94 lines
2.8 KiB
Markdown
94 lines
2.8 KiB
Markdown
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# Smart grid
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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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