1,721,081 research outputs found
A New Approach to Assess Space-Time Variability in Solar Irradiances
Get PDFOne major concern with many renewable energy sources, especially solar, is that they are unpredictable and can be unreliable as a steady energy source. For example, a passing cloud can drastically disrupt the intake of solar irradiance in solar panels, thus halting the ability to harness energy during the passing interference. One of the first steps to overcoming disadvantages such as these is to perform analyses to gain an understanding of how the data behaves. When considering time series data for solar irradiance, what does a “normal” day look like? What causes interference? How can we classify the data in a meaningful way? Answering these questions can help improve evolving technologies to become more reliable, and there are many approaches to best address these topics. In this paper, we discuss a popular analysis technique for time series data called clustering. However, our data set is highly variable, which clustering struggles to handle. Instead, we have developed a new approach using dynamic time warping and geographical distance to analyze the spatial structure of the data
Optimizing Electrified Chemical Process Scheduling with Future Wholesale Electricity Pricing
Get PDFThe chemical manufacturing industry amounts to 9.4% of all energy consumption in the United States, most of which comes from combusting fossil fuels, a high emissions process. One method of reducing emissions from this industry is electrifying its loads. Especially in regions where our power grid is increasing its penetrations of zero-marginal cost resources, it is expected to see declining, but more volatile, electricity prices. To allow chemical manufacturing plants to capitalize on these volatile electricity prices, this thesis builds a mixed integer linear programming optimization model for batch chemical manufacturing process scheduling. This thesis incorporates wholesale electricity rates into the plant’s operating costs and then maximizes the plant’s profit, allowing the model to choose the plant’s yield based on operational constraints. By using projected electricity pricing and emissions data for the years 2020, 2030, 2040 and 2050, the model shows that future grid volatility increases the cost savings and emissions reductions that scheduling models can attain. Using a case study of a fully electrified steam production process powered with electric boilers, this model shows an annual reduction in electricity costs of $58 million and reduced emissions of 23 million kg of CO2 in 2050, a significant motivator for plant owners and climate advocates alike
Inverter Current Limiting Impacts on Power System Stability
Get PDFThe need to keep the average global temperature increase to less than 1.5 °C combined with the decline in the cost of renewable energy technologies has led to an increase in the number of energy sources that have been connected to the power grid through inverters, called inverter-based resources (IBR). Because inverters are made using semiconductor switches that cannot withstand high current levels, they must have a method to limit the current that can pass through the device. This method is usually implemented digitally and is called a current limiter. While current limiters are not new, their impact on overall power system stability has not been fully explored. In addition, existing positive sequence modeling tools cannot capture fast inverter dynamics, and while electromagnetic transient studies can, studying large systems quickly becomes computationally intractable. Because of this, studying inverter interactions in large systems has been a challenge that limits our understanding of the behavior of the future power grid. A new open-source modeling tool called Sienna, built on the direct-quadrature-zero (dq0) model, is being developed to address this problem. The work in this thesis contributed instantaneous and magnitude current limiting models to Sienna and then explored the conditions under which these current limiters would cause a 14-bus system to become large signal unstable using grid forming (GFM) and grid following (GFL) IBR interacting with synchronous machines. The research found that the magnitude current limiter was less likely to cause large signal instability and that the pre-disturbance reactive conditions affected when the current limiter drove instability. It also found that a GFM IBR may enter a limit cycle when it reaches its current limit and there are synchronous machines on the system, and that there is a scenario when an unstable GFM IBR can trigger an additional source of instability in a GFL IBR. This demonstrates the importance of fully exploring how control structures like current limiting will behave and interact in a larger system.</p
Inverter Current Limiting Impacts on Power System Stability
Get PDFThe need to keep the average global temperature increase to less than 1.5 °C combined with the decline in the cost of renewable energy technologies has led to an increase in the number of energy sources that have been connected to the power grid through inverters, called inverter-based resources (IBR). Because inverters are made using semiconductor switches that cannot withstand high current levels, they must have a method to limit the current that can pass through the device. This method is usually implemented digitally and is called a current limiter. While current limiters are not new, their impact on overall power system stability has not been fully explored. In addition, existing positive sequence modeling tools cannot capture fast inverter dynamics, and while electromagnetic transient studies can, studying large systems quickly becomes computationally intractable. Because of this, studying inverter interactions in large systems has been a challenge that limits our understanding of the behavior of the future power grid. A new open-source modeling tool called Sienna, built on the direct-quadrature-zero (dq0) model, is being developed to address this problem. The work in this thesis contributed instantaneous and magnitude current limiting models to Sienna and then explored the conditions under which these current limiters would cause a 14-bus system to become large signal unstable using grid forming (GFM) and grid following (GFL) IBR interacting with synchronous machines. The research found that the magnitude current limiter was less likely to cause large signal instability and that the pre-disturbance reactive conditions affected when the current limiter drove instability. It also found that a GFM IBR may enter a limit cycle when it reaches its current limit and there are synchronous machines on the system, and that there is a scenario when an unstable GFM IBR can trigger an additional source of instability in a GFL IBR. This demonstrates the importance of fully exploring how control structures like current limiting will behave and interact in a larger system.</p
Optimizing Dynamic EV Wireless Charging Systems to Minimize Distribution Grid Stress
Get PDFDynamic Wireless Power Transfer is an exciting new technology that has the potential to extend the driving range of electric vehicles (EVs), and convince more people to make the transition away from internal combustion engine vehicles (ICEs). This technology has been primarily developed in lab settings, meaning that there are many unknowns and risks involved with integrating it into the existing power grid. This thesis aims to quantify the grid stress that these large power systems would have on a real-world distribution network. To do this, we conduct a case study in Greensboro, North Carolina using traffic and distribution system data collected along a major highway. We develop mathematical models for simulating traffic flow in the area, as well as power flow on the distribution network. Finally, we develop surrogate models to address the uncertainty in our case study's assumptions
A New Approach to Assess Space-Time Variability in Solar Irradiances
Get PDFOne major concern with many renewable energy sources, especially solar, is that they are unpredictable and can be unreliable as a steady energy source. For example, a passing cloud can drastically disrupt the intake of solar irradiance in solar panels, thus halting the ability to harness energy during the passing interference. One of the first steps to overcoming disadvantages such as these is to perform analyses to gain an understanding of how the data behaves. When considering time series data for solar irradiance, what does a “normal” day look like? What causes interference? How can we classify the data in a meaningful way? Answering these questions can help improve evolving technologies to become more reliable, and there are many approaches to best address these topics. In this paper, we discuss a popular analysis technique for time series data called clustering. However, our data set is highly variable, which clustering struggles to handle. Instead, we have developed a new approach using dynamic time warping and geographical distance to analyze the spatial structure of the data
Optimizing Electrified Chemical Process Scheduling with Future Wholesale Electricity Pricing
Get PDFThe chemical manufacturing industry amounts to 9.4% of all energy consumption in the United States, most of which comes from combusting fossil fuels, a high emissions process. One method of reducing emissions from this industry is electrifying its loads. Especially in regions where our power grid is increasing its penetrations of zero-marginal cost resources, it is expected to see declining, but more volatile, electricity prices. To allow chemical manufacturing plants to capitalize on these volatile electricity prices, this thesis builds a mixed integer linear programming optimization model for batch chemical manufacturing process scheduling. This thesis incorporates wholesale electricity rates into the plant’s operating costs and then maximizes the plant’s profit, allowing the model to choose the plant’s yield based on operational constraints. By using projected electricity pricing and emissions data for the years 2020, 2030, 2040 and 2050, the model shows that future grid volatility increases the cost savings and emissions reductions that scheduling models can attain. Using a case study of a fully electrified steam production process powered with electric boilers, this model shows an annual reduction in electricity costs of $58 million and reduced emissions of 23 million kg of CO2 in 2050, a significant motivator for plant owners and climate advocates alike
Optimizing Long Duration Energy Storage Systems: A Forecasting and Modeling Approach Using an Echo State Network
Get PDFAs a global transition towards renewables is underway, proper management and scheduling of long duration energy storage (LDES) technologies is essential to maintain grid reliability, and address uncertainty within Variable Renewable Energy (VRE) generation. This thesis employs a dynamic Echo State Network (ESN) that generates price forecasts used in an optimization problem to optimize the schedule of varying sizes of LDES devices. The objective function of the model is to maximize energy arbitrage by choosing when to charge and discharge the storage devices. The optimization model makes use of a rolling horizon, optimizing over a period with extended foresight. The ESN is trained with price, VRE and load data from NREL's 118-Bus system to generate realistic price forecasts that are used as foresight in the optimization model. This work creates a framework that is better than deterministic models, by incorporating foresight of realistic price forecasts to inform the model. A multitude of simulations were conducted analyzing various lengths of foresight and sizes of LDES devices under two different market structures, a wholesale electricity market and an ancillary services (A/S) market. The operation dynamics of devices with shorter discharge durations were captured better with less foresight, while devices with longer discharge durations required more foresight. Smaller devices participating in the A/S market were influenced minimally by foresight horizon. Whereas larger devices saw a significant increase in value when simulated with a longer foresight. A vital takeaway is the impact on the value of storage devices of varying sizes when forecast error is present.</p
Applying Dynamic Modeling, Simulation, and Advanced Controls to Improve State-of-the-Art Smart Inverter Technologies for Variable Renewable Power Systems
Get PDFThe United States Biden administration, state governments, and many countries around the world are pushing sustainability goals for as early as 2030. Given the fact that the power grid is projected to be responsible for over 50% of energy consumption, clean electricity is priority number one for transitioning to a 100% zero-emission nation. Power markets are changing to keep up with the economic advantage wind and solar have found in recent years, but there are still technological road-blocks we must overcome to keep up with sustainability needs. This thesis reviews, models, simulates, and provides new solutions to the leading technological issues of integrating renewable energy into our power grid: 1. managing system harmonic instability that results from integrating asynchronous renewable generation and 2. developing control schemes to mitigate intermittency from renewable power plants in order for them to replace fossil fuels in the niche grid-servicing ancillary markets. Specifically, grid-forming inverters --which provide synthetic inertia that mimics inherent rotational inertia found in most fossil-fueled generators-- are modeled on a test power system at different penetration levels and with or without battery storage. With knowledge of stability of a power system when integrating grid forming inverters on solar or wind power plant production, this work then presents a hierarchical control algorithm for achieving control of highly variable and intermittent, inverter-based generation sources. The aggregate of the work conducted in this thesis presents a proposed solution that paves a path for designing power grid systems that can stably and reliably manage near 100% penetration levels of renewable energy
Optimizing Long Duration Energy Storage Systems: A Forecasting and Modeling Approach Using an Echo State Network
Get PDFAs a global transition towards renewables is underway, proper management and scheduling of long duration energy storage (LDES) technologies is essential to maintain grid reliability, and address uncertainty within Variable Renewable Energy (VRE) generation. This thesis employs a dynamic Echo State Network (ESN) that generates price forecasts used in an optimization problem to optimize the schedule of varying sizes of LDES devices. The objective function of the model is to maximize energy arbitrage by choosing when to charge and discharge the storage devices. The optimization model makes use of a rolling horizon, optimizing over a period with extended foresight. The ESN is trained with price, VRE and load data from NREL's 118-Bus system to generate realistic price forecasts that are used as foresight in the optimization model. This work creates a framework that is better than deterministic models, by incorporating foresight of realistic price forecasts to inform the model. A multitude of simulations were conducted analyzing various lengths of foresight and sizes of LDES devices under two different market structures, a wholesale electricity market and an ancillary services (A/S) market. The operation dynamics of devices with shorter discharge durations were captured better with less foresight, while devices with longer discharge durations required more foresight. Smaller devices participating in the A/S market were influenced minimally by foresight horizon. Whereas larger devices saw a significant increase in value when simulated with a longer foresight. A vital takeaway is the impact on the value of storage devices of varying sizes when forecast error is present.</p
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