Optimal Scheduling and Real-Time State-of-Charge Management of Energy Storage System for Frequency Regulation

• Published in: Energies (Basel) Vol. 9; no. 12; p. 1010
• Main Authors: Yang, Jin-Sun, Choi, Jin-Young, An, Geon-Ho, Choi, Young-Jun, Kim, Myoung-Hoe, Won, Dong-Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2016
• Subjects: Algorithms; Ancillary services; Automatic control; Energy charge; energy management; Energy storage; energy storage system (ESS); frequency regulation (FR); optimal scheduling; Optimization; Real time operation; Regulations; Scheduling; Signal generation; State of charge; state-of-charge (SOC)
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en9121010

An energy storage system (ESS) in a power system facilitates tasks such as renewable integration, peak shaving, and the use of ancillary services. Among the various functions of an ESS, this study focused on frequency regulation (or secondary reserve). This paper presents an optimal scheduling algorithm for frequency regulation by an ESS. This algorithm determines the bidding capacity and base point of an ESS in each operational period to achieve the maximum profit within a stable state-of-charge (SOC) range. However, the charging/discharging efficiency of an ESS causes SOC errors whenever the ESS performs frequency regulation. With an increase in SOC errors, the ESS cannot respond to an automatic generation control (AGC) signal. This situation results in low ESS performance scores, and finally, the ESS is disqualified from performing frequency regulation. This paper also presents a real-time SOC management algorithm aimed at solving the SOC error problem in real-time operations. This algorithm compensates for SOC errors by changing the base point of the ESS. The optimal scheduling algorithm is implemented in MATLAB by using the particle swarm optimization (PSO) method. In addition, changes in the SOC when the ESS performs frequency regulation in a real-time operation are confirmed using the PSCAD/EMTDC tool. The simulation results show that the optimal scheduling algorithm manages the SOC more efficiently than a commonly employed planning method. In addition, the proposed real-time SOC management algorithm is confirmed to be capable of performing SOC recovery.