Economic MPC Optimization — Cost-Optimal Real-Time Control for Energy, Buildings, and Process Industries

Public reference use case (by others): This page summarizes publicly available reference implementations and papers. Not client results of Dr. Rafal Noga.

Why This Matters (Executive Summary)

• Business pain: Traditional controllers track fixed setpoints but cannot answer “are we running at the cheapest, most efficient operating point right now?” — leaving energy savings, throughput gains, and raw material efficiency unrealized.
• Solution class: Economic MPC replaces the tracking objective with a direct economic cost function (energy price, fuel cost, throughput value), optimizing the operating point in real time while still respecting all process constraints.
• Measurable outcomes: Published cases report approximately 17% non-renewable primary energy reduction in buildings, coal-free cement precalciner operation with over 50% reduction in temperature variability, and improved power capture with reduced structural loads on wind turbines.
• Natural next step: Economic MPC is the logical upgrade after tracking MPC is working — once the system is stable and constraints are respected, the question becomes “are we running at the best operating point?” This is where the largest financial returns live.

The Design Pattern Explained

In conventional MPC, the controller minimizes deviation from a setpoint determined by a separate optimization layer (RTO) or by operators. Economic MPC removes this separation: the objective function directly encodes the economic cost — energy consumption, fuel expenditure, profit contribution, or a weighted combination — so the controller simultaneously finds the best operating point and drives the system there.

Economic MPC is preferred over layered RTO + tracking MPC because it eliminates the steady-state assumption required by traditional RTO, responds to time-varying economics (energy prices, demand forecasts, weather) within the control horizon, and naturally handles multi-objective trade-offs (energy vs. comfort, fuel vs. emissions, throughput vs. quality) in a single optimization.

The architecture typically includes: (1) a dynamic process model capturing energy/material balances, (2) time-varying economic parameters as inputs (prices, forecasts, demand), (3) hard constraints on safety, quality, and actuator limits, and (4) longer prediction horizons (hours to days) to capture the relevant economic dynamics.

Applications & Reference Implementations

Application 1: Wind Turbine Control — Economic MPC on the NREL 5-MW Benchmark

Lecture material from the University of Freiburg describes an economically inspired tracking MPC applied to the NREL 5-MW reference wind turbine. The controller navigates the three-way trade-off between power capture, structural loads (tower oscillations), and actuator wear (pitch utilization). Closed-loop simulation benchmarks against a baseline controller showed that MPC achieves better generator-speed tracking, softer pitch utilization, improved power capture, and reduced tower oscillations — with higher power fluctuations noted as a trade-off. This demonstrates how economic objectives can be embedded directly into turbine control without a separate optimization layer. ¹

Application 2: LHC Cryogenics — Economic NMPC for Superfluid Helium at 2 K

An output-feedback economic NMPC was applied to the superfluid helium cryogenic circuit of the Large Hadron Collider at CERN. The system must maintain bath temperature below 2.1 K (magnet powering limit) with superfluidity lost above 2.16 K — an extremely tight constraint window. The NMPC used a first-principles thermo-hydraulic model paired with a Luenberger Observer and Moving Horizon Estimation for state reconstruction. Two configurations were tested: NMPC #1 with two control valves achieved set-point recovery approximately 1 hour after perturbation; NMPC #2 added 12 electric heaters in simulation. Computation times were approximately 1 s for estimation and 7-14 s for optimization. This case shows economic NMPC handling safety-critical cryogenic constraints where PI control performs poorly during large transients. ²

Application 3: Swiss Office Building — MPC Achieves 17% Energy Reduction (OptiControl-II)

The ETH Zurich OptiControl-II project implemented MPC for a fully occupied Swiss office building, controlling thermally activated building systems (TABS), air handling units (AHU), and blinds over a seven-month field deployment. Simulation-based comparisons against the installed rule-based strategy showed approximately 17% reduction in non-renewable primary energy (NRPE), translating to roughly 21.6 MWh NRPE/year for one floor or 108 MWh/year scaled to five floors. Cost-benefit analysis under stated assumptions indicated approximately 5,000 CHF/year net savings. The system maintained improved comfort compliance throughout. This is one of the most thoroughly documented building MPC deployments, conducted in collaboration with Siemens Building Technologies. ³

Application 4: Holcim Lagerdorf — Coal-Free Cement Precalciner via MPC

At Holcim’s Lagerdorf cement plant in Germany, ABB implemented Model Predictive Control (Expert Optimizer with MPC + Mixed Logical Dynamic control) on the precalciner to enable coal-free operation using alternative fuels. The controller explicitly managed delays, thermal inertia, combustion air supply, and fuel variability. Results showed precalciner temperature variability reduced by over 50%, with the temperature deviation range narrowing from -45/+80 degrees C (manual) to -30/+50 degrees C (MPC), and measurements at setpoint increasing from 6% to 10%. The plant achieved coal-free precalciner operation starting June 2007, with coal on standby for fast recovery. Qualitative benefits included reduced cyclone blockage risk and improved kiln stability. ⁴

Application 5: Airborne Wind Energy — Trajectory Optimization for Pumping-Cycle Kites

For a pumping-cycle airborne wind energy system (PN-14, rated up to 200 kW with kite areas of 90-180 m2), a 3-D optimal-control trajectory optimization was implemented using Python, CasADi, and IPOPT. The optimizer computes feasible power-cycle trajectories that serve as time-varying setpoints for supervisory control, balancing energy yield against airspace geometry, control-loop limits, and mechanical/electrical constraints. Solutions adapt substantially between low and high wind speeds, with high-efficiency cycles characterized by short reel-in phases. While quantified KPI deltas are not publicly available, the approach demonstrates economic optimization principles applied to renewable energy systems with complex, time-varying dynamics. ⁵

What This Means for Your Operations

Economic MPC delivers the largest returns in systems where energy costs are significant, operating conditions change over time (weather, demand, prices), and multi-objective trade-offs exist between cost, quality, and throughput. For DACH SMEs in buildings, process industries, and energy, this means: if your operators already run a stable process but you suspect you are not at the cost-optimal operating point, economic MPC is the systematic way to find and maintain that optimum.

Common prerequisites: a working regulatory control layer (stable base-layer PID/MPC), measurable economic drivers (energy meters, production counters), a dynamic model of the process energy/material balance, and access to forecast data (weather, prices, demand) if the economics are time-varying.

How We Deliver This (Engagement Model)

• Phase 0: NDA + data request — share process data, energy bills, control architecture, and current operating strategy.
• Phase 1: Fixed-scope discovery (2-4 weeks) — economic baseline quantification, dynamic model identification, constraint mapping, and feasibility/ROI estimate for economic MPC.
• Phase 2: Implementation + validation + commissioning — economic MPC development, simulation validation against historical data, online commissioning with operator oversight.
• Phase 3: Monitoring + training + scaling — performance dashboards tracking economic KPIs, operator training on the economic objective, and extension to additional units or sites.

Typical KPIs to Track

• Energy efficiency: kWh/unit produced, non-renewable primary energy consumption, specific energy cost
• Throughput: Production rate at quality specification, utilization of alternative fuels/feedstocks
• Quality/comfort: Temperature variability, comfort compliance hours, product quality deviation
• Emissions: CO2/ton, alternative fuel substitution rate, coal elimination milestones
• Operator burden: Manual interventions per shift, time spent adjusting setpoints

Risks & Prerequisites

• Model complexity: Economic MPC often requires longer prediction horizons and more detailed models than tracking MPC, increasing computational cost and commissioning effort.
• Economic data integration: Time-varying prices, forecasts, and demand signals must be reliably fed into the controller — data pipeline quality is critical.
• Operator trust: Operators accustomed to fixed setpoints may resist a controller that moves the operating point; training and transparent dashboards are essential.
• Baseline quantification: Without a clear energy/cost baseline, it is impossible to demonstrate ROI — invest in measurement before optimization.
• Regulatory control must work first: Economic MPC sits on top of a stable base layer; if PID loops are poorly tuned or sensors are unreliable, fix those first.

FAQ

Q: How is economic MPC different from real-time optimization (RTO)? A: Traditional RTO computes optimal setpoints at steady state and passes them to a tracking controller. Economic MPC combines both functions: it optimizes the economic objective dynamically, accounting for transients, constraints, and time-varying economics within a single framework. This eliminates the steady-state assumption and reduces the layering complexity.

Q: What energy savings can we realistically expect? A: Published results range from approximately 17% NRPE reduction in buildings to over 50% variability reduction in cement. Actual savings depend on the current baseline, process dynamics, and the magnitude of time-varying economic drivers. A Phase 1 discovery engagement quantifies the realistic potential for your specific system.

Q: Does economic MPC require a complete process model? A: The model must capture the dominant energy/material dynamics and the key economic drivers. It does not need to model every physical detail. In practice, a combination of first-principles structure and data-driven parameter identification yields models that are sufficient for economic optimization within 4-8 weeks of commissioning effort.

Q: Can economic MPC work with our existing DCS/PLC infrastructure? A: Yes. Economic MPC typically runs as a supervisory layer that sends setpoints or trajectories to existing regulatory controllers. Integration requires an OPC or similar data interface, not a control system replacement.

Book a 30-Minute Discovery Call

Ready to explore whether this pattern fits your system?

Dr. Rafal Noga — Independent APC/MPC Consultant

📧 Email me · 🌐 noga.es

Fixed-scope discovery · NDA-first · DACH on-site available

Public References

Footnotes

1. Schild, “Control-oriented modeling and controller design for wind turbines” (University of Freiburg / IAV, 2018). ↩

2. “NMPC for the Superfluid Helium Cryogenic Circuit of the LHC” (IFAC PapersOnLine, 2015). ↩

3. Sturzenegger et al., “Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost-Benefit Analysis” (IEEE TCST / ETH Research Collection, 2016). https://www.research-collection.ethz.ch/bitstreams/1a73128f-0bc3-4f40-a001-39d53e0cf491/download ↩

4. Marx, “Coal free Cement Plant Operation using Alternative Fuels — Modeling and Control of Pre-calciner under Alternative Fuels using Model Predictive Control” (ABB / AUCBM, 2008). https://library.e.abb.com/public/b72642321ab6290d852575bf00575b7a/AUCBM_2008_Paper_Marx_2008.pdf ↩

5. Airborne Wind Energy trajectory optimization (PN-14 system, CasADi + IPOPT implementation). See also: Wind Energy Science, https://wes.copernicus.org/articles/10/695/2025/ ↩