In recent years, there has been lots of emphasis put on the development of renewable energy. While considerable improvement on renewable energy has been made, there are some inherent limitations for these renewable energies. For example, for solar and wind power, there is an intermittent nature. For the fuel cell, the dynamics of electro-chemical reaction is quite slow compared to the electric load. This will not be acceptable for modern electric application, which requires constant voltage of constant frequency. This work proposed and evaluated a new power circuit that can deal with the problem of the intermittent nature and slow response of the renewable energy. The proposed circuit integrates different renewable energy sources as well as energy storage. By integrating renewable energy sources with statistical tendency to compensate each other, the effect of the intermittent nature can be greatly reduced. This integration will increase the reliability and utilization of the overall system. Moreover, the integration of energy storage solves the problem of the slow response of renewable energy. It can provide the extra energy required by load or absorb the excessive energy provided by the energy sources, greatly improving the dynamics of overall system. To better understand the proposed circuit, "Dual Active Bridge" and "Triple Active Bridge" were reviewed first. The operation principles and the modeling were presented. Analysis and design of the overall system were discussed. Controller design and stability issues were investigated. Furthermore, the function of the central controller was explained. In the end, different simulations were made and discussed. Results from the simulations showed that the proposed multi-port DC-DC power converter had satisfactory performance under different scenarios encountered in practical renewable energy application. The proposed circuit is an effective solution to the problem due to the intermittent nature and slow response of the renewable energy.

This report presents an overview of the engineering considerations involved in the design of grid-scale battery energy storage systems. Grid-scale is defined here as systems over 1 MW in rated power, typically operated by a utility, independent power producer, or Independent System Operator (ISO). The physical components of a BESS are presented and explained, including power electronics, an introduction to various commercially available battery technologies, necessary control systems, and balance of plant hardware. Also presented are a variety of real-world applications of battery energy storage systems, showing how the specific application determines what mix of technology will be selected when designing the system, as well as explaining the foundation for the control algorithms. / text

The production of oil requires tremendous amounts of energy consumption through a distributed combustion network of processes along the oil supply chain spectrum. The consequences of fossil-based fuel combustion processes are the generation of Greenhouse Gas (GHG) emissions and hazardous wastewater, which have adverse environmental effects. Potential mitigation options of GHG emissions are the application of renewable and alternative energy sources. This research deals with integrating the upstream oil supply chain with renewable power generation systems in order to assess the impact of energy demand, and CO2 emissions on the efficiency of oil operations and environment . The main focus in this thesis is to evaluate the solar energy alternative for producing part of the energy requirements in the upstream oil supply chain. The output from the research will provide an optimal mix of energy generation in the upstream oil industry in order to comply with CO2 constraints, while sustaining target production plans. An analysis of GHG emission sources and their associated flow rates in the upstream oil supply chain mainly CO2 is discussed in this study. An investigation of replacement of energy supply for some non-critical operations from fossil fuels or other conventional sources to green renewable energy sources mainly from solar energy is also carried out with special focus on enhanced oil recovery operations. An analysis of different types of solar energy and identification of the best type of solar energy technologies that best matches the oil and gas industry is investigated in this study. The thesis will also identify the challenges for solar energy integration including irradiation levels and weather conditions in addition to policy regulations

Sustainable Built Environments Senior Capstone Project / This capstone project evaluates the current state of floating solar photovoltaic technology and proposes use of the technology on water management infrastructure in Arizona. The study finds that floating solar photovoltaic has a higher energy density (100 W/m^2) than land-based, utility-scale solar and does not involve significant cost increases. The study proposes and models a small pilot installation on Lake Pleasant Reservoir, part of the Central Arizona Project, and finds that lifetime costs per unit energy are higher than what the Central Arizona Project currently pays for energy, assuming US median per-wattinstalled costs for commercial solar. This cost however does not factor in savings from water conservation, existing infrastructure, reduced land costs, or other benefits. The study recommends water reservoirs by hydropower dams as ideal locations for floating photovoltaic installations. Justified with a significant background on Arizona’s environmental, social, and economic sustainability, as well as regulations calling for increased renewable energy generation and reduced carbon emissions, this study recommends aggressive implementation of floating solar photovoltaic technology within a sustainable development paradigm.

Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue considering the integration of renewable energy resources into production planning of energy-intensive manufacturing industries. Recently, a growing number of manufacturing companies are considering renewable energies to meet their energy requirements to move towards green manufacturing as well as decreasing their energy costs. However, the intermittent nature of renewable energies imposes several difficulties in long term planning of how to efficiently exploit renewables. In this study, we propose a scheme for manufacturing companies to use onsite and grid renewable energies provided by their own investments and energy utilities as well as conventional grid energy to satisfy their energy requirements. We propose a multistage stochastic programming model and study an efficient solution method to solve this problem. We examine the proposed framework on a test case simulated based on a real-world semiconductor company. Moreover, we evaluate long-term profitability of such scheme via so called value of multistage stochastic programming.

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in recent year has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This thesis investigates the impact of smart grid technologies on the integration of wind power into the power system. A smart grid power system model is developed and validated by comparison with a real-life smart grid experiment: the Olympic Peninsula Demonstration Experiment. The smart grid system model is then expanded to include 1000 houses and a generic generation mix of nuclear, hydro, coal, gas and oil based generators. The effect of super-imposing varying levels of wind penetration are then investigated in conjunction with a market model whereby suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. It is also found that the degree to which Greenhouse Gas (GHG) emissions can be mitigated is highly dependent on the generation mix. A displacement of natural gas based generation during peak demand can for instance lead to an increase in GHG emissions. Of practical significance to power system operators, the simulations also demonstrate that Demand Response (DR) can reduce generator cycling and improve generator efficiency, thus potentially lowering GHG emissions while also reducing wear and tear on generating equipment. / Graduate

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.

Weakness analysis based on grid strength assessment is useful for identifying potential weak grid issues. However, when taking into account the impact of the interactions among Renewable Energy Sources (RESs), the weakness analysis becomes computationally challenging. Different combinations of PointsofInterconnections (POIs) of RESs may have different impacts on grid strength at each POI. Due to the combination nature, such weakness analysis may be time-consuming when identifying the weakest combination of POIs from a large number of potential candidate locations in realistic power grids. This dissertation addresses the topic of determination of the weakest RESs combinations. Based on impedance ratios as a criterion, the dissertation shows that the impacts of impedance ratios magnitudes and angles are ‘quasi-mutually exclusive’. Such a concept is then used to reduce the computational burden with a fast screening algorithm. To further understand the impact of network components on grid strength, vector-based interaction analysis is developed based on the concepts of operational transfer impedances and operational interaction operators. In particular, this dissertation shows how mathematical models of interaction of multiple RESs can be simplified by replacing them with equivalent impedances, allowing us to simplify the mathematical expressions that quantify interactions among RESs. The conclusions and concepts established based on simplified models are statistically tested for their applicability to the generalized interaction model. The result would be a more simplified mathematical representation of interaction among RESs. Finally, a new technique is presented to efficiently update the Bus Impedance Matrix (Zbus) following changes in the series impedance of a branch. Conventionally, such update requires redundant recalculations, which involve matrix inversion operations (i.e., inverting the Bus Admittance Matrix, Ybus) and thus cause high computational burden because of potential matrix ill-conditioning, especially for largescale power grids. This dissertation overcomes these shortcomings by deriving an analytical expression for changes in Zbus in terms of its old elements and the variation of the impedance of a given branch. Hence, the computation overhead is comparatively small, and no issues arise due to the new Ybus being ill-conditioned. Such contribution helps facilitate real-time applications of methods that rely on Zbus.

The Master's Thesis research focuses on analyzing the possibilities of using Direct Current distribution systems to distribute power to end users. Considering the shift in load types in the past few decades and also a growing demand of distributed generation, DC distribution can potentially offer higher efficiencies and cost savings to utilities. The incorporation of DC distribution offers the opportunity to eliminate multiple conversion stages for devices which are powered using DC electricity. The integration of power sources such as photovoltaics and fuel cells, which produce DC power, offer further incentives to consider the use of DC systems. Using DC systems can help eliminate the conversion losses associated with rectifiers and inverters which would be part of the infrastructure if AC distribution was used. In the literature, the study of DC distribution has been limited to customized systems. The objective of this research is to analyze DC distribution when applied to systems based on standard IEEE test feeder systems. The IEEE 13 node test feeder and the IEEE 37 node test feeder will be used as the basis for the analysis. Issues such as associated costs, protection and integration of appliances will also be addressed.

Solar photovoltaic (PV) is the fastest-growing energy resource. The price of energy generation from residential PV has dropped from $0.50 to $0.10 per kWh in the past decade. One challenge with this resource is that the amount of power available depends on the solar irradiance and temperature. Abrupt changes in solar irradiance can cause disturbances to the hosting electricity network and lead to voltage and frequency oscillations. The impact is more severe in a weak grid with high penetration of such resources. Evolving grid interconnection standards are imposing requirements to limit the impacts of these disturbances on the grid. Battery energy storage (BES) technology has also experienced a significant price drop (e.g., from $1100 to $156 per kWh for lithium-ion batteries) in the past decade. Therefore, complementary PV+BES solutions are increasingly considered. A BES of sufficient capacity equipped with appropriate controls can respond to both abrupt and long-term PV power variations. Properly formulating the problem and developing efficient control systems is crucial. These define the scope and objective of this research. This research develops two BES solutions. In the first one, the BES is co-located with the PV and connects to its dc output terminals. The BES controller ensures that the PV+BES system exhibits a desirable power ramp rate set by the user. In the second solution, the BES is not co-located with the PV. It detects the disturbances from their signatures on its locally measured signals and takes proper actions. An approach based on capacitor emulation combined with a droop mechanism is developed and optimally designed to provide dynamic and static supports. The BES can respond to the disturbances from more than one PV system and non-PV sources, such as load disturbances. The dissertation presents detailed modeling and control of the BES system. Optimal control techniques are developed to ensure robust and fast responses. For the simulation study, the proposed BES systems are implemented in a hybrid dc/ac study system and the effect on both dc and ac subsystems are investigated. The real-time results obtained by implementing the proposed controllers on laboratory-scale hardware prototypes are also presented.