With the high penetration of large scale distributed renewable energy generations, there is a serious curtailment of wind and solar energy in 5G internet of energy. A reasonable assessment of large scale renewable energy grid-connected capacities under random scenarios is critical to promote the efficient utilization of renewable energy and improve the stability of power systems. To assure the authenticity of the data collected by the terminals and describe data characteristics precisely are crucial problems in assessing the accommodation capability of renewable energy. To solve these problems, in this paper, we propose an L-DRL algorithm based on deep reinforcement learning (DRL) to maximize renewable energy accommodation in 5G internet of energy. LSTM as a bad data tolerant mechanism provides real state value for the solution of accommodation strategy, which ensures the accurate assessment of renewable energy accommodation capacity. DDPG is used to obtain optimal renewable energy accommodation strategies in different scenarios. In the numerical results, based on real meteorological data, we validate the performance of the proposed algorithm. Results show considering the energy storage system and demand response mechanism can improve the capacity of renewable energy accommodation in 5G internet of energy.

随着大规模分布式可再生能源的高普及率，5G能源互联网中的风能和太阳能受到严重限制。合理评估随机情况下的大规模可再生能源并网容量对于促进有效利用可再生能源和提高电力系统的稳定性至关重要。确保终端收集的数据的真实性并精确描述数据特征是评估可再生能源容纳能力的关键问题。为了解决这些问题，本文提出了一种基于深度强化学习（DRL）的L-DRL算法，以最大程度地提高5G能源互联网中的可再生能源适应能力。LSTM作为一种不良的数据容忍机制，为解决方案的解决方案提供了真实的价值，从而确保了对可再生能源调节能力的准确评估。DDPG用于在不同情况下获得最佳的可再生能源适应策略。在数值结果中，基于真实的气象数据，我们验证了该算法的性能。结果表明，考虑储能系统和需求响应机制可以提高5G能源互联网中可再生能源的容纳能力。我们验证了所提出算法的性能。结果表明，考虑储能系统和需求响应机制可以提高5G能源互联网中可再生能源的容纳能力。我们验证了所提出算法的性能。结果表明，考虑储能系统和需求响应机制可以提高5G能源互联网中可再生能源的容纳能力。