Startseite Optimized multivariable control structures for voltage regulation in active distribution networks
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Optimized multivariable control structures for voltage regulation in active distribution networks

  • Pablo Rullo , Patricio Luppi , Lautaro Braccia , Diego Feroldi ORCID logo EMAIL logo und David Zumoffen
Veröffentlicht/Copyright: 6. Mai 2025
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
International Journal of Emerging Electric Power Systems
Aus der Zeitschrift International Journal of Emerging Electric Power Systems

Abstract

The increasing penetration of distributed energy resources (DERs) into Active Distribution Networks (ADNs) demands innovative control solutions to address critical challenges such as voltage stability and power quality. Existing decentralized control strategies often fail to handle the intricate interactions between control loops in highly coupled systems, leading to suboptimal performance, excessive reactive power requirements, and operational limitations. This paper introduces a novel multivariable control framework designed for voltage regulation in ADNs. The proposed framework incorporates advanced Control Allocation and Measurement Combination modules, developed through a systematic methodology that merges Process Systems Engineering and Power Systems principles. By formulating the control design as a bilevel mixed-integer nonlinear programming (BMINLP) optimization problem, the framework minimizes voltage deviations, reactive power usage, and the reliance on actuators and sensors, ensuring system stability even under extreme conditions such as significant active power generation drops. Dynamic simulations performed on the IEEE 33-bus system demonstrate that the proposed approach outperforms traditional decentralized controllers by effectively preventing reactive power saturation and delivering superior voltage regulation. The results indicate that the voltage at all nodes stays within the permissible limits, with a maximum deviation of 0.7 %. Moreover, it achieves these results with reduced hardware and fewer control loops, highlighting its resource efficiency. This work represents a significant step forward in the control of ADNs with high DER penetration. By addressing the limitations of conventional strategies, it offers a scalable and robust solution that optimizes control resources, enhances system stability, and supports the transition to decentralized, renewable-powered distribution networks.

Keywords: active distribution networks; voltage regulation; multivariable control; variables combination; Volt/VAR control
Corresponding author: Diego Feroldi, Grupo de Ingeniería de Sistemas de Procesos (GISP), CIFASIS (CONICET-UNR), 27 de Febrero 210 bis, (S2000EZP) Rosario, Argentina; and Universidad Nacional de Rosario (UNR–FCEIA), Pellegrini 250, (S2000BTP) Rosario, Argentina, E-mail:

Acknowledgments

This work was supported by CONICET, UNR, and the PICT2019-00605 project.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: The LLM (Large Language Model) was used exclusively to enhance the writing quality of this paper.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The funding for this paper was provided by the PICT2019-00605 project, CONICET, and UNR.

  7. Data availability: The data is available upon request.

Appendix A: Data of the IEEE 33-bus distribution test system

In this section, we present the data of the IEEE 33-bus distribution test system. This data is essential for analyzing and evaluating the performance of the control methods applied to the system. Table A.2 provides information regarding the active and reactive power demands at each node, along with the permissible voltage limits. Table A.3 presents information related to the distribution network branches, including the resistance, reactance, and the maximum allowed power flow in each branch (F max).

Table A.2:

Bus data of the IEEE 33-bus distribution test system.

Bus P d [MW] Q d [Mvar] V max [p.u.] V min [p.u.]
1 0 0 1.0 1.0
2 0.1 0.06 1.05 0.95
3 0.09 0.04 1.05 0.95
4 0.12 0.08 1.05 0.95
5 0.06 0.03 1.05 0.95
6 0.06 0.02 1.05 0.95
7 0.2 0.1 1.05 0.95
8 0.2 0.1 1.05 0.95
9 0.06 0.02 1.05 0.95
10 0.06 0.02 1.05 0.95
11 0.045 0.03 1.05 0.95
12 0.06 0.035 1.05 0.95
13 0.06 0.035 1.05 0.95
14 0.12 0.08 1.05 0.95
15 0.06 0.01 1.05 0.95
16 0.06 0.02 1.05 0.95
17 0.06 0.02 1.05 0.95
18 0.09 0.04 1.05 0.95
19 0.09 0.04 1.05 0.95
20 0.09 0.04 1.05 0.95
21 0.09 0.04 1.05 0.95
22 0.09 0.04 1.05 0.95
23 0.09 0.05 1.05 0.95
24 0.42 0.2 1.05 0.95
25 0.42 0.2 1.05 0.95
26 0.06 0.025 1.05 0.95
27 0.06 0.025 1.05 0.95
28 0.06 0.02 1.05 0.95
29 0.12 0.07 1.05 0.95
30 0.2 0.6 1.05 0.95
31 0.15 0.07 1.05 0.95
32 0.21 0.1 1.05 0.95
33 0.06 0.04 1.05 0.95
Table A.3:

Branches data of the IEEE 33-bus distribution test system.

Branch From bus To bus R [Ω] X [Ω] F max [MW]
1 1 2 0.0922 0.047 5
2 2 3 0.493 0.2511 5
3 3 4 0.366 0.1864 5
4 4 5 0.3811 0.1941 5
5 5 6 0.819 0.707 5
6 6 7 0.1872 0.6188 5
7 7 8 0.7114 0.2351 5
8 8 9 1.03 0.74 5
9 9 10 1.044 0.74 2.5
10 10 11 0.1966 0.065 2.5
11 11 12 0.3744 0.1238 2.5
12 12 13 1.468 1.155 2.5
13 13 14 0.5416 0.7129 2.5
14 14 15 0.591 0.526 2.5
15 15 16 0.7463 0.545 2.5
16 16 17 1.289 1.721 2.5
17 17 18 0.732 0.574 2.5
18 2 19 0.164 0.1565 2.5
19 19 20 1.5042 1.3554 2.5
20 20 21 0.4095 0.4784 2.5
21 21 22 0.7089 0.9373 2.5
22 3 23 0.4512 0.3083 2.5
23 23 24 0.898 0.7091 2.5
24 24 25 0.896 0.7011 2.5
25 6 26 0.203 0.1034 2.5
26 26 27 0.2842 0.1447 2.5
27 27 28 1.059 0.9337 2.5
28 28 29 0.8042 0.7006 2.5
29 29 30 0.5075 0.2585 2.5
30 30 31 0.9744 0.963 2.5
31 31 32 0.3105 0.3619 2.5
32 32 33 0.341 0.5302 2.5

Appendix B: Key performance index

To evaluate the performance of the voltage controllers (VCs), it is essential to analyze the steady-state voltage deviations. The steady-state deviation percent ( Δ v S S ) of the VCs is defined as follows:

(B.1) Δ v S S ( % ) = v i s p − v i s s v i s p 100 ,

where v i s p represents the setpoint or nominal operating point of a particular voltage node and v i s s the steady state value.

References

1. Thompson, S. Strategic analysis of the renewable electricity transition: power to the world without carbon emissions? Energies 2023;16. https://doi.org/10.3390/en16176183.Suche in Google Scholar

2. Kinga, S, Megahed, TF, Kanaya, H, Mansour, DEA. A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks. Comput Electr Eng 2024;119:109574. https://doi.org/10.1016/j.compeleceng.2024.109574.Suche in Google Scholar

3. Srivastava, P, Haider, R, Nair, VJ, Venkataramanan, V, Annaswamy, AM, Srivastava, AK. Voltage regulation in distribution grids: a survey. Annu Rev Control 2023;55:165–181. https://doi.org/10.1016/j.arcontrol.2023.03.008.Suche in Google Scholar

4. Us Salam, I, Yousif, M, Numan, M, Zeb, K, Billah, M. Optimizing distributed generation placement and sizing in distribution systems: a multi-objective analysis of power losses, reliability, and operational constraints. Energies 2023;16:5907. https://doi.org/10.3390/en16165907.Suche in Google Scholar

5. Kamran, M. Fundamentals of smart grid systems. London: Academic Press; 2022.Suche in Google Scholar

6. Cai, X, Quan, C, Chen, Y. Investigation on voltage stability evaluation indicators and algorithms for power systems based on neural network algorithms. Int J Emerg Elec Power Syst 2024;25:583–91. https://doi.org/10.1515/ijeeps-2023-0148.Suche in Google Scholar

7. IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003) 2018:1–38. https://doi.org/10.1109/IEEESTD.2018.8332112.Suche in Google Scholar

8. Rocabert, J, Luna, A, Blaabjerg, F, Rodriguez, P. Control of power converters in AC microgrids. IEEE Trans Power Electron 2012;27:4734–49. https://doi.org/10.1109/tpel.2012.2199334.Suche in Google Scholar

9. Sun, H, Guo, Q, Qi, J, Ajjarapu, V, Bravo, R, Chow, J, et al.. Review of challenges and research opportunities for voltage control in smart grids. IEEE Trans Power Syst 2019;34:2790–801. https://doi.org/10.1109/tpwrs.2019.2897948.Suche in Google Scholar

10. Calderaro, V, Conio, G, Galdi, V, Piccolo, A. Reactive power control for improving voltage profiles: a comparison between two decentralized approaches. Elec Power Syst Res 2012;83:247–54. https://doi.org/10.1016/j.epsr.2011.10.010.Suche in Google Scholar

11. Liu, X, Cramer, AM, Liao, Y. Reactive power control methods for photovoltaic inverters to mitigate short-term voltage magnitude fluctuations. Elec Power Syst Res 2015;127:213–20. https://doi.org/10.1016/j.epsr.2015.06.003.Suche in Google Scholar

12. Russo, M, Fusco, G. Robust decentralized pi controllers design for voltage regulation in distribution networks with dg. Elec Power Syst Res 2019;172:129–39. https://doi.org/10.1016/j.epsr.2019.01.039.Suche in Google Scholar

13. Fusco, G, Russo, M. Applying the integral controllability property in a multi-loop control for stable voltage regulation in an active distribution network. Energies 2024;17:2455. https://doi.org/10.3390/en17112455.Suche in Google Scholar

14. Lundberg, M, Samuelsson, O, Hillberg, E. Local voltage control in distribution networks using pi control of active and reactive power. Elec Power Syst Res 2022;212:108475. https://doi.org/10.1016/j.epsr.2022.108475.Suche in Google Scholar

15. Kang, SJ, Kim, J, Park, JW, Baek, SM. Reactive power management based on voltage sensitivity analysis of distribution system with high penetration of renewable energies. Energies 2019;12:1493. https://doi.org/10.3390/en12081493.Suche in Google Scholar

16. Tang, Z, Hill, DJ, Liu, T. Distributed coordinated reactive power control for voltage regulation in distribution networks. IEEE Trans Smart Grid 2020;12:312–23. https://doi.org/10.1109/tsg.2020.3018633.Suche in Google Scholar

17. Nowak, S, Chen, YC, Wang, L. Measurement-based optimal der dispatch with a recursively estimated sensitivity model. IEEE Trans Power Syst 2020;35:4792–802. https://doi.org/10.1109/tpwrs.2020.2998097.Suche in Google Scholar

18. Mehbodniya, A, Paeizi, A, Rezaie, M, Azimian, M, Masrur, H, Senjyu, T. Active and reactive power management in the smart distribution network enriched with wind turbines and photovoltaic systems. Sustainability 2022;14:4273. https://doi.org/10.3390/su14074273.Suche in Google Scholar

19. Momoh, JA, Reddy Salkuti, S. Feasibility of stochastic voltage/var optimization considering renewable energy resources for smart grid. Int J Emerg Elec Power Syst 2016;17:287–300. https://doi.org/10.1515/ijeeps-2016-0009.Suche in Google Scholar

20. Bidgoli, HS, Van Cutsem, T. Combined local and centralized voltage control in active distribution networks. IEEE Trans Power Syst 2017;33:1374–84. https://doi.org/10.1109/tpwrs.2017.2716407.Suche in Google Scholar

21. Zhang, Z, Ochoa, LF, Valverde, G. A novel voltage sensitivity approach for the decentralized control of dg plants. IEEE Trans Power Syst 2017;33:1566–76. https://doi.org/10.1109/tpwrs.2017.2732443.Suche in Google Scholar

22. Fusco, G, Russo, M, Casolino, GM. Voltage and active power local PI control of distributed energy resources based on the effective transfer function method. Int J Electr Power Energy Syst 2023;152:109264. https://doi.org/10.1016/j.ijepes.2023.109264.Suche in Google Scholar

23. Evangelopoulos, VA, Georgilakis, PS, Hatziargyriou, ND. Optimal operation of smart distribution networks: a review of models, methods and future research. Elec Power Syst Res 2016;140:95–106. https://doi.org/10.1016/j.epsr.2016.06.035.Suche in Google Scholar

24. Farivar, M, Chen, L, Low, S. Equilibrium and dynamics of local voltage control in distribution systems. In: 52nd IEEE conference on decision and control. IEEE; 2013:4329–34 pp.10.1109/CDC.2013.6760555Suche in Google Scholar

25. Andrén, F, Bletterie, B, Kadam, S, Kotsampopoulos, P, Bucher, C. On the stability of local voltage control in distribution networks with a high penetration of inverter-based generation. IEEE Trans Ind Electron 2014;62:2519–29.10.1109/TIE.2014.2345347Suche in Google Scholar

26. Eggli, A, Karagiannopoulos, S, Bolognani, S, Hug, G. Stability analysis and design of local control schemes in active distribution grids. IEEE Trans Power Syst 2020;36:1900–9. https://doi.org/10.1109/tpwrs.2020.3026448.Suche in Google Scholar

27. Ye, L, Kosaraju, KC, Gupta, V, Trevizan, RD, Byrne, RH, Chalamala, BR. Decentralized reactive power control in distribution grids with unknown reactance matrix. IEEE Open Access J Power Energy 2024;11:154–64. https://doi.org/10.1109/oajpe.2024.3379485.Suche in Google Scholar

28. Fusco, G, Russo, M. Tuning of multivariable PI robust controllers for the decentralized voltage regulation in grid-connected distribution networks with distributed generation. Int J Dyn Contr 2020;8:278–90. https://doi.org/10.1007/s40435-019-00528-7.Suche in Google Scholar

29. Rullo, PG, Braccia, L, Feroldi, D, Zumoffen, D. Multivariable control structure design for voltage regulation in active distribution networks. IEEE Lat Am Trans 2022;20:839–47. https://doi.org/10.1109/tla.2022.9693569.Suche in Google Scholar

30. Luppi, P, del Portal, SR, Braccia, L, Zumoffen, D. Plantwide control design using latent variables: an integration between control allocation and a measurement combination approach. J Process Control 2022;120:159–76. https://doi.org/10.1016/j.jprocont.2022.11.007.Suche in Google Scholar

31. Sadat AlDavood, M, Mehbodniya, A, Webber, JL, Ensaf, M, Azimian, M. Robust optimization-based optimal operation of islanded microgrid considering demand response. Sustainability 2022;14:14194. https://doi.org/10.3390/su142114194.Suche in Google Scholar

32. Zumoffen, D, Luppi, P, Braccia, L. Advanced plant-wide control design tools applied to the fluid catalytic cracker-fractionator benchmark. Chem Eng Sci 2024;287:119732. https://doi.org/10.1016/j.ces.2024.119732.Suche in Google Scholar

33. Zumoffen, D. Plant-wide control design based on steady-state combined indexes. ISA Trans 2016;60:191–205. https://doi.org/10.1016/j.isatra.2015.10.016.Suche in Google Scholar PubMed

34. Garcia, CE, Morari, M. Internal model control. 2. Design procedure for multivariable systems. Ind Eng Chem Process Des Dev 1985;24:472–84. https://doi.org/10.1021/i200029a043.Suche in Google Scholar

35. Hacopian Dolatabadi, S, Ghorbanian, M, Siano, P, Hatziargyriou, ND. An enhanced IEEE 33 bus benchmark test system for distribution system studies. IEEE Trans Power Syst 2020;36:2565–72. https://doi.org/10.1109/tpwrs.2020.3038030.Suche in Google Scholar

36. Skogestad, S, Postlethwaite, I. Multivariable feedback control: analysis and design. Chichester, England: John Wiley & Sons; 2005.Suche in Google Scholar

37. Horn, RA, Johnson, CR. Topics in matrix analysis. New York, USA: Cambridge University Press; 1994.Suche in Google Scholar
Received: 2025-01-03
Accepted: 2025-04-22
Published Online: 2025-05-06

© 2025 Walter de Gruyter GmbH, Berlin/Boston

https://doi.org/10.1515/ijeeps-2025-0001
Schlagwörter für diesen Artikel
active distribution networks; voltage regulation; multivariable control; variables combination; Volt/VAR control
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 9.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2025-0001/html
Button zum nach oben scrollen