Motor control assets are foundational elements in many industrial operations. In the mining industry, these assets primarily consist of motor control centers and drives, which are available with a comprehensive assortment of control and monitoring devices. Various intelligent electronic devices (IEDs) are now used to prevent machine damage and downtime. As motor control devices have advanced in technology, so too have the IEDs that protect them. These advances have resulted in new standards, such as IEC 61850, that have embedded intelligence and a standard set of communication schemes by which IEDs can share information in a peer-to-peer or one-to-many fashion. This dissertation investigated the steps involved in interfacing IEDs to a mining process control network via the use of the IEC 61850 standard. As a result of this study, several key technological advancements were made including the development of (i) vendor independent system to communicate with IEDs in a mining environment over IEC 61850, (ii) command and control methods for communication based assisted automation of IEDs for mining firms, (iii) effective solutions to incorporate electrical distribution data in the process control system, (iv) enhanced safety platforms through remote operation of IEDs, (v) standard visualization faceplate graphics for HMI operators with enhanced security, and (vi) new methods for time stamped dataflow to be correctly inserted into a process historian for 'true' Sequence of Events Records. / Ph. D.

yes / The current electricity grid suffers from numerous challenges due to the lack of an effective energy management strategy that is able to match the generated power to the load demand. This problem becomes more pronounced with microgrids, where the variability of the load is obvious and the generation is mostly coming from renewables, as it depends on the usage of distributed energy sources. Building a smart microgrid would be much more economically feasible than converting the large electricity grid into a smart grid, as it would require huge investments in replacing legacy equipment with smart equipment. In this paper, application of Internet of Things (IoT) technology in different parts of the microgrid is carried out to achieve an effective IoT architecture in addition to proposing the Internet-of-Asset (IoA) concept that will be able to convert any legacy asset into a smart IoT-ready one. This will allow the effective connection of all assets to a cloud-based IoT. The role of which is to perform computations and big data analysis on the collected data from across the smart microgrid to send effective energy management and control commands to different controllers. Then the IoT cloud will send control actions to solve microgrid's technical issues such as solving energy mismatch problem by setting prediction models, increasing power quality by the effective commitment of DERs and eliminating load shedding by turning off only unnecessary loads so consumers won't suffer from power outages. The benefits of using IoT on various parts within the microgrid are also addressed.

Edge networks such as sensor, mobile, and disruption tolerant networks suffer from topological uncertainty and disconnections due to myriad of factors including limited battery capacity on client devices and mobility. Hence, providing reliable, always-on consistency for network applications in such mobile and sensor systems is non-trivial and challenging. However, the problem is of paramount importance given the proliferation of mobile phones, PDAs, laptops, and music players. This thesis identifies two fundamental deterrents to addressing the above problem. First, limited energy on client mobile and sensor devices makes high levels of consistency and availability impossible. Second, unreliable support from the network infrastructure, such as coverage holes in WiFi degrades network performance. We address these two issues in this dissertation through client and infrastructure end modifications. The first part of this thesis proposes a novel energy management architecture called Hierarchical Power Management (HPM). HPM combines platforms with diverse energy needs and capabilities into a single integrated system to provide high levels of consistency and availability at minimal energy consumption. We present two systems Triage and Turducken which are instantiations of HPM for sensor net microservers and laptops respectively. The second part of the thesis proposes and analyzes the use of additional infrastructure in the form of relays, mesh nodes, and base stations to enhance sparse and dense mobile networks. We present the design, implementation, and deployment of Throwboxes a relay system to enhance sparse mobile networks and an associated system for enhancing WiFi based mobile networks.

Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

A growing need for clean and sustainable energy is causing a significant shift in the electricity generation paradigm. In the electricity system of the future, integration of renewable energy sources with smart grid technologies can lead to potentially huge economical and environmental benefits ranging from lesser dependency on fossil fuels and improved efficiency to greater reliability and eventually reduced cost of electricity. In this context, microgrids serve as one of the main components of smart grids with high penetration of renewable resources and modern control strategies. This dissertation is concerned with developing optimal control strategies to manage an energy storage unit in a grid-connected microgrid under uncertainty of electricity demand and prices. Two methods are proposed based on the concept of rolling horizon control, where charge/discharge activities of the storage unit are determined by repeatedly solving an optimization problem over a moving control window. The predicted values of the microgrid net electricity demand and electricity prices over the control horizon are assumed uncertain. The first formulation of the control is based on the scenario-based stochastic conditional value at risk (CVaR) optimization, where the cost function includes electricity usage cost, battery operation costs, and grid signal smoothing objectives. Gaussian uncertainty is assumed in both net demand and electricity prices. The second formulation reduces the computations by taking a worst-case CVaR stochastic optimization approach. In this case, the uncertainty in demand is still stochastic but the problem constraints are made robust with respect to price changes in a given range. The optimization problems are initially formulated as mixed integer linear programs (MILP), which are non-convex. Later, reformulations of the optimization problems into convex linear programs are presented, which are easier and faster to solve. Simulation results under different operation scenarios are presented to demonstrate the effectiveness of the proposed methods. Finally, the energy management problem in network of grid-connected microgrids is investigated and a strategy is devised to allocate the resulting net savings/costs of operation of the microgrids to the individual microgrids. In the proposed approach, the energy management problem is formulated in a deterministic co-operative game theoretic framework for a group of connected microgrids as a single entity and the individual savings are distributed based on the Shapley value theory. Simulation results demonstrate that this co-operation leads to higher economical return for individual microgrids compared to the case where each of them is operating independently. Furthermore, this reduces the dependency of the microgrids on the utility grid by exchanging power locally. / Thesis / Master of Applied Science (MASc)

Energy management strategies for microgrids, containing energy storage, renewable energy sources (RES), and electric vehicles (EVs); which interact with the grid on an individual basis; are presented in Chapter 3. An optimization problem to reduce cost, formulated over a rolling time horizon, using predicted values of load demand, EV connection/disconnection times, and charge levels at time of connection, is described. The solution provides the on-site storage and EV charge/discharge powers. For the first time, both bidirectional and unidirectional charging are considered for EVs and a controller which accommodates uncertainties in EV energy levels and connection/disconnection times is presented. In Chapter 4, a stochastic chance constraints based optimization is described. It affords significant improvement in robustness, over the conventional controller, to uncertainties in system parameters. Simulation results demonstrate that the stochastic controller is at least twice as effective at meeting the desired EV charge level at specific times compared to the non-stochastic version, in the presence of uncertainties. In Chapter 5, a network of microgrids, containing RES and batteries, which trade energy among themselves and with the utility grid is considered. A novel distributed energy management system (EMS), based on a central EMS using a Multi-Objective (MO) Rolling Horizon (RH) scheme, is presented. It uses Alternating Direction Method of Multipliers (ADMM) and Quadratic Programming (QP). It is inherently more data-secure and resilient to communication issues than the central EMS. It is shown that using an EMS in the network provides significant economic benefits over MGs connected directly to the grid. Simulations demonstrate that the distributed scheme produced solutions which are very close to those of the central EMS. Simulation results also reveal that the faster, less memory intensive distributed scheme is scalable to larger networks -- more than 1000 microgrids as opposed to a few hundreds for the central EMS. / Thesis / Doctor of Philosophy (PhD)

The closed greenhouse is an innovative concept in sustainable energy management. The closed greenhouse can be considered as a large commercial solar building. In principle, it is designed to maximize the utilization of solar energy through seasonal storage. In a fully closed greenhouse, there are not any ventilation windows. Therefore, the excess sensible and latent heat must be removed, and can be stored using seasonal and/or daily thermal storage technology. The available stored excess heat can be utilized later in order to satisfy the heating demand in the greenhouse, and also in neighbouring buildings. A model for energy analysis of a greenhouse has been developed using the commercial software TRNSYS. With this model, the performance of various design scenarios has been examined. The closed greenhouse is compared with a conventional greenhouse using a case study to guide the energy analysis. In the semi-closed greenhouse, a large part of the available excess heat will be stored through thermal energy storage system (TES). However, a ventilation system can still be integrated in order to use fresh air as a rapid response indoor climate control system. The partly closed greenhouse consists of a fully closed section and a conventional section. The fully closed section will supply the heating and cooling demand of the conventional section as well as its own demand. The results show that there is a large difference in heating demand between the ideal closed and conventional greenhouse configurations. Also, it can be concluded that the greenhouse glazing type (single or double glass) and, in the case of the semi-closed and partly closed greenhouse, the controlled ventilation ratio are important for the thermal energy performance of the system.  A thermo-economic analysis has been done in order to investigate the cost feasibility of various closed greenhouse configurations. From this analysis, it was found that the load chosen for the design of the seasonal storage has the main impact on the payback period. In the case of the base load being chosen as the design load, the payback period for the ideal closed greenhouse might be reduced by 50% as compared to using peak load. Thus, future studies should explore innovative combinations of short term and seasonal storage. Finally, several energy management scenarios have been discussed in order to find alternatives for improving the energy performance of the closed greenhouses. However, no specific optimal solution has so far been defined. / <p>QC 20111115</p>

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)

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