Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response. (June 2020)

• Record Type: Journal Article
• Authors: Das, Choton K.; Mahmoud, Thair S.; Bass, Octavian; Muyeen, S.M.; Kothapalli, Ganesh; Baniasadi, Ali; Mousavi, Navid
• Is Part Of: Journal of energy storage. Volume 29(2020)
• Publication Date: 2020-06
• Subjects: Change of frequency -- Energy storage system -- Battery sizing -- Frequency response -- Frequency deviation -- Transmission network planning -- IEEE 39 bus data -- ROCOF -- Frequency nadir -- PSO -- Artificial bee colony -- DIgSILENT PowerFactory -- Generator dispatch -- Fitness-scaled chaotic -- Controller tuning -- Hybrid meta-heuristic -- BESS -- Battery storage; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101315
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Optimal sizing of a grid-scale battery energy storage system is investigated. Frequency deviation and ROCOF are minimized through the BESS sizing. Parameter tuning of BESS PQ controller (active power part) is performed. Both power and energy sizes of the BESS are estimated. Frequency response of transmission networks is improved. Abstract: The frequency response of a large power system is affected by the penetration of renewable energy sources (RESs), where a utility-scale energy storage system (ESS) can alleviate the problem. This paper presents a strategy for sizing an ESS to improve frequency response of transmission networks. The location of the ESS in the transmission network is determined through a sensitivity analysis targeting minimum line loading around a bus. The ESS sizing strategy considers the minimization of frequency deviation as well as rate of change of frequency (ROCOF) after generator or load tripping events. The tuning of PQ controller parameters of the ESS (active power part) is also performed for frequency response improvement. The proposed approach is tested in a modified IEEE-39 bus power system considering a variety of scenarios where RESs are integrated as four different schemes for peak and off-peak load conditions. DIgSILENT PowerFactory is used for developing, testing, and analyzing the system models. A fitness-scaled chaotic artificial bee colony (FSCABC) optimization algorithm (a hybrid meta-heuristic approach) is used for optimization through a Python script automating simulation events in PowerFactory. The results obtained from the FSCABC approach are verified through the application of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS sizing technique including ESS controller tuning can successfully improve the frequency response of a transmission network.