In recent years, the share of renewable energy sources (RESs) has been increasing in the electricity generation mix with a mandate to reduce greenhouse gas emissions that are released from burning fossil fuels. Indeed, a large share of electricity from renewable resources is required to de-carbonize the electricity sector. With the evolution of smart grids and microgrids, effective and efficient penetration of renewable generation such as wind and solar can possibly be attained. However, the intermittent nature of wind and solar generation makes microgrid operation and planning a complex problem and there is a need for a flexible grid to cope with the variability and uncertainty in their generation profiles. This research focuses on the coordination of distributed energy resources, such as energy storage systems (ESSs) and demand response (DR) to present an efficient solution towards improving the flexibility of microgrids, and supporting high levels of renewables generation. The overall goal of this research is to examine the influence of coordinated operation of ESS and DR on microgrid operations in the presence of high penetration levels of renewable generation. Deterministic and stochastic short-term operational planning models are developed to analyze the effects of coordinating ESS and DR, vis-à-vis their independent operation, on microgrids with high renewable generation. Special focus is on operation costs, scheduling and dispatching of controllable distributed generators, and levels of renewable generation. A set of valid probabilistic scenarios is considered for the uncertainties of load, and intermittency in wind and solar generation sources. The numerical results considering a benchmark microgrid indicate that coordinated operation of ESS and DR is beneficial in terms of operation costs, vis-à-vis their independent presence in the microgrid, when there is sufficient renewable generation. The coordinated operation reduces the risk in scheduling and increases the flexibility of the microgrid in supporting high levels of renewable generation.

Driven by technical, economic and environmental benefits for different stakeholders in the power industry, the electric distribution system is currently undergoing a major paradigm shift towards having an increasing portion of its growing demand supplied via distributed generation (DG) units. As the number of DG units increase; microgrids can be defined within the electric distribution system as electric regions with enough generation to meet all or most of its local demand. A microgrid should be able to operate in two modes, grid-connected or islanded. The IEEE standard 1547.4 enumerates a list of potential benefits for the islanded microgrid operation. Such benefits include: 1) improving customers’ reliability, 2) relieving electric power system overload problems, 3) resolving power quality issues, and 4) allowing for maintenance of the different power system components without interrupting customers. These benefits motivate the operation of microgrid systems in the islanded mode. However the microgrid isolation from the main grid creates special technical challenges that have to be comprehensively investigated in order to facilitate a successful implementation of the islanded microgrid concept. Motivated by these facts, the target of this thesis is to introduce new analysis and operational control algorithms to tackle some of the challenges associated with the practical implementation of the islanded microgrid concept. In order to accomplish this target, this study is divided into four perspectives: 1) developing an accurate steady-state analysis algorithm for islanded microgrid systems, 2) maximizing the possible utilization of islanded microgrid limited generation resources, 3) allowing for the decentralized operation of islanded microgrid systems and 4) enabling the islanded microgrid operation in distribution systems with high penetration of plug-in electric vehicles (PEVs). First for the steady-state analysis of islanded microgrid systems, a novel and generalized algorithm is proposed to provide accurate power flow analysis of islanded microgrid systems. Conventional power flow tools found in the literature are generally not suitable for the islanded microgrid operating mode. The reason is that none of these tools reflect the islanded microgrid special philosophy of operation in the absence of the utility bus. The proposed algorithm adopts the real characteristics of the islanded microgrid operation; i.e., 1) Some of the DG units are controlled using droop control methods and their generated active and reactive power are dependent on the power flow variables and cannot be pre-specified; 2) The steady-state system frequency is not constant and is considered as one of the power flow variables. The proposed algorithm is generic, where the features of distribution systems i.e. three-phase feeder models, unbalanced loads and load models have been taken in consideration. The effectiveness of the proposed algorithm, in providing accurate steady-state analysis of islanded microgrid systems, is demonstrated through several case studies. Secondly, this thesis proposes the consideration of a system maximum loadability criterion in the optimal power flow (OPF) problem of islanded microgrid systems. Such consideration allows for an increased utilization of the islanded microgrid limited generation resources when in isolation from the utility grid. Three OPF problem formulations for islanded microgrids are proposed; 1) The OPF problem for maximum loadability assessment, 2) The OPF for maximizing the system loadability, and 3) The bi-objective OPF problem for loadability maximization and generation cost minimization. An algorithm to achieve a best compromise solution between system maximum loadability and minimum generation costs is also proposed. A detailed islanded microgrid model is adopted to reflect the islanded microgrid special features and real operational characteristics in the proposed OPF problem formulations. The importance and consequences of considering the system maximum loadability in the operational planning of islanded microgrid systems are demonstrated through comparative numerical studies. Next, a new probabilistic algorithm for enabling the decentralized operation of islanded microgrids, including renewable resources, in the absence of a microgrid central controller (MGCC) is proposed. The proposed algorithm adopts a constraint hierarchy approach to enhance the operation of islanded microgrids by satisfying the system’s operational constraints and expanding its loading margin. The new algorithm takes into consideration the variety of possible islanded microgrid configurations that can be initiated in a distribution network (multi-microgrids), the uncertainty and variability associated with the output power of renewable DG units as well as the variability of the load, and the special operational philosophy associated with islanded microgrid systems. Simulation studies show that the proposed algorithm can facilitate the successful implementation of the islanded microgrid concept by reducing customer interruptions and enhancing the islanded microgrid loadability margins. Finally, this research proposes a new multi-stage control scheme to enable the islanded microgrid operation in the presence of high PEVs penetration. The proposed control scheme optimally coordinates the DG units operation, the shedding of islanded microgrid power demand (during inadequate generation periods) and the PEVs charging/discharging decisions. To this end, a three-stage control scheme is formulated in order to: 1) minimize the load shedding, 2) satisfy the PEVs customers’ requirements and 3) minimize the microgrid cost of operation. The proposed control scheme takes into consideration; the variability associated with the output power of renewable DG units, the random behaviour of PEV charging and the special features of islanded microgrid systems. The simulation studies show that the proposed control scheme can enhance the operation of islanded microgrid systems in the presence of high PEVs penetration and facilitate a successful implementation of the islanded microgrid concept, under the smart grid paradigm.

The research focus of this thesis is on modelling techniques for river kinetic turbines, to develop predictive numerical tools to further the design of this emerging hydro technology. The performance benefits of enclosing the turbine in a shroud are quantified numerically and an optimized shroud design is developed. The optimum performing model is then used to study river kinetic turbines, including different anchoring systems to enhance performance. Two different turbine numerical models are studied to simulate the rotor. Four different computational fluid dynamics (CFD) turbulence models are compared against a series of particle image velocimetry (PIV) experiments involving highly-separated diffuser-flow and nozzle-flow conditions. The risk of cavitation is briefly discussed as well as riverbed boundary layer losses. This study is part of an effort to develop this emerging technology for distributed power generation in provinces like Manitoba that have a river system well adapted for this technology.

With the increase of world population and industrial growth of developing countries, demand for energy, in particular electric power, has gone up at an unprecedented rate over the last decades. To meet the demand, electric power generation by use of fossil fuel has increased enormously thereby producing increased quantity of greenhouse gases. This contributes more and more to atmospheric pollution, which climate scientists believe can adversly affect the global climate, as well as health and the welfare of the world population. In view of these issues, there is global awareness to look for alternate sources of energy such as natural gas, hydropower, wind, solar, geothermal and biomass. It is recognized that this requires replacement of existing infrastructure with new systems, which cannot be achieved overnight. Optimal control theory has been widely used in diverse areas of physical sciences, medicine, engineering and economics. The main motivation of this thesis is to use this theory to find the optimum strategy for integration of all currently available renewable energy sources with the existing electric power generating systems. The ultimate goal is to eliminate fossil fuels. Eight main energy sources namely, Coal, Petroleum, Natural Gas, Conventional Hydro, Wind, Solar, Geothermal and Biomass are considered in a dynamic model. The state of the dynamic model represents the level of energy generation from each of the sources. Different objective functions are proposed in this thesis. These range from meeting the desired target level of power generation from each of the available sources at the end of a given plan period, to reducing the implementation and investment costs; from minimizing the production from polluted energy sources to meeting the electricity demand during a whole plan period. Official released data from the U.S. Energy Information Administration have been used as a case study. Based on real life data and the mathematics of optimal control theory, we present an optimal policy for integration of renewable energy sources to the national power grid.

This research examines energy poverty in the Daniel McIntyre and St. Matthews (DMSM) neighbourhoods in the city of Winnipeg. Energy poverty, defined as households spending more than 6% of their income on energy expenditures, affects as many as 50% of households in DMSM. Energy poverty can be alleviated through energy retrofits for dwellings such as weather stripping; increasing insulation in exterior walls, the attic and basement; and installing a high-efficiency furnace. The recommendations include: establishing consistent housing and energy efficiency policies; increasing the flexibility of utility on-bill financing; levying the necessary capital for energy retrofits through municipal financing mechanisms; increasing the knowledge and capacity of local residents; increasing the knowledge and capacity of local contractors for sustainable design and construction; creating a provincial strategy to increase the energy efficiency of social housing; developing low-income energy efficiency programs for rental properties; and increasing access to renewable energy sources.

Interest is growing worldwide among utility companies and governments of maritime countries in assessing the power potential of tidal streams. While the latest assessment for Canadian coastlines estimates a resource of approximately 42 GW, these results are based on the average kinetic energy flux through the channel. It has been shown, however, that this method cannot be used to obtain the maximum extractable power for electricity generation. This work presents an updated theory for the extractable power from a channel linking a bay to the open ocean. A mathematical model is developed for one-dimensional, non-steady flow through a channel of varying cross-section. Flow acceleration, bottom drag, and exit separation effects are included in the momentum balance. The model is applied to Masset Sound and Masset Inlet in Haida Gwaii, a remote island region, to determine the extractable power and its associated impacts to the tidal amplitude and volume flow rate through the channel.

This thesis explores the integration and dynamics of residential scale renewable-regenerative energy systems which employ hydrogen for energy buffering. The development of the Integrated Renewable Energy Experiment (IRENE) test-bed is presented. IRENE is a laboratory-scale distributed energy system with a modular structure which can be readily re-configured to test newly developed components for generic regenerative systems. Key aspects include renewable energy conversion, electrolysis, hydrogen and electricity storage, and fuel cells. A special design feature of this test bed is the ability to accept dynamic inputs from and provide dynamic loads to real devices as well as from simulated energy sources/sinks. The integration issues encountered while developing IRENE and innovative solutions devised to overcome these barriers are discussed. Renewable energy systems that employ a regenerative approach to enable intermittent energy sources to service time varying loads rely on the efficient transfer of energy through the storage media. Experiments were conducted to evaluate the performance of the hydrogen energy buffer under a range of dynamic operating conditions. Results indicate that the operating characteristics of the electrolyser under transient conditions limit the production of hydrogen from excess renewable input power. These characteristics must be considered when designing or modeling a renewable-regenerative system. Strategies to mitigate performance degradation due to interruptions in the renewable power supply are discussed. Experiments were conducted to determine the response of the IRENE system to operating conditions that are representative of a residential scale, solar based, renewable-regenerative system. A control algorithm, employing bus voltage constraints and device current limitations, was developed to guide system operation. Results for a two week operating period that indicate that the system response is very dynamic but repeatable are presented. The overall system energy balance reveals that the energy input from the renewable source was sufficient to meet the demand load and generate a net surplus of hydrogen. The energy loss associated with the various system components as well as a breakdown of the unused renewable energy input is presented. In general, the research indicates that the technical challenges associated with hydrogen energy buffing can be overcome, but the round trip efficiency for the current technologies is low at only 22 percent.

Penetration of renewable energy generators into energy systems is increasing. The intermittency and variability of these generators makes supplying energy reliably and cost effectively difficult. As a result, storage technologies are proposed as a means to increase the penetration of renewable energy, to minimize the amount of curtailed renewable energy, and to limit the amount of back-up supply. Therefore, methods for determining an energy system’s storage requirements are being developed. This thesis investigates and details four existing methods, proposes and develops a fifth method, and compares the results of all five methods. The results show that methods which incorporate cost, namely the Dynamic Optimization and the Abbey method, consistently yield the most cost effective solutions. Under excellent renewable energy conditions the results show that the cost-independent methods of Korpaas, Barton, and the Modified Barton method produce solutions that are nearly as cost effective but have greater reliability of energy supply than the Dynamic Optimization and Abbey solutions. This thesis recommends a new path of research for the Modified Barton method: the incorporation of cost through the confidence level. This thesis also recommends the development of new sizing methods from various aspects of the methods presented. / Graduate

Future electrical power networks should support the integration of distributed renewable energy sources, which may be contributed by individual customers instead of utility companies. Such a demand poses new challenges to power distribution network design, since the energy generation, energy consumption, and power flow all become highly dynamic. An inappropriate network design may not only waste much energy in power distribution but also incur high cost in network construction. In this thesis, we study the optimal network design problem under a dynamic current injection model. We investigate different optimization methods to obtain the optimal network structure that can better adapt to dynamic energy generation/consumption requirements and is more efficient than traditional tree-structured power networks. By predicting users' potential load in the network, network design with our method results in significant energy saving. / Graduate / 0984

An increased penetration of renewable energy into the electric power grid is desirable from an environmental standpoint as well as an economical one. However, renewable sources such as wind and solar energy are often variable and intermittent, and additionally, are non-dispatchable. Also, the locations with the highest amount of available wind or solar may be located in areas that are far from areas with high levels of demand, and these areas may be under the control of separate, individual entities. In this dissertation, a method that coordinates these areas, accounts for the variability and intermittency, reduces the impact of renewable energy forecast errors, and increases the overall social benefit in the system is developed. The approach for the purpose of integrating intermittent energy sources into the electric power grid is considered from both the planning and operations stages. In the planning stage, two-stage stochastic optimization is employed to find the optimal size and location for a storage device in a transmission system with the goal of reducing generation costs, increasing the penetration of wind energy, alleviating line congestions, and decreasing the impact of errors in wind forecasts. The size of this problem grows dramatically with respect to the number of variables and constraints considered. Thus, a scenario reduction approach is developed which makes this stochastic problem computationally feasible. This scenario reduction technique is derived from observations about the relationship between the variance of locational marginal prices corresponding to the power balance equations and the optimal storage size. Additionally, a probabilistic, or chance, constrained model predictive control (MPC) problem is formulated to take into account wind forecast errors in the optimal storage sizing problem. A probability distribution of wind forecast errors is formed and incorporated into the original storage sizing problem. An analytical form of this constraint is derived to directly solve the optimization problem without having to use Monte-Carlo simulations or other techniques that sample the probability distribution of forecast errors. In the operations stage, a MPC AC Optimal Power Flow problem is decomposed with respect to physical control areas. Each area performs an independent optimization and variable values on the border buses between areas are exchanged at each Newton-Raphson iteration. Two modifications to the Approximate Newton Directions (AND) method are presented and used to solve the distributed MPC optimization problem, both with the intention of improving the original AND method by improving upon the convergence rate. Methods are developed to account for numerical difficulties encountered by these formula- tions, specifically with regards to Jacobian singularities introduced due to the intertemporal constraints. Simulation results show convergence of the decomposed optimization problem to the centralized result, demonstrating the benefits of coordinating control areas in the IEEE 57- bus test system. The benefit of energy storage in MPC formulations is also demonstrated in the simulations, reducing the impact of the fluctuations in the power supply introduced by intermittent sources by coordinating resources across control areas. An overall reduction of generation costs and increase in renewable penetration in the system is observed, with promising results to effectively and efficiently integrate renewable resources into the electric power grid on a large scale.