Master Plan for Electricity Distribution Networks Based on Micro-Spatial Projection of Energy Demand
DOI:
https://doi.org/10.26555/jiteki.v7i3.22244Keywords:
Master Plan, Energy Growth, Micro-spatial, Transformer, Distribution SubstationAbstract
The existing method of the Master plan for electricity distribution networks is sectoral and macro-based, which means it is unable to show load centers in micro-grids. The inaccurate and bias results lead to the failure of determining the capacity of transformers, the total of transformers, and the locations of distribution substations, and thus it will complicate the master planning of the distribution network. Therefore, a micro-spatial-based method in electricity master planning is needed, as it will generate more accurate forecasting, energy projection and estimate the numbers of load centers at each grid based on the geographical structure. The research contribution is to produce a master planning of distribution network that will help in determining transformer capacity, the placement of substations and distribution substations, evaluation, and orientation of electricity distribution system development to a smaller area. The results of the load growth become the basis for determining the capacity and the total of transformers in the area. The methodology developed in this research has analyzed the transformer rating, transformer capacity, total of transformers, and the location of transformer with growing energy demand in the smaller range. The results can be developed into the design planning of distribution network systems with better accuracy.References
S. Zhang, H. Cheng, D. Wang, L. Zhang, F. Li, and L. Yao, “Distributed generation planning in active distribution network considering demand side management and network reconfiguration,†Appl. Energy, vol. 228, no. July, pp. 1921–1936, 2018. https://doi.org/10.1016/j.apenergy.2018.07.054
M. Li, D. Allinson, and M. He, “Seasonal variation in household electricity demand: A comparison of monitored and synthetic daily load profiles,†Energy Build., vol. 179, pp. 292–300, Nov. 2018. https://linkinghub.elsevier.com/retrieve/pii/S037877881831346X
M. A. McNeil, N. Karali, and V. Letschert, “Forecasting Indonesia’s electricity load through 2030 and peak demand reductions from appliance and lighting efficiency,†Energy Sustain. Dev., vol. 49, pp. 65–77, 2019. https://doi.org/10.1016/j.esd.2019.01.001
M. Q. Raza, N. Mithulananthan, J. Li, and K. Y. Lee, “Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days,†IEEE Trans. Sustain. Energy, vol. 11, no. 1, pp. 27–36, Jan. 2020. https://doi.org/10.1016/j.esd.2019.01.001
A. Gligor, I. Vlasa, C.-D. Dumitru, C. E. Moldovan, and C. Damian, “Power Demand Forecast for Optimization of the Distribution Costs,†Procedia Manuf., vol. 46, pp. 384–390, 2020. https://linkinghub.elsevier.com/retrieve/pii/S2351978920309343
C. Wang et al., “Load Capacity Evaluation of Power Transformer via Temperature Rise Characteristics,†in 2020 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), 2020, vol. 2020-Octob, pp. 512–515. https://ieeexplore.ieee.org/document/9437520/
K. Prakash, F. R. Islam, K. A. Mamun, and H. R. Pota, “Configurations of Aromatic Networks for Power Distribution System,†Sustainability, vol. 12, no. 10, p. 4317, May 2020. https://www.mdpi.com/2071-1050/12/10/4317
M. Srndovic, A. Zhetessov, T. Alizadeh, Y. L. Familiant, G. Grandi, and A. Ruderman, “Simultaneous Selective Harmonic Elimination and THD Minimization for a Single-Phase Multilevel Inverter With Staircase Modulation,†IEEE Trans. Ind. Appl., vol. 54, no. 2, pp. 1532–1541, Mar. 2018. http://ieeexplore.ieee.org/document/8114251/
K. Gajowniczek and T. Zabkowski, “Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting,†Complexity, vol. 2018, no. iii, 2018. https://doi.org/10.1155/2018/3683969
P. P. Patil, R. K. Patil, and N. Patil, “Dc House: An Alternate Solution for Rural Electrification,†Int. J. Trend Sci. Res. Dev., vol. Volume-3, no. Issue-4, pp. 4–7, Jun. 2019.
M. Afrasiabi, M. Mohammadi, M. Rastegar, L. Stankovic, S. Afrasiabi, and M. Khazaei, “Deep-Based Conditional Probability Density Function Forecasting of Residential Loads,†IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3646–3657, Jul. 2020. https://ieeexplore.ieee.org/document/8988175/
W. Pavón, E. Inga, and S. Simani, “Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration,†Sustainability, vol. 11, no. 6, p. 1607, Mar. 2019. https://www.mdpi.com/2071-1050/11/6/1607
S. Abeysinghe, J. Wu, and M. Sooriyabandara, “A Statistical Assessment Tool for Electricity Distribution Networks,†Energy Procedia, vol. 105, pp. 2595–2600, 2017. http://dx.doi.org/10.1016/j.egypro.2017.03.747
Q. Xie, J. Xiao, P. Gardoni, and K. Hu, “Probabilistic Analysis of Building Fire Severity Based on Fire Load Density Models,†Fire Technol., vol. 55, no. 4, pp. 1349–1375, Jul. 2019. http://link.springer.com/10.1007/s10694-018-0716-0
A. G. Ismail, M. A. El-Dabah, and I. A. Nassar, “Enhancement of Electrical Distribution Networks Performance Using the Load Management Methodology,†Energy Reports, vol. 6, pp. 2066–2074, 2020. https://doi.org/10.1016/j.egyr.2020.07.018
G.-J. Cho, C.-H. Kim, Y.-S. Oh, M.-S. Kim, and J.-S. Kim, “Planning for the Future: Optimization-Based Distribution Planning Strategies for Integrating Distributed Energy Resources,†IEEE Power Energy Mag., vol. 16, no. 6, pp. 77–87, Nov. 2018. https://ieeexplore.ieee.org/document/8495043/
G. Parise, L. Parise, L. Martirano, P. Ben Chavdarian, Chun-Lien Su, and A. Ferrante, “Wise Port and Business Energy Management: Port Facilities, Electrical Power Distribution,†IEEE Trans. Ind. Appl., vol. 52, no. 1, pp. 18–24, Jan. 2016. http://ieeexplore.ieee.org/document/7167710/
K. Farahzad, A. Shahbahrami, and M. Ashouri, “Optimal Capacity Determination For Electrical Distribution Transformers Based On IEC 60076-7 And Practical Load Data,†Int. J. Eng. Manuf., vol. 10, no. 1, pp. 1–11, Feb. 2020. http://www.mecs-press.org/ijem/ijem-v10-n1/v10n1-1.html
A. Senen, C. Widyastuti, O. Handayani, and P. Putera, “Development of Micro-Spatial Electricity Load Forecasting Methodology Using Multivariate Analysis for Dynamic Area in Tangerang , Indonesia,†vol. 29, no. 4, pp. 2565–2578, 2021. https://doi.org/10.47836/pjst.29.4.18
C. Widyastuti, A. Senen, and O. Handayani, “Micro-Spatial Electricity Load Forecasting Using Clustering Technique,†Proceeding - 2020 2nd Int. Conf. Ind. Electr. Electron. ICIEE 2020, vol. 11005, pp. 17–21, 2020. https://doi.org/10.1051/e3sconf/202020211005
I. Gde Made Yoga Semadhi Artha, “Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method,†J. Electr. Electron. Eng., vol. 7, no. 1, p. 1, 2019. http://www.sciencepublishinggroup.com/j/jeee
M. Lekshmi and K. N. A. Subramanya, “Short-Term Load Forecasting of 400kV Grid Substation Using R-Tool and Study of Influence of Ambient Temperature on the Forecasted Load,†in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, pp. 1–5. https://ieeexplore.ieee.org/document/8883005/
Q. Fu, R. Lai, Y. Shan, and X. Geng, “A Spatial Forecasting Method for Photovoltaic Power Generation Combined of Improved Similar Historical Days and Dynamic Weights Allocation,†in 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2018, pp. 1195–1198. https://ieeexplore.ieee.org/document/8883005/
Q. Hui, T. Haibo, F. Wei, W. Beibei, and W. Qian, “Research on Apache Spark Based Transformer Areas Load Forecasting,†in 2018 China International Conference on Electricity Distribution (CICED), 2018, no. 201804260000077, pp. 2437–2441. https://ieeexplore.ieee.org/document/8467889/
A. Sbravati, M. H. Oka, J. A. Maso, and J. Valmus, “Enhancing Transformers Loadability for Optimizing Assets Utilization and Efficiency,†in 2018 IEEE Electrical Insulation Conference (EIC), 2018, no. June, pp. 144–149. https://ieeexplore.ieee.org/document/8592094/
S. Das and T. Malakar, “A Probabilistic Load Flow with Uncertain Load Using Point Estimate Method,†in 2018 15th IEEE India Council International Conference (INDICON), 2018, pp. 1–5. https://ieeexplore.ieee.org/document/8987005/
M. Djamali, S. Tenbohlen, E. Junge, and M. Konermann, “Real-Time Evaluation of the Dynamic Loading Capability of Indoor Distribution Transformers,†IEEE Trans. Power Deliv., vol. 33, no. 3, pp. 1134–1142, Jun. 2018. https://ieeexplore.ieee.org/document/7984823/
S. Bahramara and F. G. Mohammadi, “Optimal sizing of distribution network transformers considering power quality problems of non-linear loads,†CIRED - Open Access Proc. J., vol. 2017, no. 1, pp. 2471–2475, Oct. 2017. https://digital-library.theiet.org/content/journals/10.1049/oap-cired.2017.0258
Y. Tan et al., “Repulsive firefly algorithm-based optimal switching device placement in power distribution systems,†Glob. Energy Interconnect., vol. 2, no. 6, pp. 489–495, Dec. 2019. https://linkinghub.elsevier.com/retrieve/pii/S2096511720300025
J. Zhang, K. Liu, G. Liu, B. Xu, and Y. Kang, “Research on the Influence of Primary Load Imbalance on the Combined Transformer’s Error,†in 2018 International Conference on Power System Technology (POWERCON), 2018, no. 201804270000511, pp. 1504–1511. https://ieeexplore.ieee.org/document/8602069/
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