Multi Energy Dispatch Optimiser (MEDO)
MEDO (Multi Energy Dispatch Optimiser) is a flexible optimization engine, built by the Thermochemical Power Group at the University of Genova, that transforms complex energy system modelling into a repeatable, auditable process, particularly suitable for Energy Management and Market Dispatching strategies. Designed for engineers, asset managers, and energy traders, the software automates the mathematical formulation of multi-energy-vector systems (e.g., electricity, heat, H2, NH3, traditional fossil fuels). Capturing off-design performance, interconnections, and operational constraints. MEDO optimizes time-varying problems; the time step can be set arbitrarily, and the software automatically adjusts the energy and power balances. The software’s internal architecture uses Mixed Integer Linear Programming (MILP) formulation, making it highly robust and reliable for optimisation problems. The MILP formulation enables single assets, microgrids and multi-energy-vectors systems to be evaluated using the same framework. MEDO uses Piecewise Linearisation, Big-M, and rolling-horizon methods to handle the nonlinearities in modelled systems. These methods keep the problem within the linear domain while accounting for the system’s nonlinear behaviour. The tool structure has a modular structure that allows users to integrate new modules.
MEDO represents the updated and advanced evolution of W-ECoMP, the thermo-economic modelling and optimisation tool previously developed by the Thermochemical Power Group at the University of Genova [1-3]. MEDO extends the original framework to address the increasing complexity of modern multi-energy systems. The updated tool introduces a MILP-based optimisation approach, enabling more flexible and computationally efficient energy dispatch strategies while explicitly accounting for operational constraints, energy-vector interactions, and time-dependent market conditions.
Key Features
• Automated model generation: converts technology performance curves and interconnection rules into a mathematically consistent optimisation problem, reducing manual equation coding and model setup time.
• Multi scenario support: run Demand Driven, Market Driven, or Hybrid dispatching problems.
• Off design fidelity: native handling of off design efficiency and cost curves for realistic operational profiles, using piecewise linearisation.
• Multi node and multi vector: Manage multiple demand profiles per BUS and enable energy exchanges between BUSs, and the technologies are used as converters.
Fields of application
The software is appropriate for modelling energy dispatch in multi-carrier systems. Below is a summary of the most significant results obtained using the new software. The choice of the optimization algorithm was developed and extensively discussed in the paper, where it was demonstrated that MILP was the best algorithm in terms of both solution robustness and computation time for dispatching problems [5].
The sizing and impact of the island’s interconnection in Greece were studied by evaluating the maximum installed RES capacity. The scenarios comparison has been done using the common metrics LCOE, RESshare and REScurtailment. The problem falls under Demand-Driven problems, where the main objective is to reduce operating costs while fulfilling Demand requirements.
MEDO’s flexibility has enabled modelling of the CCGGT’s behaviour by introducing various operating modes, including Peak Firing (PF) and Warm Keeping (WK). In this application, particular attention was paid to replicating all nonlinear behaviours of the plant’s actual operation within the dispatch model to assess the overall impact on the system. This application falls under Market-Driven optimisation, in which the objective is to maximise the revenue of a specific technology [11].
The CAES storage system has been modelled under different configurations, technical and market conditions. The objective of this study was to evaluate the economic potential of the technology for arbitrage strategies in the European and the USA DAM markets. The simulation output has been validated for revenue stacking and payback periods [12].
MEDO has also been used in the maritime sector, where the goal is to use vessels’ electrical and thermal demand profiles as constraints to be met at the lowest possible cost. The focus of this study was on the testing of alternative technologies for the propulsion system, including fuel cells and battery energy storage, as well as the corresponding sizing of components [10].
Projects
SINNOGENES – Storage INNOvations for Green ENErgy Systems
(Horizon Europe – GA 101096992, 2023 – 2026, https://sinnogenes.eu/)
EVELIXIA – Smart Grid-Efficient Interactive Buildings
(Horizon Europe – GA 101123238, 2023 – 2027, https://www.evelixia-project.eu/)
POSEIDON (Photo-driven Optimal Seawater Electrolysis for energy-IslanDs OperatioN)
(PRIN 2022 PNRR – P2022ER7KM, 2023 – 2025, https://poseidon.prin.unige.it/)
I-UPS – High temperature heat pump towards industrial systems decarbonization
(Horizon Europe – GA 101147078, 2024 – 2027, https://i-ups.eu/project)
USES4HEAT – Underground Large Scale Seasonal Energy Storage for Decarbonized and Reliable Heat
(Horizon Europe – GA 101136136, 2024 – 2027, https://uses4heat.eu/)
Publications
[1] “Time-dependent optimization of a large hydrogen generation plant using ‘spilled’ water at Itaipu14 GW hydraulic plant“
Rivarolo M., Bogarin J., Magistri L., Massardo A. F
International Journal of Hydrogen Energy, 2012 (37), 5434-5443.
[2] “Thermo-economic optimization of the impact of renewable generators on poly-generation smart-grids including hot thermal storage”
Rivarolo M., Greco A., Massardo A. F.
Energy Conversion and Management, 2013 (65), 75-83.
[3] “Feasibility study of methanol production from different renewable sources and thermo-economic analysis”
Rivarolo M., Bellotti D., Magistri L., Massardo A. F.
International Journal of Hydrogen Energy, 41 (2016), 2105-2116
[4] “Large size heat pumps advanced cost functions introducing the impact of design COP on capital costs ”
Vannoni A., Sorce A., Traverso A., Massardo A.F.
Energy, 2023 (284)
[5] “Best Practices for Electricity Generators and Energy Storage Optimal Dispatch Problems”
Vasylyev A., Vannoni A., Sorce A.
Journal of Engineering for Gas Turbines and Power, 2024 (284)
[6] “Integrated energy and ancillary services optimized management and risk analysis within a pay-as-bid market”
Vannoni A., Sorce A.
Applied Energy, 2024 (146)
[7] “Optimal dispatch of Li-Ion battery energy storage, reviewing and considering cycling and calendar ageing models”
Vasylyev A., Vannoni A., Sorce A.
Applied Thermal Engineering, 2025 (265)
[8] “Techno-economic analysis of pressure gain combustion gas turbine for island grids with high renewable solar share”
Dubey A., Sorce. A, Rivarolo M., Traverso A., Sala S.
Applied Thermal Engineering, 2025 (265)
[9] “MEDO (Multi-Energy Dispatch Optimiser): A MILP-Based Optimisation Framework for Energy Management in the Maritime Sector and Isolated Islands”
Vasylyev A., Sorce A.
PhD thesis, 2026
[10] “Feasibility analysis of hydrogen fuel cell-powered passenger vessels: a multi-criteria and MILP-based analysis for sustainable inland navigation”
Saadeldin M., Vasylyev A., Rivarolo M., Sorce A.
Ocean Engineering, 2026 (349)
[11] “Economic potential of combined cycle flexibility: bottoming cycle hot standby mode and gas turbine peak firing in the European and US markets”
Vasylyev A., Dubey A., Sorce A.
Journal of Engineering for Gas Turbines and Power, 2027
[12] “Comparative performance and operating strategies of electric heating-assisted compressed air energy storage”
Kim H., Vasylyev A., Sorce A.
Journal of Engineering for Gas Turbines and Power, 2027