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A multi-period optimization model for energy planning with CO(2) emission consideration.

H Mirzaesmaeeli¹, A Elkamel, P L Douglas

¹Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Journal of Environmental Management

|February 13, 2010

Summary

This study presents a new optimization model for electric power generation planning. It determines the most cost-effective energy mix to meet demand and CO2 emission targets, considering various economic and environmental factors.

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Area of Science:

Operations Research
Environmental Engineering
Energy Systems Analysis

Background:

Electric power systems face increasing demand and environmental regulations.
Optimizing energy generation mix is crucial for economic viability and sustainability.
Carbon dioxide (CO2) emission reduction targets necessitate strategic planning.

Purpose of the Study:

To develop a novel deterministic multi-period mixed-integer linear programming (MILP) model for power generation planning.
To determine the optimal mix of energy supply and pollution control strategies.
To minimize costs while meeting electricity demand and CO2 emission targets.

Main Methods:

Formulation of a deterministic multi-period mixed-integer linear programming (MILP) model.
Inclusion of time-dependent parameters: demand forecasts, fuel prices, lead times, and operational costs.
Application of the model to two distinct case studies for evaluation.

Main Results:

The model successfully identifies optimal energy supply mixes and mitigation options.
Case studies reveal significant economical, structural, and environmental impacts of CO2 emission reduction policies.
The model provides insights into cost-effective strategies for achieving emission targets.

Conclusions:

The developed MILP model is an effective tool for strategic power generation planning under emission constraints.
Economic and structural adjustments are necessary in the electricity sector to meet environmental goals.
The model aids in understanding the trade-offs between cost, demand, and environmental impact.