Cycle-Life-Aware Optimal Sizing of Grid-Side Battery Energy Storage

Abstract

Grid-side electrochemical battery energy storage systems (BESS) have been increasingly deployed as a fast and flexible solution to promoting renewable energy resources penetration. However, high investment cost and revenue risk greatly restrict its grid-scale applications. As one of the key factors that affect investment cost, the cycle life of battery heavily depends on its charging/discharging actions during the operation, particularly in the presence of uncertain renewable generation. In this context, it is necessary to consider the operation-dependent cycle life of batteries in optimal BESS sizing, which imposes great challenges to the modeling and solving of the planning problems. In this paper, we propose a novel two-level optimal sizing model for grid-scale BESS, considering its operation under uncertainties induced by volatile wind generation. In the lower level, a long-term chronological operation simulation of BESS is processed with an accurate cycle life model of batteries; in the upper level, marginal economic utility analysis and BESS size reforming are conducted to approach the optimal size of BESS. An iterative algorithm is designed to solve the model effectively. The proposed method is verified on a modified IEEE RTS-24 system and a real provincial power grid of China. INDEX TERMS battery energy storage, optimal sizing, cycle life, marginal utility analysis I. INTRODUCTION A. BACKGROUND VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

Sizing and operation co‐optimization strategy for flexible traction power supply system

“…The traction load is demonstrated in Figure 5a, generated with the measured load of the single train and headway modelled by the gamma distribution $$\mathcal{G}( {1.53,\ 6.94} )$$. Considering the stochastic nature, the PV outputs are represented by the power profiles of four typical scenarios [9], the magnitudes and probabilities of which are shown in Figure 5b. The specifications of HESS are given in Table 1.…”

“…In order to test and verify the necessity of the operational‐planning co‐optimization framework for the presented FTPSS considering the degradation model of BESS, the popular bi‐level frame with the rain flow counting algorithm is adopted as method 1 [9]. Note that the particle swarm optimization algorithm in the master level is used to obtain optimized sizes within the bounds $${N}_b \in [ {10,\ 2000} ]$$ and $${N}_u \in [ {10,800} ]$$.…”

“…Based on the above results, the aging characteristic of ESS is estimated by the semi‐experiential method and a battery sizing‐oriented cost‐benefit analysis is built in the external system. Besides, references [9] and [10] formulate the optimal operation strategy as a mixed integer linear programming (MILP) model in the slave layer, and then evaluate the lifespan of ESS by the rain flow counting method, thus modelling the investment cost in the master layer. A similar bi‐level frame is adopted for the sizing of the hybrid energy storage system (HESS) with the state machine‐based power flow control strategy and rain flow counting method in [11].…”

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

“…A two-level sizing of grid-scale BESS is proposed in [23], considering the effect of the uncertainty of wind power in the operation of the BESS. The proposed strategy involved lower level and upper level operations.…”

“…Although the sizing and operation of BESS in a wind-penetrated microgrid have been tremendously studied in the literature, only few of the previous studies have used the BO algorithm to solve the optimization problem. Specifically, based on the limitations cited in [23,26], we present the BOA as another flexible solution for solving the problem of BESS wind power integration in the microgrid. Moreover, the contributions to study in this work are summarised as follows:…”