  Considering widely dispersed uncertain renewable energy sources (RESs),
scenario-based stochastic optimization is an effective method for the economic
dispatch of renewables-rich power systems. However, on classic computers, to
simulate RES uncertainties with high accuracy, the massive scenario generation
is very time-consuming, and the pertinent optimization problem is
high-dimensional NP-hard mixed-integer programming. To this end, we design a
quantum-assisted scheme to accelerate the stochastic optimization for power
system economic dispatch without losing accuracy. We first propose the unified
quantum amplitude estimation to characterize RES uncertainties, thereby
generating massive scenarios by a few qubits to reduce state variables. Then,
strong Benders cuts corresponding to some specific scenarios are selected to
control the solution scale of Benders master problem in the iterative process,
all of which are implemented by customized quantum approximation optimization
algorithms. Finally, we perform numerical experiments on the modified IEEE
6-bus system to test the designed scheme.
