Optimal Site Selection of Wind-Solar Complementary Power Generation Project for a Large-Scale Plug-In Charging Station
Abstract
:1. Introduction
2. Literature Review
3. Site Selection Index System for Wind-Solar Hybrid Power Generation Project Interconnected to Large Battery-Replacement Stations
3.1. Location Index of Wind-Solar Power Generation
3.2. Site Selection Indexes for Large Network of Power Station in Wind-Solar Hybrid Power Generation Project
4. Construction of Optimal Location Model under Uncertain Linguistic Environment
4.1. Location Decision Procedure under Uncertain Linguistic Setting
- (1)
- Building the AHP structure introduction chart: Considering the site of the wind and solar power generation project for a network of large-scale recharge stations as the target, a hierarchical structure is established from the perspectives of economy, nature, geography, reliability of power supply, satisfaction of traffic, society and environment and the impact correlation between indicators is also established.
- (2)
- Pair comparison of indicators: Saaty 1–9 is used as the relative scale for measurement, as presented in Table 3. During this process, expert opinions are first inquired and subsequently, middle managers are responsible for organizing special seminars to discuss and negotiate these opinions when there are significant differences of opinions among experts.
- (3)
- Consistency test: We consider the deviation from the estimation of comparative matrix weight to be acceptable when Consistency Ratio , that represents the consistency. The estimation of pair comparison must be corrected till the requirement of consistency is satisfied when the value of CR exceeds the above value.
- (4)
- Calculation of indicator weights: A weighted supermatrix is generated after the consistency test is successful and an indicator weight sheet is generated by using Super Decision.
- (1)
- Suppose that is a linguistic value in the language collection of , the mapping from to the parameter of is defined as follows:The value of the parameter can be obtained through an experiment. Many experiments show that, if the scale of the assessment of language is 7, . [57].
- (2)
- The effective domain of discourse and the calculation of is obtained as follows:
- (3)
- Using to indicate a drop of the cloud and follows the principle of for a normal distribution curve, and can be indicated as and thus, the following equation can be obtained:
- (4)
- As , the excess entropy follows the principle of for a normal distribution curve and thus, the calculation method of can be obtained as follows:
- (1)
- Input: Three parameters of and the quantity of generation of cloud drops n into Generator of normal affiliated cloud;
- (2)
- The generation of random number ,
- (3)
- The generation of random number ;
- (4)
- ;
- (5)
- Repeat the above stated steps from (1) to (3) until the generation of n drops of cloud.The expected value s for the global score of cloud A can be obtained as follows:Given two pieces of cloud A and B, if , .
4.2. Analysis of Example
4.2.1. Example Overview
4.2.2. Application of Optimized Siting Model for the Wind and Solar Power Generation Project
4.3. Evaluation Result Analysis
4.3.1. Comparison and Analysis
4.3.2. Sensitivity Analysis
5. Conclusions
- (1)
- Using the statistical indexes of site selection of traditional wind and solar power generation in the literature, the statistical analysis and selection were carried out and subsequently, the impact of large charging power stations on the site selection of the wind-solar hybrid power generation project was analyzed to set up a site selection index system for this project.
- (2)
- The traditional PROMETHEE method was extended based on the cloud model and uncertain linguistic environment and considering the interaction between the index weights, the AHP method was used to evaluate and improve the feasibility of the wind-solar complementary project and the effectiveness of location decisions.
- (3)
- Based on the analysis of the site selection decision of the landscape complementary power generation project for the network of large-scale charging stations in Shanghai, the feasibility and effectiveness of the decision-making system proposed in this paper were demonstrated. Subsequently, the comparative analysis and sensitivity analysis were carried out, which demonstrated the superiority and stability of the decision-making model proposed in this paper.
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Dong, J.; Feng, T.T.; Yang, Y.S.; Ma, Y. Macro-site selection of wind/solar hybrid power station based on ELECTRE-II. Renew. Sustain. Energy Rev. 2014, 35, 194–204. [Google Scholar]
- Wu, Y.N.; Yang, Y.S.; Feng, T.T.; Kong, L.N.; Liu, W.; Fu, L.J. Macro-site selection of wind/solar hybrid power station based on ideal matter-element model. Int. J. Electr. Power Energy Syst. 2013, 5, 76–84. [Google Scholar]
- Wu, Y.N.; Yang, Y.S.; Feng, T.T.; Gao, M.; Zhang, J.Y. Research on the macro location of the landscape complementary power station. Power Syst. Technol. 2013, 2, 319–326. [Google Scholar]
- Tang, J.; Zeng, S.Q.; Sun, Q.Y.; Huang, X. A decentralized wind power plant selection method based on fuzzy chromatography. Chin. Electr. Power Press 2015, 4, 151–155. [Google Scholar]
- Qin, L.; Su, M.; Li, S.; Zhu, X.; Wang, G. Based on hybrid intelligent algorithm, the multi-objective planning of scenic power supply is considered. Renew. Energy Resour. 2015, 6, 843–850. [Google Scholar]
- Yang, B.; Wang, L.F.; Liao, C.L. Coordinated control of electric vehicle and distributed power supply. Trans. Chin. Electrotech. Soc. 2015, 14, 419–426. [Google Scholar]
- Saber, A.Y.; Venayagamoorthy, G.K. Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. IEEE Syst. J. 2012, 1, 103–109. [Google Scholar] [CrossRef]
- Zhang, Y.D.; Liu, N.; Zhang, J.H.; Li, Y. The capacity optimization of the power station of electric vehicle charging station is optimized. Power Syst. Prot. Control. 2013, 15, 126–134. [Google Scholar]
- Wu, J.K.; Lin, Y.X.; Wu, Z.S.; Liu, X.D. Considering the uncertainty of DG and EV distribution network reactive voltage coordination optimization method. Mod. Electr. Power. 2016, 4, 23–29. [Google Scholar]
- Zhu, Z.; Lia, Q.F.; Liu, D.C.; Jia, J.; Tang, F.; Zong, H.L. Considering the new energy and electric vehicle access to the active distribution network reconstruction strategy. Autom. Electr. Power Syst. 2015, 14, 12. [Google Scholar]
- Hou, J.C.; Hu, Q.F.; Tan, Z.F. A multi-objective optimization model for the coordinated scheduling of wind power electric vehicle with demand response. Electr. Power Autom. Equip. 2016, 7, 22–27. [Google Scholar]
- Chen, G.; Dai, P.; Zhou, H.; Sun, K.; Shen, Y. Plan and refactor power distribution system of electric vehicle and distributed power supply. Power Syst. Technol. 2013, 1, 82–88. [Google Scholar]
- Hu, Z.C.; Song, Y.H. The business model and the optimization scheduling strategy of the electric vehicle and the power network are reduced. Proc. Chin. Soc. Electr. Eng. 2015, 24, 6293–6303. [Google Scholar]
- Peng, X.G.; Liu, J.X.; Liu, Y.; Lin, Z.Q. A multi-objective distributed power optimization configuration with uncertain factors for electric vehicles and renewable energy. Power Syst. Technol. 2015, 8, 2188–2194. [Google Scholar]
- Liu, B.L.; Hu, J.L.; Liu, J.; Qian, X.; Cheng, J. Research on multi-objective planning of distribution network with distributed power supply and electric vehicle charging stations. Power Syst. Technol. 2015, 39, 450–456. [Google Scholar]
- Kabir, G.; Sumi, R.S. Power substation location selection using fuzzy analytic hierarchy process and PROMETHEE: A case study from Bangladesh. Energy 2014, 72, 717–730. [Google Scholar] [CrossRef]
- Kurt, Ü. The fuzzy TOPSIS and generalized Choquet fuzzy integral algorithm for nuclear power plant site selection—A case study from Turkey. J. Nucl. Sci. Technol. 2014, 10, 1241–1255. [Google Scholar] [CrossRef]
- Liu, H.C.; You, J.X.; Fan, X.J.; Chen, Y.Z. Site selection in waste management by the VIKOR method using linguistic assessment. Appl. Soft Comput. 2014, 21, 453–461. [Google Scholar] [CrossRef]
- Devi, K.; Yadav, S.P. A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. Int. J. Adv. Manuf. Technol. 2013, 66, 1219–1229. [Google Scholar] [CrossRef]
- Mokhtarian, M.N.; Sadi-Nezhad, S.; Makui, A. A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suif location for digging some pits for municipal wet waste landfill. Comput. Ind. Eng. 2014, 78, 213–233. [Google Scholar] [CrossRef]
- Li, Y.; Liu, X.; Chen, Y. Selection of logistics center location using Axiomatic Fuzzy Set and TOPSIS methodology in logistics management. Expert Syst. Appl. 2011, 6, 7901–7908. [Google Scholar] [CrossRef]
- Liu, S.; Chan, F.T.S.; Chung, S.H. A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robot. Comput. Integr. Manuf. 2011, 2, 426–433. [Google Scholar] [CrossRef]
- Li, D.Y.; Liu, C.Y. Uncertain artificial intelligence. J. Softw. 2004, 11, 1583–1594. [Google Scholar]
- Capilla, J.A.J.; Carrión, J.A.; Alameda-Hernandez, E. Optimal site selection for upper reservoirs in pump-back systems, using geographical information systems and multicriteria analysis. Renew. Energy 2016, 86, 429–440. [Google Scholar] [CrossRef]
- Ghoseiri, K.; Lessan, J. Waste disposal site selection using an analytic hierarchal pairwise comparison and ELECTRE approaches under fuzzy environment. J. Intell. Fuzzy Syst. 2014, 2, 693–704. [Google Scholar]
- Suh, J.; Jeffrey, R.S.B. Solar Farm Suitability Using Geographic Information System Fuzzy Sets and Analytic Hierarchy Processes: Case Study of Ulleung Island, Korea. Energies 2016, 9, 648. [Google Scholar] [CrossRef]
- Guo, S.; Zhao, H. Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective. Appl. Energy 2015, 158, 390–402. [Google Scholar] [CrossRef]
- Wu, Y.; Chen, K.; Zeng, B.; Yang, M.; Geng, S. Cloud-based decision framework for waste-to-energy plant site selection—A case study from China. Waste Manag. 2016, 48, 593–603. [Google Scholar] [CrossRef] [PubMed]
- Wu, Y.; Geng, S.; Zhang, H.; Gao, M. Decision framework of solar thermal power plant site selection based on linguistic Choquet operator. Appl. Energy 2014, 136, 303–311. [Google Scholar] [CrossRef]
- Xue, L.; Qian, P. The effectiveness evaluation algorithm and its application are based on fuzzy ANP system. Math. Pract. Theory 2016, 10, 69–77. [Google Scholar]
- Betrie, G.D.; Sadiq, R.; Morin, K.A.; Tesfamariam, S. Selection of remedial alternatives for mine sites: A multicriteria decision analysis approach. J. Environ. Manag. 2013, 119, 36–46. [Google Scholar] [CrossRef] [PubMed]
- Peng, A.H.; Xiao, X.M. Material selection using PROMETHEE combined with analytic network process under hybrid environment. Mater. Des. 2013, 47, 643–652. [Google Scholar] [CrossRef]
- Liao, H.; Xu, Z. Multi-criteria decision making with intuitionistic fuzzy PROMETHEE. J. Intell. Fuzzy Syst. 2014, 4, 1703–1717. [Google Scholar]
- Elevli, B. Logistics freight center locations decision by using Fuzzy-PROMETHEE. Transport 2014, 29, 412–418. [Google Scholar] [CrossRef]
- Chen, T.Y. An interval type-2 fuzzy PROMETHEE method using a likelihood-based outranking comparison approach. Inf. Fusion 2015, 25, 105–120. [Google Scholar] [CrossRef]
- Mousavi, S.M.; Tavakkoli-Moghaddam, R.; Heydar, M.; Ebrahimnejad, S. Multi-criteria decision making for plant location selection: An integrated Delphi–AHP–PROMETHEE methodology. Arab. J. Sci. Eng. 2013, 5, 1255–1268. [Google Scholar] [CrossRef]
- Li, D.; Liu, C.; Gan, W. A new cognitive model: Cloud model. Int. J. Intell. Syst. 2009, 24, 357–375. [Google Scholar] [CrossRef]
- Latinopoulos, D.; Kechagia, K. A GIS-based multi-criteria evaluation for wind farm site selection. A regional scale application in Greece. Renew. Energy 2015, 78, 550–560. [Google Scholar] [CrossRef]
- Atici, K.B.; Simsek, A.B.; Ulucan, A.; Tosun, M.U. A GIS-based Multiple Criteria Decision Analysis approach for wind power plant site selection. Util. Policy 2015, 37, 86–96. [Google Scholar] [CrossRef]
- Gorsevski, P.V.; Cathcart, S.C.; Mirzaei, G.; Jamali, M.M.; Ye, X.; Gomezdelcampo, E. A group-based spatial decision support system for wind farm site selection in Northwest Ohio. Energy Policy 2013, 55, 374–385. [Google Scholar] [CrossRef]
- Fetanat, A.; Khorasaninejad, E. A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. Ocean Coast. Manag. 2015, 109, 17–28. [Google Scholar] [CrossRef]
- Sánchez-Lozano, J.M.; García-Cascale, M.S.; Lamata, M.T. GIS-based onshore wind farm site selection using Fuzzy Multi-Criteria Decision Making methods. Evaluating the case of Southeastern Spain. Appl. Energy 2016, 171, 86–102. [Google Scholar]
- Azizi, A.; Malekmohammadi, B.; Jafari, H.R.; Nasiri, H.; Parsa, V.A. Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: Case study of Ardabil province, Iran. Environ. Monit. Assess. 2014, 10, 6695–6709. [Google Scholar] [CrossRef] [PubMed]
- Wu, Y.; Zhang, J.; Yuan, J.; Geng, S.; Zhang, H. Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China. Energy Convers. Manag. 2016, 113, 66–81. [Google Scholar] [CrossRef]
- Gamboa, G.; Munda, G. The problem of windfarm location: A social multi-criteria evaluation framework. Energy Policy 2007, 3, 1564–1583. [Google Scholar] [CrossRef]
- Cebi, S.; Kahraman, C. Using Multi Attribute Choquet Integral in Site Selection of Wind Energy Plants: The Case of Turkey. J. Mult. Valued Log. Soft Comput. 2013, 20, 423–443. [Google Scholar]
- Wang, M.Z.; Liu, Z. Wind farms affect birds. J. Northwest Norm. Univ. Nat. Sci. Bimon. 2011, 3, 87–91. [Google Scholar]
- Lu, Z.N.; Wang, J. Based on the GIS—PPE model, the development of China’s wind energy resources development site evaluation. East Chin. Econ. Manag. 2012, 12, 141–144. [Google Scholar]
- Deng, Y.C.; Yu, Z. The evaluation method of the macro location resource of wind farm based on the reference wind power unit. Acta Energiae Solaris Sin. 2010, 11, 1516–1520. [Google Scholar]
- Wang, Z.L.; Ma, P. Optimal location decision of wind farm based on fuzzy analytic hierarchy process. Renew. Energy Res. 2013, 3, 239–242. [Google Scholar]
- Ma, P.; You, X.P. The application of fuzzy multi-objective decision making method in the location of wind farm. Renew. Energy Res. 2013, 11, 20–25. [Google Scholar]
- Chen, C.R.; Huang, C.C.; Tsuei, H.J. A hybrid MCDM model for improving GIS-based solar farms site selection. Int. J. Photoenergy 2014, 1, 925370–925378. [Google Scholar] [CrossRef]
- Carrión, J.A.; Estrella, A.E.; Dols, F.A.; Toro, M.Z.; Rodríguez, M.; Ridao, A.R. Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renew. Sustain. Energy Rev. 2008, 9, 2358–2380. [Google Scholar] [CrossRef]
- Sánchez-Lozano, J.M.; Antunes, C.H.; García-Cascales, M.S.; Dias, L.C. GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renew. Energy 2014, 6, 478–494. [Google Scholar] [CrossRef]
- Xiao, J.H.; Yao, Z.; Qu, J.; Sun, J. Research on an optimal site selection model for desert photovoltaic power plants based on analytic hierarchy process and geographic information system. J. Renew. Sustain. Energy 2013, 2, 023132–023146. [Google Scholar] [CrossRef]
- Xiao, J.H.; Yao, Z.Y.; Sun, J.H. Research and evaluation of site selection for solar photovoltaic power station. J. Desert Res. 2011, 6, 1598–1605. [Google Scholar]
- Wang, J.; Peng, L.; Zhang, H. Method of multi-criteria group decision-making based on cloud aggregation operators with linguistic information. Inf. Sci. 2014, 274, 177–191. [Google Scholar] [CrossRef]
- Shanghai Development and Reform Commission. Shanghai Electric Vehicle Charging Infrastructure Special Planning. Available online: http://auto.sina.com.cn/news/hy/2016-04-08/detail-ifxrcizs7044762.shtml (accessed on 8 April 2016).
Primary Index | Secondary Index | Frequency |
---|---|---|
Natural resources | Annual average wind speed | 11 |
Wind power density | 9 | |
Total solar radiation | 8 | |
Wind resources | 7 | |
Available wind energy time | 5 | |
Turbulence intensity | 5 | |
Average temperature | 4 | |
Extreme climate | 3 | |
Illumination time | 3 | |
Scattering | 2 | |
Equivalent solar time | 2 | |
Sunshine stability | 1 | |
Economic factors | Construction cost | 16 |
Operational and maintenance cost | 10 | |
Payback period of investment | 6 | |
Net profit rate | 2 | |
Economic externalities | 2 | |
Commercial feasibility | 1 | |
Technical requirements | Distance to the transmission line | 15 |
Power source | 6 | |
Distance to the load center | 4 | |
Distance to the transformer substation | 4 | |
Capacity coefficient | 3 | |
Network loss | 1 | |
Traffic factors | Transportation convenience | 20 |
Geographical factors | Geological and topographical conditions | 12 |
Slope | 8 | |
Regional stability | 6 | |
Altitude | 3 | |
Orientation | 2 | |
Land scale | 1 | |
Social factors | Regional economic developmental level | 12 |
Public acceptance | 7 | |
Distance to the city/urban area | 7 | |
Government policy | 5 | |
Distance to the tourist area | 2 | |
Population density | 1 | |
Environmental factors | Impact on the ecological environment | 15 |
Pollutant emission reduction benefits | 7 | |
Energy conservation benefits | 6 | |
Current land use | 5 | |
Noise pollution | 4 | |
Electromagnetic radiation effects | 1 |
Economic indicators C1 | Project construction cost C11 | Construction costs include land requisition fees, demolition charges, other investment expenses such as the purchase , installation of equipment and line |
Project operation and maintenance cost C12 | Mainly including wind-light complementary project unit, filling in the plant equipment and power network maintenance and when scenery complementary project of power can meet the load demand fill in power station, every year need spending to the superior power grid electricity purchasing cost | |
Payback period C13 | The amount of time that the net income of the complementary power generation project offsets the total investment | |
Natural factors C2 | Wind energy resources C21 | The annual mean wind speed, wind power density and wind power can be measured by the annual mean of the observation point |
Solar resources C22 | Mainly through the total radiation of the sun, the proportion of the scattering in total radiation and the equivalent of the sun | |
Battery operating temperature C23 | Through environmental temperature, daylight illumination and wind speed and other factors | |
Geographical factors C3 | Geological and topographical conditions C31 | The height of the underground water table is evaluated mainly through the thickness of strata or soil, whether the soil is uniform and bearing capacity |
Terrain and topography C32 | By slope size, whether there are occlusion objects to evaluate | |
Regional stability C33 | Whether there is a fault or earthquake in the area | |
Reliability factor C4 | Network loss in the distribution network C41 | The line distance between the landscape complementary power generation project and the charging station is evaluated |
Voltage stability C42 | The voltage stability of the power generation project and the anti-jamming capability of the voltage | |
Installed capacity of power generation projects C43 | The installed capacity of the complementary power generation project | |
Traffic satisfaction factors C5 | Traffic convenience C51 | Mainly through main road condition, road condition, lane number, intersection number and other factors |
Charge for power plant services C52 | The maximum number of electric vehicles that can be serviced by electric vehicles at the same time and daily | |
Charge for power station service RADIUS C53 | The distance between two adjacent charging stations | |
Social factors C6 | Supporting policy C61 | Subsidies, electricity prices, land concessions and tax incentives |
Residents’ acceptance C62 | Survey the residents’ recognition of the complementary power generation project and the charging station | |
Influence on the regional development C63 | The development of the project to the regional economy, the function and the size of the employment problem | |
Environmental factors C7 | Ecological and Environmental impact C71 | It mainly refers to the influence and size of birds, soil and vegetation, etc. |
Energy-saving benefits C72 | Energy savings compared to coal-fired and internal-combustion vehicles | |
Pollutant emission reduction effect C73 | Compared with conventional power generation technology and internal combustion electric vehicles, the reduction of pollutants such as SO2, CO2, NOX, CO and TSP is compared |
Importance Degree | Definition | Explanation |
---|---|---|
1 | Equally important | Both indicators make the same contribution to the target. |
3 | Slightly important | Experience and judgment tend to be slightly more important. |
5 | Important | Experience and judgment tend to be more important. |
7 | Very important | Experience and judgment tend to be more important and the degree of importance is very evident in practice. |
9 | Extremely important | Experience and judgment tend to be more important and the degree of importance is extremely evident in practice. |
2, 4, 6, 8 | Middle values | Between other degrees of importance |
Language Value | Cloud |
---|---|
Very Poor (VP) | (0.00, 2.959, 0.125) |
Poor (P) | (2.25, 2.655, 0.226) |
Moderately Poor (MP) | (3.85, 2.100, 0.411) |
Moderate (M) | (5.00, 1.922, 0.471) |
Moderately Good (MG) | (6.15, 2.100, 0.411) |
Good (G) | (7.75, 2.655, 0.226) |
Very Good (VG) | (10.00, 2.959, 0.125) |
Factor | Affected Factor |
---|---|
C11 | C12, C13, C31, C32, C33, C43, C51, C52, C61, C62, C71 |
C12 | C11, C13, C31, C41, C42, C51, C52, C53, C61, C62, C63, C71, C73 |
C13 | C31, C32, C41, C42, C43, C51, C52, C53, C61, C63, C72, C73 |
C21 | C12, C13, C23, C32, C41, C42, C43, C61, C72, C73 |
C22 | C12, C13, C32, C41, C42, C43, C61, C72, C73 |
C23 | C12, C13, C22, C31, C32, C41, C42, C43, |
C31 | C11, C13, C33, C43, C61 |
C32 | C12, C13, C21, C22, C23, C31, C43, C61, C72, C73 |
C33 | C11, C12, C13, C31, C42, |
C41 | C12, C13, C43, C52, C63, C72, C73 |
C42 | C12, C13, C21, C22, C23, C31, C33, C43, C52, C63 |
C43 | C11, C12, C13, C21, C22, C23, C31, C32, C41, C42, C51, C52C53, C61, C62, C63, C71, C72, C73 |
C51 | C11, C12, C13, C31, C32, C33, C43, C52, C53, C62, C72, C73 |
C52 | C11, C12, C13, C21, C22, C23, C33, C41, C42, C43C51, C53, C62, C63, C71, C72, C73 |
C53 | C12, C13, C43, C51, C52, C63 |
C61 | C11, C12, C13, C43, C51, C63, C71 |
C62 | C12, C13, C33, C43, C52, C53, C61, C62, C63, C71 |
C63 | C12, C13, C52, C61, C62, C71, C72, C73 |
C71 | C11, C13, C21, C22, C23, C31, C43, C61, C62 |
C72 | C11, C12, C13, C21, C22, C23, C31, C32, C33, C41, C42, C43, C51, C52, C53, C61, C62, C63, C73 |
C73 | C12, C13, C21, C22, C23, C32, C33, C41, C42, C43, C52, C61, C62, C63, C72 |
Index | Weight | Index | Weight |
---|---|---|---|
C11 Construction cost | 0.011037 | C43 Installed capacity | 0.095162 |
C12 Operating maintenance cost | 0.013562 | C51 Traffic convenience | 0.033413 |
C13 Investment payback period | 0.054911 | C52 Service capability | 0.049730 |
C21 Wind resource conditions | 0.081353 | C53 Radius of service | 0.009904 |
C22 Solar resource conditions | 0.058523 | C61 Government policy | 0.02470 |
C23 Battery operating temperature | 0.040059 | C62 Residents around identification degree | 0.017064 |
C31 Geological and topographical conditions | 0.034506 | C63 Impact on regional development | 0.012621 |
C32 Topography and slope | 0.043445 | C71 Ecological and environmental impact | 0.059396 |
C33 Regional stability | 0.020278 | C72 Energy-saving benefit | 0.147163 |
C41 Network loss | 0.028950 | C73 Pollutant emission reduction benefit | 0.091685 |
C42 Voltage stability | 0.072538 |
District | C11/Ten Thousand Yuan | C12/Ten Thousand Yuan | C13/Year | C41/Ten Thousand Yuan | C52/(Car/Day) | C53/km |
---|---|---|---|---|---|---|
A1 | 2880 | 240 | 5 | 6.99 | 360 | 8 |
A2 | 2070 | 220 | 4 | 6.29 | 330 | 13 |
A3 | 2010 | 235 | 4.5 | 6.09 | 400 | 10 |
A4 | 1920 | 210 | 3 | 5.98 | 310 | 15 |
A1 | A2 | A3 | A4 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
DM1 | DM2 | DM3 | DM1 | DM2 | DM3 | DM1 | DM2 | DM3 | DM1 | DM2 | DM3 | |
C11 | P | MP | P | M | M | MP | M | MG | M | G | MG | MG |
C12 | M | MG | MG | G | G | MG | MG | G | MG | VG | G | G |
C13 | MG | G | MG | G | VG | G | G | G | MG | VG | G | VG |
C21 | M | M | MP | MG | MG | G | M | MG | M | G | G | VG |
C22 | MG | G | MG | MG | G | G | G | VG | G | G | VG | VG |
C23 | MP | P | MP | MP | M | M | M | MG | M | G | MG | G |
C31 | M | MP | MP | M | MG | M | M | M | MP | MG | MG | M |
C32 | MG | M | M | G | MG | MG | MG | G | G | VG | G | G |
C33 | MG | G | MG | VG | G | G | G | MG | G | VG | VG | G |
C41 | MG | G | MG | MG | G | G | G | VG | G | G | VG | VG |
C42 | P | P | VP | P | MP | MP | MP | M | MP | M | MG | MG |
C43 | MP | M | M | M | M | MG | MP | M | MP | MG | G | G |
C51 | P | P | VP | P | G | G | G | P | P | VG | G | G |
C52 | M | M | MP | MG | MG | G | M | MG | M | G | G | VG |
C53 | MG | M | M | MG | G | G | MG | M | MG | G | G | VG |
C61 | VP | MP | MP | MG | MP | MG | MP | MG | MP | MG | VG | MG |
C62 | MP | M | MP | M | M | MP | M | MG | M | M | MG | MG |
C63 | M | M | MG | MG | MG | G | G | G | MG | VG | G | VG |
C71 | P | MP | MP | M | M | MP | MG | M | M | MG | G | MG |
C72 | MP | P | P | G | MG | MG | G | MG | G | G | VG | G |
C73 | M | M | MG | MG | M | MG | MG | G | MG | MG | G | G |
A1 | A2 | A3 | A4 | |
---|---|---|---|---|
C11 | (2.7833, 2.4838, 0.3006) | (4.6167, 1.9831, 0.4519) | (5.3833, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) |
C12 | (5.7667, 2.0424, 0.4319) | (7.2167, 2.4838, 0.3006) | (6.6833, 2.2999, 0.3601) | (8.5000, 2.7601, 0.1981) |
C13 | (6.6833, 2.2999, 0.3601) | (8.5000, 2.7601, 0.1981) | (7.2167, 2.4838, 0.3006) | (9.2500, 2.8613, 0.1657) |
C21 | (4.6167, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) | (5.3833, 1.9831, 0.4519) | (8.5000, 2.7601, 0.1981) |
C22 | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) | (8.5000, 2.7601, 0.1981) | (9.2500, 2.8613, 0.1657) |
C23 | (3.3167, 2.2999, 0.3601) | (4.6167, 1.9831, 0.4519) | (5.3833, 1.9831, 0.4519) | (7.2167, 2.4838, 0.3006) |
C31 | (4.2333, 2.0424, 0.4319) | (5.3833, 1.9831, 0.4519) | (4.6167, 1.9831, 0.4519) | (5.7667, 2.0424, 0.4319) |
C32 | (5.3833, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) | (8.5000, 2.7601, 0.1981) |
C33 | (6.6833, 2.2999, 0.3601) | (8.5000, 2.7601, 0.1981) | (7.2167, 2.4838, 0.3006) | (9.2500, 2.8613, 0.1657) |
C41 | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) | (8.5000, 2.7601, 0.1981) | (9.2500, 2.8613, 0.1657) |
C42 | (1.5000, 2.7601, 0.1981) | (3.3167, 2.2999, 0.3601) | (4.2333, 2.0424, 0.4319) | (5.7667, 2.0424, 0.4319) |
C43 | (4.6167, 1.9831, 0.4519) | (5.3833, 1.9831, 0.4519) | (4.2333, 2.0424, 0.4319) | (7.2167, 2.4838, 0.3006) |
C51 | (1.5000, 2.7601, 0.1981) | (5.9167, 2.6550, 0.2260) | (4.0833, 2.6550, 0.2260) | (8.5000, 2.7601, 0.1981) |
C52 | (4.6167, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) | (5.3833, 1.9831, 0.4519) | (8.5000, 2.7601, 0.1981) |
C53 | (5.3833, 1.9831, 0.4519) | (7.2167, 2.4838, 0.3006) | (5.7667, 2.0424, 0.4319) | (8.5000, 2.7601, 0.1981) |
C61 | (2.5667, 2.4204, 0.3433) | (5.3833, 2.1000, 0.4110) | (4.6167, 2.1000, 0.4110) | (7.4333, 2.4204, 0.3433) |
C62 | (4.2333, 2.0424, 0.4319) | (4.6167, 1.9831, 0.4519) | (5.3833, 1.9831, 0.4519) | (5.7667, 2.0424, 0.4319) |
C63 | (5.3833, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) | (9.2500, 2.8613, 0.1657) |
C71 | (3.3167, 2.2999, 0.3601) | (4.6167, 1.9831, 0.4519) | (5.3833, 1.9831, 0.4519) | (6.6833, 2.2999, 0.3601) |
C72 | (2.7833, 2.4838, 0.3006) | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) | (8.5000, 2.7601, 0.1981) |
C73 | (5.3833, 1.9831, 0.4519) | (5.7667, 2.0424, 0.4319) | (6.6833, 2.2999, 0.3601) | (7.2167, 2.4838, 0.3006) |
A1 | A2 | A3 | A4 | |
---|---|---|---|---|
A1 | (0.0000, 0.8001, 0.1340) | (0.0023, 0.8014, 0.1339) | (0.0000, 0.8606, 0.1176) | |
A2 | (0.1120, 0.8001, 0.1340) | (0.0315, 0.8034, 0.1328) | (0.0000, 0.8625, 0.1164) | |
A3 | (0.1120, 0.8014, 0.1339) | (0.0293, 0.8034, 0.1328) | (0.0000, 0.8636, 0.1163) | |
A4 | (0.2236, 0.8606, 0.1176) | (0.1116, 0.8625, 0.1164) | (0.1138, 0.8636, 0.1163) |
A1 | (0.0023, 1.4223, 0.2230) | (0.4476, 1.4223, 0.2230) | (−0.4453, 2.0115, 0.3153) |
A2 | (0.1435, 1.4246, 0.2216) | (0.1409, 1.4246, 0.2216) | (0.0026, 2.0147, 0.3135) |
A3 | (0.1413, 1.4260, 0.2216) | (0.1477, 1.4260, 0.2216) | (−0.0063, 2.0166, 0.3134) |
A4 | (0.4490, 1.4934, 0.2022) | (0.0000, 1.4934, 0.2022) | (0.4490, 2.1120, 0.2860) |
1st | 2nd | 3rd | 4th | 5th | Average Value | |
---|---|---|---|---|---|---|
−0.3147 | −0.3091 | −0.3192 | −0.3165 | −0.3292 | −0.3177 | |
0.0020 | 0.0060 | 0.0033 | 0.0128 | 0.0033 | 0.0055 | |
−0.0125 | −0.0104 | −0.0069 | −0.0057 | −0.0093 | −0.0090 | |
0.3325 | 0.3159 | 0.3109 | 0.3163 | 0.2933 | 0.3137 |
Language Value | Triangular Fuzzy Numbers |
---|---|
Very poor | (0.00, 0.00, 0.15) |
Poor | (0.00, 0.15, 0.30) |
Moderately poor | (0.15, 0.30, 0.50) |
Moderate | (0.30, 0.50, 0.65) |
Moderately good | (0.50, 0.65, 0.80) |
Good | (0.65, 0.80, 1.00) |
Very good | (0.80, 1.00, 1.00) |
A1 | A2 | A3 | A4 | |
---|---|---|---|---|
C11 | (0.0000, 0.2000, 0.5000) | (0.1500, 0.4333, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7000, 1.0000) |
C12 | (0.3000, 0.6000, 0.8000) | (0.5000, 0.7500, 1.0000) | (0.5000, 0.7000, 1.0000) | (0.6500, 0.8667, 1.0000) |
C13 | (0.5000, 0.7000, 1.0000) | (0.6500, 0.8667, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.9333, 1.0000) |
C21 | (0.1500, 0.4333, 0.6500) | (0.5000, 0.7000, 1.0000) | (0.3000, 0.5500, 0.8000) | (0.6500, 0.8667, 1.0000) |
C22 | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.8667, 1.0000) | (0.6500, 0.9333, 1.0000) |
C23 | (0.0000, 0.2500, 0.5000) | (0.1500, 0.4333, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7500, 1.0000) |
C31 | (0.1500, 0.3667, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.1500, 0.4333, 0.6500) | (0.3000, 0.6000, 0.8000) |
C32 | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.8667, 1.0000) |
C33 | (0.5000, 0.7000, 1.0000) | (0.6500, 0.8667, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.9333, 1.0000) |
C41 | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.8667, 1.0000) | (0.6500, 0.9333, 1.0000) |
C42 | (0.0000, 0.1000, 0.3000) | (0.0000, 0.2500, 0.5000) | (0.1500, 0.3667, 0.6500) | (0.3000, 0.6000, 0.8000) |
C43 | (0.1500, 0.4333, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.1500, 0.3667, 0.6500) | (0.5000, 0.7500, 1.0000) |
C51 | (0.0000, 0.1000, 0.3000) | (0.0000, 0.5833, 1.0000) | (0.0000, 0.3667, 1.0000) | (0.6500, 0.8667, 1.0000) |
C52 | (0.1500, 0.4333, 0.6500) | (0.5000, 0.7000, 1.0000) | (0.3000, 0.5500, 0.8000) | (0.6500, 0.8667, 1.0000) |
C53 | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7500, 1.0000) | (0.3000, 0.6000, 0.8000) | (0.6500, 0.8667, 1.0000) |
C61 | (0.0000, 0.2000, 0.5000) | (0.1500, 0.5333, 0.8000) | (0.1500, 0.4167, 0.8000) | (0.5000, 0.7667, 1.0000) |
C62 | (0.1500, 0.3667, 0.6500) | (0.1500, 0.4333, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.3000, 0.6000, 0.8000) |
C63 | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.9333, 1.0000) |
C71 | (0.0000, 0.2500, 0.5000) | (0.1500, 0.4333, 0.6500) | (0.3000, 0.5500, 0.8000) | (0.5000, 0.7000, 1.0000) |
C72 | (0.0000, 0.2000, 0.5000) | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) | (0.6500, 0.8667, 1.0000) |
C73 | (0.3000, 0.5500, 0.8000) | (0.3000, 0.6000, 0.8000) | (0.5000, 0.7000, 1.0000) | (0.5000, 0.7500, 1.0000) |
A1 | A2 | A3 | A4 | |
---|---|---|---|---|
A1 | (0.0000, 0.0000, 0.2924) | (0.0000, 0.0063, 0.2899) | (0.0000, 0.0000, 0.1287) | |
A2 | (0.0000, 0.2171, 0.6955) | (0.0000, 0.0622, 0.5066) | (0.0000, 0.0000, 0.3116) | |
A3 | (0.0000, 0.2141, 0.6962) | (0.0000, 0.0529, 0.5098) | (0.3123, 0.0000, 0.3123) | |
A4 | (0.0338, 0.4069, 0.8008) | (0.0000, 0.1898, 0.6144) | (0.0000, 0.1991, 0.6119) |
De-fuzziness | ||||
---|---|---|---|---|
A1 | (0.0000, 0.0063, 0.7110) | (0.0338, 0.8381, 2.1925) | (−0.0338, −0.8317, −1.4815) | −0.7947 |
A2 | (0.0000, 0.2793, 1.5137) | (0.0000, 0.2427, 1.4167) | (0.0000, 0.0366, 0.0970) | 0.0426 |
A3 | (0.3123, 0.2670, 1.5184) | (0.0000, 0.2676, 1.4084) | (0.3123, −0.0006, 0.1100) | 0.1053 |
A4 | (0.0338, 0.7958, 2.0271) | (0.3123, 0.0000, 0.7526) | (−0.2786, 0.7958, 1.2745) | 0.6469 |
Algorithm | Sorting Result |
---|---|
PROMETHEE method based on fuzzy numbers | A4 > A3 > A 2> A1 |
PROMETHEE method based on the cloud model | A4 > A2 > A3 > A1 |
County | Public Charging Pile Construction Plan in 2017 | Public Charging Pile Construction Plan in 2017 | Public Charging Pile Construction Plan before 2020 |
---|---|---|---|
Jinshan District (A2) | 600 | 1300 | ≥6 |
Chongming District (A3) | 200 | 450 | ≥2 |
Increasing of Index | Ranking |
---|---|
−30% | A4 > A2 > A3 > A1 |
−20% | A4 > A2 > A3 > A1 |
−10% | A4 > A2 > A3 > A1 |
0 | A4 > A2 > A3 > A1 |
10% | A4 > A2 > A3 > A1 |
20% | A4 > A2 > A3 > A1 |
30% | A4 > A2 > A3 > A1 |
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Share and Cite
Chen, W.; Zhu, Y.; Yang, M.; Yuan, J. Optimal Site Selection of Wind-Solar Complementary Power Generation Project for a Large-Scale Plug-In Charging Station. Sustainability 2017, 9, 1994. https://doi.org/10.3390/su9111994
Chen W, Zhu Y, Yang M, Yuan J. Optimal Site Selection of Wind-Solar Complementary Power Generation Project for a Large-Scale Plug-In Charging Station. Sustainability. 2017; 9(11):1994. https://doi.org/10.3390/su9111994
Chicago/Turabian StyleChen, Wenjun, Yanlei Zhu, Meng Yang, and Jiahai Yuan. 2017. "Optimal Site Selection of Wind-Solar Complementary Power Generation Project for a Large-Scale Plug-In Charging Station" Sustainability 9, no. 11: 1994. https://doi.org/10.3390/su9111994