DO-IT Optimisation Model The Design and Operation of Integrated Technologies (DO-IT) optimisation model is a holistic decision-support tool for planning and operating energy systems in buildings. Developed within the framework of applied energy systems research, the model integrates multi-energyvector (e.g., electricity, heating, cooling, hydrogen) technologies to enable optimal strategies for decarbonisation, cost reduction, and resilience under uncertainty. DO-IT is based on mixed-integer linear programming (MILP) implemented in Pyomo. It supports multi-year and multi-scenario planning, capturing both investment (what, when, and how much to install) and operation (how to run technologies optimally) decisions. The model is stochastic in nature and considers generation, conversion and storage technologies simultaneously, along with resource prices, carbon costs, weather variability, and environmental or budget constraints. It includes options for setting annual carbon budgets, investment cost caps, forced technology installations, or self-sufficiency requirements. The framework also incorporates the ability to model inter-seasonal energy storage, allowing exploration of scenarios involving large-scale thermal, hydrogen, or battery energy storage and their role in enhancing system flexibility. The modular architecture and vector-agnostic structure of the DO-IT model means that it can be applied to a wide range of energy systems and contexts, from individual buildings and campuses to multi-building districts or microgrids, and from short-term operational optimisation to long-term decarbonisation roadmaps. Its objective can combine expected system cost with financial risk to enable robust, cost-efficient, and environmentally sustainable strategies for diverse energy applications. Key Features The provided Python file defines a single function, `optimisation_model`, which builds and returns a Pyomo ConcreteModel. This model includes all sets, parameters, decision variables, constraints, and the objective function. Once instantiated with the relevant configuration and data objects, the model can be solved with any MILP solver (e.g. GLPK, CBC, Gurobi, CPLEX). The result provides detailed outputs for costs, emissions, technology capacities, storage states, and energy exchanges. Scientific References If you use this version of the DO‑IT model, please cite the following works: 1. Olympios AV, Kourougianni F, Arsalis A, Papanastasiou P, Pantaleo AM, Markides CN, Georghiou GE. A holistic framework for the optimal design and operation of electricity, heating, cooling and hydrogen technologies in buildings. Applied Energy 2024; 370: 123612. https://doi.org/10.1016/j.apenergy.2024.123612. 2. Olympios AV, Mersch M, Kourougianni F, Pantaleo AM, Aunedi M, Markides CN, Georghiou GE. A stochastic optimisation approach for the integration of PV systems with heat pumps and energy storage in buildings. Applied Thermal Engineering 2025; 278(D): 127312. https://doi.org/10.1016/j.applthermaleng.2025.127312.
Acknowledgements This research was supported by the ONISILOS COFUND Fellowship programme, which is funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement No 101034403. The authors also acknowledge the PHAETHON Research and Innovation Centre of Excellence for Intelligent, Efficient and Sustainable Energy Solutions Horizon Europe Wide spread Teaming project, which has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No 101059898, from the Government of the Republic of Cyprus through the Deputy Ministry of Research and Innovation and the University of Cyprus. Contact and Support For questions, collaboration requests, or data access, please contact: Dr. Andreas V. Olympios University of Cyprus Email:
[email protected]