MILP optimisation for energy systems: what it is and why it matters
Mixed-Integer Linear Programming (MILP) is the mathematical method used to optimise multi-energy system design. This page explains what MILP is, why it is used for energy planning, and how it compares to other approaches.
What is Mixed-Integer Linear Programming?
Mixed-Integer Linear Programming (MILP) is a mathematical optimisation method used in engineering, logistics, and operations research. It finds the best solution to a problem that involves both continuous decisions — how much capacity to install, how much energy to dispatch each hour — and binary decisions — whether to install a particular technology at all.
It does this by solving a system of linear equations and inequalities subject to constraints, with some variables restricted to integer values.
MILP has been used in industrial engineering for decades. It underpins production scheduling, supply chain optimisation, and network routing problems. In energy systems, it has become the standard method for technology selection and dispatch optimisation because energy planning involves precisely the mix of continuous and binary decisions that MILP handles well.
Scale, solve times, and published solutions
For realistic district energy problems — multiple buildings, multiple energy carriers, hourly resolution over a full year — MILP models can be large. A district-scale problem with 20 buildings, 15 technology candidates, and 8,760 hourly time steps involves hundreds of thousands of variables and constraints.
Published research from the ETH Domain has addressed this with multi-scale decomposition and rolling-horizon methods that reduce computation time by 10–100x compared to brute-force single-period models (Marquant et al., Applied Energy, 2017). Grid-aware formulations have also been validated: including electrical grid constraints in the MILP model was shown to enable 40% more renewable integration and reduce operational emissions by 18% compared to grid-naive approaches (Morvaj et al., Applied Energy, 2016).
These methods make it practical to run detailed district-scale optimisation within normal project timelines — not as a research exercise, but as a routine part of a feasibility study or energy master plan. For the broader planning context in which these runs sit, see the page on district energy planning software .
Multi-scale decomposition and rolling-horizon methods reduce solve times without meaningful loss of solution quality.
Grid-aware MILP formulations can integrate substantially more renewables by coordinating system design and operation with electrical network constraints.
Including electrical grid constraints in operating schedules reduced emissions in the published test case while managing voltage and current within bounds.
Frequently asked questions
What is MILP and why is it used for energy system optimisation?
Mixed-Integer Linear Programming (MILP) is a mathematical optimisation method that handles problems combining binary decisions (install or don't install a technology) with continuous decisions (how much capacity, how much to dispatch each hour). Energy system design involves both types of decision simultaneously, making MILP well-suited to the problem.
Is MILP the same as AI or machine learning?
No. MILP is a deterministic mathematical method from operations research. It does not learn from data, does not use neural networks, and does not produce probabilistic outputs. The same inputs always produce the same outputs, and every result is traceable to its underlying equations. This distinguishes it from AI-based approaches and makes it particularly appropriate for engineering applications where auditability matters.
What are the limitations of MILP for energy planning?
For large problems — many buildings, many technologies, full hourly resolution over a year — MILP models can be computationally expensive. Research has addressed this through multi-scale decomposition and rolling-horizon methods that reduce solve times significantly. Sympheny's engine incorporates these techniques to make detailed district-scale optimisation practical within normal project timelines.
How does MILP compare to simulation-based energy tools?
Simulation tools evaluate a defined system and show how it performs. MILP optimisation tools search across a space of possible systems to find the one that best meets your targets. For feasibility and planning work — where the goal is to identify the right system, not just evaluate a pre-specified one — MILP-based optimisation is the more appropriate method.
What solver does Sympheny use for MILP?
Sympheny uses Gurobi, one of the leading commercial MILP solvers used in industrial engineering and academic research. Sympheny's V3 platform includes Sense, a proprietary energy hub solver built on top of Gurobi that delivers faster execution, more flexible hub topologies, and richer constraint handling than the previous solver generation.
See Sympheny's MILP engine on a project like yours
Book a 30-minute demo. We'll run the optimiser on a project setup like yours so you can see the design space search, the Pareto front, and the auditable outputs.