a taxonomy of threat parameters. This paper proposes a method for constructing a Bayesian network that extends FAIR, for obtaining quantitative LEF results of high granularity, by means of a traceable and repeatable process, even for fuzzy input. Moreover, the proposed encoding enables sensitivity analysis to show how changes in fuzzy input contribute to the LEF. Finally, the method can highlight the most influential elements of a particular threat to help plan countermeasures better. The numerical results of applying the method to a smart grid show that our Bayesian model can not only provide evaluation consistent with FAIR, but also supports more flexible input, more granular output, as well as illustrates how individual threat components contribute to the LEF.
Anhtuan Le, Yue Chen, Kok Keong Chai, Alexandr Vasenev, Lorena Montoya

Replay Attack Impact on Advanced Metering Infrastructure (AMI)

Advanced Metering Infrastructure (AMI) has currently become the most popular element in smart grid implementations both in home area network (HAN) and Neighborhood Area Network (NAN) environment as well as in large commercial/industrial establishments. The security of AMI has been an issue for several years, and many tools and utilities have been proposed to ensure the security of AMI networks. However, no network is completely safe from malicious users (hackers). Smart Meters (SM) in the NAN are typical targets for attackers, their objective being the acquisition of authentication information and attempting to successfully authenticate to become a part of the NAN. Such attacks are easy to launch and can cause significant impact since false data can be injected into the system. We explore the impact of such an attack on a previously developed authentication scheme and demonstrate that packet replays at a very fast rate can drain resources in a fashion similar to a Denial of Service (DoS) attack. The effect is pronounced since the authentication scheme uses a multi-hop path to reach the central authentication server. The intermediate nodes partially process each packet before forwarding it, causing an increase in the end-to-end delays as well as increased energy consumption. The authentication scheme is coded in C and the replay attacks are launched using an existing open source security tools.
Bashar Alohali, Kashif Kifayat, Qi Shi, William Hurst

D2Sketch: Supporting Efficient Identification of Heavy Hitters Over Sliding Windows

Heavy hitters can provide an important indicator for detecting abnormal network events. Most of existing algorithms for heavy hitter identification are implemented to deal with static datasets generated within a fixed time frame, lacking the ability to handle the latest arrivals of data streams adaptively. Considering the rigid demand for accurate and fast detection of outlier events in some networks like Smart Grids, these existing algorithms are not suitable to be deployed straightforward. To this end, this paper presents a new algorithm called D2Sketch for efficient heavy hitter identification over an adaptive sliding window for flexible dataset input. D2Sketch provides a novel framework that combines the Count-Min Sketch to get the connection degree of each host, with the stream-summary structure of Space Saving algorithm to get a more accurate list of Top-K heavy hitters. Moreover, it can adjust its measurement window to the most recent datasets automatically. Extensive experimental results show that the D2Sketch algorithm outperforms the related algorithm in terms of false positive rate, ordering deviation and estimate error.
Haina Tang, Yulei Wu, Tong Li, Hongbin Shi, Jingguo Ge

Short Term Load Forecasting for Residential Buildings

An Evaluation Based on Publicly Available Datasets
Short Term Load Forecasting is an essential component for optimizing the energy management of individual houses or small micro grids. By learning consumption patterns on smart metering data, smart grid applications such as Demand-Side-Management can be applied. However, most of the research done in this field is based on data which is not publicly available. Moreover, the evaluations also vary in the evaluation settings and the error measurements. In this work, five state-of-the-art approaches are compared on three publicly available datasets in the most common scenarios. By doing this, the most promising methods and model settings are pointed out. Furthermore, it can be seen that forecasting the consumption 24 h ahead achieves about the same accuracy as doing it four hours ahead. Still, the best results for individual households are rather inaccurate. By aggregating ten households, the results enhance by a factor of about 60%.
Carola Gerwig

Short-Term Electrical Load Forecasting Based on Fuzzy Logic Control and Improved Back Propagation Algorithm

The short-term electrical load forecasting plays a significant role in the management of power system supply for countries and regions. A new model which combines the fuzzy logic control with an improved back propagation algorithm (FLC-IBP) is proposed in this paper to improve the accuracy of the short-term load forecasting (STLF). Specifically, the composite-error-function-based method and the dynamic learning rate approach are designed to achieve a better predictable result, which mainly applies the improved back propagation algorithm (BP). Besides, the fuzzy logic control theory is used to build up a good optimization process. Experimental results demonstrate that the proposed method can improve the accuracy of load prediction.
Lei Wan, Junxiu Liu, Senhui Qiu, Mingcan Cen, Yuling Luo

On the Study of Secrecy Capacity with Outdated CSI

In this paper, we study the secrecy capacity of the proposed transmission scheme. It is provided that the SNR at the selected best relay, destination and eavesdropper are same due to the symmetrical setup. The outage probability is considered here to evaluate the performance of such a secrecy communication system. Then the system will be extended into outdated channel state information (CSI) scenarios. The correlation coefficients between the actual and outdated channel will influence the system performance. The closed-form expressions of the achievable outage probability will be derived to demonstrate the performance of the proposed secure transmission scheme, and numerical results are presented.
Tong Chen, Peng Xu, Zhiguo Ding, Xuchu Dai

The Role of Analog Beamwidth in Spectral Efficiency of Millimeter Wave Ad Hoc Networks

This work considers a millimeter wave (mmWave) ad hoc network, where the analog beam-codebook based beam training is employed to determine the analog beamforming direction for each node. In the analog beam training, a narrow analog beam pattern achieves a high main lobe gain and also reduces the probability of receiving the interference from its main lobe, but it requires high beam training overhead which might in return degrade the spectral efficiency of the network. Thus, there exists a trade-off between the analog beamwidth and the spectral efficiency performance. This motivates us to characterize the spectral efficiency as a function of the analog beamwidth and analyze the role of the beamwidth in the spectral efficiency. Numerical results confirm the effectiveness of the analysis and also provide a suggestion on how to choose the best beamwidth for a given setup.
Pan Cao, John S. Thompson

MASTERING Workshop

Frontmatter

An ANN-Based Energy Forecasting Framework for the District Level Smart Grids

This study presents an Artificial Neural Network (ANN) based district level smart grid forecasting framework for predicting both aggregated and disaggregated electricity demand from consumers, developed for use in a low-voltage smart electricity grid. To generate the proposed framework, several experimental study have been conducted to determine the best performing ANN. The framework was tested on a micro grid, comprising six buildings with different occupancy patterns. Results suggested an average percentage accuracy of about 96%, illustrating the suitability of the framework for implementation.
Baris Yuce, Monjur Mourshed, Yacine Rezgui

Future Demand Response Services for Blocks of Buildings

Research surrounding demand response (DR) is beginning to consider how blocks of buildings can operate collectively within energy networks. DR at the level of a block of buildings involves near real-time optimisation of energy demand, storage and supply (including self-production) using intelligent energy management systems with the objective of reducing the difference between peak-power demand and minimum night-time demand, thus reducing costs and greenhouse gas emissions. To enable this it will be necessary to integrate and augment the telemetry and control technologies embedded in current building management systems and identify potential revenue sources: both of which vary according to local and national contexts. This paper discusses how DR in blocks of buildings might be achieved. The ideas proposed are based on a current EU funded collaborative research project called “Demand Response in Blocks of Buildings” (DR-BOB), and are envisaged to act as a starting-point for future research and innovation.
Tracey Crosbie, Vladimir Vukovic, Michael Short, Nashwan Dawood, Richard Charlesworth, Paul Brodrick

Use Cases and Business Models of Multi-Agent System (MAS) ICT Solutions for LV Flexibility Management

This paper describes the use cases and business models opportunities of a Multi-Agent System (MAS) ICT solution for LV Flexibility Management. The MAS platform provides a technological solution that enables new collaboration opportunities between actors in the LV portion of the grid, namely, distribution system operators, ESCOs (in particular Telecoms) and consumers/prosumers. MAS have potential for efficient decision-making in the LV part of the grid due to the large number devices, users and variables and which makes more efficient a decentralized decision making approach. To support the new collaborations and business strategies amongst these actors, new business models are required and the ecosystem forms series of multi-sided platform business models. In this paper, the approach to business model development is detailed and 17 resultant business model opportunities are identified. These business models are then mapped to the use cases for future analysis.
Juan Manuel Espeche, Thomas Messervey, Zia Lennard, Riccardo Puglisi, Mario Sissini, Meritxell Vinyals

Combination of Standards to Support Flexibility Management in the Smart Grid, Challenges and Opportunities

This paper presents the results of an assessment of a wide range of standards in the smart grid and telecommunications domains that may be jointly used to implement three use cases focusing on the use of the multi-agent systems paradigm in enhancing the smart grid particularly in the area of flexibility management. In addition to supporting a decentralised grid, multi-agent systems can improve other aspects such as reliability, performance and security. The paper identifies relevant standards based on a set of key smart grid use cases from the EU Mas2tering project. The evaluation aims to provide recommendations on combining those standards. This will enable new collaboration opportunities between grid operators, telecom and energy companies, both from technology and business perspectives. The set of telecommunication and energy standards is identified based on existing EU smart grid implementations, models (such as the Smart Grid Architecture Model) and reports published by the European and International standardisation bodies and coordination groups.
Hisain Elshaafi, Meritxell Vinyals, Michael Dibley, Ivan Grimaldi, Mario Sisinni

Special Session Track

Frontmatter

A Load Balanced Charging Strategy for Electric Vehicle in Smart Grid

As the number of Electric Vehicle (EV) increases, the uncoordinated charging behaviors may cause the charging demand fluctuations and the charging load unbalanced. Besides, the users’ charging behaviors are affected by many factors. For example, the residual energy of battery decides the travel distance of EV and if an EV has more residual energy, the charging willing is lower. Because EV users don’t have much willing to change their charging time and place just as in the past, the charging habit may also affect the charging decision. In this paper, we propose a smart charging startegy CDF (Charging Decision Function), where three sub-functions related to the residual energy of battery, EV’s charging habit, and the charging efficiency of charging station are all weighted and involved, for improving the balance of charging load and reducing the charging demand fluctuations. The charging decision is resulted from the CDF’s value, and if an EV decides to charge, the charging time as well as charging place is also calculated. Compared with other two related strategies, CDF has the best performance in terms of reducing the charging demand fluctuations. The load balance among different charging stations is also improved.
Qiang Tang, Ming-zhong Xie, Li Wang, Yuan-sheng Luo, Kun Yang

Optimized Energy-Aware Window Control for IEEE 802.11ah Based Networks in Smart Grid Enabled Cities

IEEE 802.11ah brings Restricted Access Window (RAW) to decrease collision probability for smart grid applications. The RAW size affects transmission energy consumption and data rate for the different number of devices per group. In this paper, we investigate an energy efficient RAW optimization problem for IEEE 802.11ah based uplink communications. We formulate the problem based on overall energy consumption and the data rate of each RAW by applying probability theory. Then, we derive the energy efficiency of the uplink transmission. Last but not the least, a dynamic energy-aware window algorithm to adapt the RAW size is proposed to optimize the energy efficiency by identifying the number of slots in each RAW for different group scales. Simulation results show that our proposed algorithm outperforms existing RAW on uplink energy efficiency and delivery ratio.
Yanru Wang, Chao Liu, Kok Keong Chai, Yue Chen, Jonathan Loo

Smart Home System Network Architecture

A four-tier based Smart Home System is proposed in this paper; this type of network architecture is inspired by both the concept of the Internet of Things (IoT) and Machine to Machine (M2M) technologies. The main purpose of this structure is to establish a centralized resource management system, which can monitor and control each home appliance within a house. This aim and structure can help to overcome the most notable research challenge which is called interoperability of the system. The interoperability can be reflected from how other services such as assisted living and vehicle tracking are able to share and utilize the same resources. One of the structure advantages is to help existing house owners to live in a modern and convenient environment; it is also environmentally friendly by reducing the overall energy consumption within a household. A novel Home System Data Reflect Arc is proposed to minimalize the response time and limit unnecessary data transmission. Finally, with the aid and optimization of wireless sensor networks, a Received Signal Strength Indicator (RSSI) based indoor localization approach is proposed which is novel based on the fact that it does not require specialized implementation, nor does it require people to wear specialized hardware as it is part of a passive device free environment.
Chenqi Yang, Emilio Mistretta, Sara Chaychian, Johann Siau

Threat Navigator: Grouping and Ranking Malicious External Threats to Current and Future Urban Smart Grids

Deriving value judgements about threat rankings for large and entangled systems, such as those of urban smart grids, is a challenging task. Suitable approaches should account for multiple threat events posed by different classes of attackers who target system components. Given the complexity of the task, a suitable level of guidance for ranking more relevant and filtering out the less relevant threats is desirable. This requires a method able to distil the list of all possible threat events in a traceable and repeatable manner, given a set of assumptions about the attackers. The Threat Navigator proposed in this paper tackles this issue. Attacker profiles are described in terms of Focus (linked to Actor-to-Asset relations) and Capabilities (Threat-to-Threat dependencies). The method is demonstrated on a sample urban Smart Grid. The ranked list of threat events obtained is useful for a risk analysis that ultimately aims at finding cost-effective mitigation strategies.
Alexandr Vasenev, Lorena Montoya, Andrea Ceccarelli, Anhtuan Le, Dan Ionita

Invited Papers Track

Frontmatter

A New Dynamic Weight-Based Energy Efficient Algorithm for Sensor Networks

Since sensor nodes have limited energy resources, prolonging network lifetime and improving scalability are essential elements in energy- efficient Wireless Sensor Networks (WSNs). Most existing approaches consider the residual energy of a single node when electing a cluster head (CH), omitting other factors associated with the node, such as its location within the WSN topology and its nodal degree. Thus, this paper proposes a new Dynamic Weight Clustering based Algorithm (DWCA) for WSNs to reduce the overall energy consumption, balance the energy consumption among all nodes and improve the network scalability. The study has examined the performance of the proposed DWCA algorithm using simulation experiments. We compare the performance of our DWCA against some counterparts. The results demonstrate that our algorithm outperforms its counterparts in terms of energy efficiency and scalability.
Alsnousi Essa, Ahmed Y. Al-Dubai, Imed Romdhani, Mohamed A. Eshaftri

LTE Delay Assessment for Real-Time Management of Future Smart Grids

This study investigates the feasibility of using Long Term Evolution (LTE), for the real-time state estimation of the smart grids. This enables monitoring and control of future smart grids. The smart grid state estimation requires measurement reports from different nodes in the smart grid and therefore the uplink LTE radio delay performance is selected as key performance indicator. The analysis is conducted for two types of measurement nodes, namely smart meters (SMs) and wide area monitoring and supervision (WAMS) nodes, installed in the (future) smart grids. The SM and WAMS measurements are fundamental input for the real-time state estimation of the smart grid. The LTE delay evaluation approach is via ‘snap-shot’ system level simulations of an LTE system where the physical resource allocation, modulation and coding scheme selection and retransmissions are modelled. The impact on the LTE delay is analyzed for different granularities of LTE resource allocation, for both urban and suburban environments. The results show that the impact of LTE resource allocation granularity on delay performance is more visible at lower number of nodes per cell. Different environments (with different inter-site distances) have limited impact to the delay performance. In general, it is challenging to reach a target maximum delay of 1 s in realistic LTE deployments (This work is partly funded by the FP7 SUNSEED project, with EC grant agreement no: 619437.).
Ljupco Jorguseski, Haibin Zhang, Manolis Chrysalos, Michal Golinski, Yohan Toh

Big Data Processing to Detect Abnormal Behavior in Smart Grids

This paper proposes a methodology to effectively detect abnormal behavior in Smart Grids. The approach uses a cyber attack impact assessment technique to rank different assets, a cross-association decomposition technique for grouping assets and ultimately to reduce the number of monitored parameters, and an anomaly detection system based on the Gaussian clustering technique. The developed methodology is evaluated in the context of the IEEE 14-bus electricity grid model and three distinct classes of cyber attacks: bus fault attacks, line breaker attacks, and integrity attacks.
Béla Genge, Piroska Haller, István Kiss

[Invited Paper] Native IP Connectivity for Sensors and Actuators in Home Area Network

This paper discusses the requirements and trends in the building automation industry and in general shift of focus to provide native IP connectivity in low power devices. It then provides experimental results that validate the readiness of protocols for Internet of things developed by IETF and available in some chipset to meet the requirements from the industry. The evaluation criteria that was selected in this paper is latency, packer delivery rate, coverage and power consumption. It can be seen from the results that maximum 3 hops can be supported in order to achieve a PDR between 80–90 percent for 150 ms deadline.
Pang Zhibo, Gargi Bag, Edith Ngai, Victor Leung

Backmatter

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