access icon free Coordinated dispatch of networked energy storage systems for loading management in active distribution networks

© The Institution of Engineering and Technology
Received 08/04/2016, Accepted 31/05/2016, Revised 12/05/2016, Published 31/05/2016

System overloading is becoming a critical issue in distribution system due to outdated infrastructure and growing electricity demand. Although renewable-based distributed generation is a promising solution to relieve this issue, its intermittency and uncertainty impose significant challenges on system operations. This study attempts to coordinate networked energy storage systems (NESSs) to manage network loading in distribution networks. The NESS can act as a buffer to absorb surplus energy during high generation periods and serve the demand during peak load periods. A consensus-based dispatch strategy is proposed to coordinate NESSs. Through limited communication among neighbouring NESSs, required active power curtailment is shared. The mathematical formulation of a state-of-charge weighting factor is introduced to improve the efficiency of NESSs. A sensitivity study is also conducted to demonstrate the performance of the proposed strategy.

Inspec keywords: distributed power generation; load management; distribution networks; load dispatching; energy storage; power generation dispatch

Other keywords: consensus-based dispatch strategy; active power curtailment; coordinated dispatch; NESSs; renewable-based distributed generation; networked energy storage systems; weighting factor; network loading management; active distribution networks

Subjects: Distribution networks; Distributed power generation; Power system management, operation and economics

References

    1. 1)
      • 32. Xiao, L., Boyd, S. : ‘Faster linear iterations for distributed averaging systems’, Syst. Control Lett., 2004, 53, pp. 65–78.
    2. 2)
      • 12. Lu, X., Sun, K., Guerrero, J.M., et al: ‘State-of-charge balance using adaptive droop control for distributed energy storage systems in DC microgrid applications’, IEEE Trans. Ind. Electron., 2014, 61, (6), pp. 2804–2815.
    3. 3)
      • 19. He, W., Chen, G., Han, Q.L., et al: ‘Network-based leader following consensus of nonlinear multi-agent system via distributed impulsive control’, Inf. Sci. (in press), 2015, http://dx.doi.org/10.1016/j.ins.2015.06.005.
    4. 4)
      • 1. World Energy Council: ‘Global electricity initiative 2014 report’ (GEI Secretariat, 2014).
    5. 5)
      • 30. Zhao, Y., Chen, B., Fan, Y., et al: ‘Convergence speed analysis and optimization for distributed control of virtual power plant’. 10th IET Int. Conf. on Advances in Power System Control, Operation and Management (APSCOM2015), Hong Kong, November 2015.
    6. 6)
      • 6. Luo, F., Dong, Z., Meng, K., et al: ‘Short-term operational planning framework for virtual power plants with high renewable penetrations’, IET Renew. Power Gener., 2016, 10, (5), pp. 623–633.
    7. 7)
      • 14. Zheng, Y., Dong, Z.Y., Luo, F., et al: ‘Optimal allocation of energy storage system for risk mitigation of DISCOs with high renewable penetrations’, IEEE Trans. Power Syst., 2013, 29, (1), pp. 212–220.
    8. 8)
      • 20. Fiedler, M.: ‘Algebraic connectivity of graphs’, Czechoslovak Math. J., 1973, 23, (98), pp. 298–305.
    9. 9)
      • 13. Mokhtari, G., Nourbakhsh, G., Zare, F., et al: ‘Smart coordination of energy storage units(ESUs) for voltage and loading management in distribution networks’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 4812–4820.
    10. 10)
      • 16. Moghadam, M.R.V., Ma, R.T.B., Zhang, R.: ‘Distributed frequency control in smart grids via randomized demand response’, IEEE Trans. Smart Grid, 2014, 5, (6), pp. 2798–2809.
    11. 11)
      • 18. Meng, K., Dong, Z.Y., Xu, Z., et al: ‘Cooperation-driven distributed model predictive control for energy storage systems’, IEEE Trans. Smart Grid, 2015, 6, (6), pp. 2583–2585.
    12. 12)
      • 10. Toledo, O.M., Filho, D.O., Diniz, A.S.A.C.: ‘Distributed photo-voltaic generation and energy storage systems: A review’, Renew. Sustain. Energy Rev., 2010, 14, (1), pp. 506–511.
    13. 13)
      • 21. Olfati-Saber, R., Fax, J., Murray, R.: ‘Consensus problems in networks of agents with switching topology and time-delays’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 1520–1533.
    14. 14)
      • 17. Diaz, N.L., Dragicevic, T., Vasquez, J.C.: ‘Intelligent distributed generation and storage units for dc microgrids—a new concept on cooperative control without communications beyond droop control’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 2476–2485.
    15. 15)
      • 2. Belhaiza, S., Baroudi, U.: ‘A game theoretic model for smart grids demand management’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 1386–1393.
    16. 16)
      • 31. Meng, L., Dragicevic, T., Roldan-Perez, J., et al: ‘Modeling and sensitivity study of consensus algorithm-based distributed hierarchical control for DC microgrids’, IEEE Trans. Smart Grid, 2016, 7, (3), pp. 1504–1515.
    17. 17)
      • 27. Mokhtari, G., Ghosh, A., Nourbakhsh, G., et al: ‘Smart robust resources control in LV network to deal with voltage rise issue’, IEEE Trans. Sustain. Energy, 2013, 4, (4), pp. 1043–1050.
    18. 18)
      • 9. Rahbari-Asr, N., Ojha, U., Zhang, Z., et al: ‘Incremental welfare consensus algorithm for cooperative distributed generation/demand response in smart grid’, IEEE Trans. Smart Grid, 2014, 5, (6), pp. 2836–2845.
    19. 19)
      • 25. Li, H., Chen, G., Liao, X., et al: ‘Leader-following consensus of discrete-time multiagent systems with encoding-decoding’, IEEE Trans. Circuits Syst. II Express Briefs, 2015, 63, (4), pp. 401–405.
    20. 20)
      • 28. Xu, Y., Zhang, W., Liu, W., et al: ‘Distributed subgradient-based coordination of multiple renewable generators in a microgrid’, IEEE Trans. Power Syst., 2014, 29, (1), pp. 23–33.
    21. 21)
      • 7. Teng, J.H., Luan, S.W., Lee, D.J., et al: ‘Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1425–1433.
    22. 22)
      • 33. Xiao, L., Boyd, S., Kim, S.J.: ‘Distributed average consensus with least-mean-square deviation’. Proc. of the 17th Int. Symp. on Mathematical Theory of Networks and Systems (MTNS), Kyoto, Japan, July 2006, pp. 2768–2776.
    23. 23)
      • 8. Mancarella, P., Chicco, G.: ‘Real-time demand response from energy shifting in distributed multi-generation’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 1928–1938.
    24. 24)
      • 5. Luo, F., Meng, K., Dong, Z., et al: ‘Coordinated operational planning for wind farm with battery energy storage system’, IEEE Trans. Sustain. Energy, 2015, 6, (1), pp. 253–262.
    25. 25)
      • 23. Olfati-Saber, R.: ‘Ultrafast consensus in small-world networks’. Proc. American Control Conf., Portland, USA, 2005, vol. 4, pp. 2371–2378.
    26. 26)
      • 26. Li, H., Chen, G., Huang, T., et al: ‘Event-triggered distributed average consensus over directed digital networks with limited communication bandwidth’, IEEE Trans. Cybern., 2016, (in press).
    27. 27)
      • 24. Olfati-Saber, R., Fax, J., Murray, R.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, (1), pp. 215–233.
    28. 28)
      • 3. Meng, K., Yang, H.M., Dong, Z.Y., et al: ‘Flexible operational planning framework considering multiple wind energy forecasting service providers’, IEEE Trans. Sustain. Energy, 2016, 7, (2), pp. 708–717.
    29. 29)
      • 4. Mokhtari, G., Nourbakhs, G., Ledwich, G., et al: ‘A supervisory load-leveling approach to improve the voltage profile in distribution network’, IEEE Trans. Sustain. Energy, 2015, 6, (1), pp. 245–252.
    30. 30)
      • 29. Zhang, Z., Chow, M.Y.: ‘Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 1761–1768.
    31. 31)
      • 11. Toledo, O.M., Filho, D.O., Diniz, A.S.A.C., et al: ‘Methodology for evaluation of grid-tie connection of distributed energy resources-case study with photovoltaic and energy storage’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1132–1139.
    32. 32)
      • 22. Godsil, C., Royle, G.: ‘Algebraic graph theory (graduate texts in mathematics)’ (Springer-Verlag, New York, 2000, 1st edn.).
    33. 33)
      • 15. Le, H.T., Santoso, S., Nguyen, T.Q.: ‘Augmenting wind power penetration and gird voltage stability limits using ESS: application design, sizing, and a case study’, IEEE Trans. Power Syst., 2011, 27, (1), pp. 161–171.
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