针对电力物联网下多能源协同优化问题，建立了风光蓄协同交易的优化模型。利用抽水蓄能(pumped storage power, PSP)电厂出色的调节性能，将其按比例与风电厂和光伏电厂进行打捆，以最大化消纳新能源；另一部分参与主体侧市场调节以实现协同优化。通过构建风光蓄联盟，旨在消除风光新能源出力偏差，从而解决物联网中的信息交流安全问题。此外，采用沙普利值法对剩余价值进行分配。在此过程中，还考虑到碳排放要求，在负荷侧引入电动汽车(electric vehicle, EV)的减碳效能，并将其与火电出力形成约束；同时，在电网侧加入电储能调节，以确保在电力物联网环境下系统的安全运行。本研究采用主体侧报量报价方式，使得新能源预测全部出清，而负荷侧则跟随市场价格波动进行清算。进一步考虑波动偏差，对多能源主体的出力进行了优化。通过多个不同情景下的仿真结果验证了所提优化模型的可行性和适应性。

Aiming at the problem of multi-energy collaborative optimization under the power internet of things, an optimization model of collaborative trading of wind and solar storage was established. The excellent regulation performance of pumped storage power (PSP) plant is used to bundle it with wind power plant and photovoltaic power plant in proportion to maximize the absorption of new energy; The other part participates in the main side market regulation to achieve collaborative optimization. Through the construction of landscape storage alliance, it aims to eliminate the deviation of landscape new energy output, so as to solve the security problem of information exchange in the internet of things. In addition, the Shapley value method is used to allocate the surplus value. In this process, considering the carbon emission requirements, the carbon reduction efficiency of electric vehicles(EV) is introduced on the load side, and it is constrained by the output of thermal power. At the same time, the electric energy storage regulation is added to the grid side to ensure the safe operation of the system in the power internet of things environment. This study adopts the volume quotation method on the subject side, so that all the new energy forecasts are cleared, while the load side is cleared following the market price fluctuations. Further considering the fluctuation deviation, the output of the multi-energy subject is optimized. Simulation results under different scenarios verify the feasibility and adaptability of the proposed optimization model.