Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Abstract

As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

“…In existing literature, most of the studies related to battery modelling and control concentrate on long-term cost-based optimization techniques [13][14][15][16][17][18]. Some of the recent studies [19][20][21] have also utilised battery aging models, i.e., their capacity degradation and resistance increase as aging parameters in the power system planning and control studies (predominantly considered as the tertiary control of microgrids).…”

Predictive energy management strategy for battery energy storage considering battery degradation cost

“…Nevertheless, the BES calendar aging cost and the effect of complete/incomplete cycles on the BES cycle aging cost are ignored. In [42,43], the BES degradation cost is calculated according to the BES cycle aging. However, the BES calendar aging and the impact of the BES complete/incomplete cycles on the cycle aging process are neglected in computing the BES degradation cost.…”

Correction: Lee et al. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies 2021, 14, 470

“…In the original publication [1], References [2][3][4][5] were not cited, and proper credit was not given for the Algorithm steps in Section 3.2.1 (Reference [5]). The correct citation has now been inserted into Section 3, BESS Modeling, Section 3.2.1, Rainflow-Counting Algorithm, Paragraph 1, and should read as follows:…”