Academic literature on the topic '090607 Power and Energy Systems Engineering (excl. Renewable Power)'

Renewable energy (i.e., biomass, wind and solar) and Battery Energy Storage (BES) are emerging as sustainable solutions for electricity generation. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The smart grid is formulated as a combination of power systems, telecommunication communication and information technology. As an integral part of the smart grid, a smart integration approach is presented in this thesis. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources and associated BES units in distribution networks strategically by considering various technical, economical and environmental issues. Hence, the aim of the thesis is to develop methodologies for strategic planning and operations of high renewable DG penetration along with an efficient usage of BES units. The first contribution of the thesis is to present three alternative analytical expressions to identify the location, size and power factor of a single DG unit with a goal of minimising power losses. These expressions are easily adapted to accommodate different types of renewable DG units for minimizing energy losses by considering the time-varying demand and different operating conditions of DG units. Both dispatchable and non-dispatchable renewable DG units are investigated in the study. Secondly, a methodology is also introduced in the thesis for the integration of multiple dispatchable biomass and nondispatchable wind units. The concept behind this methodology is that each nondispatchable wind unit is converted into a dispatchable source by adding a biomass unit with sufficient capacity to retain the energy loss at a minimum level. Thirdly, the thesis studies the determination of nondispatchable photovoltaic (PV) penetration into distribution systems while considering time-varying voltage-dependent load models and probabilistic generation. The system loads are classified as an industrial, commercial or residential type or a mix of them with different normalised daily patterns. The Beta probability density function model is used to describe the probabilistic nature of solar irradiance. An analytical expression is proposed to size a PV unit. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation. The IMO is minimised in determining the optimal size and power factor of a PV unit. Fourthly, the thesis discusses the integration of PV and BES units considering optimal power dispatch. In this work, each nondispatchable PV unit is converted into a dispatchable source by adding a BES unit with sufficient capacity. An analytical expression is proposed to determine the optimal size and power factor of PV and BES units for reducing energy losses and enhancing voltage stability. A self-correction algorithm is then developed for sizing multiple PV and BES units. Finally, the thesis presents a comprehensive framework for DG planning. In this framework, analytical expressions are proposed to efficiently capture the optimal power factor of each DG unit with a standard size for minimising energy losses and enhancing voltage stability. The decision for the optimal location, size and number of DG units is obtained through a benefit-cost analysis over a given planning horizon. Here, the total benefit includes energy sales, loss reduction, network investment deferral and emission reduction, while the total cost is a sum of capital, operation and maintenance expenses. The study reveals that the time-varying demand and generation models play a significant role in renewable DG planning. Depending on the characteristics of demand and generation, a distribution system would accommodate up to an estimated 48% of the nondispatchable renewable DG penetration. A higher penetration level could be obtained for dispatchable DG technologies such as biomass and a hybrid of PV and BES units. More importantly, the study also indicates that optimal power factor operation could be one of the aspects to be considered in the strategy of smart renewable DG integration. A significant energy loss reduction and voltage stability enhancement can be achieved for all the proposed scenarios with DG operation at optimal power factor when compared to DG generation at unity power factor which follows the current standard IEEE 1547. Consequently, the thesis recommends an appropriate modification to the grid code to reflect the optimal or near optimal power factor operation of DG as well as BES units. In addition, it is shown that inclusion of energy loss reduction together with other benefits such as network investment deferral and emission reduction in the analysis would recover DG investments faster.

Wireline telecommunications infrastructure in the customer access network or CAN is undergoing a veritable technological and commercial revolution. The paired-copper CAN is being modernised with optical fibre deployed ever closer to customers, culminating soon with fibre-to-the-home networks or some variant thereof. Although bandwidth ceases to be a scarce commodity, the underlying natural monopoly will most likely be strengthened. National competition policy desires open access to multiple service providers yet commercial pressure calls for closure. This has been the recent experience with the hybrid fibre coaxial networks delivering pay television and Internet access. This research asks the question: What are the factors that prevent open access to the broadband services of next generation wireline infrastructure? How can these obstacles be overcome? A particular focus is given to non-price considerations which come to the fore due to the unique strategic and technological characteristics of optical fibre in the access network. The methodological approach involves data gathering via three case studies - that of the Telstra/Foxtel pay television network, the TransACT broadband network and fibre-to-the-home networks in general. Although the ultimate focus is on the research question above, these cases are discussed in a holistic way with consideration of a number of contextual factors. The research also examines the relationship between the concepts of ‘open access’ and ‘network neutrality’, visiting the concept of ‘common carriage’ in doing so. Several findings are reached that illuminate the field of telecommunications access regulation as applied to infrastructure capable of delivering truly next generation broadband services. Since 1993, our politicians have only paid lip service to the importance of competition and have deferred to the demands of the dominant builder of telecommunications infrastructure. From the viewpoints of end-users and access seekers, the access regime is found to be incapable of dealing with the technical and commercial bottlenecks arising from optical fibre in the CAN. It is concluded that communication between users should be recognised as the prime purpose of telecommunications and that the regulatory regime should not reward discriminatory practices detracting from the development of a networked information economy. It is also concluded that dominant players should never be rewarded with access holidays which could otherwise entrench market dominance through the creation of new bottlenecks. Access regulation is ill-equipped to cope with optical fibre in the CAN until it also recognizes the strategic potential of such infrastructure.

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components.

Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of

This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered.
threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of
generation events from transient swings caused by other events.


This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.

Recent developments in semiconductor device technology have seen the advent of wide-bandgap (WBG) based devices that enable operation at high switching frequencies. These devices, such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs), are becoming a favored choice in inverters for electric drive systems because of their lower switching losses and higher allowable operating temperature. However, the fast switching of such devices implies increased voltage edge rates (high dv/dt) that give rise to various undesirable effects including large common-mode currents, electromagnetic interference, transient overvoltages, insulation failure due to the overvoltages, and bearing failures due to

microarcs. With increased use of these devices in transportation and industrial applications, it is imperative that accurate models and efficient simulation tools, which can predict these high-frequency effects and accompanying system losses, be established. This research initially focuses on establishing an accurate wideband model of a surface-mount permanent-magnet

ac machine supplied by a WBG-based inverter. A new multirate simulation framework for predicting the transient behavior and estimating the power losses is then set forth. In this approach,

the wideband model is separated into high- and low-frequency models implemented using two different computer programs that are best suited for the respective time scales. Repetitive execution of the high-frequency model yields look-up tables for the switching losses in the semiconductors, electric machine, and interconnecting cable. These look-up tables are then incorporated into the low-frequency model that establishes the conduction

losses. This method is applied to a WBG-based electric drive comprised of a SiC inverter and permanent-magnet ac machine. Comparisons of measured and simulated transients are provided.

Increase in the awareness environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed and SOC trajectory, as 50 element time variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for given drive cycle. Observations of the trade-off is discussed in the case of Multi-Objective Optimizations.

In this research, an attempt is made to evaluate alternative model-predictive microgrid control approaches and to understand the trade-offs that emerge between model complexity and the ability to achieve real-time optimized system performance. Three alternative controllers are considered and their computational and optimization performance compared. In the first, nonlinearities of the generators are included within the optimization. Subsequently, an approach is considered wherein alternative (non-traditional) states and inputs of generators are used which enables one to leverage linear models with the model predictive control (MPC). Nonlinearities are represented outside the control in maps between MPC inputs and the physical inputs. Third, a recently proposed linearized trajectory (LTMPC) is considered. Finally, the performance of the controllers is examined utilizing alternative models of the synchronous machine that have been proposed for power system analysis.