Quantum-Assisted Stochastic Economic Dispatch for Renewables-Rich Power Systems
Abstract
Scenario-based stochastic optimization is commonly deemed as an effective method for the economic dispatch of a renewables-rich power system which features a massive number of widely dispersed renewable energy sources (RESs). However, it is rather computationally challenging to simulate RES uncertainties with high accuracy on classic computers, due primarily to the generation of massive RESs scenarios and the solution of pertinent optimization problems that are by nature NP-hard mixed-integer programs. 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 are selected and formed under some specific scenarios so as to tune the solution scale of Benders master problem in the iterative process, all of which are implemented by customized quantum approximation optimization algorithms. Finally, numerical experiments are conducted on the modified IEEE 6-bus system to test the efficacy of the proposed quantum-assisted scheme.
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