Modelling study to assess the potential benefits of trading in and between local energy communities in Germany

In collaboration with Bündnis Bürgerenergie e.V. (BBEn), Grid Singularity conducted a study, using its open source energy exchange simulation tool and the dataset provided by the EWS Elektrizitätswerke Schönau eG, to assess the feasibility of local energy markets in Germany, reducing overall grid usage while maximising economic and social benefits of distributed renewable resources for citizens. This article, co-authored by BBEn and Grid Singularity, presents the study approach and the main findings.

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The Approach

The approach taken in the study and in Grid Singularity’s software solutions is that the energy market design should reflect today’s user-centric model, accentuated by the rise of prosumers and recognised in the new European legislation (RED II). This approach posits that traditional consumers, whether they be prosumers or just optimising a base load, ought to be enabled to participate in the local electricity market and benefit from trading on an equal footing with the large incumbents. Grid operators continue to play a critical role in this system, benefiting from optimised access to flexibility and reduced grid congestion. The only economic boundaries in such a system relate to the applicable Feed-in-Tariff (FiT) and Market Maker rates (the local wholesale or utility market rate of energy), as explained below.

In Grid Singularity’s energy exchange engine (d3a.io), energy resources are registered at the household and energy community participant level with specified technical parameters, such as maximum storage power, load and generation power profiles and trading preferences. Each community participant is then registered under the relevant community market, which connects to the wider distribution grid at the next level (Figure 1).

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Figure 1: Hierarchical structure of bottom-up markets in Grid Singularity’s energy exchange engine, d3a.io, as modelled for the EWS Schönau community

Each energy consuming resource such as load and storage places bids, while each energy producing resource such as photovoltaics (PV) and combined heat and power (CHP) places offers to exchange energy within and between markets. Bids are posted in the energy resource’s immediate market first, such as the household market, and then propagated through the rest of the layered markets, to the local EWS Schönau community market and then to the external distribution grid market until a match is found. The simulation is divided into 672 market slots of 15 minute length for one week, in which bids and offers are posted and updated according to their defined strategy. In order to trade at a beneficial rate, bids are posted at the beginning of each market slot at a low price and slowly increased until they are matched with an offer or reach the Market Maker price at 29.2 cts/kWh. Offers in turn begin with a high price and decreased during the market slot until either matching with a bid or reaching the FiT price of 11 cts/kWh. The battery strategy reacted to changes in prices while the load, PV, and CHP strategies remained inflexible, which means that additional demand side management opportunities could be explored with more intelligent energy asset trading strategies.

A digital twin of the EWS Schönau community, as a virtual representation of the real dataset, was simulated using Grid Singularity’s software in multiple scenarios. To account for seasonal variability, one week of data from November and May were selected. The dataset represents a smart community of 25 members with very diverse consumption patterns and included 14 family households, 4 households with retirees, 2 family households with offices, 1 multi-family house, 1 two-person household, 1 church, and 2 agricultural units. The community has significant energy production and storage resources installed with a total of 378 kW decentralised PV owned by 21 members, 286 kWh decentralised storage owned by 19 members, and 27.5 kW decentralised CHP owned by 5 members. One of the agricultural units is a major market actor with a large consumption, 200 kW PV generation and 150 kWh capacity storage.

“EWS Elektrizitätswerke Schönau eG was pleased to collaborate with Bündnis Bürgerenergie and Grid Singularity to explore the potential of local electricity markets in Germany. We will continue to seek innovations like the one presented in this study in order to provide benefits to energy community participants and accelerate the transition to renewable energy to protect the environment.”

— Peter Ugolini-Schmidt, Energy Policy Expert at EWS Elektrizitätswerke Schönau eG.

A total of 34 simulations were conducted for the following five scenarios, all for both seasons (a week in May and a week in November):

  1. Comparison of baseline community with no local energy trading and the same community with an enabled local electricity market (LEM), with and without participation of a large agricultural prosumer
  2. Scenario 1 + additional uni-directional electric vehicle (EV) profiles and community wind turbine of 3 MW
  3. Scenario 1 + additional community PV and storage capacity
  4. Scenario 2 + additional community storage
  5. Scenario 1 + trading with nearby community that has limited resources

The pricing strategy in the simulation fostered local consumption. The preset EWS grid fees of 4 cts/kWh were added to each of the trades transacted within or through the community market in order to cover the grid cost that is paid to the grid operator for using the grid infrastructure. The fee also provides a financial motivation to community participants to self-consume. Therefore, households or other community participants will first self-consume, then store, and then discharge energy locally. After balancing their own energy requirements, if there is a deficit or a surplus, they will trade on the community market, intending to supply or be supplied by a neighbour. Finally, during the last ticks of the market slot, the community participants will trade directly with the Market Maker and the FiT to avoid any power outage or curtailment.

Discussion of Results

a. Reduced energy bills with LEM implementation

In the existing grid and market structure, to balance the mismatches between the load consumption pattern and the renewable resource generation, a household exchanges energy exclusively with the wider grid. If it requires energy (the load is greater than the PV generation and battery stored energy), the household must purchase it from its utility at a fixed Market Maker rate of 29.2 cts/kWh. In turn, if the household is generating more energy than it consumes and is able to store, this surplus is sold at the FiT of 11 cts/kWh, depending on the age and the size of the installation. By enabling LEMs, a household can purchase and sell locally, to and from its neighbours. In LEMs, energy can be purchased at a lower price than the Market Maker rate and sold at a higher price than the FiT, which generates economic benefits for both trading parties.

The Grid Singularity simulation of an enabled LEM in the EWS Schönau community resulted in reduced energy bills on average compared to the baseline of 20.8% for the week of November and in increased prosumer revenue of 8.1% for the week of May, with the vast majority of community participants benefiting (Table 1). Some members encountered slightly increased bills. Only one member’s bill increased in November, by 0.07 €, and in May there was an average increase of 0.86 € for 11 members. This is a result of the espoused storage strategy, set to store all excess generation within the community and discharge whenever needed, disregarding the household’s future consumption needs. Adding intelligence to the storage strategy such as an energy requirement forecast for the house would eliminate this externality.

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Table 1 : Cumulative electricity bills and cost reduction of the simulated EWS Schönau community for the weeks in November and May, with and without LEM (note: negative value indicates revenue)

Community members who own CHP generation enjoyed the most significant bill reduction and revenue increase benefiting from a different generation pattern compared to the PV. The benefits ranged from 16.47 € to 32.44 € in absolute terms which corresponded to 59% to 274% per household with CHP in November and from 5.55 € to 13.61 € or 44% to 342% per household in May. This benefit was due to the CHP which generated energy at different times of the day and night, when there was limited competition among generation resources within the community.

b. Enhanced storage usage and improved self-consumption and self-sufficiency with LEM implementation

Local electricity markets enable community members to trade among each other rather than exclusively be supplied by one retailer from which they must consume or export their surplus energy at the FiT. Consequently, storage capacity is no longer limited to the generation and consumption of a single house. In the baseline week of November, it was not fully used, charging only up to 50% on multiple days due to low house generation. Once local trades were enabled, this storage was able to also store the community’s surplus generation, reaching 100% state of charge (SOC) on 5 days of the November week (two days were not optimized due to poor weather conditions and low generation within the community). Consequently, the net export of the community decreased and the self-consumption increased by 7.7%, as shown in Figure 2.

Figure 3 below shows the evolution of a storage’s SOC over one week of trading with 6 kWh capacity in a family house. With local trading this storage can store more generation that can be used by the community after the sunset, for example, reducing their reliance on the wider grid and improving the self-sufficiency of the community by 5.7%, as presented in Table 2.

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Figure 2: Community import and export in a week in November; Grid Singularity’s simulation based on EWS Schönau community dataset.
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Figure 3: Storage use improvements with LEMs in a week in November; Grid Singularity’s simulation based on EWS Schönau community dataset.

The simulated week in May is very different in terms of generation intensity compared to the week in November. Due to sunnier weather in May, PV installations generate more energy. Since the total capacity of installed PV is high compared to the community demand, the surplus is exported to the wider grid at the FiT. Compared to November, the community imports less energy from the Market Maker (228 kWh in May vs. 3177 kWh in November) and the export is higher (6830 kWh in May vs. 1323 kWh in November).

Figure 4 illustrates that due to higher generation during the week of May, the storage becomes saturated for four days. Since the majority of the energy community participants have PV generation and storage capacity, they also have the same generation pattern and experience a surplus at the same time. Consequently, there are limits to storage usage improvements during days when there is overgeneration. Nevertheless, enabling local trades still improves the storage use for the remaining days in the week in May. Self-sufficiency is improved by 2.2% and the self-consumption by 0.7 %, as shown in Table 2.

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Figure 4: Storage use improvements with LEMs in a week in May; Grid Singularity’s simulation of EWS Schönau community dataset.
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Table 2: Self-consumption and self-sufficiency with and without enabled LEMs; Grid Singularity’s simulation based on EWS Schönau community dataset.

c. Community resource optimisation

While enabling local trading already optimises the economic and social metrics, there could be further improvements by adding additional energy storage and more diverse energy resources.

The first consideration in the framework of this study was to investigate the impact of acquiring additional energy resources such as a central PV farm or central storage. Multiple configurations of adding PV were tested in order to see the impact on two key performance indicators (KPIs), namely self-consumption and self-sufficiency. For instance, increasing the total PV capacity by 26%, by adding a 100 kW community PV, slightly reduces the net import of the community, improving the self-sufficiency by 1.7% for the week of November and 0.2% for the week of May. Despite this improvement, adding more generation from the same source (solar) also increases the mismatch between the load consumption pattern and the generation and thus more energy is exported to the wider grid. The net export of the community is increased by 82% for November and by 35% for May, reducing the self-consumption in both cases (Table 3) as more excess energy is exported from the community. This finding demonstrates that diversification of energy generation patterns is critical for the efficiency of local electricity markets.

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Table 3: Self-consumption and self-sufficiency baseline LEM compared to LEM with an additional 100 kW PV; Grid Singularity’s simulation based on EWS Schönau community dataset.

Adding community storage, with various capacities, was also analysed in one simulation scenario, shown in Figure 5. An additional 300 kWh storage, without additional PV, increased self-sufficiency from 61.1% to 70.1% and self-consumption from 82.5% to 94.6% for the week of November, as illustrated by orange lines. Since the community has a very small import and a high net export value in May, adding storage does not improve these KPIs in that season, illustrated by grey and yellow horizontal lines, respectively. It only slightly reduces the export to the wider grid by storing excess generation for use during the week and month that follows.

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Figure 5: Self-sufficiency and self-consumption values with added storage capacity; Grid Singularity’s simulation based on EWS Schönau community dataset.

Thirdly, six EVs were added to the community to mimic the emergence of charging stations. They were modelled as a consumption only (one-way energy flow), and integrated into six different family houses. The pattern of EVs can differ and some have high power demand over short periods of time. Depending on when they are charging during the day, they can either be supplied with household PV generation, battery stored energy, neighbours’ generation or by the wider grid and Market Maker. Adding EV profiles into the simulation means increasing the consumption of their owners and thus the consumption of the overall community. Therefore, EVs can absorb more local generation that would otherwise be exported to the wider grid, improving the community self-consumption for both the week in November and May. As EVs also increase the community import, the self-sufficiency slightly reduced in November, but increased in May, due to the PV over-generation. As a result of increased demand, EV owners have higher bills and reduced revenue (note that the study did not account for opportunity costs such as the financial and environmental cost of petrol). Nevertheless, it should be emphasised that adding intelligence to the EV charging such as only charging during the day, when PVs are producing electricity, or integrating new technologies such as Vehicle-to-Grid (V2G) which would allow the EV to buy and sell energy like a storage device, would likely improve the social and financial metrics of the EVs owners and the community.

Fourthly, the study researched the effects of adding a 3 MW wind turbine, which has a different generation pattern compared to solar resources. The PV generates electricity only during the day and when it is sunny. In contrast, the wind turbine functions when the wind is blowing, which frequently occurs when there is no sun, in winter or at night. Therefore, these two types of generation are independent and complementary. (Note: the provided dataset from the wind turbine which is also located in Schönau is from a different year which may impact results.)

Adding this large wind turbine of 3 MW in the community creates a very high over-generation due to the low demand of the community. The net import is slightly reduced for both weeks, improving the self-sufficiency to 72.1% in November and 100% in May, but the net export has consequently increased as well, resulting in a very low self-consumption of 8.2% for the week in November and 2.4% for the week in May.

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Figure 6. SOC of the large agricultural unit storage (150 kWh) during the week of November; Grid Singularity’s simulation based on EWS Schönau community dataset.
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Figures 7: SOC of the large agricultural unit storage (150 kWh) during the week of May; Grid Singularity’s simulation based on EWS Schönau community dataset.

The two figures above show the usage of the storage with the addition of the wind turbine. At first, the storage resources were limited to the generation of the PV and the CHP, but with the wind turbine, they can store the surplus it produces. There is a critical zone on the 3rd November, when the storages are mainly empty due to a lack of generation within the community causing the energy deficit to be imported from the wider grid. The cause is that during this timeframe, the wind turbine is consuming energy (wind turbines’ auxiliary systems, such as sensors, cooling and heating systems can indeed consume some energy). On the other hand, during the week of May there is a very high overgeneration causing the storages to be saturated for most of the week.

Having a wind turbine within the community market consequently boosts the supply and thus increases the competition among generation resources. With the wind turbine, the bills of the community members decreased in November. However, their revenue also decreased in May (Table 4) due to an increase of supply and competition causing some PV owners to be unable to sell locally and have to export the excess at a lower, FiT price. In conclusion, if the wind turbine is directly owned by the community and its revenue is shared among the community members, it would bring a financial benefit to the community. If this is not the case, the decrease in bills would need to be compared to the revenue reduction to assess the net economic result.

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Table 4: Cumulative electricity bills with and without additional wind turbine; Grid Singularity’s simulation based on EWS Schönau community dataset.

These community social metrics could be improved by adding a community storage to absorb the overgeneration of the wind turbine. Notably, with a simulated additional storage of 3 MWh the community can become completely self-sufficient and increase its self-consumption to 11.4% for the week of November. During the week of May the community is already 100% self-sufficient, so no improvements can be made in that season. These findings demonstrate that there are limits to the community’s technical optimisation by means of adding generation or storage capacity. Due to seasonal generation and demand pattern variability, it is difficult to determine the appropriate energy resource sizing. Grid Singularity’s energy exchange simulation tool allows users to easily make changes to their configuration, such as by adding generation and storage capacity, which could serve to approximate the most optimal sizing of a house or a community, especially when one-year simulations are enabled in the software.

d. Inter-community trading benefits

Another solution to enhance the LEM financial, environmental and social metrics, complementary to that of optimising local energy resources, is to enable one energy community to trade with a nearby community. This scenario was simulated in Grid Singularity’s energy exchange software, basing the second community on the EWS Schönau community dataset but without generation or storage capacity. This community, composed of 24 consumers was added on the same grid level with inter-community trade activated, creating value for both communities. EWS Schönau community could now export some of its excess generation to the other community rather than exporting it all to the wider grid, achieving a price that is higher than the FiT. In turn, the exclusively consumer community could purchase some electricity from the EWS Schönau community rather than just from the Market Maker, at a lower price.

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Figure 8: Maximum, minimum and average price of energy in an exclusively consumer community, comparing the baseline scenario (above) and the scenario with inter-community trade (below); Grid Singularity’s simulation based on EWS Schönau community dataset.

As illustrated by circles in Figure 8, the exclusively consumer community can purchase electricity from EWS Schönau community at a lower price of around 24 cts/kWh during periods of excess generation, decreasing its cumulative bills by 49.5 € for the week in November and by 87.1 € for the week in May (Table 5). In turn, the EWS Schönau cumulative bills increased very slightly for the week in November by 6.6 €. This small cost can be reduced or removed by integrating smarter trading strategies based on energy demand, generation and price forecasts. Importantly, the cumulative bills of EWS Schönau in the week of May decreased by 110.2 €, creating an overall positive financial outcome, which could be further improved with smart trading strategies (Table 5).

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Table 5: EWS Schönau and consumer community electricity bills with and without inter-community trading for the weeks in November and May; Grid Singularity simulation

Moreover, there are environmental and social benefits of inter-community trading in that renewable resources are used more optimally and grid congestion is reduced, increasing overall energy efficiency and sustainability. The generated savings and revenue could also promote additional investments in local renewable energy.

In Conclusion

The study to assess the potential of local energy trading in Germany, conducted by Grid Singularity using its energy exchange simulation tool based on a dataset provided by EWS Elektrizitätswerke Schönau eG and with support from Bündnis Bürgerenergie e.V, concluded the following:

  1. There are strong economic and environmental benefits of enabling local electricity markets:
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2. Local electricity markets could be optimised by investing in additional energy resources, and especially by diversifying local energy sources. Simulations should be conducted to assess the appropriate level and type of local energy generation and storage.

3. Enabling inter-community trading creates economic and social value for both resource rich and predominantly consumer communities.

4. Further energy market optimisation can be sought in connecting more communities and introducing smarter trading strategies. This is a topic of future research, with final validation provided in a pilot deployment.

According to Katharina Habersbrunner, Bündnis Bürgerenergie Board Member:

“This study demonstrates the feasibility of local energy markets and how optimisation of energy sources, storage capacity and inter-community trading triggers efficiency, producing economic and environmental benefits.”

Written by

Engineering d3a.io, open source software that simulates and operates custom energy exchanges, creating local marketplaces that interconnect to form a smart grid

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