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Home > Archive>Volume 9, Issue 4, 2021 >788-799. DOI:10.35833/MPCE.2020.000910
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Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks
Author:
Affiliation:

1.Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China;2.Center for Electric Power and Energy (CEE), Department of Electrical Engineering, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark;3.Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore;4.State Grid Yantai Power Supply Company, Yantai, China

Fund Project:

This work was supported by the Science and Technology Project of State Grid Corporation of China “Research on resilience technology and application foundation of intelligent distribution network based on integrated energy system” (No. 52060019001H).

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    Abstract:

    Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

    表 6 Table 6
    表 3 Table 3
    表 7 Table 7
    表 4 Table 4
    表 2 Table 2
    图1 Framework of proposed model.Fig.1
    图2 Flowchart of C&CG algorithm.Fig.2
    图3 Flowchart of proposed hybrid ESS allocation strategy.Fig.3
    图4 Modified IEEE 33-node DN.Fig.4
    图5 Information of DNs. (a) TOU price. (b) Typical daily load.Fig.5
    图6 PV and load data of scenario clustering. (a) PV data. (b) Load data.Fig.6
    图7 Clustering error curves.Fig.7
    图8 SOC curves of ESS in scenario 1.Fig.8
    图9 Convergence process of C&CG algorithm.Fig.9
    图10 Hybrid ESS allocation results.Fig.10
    图11 Annual operation cost. (a) Electricity purchase cost. (b) ESS allocation cost. (c) Network loss cost. (d) Total annual operation cost.Fig.11
    图12 SOC of ESSs in failure scenario 4. (a) Strategy 1. (b) Strategy 2. (c) Strategy 3. (d) Proposed strategy.Fig.12
    图13 Comparison of CLRR with and without MESS.Fig.13
    图14 Failure recovery of proposed hybrid ESS allocation strategy.Fig.14
    图15 Results of failure recovery.Fig.15
    表 5 Table 5
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History
  • Received:December 28,2020
  • Online: August 04,2021

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