Scholarly Work - Electrical & Computer Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8814
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Item Radiometry and the Friis transmission equation(American Association of Physics Teachers, 2013) Shaw, JosephTo more effectively tailor courses involving antennas, wireless communications, optics, and applied electromagnetics to a mixed audience of engineering and physics students, the Friis transmission equation—which quantifies the power received in a free-space communication link—is developed from principles of optical radiometry and scalar diffraction. This approach places more emphasis on the physics and conceptual understanding of the Friis equation than is provided by the traditional derivation based on antenna impedance. Specifically, it shows that the wavelength-squared dependence can be attributed to diffraction at the antenna aperture and illustrates the important difference between the throughput (product of area and solid angle) of a single antenna or telescope and the throughput of a transmitter-receiver pair.Item A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids(2017-05-01) Dehghanpour, Kaveh; Colson, Christopher; Nehrir, HashemThis paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.Item Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response(2018-08-01) Nehrir, Hashem; Dehghanpour, Kaveh; Sheppard, John W.; Kelly, NathanIn this paper, we study the behavior of a Day-Ahead (DA) retail electrical energy market with price-based Demand Response (DR) from Air Conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a Q-learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users' comfort preferences. Since the retailer agent does not have direct access to the AC loads' underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer's decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer's profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of Photo-Voltaic (PV) power in the distribution feeder.Item Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework(2018-11-01) Dehghanpour, Kaveh; Nehrir, HashemIn this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.Item A Market-Based Resilient Power Management Technique for Distribution Systems with Multiple Microgrids Using a Multi-Agent System Approach.(2019-01-24) Dehghanpour, Kaveh; Nehrir, HashemIn this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.Item MEMS-in-the-lens architecture for a miniature high-NA laser scanning microscope(2019-06) Liu, Tianbo; Rajadhyaksha, Milind; Dickensheets, David L.Laser scanning microscopes can be miniaturized for in vivo imaging by substituting optical microelectromechanical system (MEMS) devices in place of larger components. The emergence of multifunctional active optical devices can support further miniaturization beyond direct component replacement because those active devices enable diffraction-limited performance using simpler optical system designs. In this paper, we propose a catadioptric microscope objective lens that features an integrated MEMS device for performing biaxial scanning, axial focus adjustment, and control of spherical aberration. The MEMS-in-the-lens architecture incorporates a reflective MEMS scanner between a low-numerical-aperture back lens group and an aplanatic hyperhemisphere front refractive element to support high-numerical-aperture imaging. We implemented this new optical system using a recently developed hybrid polymer/silicon MEMS three-dimensional scan mirror that features an annular aperture that allows it to be coaxially aligned within the objective lens without the need for a beam splitter. The optical performance of the active catadioptric system is simulated and imaging of hard targets and human cheek cells is demonstrated with a confocal microscope that is based on the new objective lens design.Item An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids(2019-01) Deghganpour, Kaveh; Nehrir, HashemIn this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.Item All-sky polarization imaging of cloud thermodynamic phase(2019-02) Eshelman, Laura M.; Tauc, Martin J.; Shaw, Joseph A.Knowing the cloud thermodynamic phase (if a cloud is composed of ice crystals or liquid droplets) is crucial for many cloud remote sensing measurements. Further, this knowledge can help in simulating and interpreting cloud radiation measurements to better understand the role of clouds in climate, weather, and optical propagation. Knobelspiesse et al. [Atmos. Meas. Tech. 8, 1537 (2015)] showed that, for simulated zenith observations, the algebraic sign of the S1 Stokes parameter (related to the difference between perpendicular and parallel linear polarization in the scattering plane) can be used to detect cloud thermodynamic phase when observed with a ground-based passive polarimeter. In this paper, we describe the use of our all-sky imaging polarimeter to experimentally test this proposed method of detecting cloud thermodynamic phase in the entire sky dome. The zenith cloud phase was validated with a dual-polarization lidar instrument.Item A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techniques and transport models(2010-03) Spangler, Lee H.; Dobeck, Laura M.; Repasky, Kevin S.; Nehrir, Amin R.; Humphries, Seth D.; Barr, Jamie L.; Keith, Charlie J.; Shaw, Joseph A.; Rouse, Joshua H.; Cunningham, Alfred B.; Benson, Sally M.; Oldenburg, Curtis M.; Lewicki, Jennifer L.; Wells, Arthur W.; Diehl, J. Rodney; Strazisar, Brian R.; Fessenden, Julianna E.; Rahn, Thom A.; Amonette, James E.; Barr, Jon L.; Pickles, William L.; Jacobson, James D.; Silver, Eli A.; Male, Erin J.; Rauch, Henry W.; Gullickson, Kadie S.; Trautz, Robert; Kharaka, Yousif; Birkholzer, Jens; Wielopolski, LucienA controlled field pilot has been developed in Bozeman, Montana, USA, to study near surface CO2 transport and detection technologies. A slotted horizontal well divided into six zones was installed in the shallow subsurface. The scale and CO2 release rates were chosen to be relevant to developing monitoring strategies for geological carbon storage. The field site was characterized before injection, and CO2 transport and concentrations in saturated soil and the vadose zone were modeled. Controlled releases of CO2 from the horizontal well were performed in the summers of 2007 and 2008, and collaborators from six national labs, three universities, and the U.S. Geological Survey investigated movement of CO2 through the soil, water, plants, and air with a wide range of near surface detection techniques. An overview of these results will be presented.Item Using hyperspectral plant signatures for CO2 leak detection during the 2008 ZERT CO2 sequestration field experiment in Bozeman, MT(2010-03) Male, Erin J.; Pickles, William L.; Silver, Eli A.; Hoffmann, Gary D.; Lewicki, Jennifer; Apple, Martha E.; Repasky, Kevin S.; Burton, Elizabeth A.Hyperspectral plant signatures can be used as a short-term, as well as long-term (100-year timescale) monitoring technique to verify that CO2 sequestration fields have not been compromised. An influx of CO2 gas into the soil can stress vegetation, which causes changes in the visible to near-infrared reflectance spectral signature of the vegetation. For 29 days, beginning on July 9, 2008, pure carbon dioxide gas was released through a 100-m long horizontal injection well, at a flow rate of 300 kg day−1. Spectral signatures were recorded almost daily from an unmown patch of plants over the injection with a “FieldSpec Pro” spectrometer by Analytical Spectral Devices, Inc. Measurements were taken both inside and outside of the CO2 leak zone to normalize observations for other environmental factors affecting the plants. Four to five days after the injection began, stress was observed in the spectral signatures of plants within 1 m of the well. After approximately 10 days, moderate to high amounts of stress were measured out to 2.5 m from the well. This spatial distribution corresponded to areas of high CO2 flux from the injection. Airborne hyperspectral imagery, acquired by Resonon, Inc. of Bozeman, MT using their hyperspectral camera, also showed the same pattern of plant stress. Spectral signatures of the plants were also compared to the CO2 concentrations in the soil, which indicated that the lower limit of soil CO2 needed to stress vegetation is between 4 and 8% by volume.