Team 1709: Utility Grid Enhancement for Deep Integration of - - PowerPoint PPT Presentation

Team 1709: Utility Grid Enhancement for Deep Integration

• f Distributed Photovoltaic Power Generation

Sponsored by: The United Illuminating Company Alsandy Jacot (EE) Derek McCormack (EE) Rahul Vachhani (EE) Joel Velez (EE) Project Advisor: Dr. Peng Zhang

Overview

Background Design Examples Conclusion Introduction

Introduction - Sponsor

• We are being sponsored by United Illuminating
• Southwest area of Connecticut
• Territory covers 17 towns
• 9 of which are along the shore

Introduction - Our Project

• Perform big data analytics to develop a PV capacity factor prediction tool

Algorithm based on Big Data Analytics Input Specific Parameters Output expected values Time/Season Size (kW) Efficiency Inv. Tilt (Deg) Azimuth (Deg) Output (kW) Capacity factor Machine learning

Background - Photovoltaics and the Distribution System

• Excess energy from photovoltaics flow back into the power distribution system.
• Due to the intermittent and fast ramping nature of PVs they seldom reach their

nameplate capacity value in daily operations.

• This can have negative effects on the system.

○ Voltage issues ○ Overloading ○ Protection schemes not working as designed

Background - Photovoltaics and the Distribution System

Background - Importance

• It is important for UI to have a good understanding of how much power is

flowing back into the system during specific operating conditions so that they may: ○ Mitigate potentially negative effects of PV. ○ Make changes and upgrades to system without over engineering($$). ○ Perform more accurate analysis of their system.

Background - Capacity Factor

• To understand how much power is flowing into the system we will be using

capacity factors.

• Capacity Factor - is the predicted output of a PV system over a given time

period compared to that of the nameplate rating of the PV system. ○ Nameplate rating is typically much higher than the actual output. ○ Actual output will vary based on several variables.

Design - Data

• Perform big data analytics

○ Determine which variables most affect PV output. ○ Determine number of residential sites and locations to analyze. ○ Determine what times to analyze. ○ Determine which algorithm to best model our data. ○ Obtain data from CT Greenbank ○ Use analytics to determine how each variable affects the output so a weighting can be assigned to the variables.

Design - Data - Variables

• Decide which variables most affect PV output.

○ Irradiance ○ Tilt angle ○ Azimuth angle ○ Manufacturer of PV System ○ Time/ Season ○ Specifics of PV System (Size, quantity, etc…)

Design - Data - Sites

• Determine number of residential sites and locations to analyze.

○ Decided on analyzing 75 sites split into 3 areas. ■ Data will be at 5 minutes intervals. ○ Chose 25 sites each from Fairfield, Hamden, and Milford. ■ Chosen due to high concentration of PV sites. ■ Further narrowed locations geographically based on relativity to shore.

Design - Data - Sites

• Determine number of residential sites and locations to analyze.

Design - Data - Time

• Determine what times and seasons to analyze.

○ May 17, 2015 → May 23, 2015 10:00AM → 8:00PM ○ May 15, 2016 → May 21, 2016 10:00AM → 8:00PM ■ High PV output with low power consumption. ○ July 26, 2015 → August 1, 2015 10:00AM → 8:00PM ○ July 17, 2016 → July 23, 2016 10:00AM → 8:00PM ■ High PV output with high power consumption ○ February 15, 2015 → February 21, 2015 10:00AM → 8:00PM ○ February 14, 2016 → February 20, 2016 10:00AM → 8:00PM ■ Low PV output with low power consumption

Design - Data - Modeling

• Determine which algorithm to best model our data.

○ K-means clustering ■ Popular, efficient, simple and intuitive in nature. ■ Determined to be effective for smaller sample sizes (75 sites) ○ Random forest (decision trees) ■ Good for studying relationships among variables. ■ Applicable to both regression and classification problems ○ SVM (Support vector machine) ■ It works really well with clear margin of separation ■ Applicable to both regression and classification problems

Design - Prediction Tool

• Based on our analysis we intend to assign weights to our variables so that we

can derive a tool that with given variables input can output a predicted capacity factor.

• Once we have a working tool we would like to implement it into a user friendly

interface that UI can easily utilize. ○ Excel? ○ Java? ○ Etc..?

Examples - SVM

Conclusion - Where we are now

• Currently have a request out to CT Greenbank for the required data.
• While we wait we will be doing more research on which machine learning

algorithm will be best for our project and we will be doing more sample analysis in Matlab.

• Keep working on this semester's final report.

Conclusion - Where we intend to be

• By the end of this semester we intend on having all the needed data and plan on

preparing it for analysis.

• By the end of the Fall semester we intend on having an easy to use tool for

capacity factor prediction that UI can use for in regards to their system.

• If time permits we will also perform basic research and analysis on the

economical impact of distributed energy resources. PV in particular.

Conclusion - Gantt

References

1. https://cleantechnica.com/solar-power/ 2. http://www.ipsi.net/commercial-power/cogeneration-systems 3. http://www.decodingsustainability.com/blog/2016/2/28/does-combined-heat-and-power-fit-into-the-future-low-carbon-world 4. https://www.civicsolar.com/support/installer/articles/microgrid-regulatory-policy-us 5. https://www.emaze.com/@AQWLRIT/Wind-Turbine 6. http://www.spheralsolar.com/ 7. http://www.nyiso.com/public/webdocs/media_room/publications_presentations/Other_Reports/Other_Reports/A_Review_of_Distributed_Energ y_Resources_September_2014.pdf 8. http://pvwatts.nrel.gov/index.php 9 https://maps.nrel.gov/nsrdb-viewer/#/?aL=8VWYIh%255Bv%255D%3Dt&bL=groad&cE=0&lR=0&mC=29.305561325527698%2C-84.63867 10. http://gizmodo.com/rooftop-solar-panels-are-almost-all-facing-the-wrong-di-1644518413 11. http://costofsolar.com/best-direction-to-face-solar-panels-south-or-west/ 12. http://energy.gov/eere/energybasics/articles/solar-radiation-basics 13. http://brightstarsolar.net/2014/02/common-sizes-of-solar-panels/ 14. http://www.weatherquestions.com/What_causes_the_seasons.htm 15. http://news.energysage.com/best-solar-panel-manufacturers-usa/

• 16. https://www.wunderground.com/history

Questions?