Abstract: Owing to the evolution of 6G networks toward the deep integration of communication and sensing， integrated sensing and communication （ISAC） technology has demonstrated significant potential in various wireless applications such as vehicular networks and low-altitude unmanned aerial systems. However， current ISAC systems are confronted by the challenge of excessive power consumption due to intensified multi-user interference. Hence， this paper proposes an ISAC system model assisted by reconfigurable intelligent surfaces （RISs） and rate-splitting multiple access （RSMA）. The power-optimization problem is rigorously formulated as a convex programming framework for minimizing the long-term average transmit power at the base station by jointly optimizing the BS transmit beamforming vectors， RIS phase-shift matrix， and RSMA ratios， subject to the minimum communication rate constraints of users， minimum radar-beam gain constraints， and minimum user-decoding capability constraints. The resulting optimization problem is nonconvex and involves highly coupled variables， thus rendering it difficult to be solved directly. To tackle this challenge， an optimization algorithm based on an alternating optimization framework is proposed to decouple and solve the problem. First， the original problem is transformed into a semidefinite programming problem using semidefinite relaxation （SDR）， and feasible solutions are obtained via Gaussian randomization. Second， the nonconvex terms in the constraints are addressed using successive convex approximation （SCA） to iteratively construct locally optimal solutions with guaranteed convergence. Finally， alternating iterations are performed to progressively reduce the average transmit power at the base station. Simulation results show that the proposed scheme significantly reduces power consumption compared with conventional schemes combining RISs with spatial division multiple access and RISs with non-orthogonal multiple access while ensuring the required communication rate and target sensing gain. Furthermore， the proposed SDR-SCA algorithm exhibits rapid convergence， thus further enhancing its practical applicability in green ISAC systems.