Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density
Abstract
:1. Introduction
2. The Construction of an Electric/Thermal/Cold Load Model in a Typical New District Based on Timing-Phasing and Hybrid Clustering
2.1. Analysis of the Electric/Thermal/Cold Load Characteristics
2.2. The Seasonal Characteristics of the Electric/Thermal/Cold Load
2.3. Multi-Scenario Construction of the Electric/Thermal/Cold Load in a Typical New District Based on Timing-Phasing and Hybrid Clustering
- (1)
- Initialize the membership matrix’s degree, U, determine the initial clusterings’ number, c, and the initial clustering centres’ number, vi.
- For the set of clustering data, define the upper limit number of the initial clustering, lmax, and set the neighbourhood radius, Eps, and the density threshold, Minpts.
- Determine whether there are unmarked points in the data set. If no such points exist, the search ends; otherwise, randomly select an unmarked data point, d, to determine whether the data exceed the density threshold Minpts within the neighbourhood radius, Eps. If not, go to step d; otherwise, go to step c.
- Mark point d as a noise point that is no longer processed and return to step b.
- Mark point d as a core point, form the clusters and place the objects within the neighbourhood radius, Eps, into the clusters. Then, find the density reachable point in the cluster and add to the clusters until all of the data clustering is complete or the upper limit is reached. Return to step b.
- The clustering number, c, is the total number in the final cluster, and the initial clustering centre, vi, is defined in Equation (1).
- (2)
- Determine the weighting index, w, which can be regared as the final clustering effect’s fuzzy degree.
- (3)
- Update the clustering centre and the membership matrix degree, which are shown in Equation (4).
- (4)
- Calculate the objective function through the Equation (3).
- (5)
- Calculate the iteration error of the two iterations of the objective function, ∆Jw(U, V), then determine whether it is less than the given positive number, , and if this condition is not met, return to step 3. Otherwise, the clustering process is finished.
3. The Location Optimization Model of the Integrated Energy Supply Centre in a Typical New District
3.1. The Integrated Load Density of Each Functional District in a Typical New District
3.1.1. The Integrated Load Density
3.1.2. The Integrated Load Density of Each Functional District in a Typical New District Based on the Construction of a Timing Multi-Scenario
3.2. The Multi-Objective Location Optimization Model of the Integrated Energy Supply Centre in a Typical New District
3.2.1. The Objective Functions
3.2.2. The Constraint Conditions
3.2.3. The Model Solution
- (1)
- Input the original planning data and initialize the population. Then, randomly generate N antibodies and extract m individuals from the memory library to form the initial population.
- (2)
- Calculate the antibody fitness value and the expected reproductive probability; then arrange the antibodies in descending order.
- (3)
- Form the parent population, and arrange the initial population in descending order according to the expected reproductive probability, P. Extract the first N individuals to form the parent population. The first m individuals form the memory library.
- (4)
- Determine whether the number of iterations has reached the upper limit. If not, the next step will be continued; otherwise, the iterative process is finished, and a non-inferior solution set will be output.
- (5)
- Implement adaptive selection, crossover and mutation through Equations (21) and (22). Then, return to step 3.
- (6)
- Calculate the inner interval ratio in each partition result according to Equation (13). Then, compare the inner interval ratio’s value, and obtain the optimal partition results of the typical new district.
4. Example Analysis
4.1. Analysis of the Electric/Thermal/Cold Load Characteristics
4.2. The Location Optimization of the Integrated Energy Supply Centre in a Typical New District
5. Conclusions
- (1)
- The timing multi-scenario characteristics of the electric/thermal/cold loads, the load density values and the load weight ratios for different functional districts are distinct.
- (2)
- The different optimized numbers of integrated energy centres correspond to the different inner interval ratios of energy supply partitions. The smaller the inner interval ratio value is, the better the partition situation will be.
- (3)
- The location optimization method with the minimum improved integrated load moment of the system and the minimum inner interval ratio of the partition can suitably balance the uncertainty of the electric/thermal/cold load fluctuation and the spatial location in each functional district. Therefore, the location of optimization of the integrated energy supply centre in a typical new district based on the load density has been realized.
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
FCM | Fuzzy C means |
LAN | Local area network |
DBSCAN | Density-based spatial clustering of applications with noise |
Appendix A
Label | Functional District Type | Covered Area/×105 m2 | Spatial Location | Label | Functional District Type | Covered Area/×105 m2 | Spatial Location | ||
---|---|---|---|---|---|---|---|---|---|
X Coordinate/m | Y Coordinate/m | X Coordinate/m | Y Coordinate/m | ||||||
1 | Residential district-1 | 22 | 2550 | 9100 | 16 | Residential district-6 | 32.8 | 5600 | 5600 |
2 | Municipal district-1 | 32 | 3450 | 9000 | 17 | Residential district-7 | 41.6 | 7600 | 6350 |
3 | Industrial district-1 | 48.8 | 4750 | 8800 | 18 | Industrial district-3 | 15.6 | 8700 | 6800 |
4 | Other district-1 | 17.2 | 1100 | 7850 | 19 | Industrial district-4 | 33.2 | 4650 | 4800 |
5 | Commercial district-1 | 27.2 | 2300 | 7150 | 20 | Municipal district-4 | 34.8 | 7350 | 5100 |
6 | Residential district-2 | 18.4 | 3550 | 7450 | 21 | Commercial district-4 | 11.6 | 3800 | 3600 |
7 | Commercial district-2 | 28.4 | 5100 | 7500 | 22 | Commercial district-5 | 49.2 | 6350 | 3950 |
8 | Residential district-3 | 30.4 | 6350 | 8100 | 23 | Residential district-8 | 25.6 | 8700 | 4400 |
9 | Industrial district-2 | 17.6 | 1600 | 6050 | 24 | Industrial district-5 | 14.4 | 8150 | 3600 |
10 | Municipal district-2 | 76.8 | 3050 | 4900 | 25 | Other district-3 | 8.8 | 2600 | 3300 |
11 | Residential district-4 | 23.6 | 3450 | 6250 | 26 | Commercial district-6 | 25.6 | 3300 | 2700 |
12 | Residential district-5 | 34.4 | 4800 | 6100 | 27 | Municipal district-5 | 26.4 | 4250 | 2800 |
13 | Commercial district-3 | 28.8 | 6200 | 6300 | 28 | Other district-4 | 28.4 | 5150 | 3000 |
14 | Other district-2 | 20.8 | 6700 | 7100 | 29 | Industrial district-6 | 45.6 | 4900 | 1200 |
15 | Municipal district-3 | 15.2 | 7850 | 8000 | 30 | Industrial district-7 | 29.2 | 6150 | 2200 |
Label | Electric Load Density/(w/m2) | Thermal Load Density/(w/m2) | Cold Load Density/(w/m2) | INTEGRATED Load Density/(w/m2) | Label | Electric Load Density/(w/m2) | Thermal Load Density/(w/m2) | Cold Load Density/(w/m2) | Integrated Load Density/(w/m2) |
---|---|---|---|---|---|---|---|---|---|
1 | 122.3 | 78.8 | 86.5 | 103.984 | 16 | 142.4 | 105.3 | 88.6 | 121.66 |
2 | 204.3 | 168.6 | 103.2 | 173.727 | 17 | 204.5 | 97.2 | 113.5 | 158.728 |
3 | 407.8 | 98.6 | 72.3 | 289.188 | 18 | 253.8 | 67.8 | 172.3 | 201.7 |
4 | 78.9 | 65.4 | 78.6 | 74.775 | 19 | 396.8 | 123.5 | 64.8 | 286.287 |
5 | 186.3 | 96.2 | 102.7 | 134.831 | 20 | 212.5 | 168.2 | 86.5 | 174.453 |
6 | 142.3 | 98.7 | 112.4 | 125.258 | 21 | 196.2 | 87.2 | 89.6 | 132.514 |
7 | 172.1 | 92.4 | 96.8 | 126.221 | 22 | 147.5 | 76.5 | 69.4 | 103.764 |
8 | 123.4 | 86.2 | 94.6 | 108.136 | 23 | 90.8 | 46.3 | 65.7 | 74.598 |
9 | 507.8 | 68.7 | 89.2 | 348.613 | 24 | 262.5 | 88.4 | 77.6 | 196.355 |
10 | 192.4 | 154.2 | 123.2 | 167.482 | 25 | 66.2 | 40.7 | 57.8 | 56.45 |
11 | 176.2 | 90.8 | 102.4 | 139.468 | 26 | 104.2 | 62.3 | 70.8 | 81.689 |
12 | 184.5 | 82.2 | 98.7 | 141.072 | 27 | 192.7 | 146.8 | 102.7 | 161.389 |
13 | 164.5 | 97.8 | 90.2 | 123.171 | 28 | 96.3 | 50.8 | 42.7 | 69.25 |
14 | 96.8 | 72.4 | 89.7 | 87.705 | 29 | 396.2 | 172.5 | 102.4 | 302.215 |
15 | 162.4 | 78.6 | 64.3 | 118.478 | 30 | 276.8 | 67.5 | 89.2 | 202.831 |
References
- Hong, L. Research on the Optimal Scheduling of CCHP Microgrid Oriented to Large Industrial Park; Nanjing University of Science & Technology: Nanjing, China, 2017. [Google Scholar]
- A Case Analysis of the Construction of New Foreign Area. 2014. Available online: https://wenku.baidu.com/view/5fb1de b676eeaeaad1f330b4.html (accessed on 22 February 2018).
- The Case of the New Industrial Area’s Development Experience at Home and Abroad. 2015. Available online: https://wenku.baidu.com/view/edabc911daef5ef7ba0d3cdf.html (accessed on 24 February 2018).
- Kiviluoma, J.; Heinen, S.; Qazi, H.; Madsen, H.; Strbac, G.; Kang, C.; Zhang, N.; Patteeuw, D.; Naegler, T. Harnessing Flexibility from Hot and Cold: Heat Storage and Hybrid Systems Can Play a Major Role. IEEE Power Energy Mag. 2017, 15, 25–33. [Google Scholar] [CrossRef]
- Dall’Anese, E.; Mancarella, P.; Monti, A. Unlocking Flexibility: Integrated Optimization and Control of Multienergy Systems. IEEE Power Energy Mag. 2017, 15, 43–52. [Google Scholar] [CrossRef]
- Zarif, M.; Khaleghi, S.; Javidi, M.H. Assessment of electricity price uncertainty impact on the operation of multi-carrier energy systems. IET Gen. Transm. Distrib. 2015, 9, 2586–2592. [Google Scholar] [CrossRef]
- Qian, A.; Ran, H. Key Technologies and Challenges for Multi-energy Complementarity and Optimization of Integrated Energy System. Autom. Electr. Power Syst. 2018, 42, 2–10. [Google Scholar]
- Hussain, A.; Bui, V.H.; Kim, H.M.; Im, Y.H.; Lee, J.Y. Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations. Energies 2017, 10, 789. [Google Scholar] [CrossRef]
- Tang, X.; Liu, J.; Wang, X.; Xiong, J. Electric vehicle charging station planning based on weighted Voronoi diagram. In Proceedings of the International Conference on Transportation, Mechanical, and Electrical Engineering, Changchun, China, 16–18 December 2012; pp. 1–5. [Google Scholar]
- Fu, Y.; Wei, C.; Li, Z.K.; Jiang, Y. Optimal Partitioning of Substation Service Areas Considering Impacts of Geographic Information and Administrative Boundaries. Autom. Electr. Power Syst. 2014, 38, 126–131. [Google Scholar]
- Liu, H.; Wang, B.; Li, M.; Qu, G. Substation Planning of Active Distribution Network Based on Improved Weighted Voronoi Diagram Method. Autom. Electr. Power Syst. 2017, 41, 45–52. [Google Scholar]
- Wang, J.; Gu, W.; Lu, S.; Tang, Y. Coordinated Planning of Multi-district Integrated Energy System Combining Heating Network Model. Autom. Electr. Power Syst. 2016, 40, 17–24. [Google Scholar]
- Chen, J. Research on Planning of Regional Distributed Energy System under Energy Internet; North China Electric Power University: Beijing, China, 2017. [Google Scholar]
- Jia, C.; Wu, C.; Zhang, C.; Zhou, J.; Liu, G. Optimum configuration of energy station in urban hybrid area of commerce and residence based on integrated planning of electricity and heat system. Power Syst. Prot. Control 2017, 45, 30–36. [Google Scholar]
- Wu, C.; Tang, W.; Bai, M.; Cai, Y. Energy Router Based Planning of Energy Internet at User Side. Autom. Electr. Power Syst. 2017, 41, 20–28. [Google Scholar]
- Ko, B.; Utomo, N.P.; Jang, G.; Kim, J.; Cho, J. Optimal Scheduling for the Complementary Energy Storage System Operation Based on Smart Metering Data in the DC Distribution System. Energies 2013, 6, 6569–6585. [Google Scholar] [CrossRef]
- Huang, Y.; Söder, L. Evaluation of economic regulation in distribution systems with distributed generation. Energy 2017, 126, 192–201. [Google Scholar] [CrossRef]
- Soroudi, A. Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment. IEEE Trans. Power Syst. 2012, 27, 1283–1293. [Google Scholar] [CrossRef]
- Hemmati, R.; Hooshmand, R.A.; Taheri, N. Distribution network expansion planning and DG placement in the presence of uncertainties. Int. J. Electr. Power Energy Syst. 2015, 73, 665–673. [Google Scholar] [CrossRef]
- Yang, Y.; Wang, X.; Luo, J.; Duan, J.; Gao, Y.; Li, H.; Xiao, X. Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics. Energies 2017, 10, 2137. [Google Scholar] [CrossRef]
- Liu, K.Y.; Sheng, W.; Liu, Y.; Meng, X.; Liu, Y. Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs. Int. J. Electr. Power Energy Syst. 2015, 69, 430–440. [Google Scholar] [CrossRef]
- Hao, X.; Wei, P.; Li, K. Multi-time Scale Coordinated Optimal Dispatch of Microgrid Based on Model Predictive Control. Autom. Electr. Power Syst. 2016, 40, 7–14. [Google Scholar]
- Zeng, B.; Zhang, J.; Yang, X.; Wang, J.; Dong, J.; Zhang, Y. Integrated Planning for Transition to Low-Carbon Distribution System With Renewable Energy Generation and Demand Response. IEEE Trans. Power Syst. 2014, 29, 1153–1165. [Google Scholar] [CrossRef]
- Wang, J.; Liu, X.; Cui, R. Spatial Load Forecasting Method for The New Urban Area Based on Information Classification Approach. Int. J. Digit. Content Technol. Its Appl. 2013, 7, 527–533. [Google Scholar]
- Gao, Y.; Hu, X.; Yang, W.; Liang, H.; Li, P. Multi-Objective Bi-level Coordinated Planning of Distributed Generation and Distribution Network Frame Based on Multi-Scenario Technique Considering Timing Characteristics. IEEE Trans. Sustain. Energy 2017, 8, 1415–1429. [Google Scholar] [CrossRef]
- Zeng, B.; Zhang, J.; Ding, L.; Dong, J. An Improved Adaptive Fuzzy C-means Algorithm for Load Characteristics Classification. Autom. Electr. Power Syst. 2011, 35, 42–46. [Google Scholar]
- Sun, X.; Zheng, H.; Li, H.; Wang, Z.; Li, J.; Wang, J. Bad Data Identification for Leakage Reactance Parameters of Transformer Based on Improved DBSCAN Algorithm. Autom. Electr. Power Syst. 2017, 41, 96–101. [Google Scholar]
- Chen, S.H. Analysis on the Influence Factors of Indoor Temperature and Study on Thermal Load Forecasting; Dalian Maritime University: Dalian, China, 2015. [Google Scholar]
- Gao, Y.; Liu, J.; Yang, J.; Liang, H.; Zhang, J. Multi-Objective Planning of Multi-Type Distributed Generation Considering Timing Characteristics and Environmental Benefits. Energies 2014, 7, 6242–6257. [Google Scholar] [CrossRef]
- Hussain, A.; Bui, V.H.; Kim, H.M. Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties. Energies 2016, 9, 278. [Google Scholar] [CrossRef]
- Yu, D.; Yang, M.; Zhai, H.; Han, X. An Overview of Robust Optimization Used for Power System Dispatch and Decision-making. Autom. Electr. Power Syst. 2016, 40, 134–143. [Google Scholar]
- Núñezmata, O.; Palmabehnke, R.; Valencia, F.; Mendoza-Araya, P.; Jiménez-Estévez, G. Adaptive Protection System for Microgrids Based on a Robust Optimization Strategy. Energies 2018, 11, 308. [Google Scholar] [CrossRef]
Weight Value | Residential District | Commercial District | Industrial District | Municipal District | Other District |
---|---|---|---|---|---|
Electric load | 0.54 | 0.41 | 0.63 | 0.51 | 0.44 |
Thermal load | 0.24 | 0.33 | 0.21 | 0.28 | 0.32 |
Cold load | 0.22 | 0.26 | 0.16 | 0.21 | 0.24 |
Method | Partition Marking | X coor dinate/m | Y coor dinate/m | Functional District Marking | Sum of Integrated Load Density/(w/m2) | Iteration Number | Simulink Time/s |
---|---|---|---|---|---|---|---|
Planning method in this paper | Partition one | 4550.7 | 8620.9 | 1, 2, 3, 7, 8 | 801.256 | 1258 | 160.23 |
Partition two | 2349.6 | 6663.4 | 4, 5, 6, 9, 11 | 822.945 | |||
Partition three | 4096.9 | 5046.6 | 10, 12, 16, 19, 21, 25 | 905.465 | |||
Partition four | 7579.9 | 5862.8 | 13, 14, 15, 17, 18, 20, 23, 24 | 1135.188 | |||
Partition five | 5177.2 | 2209.9 | 22, 26, 27, 28, 29, 30 | 921.138 | |||
Adaptive robust optimization method | Partition one | 4476.6 | 8534.2 | 1, 2, 3, 6, 7, 8 | 926.514 | 1426 | 1960.86 |
Partition two | 2157.2 | 6537.4 | 4, 5, 9, 11 | 697.687 | |||
Partition three | 4446.7 | 4977.6 | 10, 12, 16, 19, 22 | 820.265 | |||
Partition four | 7579.9 | 5862.8 | 13, 14, 15, 17, 18, 20, 23, 24 | 1135.188 | |||
Partition five | 4864.9 | 2003.5 | 21,25, 26, 27, 28, 29, 30 | 1006.338 |
© 2018 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, H.; Wang, X.; Duan, J.; Chen, F.; Gao, Y. Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density. Energies 2018, 11, 934. https://doi.org/10.3390/en11040934
Li H, Wang X, Duan J, Chen F, Gao Y. Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density. Energies. 2018; 11(4):934. https://doi.org/10.3390/en11040934
Chicago/Turabian StyleLi, Hong, Xiaodan Wang, Jie Duan, Feifan Chen, and Yajing Gao. 2018. "Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density" Energies 11, no. 4: 934. https://doi.org/10.3390/en11040934