In the context of the rapid development of the global economy, the continuous growth of energy demands, the gradual depletion of fossil fuels and the increasing environmental pressure, many countries and regions are vigorously advocating for the reform and transformation of current energy structures, and have introduced various policies to promote the use and development of clean energy. Photovoltaic (PV) power generation technology has been rapidly developed and applied during this period [
1,
2,
3]. With the increasing penetration rate and number of distributed PVs in the distribution network, adverse effects such as power backflows, the bus voltage being out of limit, power loss increases, harmonic increases and power supply reliability reductions may occur [
4,
5]. At the same time, the PV power output varies over time due to intermittency, randomness and volatility. The load changes also have time sequence characteristics, and the peak and valley of the PV power output and those of the load in the distribution network are not completely matched [
6]. The double uncertainty of renewable energy and load leads to greater uncertainty regarding the power flow in the grid, which restricts the consumption of PV power in the distribution network. An energy storage system with a rapid energy response ability can, to a certain extent, ease PV grid power, shift the peak load, decrease the power loss, improve the voltage quality, reduce the injected power of the upper-level grid, improve the system stability, and optimize the operations of the distribution network, which involves the flexible scheduling of load side resources and an effective means of achieving demand-side management [
7,
8,
9,
10]. On the other hand, the energy storage system is a solution to increase the share of self-consumption in a developed market which is conducive to the economic feasibility of PV systems, and it can lead to less pollution of the environment and lower carbon dioxide (CO
2) emissions [
11,
12]. Therefore, under the conditions of a high penetration multi-access point grid-connected PV, it is important to achieve optimal allocation and operational planning for energy storage systems to optimize the power flow of the grid, improve the absorption capacity and peak-cutting benefit of PV power in the distribution network, and promote the safety, stability and economic operations of the power system [
13,
14].
Many scholars have studied the optimal allocation and operation of distributed energy storage in a distribution network. Kabir et al. [
15] determined the capacity of an energy storage system to solve the problem of the overvoltage produced by high penetration grid-connected PVs and the lower voltage brought by the peak load. Bennett et al. [
16] optimized the capacity and the charging and discharging operational strategy of a battery energy storage system to achieve the load balancing, peak and valley filling, and rooftop PV optimization management in a low-voltage distribution network. The authors of [
15,
16] optimized the capacity of energy storage, but the locations of energy storage were given in advance, and the optimal location was not selected. Sardi et al. [
17] determined the location and capacity of energy storage to maximize the net present value and increase the load factor and voltage level based on a cost–benefit analysis. Hashemi et al. [
18] calculated the minimum energy storage capacity at different locations based on the voltage sensitivity analysis method to avoid overvoltage under the conditions of a high penetration grid-connected PV system. Yuan et al. [
19] used the minimum power loss as their optimization objective, and applied the Coyote optimization algorithm to solve and obtain the optimal location and capacity of a lithium-ion battery energy storage system. Tang and Low [
20] determined the optimal location and capacity of energy storage in a distribution network based on the continuous tree theory of the linear discrete flow model to minimize the total active power loss of the system. Giannitrapani et al. [
21] determined the location and quantity of energy storage systems according to voltage sensitivity, determined the capacity of energy storage based on the multi-stage power flow, and then determined the optimal installation location, quantity, and capacity of energy storage systems in a low-voltage distribution network with the minimum total cost as the optimization objective. Das et al. [
22] used the voltage deviation, flicker, power loss and line loading as the optimization objectives, and utilized a fitness-scaled chaotic artificial bee colony algorithm to solve the objective function to achieve the optimal configuration of the energy storage system. Wong et al. [
23] used the minimum active power loss of the distribution network as their optimization objective, and applied the whale optimization algorithm to obtain the optimal location and capacity of energy storage. The optimization process was carried out by first optimizing the position and then optimizing the capacity, while simultaneously optimizing the position and the capacity. The results obtained were compared with the results from using the firefly algorithm and particle swarm optimization algorithm, thus verifying the superiority of the proposed algorithm. In the literature [
17,
18,
19,
20,
21,
22,
23], the location and capacity of the energy storage were optimized, but the optimal operation of energy storage charging and discharging was not considered. Reihani and Ghorbani [
24] controlled the optimal charging and discharging operations of energy storage by load power predictions to achieve peak adjustment and power leveling with the access of high penetration and multi-point PV systems to the distribution network. Nagarajan and Ayyanar [
25] used the convex optimization method to optimize the charging and discharging strategy of a Li-ion battery energy storage system to reduce the impact of intermittent PV power generation on the system. In the literature [
24,
25], a charging and discharging operational strategy optimization for energy storage was realized, but this study was carried out under the premise that the location and capacity of the energy storage are pre-established. Mahani et al. [
26] optimized the planning and operation of the energy storage location and capacity in the distribution network with a high penetration rate of renewable energy to realize the peak adjustment and reduce the reverse power flow and price arbitrage. Grisales-Noreña et al. [
27] selected the minimum energy loss as the optimization objective, and used a master–slave strategy to solve the problem to obtain the optimal position and the charge/discharge operational control strategies for energy storage batteries and capacitors banks. In the literature [
26,
27], the voltage regulation of a distribution network before and after the configuration of the energy storage system was not considered.
To our knowledge, there are few studies on energy storage optimization allocation and operation that simultaneously consider the joint optimization of the installation location, capacity, charge/discharge power of an energy storage system, and power flow. Usually, the studies are only part of them, while other factors are given in advance. In addition, in the present study, the location of the PV system in the power grid is composed of one point or several points, and multi-point cases are not considered.
The objective of the paper is to propose a new optimization approach that used an improved particle swarm optimization algorithm to determine the energy storage systems’ locations, capacities and charge/discharge strategy simultaneously to minimize the voltage deviations and total active power loss for the access of high penetration single-point and multi-point PV systems in the distribution network. The membership function and weighting method are used to combine the two objectives into a single objective. The differential inertial weights and global guided cross search mechanism are utilized in the improved algorithm. The Institute of Electrical and Electronic Engineers (IEEE)-33 bus feeder system is examined. The power flow optimization results under different scenarios in the distribution network are achieved to verify its efficiency.