ENERGIDRICA: Energy Efficiency in Water Network Management

Details
Funded by: MUR	Reference: MUR-ENERGIDRICA
Start: 2021-01-01	Duration: 24 Months
Partners
Coordinator: DHITECH	Italy
Contractor: CRS4	Italy

Abstract

Water infrastructure management is now one of the key areas to evaluate the application and development of new models and technologies for energy efficiency. It is estimated that effective control of facilities can save 10% water and 12% to 30% energy. Therefore, increasing efficiency in water supply and distribution offers great potential for reducing energy consumption and CO2 emissions. This requires innovations that affect the entire process from energy rationalization, supply schemes for consumption centers powered by multiple sources to the optimization of pumping in the networks of adduction and distribution to the use of renewable energy. ENERGIDRICA will develop a decision support system for the energy efficiency of water supply and distribution networks by generating process innovations according to the principles of energy saving, energy reduction and integration with sustainable energy sources, in three complementary decision-making areas: [a] Urban center supply schemes from multiple sources. ENERGIDRICA will produce a structured and replicable methodology to energy rationalize the resource rates from each source, respecting the needs, the constraints of resource availability and the capacity of hydraulic carriers; [b] - Management of pumping in adduction and distribution networks. ENERGIDRICA will develop methodologies for the analysis of energy inefficiencies and for the support of plant management that integrate advanced management-oriented hydraulic analysis tools, in which it is possible to physically based modeling of water losses in the network as indicators of energy and management efficiency; [c] Integration with sustainable energy sources. ENERGIDRICA will develop tools for the integration with sustainable energy sources in a logic of self-consumption to feed pumping systems. Different functionalities will be addressed to support the realization from scratch of sustainable energy recovery plants or to promote their use in the operational optimization of pumping. As part of the project, CRS4 will develop a forecasting system of generation from renewable sources for specific plants, based on the processing of the output of the GFS model at global scale, through machine learning systems based on historical data of energy production and weather forecasts. The forecasts, also of probabilistic type, will be made available through a web interface for the partners involved. CRS4 Visual and Data-intensive Computing Activities are related to data visualization and presentation, also on the web platform.

Publications

[1] Luca Massidda, Fabio Bettio, and Marino Marrocu. Probabilistic day-ahead prediction of PV generation. A comparative analysis of forecasting methodologies and of the factors influencing accuracy. Solar Energy, 271: 112422, March 2024. DOI: 10.1016/j.solener.2024.112422.