# 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 improve the voltage quality and power loss, the active network loss and voltage drop can be established as the objective function, and the particle swarm algorithm can be used to solve the access capacity [40]. The results show that this method can significantly reduce the power loss and alleviate voltage drop throughout the whole network, which can provide guidance for the actual site selection. There are more solution algorithms available today, and there are numerous innovative ways to use them. Some authors have used the modified Jaya algorithm to find the optimal PV capacity and bus location [41] and proposed a new discriminative method to meet the requirements of improving voltage distribution and reducing network losses at high penetration rates. Some scholars zoomed out to the whole world to evaluate the combination of technology and geographic distribution [42], which ultimately resulted in the most environmentally friendly strategy, identifying Kosovo, South Africa, and Australia as the best locations for PV system installation. For building PV, multi-scale roof characterization using LiDAR data and aerial orthophotos [43] allows for rapid calculation of PV capacity. Combining LiDAR and orthoimage data processing for geometrically characterizing roofs at the slope level and calculating their PV solar potential, the method is a product of the combination of new energy and control fields. A new approach for off-grid PV DC microgrids has been proposed by [PERSON] et al. [44]. By comparing some of the existing methods, the proposed method is highly adaptable, allowing the design of installations and the ability to adjust them after the fact for new buildings. Among the experimental and numerical analyses of the retrofitting of photovoltaic systems in Polish cities [45], there are results that show that changing the direction of solar radiation can increase the annual energy production by about 20-45%, effectively reducing the operating costs of buildings, as well as having environmental benefits. The possibility of transition from centralized to distributed energy systems in large Polish cities has been analyzed [46], and the results show that the application of micro PV systems will contribute to more energy-efficient residential buildings and a significant reduction of fossil-fuel-based energy consumption and that the development of micro PV systems seems to be one of the most effective options for the rapid transition from centralized to distributed energy systems based on individual renewable energy sources in large Polish cities. One of them. A greedy algorithm based on voltage deviation minimization was proposed to determine the appropriate PV storage capacity [47]. The results show that the level of PV capacity can be changed by time series analysis. Furthermore, incorrect capacity and location selection may lead to power quality deterioration problems in high-penetration power systems. By analyzing the PV connected to the Dutch access power system, it was concluded that the magnitude of voltage fluctuations depends on the location of the PV in the grid, the capacity, and the grid configuration [48]. In order to solve this problem, researchers have proposed the introduction of electric vehicle technology to reduce voltage fluctuations, which is effective but raises the cost of charging for electric vehicle owners. An optimization method based on interwoven thermodynamic building models with genetic algorithms has been utilized in the Netherlands [49]. This method can effectively reduce the energy loss at the access point, increase the PV access capacity, and also reduce the cost of microgrid users, which has a good economy. PV systems are also being studied in other European countries. In France, someone combined the PV carrying capacity of low-voltage grids with user voltage sensitivity and reliability analysis [50].