Energy sharing refers to the coordinated use and generation of electricity, independent of established market roles, including the inclusion of the public grid. Energy sharing can intrinsically combine several positive repercussions. Among other things, it can enable greater participation of private individuals in the energy transition, thereby increasing acceptance of renewable energies and private investment. Energy sharing is also expected to create incentives for the grid-friendly use of flexible consumption equipment at local level.

In November 2024, a legislative proposal was made to amend the Energy Industry Act (EnWG) to enable energy sharing using the public grid. This raises various questions regarding implementation, pricing and possible system repercussions. In this article series, we provide an insight into current developments and explain possible forms of design and the implications for the energy system.

Background

The aim of time-variable and dynamic tariffs is to incentivize flexible consumers to shift their consumption through variable prices and give them the opportunity to significantly reduce their electricity bill.

The tariff models, as described in our article to dynamic tariffs, are usually based on spot market electricity prices and thus reflect the supply and demand of electricity within the German bidding zone. In this way, the integration of electricity from renewable sources can be improved and price fluctuations on the market can be smoothed out. This reduces the electricity price for all consumers and increases price certainty for all market participants.

However, these price signals at national level can also have counterproductive consequences, as described below.

Simultaneous reactions to the signals are a challenge for the grid

Particularly in areas with many flexible consumers, low dynamic prices can lead to a simultaneous increase in the load of many consumers and thus to peak loads and grid overload in the distribution grid. Conversely, in areas with high storage penetration and very high prices, distribution grid overload can also be caused by simultaneous feed-in.

Competition between market signals and redispatch measures

In addition, the price signals based on the German bidding zone do not reflect the regional distribution of the generation surplus and can therefore lead to an increase in the necessary redispatch measures. If, for example, there is a high level of wind energy generation in the north of Germany at one point in time, while at the same time no solar power is being generated in the south, low prices in the context of spot market based dynamic tariffs at these times can incentivize an increase in consumption, also in southern Germany. This can trigger or intensify an overload of the transmission grids and additional redispatch measures become necessary to maintain grid stability. These are then typically provided by gas power in the south.

Basic components of a local dynamic tariff

Dynamic tariffs with a local component offer a solution to the aforementioned problem. In particular, local dynamic prices could be offered within the framework of energy communities, which are derived on the basis of the combination of local generation, local consumption and the market value of the necessary residual electricity. In this case, the energy community would assume full supply to its members.

It must be emphasized that the current regulatory framework in Germany does not yet permit local dynamic tariffs in the context of energy communities. However, as the regulatory framework is currently changing very dynamically in this regard, we believe it would be helpful to highlight the most important framework conditions for the implementation of local dynamic tariffs. In addition, the industry is currently evaluating other options for offering local dynamic tariffs outside of energy communities and thus entering this business model at an early stage.

Price components

A local dynamic tariff generally sets its price based on the local availability of electricity, i.e. the ratio of local generation to consumption, as illustrated in Figure 1. This can incentivize consumption at times of local surpluses of renewable electricity and thus also relieve the burden on higher grid levels. In addition, at times when local demand cannot be met by local consumption, electricity has to be imported and at times when more electricity is generated locally than is consumed, surplus electricity can be exported.

The prices for electricity imports and exports are therefore the second important factor influencing local tariff formation. In principle, numerous pricing mechanisms are conceivable. In [1], for example, the authors describe three of these mechanisms in detail and show the different effects on profitability for consumers and prosumers. A possible price curve according to the price mechanism of the ‘supply-demand ratio’, as described in [1] and [2], is shown in Figure 2.

In addition, the tariff is subject to taxes, levies, surcharges and grid fees, also shown in Figure 2. Exemptions for locally marketed electricity are being called for by various parties, but are not yet being implemented.

Marketing of local flexibilities

In addition to the local balancing of supply and demand, the price incentives can optionally also be used for other purposes. For example, it is conceivable that the provider of the local dynamic tariff could also aggregate the local flexibilities and market them further, for example on the power exchange or for the provision of ancillary services. In this example, the signals for adjusting behavior in line with the market or the system would also be reflected in the local dynamic tariffs.

Business model perspective

For a local dynamic tariff to be successful as a business model in the long term, the price must be designed in such a way that all participants benefit equally.

On the one hand, this means that producers and consumers must not be financially worse off than if they did not participate in the local dynamic tariff, so that there is a long-term incentive to participate. The price must therefore be attractive for producers compared to other marketing options and for consumers compared to normal tariffs.

In the tariff shown in Figure 1, the import price therefore represents the upper limit of the price, as it is assumed that consumers are not willing to pay more for their electricity purchases. The export price represents the lower limit, as system operators want to achieve at least this level of revenue in order to pass on their electricity. The price fluctuates within these limits depending on local supply and demand.

On the other hand, the price must also allow a sufficient margin for providers of the tariff. In particular, this must also include a risk premium, which also takes into account the risk of residual electricity procurement, as described below.

Technical challenges in the implementation of local dynamic tariffs

Forecasts as a central condition for  local dynamic tariffs

Until now, the main tasks of providers of traditional household electricity tariffs have been to procure and balance the quantities of electricity consumed. For the most part, this could be done on the basis of standardized standard load profiles and, if necessary, adjustments could be made in case of deviations.

Providers of local dynamic tariffs, on the other hand, must also forecast both local generation and local consumption with high temporal and local resolution for pricing, balancing and procurement of residual electricity.

This is aggravated by feedback effects due to the reaction of flexible consumers to local tariffs and the use of home storage systems. For example, if low prices stimulate a greater increase in consumption than predicted when there is a large local surplus of electricity, this can lead to the surpluses no longer being able to cover demand and additional quantities having to be purchased.

In addition, the exact forecasting of generation and consumption for a small number of players is significantly more complex and prone to error than for a large number of load and generation profiles. On the one hand, forecasting errors average out with larger numbers of players. On the other hand, smaller groups generate less data from which forecasting algorithms can learn the behaviour of specific communities and thus create better future forecasts.

High-quality consumption forecasts are necessary to achieve the goals pursued by local dynamic tariffs. These also have a high impact on economic efficiency, as

• residual electricity has to be procured
• optional flexibilities can also be marketed and consumption behavior must actually correspond to the forecasts.

Processual implementation

The implementation of energy sharing in general and specifically of local dynamic tariffs is associated with further technical and procedural challenges.

In particular:

• High-resolution measurement data in terms of time and location must be collected regularly or even in real time in order to serve as input for forecasts.
• Prices must be communicated to customers, for example via an app, and be automatically available as input for their systems or their energy management system.
• Residual electricity is procured and surplus electricity is marketed.
• All electricity volumes are balanced.

This requires adjustments or further development of the current communication and balancing processes.

Conclusion and role of the FfE

Local dynamic tariffs could intrinsically incentivize the consumption of electricity from renewable energy sources and grid-friendly behavior by mapping local electricity availability. However, their implementation depends heavily on the quality of the forecasts of local consumption and requires further development of the communication processes.

FfE has developed forecasting models for various energy industry contexts as part of numerous projects and is currently also working on simulations of energy communities. We also have expertise in process development and market analyses. We offer consulting services to help you develop personalized business models and roadmaps in the complex field of energy sharing.

Bibliography

[1] Bogensperger, A., Ferstl, J., & Yu, Y. (2021, August). Comparison of Pricing Mechanisms in Peer-to-Peer  Energy Communities. Forschungsstelle für Energiewirtschaft e.V. https://iewt2021.eeg.tuwien.ac.at/download/contribution/fullpaper/128/128_fullpaper_20210831_085106.pdf

[2] Liu, N., Yu, X., Wang, C., Li, C., Ma, L., & Lei, J. (2017). Energy Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers. IEEE Transactions on Power Systems, 32. https://doi.org/10.1109/TPWRS.2017.2649558