A validated methodology using real electric consumption for simulating building demand and shared energy in Renewable Energy Communities (REC)

The significant impact of building energy consumption is widely acknowledged. Among various sustainable solutions, the growing number of Renewable Energy Communities (RECs) highlights the potential of citizen-led initiatives towards a more sustainable future. To thoroughly and accurately analyze the behavior of a REC, it is crucial to perform highly detailed simulations of the community, ideally on an hourly basis. This requires estimating both the energy production from renewable sources – primarily photovoltaic (PV) panels – and the energy consumption by users on an hourly scale. However, obtaining hourly consumption data from REC members remains challenging, hindering accurate feasibility studies for developing new communities.

To address the gap in predictive simulation of community energy behavior, a new analysis workflow has been developed. This study employs Building dynamic Energy Simulations (BES) to model hourly thermal energy requirements of buildings and subsequently converts these requirements into electrical loads by simulating the corresponding HVAC systems. By incorporating other buildings electrical loads (such as lighting and appliances, among others), it is possible to reconstruct the overall electrical consumption of each user, regardless of their use case (e.g., school, office, residential). Therefore, using a MATLAB code, all energy flows, including shared energy, can be studied on an hourly basis, along with the economic outcomes derived from cash flows for sold energy and, if applicable, state incentives for shared energy.

A case study was developed for a REC composed of various users, each associated with different photovoltaic systems. The new methodology was applied by varying certain input data, allowing for the prediction of the REC configuration that optimizes shared energy.

This methodology provides solid predictive results and serves as a practical tool for simulating an energy community. It is particularly useful when electrical consumption data is unavailable due to new construction, data unavailability, or limited time and economic resources for gathering hourly consumption data for each user.

As a future development, this methodology is expected to be integrated with machine learning techniques to identify the most efficient compositions of energy communities to maximize shared energy.