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A Multi-Stage Optimization Model for Flexibility in Engineering - - PowerPoint PPT Presentation

Apr 30, 2023 279 likes •525 views

A Multi-Stage Optimization Model for Flexibility in Engineering Design Ramin Giahi, Cameron A. MacKenzie, Chao Hu Iowa State University Industrial and Manufacturing Systems Engineering 1 Engineering System Design Power generation 25 de

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Industrial and Manufacturing Systems Engineering

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A Multi-Stage Optimization Model for Flexibility in Engineering Design

Ramin Giahi, Cameron A. MacKenzie, Chao Hu

Iowa State University

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Industrial and Manufacturing Systems Engineering

Engineering System Design

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Power generation 25 de Abril bridge

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  • Flexibility in engineering system design:
  • Flexibility in system design and implications for aerospace

systems (Saleh et.al 2003)

  • A flexible and robust approach for preliminary engineering

design based on designer's preference (Nahm et.al, 2007)

  • A real options approach to hybrid electric vehicle

architecture design for flexibility (Kang et.al 2016)

Previous Works

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Industrial and Manufacturing Systems Engineering

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  • Challenges with flexible design:
  • Operation of engineered systems for long time
  • Evaluation of the objective function with the use of

computationally expensive simulation

  • Our contribution:

Optimize the design when the objective function must be evaluated via simulation considering long range uncertainty and flexibility in design

Our Research Contribution To The Engineering Design

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Industrial and Manufacturing Systems Engineering

Research Framework

Real world application Identify key and long- range uncertainty (forecast and simulate future condition) Optimization with long- range uncertainty Simulation Optimization Black box simulation optimization Optimal design with flexibility Optimal design without flexibility 5

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Application: Hybrid Renewable Energy System

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Industrial and Manufacturing Systems Engineering

Sharafi, Masoud, and Tarek Y. ELMekkawy. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach." Renewable Energy 68 (2014): 67-79.

Hybrid Renewable Energy Systems

Solar panel Wind turbine Demand Battery Hydrogen Tank Electrolyzer Fuel cell Energy to load Excess energy to battery Energy to load Excess energy to Electrolyzer Hydrogen Energy to load Hydrogen Energy to load

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Application: Hybrid Renewable Energy System

  • Design of hybrid renewable energy system
  • Hybrid renewable system includes: PV panels, wind

turbine, battery storage, electrolyzer, and fuel cell

  • Design variables: capacity of the components of the

system

  • Identify the optimal capacity of each component to

minimize the expected discounted cost

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Industrial and Manufacturing Systems Engineering

Research Framework

Real world application Identify key and long- range uncertainty (forecast and simulate future condition) Optimization with long- range uncertainty Simulation Optimization Black box simulation optimization Optimal design with flexibility Optimal design without flexibility 9

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Industrial and Manufacturing Systems Engineering

Simulation of Energy Demand for California, 2017-2036

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Historical demand Forecasted demand

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Industrial and Manufacturing Systems Engineering

Research Framework

Real world application Identify key and long- range uncertainty (forecast and simulate future condition) Optimization with long- range uncertainty Simulation Optimization Black box simulation optimization Optimal design with flexibility Optimal design without flexibility 11

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Mathematical Model

  • Goal: Find the optimal design of hybrid

renewable energy system

  • Minimize expected discounted costs
  • Investment
  • Replacement
  • Maintenance
  • Decision variables: Capacity of solar, wind,

battery, fuel cells, electrolyzer, and hydrogen tank

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Industrial and Manufacturing Systems Engineering

Randomly select decision variables

Simulation Optimization

Monte Carlo simulation Cost

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Cost Update decision variables Cost

New decision variables Monte Carlo simulation

Cost

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Industrial and Manufacturing Systems Engineering

Research Framework

Real world application Identify key and long- range uncertainty (forecast and simulate future condition) Optimization with long- range uncertainty Simulation Optimization Black box simulation optimization Optimal design with flexibility Optimal design without flexibility 14

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Optimal Solution

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Components Capacity (Giga Watt) Percentage (%) Solar panel 392 78 Wind turbine 146 Battery 89 17 Electrolyzer 104

  • Hydrogen tank

322

  • Fuel cell

138 4 Diesel

  • 1

Expected cost $ 40.66 trillion

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Demand Fulfillment Analysis: 1 Scenario

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Parallel Coordinate Plot for Hybrid Renewable Design

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Industrial and Manufacturing Systems Engineering

Research Framework

Real world application Identify key and long- range uncertainty (forecast and simulate future condition) Optimization with long- range uncertainty Simulation Optimization Black box simulation optimization Optimal design with flexibility Optimal design without flexibility 18

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Design Optimization with Flexibility

Case 1: One time design modification at 2027

2017 2037 2027

Optimize design

  • ver 2017-2027

High demand Medium demand Low demand

Optimize additional capacity for low demand profile Optimize additional capacity for medium demand profile Optimize additional capacity for high demand profile

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Design Optimization with Flexibility

  • Case 2: Two times design modifications at 2027

and 2032

  • Stage 1: Optimize design for 2017-2027
  • Stage 2: Optimize additional capacity for

2027-2032, given the optimal initial design

  • Stage 3: Optimize additional capacity for

2032-2037, given the optimal initial design and each optimal expansion amounts of stage 2

  • Find the expected cost of design (initial design

cost + average expansion cost at stage 2 + average expansion cost at stage 3)

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Expected Cost of Design with and without Flexibility

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Conclusions

  • Design under long-range uncertainty
  • Hybrid renewable energy system
  • Monte Carlo simulation of uncertainties (e.g.,

demand)

  • Optimize design with and without flexibility
  • Compare the design without flexibility with design

with flexibility

  • Funding through the NSF-funded Center for e-design

rgiahi@iastate.edu

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Reference

  • Saleh, Joseph H., Daniel E. Hastings, and Dava J. Newman. "Flexibility in system

design and implications for aerospace systems." Acta astronautica 53.12 (2003): 927-944.

  • Nahm, Yoon-Eui, Haruo Ishikawa, and Young-Soon Yang. "A flexible and robust

approach for preliminary engineering design based on designer's preference." Concurrent Engineering 15.1 (2007): 53-62.

  • Kang, Namwoo, Alparslan Emrah Bayrak, and Panos Y. Papalambros. "A Real

Options Approach to Hybrid Electric Vehicle Architecture Design for Flexibility." ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016.

  • Sharafi, Masoud, and Tarek Y. ELMekkawy. "Multi-objective optimal design of

hybrid renewable energy systems using PSO-simulation based approach." Renewable Energy 68 (2014): 67-79.