Abstract: At present, China is accelerating the construction of a new type of power system with new energy as the main body, and the large amount of new energy connected has led to the prominent phenomenon of wind and light abandonment. To improve the level of new energy consumption in the power system, this paper proposes a new energy consumption method based on load alignment and deep reinforcement learning. Firstly, this article proposes a node load line formation model based on linearized power flow calculation method. This model can guide adjustable loads to adjust the electricity consumption period, thereby promoting the improvement of new energy consumption level. Meanwhile, this model is an AC power flow model, which has the advantage of considering voltage constraints and other related constraints of the power system compared to the DC power flow model. Compared to other AC power flow models, this model linearizes all nonlinear constraints and has lower computational costs. Secondly, this article constructs a market framework for load alignment mechanism, and studies the solution method for load alignment incentive prices using electric vehicle clusters as adjustable loads. The load alignment mechanism framework includes three main entities: independent system operators, regional power grid sellers, and electric vehicle adjustable load aggregators. The solution of the load benchmark incentive price involves a master-slave game between three entities, and the mathematical analysis method is difficult to solve. Therefore, it is considered to use deep reinforcement learning algorithm to solve. The deep reinforcement learning algorithm takes the marginal electricity price of each node as the state space, the load benchmark incentive price as the action space, and the cost of regional power grid sellers as feedback. By continuously training the intelligent agent, the agent can find the load line incentive price that maximizes the benefits of regional power grid sellers. Finally, the example analysis shows that the load alignment mechanism can not only effectively promote the improvement of new energy consumption level, but also enhance the interests of independent system operators, regional power grid sellers, and electric vehicle aggregators. At the same time, examples show that the deep reinforcement learning algorithm can maximize the benefits of regional power grid sellers.

Key words: load alignment, renewable energy consumption, deep reinforcement learning, demand response