Decision-making model based on conditional risks and conditional costs in power system probabilistic planning

The risk management and decision making for power systems have been evolving from the qualitative methods to the quantitative methods. The traditional decision-making model based on the system average risk and the average cost has commensurated events of low-probability/high-damage and events of high-probability/low-damage, which ignores the relative importance of these events as they are viewed by the decision makers. In this paper, we propose the concepts of conditional risk indices and conditional costs of power systems, and establish a novel risk decision-making model based on these indices. The conditional indices will be helpful for the risk decision makers to thoroughly tradeoff between different risk domains according to their preferences. Further, the system risk is monetarily represented as the reliability cost by the customer composite damage function, and the decision-making model is solved by sequential Monte Carlo simulation. Finally, the modified IEEE-RTS incorporating wind energy is investigated by the proposed model and method. The effects of decision preferences, the wind power capacity and cost on the system decision making are discussed.