Monte-Carlo optimization framework for assessing electricity generation portfolios

This paper proposes a stochastic approach based on Monte-Carlo Simulation (MCS) to account for various uncertainties when determining the overall generation cost of electricity generation portfolios. This approach extends the traditional deterministic methods for solving optimal generation mix by incorporating uncertainty into key cost assumptions and therefore solving the probability distribution of the expected generation costs for different generation technology portfolios consisting of different generation technologies. The overall cost output is represented by a probability distribution in which the statistical features of mean and standard deviation are used to measure the cost and risk profile for each generation portfolio. The model is applied to a case study of electricity generation portfolios consisting of different mixes of the three most common generation technologies: coal, Combined Cycle Gas Turbine (CCGT) and Open Cycle Gas Turbine (OCGT), taking into account fuel and carbon prices uncertainty. The case study demonstrates the capability of this model in addressing the impact of uncertainty on the cost and risk across different possible electricity generation portfolios. Therefore, it provides a comprehensive basis to assist decision making in generation investment in order to identify appropriate generation technology and/or the generation technology portfolio mixes that most likely to achieve the objectives in terms of expected costs, risks and CO₂ emissions.