The development of electric vehicle (EV) greatly promotes building a green and sustainable society. The new technology also brings new challenges. With the penetration of electric vehicles, the charging demands are increasing, and how to efficiently coordinate EVs' charging activities is a major challenge and sparks numerous research efforts. In this dissertation, we investigate the EV charging scheduling problem under the public charging and home charging scenarios from different perspectives. First, we investigate the EV charging scheduling problem under a charging station scenario by jointly considering the revenue of the charging station and the service requirements of charging customers. We first propose an admission control algorithm to guarantee the non-flexible charging requirements of all admitted EVs being satisfied before their departure time. Then, a utility based charging scheduling algorithm is proposed to maximize the profit for the charging station. With the proposed charging scheduling algorithm, a win-win situation is achieved where the charging station enjoys a higher profit and the customer enjoys more cost savings. Second, we investigate the EV charging scheduling problem under a parking garage scenario, aiming to promote the total utility of the charging operator subject to the time-of-use pricing. By applying the analyzed battery charging characteristic, an adaptive utility oriented scheduling algorithm is proposed to achieve a high profit and low task declining probability for the charging operator. We also discuss a reservation mechanism for the charging operator to mitigate the performance degradation caused by charging information mismatching. Third, we investigate the EV charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery degradation cost during the charging process while satisfying the battery charging characteristic. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to achieve the least battery degradation cost and alleviate the peak power load, which is beneficial for both the customers and charging operator. Fourth, we investigate the EV charging scheduling problem under a residential community scenario. By jointly considering the charging energy and battery performance degradation during the charging process, we propose a utility maximization problem to optimize the gain of the community charging network. A utility maximized charging scheme is correspondingly proposed to achieve the utility optimality for the charging network. In summary, the research outcomes of the dissertation can contribute to the effective management of the EV charging activities to meet increasing charging demands. / Graduate

Sensor network web services have recently emerged as promising tools to provide remote management, data collection and querying capabilities for sensor networks. They can be utilized in a large number of elds among which Demand-Side Energy Management (DSEM) is an important application area that has become possible with the smart electrical power grid. DSEM applications generally aim to reduce the cost and the amount of power consumption. In the traditional power grid, DSEM has not been implemented widely due to the large number of households and lack of ne-grained automation tools. However by employing intelligent devices and implementing communication infrastructure among these devices, the smart grid will renovate the existing power grid and it will enable a wide variety of DSEM applications. In this thesis, we analyze various DSEM scenarios that become available with sensor network web services. We assume a smart home with a Wireless Sensor Network (WSN) where the sensors are mounted on the appliances and they are able to run web services. The web server retrieves data from the appliances via the web services running on the sensor nodes. These data can be stored in a database after processing, where the database can be accessed by the utility, as well as the inhabitants of the smart home. We showthat our implementation is e cient in terms of running time. Moreover, the message sizes and the implementation code is quite small which makes it suitable for the memory-limited sensor nodes. Furthermore, we show the application scenarios introduced in the thesis provide energy saving for the smart home.

The cyber-physical systems (CPS), such as smart grid and intelligent transportation system, permeate into our modern societies recently. The infrastructures in such systems are closely interconnected and related, e.g., the intelligent transportation system is based on the reliable communication system, which requires the stable electricity provided by power grid for the proper function. We call such mutually related systems interdependent networks. This thesis addresses the cascading failure issue in interdependent cyber physical system. We consider CPS as a system that consists of physical-resource and computational-resource networks. The failure in physical-resource network might cause the failures in computational-resource network, and vice versa. This failure may recursively occur and cause a sequence of failures in both networks. In this thesis, we propose two novel interdependence models that better capture the interdependent networks. Then, we study the effect of cascading failures using percolation theory and present the detailed mathematical analysis on failure propagation in the system. By calculating the size of functioning parts in both networks, we analyze the robustness of our models against the random attacks and failures. The cascading failures in smart grid is also investigated, where two types of cascading failures are mixed. We estimate how the node tolerance parameter T (ratio of capacity to initial workload) affect the system performance. This thesis also explores the small clusters. We give insightful views on small cluster in interdependent networks, under different interdependence models and network topologies.

Smart grid is an emerging technology that aims to empower the current power grid with the integration of two-way communication and computer technology. This thesis deals with energy management in smart grid with focus on the smart home and the Intelligent Transportation System (ITS). The smart home contains a network that connects home elements like smart appliances, HVAC (heating, ventilation, and air conditioning), thermostat, smart meter, sensors, solar panels and energy storage. ITS integrates computer and communication technologies for advanced traffic management and communication among road infrastructure, vehicles and users. A web service describes a collection of operations that are accessible via the Internet. Web services can also provide security and interoperability. Due to the rising cost of energy, more and more residential consumers are interested in controlling temperature or appliances in order to reduce energy consumption. In this thesis, we propose an approach that uses Web services to remotely and efficiently interact with smart home devices in order to manage energy consumption, in a smart grid environment. Consequently, the user is able to adjust the temperature, control appliances or read energy consumption values quickly, remotely and securely. A smart home with a wireless network based on ZigBee and XMPP (eXtensible Messaging and Presence Protocol) is simulated. The advantage of XMPP is that it provides near real-time communication and security. There is a central computer that can communicate with all home elements. Business Process Execution Language (BPEL) is used to implement the Web service on a central computer. Furthermore, quality of service is offered. Therefore, different levels of security and an access control mechanism are provided. An algorithm is proposed that can sell stored energy back to the grid from smart home. Another algorithm is proposed that can facilitate demand response. Moreover, dynamic programming is used to minimize energy consumption. Also, a broadcasting algorithm is presented that can be used by an electric vehicle to find the most suitable charging station in ITS. Simulation and analytical study is undertaken to demonstrate the performance and advantage of the proposed approach and algorithms.

Power demands will increase day-by-day because of widely adopting of Plug-in Electric Vehicles (PEVs) in the world and growing population. Finding and managing additional power resources for upcoming demands is a challenge. Renewable power is one of the alternatives. However, to manage and control renewable resources, we need suitable Energy Storage System (ESS). PEVs have a large battery pack that is used mainly to supply electric motor. Moreover, PEV battery could be used as an ESS to store power at a certain time and use it at another time. Nevertheless, it can play the same role with electric power grids, so it can store power at a time and return it at another time. This role might help the grid to meet the growing demands. In this thesis, we propose a charging and discharging coordination algorithm that effectively addresses the problem of power demand on peak time using the PEV’s batteries as a backup power storage, namely, Flexible Charging and Discharging (FCD) algorithm. The FCD algorithm aims to manage high power demands at peak times using Vehicle to Home (V2H) technologies in Smart Grid and PEV’s batteries. Intensive computer simulation is used to test FCD algorithm. The FCD algorithm shows a significant reduction in power demands and total cost, in proportion to two other algorithms, without affecting the performance of the PEV or the flexibility of PEV owner’s trip schedule.

To achieve a high level of reliability, efficiency, and robustness in electric systems, the concept of smart grid has been proposed. It is an update of the traditional electric grid designed to meet current and future customers' requirements. With the smart grid, demand management has been adopted in order to shape the load pattern of the consumers, maintain supply-demand balance, and reduce the total energy cost. In this thesis, we focus mainly on energy savings by critically investigating the problem of load management in the smart grid. We first propose a user aware demand management approach that manages residential loads while taking into consideration users' comfort. This latter is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort or savings. The proposed approach is based on game theory using a modified regret matching procedure. It provides users with high incentives to participate actively in load management and borrows advantages of both centralized and decentralized schemes. Then, we investigate the issue of fairness within demand response programs. The fair division of the system bill stemming from the use of shared microgrid resources with different costs is examined. The Shapley value provides one of the core solutions to fairness problems; however, it has been known to be computationally expensive for systems such as microgrids. Therefore, we incorporate an approximation of the Shapley value into a demand response algorithm to propose a fair billing mechanism based on the contribution of each user towards attaining the aggregated system cost. Finally, we study energy trading in the smart grid as an alternative way to reduce the load on the grid by efficiently using renewable energy resources. We propose a solution that takes into account the smart grid physical infrastructure, in addition to the distribution of its users. Different constraints stemming from the nature of the smart grid have been considered towards a realistic solution. We show through simulation results that all of the proposed schemes reduce the load on the grid, the energy bills, and the total system energy cost while maintaining the users' comfort as well as fairness.

The unprecedented rise of mobile user demand over the years have led to an enormous growth of the energy consumption of wireless networks as well as the greenhouse gas emissions which are estimated currently to be around 70 million tons per year. This significant growth of energy consumption impels network companies to pay huge bills which represent around half of their operating expenditures. Therefore, many service providers, including mobile operators, are looking for new and modern green solutions to help reduce their expenses as well as the level of their CO2 emissions. Base stations are the most power greedy element in cellular networks: they drain around 80% of the total network energy consumption even during low traffic periods. Thus, there is a growing need to develop more energy-efficient techniques to enhance the green performance of future 4G/5G cellular networks. Due to the problem of traffic load fluctuations in cellular networks during different periods of the day and between different areas (shopping or business districts and residential areas), the base station sleeping strategy has been one of the main popular research topics in green communications. In this presentation, we present several practical green techniques that provide significant gains for mobile operators. Indeed, combined with the base station sleeping strategy, these techniques achieve not only a minimization of the fossil fuel consumption but also an enhancement of mobile operator profits. We start with an optimized cell planning method that considers varying spatial and temporal user densities. We then use the optimal transport theory in order to define the cell boundaries such that the network total transmit power is reduced. Afterwards, we exploit the features of the modern electrical grid, the smart grid, as a new tool of power management for cellular networks and we optimize the energy procurement from multiple energy retailers characterized by different prices and pollutant levels in order to achieve green goals. Finally, we introduce the notion of green mobile operator collaboration as a new aspect of the green networking where competitive cellular companies might cooperate together in order to achieve green goals.

Cyber security is a major concern due to global incidents of intrusion. The impact of the attacks on the electricity grid can be significant, resulting in the collapsing of the national economy. Electricity network is needed by banks, government security agencies, hospitals and telecommunication operators. The purpose of this research is to investigate the various types of cyber security threats, including ICT technologies required for safe operation of the smart grid to protect and mitigate the impact of cyber security. The modelling of cyber security using the Matlab/SimPowerSystem simulates the City of Tshwane power system. Eskom components used to produce energy, interconnect to the City of Tshwane power distribution substations and simulated using Simulink SimPowerSystem. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

With the rapid adoption of electric vehicles and the rise of power generation from re- newable sources, intelligent management of power demand on a household level is gaining importance. Current algorithms used for that purpose have negative privacy implications and focus only on controlling the charging of electric vehicles while ignoring other ap- pliances. We describe a decentralized algorithm designed to control the power demand of different types of household appliances along with the charging of electric vehicles while preserving the privacy of the subscribers. We also present a smart grid simulator to evaluate the algorithm's effectiveness along with results of simulating a scale model of the power grid of the state of Texas. 1

Energy consumption by buildings, due to various factors such as temperature regulation, lighting, poses a threat to our environment and energy resources. In the United States, statistics reveal that commercial and residential buildings combined contribute about 40 percent of the overall energy consumption, and this figure is expected to increase. In order to manage the growing demand for energy, there is a need for energy system optimization, which would require a realistic, high-resolution energy-demand model. In this work, we investigate and model the energy consumption of buildings by taking into account physical, structural, economic, and social factors that influence energy use. We propose a novel activity based modeling framework that generates an energy demand profile on a regular basis for a given nominal day.  We use this information to generate a building-level energy demand profile at highly dis-aggregated level. We then investigate the different possible uses of generated demand profiles in different What-if scenarios like urban-area planning, demand-side management, demand sensitive pricing, etc. We also provide a novel way to resolve correlational and consistency problems in the generation of individual-level and building-level "shared" activities which occur due to individuals\' interactions. / Master of Science