In recent literature, numerous optimization models and frameworks have been presented, for example the oemof framework, Ehub Modeling Tool by EMPA (Switzerland), or the DER-CAM model by Lawrence Berkeley National Laboratory. Instead, advanced techno-economic models based on mathematical optimization, for example, mixed-integer linear programs (MILPs), are increasingly used to design energy hubs and estimate their economic performance and environmental impact. Designing energy hubs is a challenging task for which conventional (static) planning methods are not sufficient. Energy hubs supply different forms of energy (heating, cooling, and electricity) to local energy distribution systems, for example, of a building complex or a district. An energy hub consists of multiple energy conversion and storage technologies, which enable bidirectional energy flows between different energy sectors. At the district and building level, multi-energy systems are often realized as energy hubs. These cross-sectoral supply systems, also called multi-energy systems, are a promising approach to increase the economic and environmental performance of energy supply systems.
Sustainable energy supply systems with low or zero emissions are achieved by linking different energy sectors, for example, heating, cooling, and electricity, with each other. To slow down the global climate change, greenhouse gas emissions need to be reduced drastically. With the tool, students understand how energy supply systems with intermittent renewable energies and cross-sectoral conversion and storage technologies can be designed with innovative planning approaches. The webtool has been developed to support academic teaching in energy engineering courses.
Homer energy software free download for free#
The webtool is accessible online for free and the optimization model is open-source. The calculation is based on mathematical optimization and uses a mixed-integer linear program. The objective functions of the design optimization are total annualized costs, CO 2 emissions, or trade-offs between both objectives (multi-objective optimization). Energy demands are provided by the user with an hourly resolution and all technical and economic model parameters can be tailored to specific use cases. A large variety of different technologies including renewable energies (wind power, photovoltaic, solar thermal, and hydroelectricity) and energy carriers like natural gas, hydrogen, biomass, and waste can be considered. The tool determines the optimal technology selection and sizing of all energy conversion units of a supply system while satisfying heat, cold, electricity, and hydrogen demands. This paper presents a novel webtool, called Energy Hub Design Optimization tool, for designing and optimizing complex multi-energy systems.