Journal
IET GENERATION TRANSMISSION & DISTRIBUTION
Volume 10, Issue 3, Pages 638-644Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2015.0411
Keywords
relaxation theory; integer programming; convex programming; reactive power; on load tap changers; power system management; demand side management; energy storage; power generation planning; distributed power generation; power distribution planning; active distribution network expansion planning; dispersed energy storage system; convex model; DESS; active management scheme; distributed generation curtailment; generation curtailment; demand side management; on-load tap changer tap adjustment; reactive power compensation; peak shaving; operation cost decrement; DG installation; substation expansion; DNEP problem; mixed integer nonlinear programming problem; second-order cone programming model; distflow equation; constraint relaxation
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Funding
- National High Technology Research and Development Program (863 Program) of China [2014AA051901]
- National Natural Science Foundation of China [51261130473]
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This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on-load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non-linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second-order cone programming model through distflow equations and constraints relaxation. A modified 50-bus application example is used to verify the proposed model.
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