HYB25-70
Capacity-Based Grid Tariffs
01 HYB25-70
Presented by: Magnus Jennerholm
Energicentrum Gotland, Sweden
This abstract describes the Tariff 1.0 and 2.0 project on Gotland, Sweden, which aims to develop capacity-based electricity grid tariffs to create a more resource-efficient energy system. The project has been implemented on Gotland through a collaboration between Energicentrum Gotland, Gotlands Elnät AB, Plexigrid and Ngenic. The goal has been to create the conditions for testing a grid tariff model based on real-world circumstances in the electricity grid. The focus has been on promoting flexibility, reducing grid costs and supporting the transition to renewable energy.
The project has used advanced technology to implement the tariff model. Smart meters were installed early in the trial area on eastern Gotland, where both customer meters and metering in grid stations enabled the collection of data for at least a year. This data was used to create location- and time-specific price signals based on the station load at each grid station.
In accordance with the Energy Market Inspectorate's regulations, the proposed tariff model has four components: fixed fee, energy fee, customer-specific fee and power fee. All except the customer-specific fee have been designed to create incentives for better use of the electricity grid's capacity. Subscribers are thereby offered the opportunity to act flexibly based on dynamic price signals. The price signals consist of a cost or compensation in öre/kWh, for utilization (imports) and generation (exports). An AI-based engine has been used to generate price signals, tailored to the forecasted station load at each grid station. A customized interface then communicates these price signals to the project’s test pilots.
Possibilities with dynamic grid tariffs
The project shows that it is entirely possible to use dynamic capacity-based electricity grid tariffs from a technical point of view. The result is also expected to contribute to efficient grid utilization. A contributing reason for this is that the project's grid tariff does not affect subscribers unnecessarily, while there are strong incentives to act for flexibility when it is really needed.
Differences from traditional models
The designed grid tariff differs significantly from how traditional grid tariffs are designed. The project's tariff is based on artificial intelligence learning what the station load for the coming day will look like. Based on this, time- and location-specific price signals are calculated, which provides incentives to solve real challenges in a cost-reflective manner.
Social benefits and fairness
From a social context, it is shown that all subscribers are given an opportunity to participate in maintaining balance in the grid. Expanded opportunities are also given to be part of the energy transition, while costs are expected to be distributed fairly. Even though the model is complex, it is no more difficult than following the spot price. The project advocates automation as the only possible way to balance the electricity grid. With these conditions, subscribers will need to be even less involved in managing their energy needs in the future.
Expected effects of the model
The project has used advanced technology to implement the tariff model. Smart meters were installed early in the trial area on eastern Gotland, where both customer meters and metering in grid stations enabled the collection of data for at least a year. This data was used to create location- and time-specific price signals based on the station load at each grid station.
In accordance with the Energy Market Inspectorate's regulations, the proposed tariff model has four components: fixed fee, energy fee, customer-specific fee and power fee. All except the customer-specific fee have been designed to create incentives for better use of the electricity grid's capacity. Subscribers are thereby offered the opportunity to act flexibly based on dynamic price signals. The price signals consist of a cost or compensation in öre/kWh, for utilization (imports) and generation (exports). An AI-based engine has been used to generate price signals, tailored to the forecasted station load at each grid station. A customized interface then communicates these price signals to the project’s test pilots.
Possibilities with dynamic grid tariffs
The project shows that it is entirely possible to use dynamic capacity-based electricity grid tariffs from a technical point of view. The result is also expected to contribute to efficient grid utilization. A contributing reason for this is that the project's grid tariff does not affect subscribers unnecessarily, while there are strong incentives to act for flexibility when it is really needed.
Differences from traditional models
The designed grid tariff differs significantly from how traditional grid tariffs are designed. The project's tariff is based on artificial intelligence learning what the station load for the coming day will look like. Based on this, time- and location-specific price signals are calculated, which provides incentives to solve real challenges in a cost-reflective manner.
Social benefits and fairness
From a social context, it is shown that all subscribers are given an opportunity to participate in maintaining balance in the grid. Expanded opportunities are also given to be part of the energy transition, while costs are expected to be distributed fairly. Even though the model is complex, it is no more difficult than following the spot price. The project advocates automation as the only possible way to balance the electricity grid. With these conditions, subscribers will need to be even less involved in managing their energy needs in the future.
Expected effects of the model
- Continued opportunity for subscribers to participate in the spot price market
- Existing and future flexibility can also be used to balance the local electricity grid
- Reduced need for electricity grid expansion, which strengthens electrification
- Increased demand for green renewable energy
- Lower economic and resource costs through efficiency improvements