Optimal ESS allocation in distribution network using accelerated generalised Benders decomposition

被引:23
|
作者
Lin, Zhe [1 ]
Hu, Zechun [1 ]
Zhang, Hongcai [1 ]
Song, Yonghua [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Univ Macau, Dept Elect & Comp Engn, Macau, Peoples R China
关键词
investment; load flow; energy storage; integer programming; distributed power generation; power distribution economics; power generation economics; pattern clustering; distributed ESSs; distribution system operator; mixed integer investment problem; optimal operation problems; base load; second-order cone programming model; 17-bus test system; acceleration method; optimal ESS allocation; distribution network; energy storage technologies; energy storage system; distributed renewable generations; optimal allocation; rebinding integer constraints; optimal ESS allocation problem; accelerated generalised Benders decomposition algorithm; daily charging-discharging schedule; distributed generation output uncertainties; clustering method; GBD algorithm; branch-and-bound algorithm; ENERGY-STORAGE SYSTEMS; ACTIVE DISTRIBUTION NETWORKS; OPTIMAL POWER-FLOW; OPTIMAL PLACEMENT; CONVEX RELAXATION; HIGH PENETRATION; BATTERY STORAGE; UNITS; EXPANSION;
D O I
10.1049/iet-gtd.2018.5863
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In this study, the optimal allocation of distributed ESSs is studied to maximise the benefit of the distribution system operator. The ESS allocation problem is divided into two stages: the mixed integer investment problem as the first stage and the optimal operation problems considering daily charging/discharging schedule of ESSs as the second stage. To tackle the uncertainties of distributed generation output and base load, typical days (scenarios) are firstly obtained by the clustering method and thereafter the operation problems include a number of scenarios, each with the corresponding possibility. Then each second stage problem is relaxed to a second-order cone programming model. To efficiently solve the whole problem considering multiple scenarios, the generalised Benders decomposition (GBD) algorithm is adopted, which is further accelerated by relaxing and rebinding integer constraints. Numerical experiments are conducted on a 17-bus test system to demonstrate the effectiveness of the proposed method. Additionally, comparisons between different algorithms are performed to verify the merits of the proposed acceleration method with respect to the original GBD and the branch-and-bound algorithm.
引用
收藏
页码:2738 / 2746
页数:9
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