# Multi-Objective Configuration Optimization of
## 1 Introduction  
Wind power generation and its techniques have attracted increasing worldwide attention because of their effective reduction in environmental pollution, fossil fuel consumption, and the costs of overall electricity generation [1]. Technology for wind power generation system is regarded to be much more mature than that of other distributed generation systems [2]. Photovoltaic generation also develops fast due to low emissions, high durability, and low maintenance requirements. Its technology has been considered as another one of the most mature renewable distributed generations (RDGs) and widely applied as a mean to reach emission reduction with the increasing concern of environmental protection. However, they are characteristically fluctuating and intermittent. Their generated uneven power over time increases the difficulties in the controllability and dispatch ability. Consequently, serious challenges resulting from their integration to the grid are mounting on the power balance, system safety, and power quality [3,4]. In order to promote grid penetration capability and meet standard requirements for integration of renewable distributed generations, it's necessary to apply appropriate allocation of distributed energy storage systems (ESSs) for decreasing negative effects on the grid that are brought about by fluctuating power and improving the power quality and system stability of the distribution network penetrated with such RDGs. According to the related studies [5], distribution systems suffer most from a frequency band from 0.01 Hz to 1 Hz of the fluctuating power output. Hence, the corresponding output power needs to be compensated with ESSs. Although lead-acid battery has been widely used because of the most maturetechnology and the lowest cost [6], its lifetime is badly affected by frequent charging/discharging when used for compensating fluctuation of wind power output, especially deep discharging resulting in rising temperature of the equipment. The power-type ESS is quite different from the energy-type ESSs. Power-type ESSs, such as super-capacitor and superconducting magnet ESSs, have a high power density and quick response speed, but cannot have large energy capacity. While energy-type ESSs such as batteries have high energy density, its speed for compensating power is not ideal. While hybrid energy storage system (HESS) composed of the two types of ESSs that fully take advantage of their complementation can compensate for each other's weaknesses [3; 4; 7]. Consequently, researchers proposed [3; 4; 7; 8] an HESS that contains a supercapacitor and a battery; the supercapacitor with a quick response compensates for the output power fluctuation of high frequency, and the battery with high energy density compensates for the fluctuation of lower frequency. It is theoretically and experimentally proven that an HESS with an appreciable power allocation strategy allows for decreasing charging and discharging cycles and effectively compensates for the fluctuation of the wind power output. The key issue is how to reasonably allocate an HESS to balance the compensation effect and investment economy. Basically, the configuration of an HESS is classified into aggregated and distributed [10] as illustrated in Figure 1. Simulation results demonstrate that the distributed configuration is as effective as aggregated in wind power smoothing and system frequency deviation reduction. Figure 2 depicts the control diagram of a wind-HESS [11]. The HESS implements power control and distribution according to the difference between the wind power, \(P_{WD}\), and the reference power, \(P_{WDerf}\), that is computed via the low pass filter (LPF), and the state of charge (SOC) of each ESS device. The HESS output power, \(P_{SC}\) + \(P_{B}\), either injects to or takes form the AC bus to compensate the wind power fluctuation, \(\Delta P_{WD}=P_{WD}-P_{WDerf}\). Finally, we get the compensated wind power, \(P_{WDCom}\).