A two-stage stochastic programming model with chance constraint for electricity capacity expansion considering supply reliability and sustainability perspectives

This paper presents a two-stage stochastic programming model with a chance constraint for the expansion of electricity generation capacity and transmission network, considering sustainability issues. The objective function is to minimize the average yearly operational and investment costs, considering all technical and operational constraints. The proposed model becomes a mixed-integer linear programming (MILP) problem that is solved using an accelerated Benders’ decomposition algorithm. A finite number o f scenarios is considered to represent the uncertainty in the power demand. The reliability constraint ensures meeting the demand with a high probability at peak hours. The cost of the expected power shortage is also considered in the objective function to control the amount of allowable shortage.Furthermore, a novel optimality cut inspired by the sub-problem structure has been added to solve the problem efficiently. The sustainability factors have been taken into account in the electricity capacity expansion problem. We have also examined and compared the share of renewable energy generation by changing the capacity factor parameter, as well as the amount of carbon dioxide emission, in the sensitivity analysis. Incorporating the chance constraint into the two-stage stochastic model enables us to control the probability of power shortage under various scenarios without having to invest in extra capacity. In addition to a test bench IEEE 118-bus system data, a real-world case study is used to implement the models and analyze the results. The computational results show that the addition of the reliability constraint could lead to an 89% reduction in load shedding during peak hours.