A Stochastic Geometry Analysis of Energy Harvesting in Large Scale Wireless Networks

In this paper, the theoretical sustainable capacity of wireless networks with radio frequency (RF) energy harvesting is analytically studied. Specifically, we consider a large scale wireless network where base stations (BSs) and low power wireless devices are deployed by homogeneous Poisson point process (PPP) with different spatial densities. Wireless devices exploit the downlink transmissions from the BSs for either information delivery or energy harvesting. Generally, a BS schedules downlink transmission to wireless devices. The scheduled device receives the data information while other devices harvest energy from the downlink signals. The data information can be successfully received by the scheduled device only if the device has sufficient energy for data processing, i.e., the harvested energy is larger than a threshold. Given the densities of BSs and users, we apply stochastic geometry to analyze the expected number of users per cell and the successful information delivery probability of a wireless device, based on which the total network throughput can be derived. It is shown that the maximum network throughput per cell can be achieved under the optimal density of BSs. Extensive simulations validate the analysis.Comment: This paper has been accepted by Greencom 201

Virtual Impedance Based Decentralized Control for a Microgrid System

As greenhouse gases produced from the conventional power plant causes global climate change, using renewable energy sources (RESs) in the future power system is inevitable. To minimize greenhouse gases emission, the interest in renewable grid or micro-grid is growing nowadays. In the microgrid, especially in AC microgrids, converters play an important role in many areas, including microgrid integration, uninterrupted power supply, and flexible alternating current transmission systems. Inverter plays an essential role in grid integration because it serves the interface between the energy source and the power grid. The important aspect of the inverter is control. This thesis studies several control strategies for the microgrid in both islanded and grid-connected modes while considering the intermittency of demand power and RESs. Generally, there are two types of control strategies: centralized control, the other is decentralized control. Decentralize control schemes are robust compare to other control systems and require low bandwidth e.g., only the nearest neighbor information is required. Droop control, one of the conventional decentralized control, is a well-established technique used extensively in power systems ever since synchronous generators were utilized. Since the features between the synchronous generator and the inverter are different, e.g., the inertia of the inverter is low, the traditional droop control needs to be modified in controlling the inverter. In this thesis, to eliminate the traditional droop tradeoff between power-sharing and grid voltage, we introduce virtual droop control. In case of transmission impedance mismatch between DG inverter, we use a large virtual resistance in both inverters to match the transmission impedance. The virtual impedance control is implemented and validated in MATLAB/Simulink and experiment. In this thesis, a microgrid consists of several units are selected for testing In conventional droop control, we observe a phase shift between inverter current which causes the circulation current inside the grid. But by using virtual droop control, this circulation current is minimized as well as we obtain a proper power-sharing among DG’ inverter

Planar array design and analysis on direction of arrival estimation for mobile communication systems

The demand of wireless communication has increased significantly in the past few decades due to huge demand to deliver multimedia content instantly. The expansion of mobile content paired with affordable mobile devices has opened a new trend for having access to the latest information on mobile devices. This trend is made possible by the technology of smart antenna systems as well as array signal processing algorithms. Array signal processing is not limited to wireless communication, but also found in other applications such as radar, sonar and automotive. One of the important components in array signal processing is its ability to estimate the direction of incoming signals known as directional-of-arrival (DOA). The performance of DOA algorithms depends on the steering vector since it contains information about the direction of incoming signals. One of the main factors to affect the DOA estimation is the array geometries since the array factor of the array geometries determines the definition of the steering vector. Another issue in DOA estimation is that the DOA algorithms are designed based on the ideal assumption that the antenna arrays are free from imperfection conditions. In practice, ideal conditions are extremely difficult to obtain and thus the imperfect conditions will severely degraded the performance of DOA estimation. The imperfect conditions include the presence of mutual coupling between elements and are also characteristic of directional antenna. There are three topics being discussed in this thesis. The first topic being investigated is new geometry of antenna array to improve the performance of DOA estimation. Two variants of the circular-based array are proposed in this thesis: semi-circular array and oval array. Another proposed array is Y-bend array, which is a variant of V-shape array. The proposed arrays are being put forward to offer a better performance of DOA estimation and have less acquired area compared with the circular array. It is found out that the semi-circular array has 5.7% better estimation resolution, 76% lower estimation error, and 20% higher estimation consistency than the circular array. The oval array improves the estimation resolution by 33%, estimation error by 60%, and estimation consistency by 20% compared with the circular array. In addition, for the same number of elements, the oval array requires 12.5% to 15% less area than the circular array. The third proposed array, Y-bend array, has 23% smaller estimation resolution, 88% lower estimation error, and 7% higher estimation consistency than the V-shape array. Among the proposed arrays, the semi-circular possessed the best performance with 25% smaller estimation resolution, ten times smaller estimation error, and 5% higher estimation consistency over the other proposed arrays. Secondly, this thesis investigates the DOA estimation algorithm when using the directional antenna array. In this case, a new algorithm is proposed in order to suit the characteristics of the directional antenna array. The proposed algorithm is a modified version of the Capon algorithm, one of the algorithms in beamforming category. In elevation angle estimation, the proposed algorithm achieves estimation resolution up to 1°. The proposed algorithm also manages to improve the estimation error by 80% and estimation consistency by 10% compared with the Capon algorithm. In azimuth angle estimation, the proposed algorithm achieves 20 times lower estimation error and 20% higher estimation consistency than the Capon algorithm. These simulation results show that the proposed algorithm works effectively with the directional antenna array. Finally, the thesis proposes a new method in DOA estimation process for directional antenna array. The proposed method is achieved by means of modifying covariance matrix calculation. Simulation results suggest that the proposed method improves the estimation resolution by 5° and the estimation error by 10% compared with the conventional method. In summary, this thesis has contributed in three main topics related to DOA estimation; array geometry design, algorithm for the directional antenna array, and method in DOA estimation process for the directional antenna array

Cyber- Physical Robustness Enhancement Strategies for Demand Side Energy Systems

An integrated Cyber-Physical System (CPS) system realizes the two-way communication between end-users and power generation in which customers are able to actively re-shaped their consumption profiles to facilitate the energy efficiency of the grid. However, large-scale implementations of distributed assets and advanced communication infrastructures also increase the risks of grid operation. This thesis aims to enhance the robustness of the entire demand-side system in a cyber-physical environment and develop comprehensive strategies about outage energy management (i.e., community-level scheduling and appliance-level energy management), communications infrastructure development, and cybersecurity controls that encounter virus attacks. All these aspects facilitate the demand-side system’s self-serve capability and operational robustness under extreme conditions and dangerous scenarios. The research that contributes to this thesis is grouped around and builds a general scheme to enhance the robustness of CPS demand-side energy system with outage considerations, communication network layouts, and virus intrusions. Under system outage, there are two layers for maximizing the duration of self-power supply duration in extreme conditions. The study first proposed a resilient energy management system for residential communities (CEMS), by scheduling and coordinating the battery energy storage system and energy consumption of houses/units. Moreover, it also proposed a hierarchical resilient energy management system (EMS) by fully considering the appliance-level local scheduling. The method also takes into account customer satisfaction and lifestyle preferences in order to form the optimal outcome. To further enhance the robustness of the CPS system, a complex multi-hop wireless remote metering network model for communication layout on the CPS demand side was proposed. This decreased the number and locations of data centers on the demand side and reduced the security risk of communication and the infrastructure cost of the smart grid for residential energy management. A novel evolutionary aggregation algorithm (EAA) was proposed to obtain the minimum number and locations of the local data centers required to fulfill the connectivity of the smart meters. Finally, the potential for virus attacks has also been studied as well. A trade-off strategy to confront viruses in the system with numerous network nodes is proposed. The allocation of antivirus programs and schemes are studied to avoid system crashes and achieve the minimum potential damages. A DOWNHILL-TRADE OFF algorithm is proposed to address an appropriate allocation strategy under the time evolution of the expected state of the network. Simulations are conducted using the data from the Smart Grid, Smart City national demonstration project trials

Robust wireless sensor network for smart grid communication : modeling and performance evaluation

Our planet is gradually heading towards an energy famine due to growing population and industrialization. Hence, increasing electricity consumption and prices, diminishing fossil fuels and lack significantly in environment-friendliness due to their emission of greenhouse gasses, and inefficient usage of existing energy supplies have caused serious network congestion problems in many countries in recent years. In addition to this overstressed situation, nowadays, the electric power system is facing many challenges, such as high maintenance cost, aging equipment, lack of effective fault diagnostics, power supply reliability, etc., which further increase the possibility of system breakdown. Furthermore, the adaptation of the new renewable energy sources with the existing power plants to provide an alternative way for electricity production transformed it in a very large and complex scale, which increases new issues. To address these challenges, a new concept of next generation electric power system, called the "smart grid", has emerged in which Information and Communication Technologies (ICTs) are playing the key role. For a reliable smart grid, monitoring and control of power system parameters in the transmission and distribution segments are crucial. This necessitates the deployment of a robust communication network within the power grid. Traditionally, power grid communications are realized through wired communications, including power line communication (PLC). However, the cost of its installation might be expensive especially for remote control and monitoring applications. More recently, plenty of research interests have been drawn to the wireless communications for smart grid applications. In this regard, the most promising methods of smart grid monitoring explored in the literature is based on wireless sensor network (WSN). Indeed, the collaborative nature of WSN brings significant advantages over the traditional wireless networks, including low-cost, wider coverage, self-organization, and rapid deployment. Unfortunately, harsh and hostile electric power system environments pose great challenges in the reliability of sensor node communications because of strong RF interference and noise called impulsive noise. On account of the fundamental of WSN-based smart grid communications and the possible impacts of impulsive noise on the reliability of sensor node communications, this dissertation is supposed to further fill the lacking of the existing research outcomes. To be specific, the contributions of this dissertation can be summarized as three fold: (i) investigation and performance analysis of impulsive noise mitigation techniques for point-to-point single-carrier communication systems impaired by bursty impulsive noise; (ii) design and performance analysis of collaborative WSN for smart grid communication by considering the RF noise model in the designing process, a particular intension is given to how the time-correlation among the noise samples can be taken into account; (iii) optimal minimum mean square error (MMSE)estimation of physical phenomenon like temperature, current, voltage, etc., typically modeled by a Gaussian source in the presence of impulsive noise. In the first part, we compare and analyze the widely used non-linear methods such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for point-to-point communication systems with low-density parity-check (LDPC) coded single-carrier transmission. While, the performance of these mitigation techniques are widely investigated for multi-carrier communication systems using orthogonal frequency division multiplexing (OFDM) transmission under the effect of memoryless impulsive noise, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which likely exists in contemporary communication systems including smart grid communications. Likewise, the assumption of memoryless noise model is not valid for many communication scenarios. Moreover, we propose log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. We show that the memory property of the noise can be exploited in the LLR calculation through maximum a posteriori (MAP) detection. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the simple clipping/blanking schemes. The second contribution can be divided into two aspects: (i) we consider the performance analysis of a single-relay decode-and-forward (DF) cooperative relaying scheme over channels impaired by bursty impulsive noise. For this channel, the bit error rate (BER) performances of direct transmission and a DF relaying scheme using M-PSK modulation in the presence of Rayleigh fading with a MAP receiver are derived; (ii) as a continuation of single-relay collaborative WSN scheme, we propose a novel relay selection protocol for a multi-relay DF collaborative WSN taking into account the bursty impulsive noise. The proposed protocol chooses the N’th best relay considering both the channel gains and the states of the impulsive noise of the source-relay and relay-destination links. To analyze the performance of the proposed protocol, we first derive closed-form expressions for the probability density function (PDF) of the received SNR. Then, these PDFs are used to derive closed-form expressions for the BER and the outage probability. Finally, we also derive the asymptotic BER and outage expressions to quantify the diversity benefits. From the obtained results, it is seen that the proposed receivers based on the MAP detection criterion is the most suitable one for bursty impulsive noise environments as it has been designed according to the statistical behavior of the noise. Different from the aforementioned contributions, talked about the reliable detection of finite alphabets in the presence of bursty impulsive noise, in the thrid part, we investigate the optimal MMSE estimation for a scalar Gaussian source impaired by impulsive noise. In Chapter 5, the MMSE optimal Bayesian estimation for a scalar Gaussian source, in the presence of bursty impulsive noise is considered. On the other hand, in Chapter 6, we investigate the distributed estimation of a scalar Gaussian source in WSNs in the presence of Middleton class-A noise. From the obtained results we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian channel

Learning and Control Applied to Demand Response and Electricity Distribution Networks

Balancing the supply and demand of electrical energy in real-time is a core task in power system operation. Traditionally, this balance has been achieved by controlling power plants, but increasing amounts of renewable energy generation increases the variability in generation and requires additional energy balancing capacity. An alternative to providing this additional capacity via power plants is to provide signals to loads that induce changes in their demand, which is referred to as demand response. There exists a large potential capacity for demand response using residential loads, but enabling these loads to participate in demand response requires communication and sensing capabilities. Thermostatically controlled loads (TCLs) are ubiquitous in residences and have inherent flexibility as they cycle on and off during normal operation. Coordinating on/off switching of TCL aggregations can provide energy balancing. However, TCLs are a spatially distributed resource that require sensing and communication infrastructure to enable demand response capabilities. A key to realizing cost effective residential demand response is minimizing infrastructure costs while maximizing the accuracy of the provided energy balancing, which results in increased revenue while improving reliability in the power system. The main contribution of this dissertation is to show that advanced algorithms can leverage existing infrastructure to make energy balancing with loads feasible in the near-term, which improves the reliability, economics, and environmental impact of the power grid. The dissertation first presents control algorithms, estimation algorithms, and models for residential demand response on fast timescales, i.e., on the order of seconds. Following this, the dissertation presents online learning algorithms for real-time feeder-level energy disaggregation within an electricity distribution network, which can be used to estimate the demand-responsive load in real-time. Methods for both topics are developed to operate within the capabilities of existing communication and sensing infrastructure, which reduces the implementation costs of the methods. Control and estimation algorithms are developed that address communication delays while taking into account realistic measurement availability. Results indicate that incorporating delay information into the algorithms can mitigate the effects of communication delays, allowing demand response providers to reduce infrastructure costs by using less expensive, lower quality communication networks. Additional work adapts three existing residential demand response models for a more detailed simulation environment, modifies each model to be more accurate in this environment, and benchmarks the model variations against each other. Results indicate that the model modifications produce more accurate predictions versus the unmodified models. Improving modeling accuracy can improve the reliability of the system and increase revenues for a demand response provider by improving the performance of model-based control and estimation algorithms. The energy disaggregation algorithms seek to separate the measured demand of a distribution feeder into components (e.g., the demand-responsive load and the remaining load) as feeder-level measurements become available. An online learning algorithm is adapted to perform real-time energy disaggregation using active power measurements of the total demand on the distribution feeder. Results indicate that the algorithm is able to effectively separate the air conditioning demand from the remaining demand connected to a distribution feeder. This algorithm is then extended to include reactive power, voltage, and smart meter measurements. Results indicate that the availability of additional real-time measurements leads to more accurate disaggregation of the demand components. Additional work in state estimation establishes connections between the online learning methods used and Kalman filtering algorithms.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149905/1/gsledv_1.pd