Strategic Planning for Power System Decarbonization Using Mixed-Integer Linear Programming and the William Newman Model

This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation...

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Veröffentlicht in:Energies (Basel) Jg. 18; H. 18; S. 5018
Hauptverfasser: Valdez Castro, Jairo Mateo, Aguila Téllez, Alexander
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2025
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ISSN:1996-1073, 1996-1073
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Abstract This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation cost, (ii) expected cost of energy not supplied (Value of Lost Load, VoLL), (iii) demand response cost, and (iv) CO2 emissions, subject to power balance, technical limits, and binary unit commitment decisions. The methodology is validated on the IEEE RTS 24-bus system with increasing demand profiles and representative cost and emission parameters by technology. Three transition pathways are analyzed: baseline scenario (no environmental restrictions), gradual transition (−50% target in 20 years), and accelerated transition (−75% target in 10 years). In the baseline case, the oil- and coal-dominated mix concentrates emissions (≈14 ktCO2 and ≈12 ktCO2, respectively). Under gradual transition, progressive substitution with wind and hydro reduces emissions by 15.38%, falling short of the target, showing that renewable expansion alone is insufficient without storage and demand-side management. In the accelerated transition, the model achieves −75% by year 10 while maintaining supply, with a cost–emissions trade-off highly sensitive to the carbon price. Results demonstrate that decarbonization is technically feasible and economically manageable when three enablers are combined: higher renewable penetration, storage capacity, and policy instruments that both accelerate fossil phase-out and valorize demand-side flexibility. The proposed framework is replicable and valuable for outlining realistic, verifiable transition pathways in power system planning.
AbstractList This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation cost, (ii) expected cost of energy not supplied (Value of Lost Load, VoLL), (iii) demand response cost, and (iv) CO2 emissions, subject to power balance, technical limits, and binary unit commitment decisions. The methodology is validated on the IEEE RTS 24-bus system with increasing demand profiles and representative cost and emission parameters by technology. Three transition pathways are analyzed: baseline scenario (no environmental restrictions), gradual transition (−50% target in 20 years), and accelerated transition (−75% target in 10 years). In the baseline case, the oil- and coal-dominated mix concentrates emissions (≈14 ktCO2 and ≈12 ktCO2, respectively). Under gradual transition, progressive substitution with wind and hydro reduces emissions by 15.38%, falling short of the target, showing that renewable expansion alone is insufficient without storage and demand-side management. In the accelerated transition, the model achieves −75% by year 10 while maintaining supply, with a cost–emissions trade-off highly sensitive to the carbon price. Results demonstrate that decarbonization is technically feasible and economically manageable when three enablers are combined: higher renewable penetration, storage capacity, and policy instruments that both accelerate fossil phase-out and valorize demand-side flexibility. The proposed framework is replicable and valuable for outlining realistic, verifiable transition pathways in power system planning.
This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation cost, (ii) expected cost of energy not supplied (Value of Lost Load, VoLL), (iii) demand response cost, and (iv) CO[sub.2] emissions, subject to power balance, technical limits, and binary unit commitment decisions. The methodology is validated on the IEEE RTS 24-bus system with increasing demand profiles and representative cost and emission parameters by technology. Three transition pathways are analyzed: baseline scenario (no environmental restrictions), gradual transition (−50% target in 20 years), and accelerated transition (−75% target in 10 years). In the baseline case, the oil- and coal-dominated mix concentrates emissions (≈14 ktCO[sub.2] and ≈12 ktCO[sub.2], respectively). Under gradual transition, progressive substitution with wind and hydro reduces emissions by 15.38%, falling short of the target, showing that renewable expansion alone is insufficient without storage and demand-side management. In the accelerated transition, the model achieves −75% by year 10 while maintaining supply, with a cost–emissions trade-off highly sensitive to the carbon price. Results demonstrate that decarbonization is technically feasible and economically manageable when three enablers are combined: higher renewable penetration, storage capacity, and policy instruments that both accelerate fossil phase-out and valorize demand-side flexibility. The proposed framework is replicable and valuable for outlining realistic, verifiable transition pathways in power system planning.
Audience Academic
Author Valdez Castro, Jairo Mateo
Aguila Téllez, Alexander
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SourceType Open Website
Aggregation Database
Index Database
StartPage 5018
SubjectTerms Air quality management
Alternative energy
Carbon
Case studies
Clean technology
decarbonization
Decision making
Ecuador
Electric power systems
Electricity
Emissions control
Energy industry
Energy storage
Energy transition
Flexibility
Force and energy
Fossil fuels
fossil generation
Greenhouse gases
Hydrogen
Investment analysis
Linear programming
mixed-integer linear programming
Operating costs
operational costs
Optimization
Renewable resources
Strategic management
Strategic planning
Strategic planning (Business)
wind energy
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Title Strategic Planning for Power System Decarbonization Using Mixed-Integer Linear Programming and the William Newman Model
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