July 9th 2024

TwinERGY Project Showcases Application of IES Digital Twin Technology for Demand Response Optimisation

TwinERGY Project Showcases Application of IES Digital Twin Technology for Demand Response Optimisation

As the EU Horizon 2020 funded project, TwinERGY, draws to a close, IES R&D are excited to share some final outputs from the project, which has empowered citizens across four European communities to take better control of their energy consumption.

The TwinERGY project set out to prove that a better energy ecosystem is possible, by developing a first-of-its-kind demand response framework to help citizens actively adapt their energy consumption to market fluctuations, supported by data and automation.

The project consortium, comprising 18 different partners from 12 countries, worked collaboratively to develop an interoperable demand response platform which has been successfully deployed across four pilot demonstration sites in Bristol (UK), Steinheim (Germany), Sardegna (Italy) and Athens (Greece).

To achieve this, the project leveraged various technologies, connecting smart grid technology, energy management systems, renewables and smart home devices, together with IES’ Digital Twin software, to better understand the energy supply and demand across each site. The interconnected platform has made it possible to track and predict citizens’ energy consumption patterns and promote more sustainable energy behaviours, which balance occupant comfort needs alongside lower energy costs and increased grid flexibility.

Within the TwinERGY project, IES Digital Twin technology was used to create the building and community level digital twins for each pilot site. These leveraged IES’ physics-based and data-driven simulation capabilities to calculate and forecast accurate energy demand profiles at an individual building level, including the demand from individual appliances used in the home. This demand was then aggregated within an overarching community digital twin, integrating the local energy network and any renewable installations to explore the potential for flexible load-shifting.

The project leveraged multiple tools within IES’ technology suite, with the VE being used to create the detailed dynamic simulation models at the building level, and iCD and iVN enabling the creation of community scale digital twins, incorporating the connection to the grid and any local generation. iSCAN was also used to support the integration and analysis of both real and simulated time-series data across the pilot buildings.

To substitute a lack of metered data during the COVID-19 period, the project employed two approaches to ensure the digital twins reflected the actual energy demand of the buildings as closely as possible. This included using the dynamic simulation capabilities of the VE, to incorporate schedules of use for each appliance and extract the disaggregated demand profiles over the period of simulation, or alternatively by using the StROBe open-source residential occupancy behaviour library to generate probabilistic schedules of use.

Using the simulation capabilities within the digital twin platform, it was possible to see where demand from appliances could be shifted towards periods of the day where renewable production and cost are optimal. While appliance use was optimised singularly, operational costs were evaluated first for each building in isolation and then aggregated across the community.

The graph below shows example results from one pilot site, outlining a comparison of the community’s energy demand without optimisation (orange line) and with optimisation (blue line). In the same graph the energy cost (green line) and the renewable generation (grey line) is visualised. As observed here, the algorithm attempts to shift the demand to hours of the day where the renewable generation is maximum and there is a good trade-off with the electricity price at the same time.

Figure 1: Example of optimisation of cost and renewable generation (Bristol)

Through the interconnected digital twin platform, users can access data which can help them make more informed decisions around how and when to optimise their energy consumption:

Building level:

  • Energy demand profile for single appliances within the home
  • Aggregated appliance profile to show the total demand
  • Energy price forecasting
  • Renewable generation forecasting (if available)
  • Optimal next day aggregated profile for appliances
  • Flexibility scenarios

Community level:

  • Aggregated profile at community level
  • Energy price forecasting
  • Total on site renewable generation (when available)
  • Optimal aggregated demand profile
  • Demand profiles with different flexibility scenarios
  • Cost evaluation baseline and cost for different flexibility scenarios

Niall Byrne, Associate Director – Head of R&D at IES, commented: "To achieve a secure and decarbonised energy system, we must rapidly scale up capacity of our renewable energy sources while carefully balancing consumer demand. Through the TwinERGY project, we've showcased how citizens can actively engage in shaping our energy future. Harnessing intelligent digital twin technologies, such as ours, citizens can make informed decisions to optimise their energy consumption, reduce costs, and contribute to creating a greener, more resilient energy ecosystem."

To learn more about the TwinERGY project and pilot sites, you can check out the project videos on YouTube.

For more information on our research & development activities, connect with our R&D Team.