Browsing by Author "Zimmerle, Dan, committee member"

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Open Access
A data-driven approach for maximizing available wind energy through a dedicated pricing mechanism for charging residential plug-in electric vehicles
(Colorado State University. Libraries, 2019) Eldali, Fathalla, author; Suryanarayanan, Siddharth, advisor; Collins, George J., committee member; Zimmerle, Dan, committee member; Abdel-Ghany, Salah, committee member
Wind energy generation is growing significantly because of its favorable attributes such as cost-effectiveness and environment-friendliness. Electricity is the most perishable commodity as it must be consumed almost instantaneously as it is produced. Because of that, the variable nature of wind power generation and the challenges in forecasting the output power of wind impose problems of curtailment (excess of available wind energy than forecast) and deployment of reserves (deficit of available wind energy than forecast). Energy storage for wind power installations is a potential solution; however, storing large amounts of energy over long time periods is an expensive and inefficient solution. Plug-in electric vehicles (PEVs) are recognized as one of the assets to integrate energy storage on the distribution side of the electricity grid. Thus, PEVs charging presents an alternative solution for managing this excess energy in wind energy-rich grids. An accurate wind power forecasting (WPF) in the day-ahead market leads to a more predictable dispatch and unit-commitment (UC) of generators, thus reducing the need for reserves and storage. Typically, reserves to match the imbalance in supply and demand of electricity are provided by generators that are more expensive than the ones engaged in primary services. Markets in different regions of the world have specific designs, operation policies, and regulations when it comes to variable sources (e.g., wind, and solar). Independent system operators (ISOs), tasked with handling electricity markets in the US, must meet regulating reserve as directed by the North America Electric Reliability Council (NERC). One of these requirements is that the sufficient reserve must be available to cover the generation deficit. This deficit can be due to under-forecasting. There is also a case when ISOs need to curtail wind energy generation because of over-forecasting. In the first part of this dissertation, wind power data from the Electric Reliability Council of Texas (ERCOT) market is used to improve WPF as Texas has the highest installed wind energy capacity in the North American electricity grid. Autoregressive integrated moving average (ARIMA) model is used for WPF improvement. There is also a need to develop a coherent metric to quantify the improvements to WPF because different studies use different metrics. Also, using the statistical representation of the reduction in error does not necessarily reflect the overall benefit, especially the economic benefit, for ISOs. In the second part of this dissertation work, modifications of on risk-adjusted metrics used in investments assessments are developed and applied to the operation cost (OC). OC is the result of running the economic dispatch (ED) on realistic synthetic models of the actual Texas grid to evaluate the impact of the WPF improvement on the cost of operation. The modifications of the above-mentioned risk-adjusted metrics are also applied to deferring the capital investment on the distribution systems. Then, the metrics are used to assess the combination of photovoltaic (PV) and battery energy storage system (BESS) at the residential section of the distribution grid as explained in appendix A. The third part of this dissertation uses a data-driven approach to investigate existing pricing mechanisms for a Texan city (i.e., Austin) located in a wind energy-rich grid such as ERCOT with an increased adoption rate of PEVs. The study performed indicates the need for an alternative dynamic pricing mechanism dedicated to PEVs than the existing choices for maximizing the utility of available energy from wind in the absence of grid-level energy storage. Dynamic pricing produces an opportunity to avoid high costs for the power provider and benefits the consumers if they respond to the change of the price. However, achieving these benefits needs smart rate design and real data. After justifying the need for fair pricing mechanisms to benefit the utility and the customers for the coordination of wind energy and PEVs charging in wind energy-rich grid, this dissertation designs a time-varying pricing mechanism. This dissertation employs a data decomposition technique to design a dedicated pricing mechanism for PEVs. We use real data of a city with high projections of PEVs (Austin, Texas) located in a wind-rich electricity grid (ERCOT) to demonstrate this design of a dynamic pricing method.
Open Access
A systems engineering approach to community microgrid electrification and sustainable development in Papua New Guinea
(Colorado State University. Libraries, 2019) Anderson, Alexander A., author; Suryanarayanan, Siddharth, advisor; Cale, James, committee member; Zimmerle, Dan, committee member; Chen, Suren, committee member
Electrification of remote communities worldwide represents a key necessity for sustainable development and advancement of the 17 United Nations Sustainable Development Goals (SDGs). With over 1 billion people still lacking access to electricity, finding new methods to provide safe, clean, reliable, and affordable energy to off-grid communities represents an increasingly dynamic area of research. However, traditional approaches to power system design focused exclusively on traditional metrics of cost and reliability do not provide a sufficiently broad view of the profound impact of electrification. Installation of a single microgrid is a life-changing experience for thousands of people, including both residents who receive direct electricity service and numerous others who benefit from better education, new economic opportunities, incidental job creation, and other critical infrastructure systems enabled by electricity. Moreover, an electrification microgrid must directly satisfy community needs, be sensitive to local environmental constraints, mitigate possible risks, and plan for at least a decade of sustainable operations and maintenance. These considerations extend beyond the technical and optimization problems typically addressed in microgrid design. An enterprise system-of-systems framework for microgrid planning considering technical, economic, environmental, and social criteria is developed in response to the need for a comprehensive methodology for planning of community electrification projects. This framework spans the entire systems engineering discipline and incorporates elements from project management, risk management, enterprise architecture, numerical optimization, and multi-criteria decision-making, and sustainable development theory. To support the creation of the systems engineering framework, a comprehensive survey of multi-objective optimization formulations for planning and dispatch of islanded microgrids was conducted to form a baseline for further discussion. This survey identifies that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions, 14 constraints, and 13 control variables. A sufficient group of decision-making elicitees are formed from the group of nearly 250 publications surveyed to create a comprehensive optimization framework based on technical, economic, environmental, and social attributes of islanded microgrids. This baseline enables the formulation of a flexible, computationally lightweight methodology for microgrid planning in consideration of multiple conflicting objectives using the simple multi-attribute ranking technique exploiting ranks (SMARTER). Simultaneously, the identified technical, economic, environmental, and social decision criteria form a network of functional, operational, and performance requirements in an enterprise system-of-systems structure that considers all stakeholders and actors in the development of community electrification microgrids. This framework considers community capacity building and sustainable development theory as a hierarchical structure, where each layer of the hierarchy is mapped both to a set of organizational, financial, and physical subsystems and to a corresponding subset of the 17 SDGs. The structure presents the opportunity not only to integrate classical project management and risk management tools, but also to create a new lifecycle for planning, funding, executing, and monitoring multi-phase community infrastructure projects. Throughout the research, a case study of the Madan Community in Jiwaka Province, Papua New Guinea is used to demonstrate the systems engineering concepts and tools developed by the research. The community is the center of multi-phase community capacity building project addressing critical needs of the deep rural community, including electricity, education, water, sanitation, healthcare, and economic opportunities. The researcher has been involved as a pro-bono consultant for the project since 2013 and helped raise over $1M USD in infrastructure materials, equipment, and consulting. The structure of the community-based organization and numerical optimization of a series of islanded microgrids are used to illustrate both the system-of-systems hierarchy and microgrid planning techniques based on both single-objective optimization using linear programming and the SMARTER methodology for consideration of multiple qualitative and quantitative decision criteria.
Open Access
Assessment, design and control strategy development of a fuel cell hybrid electric vehicle for CSU's ecocar
(Colorado State University. Libraries, 2013) Fox, Matthew D., author; Bradley, Thomas H., advisor; Labadie, John, committee member; Zimmerle, Dan, committee member
Advanced automotive technology assessment and powertrain design are increasingly performed through modeling, simulation, and optimization. But technology assessments usually target many competing criteria making any individual optimization challenging and arbitrary. Further, independent design simulations and optimizations take considerable time to execute, and design constraints and objectives change throughout the design process. Changes in design considerations usually require re-processing of simulations and more time. In this thesis, these challenges are confronted through CSUs participation in the EcoCAR2 hybrid vehicle design competition. The complexity of the competition's design objectives leveraged development of a decision support system tool to aid in multi-criteria decision making across technologies and to perform powertrain optimization. To make the decision support system interactive, and bypass the problem of long simulation times, a new approach was taken. The result of this research is CSU's architecture selection and component sizing, which optimizes a composite objective function representing the competition score. The selected architecture is an electric vehicle with an onboard range extending hydrogen fuel cell system. The vehicle has a 145kW traction motor, 18.9kWh of lithium ion battery, a 15kW fuel cell system, and 5kg of hydrogen storage capacity. Finally, a control strategy was developed that improves the vehicles performance throughout the driving range under variable driving conditions. In conclusion, the design process used in this research is reviewed and evaluated against other common design methodologies. I conclude, through the highlighted case studies, that the approach is more comprehensive than other popular design methodologies and is likely to lead to a higher quality product. The upfront modeling work and decision support system formulation will pay off in superior and timely knowledge transfer and more informed design decisions. The hypothesis is supported by the three case studies examined in this thesis.
Open Access
Battery identification, prediction and modelling
(Colorado State University. Libraries, 2018) Azam, Syed Mahdi, author; Young, Peter M., advisor; Collins, George, committee member; Zimmerle, Dan, committee member
In this paper, a process of modelling batteries for energy management systems has been discussed. With the increasing demand of energy management modelling, it is crucial that modelling of the components in an energy management model be done properly, effectively, and with least amount of time. The process introduced in this paper requires only one discharge data to model a battery. The internal parameters identified focuses on the electrical behavior rather than on electrochemical aspects of the battery. The battery model presented here helps to predict the discharge behavior of the battery in multiple discharging scenarios. In this modelling process, Online Parameter Identification technique has been used to identify the parameters of the battery. The parameters of the battery identified in this paper to predict the discharge behavior of a battery are internal resistance, polarization constant, nominal voltage and the actual capacity of a battery. Shepherd's equation and MATLAB's optimization toolbox was used to identify the parameters.
Open Access
Exploring remote sensing data with high temporal resolutions for wildfire spread prediction
(Colorado State University. Libraries, 2024) Fitzgerald, Jack, author; Blanchard, Nathaniel, advisor; Krishnaswamy, Nikhil, committee member; Zimmerle, Dan, committee member
The severity of wildfires has been steadily increasing in the United States over the past few decades, burning up many millions of acres and costing billions of dollars in suppression efforts each year. However, in the same few decades there have been great strides made to advance our technological capabilities. Machine learning is one such technology that has seen spectacular improvements in many areas such as computer vision and natural language processing, and is now being used extensively to model spatiotemporal phenomena such as wildfires via deep learning. Leveraging deep learning to model how wildfires spread can help facilitate evacuation efforts and assist wildland firefighters by highlighting key areas where containment and suppression efforts should be focused. Many recent works have examined the feasibility of using deep learning models to predict when and where wildfires will spread to, which has been enabled in part due to the wealth of geospatial information that is now publicly available and easily accessible on platforms such as Google Earth Engine. In this work, the First Week Wildfire Spread dataset is introduced, which seeks to address some of the limitations with previously released datasets by having an increased focus on geospatial data with high temporal resolutions. The new dataset contains weather, fuel, topography, and fire location data for the first 7 days of 56 megafires that occurred in the Contiguous United States from 2020 to 2024. Fire location data is collected by the Advanced Baseline Imager aboard the GOES-16 satellite, which provides updates every 5 minutes. Baseline experiments are performed using U-Net and ConvLSTM models to demonstrate some of the various ways that the First Week Wildfire Spread dataset can be used and to highlight its versatility.
Open Access
Identification of spatial and topographical metrics for micro hydropower applications in irrigation infrastructure
(Colorado State University. Libraries, 2012) Campbell, Brian, author; Grigg, Neil, advisor; Catton, Kimberly, advisor; Zimmerle, Dan, committee member
A recent agreement between the Federal Energy Regulatory Commission and the State of Colorado seeks to streamline regulatory review of small, low-head hydropower (micro hydropower) projects located in constrained waterways, (Governor's Energy Office, 2010). This regulatory change will likely encourage the development of micro hydropower projects, primarily as upgrades to existing infrastructure. Previous studies of low-head hydropower projects have estimated the combined capacity of micro hydro projects in Colorado between 664 MW to 5,003 MW (Connor, A.M., et al. 1998; Hall, D.G., et al. 2004, 2006). However, these studies did not include existing hydraulic structures in irrigation canals as possible hydropower sites. A Colorado Department of Agriculture study (Applegate Group, 2011) identified existing infrastructure categories for low head hydropower development in irrigation systems, which included diversion structures, line chutes, vertical drops, pipelines, check structures and reservoir outlets. However, an accurate assessment of hydropower capacity from existing infrastructures could not be determined due to low survey responses from irrigation water districts. The current study represents the first step in a comprehensive field study to quantify the type and quantity of irrigation infrastructure for potential upgrade to support micro hydropower production. Field surveys were conducted at approximately 230 sites in 6 of Colorado's 7 hydrographic divisions at existing hydraulic control structures. The United States Bureau of Reclamation contributed approximately 330 additional sample sites from the 17 western states. The work presented here describes a novel method of identifying geospatial metrics to support an estimation of total site count and resource availability of potential micro hydropower. The proposed technique is general in nature and could be utilized to assess micro hydropower resources in any region.
Open Access
Integrated water and power modeling framework for renewable energy integration
(Colorado State University. Libraries, 2012) Dozier, André, author; Labadie, John W., advisor; Zimmerle, Dan, committee member; Salas, Jose, committee member
Increasing penetration of intermittent renewable energy sources into the bulk electricity system has caused new operational challenges requiring large ramping rate and reserve capacity as well as increased transmission congestion due to unscheduled flow. Contemporary literature and recent renewable energy integration studies indicate that more realism needs to be incorporated into renewable energy studies. Many detailed water and power models have been developed in their respective fields, but no free-of-charge integrated water and power system model that considers constraints and objectives in both systems jointly has been constructed. Therefore, an integrated water and power model structure that addresses some contemporary challenges is formulated as a long-term goal, but only a small portion of the model structure is actually implemented as software. A water network model called MODSIM is adapted using a conditional gradient method to be able to connect to an overarching optimization routine that decomposes the water and power problems. The water network model is connected to a simple power dispatch model that uses a linear programming approach to dispatch hydropower resources to mitigate power flows across a transmission line. The power dispatch model first decides optimal power injections from each of the hydropower reservoirs, which are then used as hydropower targets for the water network model to achieve. Any unsatisfied power demand or congested transmission line is assumed to be met by imported power. A case study was performed on the Mid-Columbia River in the U.S. to test the capabilities of the integrated water and power model. Results indicate that hydropower resources can accommodate transmission congestion and energy capacity on wind production up until a particular threshold on the penetration level, after which hydropower resources provide no added benefit to the system. Effects of operational decisions to mitigate wind power penetration level and transmission capacity on simulated total dissolved gases were negligible. Finally, future work on the integrated water and power model is discussed along with expected results from the fully implemented model and its potential applications.
Open Access
Real-time modeling and simulation of distribution feeder and distributed resources
(Colorado State University. Libraries, 2015) Singh, Pawan, author; Suryanarayanan, Siddharth, advisor; Chakraborty, Sudipta, committee member; Zimmerle, Dan, committee member
The analysis of the electrical system dates back to the days when analog network analyzers were used. With the advent of digital computers, many programs were written for power-flow and short circuit analysis for the improvement of the electrical system. Real-time computer simulations can answer many what-if scenarios in the existing or the proposed power system. In this thesis, the standard IEEE 13-Node distribution feeder is developed and validated on a real-time platform OPAL-RT™. The concept and the challenges of the real-time simulation are studied and addressed. Distributed energy resources include some of the commonly used distributed generation and storage devices like diesel engine, solar photovoltaic array, and battery storage system are modeled and simulated on a real-time platform. A microgrid encompasses a portion of an electric power distribution which is located downstream of the distribution substation. Normally, the microgrid operates in paralleled mode with the grid; however, scheduled or forced isolation can take place. In such conditions, the microgrid must have the ability to operate stably and autonomously. The microgrid can operate in grid connected and islanded mode, both the operating modes are studied in the last chapter. Towards the end, a simple microgrid controller modeled and simulated on the real-time platform is developed for energy management and protection for the microgrid.
Open Access
Some aspects of the computational complexity in the design of islanded microgrids, design and analysis of blackstart sequences for a notional microgrid
(Colorado State University. Libraries, 2012) Natarajan, Sudarshan Ananda, author; Suryanarayanan, Siddharth, advisor; Rajopadhye, Sanjay, advisor; Zimmerle, Dan, committee member; Putkaradze, Vakhtang, committee member
The US grid represents more than $1 trillion in assets and serves over 100 million customers. But the grid is an aging system, and was built as centralized system architecture. The Title XIII of the Energy Independence and Security Act of 2007 outlined the 'Smart Grid Initiative' (SGI) as an official policy for modernization of the United States electricity transmission and distribution system to improve reliability and upgrade infrastructure to meet the ever increasing demand in electricity. The distribution feeder is the final link between the generation units and the end user. Distribution networks were traditionally designed in a radial topology since such configurations resulted in simpler protective schemes. More recently, there has been a renewed focus on distribution feeder reconfiguration. Reconfiguration at the distribution level can be achieved by the use of switches and sectionalizers. This enables portions of the distribution network to be reconfigured dynamically to improve reliability, hence enabling control on the topological structure of the distribution feeder. A related feature is the microgrid, or an islanded distributed resource (DR). The IEEE 1547.4 Standard on 'The guide for design, operation and integration of DR island systems with electric power supply' defines a microgrid as an electric power system that has the following properties: • a local distributed resource and load • the ability to disconnect from and parallel with the area electric power supply • include the local electric power supply and portions of area electric power supply A microgrid is capable of disconnecting from the main grid, on sensing a disturbance on the main grid, to maintain reliable supply of electricity to the constituent end-user loads. In this work, the computational complexity of the design of islanded microgrids by the optimal addition of network feeders in a legacy radial electric distribution system is identified, and a technique to accelerate the process of finding Pareto optimal solutions to the problem is provided. The next part of this thesis models a notional microgrid with blackstart capabilities. Microgrids that cannot continue uninterrupted supply to all local loads on disconnection from main grid are required to follow a sequence for startup which is known as the blackstart sequence. In this work, the various generation resources in the notional microgrid are studied and a blackstart sequence is engineered.
Open Access
Techno-economic analysis and decision making for PHEV benefits to society, consumers, policymakers and automakers
(Colorado State University. Libraries, 2012) Al-Alawi, Baha Mohammed, author; Bradley, Thomas, advisor; Duff, William, advisor; Olsen, Daniel, committee member; Zimmerle, Dan, committee member; Labadie, John, committee member
Plug-in hybrid electric vehicles (PHEVs) are an emerging automotive technology that has the capability to reduce transportation environmental impacts, but at an increased production cost. PHEVs can draw and store energy from an electric grid and consequently show reductions in petroleum consumption, air emissions, ownership costs, and regulation compliance costs, and various other externalities. Decision makers in the policy, consumer, and industry spheres would like to understand the impact of HEV and PHEV technologies on the U.S. vehicle fleets, but to date, only the disciplinary characteristics of PHEVs been considered. The multidisciplinary tradeoffs between vehicle energy sources, policy requirements, market conditions, consumer preferences and technology improvements are not well understood. For example, the results of recent studies have posited the importance of PHEVs to the future US vehicle fleet. No studies have considered the value of PHEVs to automakers and policy makers as a tool for achieving US corporate average fuel economy (CAFE) standards which are planned to double by 2030. Previous studies have demonstrated the cost and benefit of PHEVs but there is no study that comprehensively accounts for the cost and benefits of PHEV to consumers. The diffusion rate of hybrid electric vehicle (HEV) and PHEV technology into the marketplace has been estimated by existing studies using various tools and scenarios, but results show wide variations between studies. There is no comprehensive modeling study that combines policy, consumers, society and automakers in the U.S. new vehicle sales cost and benefits analysis. The aim of this research is to build a potential framework that can simulate and optimize the benefits of PHEVs for a multiplicity of stakeholders. This dissertation describes the results of modeling that integrates the effects of PHEV market penetration on policy, consumer and economic spheres. A model of fleet fuel economy and CAFE compliance for a large US automaker will be developed. A comprehensive total cost of ownership model will be constructed to calculate and compare the cost and benefits of PHEVs, conventional vehicles (CVs) and HEVs. Then a comprehensive literature review of PHEVs penetration rate studies will be developed to review and analyze the primary purposes, methods, and results of studies of PHEV market penetration. Finally a multi-criteria modeling system will incorporate results of the support model results. In this project, the models, analysis and results will provide a broader understanding of the benefits and costs of PHEV technology and the parties to whom those benefits accrue. The findings will provide important information for consumers, automakers and policy makers to understand and define HEVs and PHEVs costs, benefits, expected penetration rate and the preferred vehicle design and technology scenario to meet the requirements of policy, society, industry and consumers.
Open Access
Unconventional oil and gas development and student performance on standardized tests
(Colorado State University. Libraries, 2021) Koss, Gal, author; Suter, Jordan, advisor; Mannings, Dale, advisor; Zimmerle, Dan, committee member
This paper evaluates the impacts of unconventional oil and gas extraction on academic achievement. The preparation, drilling, and fracturing of oil and gas wells has been found to create air and noise pollution—which can have negative effects on cognitive performance. Analyzing public school standardized test scores in Colorado, we find that additional drilling activity within 3 km of schools before tests decreases the portion of students who meet statewide standards by 0.75 percentage points, implying 1,857 fewer students met expectations statewide over the analysis period or 1.28% of all students who failed to meet expectations at treated schools. These findings impact how we view the scope of externalities from oil and gas development and informs the ongoing policy debate about minimum distance requirements between new wells and schools.