## 1 Introduction
### Coordination of ESs or ES with renewable energies  
As for cooperation of various ESs, [PERSON] et al. [19] put forward a distributed control method for the coordination of multiple ES units to avoid violation of voltage and thermal constraints. In order to solve over-voltage problems with multiple suitably sized ES systems in low-voltage distribution networks, an approach to coordinate multiple battery ES systems was proposed, and the performance of coordinated control and non-coordinated control was compared [20]. A coordination approach of ES systems was presented for network operation including normal, over-voltage, and under-voltage control modes [21]. Considering that ES can not only contribute to their owners' inherent activities, but also to a more flexible and efficient distribution network operation, [PERSON] et al. [22] constructed a model for the coordination of distributed ES systems in distribution networks and applied mixed-integer linear programming to maximize the technical and economic value of the distributed storage. [PERSON] et al. [23] put forward a scalable wide area control scheme applying distributed model predictive control so as to enhance the angle stability of power systems after large disturbances. With the optimization of EE and EUE, [PERSON] et al. [24] applied particle swarm optimization (PSO) to solve the problem of energy storage coordination (ESC) in EI. On the other hand, some other scholars studied the coordination of ES and renewable energies. Early in 1992, [PERSON] et al. [25] established multi-pass dynamic programming to solve the problem of short-term hydrothermal coordination considering pumped storage and battery ES systems aiming at economic benefits. [PERSON] et al. [26] presented a model from the technical and economic perspective for the design of a grid-connected photovoltaic plant with a battery ES system aiming at minimizing the levelized cost of electricity. In order to specify the photovoltaic plant rated power, the system capacity of the battery ES and the technical configuration (which can reduce the levelized cost of electricity and carbon footprint of energy), [PERSON] et al. [27] put forward a bi-objective design model for off-grid photovoltaic battery ES-diesel generator hybrid energy system. After that, a dynamic cost model for the energy mix planning and the electrical grid management was proposed by [PERSON] et al. [28]. [PERSON] et al. [29] presented an approach to coordinate independent non-dispatchable generators and ES systems aiming to maximize the production of the generators so as to maximize their income. A control algorithm considering both ES system capacity and grid reliability was designed to mitigate wind power fluctuations [30]. In order to minimize the total cost of the hybrid renewable energy system consisting of a wind farm, photovoltaic plant, fuel cell and battery storage, a three design criteria optimal method was proposed [31]. [PERSON] et al. [32] conducted the energy, exergy and economic analysis for a system composed of off-grid photovoltaic, wind power and fuel cells. As for the planning of ESs, most studies concentrated on the integration of ES and renewable energies [33; 34; 35; 36], ES and flexible demand [37; 38], or the cooperation of a variety of ESs in a certain power system [19; 22], and few researchers studied the coordination of multiple ESs in EI [24]. Therefore, in the context of EI, this paper optimizes energy storage coordination (ESC) from the perspective of EE and EUE. To handle these issues, multi-agent particle swarm optimization (MAPSO) is proposed in this paper. Particle swarm optimization (PSO) is derived from the study of bird predator behavior. Essentially, it belongs to an iterative random search algorithm, which can find the global optimal solution with high probability and fast computational efficiency [39].