How can electricity consumption be modelled on a district scale – shops and businesses, offices, social housing, blocks of privately owned flats, etc. – in relation to local energy production in order to reduce occupants’ bills?
It is essential to roll out renewable energy sources in order to combat climate change, and the introduction of energy transition legislation means that it is now possible to promote the expansion of new mixed use communities (housing, shops, offices, public buildings, etc.) producing locally generated clean energy. The blocker to massive development of these sustainable districts stems in part from the difficulties associated with uniting all the stakeholders around a shared project and allocating profits equitably in accordance with consumption.
reduction of emissions
at district scale
The McDonald eco-district in the 19th arrondissement of Paris is a textbook example of a new mixed community, with over 1,700 housing units, 28,000m2 of offices, 40,000m2 of retail space, a high school, day-care centre and gymnasium. The first objective of the challenge was to model the energy footprint of the district based on energy consumption for each type of use and time of year, using hourly data readings. The second aim of the challenge was to simulate electrical energy generation in a scenario where every roof was equipped with solar panels, and then to assess what proportion of usage in the district these solar panels would cover, identify the stakeholders who would derive the most benefit, and establish criteria for allocating this energy depending on individual input. Lastly, the challenge aimed to offer greater transparency around the technical, environmental and economic hypotheses behind plans to extend solar panels to the various stakeholders in the district (real estate managers, project owners, property managers, lessors, commercial property managers, and occupants).
Founded in 2015 by serial entrepreneurs Frédéric Crampé and Patrick Leguillette, BeeBryte uses artificial intelligence to help commercial and industrial buildings to reduce their energy bills and carbon footprint by managing electricity consumption more efficiently and intelligently.
The prototype is a simulation tool which uses an allocation key to divide up the economic or environmental value of solar energy among the various consumer-stakeholders in the district. The tool offers the option of selecting different allocation criteria depending on energy consumption requirements or the cost savings created for the various occupants. In the McDonald district, covering the whole of the available surface area meets 20% of energy needs for the district using a combination of green and local energy. The tool makes it possible to distribute the value generated by the solar energy between the occupants of the district according to their consumption profile and thus to propose to these actors a project of investment and equipment profitable and dimensioned for their needs.