Modeling of risk aversion linked to renewable energy policy and decision-maker behavior

In recent years particular attention has been emphasized to different diversified means of energy production for the security of supply, availability, reliability, and robustness of electrical energy systems. The attention rests on the most effective preventive organization, at the cost of an economic investment which will be all the more profitable since the consequences of the breakdown are significant. Given the random nature of the failures of the existing electricity distribution networks and the intermittency of production, the decision to invest preventively in the electricity system is similar or not to an exposure to risk. Will the network manager then take the risk of not investing in a preventive policy, saving an investment, but under the threat of a failure requiring a more costly corrective intervention? An expected utility function models the tast and/or aversion to risk. We use the model of von Neumann and Morgenstern indicating that the rational choice amounts to maximizing the expected utility. The objective is to model risk aversion to the choice of a policy leading to the integration of renewable energies into the electricity system. Following a bibliographical approach, a methodology to model the behaviour of the risk averse decision maker using the exponential utility function has been presented. We provide a decision support tool to the decision maker that allows him to choose a corrective or preventive policy that best suits the electrical system and his preferences. We take into account the probabilities of the occurrence of failures within the framework of a defined policy, the associated costs and the degree of risk aversion of the decision-maker. Based on these elements, we provide a policy proposal that is the best compromise for the decision-maker. Scilab and Matlab software as used to plot utility curves and calculate failure probabilities. Several examples are treated and allow one to become familiar with the integration of risk aversion modeling in order to define a preventive policy for the power supply system.