doi: 10.3934/dcdss.2020206

Design of energy storage coordination optimization algorithm for distributed power distribution network operation planning

1. 

Department of Communication and Electrical engineering, Shenyang Agricultural University, Shenyang 110866, China

2. 

State Grid Henan Power Company Zhumadian Power Supply Company, Xhumadian 463000, China

3. 

State Grid Henan Power Company Sanmenxia Power Supply Company, Sanmenxia 472000, China

4. 

State Grid Henan Power Company Zhoukou Power Supply Company, Zhoukou 466000, China

* Corresponding author: Jianwei Ji

Received  March 2019 Revised  May 2019 Published  December 2019

The traditional energy storage coordination optimization algorithm has long response time, low computational efficiency and high running cost, so energy storage coordination optimization algorithm for distributed power distribution network operation planning based on particle swarm is proposed. In order to minimize the power loss of the distribution network and the maximum deviation of the node voltage, the model of capacity optimization configuration with the distributed energy storage system as the objective function is established. The improved adaptive weighting method is used to transform the target problem into the single problem, which is solved by particle swarm optimization (PSO) algorithm. The experimental results show that the composite energy storage system configured by the proposed algorithm is optimized in terms of operating cost and response time, which verifies the effectiveness of the method.

Citation: Yuhe Du, Jianwei Ji, Yu Liao, Yichu Liu. Design of energy storage coordination optimization algorithm for distributed power distribution network operation planning. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020206
References:
[1]

T. L. ChengM. Chen and H. Luo, Multi-objective optimization configuration method for distribution network energy storage with renewable energy generation, Power Grid Technology, 41 (2017), 2808-2815.   Google Scholar

[2]

N. L. Diaz, A. C. Luna and J. C. Vasquez, e. al., Centralized control architecture for coordination of distributed renewable generation and energy storage in islanded ac microgrids, IEEE Transactions on Power Electronics, 32 (2017), 5202-5213. doi: 10.1109/TPEL.2016.2606653.  Google Scholar

[3]

X. Fu, F. Zhang, Z. L. Feng and et al., Research of chaos genetic algorithm based on parallel computing for anti-missile warning radar disposition, Journal of China Academy of Electronics and Information Technology, (2016), 276–282. Google Scholar

[4]

B. Hayes, A. Wilson and R. Webster, e. al.. Comparison of two energy storage options for optimum balancing of wind farm power outputs, Iet Generation Transmission and Distribution, 10 (2016), 832-839. doi: 10.1049/iet-gtd.2015.0486.  Google Scholar

[5]

S. M. HosamaniB. B. KulkarniR. G. Boli and V. M. Gadag, Qspr analysis of certain graph theocratical matrices and their corresponding energy, Applied Mathematics and Nonlinear Sciences, 2 (2017), 131-150.  doi: 10.21042/AMNS.2017.1.00011.  Google Scholar

[6]

J. Jin and W. Mi, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Applied Mathematics and Nonlinear Sciences, 12 (2019), 1101-1115.   Google Scholar

[7]

L. Kang, H. L. Du and X. Du, e. al., Study on dye wastewater treatment of tunable conductivity solid-waste-based composite cementitious material catalyst, Desalination and Water Treatment, 125 (2018), 296-301. Google Scholar

[8]

P. KouD. Liang and L. Gao, Distributed coordination of multiple pmsgs in an islanded dc microgrid for load sharing, IEEE Transactions on Energy Conversion, 32 (2017), 471-485.  doi: 10.1109/TEC.2017.2649526.  Google Scholar

[9]

S. L. LiuH. J. Zhou and R. B. Wu, Influence of distributed power supply on relay protection of distribution network, Automation and Instrumentation, 7 (2018), 9-11.   Google Scholar

[10]

I. MirandaH. Leite and N. Silva, Coordination of multifunctional distributed energy storage systems in distribution networks, Iet Generation Transmission and Distribution, 10 (2016), 726-735.   Google Scholar

[11]

A. MoeiniI. Kamwa and M. D. Montigny, Power factor-based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses, Iet Generation Transmission and Distribution, 10 (2016), 1304-1311.   Google Scholar

[12]

W. Peng, Z. Lin and L. Wang, e. al., Molecular characteristics of illicium verum extractives to activate acquired immune response, Saudi Journal of Biological Sciences, 23 (2016), 348-352. doi: 10.1016/j.sjbs.2015.10.027.  Google Scholar

[13]

N. Rahbari-AsrY. Zhang and M. Y. Chow, Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids, Iet Generation Transmission and Distribution, 10 (2016), 1268-1277.   Google Scholar

[14]

M. SinghT. Vishnuvardhan and S. G. Srivani, Adaptive protection coordination scheme for power networks under penetration of distributed energy resources, Iet Generation Transmission and Distribution, 10 (2016), 3919-3929.  doi: 10.1049/iet-gtd.2016.0614.  Google Scholar

[15]

Y. Wang, K. T. Tan and X. Y. Peng, e. al., Coordinated control of distributed energystorage systems for voltage regulation in distribution networks, IEEE Transactions on Power Delivery, 31 (2016), 1132-1141. doi: 10.1109/TPWRD.2015.2462723.  Google Scholar

[16]

Z. WangW. Wu and B. Zhang, A distributed control method with minimum generation cost for dc microgrids, IEEE Transactions on Energy Conversion, 31 (2016), 1462-1470.  doi: 10.1109/TEC.2016.2584649.  Google Scholar

[17]

K. Xu, G. He and J. Qin, e. al., High-efficient extraction of principal medicinal components from fresh phellodendron bark (cortex phellodendri), Saudi Journal of Biological Sciences, 25 (2018), 811-815. doi: 10.1016/j.sjbs.2017.10.008.  Google Scholar

[18]

B. C. YangL. Y. Shen and J. X. He, Study on the influence of pso-hs-based dg access capacity on the voltage of distribution network, Computer Simulation, 34 (2017), 143-146.   Google Scholar

[19]

D. YuH. Zhu and W. Han, Dynamic multi agent-based management and load frequency control of pv/fuel cell/ wind turbine/ chp in autonomous microgrid system, Energy, 173 (2019), 554-568.  doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

W. Zhang and Y. Liu, Observer-based distributed consensus for general nonlinear multi-agent systems with interval control inputs, International Journal of Control, 89 (2016), 84-98.  doi: 10.1080/00207179.2015.1060361.  Google Scholar

[21]

J. L. Zhao, Y. Y. Yu and e. a. Li P., Fast calculation method of energy storage system participating in distribution network operation adjustment based on cone optimization, Automation of Electric Power Systems, 40 (2016), 30-35. Google Scholar

[22]

B. Zhou, F. Zhang and L. Wang, e. al., Hdeer: A distributed routing scheme for energy-efficient networking, IEEE Journal on Selected Areas in Communications, 34 (2016), 1713-1727. doi: 10.1109/JSAC.2016.2545498.  Google Scholar

show all references

References:
[1]

T. L. ChengM. Chen and H. Luo, Multi-objective optimization configuration method for distribution network energy storage with renewable energy generation, Power Grid Technology, 41 (2017), 2808-2815.   Google Scholar

[2]

N. L. Diaz, A. C. Luna and J. C. Vasquez, e. al., Centralized control architecture for coordination of distributed renewable generation and energy storage in islanded ac microgrids, IEEE Transactions on Power Electronics, 32 (2017), 5202-5213. doi: 10.1109/TPEL.2016.2606653.  Google Scholar

[3]

X. Fu, F. Zhang, Z. L. Feng and et al., Research of chaos genetic algorithm based on parallel computing for anti-missile warning radar disposition, Journal of China Academy of Electronics and Information Technology, (2016), 276–282. Google Scholar

[4]

B. Hayes, A. Wilson and R. Webster, e. al.. Comparison of two energy storage options for optimum balancing of wind farm power outputs, Iet Generation Transmission and Distribution, 10 (2016), 832-839. doi: 10.1049/iet-gtd.2015.0486.  Google Scholar

[5]

S. M. HosamaniB. B. KulkarniR. G. Boli and V. M. Gadag, Qspr analysis of certain graph theocratical matrices and their corresponding energy, Applied Mathematics and Nonlinear Sciences, 2 (2017), 131-150.  doi: 10.21042/AMNS.2017.1.00011.  Google Scholar

[6]

J. Jin and W. Mi, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Applied Mathematics and Nonlinear Sciences, 12 (2019), 1101-1115.   Google Scholar

[7]

L. Kang, H. L. Du and X. Du, e. al., Study on dye wastewater treatment of tunable conductivity solid-waste-based composite cementitious material catalyst, Desalination and Water Treatment, 125 (2018), 296-301. Google Scholar

[8]

P. KouD. Liang and L. Gao, Distributed coordination of multiple pmsgs in an islanded dc microgrid for load sharing, IEEE Transactions on Energy Conversion, 32 (2017), 471-485.  doi: 10.1109/TEC.2017.2649526.  Google Scholar

[9]

S. L. LiuH. J. Zhou and R. B. Wu, Influence of distributed power supply on relay protection of distribution network, Automation and Instrumentation, 7 (2018), 9-11.   Google Scholar

[10]

I. MirandaH. Leite and N. Silva, Coordination of multifunctional distributed energy storage systems in distribution networks, Iet Generation Transmission and Distribution, 10 (2016), 726-735.   Google Scholar

[11]

A. MoeiniI. Kamwa and M. D. Montigny, Power factor-based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses, Iet Generation Transmission and Distribution, 10 (2016), 1304-1311.   Google Scholar

[12]

W. Peng, Z. Lin and L. Wang, e. al., Molecular characteristics of illicium verum extractives to activate acquired immune response, Saudi Journal of Biological Sciences, 23 (2016), 348-352. doi: 10.1016/j.sjbs.2015.10.027.  Google Scholar

[13]

N. Rahbari-AsrY. Zhang and M. Y. Chow, Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids, Iet Generation Transmission and Distribution, 10 (2016), 1268-1277.   Google Scholar

[14]

M. SinghT. Vishnuvardhan and S. G. Srivani, Adaptive protection coordination scheme for power networks under penetration of distributed energy resources, Iet Generation Transmission and Distribution, 10 (2016), 3919-3929.  doi: 10.1049/iet-gtd.2016.0614.  Google Scholar

[15]

Y. Wang, K. T. Tan and X. Y. Peng, e. al., Coordinated control of distributed energystorage systems for voltage regulation in distribution networks, IEEE Transactions on Power Delivery, 31 (2016), 1132-1141. doi: 10.1109/TPWRD.2015.2462723.  Google Scholar

[16]

Z. WangW. Wu and B. Zhang, A distributed control method with minimum generation cost for dc microgrids, IEEE Transactions on Energy Conversion, 31 (2016), 1462-1470.  doi: 10.1109/TEC.2016.2584649.  Google Scholar

[17]

K. Xu, G. He and J. Qin, e. al., High-efficient extraction of principal medicinal components from fresh phellodendron bark (cortex phellodendri), Saudi Journal of Biological Sciences, 25 (2018), 811-815. doi: 10.1016/j.sjbs.2017.10.008.  Google Scholar

[18]

B. C. YangL. Y. Shen and J. X. He, Study on the influence of pso-hs-based dg access capacity on the voltage of distribution network, Computer Simulation, 34 (2017), 143-146.   Google Scholar

[19]

D. YuH. Zhu and W. Han, Dynamic multi agent-based management and load frequency control of pv/fuel cell/ wind turbine/ chp in autonomous microgrid system, Energy, 173 (2019), 554-568.  doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

W. Zhang and Y. Liu, Observer-based distributed consensus for general nonlinear multi-agent systems with interval control inputs, International Journal of Control, 89 (2016), 84-98.  doi: 10.1080/00207179.2015.1060361.  Google Scholar

[21]

J. L. Zhao, Y. Y. Yu and e. a. Li P., Fast calculation method of energy storage system participating in distribution network operation adjustment based on cone optimization, Automation of Electric Power Systems, 40 (2016), 30-35. Google Scholar

[22]

B. Zhou, F. Zhang and L. Wang, e. al., Hdeer: A distributed routing scheme for energy-efficient networking, IEEE Journal on Selected Areas in Communications, 34 (2016), 1713-1727. doi: 10.1109/JSAC.2016.2545498.  Google Scholar

Figure 1.  equivalent circuit of asynchronous generator
Figure 2.  Flow chart of the Zbus method
Figure 3.  Topology diagram of the 10 node power distribution system
Figure 4.  Flow of the improving the back/foreward sweep power flow algorithm
Figure 5.  The response time of the proposed algorithm
Figure 6.  Response time of energy storage coordination optimization algorithm based on particle swarm
Figure 7.  Response time of the energy storage coordination optimization algorithm based on cone optimization
Table 1.  Operating costs of different optimization algorithms
Number of experiments/(times) Optimization cost/(Yuan)
Proposed algorithm Reference [1] algorithm Reference [21] algorithm
26.35 40.12 50.14
20 28.47 45.69 55.78
30 31.41 52.36 63.21
40 32.58 55.47 66.97
50 34.87 60.17 70.11
60 35.99 62.47 75.41
Number of experiments/(times) Optimization cost/(Yuan)
Proposed algorithm Reference [1] algorithm Reference [21] algorithm
26.35 40.12 50.14
20 28.47 45.69 55.78
30 31.41 52.36 63.21
40 32.58 55.47 66.97
50 34.87 60.17 70.11
60 35.99 62.47 75.41
Table 2.  Calculation efficiency of different optimization algorithms
Time of experiments/(min) Calculation efficiency/(%)
Proposed algorithm Reference [1] algorithm Reference [21] algorithm
98.48 85.21 90.14
10 99.12 86.24 89.25
15 99.58 88.47 87.23
20 99.69 91.22 86.11
25 99.45 92.51 84.15
30 99.87 95.58 83.55
Time of experiments/(min) Calculation efficiency/(%)
Proposed algorithm Reference [1] algorithm Reference [21] algorithm
98.48 85.21 90.14
10 99.12 86.24 89.25
15 99.58 88.47 87.23
20 99.69 91.22 86.11
25 99.45 92.51 84.15
30 99.87 95.58 83.55
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