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Two-Layer Energy Sharing Strategy in Distribution Network with Hybrid Energy Storage System

  • Xiao HanEmail author
  • Lingling SunEmail author
  • Guozhong Liu
  • Li Kang
  • Fenglei Zheng
  • Jing Qiu
Conference paper
  • 7 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 669)

Abstract

This paper addresses an energy sharing strategy in a two-layer microgrid with the renewable distributed generation and hybrid energy storage system considered to minimize the total energy bill purchased from the utility grid and reduce the peak time consumption from the utility grid. This approach considers the energy operation management of thermal energy loads and electrical energy loads, in which the thermal energy loads are supplied by electrical energy and gas energy. Moreover, this energy sharing model is designed to allow the operation with different types of end-user modes, and the participants are divided into different layers based on their characteristics. Furthermore, the modified trading method is based on the trading model of stock opening with the maximum transaction volume. The proposed two-layer model is tested in 18-bus IEEE system with the real historical data in the Australia energy market. With the proposed two-layer energy sharing strategy, the simulation results show that the two-layer energy sharing model provides economic profits to participants and encourages load schedule.

Keywords

Two-layer energy sharing strategy Hybrid energy storage system Energy market 

Abbreviations

\( \beta_{n}^{bat,ch} \& \beta_{n}^{bat,dch} \)

Charging and discharging coefficient of BESS

\( \upbeta_{\text{n}}^{\text{bat}} \)

Coefficient of BESS degradation cost

\( \beta_{c}^{\text{th}} \& \beta_{l}^{\text{th}} \)

Coefficients of the thermal energy capacity and loss

\( \upgamma_{\text{n}}^{{{\text{bat}},{\text{loss}}}} \)

Coefficient of BESS loss

A

Price of electrical and gas energy

B

Benefit and profit

C

Cost

E

Energy

P

Power

t

Time

T

Temperature

bat

Battery energy storage system (BESS)

ch

Energy charging of BESS

dch

Energy discharging of BESS

ele

Electrical energy

rate

Rate value of variable

th

Thermal energy

\( A_{n,t}^{buy} ,A_{n,t}^{sell} \)

Planning buying and selling price

\( \varvec{A}_{t}^{buy} \& \varvec{A}_{t}^{sell} \)

Sorted matrixes of buying price \( A_{n,t}^{buy} \) and selling price \( A_{n,t}^{sell} \)

\( {\text{B}}_{{{\text{n}},{\text{t}}}}^{\text{ele}} \)

Benefit from electrical energy sharing

\( {\text{E}}_{{{\text{n}},}}^{{{\text{th}},{\text{loss}}}} \)

Loss energy of thermal energy

\( E_{n,t}^{rdg} \& E_{n,t}^{dem} \)

DRG energy and demand energy for the n-th participant at time t

\( E_{n,t}^{buy} \& E_{n,t}^{sell} \)

Planning buying and selling energy

\( E_{n,t}^{t - b} \& E_{n,t}^{t - s} \)

Traded buying and selling energy for the n-th trading player at time t

\( \varvec{E}_{t}^{buy} ,\varvec{E}_{t}^{sell} \)

Sorted matrixes of buying energy \( E_{n,t}^{buy} \) and selling energy \( E_{n,t}^{sell} \)

\( f_{{A_{n,t}^{buy} }} \left\{ {E_{n,t}^{buy} } \right\} \)

Function to sort the buying energy \( E_{n,t}^{buy} \) by the same with the same sorting sequence of the buying price \( A_{n,t}^{buy} \)

\( f_{{A_{n,t}^{buy} }}^{{\prime }} \left\{ {\varvec{E}_{t}^{{{\prime }buy}} } \right\} \)

Function to sort the energy \( \varvec{E}_{t}^{{{\prime }buy}} \) with the inverse sequence of \( f_{{A_{n,t}^{buy} }} \left\{ {E_{n,t}^{buy} } \right\} \)

\( M_{t}^{buy} \& M_{t}^{sell} \)

Amount energy of the buying and selling energy

\( {\text{N}}_{{{\text{n}},{\text{lc}}}}^{\text{bat}} \)

Charging numbers during the battery lifetime

\( {\text{P}}_{{{\text{n}},{\text{t}}}}^{\text{waste}} \)

Waste energy generated by RDG

\( {\text{P}}_{{{\text{n}},{\text{t}}}}^{\text{dem}} \)

Demand energy of the n-th participant at time t

\( P_{i} ,Q_{i} ,V_{i} \)

Active power, reactive power and voltage in the i-th branch

\( T_{amb}^{th} \)

Ambient temperature

Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2017YFB0903205) and was also supported by the ARC Research Hub for Integrated Energy Storage Solutions (IH180100020), FEIT Early Career Researcher and Newly Appointed Staff Development Scheme, The University of Sydney and Sir William Tyree Foundation-Distributed Power Generation Research Fund.

References

  1. 1.
    Kang J, Yu R, Huang X, Maharjan S, Zhang Y, Hossain E (2017) Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans Industr Inf 13(6):3154–3164CrossRefGoogle Scholar
  2. 2.
    Li Q, Choi SS, Yuan Y et al (2011) On the determination of battery energy storage capacity and short-term power dispatch of a wind farm. IEEE Trans Sustain Energy 2(2):148–158CrossRefGoogle Scholar
  3. 3.
    Zhang F, Xu Z, Meng K (2016) Optimal sizing of substation-scale energy storage station considering seasonal variations in wind energy. IET Gener Transm Distrib 10(13):3241–3250Google Scholar
  4. 4.
    Castaneda J, Enslin J, Elizondo D, Abed N, Teleke S (2010) Application of statcom with energy storage for wind farm integration. In: Proceedings IEEE PES transmission and distribution conference and exposition, pp 1–6Google Scholar
  5. 5.
    Zhang C, Wang Q, Wang J, Pinson P, Morales JM, Østergaard J (2018) Real-time procurement strategies of a proactive distribution company with aggregator-based demand response. IEEE Trans Smart Grid 9(2):766–776CrossRefGoogle Scholar
  6. 6.
    Nguyen DT, Le LB (2015) Risk-constrained profit maximization for microgrid aggregators with demand response. IEEE Trans Smart Grid 6(1):135–146Google Scholar
  7. 7.
    Zhang C, Xu Y, Dong ZY, Ma J (2017) Robust operation of microgrids via two-stage coordinated energy storage and direct load control. IEEE Trans Power Syst 32(4):2858–2868CrossRefGoogle Scholar
  8. 8.
    Wang D, Meng K, Gao X, Qiu J, Lai LL, Dong ZY (2018) Coordinated dispatch of virtual energy storage systems in LV grids for voltage regulation. IEEE Trans Indust Inf 14(6):2452–2462CrossRefGoogle Scholar
  9. 9.
    Wang Z, Gu C, Li F, Bale P, Sun H (2013) Active demand response using shared energy storage for household energy management. IEEE Trans Smart Grid 4(4):1888–1897CrossRefGoogle Scholar
  10. 10.
    Castaneda J, Enslin J, Elizondo D, Abed N, Teleke S (2010) Application of statcom with energy storage for wind farm integration. In: Proceeding IEEE PES transmission and distribution conference and exposition, pp 1–6Google Scholar
  11. 11.
    Kong W, Dong ZY, Hill DJ, Luo F, Xu Y (2018) Short-term residential load forecasting based on resident behaviour learning. IEEE Trans Power Syst 33(1):1087–1088CrossRefGoogle Scholar
  12. 12.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringUniversity of SydneyCamperdownAustralia
  2. 2.School of Electrical Engineering and TelecommunicationsUniversity of New South WalesKensingtonAustralia
  3. 3.School of Electrical Engineering and IntelligentizationDongguan University of TechnologyDongguanChina
  4. 4.Dongguan Power Supply BureauDongguanChina

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