# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 1 Introduction  
Due to the severe environmental pollution caused by fossil fuels and their depletion, the utilization of distributed renewable energy resources has been considered to be a practical approach to address the supply-demand stress, and to reduce the anthropogenic greenhouse gas emissions. However, the intermittent impacts, brought about by the renewable resources, requires significant reinforcement for the current grid infrastructure to meet the peak demand. The Energy Storage Systems (ESSs) promise a wide range of benefits to the energy system, such as to accommodate the increasing integration of Distributed Energy Resources (DERs), to meet peak demand, while deferring unnecessary network reinforcement, to support grid reliability and resilience, and to provide ancillary services, etc. According to the GTM Research report on global energy storage, a total of 1.4 GW and 2.3 GW\(\cdot\)h of energy storage was deployed worldwide in 2017. The Australian market took the pole position in terms of power capacity at 246 MW, while the U.S. led the world, in terms of energy capacity at 431 MW\(\cdot\)h. The report states that the U.S. ESS market will lead the global market from 2018 through 2022 and China will become the second-largest market in 2019 [1]. The demand of the ESS will keep growingwith the development of the emerging technologies in smart grid and the integration of intermittent renewable generation, such as Photovoltaic (PV) and wind generation. The International Renewable Energy Agency (IRENA) has predicted on the basis of the 2017 report that the installed capacity of ESS, all over the world, will increase by 42%-68% by 2030, and it might increase by 155%-227% if the renewable power generation grows rapidly [2]. With the enormous utilization of ESSs, substantial research activities have been carried out, globally, to establish feasibility studies and cost--benefit analyses. Energy balance, power output smoothing, and economic optimization are frequently considered in the ESS planning and optimization methods [3]. Energy balance methods use ESS to alleviate the mismatch between the power output of generation units and power consumptions, therefore, confirming the capacity of the ESSs. [PERSON] et al. carried out an analysis on the short-term prediction errors of PV generation and loads, the configuration was obtained by using the interval estimation method [4]. [PERSON] et al. studied the capacity allocation of vanadium redox battery in a stand-alone PV system with the loss of power supply probability as an index, combined with the regional irradiance, environmental temperature, and other factors [5]. Power output smoothing methods configure the ESS capacity by stabilizing power fluctuations of the renewable power generation. The optimal sitting and sizing of the ESSs were determined by considering the Distributed Generation (DG) utilization, network losses, and voltage profiles [6; 7]. In a study by [PERSON] and [PERSON], a bi-level energy storage planning model for energy storage capacity, and a location configuration algorithm was developed by taking reliability into account, in particular, accommodating the intermittence brought by renewables [8]. At present, the high-cost of ESSs impede the wide-area deployment, consequently, the cost minimization of ESS become a main issue in energy storage planning. Economic optimization methods obtain the capacity of the ESS by establishing the operation-cost-minimization object and associated constraints. In order to minimize the overall investment and operating costs, a stochastic optimal planning method of the battery energy storage system has been presented by [PERSON] et al. [9], to determine the siting and sizing of ESS for active distribution networks with a high penetration of renewables.