To integrate large volumes of renewables and use electricity more efficiently, many industrial trials are on-going around the world that aim to realize decentralized or hierarchical control of renewable and distributed energy resources, flexible loads and monitoring devices. As the cost and complexity involved in the centralized communications and control infrastructure may be prohibitive in controlling millions of these distributed energy resources and devices, distributed optimization methods are expected to become much more prevalent in the operation of future electric power systems, as they have the potential to address this challenge and can be applied to various applications such as optimal power ow, state estimation, voltage control, and many others. While many distributed optimization algorithms are developed mathematically, little effort has been reported so far on how these methods should actually be implemented in real-world large-scale systems. The challenges associated with this include identifying how to decompose the overall optimization problem, what communication infrastructures can support the information exchange among subproblems, and whether to coordinate the updates of the subproblems in a synchronous or asynchronous manner. This research is dedicated to developing mathematical tools to address these issues, particularly for solving the non-convex optimal power flow problem. As the first part of this thesis, we develop a partitioning method that defines the boundaries of regions when applying distributed algorithms to a power system. This partitioning method quantifies the computational couplings among the buses and groups the buses with large couplings into one region. Through numerical experiments, we show that the developed spectral partitioning approach is the key to achieving fast convergence of distributed optimization algorithms on large-scale systems. After the partitioning of the system is defined, one needs to determine whether the communications among neighboring regions are supported. Therefore, as the second part of this thesis, we propose models for centralized and distributed communications infrastructures and study the impact of communication delays on the efficiency of distributed optimization algorithms through network simulations. Our findings suggest that the centralized communications infrastructure can be prohibitive for distributed optimization and cost-effective migration paths to a more distributed communications infrastructure are necessary. As the sizes and complexities of subproblems and communication delays are generally heterogeneous, synchronous distributed algorithms can be inefficient as they require waiting for the slowest region in the system. Hence, as the third part of this thesis, we develop an asynchronous distributed optimization method and show its convergence for the considered optimal power flow problem. We further study the impact of parameter tuning, system partitioning and communication delays on the proposed asynchronous method and compare its practical performance with its synchronous counterpart. Simulation results indicate that the asynchronous approach can be more efficient with proper partitioning and parameter settings on large-scale systems. The outcome of this research provides important insights into how existing hardware and software solutions for Energy Management Systems in the power grid can be used or need to be extended for deploying distributed optimization methods, which establishes the interconnection between theoretical studies of distributed algorithms and their practical implementation. As the evolution towards a more distributed control architecture is already taking place in many utility networks, the approaches proposed in this thesis provide important tools and a methodology for adopting distributed optimization in power systems.

Demand Response (DR) was proposed more than a decade ago to incentivise customers to shift their electricity usage from peak demand periods to off-peak demand periods and to curtail their electricity usage during peak demand periods. However, the lack of two-way communication infrastructure weakens the influence of DR and limits its applications. With the development of smart grid facilities (e.g. smart meters and the two-way communication infrastructure) that enable the interactions between the energy retailer and its customers, demand response shows great potential to reduce customers' bills, increase the retailer's profit and further stabilize the power systems. Given such a context, in this thesis we propose smart pricing based demand response programs to study the interactions between the energy retailer and its customers based on game-theory and machine learning techniques. We conduct the research in two different application scenarios: 1) For customers with home energy management system (HEMS) installed in their smart meters, the retailer will know the customers' energy consumption patterns by interacting with the HEMS. As a result, the smart pricing based demand response problem can be modelled as a Stackelberg game or bilevel optimization problem. Further, efficient solutions are proposed for the demand response problems and the existence of optimal solution to the Stackelberg game and the bilevel model is proved; 2) For customers without HEMS installed in their smart meters, the retailer will not know the energy consumption patterns of these customers and must learn customers' behaviour patterns via historical energy usage data. To realize this, two appliance-level machine learning algorithms are proposed to learn customers' consumption patterns. Further, distributed pricing algorithms are proposed for the retailer to solve the demand response problem effectively. Simulation results indicate the effectiveness of the proposed demand response models in both application scenarios.

Although smart meters are deployed in many countries, the data collection process from smart meters in Smart Grid (SG) still has some challenges related to consumer privacy that needs to be addressed. Referred to as Advanced Metering Infrastructure (AMI), the data collected and transmitted through the AMI can leak sensitive information about the consumers if it is sent as a plaintext. While many solutions have been proposed in the past, the deployment of these solutions in real-life was not possible since the actual AMIs were not accessible to researchers. Therefore, a lot of solutions relied on simulations which may not be able to capture the real performance of these solutions. In this thesis, two 802.11s wireless mesh-based SG AMI network testbeds are developed with Beaglebone Black and Raspberry Pi 3 boards to provide a baseline for the simulations. The Raspberry Pi 3 testbed is also configured to be remotely accessible.

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With the obvious evolution and acceleration of smart grid, it is crucial for its success to rely on a solid transmission protocol among its peripherals due to its real time streaming. TCP is the well known traditional transport protocol used for a reliable transmission, and is a major player for smart grid. However, it lacks a fault tolerance transmission method that overcomes potential failures which may mitigate smart grid progress and in its turn decrease its reliability. We propose that smart grid operators utilize SCTP as the principle transport protocol for their smart grid communications, by using the two very significant characteristics offered by SCTP multi-homing and multi-streaming respectively. Thus, we argue that they can override two major obstacles caused by TCP Head of Line Blocking (HLB) and the inability of handling automatically two or more paths to a final destination. Although SCTP resembles TCP in many aspects, SCTP can definitely play a dominant role in many current and future applications due to its key features that do not exist in TCP. We have used ns2.34 simulator as the tool whom we relied on to investigate whether or not smart grid may benefit over TCP by the two SCTP features, and have analyzed the output of simulated results by using other analytical tools. As we obtain results, we argue that smart grid operators should rely on SCTP as a feasible transmission protocol instead of TCP.

During the last century, the significant increase in electricity demand, and its consequences, has appeared as a serious concern for the utility companies, but no essential change has been applied to the conventional power grid infrastructure till now. Recently, researchers have identified efficient control and power distribution mechanisms as the immediate challenges for conventional power grids. Hence, the next step for conventional power grid toward Smart Grid is to provide energy efficiency management along with higher reliability via smart services, in which the application of Information and Communication Technology (ICT) is inevitable. ICT introduces powerful tools to comply with the smart grid requirements. Among various ICT properties, the telecommunication network plays a key role for providing a secure infrastructure. The two-way digital communication system provides an interaction between energy suppliers and consumers for managing, controlling and optimizing energy distribution. We can also define the smart grid as a two-way flow of energy and control information, where the electricity consumers can generate energy using green energy resources. The main objective of this thesis is to select an effective communication infrastructure to support the smart grid services by considering wireless and optical communication technologies. Fiber-wireless (FiWi) networks are considered as a potential solution to provide a fast and reliable network backbone with the optical access network integration and the flexibility and mobility of the wireless network. Therefore, we adopt the integration of the wireless sensor network (WSN) to Ethernet Passive Optical Network (EPON) as a broadband access network to transmit smart meter data along with the Fiber To The Home/Building/Curb (FTTX) traffic through the shared fiber. Finally, we present and analyze the simulation results for the aforementioned infrastructure based on our adopted priority-based FTTX-WSN integration model.

During the last three decades, the significant increase in electricity demand, and its consequences, has appeared as a serious concern for the utility companies, but no major changes have been applied to the conventional power grid infrastructure. Recently, researchers have identified efficient control and power distribution mechanisms as the immediate challenges for conventional power grids. The next step for conventional power grid towards the Smart Grid is to provide energy efficiency management along with higher reliability via smart services, in which the application of Information and Communication Technology (ICT) is inevitable. ICT introduces powerful tools to comply with the smart grid requirements. Among various ICT properties, the telecommunication network plays a key role for providing a secure infrastructure. The two-way digital communication system provides an interaction between energy suppliers and consumers for managing, controlling and optimizing energy distribution. We can also define the smart grid as a two-way flow of energy and control information, where the electricity consumers can generate energy using green energy resources. The main objective of this thesis is to select an effective data communication infrastructure to support the smart grid services by considering a hybrid wireless and optical communication technologies. Radio-over-Fibre (RoF) networks are considered as a potential solution to provide a fast, reliable and efficient network backbone with the optical access network integration and the flexibility and mobility of the wireless network. Therefore, we adopt the integration of RoF to Passive Optical Network (PON) as a broadband access network to transmit smart grid data along with the Fiber to the Home/Building/Curb (FTTx) traffic through the shared fibre, and utilizing Wavelength Division Multiplexing (WDM). Finally, we present and analyze the simulation results for the aforementioned infrastructure based on our enhanced ROF-PON integration model.

In planning for future electricity supplies certain issues will need to be considered such as increased energy usage, urbanisation, reduction in personnel, global warming and the conservation of natural resources. As the result, some countries have investigated the transformation of their existing power grid to the so-called Smart Grid. The Smart Grid has three main characteristics which are, to some degree, antagonistic. These characteristics are the provision of good power quality, energy cost reduction and improvement in the reliability of the grid. The need to ensure that they can be accomplished together demands much richer Information and Communications Technology (ICT) networks than the current systems available. In this research we have identified the gap in the current proposals for the ICT of the power grid. We have designed and developed an ICT architecture for the neighbourhood sub-Grid level of the electrical network, where monitoring at this level is very underdeveloped because most current grids are controlled centrally and the response of the neighbourhood area is not generally monitored or actively controlled. Our designed ICT architecture, which is based on established architectural principles, can incorporate data from heterogeneous sources. This layered architecture provides both the sensors that can directly measure the electrical activity of the network (e.g. voltage) and also the sensors that measure the environment (e.g. temperature) since these provide information that can be used to anticipate demand and improve control actions. Additionally, we have de-signed a visualisation tool as an interface for a grid operators to facilitate a better comprehension of the behaviour of the neighbourhood level of the Smart Grid. Since we have noticed that energy aware ICT is a prerequisite for an efficient Smart Grid, we have utilised two different approaches to tackle this issue. The first approach was to utilise a cluster-based communication technique for the second layer of the architecture, which comprises Wireless Sensor Networks, where energy limitation is the major problem. Accordingly, we have analysed the energy-aware topology for wireless sensor networks embedded in the mentioned layer. We provide evidence that the proposed topology will bring energy efficiency to the communication network of the Smart Grid. The second approach was to develop a data reduction algorithm to reduce the volume of data prior to data transmission. We demonstrated that our developed data reduction is suitable for Smart Grid applications which can keep the integrity and quality of data. Finally, the work presented in this thesis is based on a real project that is being implemented in the medium voltage power network of the University of Manchester where power grid instrumentation, real data and professionals in the field are available. Since the project is long-term and the environmental sensor networks in particular are not currently installed we have evaluated some of our predictions via simulation. However, where the instrumentation was available, we were able to compare our predictions and our simulations with actual experimental results.

This thesis identifies and addresses two barriers to the adoption of Long Term Evolution (LTE) Device-to-Device (D2D) communication enabled smart grid applications in out of core network coverage regions. The first barrier is the lack of accessible simulation software for engineers to develop and test the feasibility of their D2D LTE enabled smart grid application designs. The second barrier is the lack of a distributed resource allocation algorithm for LTE D2D communications that has been tailored to the needs of smart grid applications. A solution was proposed to the first barrier in the form of a simulator constructed in Matlab/Simulink used to simulate power systems and the underlying communication system, i.e., D2D communication protocol stack of Long Term Evolution (LTE). The simulator is built using Matlab's LTE System Toolbox, SimEvents, and Simscape Power Systems in addition to an in-house developed interface software to facilitate D2D communications in smart grid applications. To test the simulator, a simple fault location, isolation, and restoration (FLISR) application was implemented using the simulator to show that the LTE message timing is consistent with the relay signaling in the power system. A solution was proposed to the second barrier in the form of a multi-agent Q-learning based resource allocation algorithm that allows Long Term Evolution (LTE) enabled device-to-device (D2D) communication agents to generate orthogonal transmission schedules outside of network coverage. This algorithm reduces packet drop rates (PDR) in distributed D2D communication networks to meet the quality of service requirements of microgrid communications. The PDR and latency performance of the proposed algorithm was compared to the existing random self-allocation mechanism introduced under the Third Generation Partnership Project's LTE Release 12. The proposed algorithm outperformed the LTE algorithm for all tested scenarios, demonstrating 20-40% absolute reductions in PDR and 10-20 ms reductions in latency for all microgrid applications.

Thesis deals with the design of intelligent electrical socket. At the beginning of the thesis, there are described the characteristics of the distribution network in the Czech Republic and the ways of measuring these properties. The selected integrated circuit that realizes the measurement is described in detail. The third chapter deals with the current possibilities of power management, especially from the perspective of the distribution company and the concept of smart networks. The fourth chapter deals with the selection of communication interface suitable for smart socket as an element of smart home. The selected IEEE 802.15.4 standard and the microcontroller implementing the communication itself are described in more detail. The following part of the work is about design of the intelligent socket development board, on which the concept is tested and the design of the final hardware is described in this chapter. The last chapter describes the measurement results realized by the created device.