Improved management of energy resources and flexibility services with the support of AI
LASAGNE is a three-year EU-funded project in collaboration with partners in Switzerland and Sweden. The overall aim is to contribute to the energy transition by developing an intelligent digital platform that connects multiple stakeholders (households, energy companies, software developers and device manufacturers), making it possible for households to make money or save energy and for energy companies to maintain grid stability.
The project aims to:
1. Support decentralized energy transition: By promoting the use of renewable energy sources and creating incentives for households and microgrids to participate actively in the energy economy.
2. Develop a smart energy infrastructure: Using modern technologies such as machine learning, ML, and Grid Edge Devices, GED, to create self-adaptive systems that react to changes in energy needs and conditions.
3. Ensure that the technology and the system are accepted by users and other actors by taking social and economic factors into account right from the start.
OBJECTIVES
1. Empower and motivate users:
• Enable households and microgrids to participate in local circular business models through personalized monitoring and reporting functions in GED devices.
2. Develop a digital framework for energy:
• Develop a framework based on collaborative ML algorithms to predict energy use and production.
• Create tools for the development of user-adapted, self-adaptive energy applications.
• Support governance and collaboration between stakeholders (households, companies and manufacturers) via the Nuvla.io platform.
3. Test the LASAGNE framework in two field studies:
• Validate the framework in two field studies, in Les Vergers Ecoquartier in Geneva and ElectriCITY Innovation in Stockholm.
VISION
LASAGNE wants to create an intelligent energy ecosystem where all stakeholders can collaborate seamlessly, based on advanced technologies and socially acceptable solutions. The overall goal is to combine technological innovations and behavioral changes to accelerate the energy transition in a sustainable and inclusive way.
Project period
May 2022 - Mar 2025
Project info
Category Energy
Project nameLASAGNE, digitaL frAmework for SmArt Grid and reNewable Energie
PartnersHES-SO, UniGe and CLEMAP (Switzerland) as well as KTH, Recap Power and ElectriCITY (Sweden))
CoordinatorHES-SO, University of Applied Sciences and Arts, Switzerland
FinancingERA-Net Smart Energy Systems and Mission Innovation/EU research and innovation program Horizon 2020
Contact persons at ElectriCITY
Testbed Hammarby Sjöstad
The Swedish field study will, in collaboration with Recap Power, test solutions for control, aggregation and flex services at housing cooperatives within the energy community in Hammarby Sjöstad. The aim is to evaluate, show potential and measure the effects of the implementation. The expectation is that the results will justify introduction in additional housing cooperatives. The technical solutions in the project will be implemented gradually, starting with hardware installation and integration into systems to enable control of resources.
The next step involves testing services to reduce energy costs (for example, scheduling based on electricity prices). In addition, the ambition is to create income streams via participation in SthlmFlex and/or offer support services to Svenska Kraftnät (the authority responsible for ensuring that Sweden’s transmission system for electricity is safe, environmentally sound and cost-effective). The possibility of sharing local electricity is also planned to be investigated within the framework of the project.
More about the technique
Machine Learning is a part of artificial intelligence (AI) where computers are trained to learn from data and make predictions or decisions without being explicitly programmed for each task.
Machine learning uses algorithms that can identify patterns in data and improve their performance over time as they are exposed to more information.
In the LASAGNE project, Machine learning is used to:
• Predict energy consumption and production.
• Develop self-adaptive systems that can adapt to changing conditions.
• Coordinate and optimize the interaction between GED units.
LASAGNE uses Grid Edge Devices, a type of hardware or a combination of hardware and software that is placed at the “edge of the grid”, i.e. close to the users and energy sources, rather than in central systems as in a traditional power grid. GED plays a key role in smart grids and microgrids.
Within the LASAGNE project, Grid Edge Devices are used to create intelligent microgrids that can:
• Predict energy demand and production using machine learning (ML).
• Coordinate energy consumption and production between different households and microgrids.
• Facilitate energy transactions and negotiations between actors in the network.
In short, GED is a key technology to enable smarter, decentralized and sustainable energy systems.
Partners
