Research

Research Goals:  
Suggest optimal and equitable areas for the potential location of EV charging stations to enable investment in EV charging infrastructure that encourages utilization during times of high supply and low demand for electricity.   

Research Description:  
As ownership of electric vehicles (EVs) increases, vehicle charging has the potential to significantly impact the demand for electricity in the near to mid-range future. San Antonio has a large portfolio of solar PV generation, which leaves the city vulnerable to the ‘duck curve’ phenomenon in which the offset of peak demand from peak generation requires a steep increase in dispatchable electricity generation in a short time. Recommend areas for potential EV charging stations that encourage charging at times that lessen stress on the grid and reduce reliance on inefficient ‘peaker’ plants.  

Research Goals: 
Develop a framework/methodology for smart medium and heavy-duty electric vehicle – grid integration and managed charging.  

Research Description: 
Replacing medium- and heavy-duty vehicles (MHDVs) with zero emission vehicles would require extreme high-power charging stations (>200kW) for fast charging rates and would incur significantly large charging loads up to several megawatts on the distribution grid.  

The project will analyze the impact of MHDV charging stations and develop a framework for smart MHDV grid integration with managed charging to help reduce investment in bulk power and distribution system investments, operating costs, reduce renewable energy curtailment, increase distribution systems EV hosting capacity, and reduce peak load.  

Research Goals: 
Building upon our proven success in developing an AI generated digital twin to optimize electric vehicle (EV) charger placement, we propose expanding this innovation into a comprehensive, city-scale digital twin for San Antonio. This advanced model, will play a key role in strategic urban planning and accelerate the city’s smart city initiatives.  

Research Description: 
This project aims to design and implement a city-scale digital twin of San Antonio, powered by AI and IoT technologies. The digital twin will serve as a precise virtual replica of the city’s ecosystem, encompassing elements such as IoT instrumentation, urban heat island, vegetation growth, river conditions, and building infrastructure. The platform will support robust capabilities for predictive modeling, simulation, and optimization of various urban development strategies to promote eco-friendly, resilient, and sustainable smart city growth. 

Research Goals: 
Construct an AI-enhanced citywide distribution grid digital twin to dynamically predict, simulate and optimize electrification strategies for more effective and reliable electric vehicle fleet (EVF) management. 

Research Description: 
Integrating smart grid technologies with advanced metering infrastructure allows for intelligent electricity distribution management, enabling efficient electric vehicle (EV) charging effective load management.  This synergy between EV fleets and the power grid optimizes energy use, improves grid resilience, and supports the adoption of renewable energy – accelerating the shift to sustainable transportation.  

Research Goals:

Develop a control and communication platform integrating grid signals, solar energy generation, battery storage, and building energy management systems.

Research Description:

Distributed energy resources, such as photovoltaic (PV) systems, electric vehicles (EVs), and battery storage, are becoming increasingly prevalent. This creates uncertain electrical load patterns, contributing to grid instability and energy waste. The project addresses the need for flexible energy loads and ancillary services to ensure the reliable operation of the grid.

Research Goal: The proposed study aims to refine and implement an innovative system for harvesting energy from roadways.  Its goal is to refine and implement a novel robust self-powered Hybrid Integrated Sensing and Energy Conversion (HISEC) system for monitoring roadway structural conditions and traffic. 

Project Description: A group of researchers at UTSA civil and electrical engineering departments developed two prototypes to harvest energy from roadways and convert it into electric power. The first prototype uses the mechanical strain energy induced by traffic and the second uses the elevated asphalt pavement heat. Both prototypes have a conversion module to convert the energy into a stream of electric voltage that can be stored in a capacitor. The experimental and field investigations suggest that the produced power is sufficient for illuminating a low-watt LED. The prototypes are completely embedded in the pavement and take no public space. 

Current prototype design allows producing 1.5 watt from each passing truck (with 20kN axle wheel) and 15 mWatt per square inch of pavement during day time.  With the growing traffic volume and elevated atmospheric temperature in San Antonio the energy resources will continue to be vastly available for harvesting. Tapping into these resources and scaling up the prototypes can provide new alternatives to support CPS effort to supplement peak hour demands and future need. One of the environmental advantages of the prototypes is to absorb pavement heat and reduce its temperature yielding to cooler surrounding air and neighborhood zones.  

Research Goal: Provide day-ahead and real-time forecasting capabilities to the microgrid management system to enhance the management of site-specific solar and energy storage dispatching. 

Project Description: The UTSA SkyImager is a low cost, edge computing, all-sky imager that provides intra-hour solar irradiance forecasts which are used to predict power output from PV arrays. The SkyImager hardware is designed around a Raspberry Pi single board computer with a fully programmable, high resolution Pi Camera, housed in an all-weather enclosure. Software to process the images and make forecasts is written in Python and utilizes the open source Open Computer Vision library. As part of the Department of Energy INTEGRATE project, the SkyImager was deployed at the National Renewable Energy Laboratory (NREL) in 2015, where it successfully collected terabytes of image data. In fall of 2016, UTSA deployed a second SkyImager, a WXT520 Vaisala weather station, and a pyranometer as part of the CPS Energy MicroGrid at Joint Base San Antonio. The UTSA equipment supplied data and forecasts to the Siemens Micro Grid Management System (MGMS) and the Battery Management System. 

Research Goal: Develop a sustainability and climate change adaption plan for the City of San Antonio. 
 
Project Description: A Climate Action and Adaptation plan (CAAP) is a strategy document that outlines a collection of measures and policies that reduce GHG emissions based upon a reduction target, as well as evaluates climate-related impacts and provides strategies to adapt and build resilience. Using the GHG emissions inventory as the foundation, a CAP defines GHG reduction goals based on local priorities for reducing emissions and provides the guiding framework for achieving those goals. The CAAP will cover the community sector, as well as municipal operations. 

Description:
Building knowledge for energy conservation: Understanding residential consumption patterns by coupling behavioral sciences and technology

Greatly improving residential energy consumption behaviors begins with developing a clear and well-defined picture of current energy usage. By identifying the types of houses and households that have the most potential to conserve energy, we can deliver cost effective approaches toward energy consumption. The goal is to target specific types of houses with tailored and cost- effective retrofit strategies. 

In collaboration with CPS Energy and the National Renewable Energy Research Laboratory (NREL), faculty and staff members at UTSA have been working to model residential energy consumption by examining characteristics of houses and households that are associated with higher and lower levels of energy consumption. 

The team has coalesced information about housing structures, such as year built and square footage and about residents, such as income, education, number of people, and age. Analysis of these data will allow for identification
of neighborhoods and types
of energy customers that hold the most potential for adjusting conservation efforts. 

Additionally, using modeling capabilities developed by NREL, the team is building models for all of the major groupings of housing types in San Antonio. With these models, “virtual” retrofits are simulated to determine how much energy could be conserved with the retrofit. Assuming similar gains can be made by applying the same retrofit to other similar structures,
a retrofit strategy can be developed for larger scale energy conservation across neighborhoods. 
By exploring household characteristics associated with the different types of housing structures and with higher and lower levels of energy consumption, strategies will be developed and targeted to encourage energy customers to engage in energy conservation related behaviors. Behaviors such as patterns and levels of thermostat setting, decisions about participating in retrofit and rebate programs, and decisions about energy efficiency of appliance purchases, among others are those that might be targeted to advance energy conservation. 

By coupling behavioral sciences and technology, our research is creating an intelligent framework to accelerate effective deployment of energy efficiency programs. 
This research is making a unique and significant contribution to the field of energy conservation. The findings show where and how cost-effective opportunities can make significant advances in residential energy conservation. 

Collaborators:   

Juan Gomez, Ph.D., P.E., Texas Sustainable Energy Research Institute 

Lloyd Potter, Ph.D., Institute for Demographic and Socioeconomic Research 

Taeg Nishimoto, College of Architecture UTSA 

Description:  
The objective of this project is to examine various approaches to enable wide spread adoption of electric and hybrid vehicles in the San Antonio community. 

As a large emitter of greenhouse gases on a regional and national scale, the transportation sector has the potential to improve air quality, reduce greenhouse gas emissions, and revolutionize fuel economy through the mainstream adoption of electric vehicles, alternative fuels, and transportation systems. 

The objective of this research is to address the following areas: 

• Early adopter segmentation 
• Technology standards for vehicle charging 
• Utility tariff design for electric vehicles 
• Public charging infrastructure location selection criteria and guidelines 
• Municipal rules, regulations and policies for electric vehicles 
• Incentive design and value proposition assessment for promoting and enabling electric vehicle adoption 

In August 2011, a workshop was held to understand key challenges and continuing work associated with electrification of the transportation sector.  Feedback from the workshop encouraged the installation of a total of 11 electric vehicle charging stations on the UTSA 1604 and UTSA Downtown campuses.  The team realized a need for a research roadmap for technology deployment and an action plan and implementation schedule for conducting pilot and demonstration projects utilizing electric vehicles.  

Collaborators: 

City of San Antonio 

Larry Ball, Entrepreneurship and Technology Management Department, UTSA 

Chris Reddick, Department of Public Administration, UTSA 

Dwain Rogers, Texas Sustainable Energy Research Institute  

Carola Wenk, Computer Science Department, UTSA 

Krystal Castillo, Department of Mechanical Engineering, UTSA  

Description: 
The objective of this research is to address the following areas including: 

1. performance evaluations of current solar deployment within the CPS Energy service area 
2. smart grid demand response design to address intermittency 
3. circuit reliability analysis for the Blue Wing Solar farm 
4. energy storage evaluation (technology and economics) 
5. innovative forecasting methodologies 

Collaborators: 

Mo Jamshidi, Ph.D., College of Engineering, UTSA 

Brian Kelley, Ph.D., College of Engineering, UTSA 

Ram Krishnan, Ph.D., College of Engineering, UTSA 

Hariharan Krishnaswami, Ph.D., College of Engineering, UTSA 

Rolando Vega, Ph.D., P.E. Texas Sustainable Energy Research Institute 

Description:
The objective of this project is to develop a roadmap that contributes to the long-term carbon strategy for CPS Energy generation assets, identifying explicit technologies and capabilities that are uniquely relevant for demonstration at CPS Energy facilities.  

The UTSA Carbon Team conducted a literature review of five main categories related to carbon (capture, storage, sequestration, reutilization, and management), analyzed the information collected, and created the Integrated Carbon Solutions Index Library.  

Innovative technologies related to carbon capture, storage, sequestration, and reutilization were outlined in a report that includes an analysis of current technologies and profiles of companies in the market.

Collaborators: University of Siena, Dr. Juan Gomez, Texas Energy Institute 

Description: 
To advance education
 in secondary and public schools, a mobile and interactive Augmented Reality (AR) computer program was developed that allows energy conservation to be taught in a real world environment. Research shows that learners who are highly engaged in a task will be intrinsically motivated to problem solve. 

AR superimposes computer graphics seamlessly into the real world and thus provides users with opportunities to access information that is not readily obtainable through observations of the real world.
This technology makes use of standard hardware, such as computers, mobile phones, or tablets. 

Traditionally, energy concepts are taught through lectures, textbooks, or hands-on experimentation, but the relationship between these concepts, especially abstract concepts, is not effectively visualized. This project consists of the design, development, and field-testing of a novel mobile phone-based AR application. AR blends the virtual with reality in a playful and engaging environment to effectively bridge the cognitive gap. 

Our future emphasis in energy education is focused on mechanical cooling systems, renewable energy and the energy-water nexus. These topics address regional needs and are also national interests. 

Resources:
Augmented Reality Video 

Collaborators: 

Afamia Elnakat, Ph.D., Texas Sustainable Energy Research Institute  

Carmen Fies, Ph.D., Interdisciplinary Learning and Teaching Department, UTSA 

Description: 
A collaboration among the Texas Energy Institute, the UTSA College of Engineering, the Southwest Research Institute, and VI Design Group, this project installed a 313 kW distributed solar energy system across UTSA's 1604 and downtown campuses. The system includes 1228 solar panels, 21 inverters, and 18 smart combiner boxes, with real-time monitoring available via an online platform. The goal is to produce 242 MWh of energy annually, saving UTSA an estimated $65,000 per year.  

SmartLiving Technology Video 

Collaborators: 

Michael Cation, SmarteBuilding  

Juan Gomez, Ph.D., P.E. Texas Sustainable Energy Research Institute 

Rolando Vega, Ph.D., P.E. Texas Sustainable Energy Research Institute 

Description: 
The UTSA SmartLiving campus is working to eliminate energy waste and maximize energy and economic efficiency through systems active control and technology upgrades to ensure long-term energy sustainability. Our campus is one of a few across the nation to embrace innovative technologies and serve as a living test bed to research efficient system responses to the day-to-day campus demand. Our research uses a multi-disciplinary approach to study the relationship of behavioral sciences and technology, and has active working relationships with 6 of the 7 colleges at UTSA. 

Features: 

• UTSA Multisite Solar-Farm has three installations stretching over the 1604 and downtown campuses and totals 1228 solar panels, 21 inverters, 18 Smart combiner boxes, and a website to display a real-time monitoring system that allows researchers and students to study solar power, irradiance and variability. 
• Level III Electric Vehicle charging stations from Coulomb Technologies are deployed at both UTSA campuses for public use. 8 of the stations are available on the 1604 UTSA campus in the Jimenes Parking Garage and 3 are available at the downtown UTSA campus garage. 
• Second by Second energy monitoring system tracks real-time energy consumption in all of the UTSA downtown campus buildings from the campus level, building level, floor level, and sub-zone. Each sensor is monitoring the HVAC, lighting, and plug load energy consumption.   

Upcoming Goals: 

• LED lighting in all of the UTSA downtown campus buildings to create energy and economic savings. 
• Carbon monitoring sensors in two of the Durango building classrooms in order to manage cooling in classrooms as class size and schedules vary. 
• Thermal camera sensors in the Buena Vista building to visualize heating and cooling patterns in the downtown cafeteria as kitchen needs and walk-through traffic fluctuate during the day. 

Collaborators: 

Michael Cation, SmarteBuilding  

Juan Gomez, Ph.D., P.E. Texas Sustainable Energy Research Institute 

Rolando Vega, Ph.D., P.E. Texas Sustainable Energy Research Institute 

Description:
Energy and water are inextricably linked and an integral part of the future of Texas. 

Today, San Antonio is uniquely positioned as an energy leader in the 21st century energy economy. Few, if any, other
 cities in the world are making the level of commitment, supported by the level of investment necessary to create a sustainable energy future for a city the size and scale of San Antonio and that also sits on the doorstep of what may become the largest producing oil and gas reserve in the continental United States. No one would have predicted the impact of the Eagle Ford Shale on San Antonio and south Texas three years ago – it is the most significant example of technology insertion and innovation impacting our energy future over the last several decades. Our city and our region must adopt a long term view on the Eagle Ford that culminates in the development of a strategic roadmap for energy, water, and economic development that guides our involvement and fully captures this opportunity. 

As we move into the third energy transition from coal to gas, wind, and solar we continue to reduce both carbon emissions and water consumption. Future energy development will place significant new demands on water. The Texas Water Development Board projects water demands to increase substantially over the next 50 years – much of this new demand will come from growing metropolitan areas and increasing thermoelectric power production. 

The complexity of the energy, water, and economic development associated with the Eagle Ford Shale provides great opportunities
 and challenges for San Antonio. We look forward to partnering across our community to create a future that positions San Antonio as a national leader in these areas. 

Check out the 2013 Eagle Ford Forum ll 

Description: 
Our world and the ecosystem we live in are almost entirely dependent on solar power.  From the fresh air that we breathe to the water that we drink, the food we consume and the fossil fuels that we use, they are all available because of solar power.  Here at UTSA, we work hard to tap more and more from this renewable energy source for the benefit of human civilization.  But one of the main challenges with solar power is accurately predicting how much irradiance will be delivered by the sun in any given day, any given hour and minute.  This affects both sides of power utility operations – how much power will be generated by solar technologies (e.g., PV arrays) and how much demand for power will be created (e.g., on hot summer days at peak hours).  

We have been developing tools to more accurately forecast how much solar energy will be available, at specific locations and times.  We provide high-temporal and -spatial resolution forecasting solutions for integrated solar plant operations and maintenance (O&M). We work to make solar power a dispatchable resource, by enhancing the predictability of power it will produce.  In doing so, this will significantly increase the value of solar power to electric utilities.  In addition, the same tools that predict solar irradiance for the generation of electricity help us understand the patterns of electricity demand, so that utilities can better forecast, plan for and coordinate load demands.  We are excited to be working on one of the largest solar forecasting projects in the nation.  Funded by the largest-municipality-owned utility in the country, CPS Energy, 

UTSA has launched 3 initiatives with this project: 

1. Day-Ahead Forecasting Product 
2. Intra-hour Forecasting Product 
3. Sky imaging Forecasting Technology Product 

As a partner of choice for solar energy research in Texas, UTSA is committed to increasing the value of solar through improved economic models, better science-to-policy transfer, solar PV thin-film testing, adaptive solar plant sensor instrumentation, site-specific soiling characterization and high-resolution GIS-based solar resource assessments. Our focus and technical capabilities help us ensure that the Solar Forecasting project delivers the highest return on investment for the citizens of San Antonio, Texas, the nation and the world. 

Collaborators: 

Rolando Vega, Ph.D., P.E. Texas Sustainable Energy Research Institute, UTSA 

Hariharan Krishnaswami, Ph.D., College of Engineering, UTSA 

Mo Jamshidi, Ph.D., College of Engineering, UTSA 

Sos Agaian, Ph.D., College of Engineering, UTSA 

Yashar Sahraei-Manjili, Texas Sustainable Energy Research Institute, UTSA 

Jaro Nummikoski, Texas Sustainable Energy Research Institute, UTSA 

Alejandro Camargo, Texas Sustainable Energy Research Institute, UTSA 

Johanna Hansen, Texas Sustainable Energy Research Institute, UTSA 

Robert A. Onufrei, Texas Sustainable Energy Research Institute, UTSA 

Description: 
UTSA has over 300 publications, 12 textbooks, and obtained over 25 grants in fields that translate to better Lifecycle and Operations, this core competency is our bread and butter, where UTSA creates a value proposition and fundamental research to the wind energy industry.  Our multidisciplinary research experience in systems of systems, artificial intelligence, reliability, fatigue and fracture, computational fluid dynamics and cloud computing enable the wind energy industry sector to benefit greatly from real-time lifecycle and operations of complex, dynamic systems.  The following are the 7 cores of competency, which integrated offer the best and most comprehensive team for Lifecycle and Operations of Wind Farms: 

• Real-Time Computational Fluid Dynamics (CFD) through Cloud Computing 
• Wind Turbine Simulation using state-of-the-art aero-servo-elastic codes 
• Accelerated Life Testing and material degradation of metal and composite structures 
• Fatigue and Fracture techniques extracted from the aerospace industry and several contracts with Boeing, FAA and others. 
• Prognostics and Prediction of Failures of Drive-trains 
• Statistical and post-processing optimization of systems 
• Self-learning systems performance through Artificial Intelligence and Smart Controls 

The integrated UTSA Wind Energy Research Program layout can be seen here. 

Collaborators: 

Rolando Vega, Ph.D., Texas Sustainable Energy Research Institute 

Harry Millwater, Ph.D., Department of Mechanical Engineering 

Kiran Bhaganagar, Ph.D., Department of Mechanical Engineering 

Victor Maldonado, Ph.D., Department of Mechanical Engineering  

Arturo Montoya, Ph.D., Department of Civil and Environmental Engineering 

Bing Dong, Ph.D., Department of Civil and Environmental Engineering 

Mo Jamshidi, Ph.D., Department of Electrical and Computer Engineering 

David Han, Ph.D., Department of Management Science and Statistics 

Description:
Development of Autonomous Soft Robotic Solar Tracking System for Building-Integrated Photovoltaic Applications. 

Overview 

• 20% of greenhouse gas emissions comes from building operation. 
• Building integrated PV can provide building energy needs. 
• Solar tracker can make PV around 40% more efficient. 
• Adaptive solar facade can achieve energy efficiency while still responding to occupant’s desire. 

Why soft robotic solar tracker? 

• Light weight and higher power-to-weight ratio 
• Muscle-like actuation 
• Easy, low cost fabrication 

Collaborators: 

Wei Gao (PI) - Assistant Professor of Mechanical Engineering 

(Mechanical Design and Materials) 

Pranav A. Bhounsule (Co-PI) - Assistant Professor of Electrical & Computer and Mechanical Engineering 

(Control and Robotics) 

Description:

Supercritical Carbon Dioxide (sCO2) Power Generation for Renewable Energy Extraction 

• Summary 
  Many advantages to sCO2 cycles: 
• Higher efficiency (up to 50%) * 
• Smaller mechanical components (30%) * 
• Lower cost of electricity (20-30%) * 
• Ideal for renewable energy 
• Reduced greenhouse emissions 
• Multi-disciplinary team at UTSA is well-equipped to advance this technology 
  Opportunity for CPS Energy to be a leader in the development of this next-generation renewable energy technology

Collaborators: 

Christopher Combs & Kiran Bhaganagar 
Department of Mechanical Engineering, UTSA 

Karan Bhanot 
Department of Finance, UTSA 

Sara Ahmed 
Department of Electrical Engineering, UTSA 

Jacob Delimont 
Propulsion and Energy Section, SwRI 

Description:

Secure, Resilient and Smart Grid Cyber-Physical Situational Understanding using Data Driven Decision-Making and AI 

Executive Summary 

• Phase I - UTSA | CPS Energy Smart Grid Security Accomplishment

Edge Cloud Cyber Infrastructure Lab for Smart Grid IoT Vulnerability and Penetration Testing 

Standardized Device Vulnerability and Penetration Testing Process and Report 

Six (6) supported students graduated, 3 PhDs will graduate 2020 – Helped San Antonio’s cyber security ecosystem talent 

Publications – Two (2) high impact factor, top ranked technical journals 

Drafting of patent application on “Automated distributed machine learning-based cyber attack detection” 

• Phase II - Secure, Resilient and Smart Grid Cyber-Physical Situational Understanding using Data Driven Decision-Making and AI

• Project Summary 

• Research Aims 

• Value Proposition for CPS 

• Time Line

Collaborators: 

Dr. Paul Rad 

Dr. Raymond Choo 

Dr. Krystel Castillo