Simulation-based Optimization of Energy Flexibility in Decentralized Industrial Energy Systems Using MILP

Sammanfattning

The decarbonisation of European industry coincides with rising shares of variable renewable generation and increasing electricity prices, making industrial load flexibility a critical resource for cost-efficient, low-carbon operation. Yet many planning tools still treat demandside management in coarse, system-level terms and cannot represent the plant-specific operating rules, inter dependencies, and validity windows that determine what flexibility is actually realizable in factories. This thesis proposes a simulation-based planning framework that embeds a fine-grained model of Energy Flexibility Measures (EFMs) into an opensource environment. The formulation captures bidirectional load changes, hold and clean-up phases, recovery and minimum activation windows, cooldown dynamics, one-to-one pairings, and group-level exclusivity at 15-minute resolution, and couples them with dynamic tariffs, peak-power charges, and on-site assets such as photovoltaic (PV) system and battery storage. The framework is implemented as a modular Mixed Integer Linear Programming (MILP) component and evaluated for two real industrial systems: an oven-dominated diecasting plant and a site-infrastructure-oriented system with ventilation, electric vehicle (EV) charging station, PV system, and battery storage. For each system, multiyear simulations (2019–2024) compare baseline operation with and without EFMs under four tariff designs: static prices, static prices with peak shaving, day-ahead prices, and day-ahead prices with peak shaving. The results show that explicit EFM modeling systematically reshapes load toward low-price periods, reduces grid peak demand, and yields consistent grid-cost savings and, in most years, CO2 reductions while respecting process and staffing constraints. Batteries are found to complement rather than replace demand response, primarily shifting midday PV surplus into higher-value evening hours, and peak-shaving charges can be reduced without increasing annual consumption. Overall, the thesis contributes a transparent, Energy Flexibility Data Model (EFDM) inspired EFM module and data layer, alongside a reproducible 15-minute planning workflow. It calls on industrial planners and tool developers to integrate structured, plant-level demand-side flexibility into energy system planning and tariff design so that future factories can co-optimize production and energy use rather than treating flexibility as an afterthought.