The decarbonisation of energy supply entails a significant extension of electrical power grids on all voltage levels. Until recently mainly power generation was in the focus for the CO2 emission reduction, but more and more it becomes clear that the necessary targets can only be met when also the heating and transport sector will be decarbonised and hence electrified.
The necessary renewable energy with its low energy density requires large surfaces of land in order to meet the energy demand in cities and industry. The spatial separation of generation and consumption will hence always require a strong grid with significant capacity, irrespective of any attempts for local autarchy.
However, the degree for necessary grid upgrades highly depends on the availability of flexibilities for supporting the grid. Particularly the heating sector with power to heat/gas technologies and the transport sector triggering mobile and stationary battery applications offer a tremendous future potential.
But already today, significant interruptible loads and auto-generation in industry and in the commercial sector exists. These flexibilities are generally used to limit tariff relevant peaks in consumption (so called “peak shaving”). But neither on a local nor on a regional level the actual real physical scarcity situation in the network is taken into account when the applicable tariff-rate is determined.
The selected approach is a method that generates market based scarcity signals of grid capacity and thereby taking into account local and regional restrictions while at the same time obeying “Unbundling” requirements.
This highly innovative approach allows the grid customer to pay for security of supply according to his individual needs.
Dynamic price signals from both the power market and the grid can now be used to generate an optimal schedule (“load shaping”), where both the customer and the Distribution System Operator equally profit, and hence a macroeconomic optimum can be reached.
Within this framework, HSLU researches effective solutions for short-term load prediction at various aggregation levels of consumption and production in a regional grouping of prosumers on network levels 5 and 7.