A Game Theoretic Optimization Framework for Home Demand Management Incorporating Local Energy Resources

Facilitated by advanced ICT infrastructure and optimization techniques, smart grid has the potential to bring significant benefits to the energy consumption management. This paper presents a game theoretic consumption scheduling framework based on the use of mixed integer programming to schedule consumption plan for residential consumers. In particular, the optimization framework incorporates integration of locally generated renewable energy in order to minimise dependency on conventional energy and the consumption cost. The game theoretic model is designed to coordinatively manage the scheduling of appliances of consumers. The Nash equilibrium of the game exists and the scheduling optimization converges to an equilibrium where all consumers can benefit from participating in. Simulation results are presented to demonstrate the proposed approach and the benefits of home demand management

Universal Privacy Gurantees for Smart Meters

Smart meters (SMs) provide advanced monitoring of consumer energy usage, thereby enabling optimized management and control of electricity distribution systems. Unfortunately, the data collected by SMs can reveal information about consumer activity, such as the times at which they run individual appliances. Two approaches have been proposed to tackle the privacy threat posed by such information leakage. One strategy involves manipulating user data before sending it to the utility provider (UP); this approach improves privacy at the cost of reducing the operational insight provided by the SM data to the UP. The alternative strategy employs rechargeable batteries or local energy sources at each consumer site to try decouple energy usage from energy requests. This thesis investigates the latter approach. Understanding the privacy implications of any strategy requires an appropriate privacy metric. A variety of metrics are used to study privacy in energy distribution systems. These include statistical distance metrics, differential privacy, distortion metrics, maximal leakage, maximal $\alpha$-leakage and information measures like mutual information. We here use mutual information to measure privacy both because its well understood fundamental properties and because it provides a useful bridge to adjacent fields such as hypothesis testing, estimation, and statistical or machine learning. Privacy leakage under mutual information measures has been studied under a variety of assumptions on the energy consumption of the user with a strong focus on i.i.d. and some exploration of markov processes. Since user energy consumption may be non-stationary, here we seek privacy guarantees that apply for general random process models of energy consumption. Moreover, we impose finite capacity bounds on batteries and include the price of the energy requested from the grid, thus minimizing the information leakage subject to a bound on the resulting energy bill. To that aim we model the energy management unit (EMU) as a deterministic finite-state channel, and adapt the Ahlswede-Kaspi coding strategy proposed for permuting channels to the SM privacy setting. Within this setting, we derive battery policies providing privacy guarantees that hold for any bounded process modelling the energy consumption of the user, including non-ergodic and non-stationary processes. These guarantees are also presented for bounded processes with a known expected average consumption. The optimality of the battery policy is characterized by presenting the probability law of a random process that is tight with respect to the upper bound. Moreover, we derive single letter bounds characterizing the privacy-cost trade off in the presence of variable market price. Finally it is shown that the provided results hold for mutual information, maximal leakage, maximal-alpha leakage and the Arimoto and Sibson channel capacity

CloudMon: a resource-efficient IaaS cloud monitoring system based on networked intrusion detection system virtual appliances

The networked intrusion detection system virtual appliance (NIDS-VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS-VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade-off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS-VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS-VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS-VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS-VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS-VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS-VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS-VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in-use NIDS-VAs and physical hosts.The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions and insightful comments to improve the quality of the paper. The work reported in this paper has been partially supported by National Nature Science Foundation of China (No. 61202424, 61272165, 91118008), China 863 program (No. 2011AA01A202), Natural Science Foundation of Jiangsu Province of China (BK20130528) and China 973 Fundamental R&D Program (2011CB302600)

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio