Don’t invest unless you’re prepared to lose all the money you invest. This is a high risk investment and you are unlikely to be protected if something goes wrong. Take 2 minutes to learn more.
Don’t invest unless you’re prepared to lose all the money you invest. This is a high risk investment and you are unlikely to be protected if something goes wrong. Take 2 minutes to learn more.
Problem: Balancing the grid as the electrical supply becomes more variable requires using fossil fuel plants to meet peak demands. Electricity demand and variability is set to double by 2050.
Existing solution: Decentralised energy management via devices such as thermostats, energy meters, and human control. Devices operate in isolation with competing priorities. No demand side management.
Proposed solution: Home digital twin enabling energy use optimisation in response to energy price and grid carbon intensity. Occupant achieves savings via cheaper off-peak energy.
Possible extended benefits:
• Targeted insulation/radiator/heat-pump upgrades: Data from digital twin calculates return-of-investment time, per room and per upgrade.
• Peak shaving for the grid: Grid pays for ability to temporarily lower energy usage. See: https://www.voltalis.com/
• Heating-as-a-Service: Accurate energy usage prediction and optimisation enables customers to pay for warmth, not energy. See: https://es.catapult.org.uk/report/ssh2-introduction-to-heat-as-a-service/
• Net-zero-as-a-Service: Ownership of occupants' decarbonisation goals, delivering optimisation and upgrades in line with their personal targets. For first product of this type, see: https://www.johnsoncontrols.com/smart-buildings/net-zero-buildings
Key issues:
• Huge variety of languages (communication protocols) used by devices making control difficult. Potential solution in emerging open source platforms enabling an 'energy internet'. See: https://seita.nl/core-technology/flexmeasures/
• Computational cost of optimisation. Non-linear, uncertain processes (I.E. building heat) create mathematically complex problems requiring greater quantities of time, money, and energy to solve. Existing methods involve significant simplifications and optimisation in isolation.
