This final model in this series brings all these components together to see how the combination of demands interact and how they affect the supply and demand matching.
It also explores the contribution of two small scale stores a 7kWh electrical store (such as a Tesla power wall) and a 10kWh heatstore.
The interactive modelling tool can be opened here:
Online tool: http://openenergymonitor.org/energymodel > navigate to 7. All
The following table show the results in terms of percentage of demand supplied directly of running the model with different electric vehicle charging profiles, and storage options. There is a small 4% oversupply.
|4% OS + night charging||62.3%|
|4% OS + 1/2day 1night charging||76.2%|
|4% OS + flat charging||78.3%|
|4% OS + smartcharging||86.3%|
|4% OS + night charging + 7 kWh li-ion store||84.4%|
|4% OS + 1/2day 1night charging + 7 kWh li-ion store||86.0%|
|4% OS + flat charging + 7 kWh li-ion store||86.2%|
|4% OS + smart charging + 7 kWh li-ion store||88.6%|
|4% OS + night charging + 10 kWh heatstore||70.2%|
|4% OS + 1/2day 1night charging + 10 kWh heatstore||81.0%|
|4% OS + flat charging + 10 kWh heatstore||82.4%|
|4% OS + smartcharging + 10 kWh heatstore||87.5%|
|Liion + heatstore|
|4% OS + 7 kWh li-ion store + 10 kWh heatstore + nightcharging||85.5%|
|4% OS + 7 kWh li-ion store + 10 kWh heatstore + 1/2d 1n||86.9%|
|4% OS + 7 kWh li-ion store + 10 kWh heatstore + flatcharging||87.1%|
|4% OS + 7 kWh li-ion store + 10 kWh heatstore + smartcharging||89.3%|
Its interesting that a matching of 89.3% can be achieved with 7 kWh li-ion store, 10 kWh heatstore and electric car smartcharging up from a minimum of 62.3% with no stores and night time charging only. I think its quite impressive and encouraging that this high a level of matching can be achieved from a relatively small amount of storage and that 86% can be achieved with the smartcharging option only.
There are clearly different routes possible to achieve higher degree's of matching. If smart charging is technically possible with the flexibility used within the model and that it doesn’t provide too much of a burden on the user and only requires potentially a relatively small amount of electronics, embodied energy and cost compared to the li-ion store and heatstore then smart charging may be a more effective option.
A li-ion store and flat rate charging provides about the same benefit as smart charging and so if smart charging does not pan out to be practical then there may be an option to make up for it with a li-ion store, especially if the embodied energy and cost of storage reduces significantly.
The combination of measures provide smaller gains (if you apply smart charging first then the li-ion store only provides ~3% additional gains, however if you apply the li-ion first it looks like its the smart charging that only provides the small gain). Perhaps an important aspect is that a combination could provide important redundancy. It looks worthwhile to explore and develop each of the above solutions with a focus on how they integrate, the flexibility at which they can match supply, their costs and embodied energy.
The other blog posts in this series are linked below and the next model will explore the use of large capacity energy stores such as power to gas to reach 100% supply/demand matching. All the modelling behind this work is open source and available on github here: https://github.com/TrystanLea/zcem
- Modelling hourly demand and supply for renewable powered domestic electricity, heating with heatpumps and electric vehicles
- Hourly energy model example 1: Variable Supply
- Hourly energy model example 2: Variable supply and flat demand (python code included)
- Hourly energy model example 3: Variable supply and traditional electricity demand
- Hourly energy model example 4: Complementarity of different renewable generating technologies
- Hourly energy model example 5: Simple space heating model with heatpump's powered by renewable energy
- Hourly energy model example 6: Electric vehicles and a renewable energy supply