# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
#### 3.1.3 Distributed PV Siting and Capacity Strategy  
In order to rationally achieve the siting and sizing of distributed PV, most researchers have introduced intelligent algorithms for optimization. In order to improve the voltage quality of the system and reduce network losses, the GA-SA algorithm [13], which is a combination of genetic algorithm and simulated annealing algorithm, can be used, which can solve the distributed PV optimal allocation problem, but the objective function and constraints of the model are relatively homogeneous, and the conclusions obtained deviate from the actual results. The improved convergent multi-objective optimization algorithm [14] can be applied in practice; the researchers use the capacity limit as the main constraint to establish the siting and capacity model, which has obvious advantages in terms of network loss reduction rate and cost. However, the same problem is that the algorithm only has obvious advantages for single objective optimization. In order to fit the reality further, the single-objective intelligent algorithm is no longer a good choice, and the multi-objective optimization needs to be further improved or combined with other algorithms. Optimization for multi-objective can be achieved by improving the non-dominated solution sorting genetic algorithm [15], which ensures that the harmonic distortion rate of each node does not exceed the limit and can effectively improve the system voltage distribution and reduce the system network loss. One of the problems is that the voltage and network loss targets determined by the model itself are too high. In fact, in the current study of distributed power supply selection and capacity determination, the application of the particle swarm algorithm has been more mature, and many improved particle swarm algorithms [16; 17; 18; 19; 20; 21; 22] can obtain good optimization results, and some authors have introduced the Delphi method and fuzzy delta function to improve the algorithm's weights, in order to enhance the reasonability and objectivity of the weights when selecting weights. Some authors also integrate the annealing principle into the optimization process, which can give the algorithm the ability to make sudden jumps, thus obtaining better optimization results. Based on the idea of interactive iterative nesting, author [PERSON] adopts a double-layer iterative hybrid particle swarm algorithm embedded in AC current calculation, which reduces the operational network loss of the distribution network system, improves the node voltage minimum, and reduces the possibility of voltage crossing the lower limit. Overall, the particle swarm algorithm has been studied more maturely in the establishment of PV citing and capacity modeling, and because it is applicable to the multi-objective optimization problem, it has significant advantages for the network loss, voltage quality, harmonics and other problems in the distribution network. So, the authors mostly choose to carry out optimization of the particle swarm algorithm for PV citing and capacity determination; the basic flow of the particle swarm algorithm is shown in Figure 7[23]. Particle swarm algorithm certainly has its superiority, but other intelligent algorithms also have unique advantages in siting and sizing. In the current case of particle swarm algorithms, research is more mature; for other intelligent algorithms, research innovation will be a new way of thinking in siting and sizing. The advantages and disadvantages of intelligent algorithms that have been applied to distributed photovoltaic and microgrid siting and sizing are shown in Table 1.