Development of Energy Management and Voltage Control Algorithms for Microgrids: A Case Study with Static Voltage Regulator at Low Voltage Level

Abstract

Recently, with the increase in distributed energy production, consumers' voltage rises on the low voltage (LV) side. At the same time, problems such as reverse power flow and grid instability together with the increase in voltage cause power line losses, therefore system efficiency decreases. This paper proposes a static voltage regulator (SVR) voltage control algorithm to solve LV microgrids’ voltage problems. A simulation study has been carried out for the implementation of a voltage control algorithm together with the Rule Based and Optimization Based Energy Management System (EMS). IEEE 13 Bus test system and proposed algorithms are modeled in Simulink/MATLAB. Results of Optimization Based EMS are obtained with Python programming language. Voltage and load profiles are obtained from automatic meter reading (AMR) data. Furthermore, results demonstrate that the SVR voltage control algorithm can be used effectively to reduce microgrids’ voltage to the desired voltage level. Additionally, it has been proved by simulation results that microgrids can be managed more effectively with Optimization Based EMS.

Keywords

• Energy management system (EMS)
• microgrid
• optimization
• voltage regulation

Mikro Şebekeler için Enerji Yönetimi ve Gerilim Kontrol Algoritmalarının Geliştirilmesi: Alçak Gerilim Seviyesinde Statik Voltaj Regülatörü Örneği

Abstract

Son yıllarda dağıtık enerji üretiminin artması ile birlikte alçak gerilim (AG) tarafta, tüketici geriliminde yükselmeler meydana gelmektedir. Aynı zamanda ters güç akışı, şebeke dengesizliği gibi problemler ile birlikte gerilimdeki yükselme, enerji hatları üzerinde kayıplara yol açarak sistemi veriminin düşmesine neden olmaktadır. Bu makalede, AG mikro şebekeler için gerilim problemlerinin çözümüne yönelik, mikro şebeke önüne statik voltaj regülatörü (SVR) bağlanmıştır ve SVR gerilim kontrol algoritması geliştirilmiştir. Gerilim kontrol algoritmasının mikro şebeke için önerilen Kural Tabanlı ve Optimizasyon Tabanlı Enerji Yönetimi Sistemleri (EYS) ile birlikte uygulanmasına yönelik benzetim çalışması yapılmıştır. IEEE 13 bara test sistemi ve önerilen algoritmalar Simulink/MATLAB ortamında oluşturulmuştur. Optimizasyon tabanlı EYS sonuçları, Python programlama dili kullanılarak elde edilmiştir. Gerçek gerilim ve yük profili otomatik sayaç okuma sistemi (OSOS) verilerinden alınmıştır ve elde edilen sonuçlar SVR gerilim kontrol algoritmasının, mikro şebekelerinin gerilimini istenilen seviyeye indirilmesinde etkili bir şekilde kullanılabileceğini göstermiştir. Aynı zamanda Optimizasyon Tabanlı EYS ile mikro şebekelerin daha efektif bir şekilde yönetilebileceği benzetim çalışması sonuçları ile doğrulanmıştır.

Keywords

• Enerji yönetim sistemi (EYS)
• gerilim regülasyonu
• mikro şebeke
• optimizasyon

References

1. Akter, M. N., Mahmud, Md A., Than Oo, A. M., (2017). A hierarchical transactive energy management system for energy sharing in residential microgrids. Energies, Cilt 10, Syf. 2098-2124. https://doi.org/10.3390/en10122098
2. Aryazanezhad, M., (2018). Management and coordinating of LTC, SVR, shunt capacitor and energy storage with high PV penetration in power distribution system for voltage regulation and power loss minimization, Electric Power System Research, Cilt 100, Syf.178-192. https://doi.org/10.1016/j.ijepes.2018.02.015
3. Bektaş, E., Bayındır, K.Ç., Terciyanlı, A., Aydın, R. A., Baykal, Ş., Yılmaz, H., (2022). Energy management integrated volt var optimization for distribution systems with SVR, PV inverter, and BESS: a case study in distribution system of Elazığ/Turkey. Electrical Engineering . https://doi.org/10.1007/s00202-022-01690-6
4. Castro, M. V., Moreira C, Carvalho L. M., (2020). Hierarchical optimization for energy scheduling and volt–var control in autonomous clusters of microgrids. IET Renewable Power Generation Cilt 4, Sayı 1, Syf. 27–38. https://doi.org/10.1049/iet-rpg.2019.0357
5. Hu, J., Liu, Y., Yan, Z., (2017). Modelling on electrical power market clearing with consideration of the participation of VPP and MG in view of energy market internet, in First IEEE Conference on Energy Internet, Syf. 171-175, Beijing, China. 10.1109/ICEI.2017.37
6. Hu, J., Shan, Y, Xu, Y., Guerrero, J. M., (2019). A coordinated control of hybrid ac/dc microgrids with PV-wind-battery under variable generation and load conditions. Electrical Power and Energy Systems, Cilt 104, Syf. 583-592. 10.1016/j.ijepes.2018.07.037
7. Ibrahim, M., Salama, M. M. A., (2015). Smart distribution system volt/var control using distributed intelligence and wireless communication. IET Generation, Transmission & Distribution, Cilt. 9, Syf. 307-318. https://doi.org/10.1049/iet-gtd.2014.0513
8. Jafari, M., Olowu, T. O., Sarwat, A. I., (2018). Optimal smart inverters Volt-Var selection with a multi-objective Volt-Var optimization using evolutionary algorithm approach, 50th North American Power Symposium (NAPS), Syf. 1-6, North Dakota. 10.1109/NAPS.2018.8600542

9. Long, Q., Wang, J., Lubkeman, D., Lu, N., Chen, P., (2019). Volt-Var optimization of distribution systems for coordinating utility voltage control with smart inverters, IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Syf. 1-5, Washington, DC. 10.1109/ISGT.2019.8791600
10. Luna, A. C., Diaz, N. L., Graells, M., Vasquez, J. C., Guerrero, J. M., (2016). Cooperative energy management for a cluster of households prosumers, IEEE Trans on Consumer Elec., Cilt 62, Syf. 235-242. 10.1109/TCE.2016.7613189
11. Luna, A. C., Diaz, N. L., Savaghebi, M., Vasquez, J. C., Guerrero, J. M., Sun, K., Chen, G., Sun, L. (2016). Optimal power scheduling for a grid-connected hybrid PV-wind-battery microgrid system, in IEEE Applied Power Electronics Conference and Exposition (APEC), Syf. 1227-1234, Long Beach, CA, USA. 10.1109/APEC.2016.7468025
12. Malekpour, A. R., Niknam, T., (2011). A probabilistic multi-objective daily Volt/Var control at distribution networks including renewable energy sources. Energy-Elsevier, Cilt 36, Sayı 5, Syf. 3477-3488. https://doi.org/10.1016/j.energy.2011.03.052
13. Mónica, A., Hortensia, A., Mircea, C., (2012). A multiobjective Var/Volt Management System in Smartgrids. Energy Procedia, Cilt 14, Syf. 490-1495. https://doi.org/10.1016/j.egypro.2011.12.1122
14. Naina, P. M., Rajamani, H. S., Swarup, K. S., (2017). Modeling and simulation of virtual power plant in energy management system applications, in 7th International Conference on Power Systems (ICPS), Syf. 392-397, Pune, India. 10.1109/ICPES.2017.8387326
15. Ramakrishna, G., Rao, N.D., (1999). Adaptive neuro-fuzzy inference system for volt:var control in distribution systems. Electric Power Systems Research, Cilt 49, Syf. 87–97. https://doi.org/10.1016/S0378-7796(98)00073-X
16. Ranaweera, I., Mitgard, O. M., Korpas, M., (2017). Distributed control scheme for residential battery storage units coupled with PV systems, Renewable Energy, Cilt 113, Syf. 1099-1110. https://doi.org/10.1016/j.renene.2017.06.084
17. Resener, M., Haffner, S., Pereira, L. A., Panos, M. P., Ramos, M. J. S., (2019). A comprehensive MILP model for the expansion planning of power distribution systems-Part I: Problem formulation, Electric Power System Research, Cilt 170, Syf. 378-384. https://doi.org/10.1016/j.epsr.2019.01.040
18. Resener, M., Haffner, S., Pereira, L. A., Panos, M. P., Ramos, M. J. S., (2019). A comprehensive MILP model for the expansion planning of power distribution systems-Part II: Numerical Results, Electric Power System Research, Cilt 170, Syf. 317-325, 2019. https://doi.org/10.1016/j.epsr.2019.01.036
19. Zafar, R., Mahmood, A., Razzaq, S., Ali, W., Naeem, U., (2018). Prosumer based energy management and sharing in smart grid. Renewable and Sustaniable Energy Reviews, Cilt 82, Syf. 1675-1684. https://doi.org/10.1016/j.rser.2017.07.018
20. URL-1: https://anaconda.org/conda-forge/glpk