Enabling coordination in energy communities: A Digital Twin model

Starting from the EU vision for Energy Communities (EC), ou r purpose is to support them by proposing a Digital Twin (DT) that includes a bi-level optimization model to deliver coordination, economic, social , and environmental benefits to its members that can be quantified as Key Performance Indicators (KPI). The diversity of EC members from the size and interest perspectives leads us to consider a bi-level optimization model . It offers support to individual consumers/prosumers (first level) and coordination for EC (second level). This model is embedded into a DT that replicates the EC and the operation of individual entities such as consumers/prosumers and public assets. The DT is created as an automatic assistant with two components: iEMS - as a member's assistant and eEMS - as an EC assistant. These components optimize the schedule, generate bids for the Local Electricity Market (LEM) and control the flexible appliances of the participants to deal with deficits and surpluses. The DT receives input from EC members, LEM, metering system and improves the operation of the EC in a two-way continuous exchange data flow. Furthermore, it is a reliable framework to test and improve models, regulations and policies in emergent EC as DT provides alternatives regarding its functionalities: optimization, market operation, setting the clearing price, settlement, value sharing for distributing benefits, etc. It can be extended to support grid operators to design tariffs, testing regulation and offer additional energ y services. The proposed DT model is tested within an EC case study, both on a seasonal and an annual basis. The average trading index on LEM is 0.6 during the summer and 0.3 during the winter months, while the Degree of Local Sufficiency (DLS) for the EC is 0.45 in summer and 0.28 in winter. Moreover, the proposed LF M model reduces the exchanges with the main grid by an average of 45 kW in summer, that represents almost 15% of the average exchange at peak hours.