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Design, optimisation and reliability allocation for energy systems based on equipment function and operating

Viknesh Andiappan1, Michael Francis D Benjamin2, Raymond R Tan3

  • 1School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 62200, Putrajaya, Malaysia.

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Summary
This summary is machine-generated.

A new mixed-integer linear programming (MILP) model optimizes energy system design and reliability by integrating input-output modeling and linearized reliability expressions. This approach balances cost and performance for complex energy systems.

Keywords:
Chemical engineeringComputer-aided engineeringEnergyEnergy sustainabilityEnergy systemEquipment functionInput-output modellingPolygenerationProcess modelingSystem reliabilitySystems engineering

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Area of Science:

  • Energy Systems Engineering
  • Operations Research
  • Mathematical Modeling

Background:

  • Energy system design faces challenges balancing cost and reliability.
  • Existing models often address reliability implicitly or use computationally intensive non-linear methods.
  • A need exists for efficient models optimizing both design and reliability.

Purpose of the Study:

  • To propose a novel mixed-integer linear programming (MILP) model for optimizing energy system design and reliability.
  • To integrate input-output (I-O) modeling with linearized parallel system reliability expressions.
  • To enable optimization based on equipment function, operating capacity, and reliability.

Main Methods:

  • Developed a mixed-integer linear programming (MILP) model.
  • Combined input-output (I-O) modeling with linearized parallel system reliability expressions.
  • Applied the model to a polygeneration case study with two scenarios.

Main Results:

  • The MILP model successfully optimized energy system design and reliability.
  • Scenario 1 (specified reliability) utilized CHP, reverse osmosis, and vapor compression chiller with a TAC of $53.3 million/year.
  • Scenario 2 (increased heat reliability) required an auxiliary boiler, increasing TAC by 5.3%.

Conclusions:

  • The proposed MILP model provides an efficient method for optimizing energy system design and reliability.
  • The model effectively balances cost and reliability requirements in polygeneration systems.
  • Demonstrated the impact of increased reliability demands on system design and cost.