@ARTICLE{ iet:/content/journals/10.1049/iet-gtd.2017.1573, author = {Qianggang Wang}, author = {Jian Wang}, author = {Chao Lei}, author = {Niancheng Zhou}, keywords = {voltage 10 kV;analytical tool;objective function;minimum restructuring cost;maximum air-conditioning load estimation;population density;heat maps;self-capacity requirements;substations;total transforming capacity;geographical map;distribution network planning schemes;distribution transformer;short-term planning model;distribution network restructuring;radial structure;}, ISSN = {1751-8687}, language = {English}, abstract = {A short-term planning model for restructuring distribution network based on heat maps is proposed here. A heat map, as the analytical tool of population density in a geographical map, is used to estimate the maximum air-conditioning load. Furthermore, the maximum load of each distribution transformer can be obtained. Two criteria for evaluating distribution network planning schemes are suggested. The first criterion is reflecting on the adequacy of the total transforming capacity of substations in a region. The second criterion is reflecting on the qualification of the structure of a 10 kV distribution network. A short-term planning model of the 10 kV restructuring distribution network is established when the first criterion is satisfied, whereas the second criterion is unsatisfied. The objective function is the minimum restructuring cost, and the constraints are the total and self-capacity requirements and the connectivity and radial structure of the 10 kV distribution network. Ultimately, a practical 10 kV distribution network is used to verify the effectiveness of the model.}, title = {Short-term planning model for distribution network restructuring based on heat maps}, journal = {IET Generation, Transmission & Distribution}, issue = {14}, volume = {12}, year = {2018}, month = {August}, pages = {3569-3577(8)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=386r9kreae65.x-iet-live-01content/journals/10.1049/iet-gtd.2017.1573} }