Ecovillage Findhorn: Potential for Harnessing Renewable Energy and Excess Waste Heat through Peer-to-Peer Trading
This study quantifies the economic, environmental, and technical benefits of P2P energy trading in Ecovillage Findhorn, informing the next phase of the EU co-funded InterPED project, with Grid Singularity collaborating with pioneer European researchers and communities to further innovation and test P2P energy trading deployment.
Introduction and Methodology
Energy researchers, policymakers, and local communities are increasingly exploring ways to optimise energy usage and reduce carbon footprint. This trend is at the heart of the EU-co-funded InterPED project [1], which aims to enable Positive Energy Districts (PEDs) — urban areas that produce net-zero or surplus renewable energy through integrated systems — by advancing sector-coupling, cross-vector integration, demand flexibility, and consumer engagement, while improving the utilization of local renewable energy sources (RES), storage solutions, and excess/waste heat (E/WH) recovery. The InterPED project aims to operationalize PEDs by demonstrating innovative solutions across four pilot sites.
At the forefront of this initiative is the Ecovillage Findhorn [2] a pioneering community that blends ecological living with cutting-edge energy innovation. It is located in the northern Scotland and contains approximately 160 buildings — around 150 residential units and 10 offices — operating under a community-led governance that emphasizes sustainable living. The site already features notable renewable and low-carbon energy elements, including multiple photovoltaic (PV) installations, on-site battery storage systems, heat pumps for domestic hot water (DHW) and space heating, with ground-source heat pumps using wastewater heat, as well as a local wind park operating three wind turbines.
This article presents key findings from a comprehensive analysis of Findhorn’s market-based assessment of RES and E/WH harvesting potential, conducted using the Grid Singularity (GSY) Local Energy Market (LEM) simulation tool (also referred to as the Singularity Map) [3], simulating four distinct scenarios with and without active peer-to-peer (P2P) energy trading, across seasonal variations (winter, with data from 1–7 January 2024, and spring, with data from 1–7 April 2024).
The GSY tool creates a digital twin of energy communities, simulating real-time market dynamics and grid interactions based on configuring participating assets and their trading strategies, as well as applicable pricing. Key features applied for this study include:
- Two-Sided Pay-as-Bid Market: Buyers and sellers submit hourly bids/offers, with trades prioritised locally before grid interaction (bids and offers to the grid were matched with a 30 seconds delay relative to local trades);
- Asset Trading Strategy Modelling: Customisable profiles for PV, batteries, heat pumps, and loads (consumption) based on historical and complementary synthetic data, with additional development to model thermal storage strategies for heat pumps [3] with multiple water tanks (space heating and domestic hot water buffers) to optimise flexibility;
- Incorporation of applicable pricing at the location for wind energy purchase (agreed community price), external supply (utility rate) and surplus sale of PV generation to the grid (feed-in-tariff — FiT), as well as network cost (grid fees); This also includes incorporating the applicable time-of-use tariffs such as day time-of-use tariff shown in Figure 1 below:
To simulate a partial digital twin of the Findhorn Ecovillage as a local energy community, a total of 53 different assets were modelled
- 36 load assets corresponding to 26 consumption profiles of 26 individual apartments, 1 laundry room, 1 backup heater and 8 immersion heaters, located in four different areas;
- 7 PV systems with capacities ranging from 2.84 kW to 7.5 kW and totalling 27.36 kW;
- 4 heat pumps, out of 15 present as measurements were unavailable for all; these included one 22 kW ground-source pump, connected to two water buffers (1500L for space heating, 1000L for domestic hot water); one 14 kW air-source heat pump (ASHP) with 100L buffer (space heating) and 380L buffer (DHW), and two 14 kW ASHPs, each connected to a 100L space heating buffer and a 380L DHW buffer;
- 3 battery storages totalling 37 kWh;
- 3 wind turbines totalling 675 kW in capacity and generating up to 1.2 GWh/year collectively).
The historical data for the consumption and generation assets was supplemented with synthetic data when historical profiles were unavailable due to lack of measurements at the time of the study, approximating the following:
- PV generation: only one of seven PVs measured, while the others used synthetic generation profiles derived from GSY’s Custom PV tool [3], which models PV generation by leveraging location-based data profiles generated from the Energy Data Map [4] provided by rebase.energy [5] based on the PV’s capacity, location, azimuth, and tilt;
- Wind generation, leveraging turbine technical specifications and historical data for wind speed and ambient temperature at location, which were obtained via open-source weather application interface Open Meteo [6] and using an open source Python library windpowerlib [7] managed by Oemof [8], a modular open source framework to model energy supply systems;
- Air-source heat pump was modelled using the open-source weather application interface Open Meteo [6] for ambient temperature measurements.Ground-source heat pump was modelled assuming the same, constant value of 8.8ºC [9] for the ground temperature for the entire location. Similar values were used to configure the buffers’ minimum, maximum and initial temperatures, thus leveraging weather tools and existing measurements to supplement those that were unavailable.
- Importantly, since this site had only aggregated building-level electricity consumption data for some buildings, apartment-level consumption was disaggregated based on the available heating bills, proportioning the same share of total consumption to each apartment.
The wind turbines of the Findhorn Wind Park are a major source of the supplied energy (29.39% in April and 22.69% in January for the simulated base case scenario). It is important to underscore that since Findhorn Wind Park is not considered a community member in the current simulation set up, the energy it provides to the community is not taken into account in calculating the self-sufficiency and self-consumption rates.
Results and Analysis
The following sections present the comparative results for these four different scenarios:
- Base case: Current assets without E/WH or P2P trading;
- E/WH Integration (scenario 2 or v.2): Current assets, with heat pumps using wastewater heat, but no P2P trading (reflects actual conditions in one area of Findhorn where a treated effluent system is used, but it is extended hypothetically to all areas in this simulation study to assess broader potential).
- P2P Trading (scenario 3 or v.3): Current assets with E/WH integration and P2P trading activation.
- Expanded RES with integrated E/WH and P2P trading (scenario 4 or v.4): All conditions as in scenario 3, with a doubled capacity for each of the 7 existing PVs (total capacity increased from 27.36 kW to 54,72 kW), significantly increased battery storages (from 37kWh to 200 kWh), and heat pumps capacity (maximum power consumption increased from to 64 kW to 128 kW, with doubled buffer volumes for DHW and space heating).
The results are analysed using the following key performance indicators, shown in aggregate terms below for the simulated local energy community:
- Self-Consumption = Self-consumed energy ÷ Total generation
- Self-Sufficiency = Self-consumed energy ÷ Total demand
- Net Energy Cost = Electricity cost (import + community purchase) — Revenue (Export + community sale)
- Heat Pump Coefficient of Performance = Thermal output ÷ Electrical input.
A. Self-Consumption and Self-Sufficiency
- Base Scenario: Low self-sufficiency (January: 2.6%; April: 22.2%) and relatively high self-consumption in the winter (January: 63.9%; April: 36.6%);
- E/WH Integration: Improved self-sufficiency by 7.7% (January: 2.8%) and 13.5% (April: 25.2%) compared to base scenario; self-consumption sees a slight increase in January (from 63.9% to 64.9%) but a slight decline of 3% in April (from 36.6% to 35.5%).
- P2P Trading: Drastic self-sufficiency improvement, amounting to 57.1% in the winter period (January: from 2.8% to 4.4%) and 97.2% in the spring (April: from 25.2% to 49.7%) compared to the second, E/WH integration scenario. Similar results regard the aggregate self-consumption: 54.1% improvement in the winter (January: 100% vs. previous 64.9%) and over 100% in the spring (April: 71.0% vs. previous 35.5%);
- Expanded RES with integrated E/WH and P2P trading: Further increase of self-sufficiency by 88.6%, when compared to scenario 3, where all other conditions were equal except the RES capacity size (January: from 4.4% to 8.3%). and 54.8% (April: from 49.8% to 77.1%). Self-consumption, in turn, reached 100% in the winter (compared to 96.3% in scenario 3), and decreased in the summer, as expected, by 18.8% (April: from 71.1% to 57.7%).
B. Economic Impact
- Base Scenario: Net electricity costs of £588.6 (January) and £197.6 (April).
- E/WH Integration: Reduced net costs by 6.8% (January aggregate costs: £548.8) in winter and by 28.2% in spring (April aggregate costs: £141.9) compared to base scenario. Major cost savings occur in spring, where a combination of less heating demand and more PV generation yields outsized benefits from more efficient heat pumps.
- P2P Trading: Delivered additional savings of 3.5% in winter (January aggregate costs: £529.7) and 23.8% in spring (April aggregate costs: £108.2) compared to the second, E/WH integration scenario.
- Expanded RES with P2P: Achieved a remarkable transition to net profitability of £23.8 in the spring, representing savings of £221.4 compared to the base scenario and £132 compared to scenario 3 where P2P and E/WH is incorporated. In the winter there are also small savings when compared to scenario 3 (January aggregate cost: £511.4 vs. £529.7 in v.3), with higher savings when compared to the base case scenario of £76.9 (from a balance of -£588.3 to -£511.4).
C. Grid Interaction
- Base Scenario: January: 1406.6 kWh imported vs. 52.3 kWh exported; April: 799.9 kWh imported vs. 548.8 kWh exported (Note: The presence of higher solar irradiation in April significantly increases surplus PV generation).
- E/WH Integration: Imports drop by 6.7% in the winter period (from 1473.9 kWh to 1374.3 kWh), while exports increase slightly, by 1.6% in the same period (from 19.9 kWh to 19.7 kWh); In the Spring period imports fall by 18.7% (from 719.3 kWh to 584.6 kWh), with a negligible,1.5% increase in the spring (from 319.8 kWh to 324.7 kWh in April). Therefore, the overall grid impact is positive.
- Activation of P2P trading significantly increased exports compared to the second scenario (165.1% January, from 19.7 kWh to 52.3 kWh, and 69% April, from 324.7 kWh to 548.8 kWh), while imports see a small increase of 2.4% in January (from 1374.3 kWh to 1406.6 kWh) and a significant increase in April of 36.8% (584.6 kWh to 799.9 kWh), and this additional energy can be harnessed with secondary flexibility markets and dynamic grid tariff models.
- RES scaling further elevated exports by approximately 100% in both seasons, highlighting expanded storage and generation capacity (January: exports nearly doubled, increasing by 97.9%; April: exports increased by 105.4%, from 548.8 kWh to 1127.1 kWh). Imports, in turn, also increase — slightly in the winter (January +0.7% compared to scenario 3; April: +23.1%).
D. Impact of E/WH Integration on Heat Pump Performance
In scenarios leveraging E/WH, excess heat from wastewater significantly increased the efficiency (COP) of heat pumps. Compared to the base case scenario with no E/WH, the average COP improvements in the scenario where all community pumps leverage E/WH were notable, ranging between 5.2% in the winter and 17.1% in the spring for a ground source heat pump. These values are higher when the current air source pumps are modelled as ground source heat pumps benefiting from E/WH but that can also be attributed to a higher starting point (around 10.6% in the winter and up to 23.3% in the spring for air source heat pumps). The study found no heat pump COP variation with the activation of P2P energy trading, indicating that heat pump efficiency is not impacted by the shift in electricity trading strategy. This is expected since P2P trading primarily affects electricity cost and allocation, rather than heat pump performance parameters.
Conclusion: Key Findings and Recommendations
The study demonstrates the substantial environmental and economic potential of integrating excess waste heat with renewable energy sources and P2P energy trading, even in a location with lower than average solar radiation like northern Scotland. These findings underscore the significant role local energy markets can play in advancing transition and growth of Europe’s communities into Positive Energy Districts, leveraging technology and market innovations for energy autonomy and resilience:
- Peer-to-peer energy trading brings about significant economic and environmental benefits but requires higher granularity of measurements.
Peer-to-peer energy trading yields high cost savings, amounting to 23.8% in the spring period when there is higher PV production, accompanied by an astounding 97.2% increase in community self-sufficiency (from 25.2% to 49.7%), with higher gains expected for the summer period which will be included in future studies. The winter savings are smaller (3.5%) due to a higher demand and lower PV production.
As elucidated by Grid Singularity’s Dr Ana Trbovich:
“A P2P energy trading approach fosters a more dynamic exchange, supporting members with surplus to store or sell it locally, but also driving up net exports when local supply surpasses local demand. This results in a more effective redistribution of the community’s surplus energy both for local use and sale to other markets.” She also highlights the social benefits:
“Importantly, while those endowed with more renewable and flexible assets accrue a larger share of the gains, all Findhorn community participants are shown to benefit from P2P trading, strengthening social inclusion through novel market access.”
Since P2P benefits multiply as market scales, even stronger economic and environmental results are expected if P2P trading were enabled for all the Findhorn participants, which commands a higher availability of asset-level generation and consumption data measurements. Likewise, benefits increase with higher availability of renewable and flexible energy resources as it is elaborated below.
As described by the Assistant Project Coordinator at the Findhorn Innovation Research and Education (FIRE), Tara Pinheiro Gibsone:
“Measurement installations are in progress at Ecovillage Findhorn with the support of the InterPED project, and future studies are planned. Through InterPED, we aim to demonstrate the benefits of peer-to-peer (P2P) energy trading and plan to work with key community stakeholders to explore its implementation.”
- The expansion of renewable and flexible capacity yields substantial financial savings when combined with E/WH integration and P2P energy trading.
Increasing the PV capacity, the total battery storage, and the maximum power consumption of heat pumps, along with their storage capacity would generate weekly savings of £132 in absolute terms without active P2P trading, and £221.4 with P2P trading for the community in the simulated week in the spring, In brief, combining P2P with expanded RES and E/WH shifts community finances from a net cost to a net gain in warmer periods (weekly bill changes from -£197.6 to +£23.8). Winter performance also improves, with costs reduced by 13.1% (weekly from -£588.6 to -£511.4).
The expansion of renewable and flexible capacity also provided notable environmental benefits, particularly during the winter period when heating demand is higher, making heat pump efficiency more impactful. In our study, Findhorn’s self-sufficiency rate increased by 88.6% in the winter (from 4.4% to 8.3%) and by 54.8% in the spring (from 49.8% to 77.1%) when compared to a scenario where all other conditions except capacity were the same. These improvements are primarily attributed to the enhanced storage capacity — both battery storage and the heat pumps’ domestic hot water and space heating buffers — which allowed more efficient use of locally generated renewable energy. In contrast, the increased RES capacity led to a decrease in self-consumption rates (by 3.7% in winter and by 18.8% in spring). due to a higher amount of energy being exported to the grid, particularly in the spring when increased solar radiation boosts PV production while heating demand decreases.
These shifts indicate that the larger renewable and flexibility capacity improved Findhorn’s ability to meet local energy demand, reducing reliance on external energy sources and lowering the community’s carbon footprint.
As noted by Dr. Andrew Peacock of Heriot Watt University (HWU) who reviewed this demonstration study,
“Evaluating additional simulation scenarios in future studies conducted using the Grid Singularity Tool and other complementary applications can be leveraged to improve the asset mix for the community, which would aid community planning. HWU is also developing forecasting services that are expected to further improve P2P energy trading results and we will be jointly testing these developments in the next phase of the InterPED project.”
On a broader level, the study has shown that despite Scotland’s lower average solar irradiation (around 900 kWh per kWp installed yearly estimated for Findhorn, Scotland, [10]), PV expansions can still be profitable, especially when combined with battery storage and P2P trading. Additionally, wind remains a potent resource in Findhorn’s location, although regulatory and ownership frameworks may limit how local participants benefit directly from the existing wind park.
- Leveraging E/WH enhances heat pump efficiency, reducing consumption costs and increasing self-sufficiency.
The most prominent use of the excess waste heat is to leverage heat pumps, using the excess waste heat as source temperature for the heat pump. That way, the efficiency of the heat pump increases compared to traditional sources of temperature (ambient air, water source, ground source), and consequently the community takes advantage of the financial and environmental benefits that stem from the more optimal use of the heat pump. This is confirmed in this study:
Leveraging E/WH as a source of higher source temperature for the heat pumps of the community resulted in significant operational improvements, demonstrated in a higher average COP (17.1% for a ground source heat pump) and more stable and elevated water tank temperature for all the community heat pumps. Consequently, the pumps consumed 15–19% less energy and reduced operating hours. These efficiency gains delivered substantial financial benefits for the Findhorn community, with a 6.8% reduction in net electricity costs in the winter and an even higher, 28.2% savings in the spring period when the energy demand is lower and the benefits more pronounced. There is also a positive environmental impact (self-sufficiency increased by 7.7% in the simulated week of January and by 13.5% in April).
Recommendations
The results from the Ecovillage Findhorn simulation study highlight critical insights for future district energy developments:
- Asset-Level Data Importance: High-resolution energy data is a critical requirement for effective operational deployment of peer-to-peer energy trading and optimisation of energy management decisions, including demand response actions.
- Market Scalability: The economic and environmental benefits of P2P energy trading magnify with increased participant density and diversity, indicating substantial advantages of comprehensive local market implementations, facilitating intra-community trading for a larger number of participants (community members and assets), as well as inter-community trading to reach Positive Energy Districts — PEDs..
- RES and Storage Expansion Synergy: The synergy between expanded RES infrastructure, storage solutions, and P2P trading can achieve substantial cost reductions and higher environmental sustainability.
- E/WH Integration enhances heat pump efficiency, reducing costs and increasing self-sufficiency.
- Notably, this study does not study the potential impact of different time-of-use and dynamic tariff models, which could serve as another catalyst for PEDs, using communities as a source of flexibility to manage congestion and better distribute the energy surplus across a wider area — or incentivise more local storage and more optimal use of the flexible assets of the communities. Additional research is also needed to develop and integrate forecasting services and support complementary demand response actions.
- Finally, as P2P regulatory frameworks remain complex, EU member states need to take further steps to enable wider application of novel technologies and business models in this domain.
The article is based on a larger INTERPED study, co-authored by Spyridon Tzavikas, Hannes Diedrich, Tiago Tavares and Ana Trbovich from Grid Singularity, who are also authors of this article. We thank Tara Pinheiro Gibsone from Findhorn Innovation Research and Education and Andrew Peacock from Heriot Watt University for their review and contributions.
References
[1] INTERPED, EU Project: INTERoperable cloud-based solution for cross-vector planning and management of Positive Energy Districts, https://interped.eu/
[2] Findhorn Ecovillage, New Frontiers for Sustainability, https://www.ecovillagefindhorn.com/
[3] Grid Singularity. Grid Singularity Web-based Singularity Map tool, https://gridsingularity.com/singularity-map and Grid Singularity Wiki, https://gridsingularity.github.io/gsy-e/technical-approach/
[4] EnergyDataMap, https://mapped.energy/
[5] Rebase Energy, The Python-first energy forecasting platform, https://www.rebase.energy/
[6] Open Meteo, Open Source Weather Application Interface, https://open-meteo.com/
[7] Windpowerlib, Python library to model the output of wind turbines and farms, https://github.com/wind-python/windpowerlib
[8] Oemof, Open Energy Modelling Framework, https://oemof.org/
[9] UK shallow ground temperatures for ground coupled heat exchangers, https://www.researchgate.net/publication/283657661_UK_shallow_ground_temperatures_for_ground_coupled_heat_exchangers, (accessed 14.08.2024)
[10] Estimation leveraging Photovoltaic Geographical Information System, https://pvgis.com/ completed by InterPED project partner Veolia in 2024.
