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$1.39M Distributed Solar Energy Technology at UTSA Campus with Sensor Network Monitoring and Control. SECO-1. Large Capacity UCIII: 140 Kw Moderate Capacity EB: 12 Kw. UTSA Campus View. UCIII. EB. Networking: (1) power flows (2) information flows.
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$1.39M Distributed Solar Energy Technology at UTSA Campus with Sensor Network Monitoring and Control SECO-1 Large Capacity UCIII: 140 KwModerate Capacity EB: 12 Kw UTSA Campus View UCIII EB Networking: (1) power flows (2) information flows Energy Management: (1) Sensors (2) Control ElectricityStorage Grid Energy Optimization Component Hariharan.Krishnaswami @utsa.edu $(t) Dr.Brian.Kelley@gmail.com Mo Jamshidi <Moj@wacong.org>
Project Plan: UTSA Downtown $ 1.0 M SECO-2 Durango Parking Garage Durango Building
Vehicle Electrification SECO-2 1 Level 3 Fast DC Charging Stations 1 Level 2 Charging Stations
Year 1 R&D Work on CPS Energy – UTSA • ($ 50ML – 10-Year) Agreement • Sustainability Education Research and Outreach Plan • Energy Efficiency and Conservation • Distributed, Management and Control of a Secure Smart Grid Network • Post Combustion Capture System (separating CO2 from flue gas generated in large-scale combustion process fired with fossil fuels or in biomass-fired combustion)
Distributed, Secure Smart Grid Network (One of the tasks with our team – 6 faculty and 12 graduate students) Power Electronics Converter/Inverter System Level Integration Optimization of energy consumption for an individual building (CPS Energy Research Laboratory) Energy Storage Technologies to be modeled and evaluated Cyber-security of the system
The UTSA Microgrid Model: West Campus (CPSEnergy) Lab Desired Utility W, Var NetworkControl &Actuation Electricity storage BMS Load Demand esponse Load Photovoltaic Smart Grid Protocol Utility/CustomerPower Optimization Targets Control mP Sensor Monitoring intermittency Storage : SOC, T, V Photovoltaic sensors … UtilityGrid AMI Meter AMI Pricing data (utility) Building Load demand
Large Scale System Modeling Using Object Oriented Programming Constrained power flow computation Dynamical adjustment of power
Process for creating housing unit level data base to model and categorize energy use R&D Area 2 1) For each housing unit type (i.e. two bedroom single family) in each block group, estimate housing and household characteristics from the American Community Survey (ACS) such as: • Number householders • Income • Occupation • Educational attainment • Years at residence • Age of unit • Etc. 2) Match data from other sources, CPS Energy data, and impute ACS information to each address record (housing unit) American Community Survey Block Groups Energy use data Other Data Sources Bexar County Appraisal District Address Record The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249
R&D Area 2 Task 2: Generate tables and models for energy consumption and maps for energy consumption patterns Tables Maps Models predicting energy consumption (yi) at the housing unit level: The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249
Energy Smart Homes Home Area Network (HAN) and Wide Area Network (WAN)
Proposal Smart Grid Demonstration Model w/CPS Desired Utility W, Var NetworkControl &Actuation Electricity storage BMS Load Demand Response 100 KW-hr Load 200 KW IPC Inverter IPC: MultiportBidirectional SEGIS SEGIS Smart Grid Protocol Utility/CustomerPower Optimization Targets Control mP Utility Scale Photovoltaic intermittency Sensor Monitoring Storage : SOC, T, V Photovoltaic sensors … UtilityGrid AMI Meter AMI Pricing data (utility) Building Load demand Sun Edison Utility Scale Demonstration
A system of renewable energy systems Green Energy
Composite Controller δ: duty cycle
Modeling of Hybrid system • The common DC bus collects the total energy from the wind and PV systems and uses it partly for supplying the load and partly to charge the battery . • The solar subsystem would be considered as the main generator , while the wind subsystem would constitute the complementary generator.
The utility of energy management supervisor is to control the battery SOC by keeping the DC bus voltage , between two imposed limits(54 V , 43 V) around the rated battery voltage Vbat_num = 48 V.
CONCLUSION Sustainable Energy is one of the 4 core competencies of UTSA UTSA T_SERI will be a responsible partner in local, regional and national efforts in all aspects of sustainable energy and water resources. MOUs have been signed with DoD. Educating future Energy Engineers, Scientists, and Managers at UTSA.