Coordinated DG-Tie planning in distribution networks based on temporal scenarios. (15th September 2018)

• Record Type: Journal Article
• Authors: Xiang, Yue; Zhou, Lili; Su, Yunche; Liu, Jichun; Huang, Yuan; Liu, Junyong; Lei, Xia; Sun, Zhang; Xu, Weiting; Zhang, Wentao
• Is Part Of: Energy. Volume 159(2018)
• Page Start: 774
• Page End: 785
• Publication Date: 2018-09-15
• Subjects: Distributed renewable generation -- Tie lines -- Temporal scenarios -- Chance constrained -- Genetic algorithm; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.06.159
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Optimal planning of distributed generation (DG) in distribution networks is key for improving energy utilization efficiency and system operation benefits. Considering the uncertainties and temporal correlations of DG output and load demand, a multi-scenario chance-constrained economic model for DG planning is established in this paper. The model considers the comprehensive benefits of environmental, reliability and other aspects, as well as the active curtailment management of DGs. Thereafter, the model is extended to the coordinated planning of both DGs and tie lines, which is formulated as a multi-objective model. An improved genetic algorithm based solving strategy integrated with game multi-objective decision method is proposed. The feasibility and effectiveness of the proposed models are verified on the IEEE 33-bus distribution system. The impacts of natural resource distribution, confidence level, unit environmental cost and other parameters are investigated as well. The case studies also prove the integration of complementary DG generation could help improve the maximum capacity of DG and indicate the environmental benefit is the vital incentive to introduce DG into the distribution network. Highlights: A two-stage planning model is established. A joint probability method is presented to handle uncertainties. Temporal scenarios are formed for planning. Economic, reliability and environmental benefits are assessed.