finish smart grid, took around 2 hr?
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- ![analytics diagram](./assets/3-smart-grid-analytics.webp)
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- ![analytics diagram](./assets/3-smart-grid-analytics.webp)
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- Example:
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- Example:
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- ![Example](./assets/3-analytics-examples.webp)
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- ![Example](./assets/3-analytics-examples.webp)
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### Load shifting / Peak shaving
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- To flatten the instantaneous energy demand, by load shifting and peak shaving
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- Using the peak and off-peak electricity tariff (tax)
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### Other considerations for smart grid
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#### Privacy concern
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- The more fine grained data is, the more risk in privacy
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#### Flexibility, and comfort of saving electricity
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- Gadgets owned:
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- TV: least beneficial
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- EV charger: essential to EV owners
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- Work from home vs. Work from office
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### Use cases : Household power usage
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- Usage differs per number of occupants
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- Try to apply load shifting for each house hold
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- Predcit:
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- Timing for high / low energy usage per day
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- Same in a week and a year
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- Most energy consumption during peak
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- Can you estimate when they wake up or go to sleep
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- predict if work from home or out
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- Design dual tariff to shift load
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- Time of day to trigger
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- If it's the same every day of week
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- Every month of year
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### Example solutions
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- Digital twin: has privacy concern
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- Adaptive dual tariff: low rate before, and high rate after threshold
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- Reinforcement learning: to learn about the environment
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- These results in smoothed electric curve
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