# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 1 Introduction  
Nowadays, with the continuous aggravation of environmental problems, driven by the carbon peaking and carbon neutralization target [1], it has become an inevitable trend to build a new type of power system, vigorously develop new energy sources and improve energy utilization [2]. More attention is paid to solar energy, with its advantages of environmental protection and convenient availability, by various countries due to the increased demand for clean energy that reduces greenhouse gas emissions in the context of low-carbon development [3]. In particular, the development of distributed photovoltaic (PV) power generation systems, which are decentralized and installed in multiple locations, has effectively improved the efficiency of solar energy utilization [4]. Distributed PV systems offer a number of advantages, firstly they help to reduce greenhouse gas emissions, minimize the impact on the environment and promote sustainable development and environmental protection. Secondly, they can enable regions to reduce their dependence on traditional power supplies and increase energy independence. It can also be interconnected with the grid, injecting excess energy into the grid and reducing the cost of electricity to the users [5]. Despite the many advantages of distributed PV power systems mentioned above, they also face a series of challenges. First, the access of large-scale distributed energy devices increases the complexity of the grid system operation, and it is difficult to characterize thesystem through an accurate physical model [6]. Secondly, the intermittent nature of PV power generation and the stochastic nature of user-side behavior lead to a large number of uncertainties contained in distributed PV systems [7]. Finally, flexible loads such as electric vehicles increase the dynamic characteristics of the system, requiring an efficient real-time online energy dispatch strategy [8]. Microgrid as a kind of small-scale distribution system for managing distributed energy can effectively solve the above problems [9]. On the power side, the local consumption of distributed energy can reduce the transmission loss and the impact on the power grid [10]. On the load side, the optimized scheduling of energy can significantly improve the efficiency of energy utilization [11]. Among them, the control strategy for energy storage is a key focus in microgrid energy scheduling research. [PERSON] et al. in [12] consider uncertain outputs of photovoltaic power and charge or discharge efficiencies for the batteries, minimizing the operational cost. In [13], an energy cooperation strategy of two microgrids is developed, in which each microgrid has a separate renewable energy generator and energy storage system, which enables the microgrid to exchange energy and minimize the cost of the energy extracted from the external grid. The methods above for energy scheduling are mostly traditional optimization algorithms, which are difficult to apply when the actual scenarios are more complex and the amount of data is huge in the case of multi-parks [14; 15; 16; 17]. In recent years, deep reinforcement learning has become a research hotspot in the field of artificial intelligence, which has the advantage of self-learning through interactive trial-and-error in dynamic environments, and provides new solutions for dealing with complex microgrid control problems [18; 19]. Among them, [PERSON] et al. propose an energy sharing strategy in [20] based on energy trading to coordinate a multi-energy microgrid with integrated regional energy interconnections, and a distributed optimization of energy allocation using the Alternating Direction Method of Multipliers (ADMM) algorithm. [PERSON] et al. in [21] give the application of deep reinforcement learning algorithm based on asynchronous dominance in solving the energy scheduling and control problem of model-free, multi-objective, multi-microgrids, and illustrate the basis for the construction of the deep neural network through specific numerical examples. In [22], [PERSON] et al.