Abstract: Quantum computers serve as platforms for high-performance quantum computing and its practical applications. With unique operational characteristics, they require scientifically feasible application methods tailored to specific scenarios and problems to leverage quantum advantages. This paper presents a quadratic unconstrained binary optimization (QUBO) model construction method for distributed resource disaggregation optimization in virtual power plants, based on optical quantum computing. It provides conversion methods for penalty terms in QUBO models corresponding to optimization objectives, equality constraints, and inequality constraints, establishing a practical quantum computing paradigm for power system optimization. Furthermore, a redundant constraint identification method and qubit sharing mechanism are developed, specifically considering virtual power plant operations to minimize required qubits. Utilizing the team's self-developed optical quantum computer, application tests demonstrate the feasibility and effectiveness of solving virtual power plant disaggregation problems. This breakthrough in solving power system optimization problems with optical quantum computers paves the way for addressing large-scale power system optimization challenges in the future.