2025-01-07 12:02:19 +08:00
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# Smart grid
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2025-01-07 18:19:28 +08:00
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<!--toc:start-->
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- [Smart grid](#smart-grid)
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- [Not smart grid](#not-smart-grid)
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- [Smart grid](#smart-grid)
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- [Reason](#reason)
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- [Implementation](#implementation)
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- [Optimizing the electric grid](#optimizing-the-electric-grid)
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- [Big data and smart grid:](#big-data-and-smart-grid)
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- [Load shifting / Peak shaving](#load-shifting-peak-shaving)
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- [Other considerations for smart grid](#other-considerations-for-smart-grid)
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- [Privacy concern](#privacy-concern)
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- [Flexibility, and comfort of saving electricity](#flexibility-and-comfort-of-saving-electricity)
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- [Use cases : Household power usage](#use-cases-household-power-usage)
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- [Example solutions](#example-solutions)
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<!--toc:end-->
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2025-01-07 12:02:19 +08:00
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## Not smart grid
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- Electric grid, production, delivery and consumption has to occur
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instantaneously and in perfect balance
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- Reason for not smart:
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- Frequency matches generation and demand
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- Voltage is generator and transformer
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- Current is the upper limit of devices and have to provide spare capacity
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## Smart grid
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- Features:
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- Cost efficiency
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- Economically efficient, sustainable
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- Quality
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- Security
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- Safety
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- How to
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- Integrate the power infrastructure with Information and Communication
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Infrastructure
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- Problems present:
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- Energy Security
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- Energy Sustainability
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- Energy equity
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## Reason
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- The UK Government pledge
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- Reduce carbon emission by 68% by 2030
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- more , 2035
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- net zero by 2050
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- Renewable with the traditional grid: power flow is less predictable
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- Renewable with changing landscape: user demand is more challenging to predict
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- Just In Time Delivery
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- Dealing with excessive energy
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- Store
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- Sell
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- Run power station
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- Problem: Excess power may damage device or parts of grid
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- Not enough energy
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- Use stored energy
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- Hydro power
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- Buy
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- Problem: risk of black out
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- This means we should use smart grid:
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- 
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- 
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## Implementation
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### Optimizing the electric grid
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- Continuous: Accurate forecast of demand and production to increase cost
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efficiency, and resilience of electricity provision
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- Predictive: Informed planning of renewable energy and storage to reach net
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zero
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- Integration and collaboration: integrate with other infrastructures for
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efficiency and collaborative intelligence
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### Big data and smart grid:
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- Sources:
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- 
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- Characteristics of big data:
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- Volume: Amount of data:
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- Likely terabytes in size
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- Stores transactions
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- In form of stream or batch
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- Variety: Lots of data types:
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- Structured or unstructured
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- Multi-factor, and probabilistic
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- Velocity: Speed of data flow:
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- Real time
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- Near real time
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- In stream or batch
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- Value: Extracting insights
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- For business
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- For revenue streams
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- Has operational value
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- Uncertain: Data uncertainty
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- Authenticity on data
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- Origin of data
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- Availability of data
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- Accountability
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- Analytics: the more sophistication, the more value.
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- Hindsight: descriptive of what happened
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- Insight: diagnostic of current situation
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- Foresight: Prediction, and perspective
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- 
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- Example:
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- 
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2025-01-07 12:26:22 +08:00
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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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