Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

• We examine the electric generation expansion plans with demand uncertainties.
• Conditional value-at-risk and maximum regrets are considered as risk measures.
• Mean-risk stochastic integer programming model is used to incorporate the risk.
• Trade-off solutions between the cost and the risk measures are provided.
• Investment plans are changed when risk is involved.

This paper focused on solving electricity generation expansion planning problems where there are uncertainties associated with the electricity demand forecasts. The electricity expansion plans are long-term commitments and affects people’s living conditions and business prosperity deeply. Therefore it is reasonable that the decision makers may be risk averse. In this paper, mathematical models are developed to incorporate the risk aversion into the generation expansion planning problems. We use the conditional-value-at-risk and maximum regret as risk measures and the results shows that the investment plans are affected when the risk is considered.