Dynamic Simulations & Energy Balances

What is Dynamic Simulation?

Modeling & Simulation Services

MATLAB & Simulink

FinHighTech provides advanced modeling and simulation services for energy and process systems using MATLAB and Simulink. Our work enables data-driven design, optimization, and justification of technical and investment decisions before implementation. Our models capture time-dependent behavior across seconds, hours, days, and years—providing a realistic foundation for system design, optimization, and investment decisions.

We build dynamic system models that capture real-world behavior: loads, control logic, storage, process interfaces, and uncertainty. This allows reliable comparison of alternatives in a changing operational and market environment. Rather than relying on static averages, we analyze how systems truly operate: variable demand, weather dependency, control logic, storage interaction, and operational constraints.

Why Dynamic Energy Balances Matter

Static calculations hide critical risks. Dynamic simulation reveals:

• peak demand and ramping requirements
  
  

• storage charging/discharging conflicts
  
  

• control-driven inefficiencies
  
  

• sensitivity to weather, prices, and disturbances
  
  

This is essential when designing future-proof energy systems in a volatile operating environment.

What You Get

• Dynamic system model (digital twin at the appropriate level of detail)

• Scenario analysis: weather, prices, demand profiles, availability, disturbances
  
  

• Sizing & optimization: power, energy, storage capacity, control strategies
  
  

• Economic metrics (optional): CAPEX/OPEX, LCOE, LCOH, sensitivity analyses
  
  

• Clear documentation & reporting: assumptions, model structure, results
  
  

• Decision-ready visuals for management and steering groups

Who Is This For?

Key Applications

• District heating production and storage optimization
  
  

• Industrial heat and steam systems
  
  

• Power-to-Heat and sector coupling
  
  

• Heat pumps, electric boilers, CHP, waste heat utilization
  
  

• Energy system resilience under extreme conditions

Industry & Process Plants

• Refineries, chemical industry, metals, pulp & paper
  
  

• Steam and heat networks, process heat production and consumption
  
  

• Energy efficiency, reliability, and capacity adequacy
  
  

Utilities & District Heating

• Production mix and technology comparison
  
  

• Thermal storage (short-term & seasonal), electric boilers, heat pumps, CHP, waste heat
  
  

• Dynamic operation, control behavior, and network performance
  
  

Buildings & Portfolios

• Heating system sizing and control optimization
  
  

• Use of historical consumption data (daily to annual resolution)
  
  

• Investment impact: cost, emissions, peak power demand

Typical Questions We Answer

• What is the correct sizing (MW / MWh) for production and storage?
  
  

• How does the system behave during extreme cold periods or disturbances?
  
  

• How do control strategies affect costs, peaks, and operational stability?
  
  

• Which configuration delivers the best overall performance (€/MWh, emissions, security of supply)?
  
  

• At what point does an investment become non-viable (interest rate, delays, price sensitivities)?

Our Delivery Process

1. Objective & Scope Definition
   What decision will the model support and at what level of detail?
   
   

2. Data & Assumptions
   Load and production profiles, process data, weather, prices, capacities.
   
   

3. Model Development (MATLAB / Simulink)
   System blocks, dynamics, control, storage, constraints.
   
   

4. Validation
   Calibration against historical data or measurements when available.
   
   

5. Scenarios & Optimization
   Comparative analysis, sensitivities, and recommendations.
   
   

6. Delivery
   Report, figures, conclusions – optionally the model package and user guidance.

Delivery Formats

• Rapid feasibility study – “Is this concept viable?”
  
  

• Investment-grade simulation – sizing, scenarios, sensitivities
  
  

• Extended system model / digital twin – continuous development and updates

Why FinHighTech?

• We bridge engineering rigor and decision-making needs:
  technology → economics → implementation
  
  

• Strong background in energy systems, industrial processes, and safety-critical environments
  
  

• Results that withstand technical scrutiny and remain clear for executive audiences

Contact

Interested in seeing what a model would look like for your system?
Send us a brief description of your case (loads, production, capacities, objectives), and we will propose a suitable approach.

FinHighTech
Modeling & Simulation – MATLAB & Simulink