Economic analysis of private investor participation in long-term distribution network planning

Document Type : Original Article


1 Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

2 Department of Energy Technology, Aalborg University, Esbjerg, Denmark



Recently, distribution network planners have enacted some facilities and policies to utilize the potential of private investor participation. Network planners should propose an attractive scheme to persuade the investor to take part in the long-term planning. In this paper, a distribution network planning approach with the cooperation of the private investor is proposed. In the proposed approach, the network planner optimizes the battery energy storage systems (BESS) installed by the investor to satisfactorily shave the peak load of the system. Through this optimization, the planner provides a financial resource to support the investor during the planning horizon. The benefits of both participants are considered and evaluated through economic indices such as payback period years (PPY), profit investment ratio (PIR), internal rate of return (IRR), and net present value (NPV). Due to the presence of photovoltaic (PV) in the system, and the inherent intermittency of load, a K-means data clustering algorithm is employed to catch the uncertainty of the problem. The obtained mixed-integer nonlinear model is solved via particle swarm optimization (PSO) and the proposed approach is tested and implemented on a 16-bus distribution test system. A sensitivity analysis on the incentive price and investment cost is also performed. Finally, the obtained results are compared with the incentive price of several countries, and it is shown that the proposed approach leads to an acceptable result and reasonable incentive price, while the planner's targets are considered as well.


Main Subjects

1. A. Bosisio, A. Berizzi, E. Amaldi, C. Bovo, and X. A. Sun, “Optimal Feeder Routing in Urban Distribution Networks Planning with Layout Constraints and Losses,” J. Mod. Power Syst. Clean Energy, vol. 8, no. 5, pp. 1005–1014, 2020.
2. H. A. S. Abushamah, M. R. Haghifam, and T. G. Bolandi, “A novel approach for distributed generation expansion planning considering its added value compared with centralized generation expansion,” Sustain. Energy, Grids Networks, vol. 25, p. 100417, 2021.
3. A. Ashoomezhad, Q. Asadi, H. Falaghi, and A. Hajizadeh, “Private Investors Participation in Long-Term Distribution Network Planning,” in 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2021, pp. 1–5.
4. L. Luo et al., “Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty,” J. Energy Storage, vol. 28, p. 101306, 2020.
5. S. Kumari, P. Jain, D. Saxena, and R. Bhakar, “Dynamic Distribution Network Expansion Planning Under Energy Storage Integration Using PSO with Controlled Particle Movement,” in Advanced Engineering Optimization Through Intelligent Techniques, Springer, 2020, pp. 497–514.
6. M. Uddin, M. F. Romlie, M. F. Abdullah, C. Tan, G. M. Shafiullah, and A. H. A. Bakar, “A novel peak shaving algorithm for islanded microgrid using battery energy storage system,” Energy, vol. 196, p. 117084, 2020.
7. V. Vahidinasab et al., “Overview of electric energy distribution networks expansion planning,” IEEE Access, vol. 8, pp. 34750–34769, 2020.
8. V. H. Fan, Z. Dong, and K. Meng, “Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles,” Appl. Energy, vol. 278, p. 115720, 2020.
9. H. Tang, C. Liu, Y. Cao, K. Lv, and Q. Zhang, “Hierarchical scheduling learning optimisation of two-area active distribution system considering peak shaving demand of power grid,” Discret. Event Dyn. Syst., pp. 1–30, 2021.
10. S. Lakshmi and S. Ganguly, “Multi-objective planning for the allocation of PV-BESS integrated open UPQC for peak load shaving of radial distribution networks,” J. Energy Storage, vol. 22, pp. 208–218, 2019.
11. H. Mehrjerdi, “Simultaneous load leveling and voltage profile improvement in distribution networks by optimal battery storage planning,” Energy, vol. 181, pp. 916–926, 2019.
12. L. Viola, L. C. P. da Silva, and M. J. Rider, “Optimal Operation of Battery and Hydrogen Energy Storage Systems in Electrical Distribution Networks for Peak Shaving,” in 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), 2019, pp. 1–6.
13. R. Martins, H. C. Hesse, J. Jungbauer, T. Vorbuchner, and P. Musilek, “Optimal component sizing for peak shaving in battery energy storage system for industrial applications,” Energies, vol. 11, no. 8, p. 2048, 2018.
14. A. Ashoornezhad, H. Falaghi, M. Yousefi, and A. Hajizadeh, “Bi-Level Distribution Network Planning Integrated with Energy Storage to PV Connected Network,” in 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), 2020, pp. 1325–1329.
15. R. P. Praveen, V. Keloth, A. G. Abo-Khalil, A. S. Alghamdi, A. M. Eltamaly, and I. Tlili, “An insight to the energy policy of GCC countries to meet renewable energy targets of 2030,” Energy Policy, vol. 147, p. 111864, 2020.
16. G. Gozgor, M. K. Mahalik, E. Demir, and H. Padhan, “The impact of economic globalization on renewable energy in the OECD countries,” Energy Policy, vol. 139, p. 111365, 2020.
17. I. J. Scott, A. Botterud, P. M. S. Carvalho, and C. A. S. Silva, “Renewable energy support policy evaluation: The role of long-term uncertainty in market modelling,” Appl. Energy, vol. 278, p. 115643, 2020.
18. G. Bersalli, P. Menanteau, and J. El-Methni, “Renewable energy policy effectiveness: A panel data analysis across Europe and Latin America,” Renew. Sustain. Energy Rev., vol. 133, p. 110351, 2020.
19. K. D. Pippi, G. C. Kryonidis, and T. A. Papadopoulos, “Methodology for the Techno-Economic Assessment of Medium-Voltage Photovoltaic Prosumers Under Net-Metering Policy,” IEEE Access, vol. 9, pp. 60433–60446, 2021.
20. N. H. Umar, B. Bora, C. Banerjee, P. Gupta, and N. Anjum, “Performance and economic viability of the PV system in different climatic zones of Nigeria,” Sustain. Energy Technol. Assessments, vol. 43, p. 100987, 2021.
21. F. Barati, S. Jadid, and A. Zangeneh, “Private investor-based distributed generation expansion planning considering uncertainties of renewable generations,” Energy, vol. 173, pp. 1078–1091, 2019.
22. M. A. Alotaibi and M. M. A. Salama, “An incentive-based multistage expansion planning model for smart distribution systems,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5469–5485, 2018.
23. H. Arasteh, V. Vahidinasab, M. S. Sepasian, and J. Aghaei, “Stochastic system of systems architecture for adaptive expansion of smart distribution grids,” IEEE Trans. Ind. Informatics, vol. 15, no. 1, pp. 377–389, 2018.
24. B. R. Prusty and D. Jena, “A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach,” Renew. Sustain. Energy Rev., vol. 69, pp. 1286–1302, 2017.
25. S. Li, H. Ma, and W. Li, “Typical solar radiation year construction using k-means clustering and discrete-time Markov chain,” Appl. Energy, vol. 205, pp. 720–731, 2017.
26. Y. Latreche, H. Bouchekara, K. Naidu, H. Mokhlis, and W. M. Dahalan, “Comprehensive Review of Radial Distribution Test Systems,” TechRxiv, no. 1, pp. 1–65, 2020.
27. A. Bagheri, H. Monsef, and H. Lesani, “Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 56–70, 2015.
28. “”
29. V. V Thang, “Optimal sizing of distributed energy resources and battery energy storage system in planning of islanded micro-grids based on life cycle cost,” Energy Syst., pp. 1–20, 2020.
30. G. Coria, F. Penizzotto, and R. Pringles, “Economic analysis of photovoltaic projects: The Argentinian renewable generation policy for residential sectors,” Renew. Energy, vol. 133, pp. 1167–1177, 2019