# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
### Optimization of Distribution Grid Energy Storage with Distributed Photovoltaics
#### 3.2.2 Energy Storage Optimization for Distributed PV  
The optimization of energy storage for distributed PV is also based on a variety of intelligent algorithms, and the intelligent algorithms applied are roughly the same as those in the siting and capacity determination part of distributed PV, but when optimizing the energy storage, the model mostly adopts a two-layer structure, and in the case of determining the optimal configuration and operation strategy of the energy storage, the economic issues also need to be considered, so in the literature, the economic constraints are usually considered as a layer. In the literature, economic constraints are usually integrated into the energy storage model as a separate optimization. A multi-objective model can be established for economy and environmental protection to maximize the consumption of distributed PV [66], and the lower layer considers the friendly interaction of distributed energy storage devices to establish the daily net load deviation per unit of contact line power, which is used to establish a layered optimal allocation method for distributed energy storage in distribution networks. A distributed PV community energy-sharing optimization strategy based on a two-tier structure can also be proposed [67], where the upper tier of the strategy is operated for the energy storage price using the master-slave game of multi-community shared centralized energy storage, and the lower tier achieves the improvement of factors such as net output magnitude and net output correlation by improving the Shapley-valued distributed PV community inter-subjects' energy-sharing revenue distribution mechanism. It is also a strategy to merge the two models under the premise of high-penetration rate [68] by considering the energy storage equipment using second-order cone relaxation to establish a system day-ahead scheduling linearization model and then based on the CVaR risk assessment model to establish a try scheduling model with an optimized loss function. Finally, the two models are merged to establish a day-ahead real-time multi-timescale scheduling model for regional integrated energy systems. The establishment of a two-layer model for energy storage optimization is a more comprehensive approach, which can not only consider important factors such as the location and capacity of energy storage but also focus on the economic issues because the current stage of energy storage technology has just started, so the consideration of the cost of energy storage is the key to the success of a project. In this paper, by summarizing the model of the literature [69], the two-layer structure of the literature is drawn, as shown in Figure 9.  
Figure 9: Optimized allocation method of distributed PV energy storage system. The process shown in Figure 9 is similar to most two-layer model structures, which can visualize the process of building a two-layer model and can provide a basic idea for readers who build a two-layer structure for research. In addition to the establishment of the two-layer structure, there are also studies that only apply intelligent algorithms for optimization. By improving the particle swarm algorithm [70; 71], the hybrid planning model for the optimal allocation of photovoltaic and energy storage is established by comprehensively considering the economic benefits, peak shaving, and valley filling issues. In order to improve the global search ability, the firefly algorithm [72] can be improved to quickly determine the optimal energy storage capacity. Incorporating energy storage devices for distributed PV increases the construction cost but is relatively the most effective way to consume PV at present, both in terms of reducing the impact on the grid when PV is connected and increasing the capacity of the PV to be connected to the grid, so PV energy storage has been widely studied around the world.