Sharing energy flows at the local level is one of the most effective and accessible ways to reduce the environmental impact of the energy sector because it fosters the penetration of renewable sources and reduces the energy transmission losses.

The “Energy Community” (EC) is the legal form, introduced by the European Union in 2018, that allows the local aggregation of citizens, public entities, and private enterprises that, in synergy, organize the energy production in order to maximize energy sharing and self-consumption, and bring economic savings (in terms of reduced costs and/or incentives) in the energy bills of the community members.

The goal of this project consists in developing a tool that, given a specific geographical area, identifies the optimal aggregations of several energy users within that area to form an EC. A multi-objective approach will be applied to search for the best economic-environmental tradeoffs. In particular, the optimization problem aims at i) maximizing the economic return received by the EC in the form of an incentive on shared electricity (i.e., produced, renewably, and consumed within the EC itself) and ii) minimizing the "environmental impact" assessed, for example, as greenhouse gas emissions, or other types of pollutant emissions. The constraints are related to energy requirements of users, technical characteristics of energy conversion units, storage units and networks, and to regulatory limitations.

The input data are energy demands, primary energy costs, prices of energy vectors, availability of renewable sources (e.g., solar radiation, wind speed), techno-economic data of energy conversion units. Some of this information is subject to high uncertainty which should be accounted using a stochastic approach. The resulting huge amount of data associated with several possible scenarios may require the application of statistical tools capable of producing a suitable reduced dataset (e.g., clustering).

The project lasts 18 months and involves professors and researchers from the Department of Industrial Engineering and the Department of Mathematics, combining expertise in the optimization of the design and operation of multi-energy systems and in the mathematical modelling of finance and stochastic processes. A young graduate person will be also involved full-time in the working team through an annual research grant.

The results would provide any interested private companies and public entities (such as municipalities), as well as citizens, with detailed reporting on the operational modes to form an EC, the identification of users to be involved to maximize the benefits that can be pursued, the best practices in energy management that maximize collective self-consumption and, consequently, the incentive received, and the possible overall benefits provided by the proposed aggregations.

The availability of the “ECs-nergy” tool will contribute to increase the understanding and accessibility, and in turn acceptance, of society to renewable energy projects and facilitate the attraction of private investments in the clean energy transition.

PUBBLICAZIONI:

1. Dal Cin E., Rech S., Benetti M. (2024). Energy, economic and environmental benefits of Demand Response for improving building energy flexibility. In E3S Web of Conferences (Vol. 523, p. 02001). 53rd AiCARR International Conference on From NZEB to ZEB: The Buildings of the Next Decades for a Healthy and Sustainable Future. 12-14 March 2024, Milan. EDP Sciences.
2. Dal Cin E., Rech S., Benetti M., Carraro G., Lazzaretto A. Optimal Aggregation of Users in Energy Communities. Proc. of the 37^th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024), June 30-July 04, 2024, Rhodes, Greece.