Abstract
In this work we address the optimal operation in active distribution networks (ADNs) with high penetration of renewable energies and energy storage. The optimal performance of ADNs can include two different optimization problems: Unit Commitment (UC) and Economic Dispatch (ED). The UC problem determines the start-up and shutdown planning of all the dispatchable generation units to supply the electricity demand, minimizing the total cost of operation, while the ED problem determines the active output power of each of the committed units for each hour of the planning horizon. Both problems have the objectives of minimizing the total cost, supplying the demand and complying with the restrictions of the main network. Here the two problems are solved together to achieve the day-ahead optimal operation of active distribution networks with distributed generation and energy storage. A test system based on the IEEE 33-bus distribution network was proposed. The optimal operation problem presented here is analyzed using four scenarios with different renewable generation and load conditions and a time-varying profile for the purchase price of energy from the network. The results reveal that the proposed network together with the optimization methodology can face diverse and highly demanding load situations, with the full use of renewable energies and complying with all the restrictions imposed. The proposed methodology is suitable for use in other optimization problems such as determining the sizing of storage units and distributed generation.
-
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Chowdhury, S, Chowdhury, SP, Crossley, P. Microgrids and active distribution networks. London, United Kingdom: The Institution of Engineering and Technology; 2009.10.1049/PBRN006ESearch in Google Scholar
2. Conejo, AJ, Baringo, L. Power system operations. Cham, Switzerland: Springer; 2018.10.1007/978-3-319-69407-8Search in Google Scholar
3. Cho, Y, Ishizaki, T, Ramdani, N, Imura, J. Box-based temporal decomposition of multi-period economic dispatch for two-stage robust unit commitment. IEEE Trans Power Syst 2019;34:3109–18. https://doi.org/10.1109/TPWRS.2019.2896349.Search in Google Scholar
4. Sperstad, IB, Korpås, M. Energy storage scheduling in distribution systems considering wind and photovoltaic generation uncertainties. Energies 2019;12:1231. https://doi.org/10.3390/en12071231.Search in Google Scholar
5. 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.Search in Google Scholar
6. Agalgaonkar, YP, Pal, BC, Jabr, RA. Distribution voltage control considering the impact of PV generation on tap changers and autonomous regulators. IEEE Trans Power Syst 2013;29:182–92. https://doi.org/10.1109/tpwrs.2013.2279721.Search in Google Scholar
7. Celli, G, Pilo, F, Pisano, G, Soma, GG. Optimal operation of active distribution networks with distributed energy storage. In: 2012 IEEE international energy conference and exhibition (ENERGYCON). IEEE; 2012:557–62 pp.10.1109/EnergyCon.2012.6348215Search in Google Scholar
8. Pilo, F, Pisano, G, Soma, GG. Optimal coordination of energy resources with a two-stage online active management. IEEE Trans Ind Electron 2011;58:4526–37. https://doi.org/10.1109/tie.2011.2107717.Search in Google Scholar
9. Howlader, HOR, Matayoshi, H, Senjyu, T. Thermal units commitment integrated with reactive power scheduling for the smart grid considering voltage constraints. Int J Emerg Elec Power Syst 2015;16:323–30. https://doi.org/10.1515/ijeeps-2014-0184.Search in Google Scholar
10. Gabash, A, Li, P. Active-reactive optimal power flow in distribution networks with embedded generation and battery storage. IEEE Trans Power Syst 2012;27:2026–35. https://doi.org/10.1109/tpwrs.2012.2187315.Search in Google Scholar
11. Montoya, OD, Gil-González, W. Dynamic active and reactive power compensation in distribution networks with batteries: a day-ahead economic dispatch approach. Comput Electr Eng 2020;85:106710. https://doi.org/10.1016/j.compeleceng.2020.106710.Search in Google Scholar
12. Valverde, G, Van Cutsem, T. Model predictive control of voltages in active distribution networks. IEEE Trans Smart Grid 2013;4:2152–61. https://doi.org/10.1109/tsg.2013.2246199.Search in Google Scholar
13. Kim, Y-J, Ahn, S-J, Hwang, P-I, Pyo, G-C, Moon, S-I. Coordinated control of a dg and voltage control devices using a dynamic programming algorithm. IEEE Trans Power Syst 2012;28:42–51. https://doi.org/10.1109/tpwrs.2012.2188819.Search in Google Scholar
14. Dimishkovska, N, Iliev, A, Dimitrov, D. Unit commitment of distributed energy resources in distribution networks using the dynamic programming method. Int J Inf Technol Syst 2020;13:17–26.Search in Google Scholar
15. Vikhar, PA. Evolutionary algorithms: a critical review and its future prospects. In: 2016 International conference on global trends in signal processing, information computing and communication (ICGTSPICC). IEEE; 2016:261–5 pp.10.1109/ICGTSPICC.2016.7955308Search in Google Scholar
16. Teng, J-H, Luan, S-W, Lee, D-J, Huang, Y-Q. Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems. IEEE Trans Power Syst 2012;28:1425–33. https://doi.org/10.1109/tpwrs.2012.2230276.Search in Google Scholar
17. Golshannavaz, S, Afsharnia, S, Aminifar, F. Smart distribution grid: optimal day-ahead scheduling with reconfigurable topology. IEEE Trans Smart Grid 2014;5:2402–11. https://doi.org/10.1109/tsg.2014.2335815.Search in Google Scholar
18. Augugliaro, A, Dusonchet, L, Favuzza, S, Riva Sanseverino, E. Voltage regulation and power losses minimization in automated distribution networks by an evolutionary multiobjective approach. IEEE Trans Power Syst 2004;19:1516–27. https://doi.org/10.1109/tpwrs.2004.825916.Search in Google Scholar
19. Logenthiran, T, Srinivasan, D, Shun, TZ. Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 2012;3:1244–52. https://doi.org/10.1109/tsg.2012.2195686.Search in Google Scholar
20. Shigenobu, R, Noorzad, AS, Muarapaz, C, Yona, A, Senjyu, T. Optimal operation and management for smart grid subsumed high penetration of renewable energy, electric vehicle, and battery energy storage system. Int J Emerg Elec Power Syst 2016;17:173–89. https://doi.org/10.1515/ijeeps-2016-0013.Search in Google Scholar
21. Vaccaro, A, Zobaa, AF. Voltage regulation in active networks by distributed and cooperative meta-heuristic optimizers. Elec Power Syst Res 2013;99:9–17. https://doi.org/10.1016/j.epsr.2013.01.013.Search in Google Scholar
22. Teleke, S, Baran, ME, Bhattacharya, S, Huang, AQ. Rule-based control of battery energy storage for dispatching intermittent renewable sources. IEEE Trans Sustain Energy 2010;1:117–24. https://doi.org/10.1109/tste.2010.2061880.Search in Google Scholar
23. Elkhatib, ME, El Shatshat, R, Salama, MMA. Decentralized reactive power control for advanced distribution automation systems. IEEE Trans Smart Grid 2012;3:1482–90. https://doi.org/10.1109/tsg.2012.2197833.Search in Google Scholar
24. Wang, P, Liang, DH, Yi, J, Lyons, PF, Davison, PJ, Taylor, PC. Integrating electrical energy storage into coordinated voltage control schemes for distribution networks. IEEE Trans Smart Grid 2014;5:1018–32. https://doi.org/10.1109/tsg.2013.2292530.Search in Google Scholar
25. Magnago, FH, Alemany, J, Lin, J. Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market. Int J Electr Power Energy Syst 2015;68:142–9. https://doi.org/10.1016/j.ijepes.2014.12.035.Search in Google Scholar
26. Ji, L, Wu, Y, Liu, Y, Sun, L, Xie, Y, Huang, G. Optimizing design and performance assessment of a community-scale hybrid power system with distributed renewable energy and flexible demand response. Sustain Cities Soc 2022;84:104042. https://doi.org/10.1016/j.scs.2022.104042.Search in Google Scholar
27. Bostan, A, Nazar, MS, Shafie-Khah, M, Catalão, JPS. Optimal scheduling of distribution systems considering multiple downward energy hubs and demand response programs. Energy 2020;190:116349. https://doi.org/10.1016/j.energy.2019.116349.Search in Google Scholar
28. Nemati, M, Braun, M, Tenbohlen, S. Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl Energy 2018;210:944–63. https://doi.org/10.1016/j.apenergy.2017.07.007.Search in Google Scholar
29. Zimmerman, RD, Murillo-Sánchez, CE. Matpower Optimal Scheduling Tool (MOST) user’s manual, version 1.1 [Online]. 2020. Available from: https://matpower. org/docs/MOST-manual-1.1.pdf.Search in Google Scholar
30. Dolatabadi, SH, 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.Search in Google Scholar
31. JosepGuerrero, M, Vasquez, JC, Matas, J, De Vicuña, LG, Castilla, M. Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans Ind Electron 2010;58:158–72. https://doi.org/10.1109/tie.2010.2066534.Search in Google Scholar
32. Bidram, A, Davoudi, A. Hierarchical structure of microgrids control system. IEEE Trans Smart Grid 2012;3:1963–1976. https://doi.org/10.1109/tsg.2012.2197425.Search in Google Scholar
33. Baran, ME, Wu, FF. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng Rev 1989;9:101–2. https://doi.org/10.1109/mper.1989.4310642.Search in Google Scholar
34. Cruz, MRM, Fitiwi, DZ, Santos, SF, Catalão, JPS. Influence of distributed storage systems and network switching/reinforcement on RES-based DG integration level. In: 2016 13th International conference on the European energy market (EEM). IEEE; 2016:1–5 pp.10.1109/EEM.2016.7521337Search in Google Scholar
35. Feroldi, D, Rullo, P. Optimal operation for the IEEE 33 bus benchmark test system with energy storage. In: 2021 IEEE URUCON; 2021:1–5 pp.10.1109/URUCON53396.2021.9647175Search in Google Scholar
36. ENRE. Ente Nacional Regulador de la Electricidad, Cuadro Tarifario – Período 04/22 [Online]; 2022. Available from: https://www.enre.gov.ar/web/Tarifasd.nsf/todoscuadros/6831EEB39C0A5E2F032586CB006834CA?opendocument [acceso April 2022].Search in Google Scholar
37. Open Power System Data. Data platform time series [Online]; 2021. Available from: https://data.open-power-system-data.org/time_series/ [acceso July 2021].Search in Google Scholar
38. Zimmerman, RD, Murillo-Sánchez, CE, Thomas, RJ. MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 2010;26:12–9. https://doi.org/10.1109/tpwrs.2010.2051168.Search in Google Scholar
39. Guo, Y, Wu, Q, Gao, H, Chen, X, Østergaard, J, Xin, H. MPC-based coordinated voltage regulation for distribution networks with distributed generation and energy storage system. IEEE Trans Sustain Energy 2018;10:1731–9. https://doi.org/10.1109/tste.2018.2869932.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Computation and analysis of anisotropic solar radiation model for India
- An adaptive energy management strategy for supercapacitor supported solar-powered electric vehicle charging station
- Multi-frequency control with fuzzy 2DOFPI in HVBTB converter of LF-HVAC system
- A modified seven-level cross-connected PUC boost multilevel inverter with reduced cost factor and device count
- Control of multifunctional inverter to improve power quality in grid-tied solar photo voltaic systems
- Research on dual-mode fault suppression strategy of AC isolation and current limiting based on hybrid MMC
- A rank correlation based translation model for simulating wind speed time series
- A SDTOGI based speed control method for SPMSG in WECS
- Application of blockchain technology in autonomous electricity transaction and settlement at the end of distribution network
- Power data sampling model based on multi-layer sensing and prediction
- Day-ahead optimal operation of active distribution networks with distributed generation and energy storage
Articles in the same Issue
- Frontmatter
- Research Articles
- Computation and analysis of anisotropic solar radiation model for India
- An adaptive energy management strategy for supercapacitor supported solar-powered electric vehicle charging station
- Multi-frequency control with fuzzy 2DOFPI in HVBTB converter of LF-HVAC system
- A modified seven-level cross-connected PUC boost multilevel inverter with reduced cost factor and device count
- Control of multifunctional inverter to improve power quality in grid-tied solar photo voltaic systems
- Research on dual-mode fault suppression strategy of AC isolation and current limiting based on hybrid MMC
- A rank correlation based translation model for simulating wind speed time series
- A SDTOGI based speed control method for SPMSG in WECS
- Application of blockchain technology in autonomous electricity transaction and settlement at the end of distribution network
- Power data sampling model based on multi-layer sensing and prediction
- Day-ahead optimal operation of active distribution networks with distributed generation and energy storage