Smart grid

Not smart grid

Electric grid, production, delivery and consumption has to occur instantaneously and in perfect balance
Reason for not smart:
- Frequency matches generation and demand
- Voltage is generator and transformer
- Current is the upper limit of devices and have to provide spare capacity

Features:
- Cost efficiency
- Economically efficient, sustainable
- Quality
- Security
- Safety
How to
- Integrate the power infrastructure with Information and Communication Infrastructure
Problems present:
- Energy Security
- Energy Sustainability
- Energy equity

The UK Government pledge
- Reduce carbon emission by 68% by 2030
- more , 2035
- net zero by 2050
Renewable with the traditional grid: power flow is less predictable
Renewable with changing landscape: user demand is more challenging to predict
Just In Time Delivery
- Dealing with excessive energy
  - Store
  - Sell
  - Run power station
  - Problem: Excess power may damage device or parts of grid
- Not enough energy
  - Use stored energy
  - Hydro power
  - Buy
  - Problem: risk of black out
This means we should use smart grid:

Continuous: Accurate forecast of demand and production to increase cost efficiency, and resilience of electricity provision
Predictive: Informed planning of renewable energy and storage to reach net zero
Integration and collaboration: integrate with other infrastructures for efficiency and collaborative intelligence

Sources:
Characteristics of big data:
- Volume: Amount of data:
  - Likely terabytes in size
  - Stores transactions
  - In form of stream or batch
- Variety: Lots of data types:
  - Structured or unstructured
  - Multi-factor, and probabilistic
- Velocity: Speed of data flow:
  - Real time
  - Near real time
  - In stream or batch
- Value: Extracting insights
  - For business
  - For revenue streams
  - Has operational value
- Uncertain: Data uncertainty
  - Authenticity on data
  - Origin of data
  - Availability of data
  - Accountability
Analytics: the more sophistication, the more value.
- Hindsight: descriptive of what happened
- Insight: diagnostic of current situation
- Foresight: Prediction, and perspective
Example:

To flatten the instantaneous energy demand, by load shifting and peak shaving
- Using the peak and off-peak electricity tariff (tax)

Usage differs per number of occupants
Try to apply load shifting for each house hold
Predcit:
- Timing for high / low energy usage per day
- Same in a week and a year
- Most energy consumption during peak
- Can you estimate when they wake up or go to sleep
- predict if work from home or out
Design dual tariff to shift load
- Time of day to trigger
- If it's the same every day of week
- Every month of year