A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage
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Jitendra Singh Bhadoriya
and
Atma Ram Gupta
Abstract
In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. POSOCO. Flexibility analysis of thermal generation for renewable integration in India; 2020. Available from: http://www.cea.nic.in/reports/others/thermal/trm/flexible_operation.pdf.Search in Google Scholar
2. Jacobson, MZ. 100% Clean, Renewable Energy and Storage for Everything. Cambridge: Cambridge University Press; 2020. https://doi.org/10.1017/9781108786713.10.1017/9781108786713Search in Google Scholar
3. Cochran, J. GREENING THE GRID: a joint initiative by USAID and ministry of power; 2017. Available from: https://www.nrel.gov/docs/fy17osti/68720.pdf.Search in Google Scholar
4. Krishan, R, Singh, SK. Study of different use cases of the grid connected battery energy storage system in India; 2020.Search in Google Scholar
5. UNFCCC. Paris Agreement. FCCC/CP/2015/L.9/Rev1. Paris: United Nations Framework Convention on Climate Change (UNFCCC); 2015. https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf.Search in Google Scholar
6. Shereef, RM, Khaparde, SA. A comprehensive method to find RPO trajectory and incentive scheme for promotion of renewable energy in India with study of impact of RPO on tariff. Energy Policy 2013;61:686–696. https://doi.org/10.1016/j.enpol.2013.06.039.Search in Google Scholar
7. IRENA. Renewable power generation costs in 2019. Abu Dhabi: International Renewable Energy Agency (IRNA); 2020.Search in Google Scholar
8. Saxena, NK. Estimation of dynamic compensation for renewable-based hybrid DG in radial distribution system using least error iterative method. Iran J Sci Technol Trans Electr Eng 2020;1. https://doi.org/10.1007/s40998-020-00345-1.Search in Google Scholar
9. Murty, VVSN, Kumar, A. Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. Int J Electr Power Energy Syst 2015;69:246–56. https://doi.org/10.1016/j.ijepes.2014.12.080.Search in Google Scholar
10. POSOCO. Electricity demand pattern analysis; 2016.Search in Google Scholar
11. Bohre, AK, Agnihotri, G, Dubey, M. Optimal sizing and sitting of DG with load models using soft computing optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system. IET Gener, Transm Distrib 2016;10:2606–2621. https://doi.org/10.1049/iet-gtd.2015.1034.Search in Google Scholar
12. Kanwar, N, Gupta, N, Niazi, KR, Swarnkar, A. Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns. J Mod Power Syst Clean Energy 2015;3:475–84. https://doi.org/10.1007/s40565-015-0169-0.Search in Google Scholar
13. CEA. Growth of Electricity Sector in India from 1947-2019. Ministery of Power ,New Delhi, Govt of India: Central Electricity Authority (CEA); 2019. https://cea.nic.in/wp-content/uploads/pdm/2020/12/growth_2020.pdf.Search in Google Scholar
14. Singh, D, Singh, D, Verma, KS. Multiobjective optimization for DG planning with load models. IEEE Trans Power Syst 2009;24:427–36. https://doi.org/10.1109/TPWRS.2008.2009483.Search in Google Scholar
15. Grahn, P, Söder, L. Electric vehicle charging impact on load profile. Trita-EE; Stockholm, 2013. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-116145.Search in Google Scholar
16. Li, R, Wang, W, Chen, Z, Wu, X. Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective Bi-level optimization. J Mod Power Syst Clean Energy 2018;6:342–55. https://doi.org/10.1007/s40565-017-0332-x.Search in Google Scholar
17. Abushamah, HAS, Haghifam, MR, Ghanizadeh Bolandi, T. A novel approach for distributed generation expansion planning considering its added value compared with centralized generation expansion. Sustain Energy Grids Netw 2021;25:100417. https://doi.org/10.1016/j.segan.2020.100417.Search in Google Scholar
18. Yang, Y, Qiu, J, Jin, M, Zhang, C. Integrated grid, coal-fired power generation retirement and GESS planning towards a low-carbon economy. Int J Electr Power Energy Syst 2021;124:106409. https://doi.org/10.1016/j.ijepes.2020.106409.Search in Google Scholar
19. Ahmadi, M, Adewuyi, OB, Danish, MSS, Mandal, P, Yona, A, Senjyu, T. Optimum coordination of centralized and distributed renewable power generation incorporating battery storage system into the electric distribution network. Int J Electr Power Energy Syst 2021;125:106458. https://doi.org/10.1016/j.ijepes.2020.106458.Search in Google Scholar
20. Battapothula, G, Yammani, C. Multi-objective simultaneous optimal planning of electrical vehicle fast charging stations and DGs in distribution system. J Mod Power Syst Clean Energy 2019;7:923–934. https://doi.org/10.1007/s40565-018-0493-2.Search in Google Scholar
21. Li, X, Yao, L, Dong, H. Optimal control and management of a large-scale battery energy storage system to mitigate fluctuation and intermittence of renewable generations. J Mod Power Syst Clean Energy 2016;4:593–603. https://doi.org/10.1007/s40565-016-0247-y.Search in Google Scholar
22. Valencia, A, Hincapie, RA, Gallego, RA. Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks. J Energy Storage 2021;34:102158. https://doi.org/10.1016/j.est.2020.102158.Search in Google Scholar
23. Siostrzonek, T, Piróg, S. Energy storage system. Solid State Phenom 2009;147–149:416–20. https://doi.org/10.4028/www.scientific.net/SSP.147-149.416.Search in Google Scholar
24. Ge, S, Xu, L, Liu, H. Low-carbon benefit analysis on DG penetration distribution system. J Mod Power Syst Clean Energy 2015;3:139–48. https://doi.org/10.1007/s40565-015-0097-z.Search in Google Scholar
25. Hemmati, R, Mehrjerdi, H. Stochastic linear programming for optimal planning of battery storage systems under unbalanced-uncertain conditions. J Mod Power Syst Clean Energy 2020;8:971–80. https://doi.org/10.35833/MPCE.2019.000324.Search in Google Scholar
26. Liu, W, Niu, S, Xu, H. Optimal planning of battery energy storage considering reliability benefit and operation strategy in active distribution system. J Mod Power Syst Clean Energy 2017;5:177–86. https://doi.org/10.1007/s40565-016-0197-4.Search in Google Scholar
27. Zheng, Y, Dong, Z, Huang, S, Meng, K, Luo, F, Huang, J, et al.. Optimal integration of mobile battery energy storage in distribution system with renewables. J Mod Power Syst Clean Energy 2015;3:589–96. https://doi.org/10.1007/s40565-015-0134-y.Search in Google Scholar
28. PFCL. Seventh Annual Integrated Ratings of State Power Distribution Utilities; Power Finance Corporation Limited (PFCL), India 2019. (Retrieved March 2, 2020) Available from: https://pfcindia.com/DocumentRepository/ckfinder/files/GoI_Initiatives/Annual_Integrated_Ratings_of_State_DISCOMs/7th_Rating_Booklet_Final_13-10-2019.pdf.Search in Google Scholar
29. Venkateswaran, VB, Saini, DK, Sharma, M. Environmental constrained optimal hybrid energy storage system planning for an Indian distribution network. IEEE Access 2020;8:97793–808. https://doi.org/10.1109/ACCESS.2020.2997338.Search in Google Scholar
30. From, I, Discom, THE. Supporting discoms in implementing amendments to electricity act insights from the discom transformation; 2020.Search in Google Scholar
31. Georgilakis, PS, Hatziargyriou, ND. Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans Power Syst 2013;28:3420–8. https://doi.org/10.1109/TPWRS.2012.2237043.Search in Google Scholar
32. Elbeltagi, E, Hegazy, T, Grierson, D. Comparison among five evolutionary-based optimization algorithms. Adv Eng Inf 2005;19:43–53. https://doi.org/10.1016/j.aei.2005.01.004.Search in Google Scholar
33. Simon, D. Biogeography-based optimization. IEEE Trans Evol Comput 2008;12:702–13.10.1109/TEVC.2008.919004Search in Google Scholar
34. Storn, R. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 1997;11:341–59.10.1023/A:1008202821328Search in Google Scholar
35. Kennedy, J, Eberhart, R. Particle swarm optimisation. Stud Comput Intell 1995;927:5–13. https://doi.org/10.1007/978-3-030-61111-8_2.Search in Google Scholar
36. Dorigo, M, Birattari, M, Thomas, S. Ant colony optimization. IEEE Comput Intell Mag 2006;1:28–39. https://doi.org/10.1109/mci.2006.329691.Search in Google Scholar
37. Gonz, JR, Alejandro Pelta, D, Cruz, C. A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO). Studies in computational intelligence. Berlin, Heidelberg: Springer; 2010, 284:65–74 pp. https://doi.org/10.1007/978-3-642-12538-6_6.Search in Google Scholar
38. Karaboga, D, Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 2007;39:459–71. https://doi.org/10.1007/s10898-007-9149-x.Search in Google Scholar
39. Mirjalili, S, Lewis, A. The whale optimization algorithm. Adv Eng Software 2016;95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008.Search in Google Scholar
40. Mirjalili, S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Base Syst 2015;89:228–49. https://doi.org/10.1016/j.knosys.2015.07.006.Search in Google Scholar
41. Kaveh, A, Farhoudi, N. Advances in engineering software a new optimization method: dolphin echolocation. Adv Eng Software 2013;59:53–70. https://doi.org/10.1016/j.advengsoft.2013.03.004.Search in Google Scholar
42. Hossein, A, Hossein, A. Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 2012;17:4831–45. https://doi.org/10.1016/j.cnsns.2012.05.010.Search in Google Scholar
43. Yang, X-S. Firefly algorithms. In: Nature-inspired optimization algorithms. Elsevier; 2014:111–127 pp. https://doi.org/10.1016/b978-0-12-416743-8.00008-7.10.1016/B978-0-12-416743-8.00008-7Search in Google Scholar
44. Rashedi, E, Nezamabadi-Pour, H, Saryazdi, S. GSA: a gravitational search algorithm. Inf Sci 2009;179:2232–48. https://doi.org/10.1016/j.ins.2009.03.004.Search in Google Scholar
45. Kaveh, A, Khayatazad, M. A new meta-heuristic method: ray optimization. Comput Struct 2012;112:283–94. https://doi.org/10.1016/j.compstruc.2012.09.003.Search in Google Scholar
46. Kaveh, A, Talatahari, S. A novel heuristic optimization method: charged system. Acta Mech 2010;213:267–89. https://doi.org/10.1007/s00707-009-0270-4.Search in Google Scholar
47. Kaveh, A, Mahdavi, VR. Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 2014;139:18–27. https://doi.org/10.1016/j.compstruc.2014.04.005.Search in Google Scholar
48. Abedinpourshotorban, H, Mariyam, S, Beheshti, Z. Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 2015;26:8–22. https://doi.org/10.1016/j.swevo.2015.07.002.Search in Google Scholar
49. Kaveh, A, Dadras, A. Advances in engineering software a novel meta-heuristic optimization algorithm : thermal exchange optimization. Adv Eng Software 2017;110:69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014.Search in Google Scholar
50. Javidy, B, Hatamlou, A, Mirjalili, S. Ions motion algorithm for solving optimization problems. Appl Soft Comput J 2015;32:72–9. https://doi.org/10.1016/j.asoc.2015.03.035.Search in Google Scholar
51. Kaveh, A, Bakhshpoori, T. Water evaporation optimization : a novel physically inspired optimization algorithm. Comput Struct 2016;167:69–85. https://doi.org/10.1016/j.compstruc.2016.01.008.Search in Google Scholar
52. Eskandar, H, Ali, S, Bahreininejad, A, Hamdi, M. Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 2012;110:151–66. https://doi.org/10.1016/j.compstruc.2012.07.010.Search in Google Scholar
53. Das, S, Suganthan, PN. Differential evolution : a survey of the state-of-the-art. IEEE Trans Evol Comput 2011;15:4–31.10.1109/TEVC.2010.2059031Search in Google Scholar
54. Qais, MH. Transient search optimization : a new meta-heuristic optimization algorithm. Appl Intell 2020;50:3926–3941. https://doi.org/10.1007/s10489-020-01727-y.Search in Google Scholar
55. Satyanarayana, S, Ramana, T, Sivanagaraju, S, Rao, GK. An efficient load flow solution for radial distribution network including voltage dependent load models. Elec Power Compon Syst 2007;35:539–51. https://doi.org/10.1080/15325000601078179.Search in Google Scholar
56. Lopez, A. National renewable energy laboratory; 2016. Available from: https://data.nrel.gov/submissions/43.Search in Google Scholar
57. Kaplanis, S, Kumar, J, Kaplani, E. On a universal model for the prediction of the daily global solar radiation. Renewable Energy 2016;91:178–188. https://doi.org/10.1016/j.renene.2016.01.037.Search in Google Scholar
58. Asian Development Bank. Handbook on battery energy storage system; 2018. Available from: https://www.adb.org/publications/battery-energy-storage-system-handbook.Search in Google Scholar
59. Sharma, S, Bhattacharjee, S, Bhattacharya, A. Electrical power and energy systems quasi-oppositional swine influenza model based optimization with quarantine for optimal allocation of DG in radial distribution network. Int J Electr Power Energy Syst 2016;74:348–73. https://doi.org/10.1016/j.ijepes.2015.07.034.Search in Google Scholar
60. Selim, A, Kamel, S, Alghamdi, AS, Jurado, F. Optimal placement of DGs in distribution system using an improved Harris Hawks optimizer based on single- and multi-objective approaches. IEEE Access 2020;8:52815–29. https://doi.org/10.1109/ACCESS.2020.2980245.Search in Google Scholar
61. Marler, RT, Arora, JS. The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 2010;41:853–62. https://doi.org/10.1007/s00158-009-0460-7.Search in Google Scholar
62. Gunantara, N. A review of multi-objective optimization: methods and its applications. Cogent Eng 2018;5:1–16. https://doi.org/10.1080/23311916.2018.1502242.Search in Google Scholar
63. Mahmoud, K, Yorino, N, Ahmed, A. Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans Power Syst 2016;31:960–9. https://doi.org/10.1109/TPWRS.2015.2418333.Search in Google Scholar
64. Huy, PD, Ramachandaramurthy, VK, Jia, YY, Kang, MT, Ekanayake, JB. Optimal placement, sizing and power factor of distributed generation: a comprehensive study spanning from the planning stage to the operation stage. Energy 2020;195. https://doi.org/10.1016/j.energy.2020.117011.Search in Google Scholar
65. Ahmed, A, Nadeem, MF, Sajjad, IA, Bo, R, Khan, IA, Raza, A. Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models. Sustain Energy Grids Netw 2020;22:100358. https://doi.org/10.1016/j.segan.2020.100358.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes
- MPPT control based on improved mayfly optimization algorithm under complex shading conditions
- A cogeneration scheme with biogas and improvement of frequency stability using inertia based control in AC microgrid
- Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs
- An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm
- Design and operation of smart hybrid microgrid
- Three-state switching cell boost converter using H-inf controller
- Bayesian optimization based machine learning approaches for prediction of plug-in electric vehicle state-of-charge
- Probabilistic and deterministic analysis of single diode model of a solar cell: a case study
- A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage
- A non-cooperative game based energy management considering distributed energy resources in price-based and incentive-based demand response program
Articles in the same Issue
- Frontmatter
- Research Articles
- Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes
- MPPT control based on improved mayfly optimization algorithm under complex shading conditions
- A cogeneration scheme with biogas and improvement of frequency stability using inertia based control in AC microgrid
- Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs
- An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm
- Design and operation of smart hybrid microgrid
- Three-state switching cell boost converter using H-inf controller
- Bayesian optimization based machine learning approaches for prediction of plug-in electric vehicle state-of-charge
- Probabilistic and deterministic analysis of single diode model of a solar cell: a case study
- A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage
- A non-cooperative game based energy management considering distributed energy resources in price-based and incentive-based demand response program
