Collaborative Planning of Community Charging Facilities and Distribution Networks
Abstract
:1. Introduction
2. New Community Collaborative Optimization Construction Method Framework
- (1)
- Calculate the basic electricity load of the new community in the target year by analyzing the occupancy rate, planned number of households and residential electricity characteristics, so as to plan the platform capacity of each load node;
- (2)
- Carrying out data analysis on the number of cars owned by 1000 people and the proportion of electric vehicles in the city where the newly built community is located, combined with the prediction results of the charging probability of electric vehicles, the charging load of electric vehicles in the newly built communities in the target year can be predicted;
- (3)
- Take residential buildings as base load nodes, calibrate base load locations in newly built communities, calculate load density, and determine the location of EV charging nodes and 10 kV outlet nodes;
- (4)
- Considering constraints such as line power, voltage amplitude, charging demand and platform capacity of distribution network in newly built communities, an economic model of collaborative optimization construction between electric vehicle charging facilities and distribution network in newly built communities is established. Combining the Prim algorithm and the single-parent genetic algorithm, the topology of the distribution network grid, the line type of the feeder, etc., are determined, and the construction of the charging facilities is planned.
3. Forecasting Base Load and Charging Load in New Communities
3.1. Prediction of Basic Electricity Load
3.2. EV Charging Load Prediction
3.2.1. Prediction of Electric Vehicle Ownership in Communities
3.2.2. EV Charging Probability Prediction in Communities
- (1)
- Starting time of electric vehicle charging
- (2)
- Charging start SOC
- (3)
- Charging time
- (4)
- Monte Carlo simulation charging probability prediction
4. Mathematical Model of Collaborative Optimization Construction of Community
4.1. Objective Function
4.2. Constraints
- (1)
- Distribution network line capacity constraints
- (2)
- Distribution network node voltage constraints
- (3)
- Constraints on charging demand
- (4)
- Capacity constraints of distribution network station area
5. Prim and Single-Parent Genetic Algorithm
5.1. Prime Algorithm
5.2. Single-Parent Genetic Algorithm
6. Analysis of Examples
6.1. Forecast of Electric Vehicle Ownership in Newly Built Communities
6.2. Prediction of Base Load and Electric Vehicle Charging Load in Newly Built Communities
6.3. Planning Scheme of Charging Facilities in Community
6.4. Summary
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Year | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|
Private car ownership/thousand | 1033 | 1277 | 1572 | 1933 | 2241 | 2564 | 2853 | 3158 | 3499 |
Private car growth rate/% | 32.80 | 23.60 | 23.10 | 23.00 | 15.90 | 14.4 | 15.00 | 10.70 | 10.80 |
Line Model | Cross-Sectional Area/mm2 | Resistivity /Ω/km | Reactance /Ω/km | Ampacity /A | Line Construction Costs/Thousand/km |
---|---|---|---|---|---|
YJV22-3*150 | 150 | 0.12 | 0.103 | 358 | 900 |
YJV22-3*300 | 300 | 0.305 | 0.089 | 540 | 1500 |
Number | Number of Households | Building Area/m2 | Load Density W/m2 | Area Requirement Factor | Annual Growth Rate | Average Daily Load/kW | Peak Load/kW | Capacity Configuration/kVA |
---|---|---|---|---|---|---|---|---|
1# | 120 | 15,600 | 40 | 0.17 | 1% | 126.89 | 192.25 | 315 |
2# | 66 | 8580 | 40 | 0.17 | 1% | 69.79 | 105.74 | 160 |
3# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |
4# | 144 | 18,720 | 40 | 0.17 | 1% | 152.26 | 230.70 | 400 |
5# | 44 | 6600 | 40 | 0.17 | 1% | 53.68 | 81.34 | 125 |
6# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |
7# | 72 | 7920 | 40 | 0.17 | 1% | 64.42 | 97.61 | 160 |
8# | 72 | 7920 | 40 | 0.17 | 1% | 64.42 | 97.61 | 160 |
9# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |
10# | 120 | 15,600 | 40 | 0.17 | 1% | 126.89 | 192.25 | 315 |
11# | 180 | 27,000 | 40 | 0.17 | 1% | 219.61 | 332.75 | 500 |
12# | 174 | 26,100 | 40 | 0.17 | 1% | 212.29 | 321.65 | 500 |
13# | 174 | 26,100 | 40 | 0.17 | 1% | 212.29 | 321.65 | 500 |
Total | 1442.12 | 2185.04 | 3510 |
Electrovalency/kWh | YJV22-3*150 Construction Costs/Thousand/km | YJV22-3*300 Construction Costs/Thousand/km | Operating Life/Years | Discount Rate | Maximum Load Operating Hours per Year/Hour |
---|---|---|---|---|---|
0.5 | 900 | 1500 | 40 | 0.1 | 3000 |
Average Annual Cost of Network Loss/Thousand | YJV22-3*150 Line Construction Length/m | YJV22-3*300 Line Construction Length/m | Average Annual Cost of Line Construction/Thousand |
---|---|---|---|
104 | 3041.23 | 249.71 | 318.2 |
References
- Li, K. Government Work Report. Available online: http://www.gov.cn/zhuanti/2020lhzfgzbg/index.htm (accessed on 13 March 2021).
- Chen, J. Research on Key Issues of Electric Vehicle Charging Facility Planning and Regulation. Ph.D. Thesis, Hunan University, Changsha, China, 2021. [Google Scholar]
- Pan, M. Intelligent and Orderly Charging Solution for Large-Scale Electric Vehicles in Residential Areas [EB/OL]. Available online: http://www.car-online.cn/News/Details?Id=34619 (accessed on 17 April 2023).
- As of July, the Cumulative Number of Charging Infrastructure in My Country Has Reached 3.98 Million Units. Available online: https://www.aqsiqauto.com/newcars/info/10700.html (accessed on 12 August 2022).
- Dixon, J.; Bell, K. Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks. ETransportation 2020, 4, 100059. [Google Scholar] [CrossRef]
- Zhang, X. Research on assessment methods of distribution network’s ability of admitting electric vehicles. Power Syst. Prot. Control 2015, 43, 14–20. [Google Scholar]
- Chu, Y. Research on electric vehicle ownership prediction in China based on Bass model and GM(1,1) model. Math. Pract. Theory 2021, 51, 21–32. [Google Scholar]
- Zhao, S.; Liu, J.; Xiang, Y. Analysis and assessment considering accommodation capability of distribution network for infrastructure deployment of EV charging/swapping service network. Electr. Power Autom. Equip. 2016, 36, 94–101. [Google Scholar]
- Li, X.; Liu, Y.; Liang, Y.; Zeng, S.; Li, M. Evaluation and optimization of Electric Vehicle Load Acceptance Capacity of Distribution Network. In Proceedings of the 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China, 30 October 2020. [Google Scholar]
- Yang, J.; Zhang, Y. Optimization of charging load acceptance capacity based on ordered charging start-up mechanism and subsidy mechanism. Electr. Power Autom. Equip. 2020, 40, 122–130. [Google Scholar]
- Zuo, Q.; He, H.; Liang, C.; Zhou, M.; Zhou, Q. Quantitative Evaluation Method of System Acceptance Capacity of Electric Vehicles in Ordered Charging Mode. Proc. CSU-EPSA 2017, 29, 1–6. [Google Scholar]
- Tian, M.; Tang, B.; Yang, X.; Xia, X. Planning of Electric Vehicle Charging Stations Considering Charging Demands and Acceptance Capacity of Distribution Network. Power Syst. Technol. 2021, 45, 498–509. [Google Scholar]
- Chen, W.; Yang, B.; Zhang, Z.; Wen, M.; Chen, X. Distribution Networks Supportability Evaluation and Optimization Considering Electric Vehicles Charging Stations. Proc. CSEE 2014, 29, 27–35, 45. [Google Scholar]
- Guo, Y.; Hu, Z.; Zhang, H.; Su, W.; Zhan, K.; Xu, Z. A Statistical Method to Evaluate the Capability of Residential Distribution Network for Accommodating Electric Vehicle Charging Load. Power Syst. Technol. 2015, 39, 2458–2464. [Google Scholar]
- Qu, D.; Fan, J.; Liu, Q.; Liu, X.; Deng, W. Assessment and optimization of charging post acceptance capacity considering the comprehensive operational risk of a distribution network. Power Syst. Prot. Control 2022, 50, 131–139. [Google Scholar]
- De Hoog, J.; Alpcan, T.; Brazil, M.; Thomas, D.A.; Mareels, I. Optimal charging of electric vehicles taking distribution network constraints into account. IEEE Trans. Power Syst. 2014, 30, 365–375. [Google Scholar] [CrossRef]
- De Hoog, J.; Muenzel, V.; Jayasuriya, D.C.; Alpcan, T.; Brazil, M.; Thomas, D.A.; Mareels, I.; Dahlenburg, G.; Jegatheesan, R. The importance of spatial distribution when analysing the impact of electric vehicles on voltage stability in distribution networks. Energy Syst. 2015, 6, 63–84. [Google Scholar] [CrossRef]
- Beijing Transport Institute. 2020 Beijing Transport Development Annual Report. Available online: https://www.bjtrc.org.cn/Show/download/id/66/at/0.pdf (accessed on 1 July 2020).
- Anna, C.; Jayan, T.N.; Aoife, F.; Brian, C. Identifying optimal locations for community electric vehicle charging. Sustain. Cities Soc. 2023, 94, 104573. [Google Scholar]
- U.S. Department of Transportation, Federal Highway Administration. 2017 National Household Travel Survey. Available online: http://nhts.ornl.gov (accessed on 7 June 2019).
- Guo, J. Research on the Charging Habits of Private Electric Vehicle Users: Half of the Electricity will Cause Anxiety. Available online: https://www.sohu.com/a/397956477_100209427 (accessed on 27 May 2020).
- Tian, L.; Shi, S.; Jia, Z. A statistical model for charging power demand of electric vehicles. Power Syst. Technol. 2010, 34, 126–130. [Google Scholar]
- Zhang, X. The Study on New Product Diffusion Mechanism Based on Improved Bass Model: Evidence from Movies. Ph.D. Thesis, Beijing Institute of Technology, Beijing, China, 2017. [Google Scholar] [CrossRef]
- Sultan, F.; Farley, J.U.; Lehmann, D.R. A meta-analysis of applications of diffusion models. J. Mark. Res. 1990, 27, 70–77. [Google Scholar] [CrossRef]
- Eberhart, R.; Kennedy, J. A new optimizer using particle swarm theory. In MHS’95, Proceedings of the sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4 October 1995; IEEEs: Piscataway, NJ, USA, 1995. [Google Scholar]
- Zhang, H.; Yuan, M.; Liang, Y.; Liao, Q. A novel particle swarm optimization based on prey-predator relationship. Appl. Soft Comput. 2018, 68, 202–218. [Google Scholar] [CrossRef]
- Hua, Y.; Wang, Y. Medium- and long-term charging load prediction of electric vehicles in residential areas considering orderly charging. J. Power Syst. Its Autom. 2022, 34, 142–150. [Google Scholar]
- Jiang, B.; Zhang, L. Optimization of Minimum Spanning Tree Based on Prim Algorithm. Comput. Eng. Des. 2009, 30, 3244–3247. [Google Scholar]
- Zhang, W.; Cheng, H.; Wang, Y.; Ouyang, W. Optimal planning of distribution network based on tree structure coded parthenogenetic algorithm. J. Electrotech. Soc. 2009, 24, 154–160. [Google Scholar]
- Wang, H. An Optimization Algorithm of Fusion Simulated Annealing and Single Parent Inheritance. Comput. Meas. Control 2021, 29, 146–149. [Google Scholar]
- Chengdu Real Estate Economic Management Bureau. Survey Report on Occupancy Rate of Community Communities in Chengdu; Chengdu Real Estate Economic and Management Bureau: Chengdu, China, 2009.
Number | Number of Households | Building Area/m2 | Number | Number of Households | Building Area/m2 |
---|---|---|---|---|---|
1# | 120 | 15,600 | 8# | 72 | 7920 |
2# | 66 | 8580 | 9# | 44 | 5720 |
3# | 44 | 5720 | 10# | 120 | 15,600 |
4# | 144 | 18,720 | 11# | 180 | 27,000 |
5# | 44 | 6600 | 12# | 174 | 26,100 |
6# | 44 | 5720 | 13# | 174 | 26,100 |
7# | 72 | 7920 |
Year | Number of Charging Facilities to Build/Unit | Peak Load (Weekdays)/kW | Node Capacity Configuration/kVA |
---|---|---|---|
2022 | 2 | 14 | 315 |
2023 | 5 | 21 | 315 |
2024 | 8 | 35 | 315 |
2025 | 12 | 49 | 315 |
2026 | 18 | 70 | 315 |
2027 | 26 | 98 | 315 |
2028 | 35 | 133 | 315 |
2029 | 48 | 182 | 315 |
2030 | 62 | 238 | 315 |
2031 | 75 | 287 | 630 |
2032 | 86 | 322 | 630 |
2033 | 95 | 357 | 630 |
2034 | 102 | 385 | 630 |
2035 | 107 | 406 | 630 |
2036 | 111 | 420 | 630 |
2037 | 114 | 427 | 630 |
2038 | 117 | 441 | 630 |
2039 | 119 | 448 | 630 |
2040 | 119 | 448 | 630 |
Grid Number | Load Density (kW/km2) | Grid Number | Load Density (kW/km2) |
---|---|---|---|
1 | 10,419.44 | 6 | 2563.33 |
2 | 1687.00 | 7 | 4781.78 |
3 | 783.22 | 8 | 0.00 |
4 | 3573.89 | 9 | 7113.89 |
5 | 2563.33 | 10 | 2136.11 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Diao, X.-H.; Zhang, J.; Wang, R.-Y.; Jia, J.-W.; Chang, Z.-L.; Li, B.; Zhao, X. Collaborative Planning of Community Charging Facilities and Distribution Networks. World Electr. Veh. J. 2023, 14, 143. https://doi.org/10.3390/wevj14060143
Diao X-H, Zhang J, Wang R-Y, Jia J-W, Chang Z-L, Li B, Zhao X. Collaborative Planning of Community Charging Facilities and Distribution Networks. World Electric Vehicle Journal. 2023; 14(6):143. https://doi.org/10.3390/wevj14060143
Chicago/Turabian StyleDiao, Xiao-Hong, Jing Zhang, Rui-Yu Wang, Jiang-Wei Jia, Zhi-Liang Chang, Bin Li, and Xuan Zhao. 2023. "Collaborative Planning of Community Charging Facilities and Distribution Networks" World Electric Vehicle Journal 14, no. 6: 143. https://doi.org/10.3390/wevj14060143