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A Sustainable Chadwick

By Alexander Dean, Max Lin, David Malone, Rafael Ning, Aaron Rapaport, Harrison Bruni, and Vani Kathuria

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The Team

Harrison Bruni Vani Kathuria

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Overview

Background Research

Benefits of the Smart Grid

Research Question

Path to MAGI Implementation

Experiment Workflow

Modelling Consumption & Generation

Visualization

Graphs

Conclusions

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Background Research

  • Smart cities
    • What’s a smart city?
    • Why model Chadwick as a smart city?
  • Smart Grids
    • What’s a smart grid?
    • Responding to Changes in Production/Consumption at Chadwick

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Benefits of the Smart Grid

  • A reduction in carbon emissions
  • Better service for consumers

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Research Question

  • Can Chadwick produce enough solar energy to completely go “off the grid” and sustain itself?
    • How can we store energy and trade energy among actors to ensure power even during times of low production?
    • Examine usage/generation and model different scenarios

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Path to MAGI Implementation

  • Learning DeterLab and MAGI
    • Simple Calc
      • Introduction to NS, agents, AAL
    • Client-Server Case Study
      • Introduction to multi-node experiments, visualization
    • File Creator
      • Introduction to IDL, file I/O

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Experiment Workflow

  • Running the experiment:
    • Start 21 Building nodes and 1 Energy Broker node
    • Parse for global and building-unique parameters
    • Each building iterates through day, calculating generation and consumption
    • Writes building-unique calculation output to data file
    • Aggregate and process 21 data files to create net data file of net surpluses and deficits
  • Next Steps:
    • Energy Broker → Forecasting → Energy Market
    • Diversified generation methods

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Example building profile from parameter file

Parameter parser from building agent

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Iterating through the day, writing generation and consumption to data file

Excerpt from building’s data file

0,2633

0,2633

0,2633

1,2633

1,2633

2,2633

2,2633

3,2633

3,2633

4,2633

4,2633

5,2633

5,2633

6,2633

7,2633

7,2633

8,2633

8,2633

9,2633

10,2633

10,2633

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Manually run python script to aggregate and process data

Excerpt from net data file

-27072.0

-26426.0

-26426.0

-25301.0

-26576.0

-25151.0

-26276.0

-25301.0

-26351.0

-25301.0

-24026.0

-29496.0

-30321.0

-30396.0

-31671.0

-29571.0

-29496.0

-29271.0

-30246.0

-31596.0

-29346.0

-26608.0

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Modeling Consumption & Generation

  • Calculating Solar Generation
    • Calculates solar insulation
  • Calculating Building Consumption
    • Models: AC, Lights, Appliances, Outlets

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Modeling Solar Generation

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Visualization

  • tau.isi.edu/chadwick
    • Google Map with Waypoints for buildings allowing users to see Production/consumption and building information

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Using Google Maps APIs

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Building class containing information

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Graphs

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Conclusion

  • By optimally installing solar panels for distributed power generation and energy transfers in a marketplace--establishing a smart grid, Chadwick School’s buildings can collectively provide enough energy to be self sustaining.
  • To go completely “off the grid,” Chadwick must implement forms of energy storage and diversified electricity generation methods to supplement production during peak usage hours and solar generation downtime.

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Actions

  • Modified a simple calculator program, client-server case study, and file creator; published MAGI documentation
  • Created an interactive website to visualize models
  • Created first step towards simulating a full smart grid scenario
  • Future: send visualizations to Chadwick School to discuss implementation