Keywords: greenhouse gases, GHG emissions, environmental systems, mathematical modelling, robust optimisation, power generation, stochastic programming, capacity planning, capacity expansion, demand, fuel prices, risk aversion, Canada, CO2 emissions, carbon dioxide, coal power plants, natural gas, combined-cycle turbines, nuclear power, nuclear energy
An environmentally conscious robust optimisation approach for planning power generating systems
This study proposes a robust optimisation capacity expansion planning model that yields a less sensitive solution due to variations in model parameters such as demand and fuel prices. By adjusting the penalty parameters, the model can accommodate the decision maker's risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested closing most of the coal power plants and building new Natural Gas (NG) combined-cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and as expected, the model was found to be less sensitive to disturbances than the deterministic model.