Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response
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. © 2020 Elsevier Ltd
Keywords
[RSTDPub], Artificial bee colony, Battery sizing, Battery storage, BESS, Change of frequency, Controller tuning, DIgSILENT PowerFactory, Energy storage system, Fitness-scaled chaotic, Frequency deviation, Frequency nadir, Frequency response, Generator dispatch, Hybrid meta-heuristic, IEEE 39 bus data, PSO, ROCOF, Transmission network planning, Computer software, Electromagnetic wave emission, Energy storage, Frequency response, Heuristic methods, Particle swarm optimization (PSO), Renewable energy resources, Sensitivity analysis, Artificial bee colonies, Frequency deviation, Hybrid Meta-heuristic, Large power systems, Optimization algorithms, Rate of change of frequencies, Renewable energy source, Utility-scale energy storage systems, Data storage equipment
Document Type
Journal Article
Date of Publication
1-1-2020
Publication Title
Journal of Energy Storage
Publisher
Elsevier Ltd
School
School of Engineering
RAS ID
31698
Funders
Edith Cowan University, ECU
Copyright
subscription content
Comments
Das, C. K., Mahmoud, T. S., Bass, O., Muyeen, S. M., Kothapalli, G., Baniasadi, A., & Mousavi, N. (2020). Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response. Journal of Energy Storage, 29, Article 101315. https://doi.org/10.1016/j.est.2020.101315