Under　China＇s　current　Renewable　Portfolio　Standards　(RPS)　policy　system,　the　provincial　power　grid　is　the　assessment　subject,　and　then　the　assessment　is　conducted　on　the　user　side.　However,　in　the　centralized　clearing　power　market,　renewable　energy　(RE)　consumption　on　the　user　side　cannot　be　accurately　identified,　so　accurate　value　transmission　cannot　be　formed　to　guide　users　to　optimize　their　electricity　consumption　behavior.　To　improve　this　problem,　this　paper　identifies　the　irreplaceable　role　of　users　in　RE　consumption　through　time　series　simulation.　We　construct　a　three-layer　RPS　assessment　value　transmission　model　of　＂power　grid　company　-　retailers　-　users＂　based　on　the　identified　unique　contributions　of　each　user.　The　equilibrium　state　of　multi-market　participants　is　obtained　based　on　the　decentralized　training　framework　of　reinforcement　learning.　The　following　conclusions　are　drawn　through　simulation　analysis:(1)　the　user-side　RE　contribution　identification　method　can　effectively　identify　the　user＇s　contribution　to　RE　consumption,　which　can　reduce　about　8%　or　increase　about　7.48%　of　RPS　cost　allocation　according　to　different　contribution　degrees;　(2)　the　RPS　value　allocation　mode　can　optimize　users＇　power　consumption　behavior,　achieve　peak　shaving　and　valley　filling,　promote　RE　consumption,　and　reduce　about　0.6%　of　the　abandoned　wind　power;　(3)　Under　the　RPS　system,　the　effect　of　improving　the　response-ability　of　the　user-side　is　better　than　increasing　the　RPS　target　and　the　peak　shaving　capacity.