CoChat
Sign Up
Cited by 28Power Systems and Renewable EnergyAdvanced Battery Technologies ResearchMicrogrid Control and Optimization

Battery Energy Storage for Photovoltaic Application in South Africa: A Review

Open access
Thango, Bonginkosi A., Bokoro, Pitshou N.·2022·Energies
DOI 10.3390/en15165962
Open this paper in CoChat — free accountOpen PDF

Abstract

Despite the significant slowdown of economic activity in South Africa by virtue of the COVID-19 outbreak, load shedding or scheduled power outages remained at a high level. The trend of rising load-shedding hours has persisted throughout most of the year 2022. Operational issues within the South African power utility inflamed the unpredictable nature of generation capacity, resulting in unscheduled outages at several generating units, mostly due to multiple breakdowns. To forestall substantial spikes in energy costs, an increasing number of enterprises and homeowners have started to gradually adopt renewable energy technologies to sustain their operational demand. Therefore, there is an increase in the exploration and investment of battery energy storage systems (BESS) to exploit South Africa’s high solar photovoltaic (PV) energy and help alleviate production losses related to load-shedding-induced downtime. As a result, the current work presents a comprehensive and consequential review conducted on the BESS specifically for solar PV application and in the South African context. The research investigations carried out on BESS for PV application are crucially examined, drawing attention to their capacities, shortcomings, constraints, and prospects for advancement. This investigation probed several areas of interest where the BESS-PV scheme is adopted, viz., choice of battery technology, mitigating miscellaneous power quality problems, optimal power system control, peak load shaving, South African BESS market and status of some Real BESS-PV projects. The techno-economic case scenario has been proposed in the current research and results yield that lithium-ion batteries are more viable than Lead–acid batteries.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/en15165962

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A BESS is a combination of a voltage source converter and a battery pack, such that active and reactive power can be controlled independently.

    “The BESS is a combination of a voltage source converter and a battery pack, such that the active and reactive power can be controlled independently [4][5][6][7][8][9].”

    From Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS (2023) · cites this paper as [4]

    The BESS installation tables in the Conclusions list batteries and inverters, and §4.1 (Regulation) discusses energy storage providing reactive-power support. Neither passage identifies the inverter as a voltage-source converter or says that active and reactive power can be controlled independently. Page numbers are unavailable in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 52 · Cited by 28

Research works printed but not linked: 56

  1. Bashe, M.; Shuma-Iwisi, M.; van Wyk, M.A. Assessing the Costs and Risks of the South African Electricity Portfolio: A Portfolio Theory Approach. J. Energy S. Afr. 2016, 27, 91-100. [CrossRef] · DOI
  2. Bruwer, J.P. South African Residential Electricity Price Increase between 2013 and 2020: An Online Desktop Study. Bus. Re-Solut. 2021. [CrossRef] · DOI
  3. Dauda, L.; Long, X.; Mensah, C.N.; Salman, M.; Boamah, K.B.; Ampon-Wireko, S.; Dogbe, C.S.K. Innovation, trade openness and CO 2 emissions in selected countries in Africa. J. Clean. Prod. 2021, 281, 125143. [CrossRef] · DOI
  4. Friedrich, E.; Trois, C. GHG emission factors developed for the collection, transport and landfilling of municipal waste in South African municipalities. Waste Manag. 2013, 33, 1013-1026. [CrossRef] · DOI
  5. Gür, T.M. Review of electrical energy storage technologies, materials and systems: Challenges and prospects for large-scale grid storage. Energy Environ. Sci. 2018, 11, 2696-2767. [CrossRef] · DOI
  6. Jiang, J.; Li, Y.; Liu, J.; Huang, X.; Yuan, C.; Wen, X.; Lou, D. Recent Advances in Metal Oxide-based Electrode Architecture Design for Electrochemical Energy Storage. Adv. Mater. 2012, 24, 5166-5180. [CrossRef] · DOI · Paper page
  7. Bruce, P.; Freunberger, S.; Hardwick, L.; Tarascan, J.M. Li-O 2 and Li-S batteries with high energy storage. Nat. Mater. 2012, 11, 19-29. [CrossRef] · DOI · Paper page
  8. Sabihuddin, S.; Kiprakis, A.E.; Mueller, M. A Numerical and Graphical Review of Energy Storage Technologies. Energies 2015, 8, 172-216. [CrossRef] · DOI
  9. Agyenim, F.; Hewitt, N.; Eames, P.; Smyth, M. A review of materials, heat transfer and phase change problem formulation for latent heat thermal energy storage systems (LHTESS). Renew. Sustain. Energy Rev. 2010, 14, 615-628. [CrossRef] · DOI
  10. Zahedi, A. Maximizing solar PV energy penetration using energy storage technology. Renew. Sustain. Energy Rev. 2011, 15, 866-870. [CrossRef] · DOI
  11. Mararakanye, N.; Bekker, B. Renewable energy integration impacts within the context of generator type, penetration level and grid characteristics. Renew. Sustain. Energy Rev. 2019, 108, 441-451. [CrossRef] · DOI
  12. Rana, M.M.; Romlie, M.F.; Abdullah, M.F.; Uddin, M.; Sarkar, M.R. A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system. Energy 2021, 234, 121157. [CrossRef] · DOI
  13. Korjani, S.; Casu, F.; Damiano, A.; Pilloni, V.; Serpi, A. An online energy management tool for sizing integrated PV-BESS systems for residential prosumers. Appl. Energy 2022, 313, 118765. [CrossRef] · DOI
  14. Peng, C.-Y.; Kuo, C.-C.; Tsai, C.-T. Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application. Appl. Sci. 2021, 11, 7851. [CrossRef] · DOI
  15. Gush, T.; Kim, C.-H.; Admasie, S.; Kim, J.-S.; Song, J.-S. Optimal Smart Inverter Control for PV and BESS to Improve PV Hosting Capacity of Distribution Networks Using Slime Mould Algorithm. IEEE Access 2021, 9, 52164-52176. [CrossRef] · DOI
  16. Nair, U.R.; Sandelic, M.; Sangwongwanich, A.; Dragičević, T.; Costa-Castelló, R.; Blaabjerg, F. An Analysis of Multi Objective Energy Scheduling in PV-BESS System under Prediction Uncertainty. IEEE Trans. Energy Convers. 2021, 36, 2276-2286. [CrossRef] · DOI
  17. Thirugnanam, K.; Kerk, S.K.; Yuen, C.; Liu, N.; Zhang, M. Energy Management for Renewable Microgrid in Reducing Diesel Generators Usage with Multiple Types of Battery. IEEE Trans. Ind. Electron. 2018, 65, 6772-6786. [CrossRef] · DOI
  18. Venu, C.; Riffonneau, Y.; Bacha, S.; Baghzouz, Y. Battery Storage System sizing in distribution feeders with distributed photovoltaic systems. In Proceedings of the 2009 IEEE Bucharest PowerTech, Bucharest, Romania, 28 June-2 July 2009; pp. 1-5. [CrossRef] · DOI
  19. Wang, Z.; Gu, C.; Li, F.; Bale, P.; Sun, H. Active Demand Response Using Shared Energy Storage for Household Energy Management. IEEE Trans. Smart Grid 2013, 4, 1888-1897. [CrossRef] · DOI
  20. Kumar, D.; Zare, F.; Ghosh, A. DC Microgrid Technology: System Architectures, AC Grid Interfaces, Grounding Schemes, Power Quality, Communication Networks, Applications, and Standardizations Aspects. IEEE Access 2017, 5, 12230-12256. [CrossRef] · DOI
  21. Lezhniuk, P.; Komar, V.; Rubanenko, O. Information Support for the Task of Estimation the Quality of Functioning of the Electricity Distribution Power Grids with Renewable Energy Source. In Proceedings of the 2020 IEEE 7th International Conference on Energy Smart Systems (ESS), Kyiv, Ukraine, 12-14 May 2020; pp. 168-171. [CrossRef] 25 · DOI
  22. 450-2020; IEEE Recommended Practice for Maintenance, Testing, and Replacement of Vented Lead-Acid Batteries for Stationary Applications-Redline. IEEE: Manhattan, NY, USA, 2021; pp. 1-115.
  23. P1361/D4; IEEE Draft Guide for Selection, Charging, Test and Evaluation of Lead-Acid Batteries Used in Stand-Alone Photovoltaic (PV) Systems. IEEE: Manhattan, NY, USA, 2013; pp. 1-41.
  24. 1188-2005; IEEE Recommended Practice for Maintenance, Testing, and Replacement of Valve-Regulated Lead-Acid (VRLA) Batteries for Stationary Applications-Redline. IEEE: Manhattan, NY, USA, 2006; pp. 1-49.
  25. Li, T.; Chen, Y.; Gou, H.Y.; Chen, X.Y.; Tang, M.G.; Lei, Y. A DC Voltage Swell Compensator Based on SMES Emulator and Lead-Acid Battery. IEEE Trans. Appl. Supercond. 2019, 29, 1-4. [CrossRef] · DOI
  26. Freitas, D.C.C.; de Moraes, J.L.; Neto, E.C.; Sousa, J.R.B. Battery Charger Lead-Acid using IC BQ2031. IEEE Lat. Am. Trans. 2016, 14, 32-37. [CrossRef] · DOI
  27. McKeon, B.B.; Furukawa, J.; Fenstermacher, S. Advanced Lead-Acid Batteries and the Development of Grid-Scale Energy Storage Systems. Proc. IEEE 2014, 102, 951-963. [CrossRef] · DOI
  28. Chiu, H.; Lin, L.; Pan, P.; Tseng, M. A novel rapid charger for lead-acid batteries with energy recovery. IEEE Trans. Power Electron. 2006, 21, 640-647. [CrossRef] · DOI
  29. Hannan, M.A.; Hoque, M.M.; Hussain, A.; Yusof, Y.; Ker, P.J. State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access 2018, 6, 19362-19378. [CrossRef] · DOI
  30. Zhang, W.; Wang, L.; Wang, L.; Liao, C.; Zhang, Y. Joint State-of-Charge and State-of-Available-Power Estimation Based on the Online Parameter Identification of Lithium-Ion Battery Model. IEEE Trans. Ind. Electron. 2022, 69, 3677-3688. [CrossRef] · DOI
  31. Al-Humaid, Y.M.; Khan, K.A.; Abdulgalil, M.A.; Khalid, M. Two-Stage Stochastic Optimization of Sodium-Sulfur Energy Storage Technology in Hybrid Renewable Power Systems. IEEE Access 2021, 9, 162962-162972. [CrossRef] · DOI
  32. Saruta, K. Long-term performance of sodium sulfur battery. In Proceedings of the 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC), Auckland, New Zealand, 5-8 December 2016; pp. 1-5. [CrossRef] · DOI
  33. Hatta, T. Applications of sodium-sulfur batteries. In PES T&D 2012; IEEE: Manhattan, NY, USA, 2012; pp. 1-3. [CrossRef] · DOI
  34. Manzoni, R. Sodium Nickel Chloride batteries in transportation applications. In Proceedings of the 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), Aachen, Germany, 3-5 March 2015; pp. 1-6. [CrossRef] · DOI
  35. Marcondes, A.; Scherer, H.F.; Salgado, J.R.C.; de Freitas, R.L.B. Sodium-nickel chloride single cell battery electrical model- Discharge voltage behavior. In Proceedings of the 2019 Workshop on Communication Networks and Power Systems (WCNPS), Brasilia, Brazil, 3-4 October 2019; pp. 1-4. [CrossRef] · DOI
  36. Benato, R.; Sessa, S.D.; Necci, A.; Palone, F. A general electric model of sodium-nickel chloride battery. In Proceedings of the 2016 AEIT International Annual Conference (AEIT), Capri, Italy, 5-7 October 2016; pp. 1-6. [CrossRef] · DOI
  37. Challapuram, Y.R.; Quintero, G.M.; Bayne, S.B.; Subburaj, A.S.; Harral, M.A. Electrical Equivalent Model of Vanadium Redox Flow Battery. In Proceedings of the 2019 IEEE Green Technologies Conference (GreenTech), Lafayette, LA, USA, 3-6 April 2019; pp. 1-4. [CrossRef] · DOI
  38. Vins, M.; Sirovy, M. Assessing Suitability of Various Battery Technologies for Energy Storages: Lithium-ion, Sodium-sulfur and Vanadium Redox Flow Batteries. In Proceedings of the 2020 International Conference on Applied Electronics (AE), Pilsen, Czech Republic, 8-9 September 2020; pp. 1-5. [CrossRef] · DOI
  39. Lim, J.-U.; Lee, S.-J.; Kang, K.-P.; Cho, Y.; Choe, G.-H. A modular power conversion system for zinc-bromine flow battery based energy storage system. In Proceedings of the 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC), Taipei, Taiwan, 1-4 November 2015; pp. 1-5. [CrossRef] · DOI
  40. Nakatsuji-Mather, M.; Saha, T.K. Zinc-bromine flow batteries in residential electricity supply: Two case studies. In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22-26 July 2012; pp. 1-8. [CrossRef] · DOI
  41. Lawder, M.T.; Suthar, B.; Northrop, P.W.C.; De, S.; Hoff, C.M.; Leitermann, O.; Crow, M.L.; Santhanagopalan, S.; Subramanian, V.R. Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications. Proc. IEEE 2014, 102, 1014-1030. [CrossRef] · DOI
  42. 1-2019; IEEE Guide for Design, Operation, and Maintenance of Battery Energy Storage Systems, Both Stationary and Mobile, and Applications Integrated with Electric Power Systems. IEEE: Manhattan, NY, USA, 2019; pp. 1-45. [CrossRef] · DOI
  43. Wong, Y.S.; Lai, L.L.; Gao, S.; Chau, K.T. Stationary and mobile battery energy storage systems for smart grids. In Proceedings of the 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Weihai, China, 6-9 July 2011; pp. 1-6. [CrossRef] · DOI
  44. Shi, M.; Hu, J.; Han, H.; Yuan, X. Design of Battery Energy Storage System based on Ragone Curve. In Proceedings of the 2020 4th International Conference on HVDC (HVDC), Xi'an, China, 6-9 November 2020; pp. 37-40. [CrossRef] · DOI
  45. Akhil, A. Trends and status of battery energy storage for utility applications. In Proceedings of the Tenth Annual Battery Conference on Applications and Advances, Long Beach, CA, USA, 10-13 January 1995; pp. 273-277. [CrossRef] · DOI
  46. Helling, F.; Götz, S.; Weyh, T. A battery modular multilevel management system (BM3) for electric vehicles and stationary energy storage systems. In Proceedings of the 2014 16th European Conference on Power Electronics and Applications, Lappeenranta, Finland, 26-28 August 2014; pp. 1-10. [CrossRef] · DOI
  47. Akhil, A.; Kraft, S.; Symons, P.C. Market feasibility study of utility battery applications: Penetration of battery energy storage into regulated electric utilities. In Proceedings of the Twelfth Annual Battery Conference on Applications and Advances, Long Beach, CA, USA, 14-17 January 1997; pp. 195-200. [CrossRef] · DOI
  48. Li, X.; Hui, D.; Lai, X. Battery Energy Storage Station (BESS)-Based Smoothing Control of Photovoltaic (PV) and Wind Power Generation Fluctuations. IEEE Trans. Sustain. Energy 2013, 4, 464-473. [CrossRef] · DOI
  49. Oudalov, A.; Cherkaoui, R.; Beguin, A. Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application. In Proceedings of the 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland, 1-5 July 2007; pp. 621-625. [CrossRef] · DOI
  50. Mejía-Giraldo, D.; Velásquez-Gomez, G.; Muñoz-Galeano, N.; Cano-Quintero, J.B.; Lemos-Cano, S. A BESS Sizing Strategy for Primary Frequency Regulation Support of Solar Photovoltaic Plants. Energies 2019, 12, 317. [CrossRef] · DOI
  51. Gilmanova, A.; Wang, Z.; Gosens, J.; Lilliestam, J. Building an internationally competitive concentrating solar power industry in China: Lessons from wind power and photovoltaics. Energy Sources Part B Econ. Plan. Policy 2021, 16, 515-541. [CrossRef] · DOI
  52. Berger, M.; Kocar, I.; Farantatos, E.; Haddadi, A. Dual Control Strategy for Grid-tied Battery Energy Storage Systems to Comply with Emerging Grid Codes and Fault Ride through Requirements. J. Mod. Power Syst. Clean Energy 2022, 10, 977-988. [CrossRef] · DOI
  53. Li, J.; You, H.; Qi, J.; Kong, M.; Zhang, S.; Zhang, H. Stratified Optimization Strategy Used for Restoration with Photovoltaic- Battery Energy Storage Systems as Black-Start Resources. IEEE Access 2019, 7, 127339-127352. [CrossRef] · DOI
  54. Abdullah, W.S.W.; Osman, M.; Ab Kadir, M.Z.A.; Verayiah, R. Battery energy storage system (BESS) design for peak demand reduction, energy arbitrage and grid ancillary services. Int. J. Power Electron. Drive Syst. 2020, 11, 398-408. [CrossRef] · DOI
  55. Shi, L.; Fa, L.; Zhu, H.; Shi, J.; Wu, F.; He, W.; Wang, C.; Lee, K.Y.; Lin, K. Photovoltaic active power control based on BESS smoothing. IFAC-PapersOnLine 2019, 52, 443-448. [CrossRef] · DOI
  56. Zhuo, W. An Approximate Dynamic Programming Approach for Wind Power Dispatch in Wind Farms. In Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China, 25-27 July 2018; pp. 7502-7508. [CrossRef] · DOI

Authors
Thango, B. A., & Bokoro, P. N.
Affiliations
University of Johannesburg (ZA)
Corresponding author
Thango, Bonginkosi A.
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2022-08-17
Volume
15
Issue
16
Pages
5962
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en15165962
OpenAlex
W4292163405
Cited by
28
References
52
Citations by year
2026: 7 · 2025: 8 · 2024: 7 · 2023: 6
FWCI
0.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
Keywords
battery energy storage, photovoltaic systems, solar energy, load shedding, South Africa, lithium-ion batteries, lead-acid batteries, battery technology selection, power quality, peak shaving, energy storage market, techno-economic analysis, renewable energy adoption

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review