access icon free Two-stage robust optimisation of user-side cloud energy storage configuration considering load fluctuation and energy storage loss

Recently, many industrial users have spontaneously built energy storage (ES) systems for participation in demand-side management, but it is difficult for users to benefit from participating in demand response (DS) because of the expensive costs of ES construction. Therefore, this study proposes a cloud ES (CES) architecture that can reduce these costs by utilising users' complementary load characteristics and the scale benefits resulting from large-scale construction of ES equipment. Considering the DR and the uncertainty of the user load, this study applies two-stage robust optimisation to solve for the optimal configuration of CES. The proposed optimisation model is verified using the load data of an industrial park in Jiangsu Province, and the results clearly indicate that the proposed CES can be more beneficial than self-built distributed ES during the warranty period. The robust configuration results can be applicable even when the load changes within the maximum and minimum range.

Inspec keywords: optimisation; cloud computing; energy storage; demand side management

Other keywords: cloud ES architecture; energy storage systems; optimisation model; energy storage loss; ES equipment; CES; load fluctuation; load data; scale benefits; demand-side management; industrial users; user load; robust configuration results; large-scale construction; two-stage robust optimisation; ES construction; user-side cloud energy storage configuration; demand response; load changes; industrial park; expensive costs; optimal configuration

Subjects: Power system management, operation and economics; Internet software; Optimisation techniques; Optimisation techniques

References

    1. 1)
      • 19. Hedman, K., Korad, A.S., Zhang, M., et al: ‘The application of robust optimization in power systems’ (PSERC Publication, Arizona, 2014), pp. 614.
    2. 2)
      • 13. Liu, J., Zhang, N., Kang, C., et al: ‘Cloud energy storage for residential and small commercial consumers: a business case study’, Appl. Energy, 2017, 188, pp. 226236.
    3. 3)
      • 27. Duggal, I., Venkatesh, B.: ‘Short-term scheduling of thermal generators and battery storage with depth of discharge-based cost model’, IEEE Trans. Power Syst., 2014, 30, (4), pp. 21102118.
    4. 4)
      • 2. Kazemi, M., Zareipour, H.: ‘Long-term scheduling of battery storage systems in energy and regulation markets considering batterys lifespan’, IEEE Trans. Smart Grid, 2018, 9, (6), pp. 68406849.
    5. 5)
      • 36. Qiu, H., Gu, W., Xu, Y., et al: ‘Interval-partitioned uncertainty constrained ro-bust dispatch for ac/dc hybrid microgrids with uncontrollable renewable generators’, IEEE Trans. Smart Grid, 2019, 10, pp. 46034614.
    6. 6)
      • 15. Kim, T., Makwana, D., Adhikaree, A., et al: ‘Cloud-based battery condition monitoring and fault diagnosis platform for large-scale lithium-ion battery energy storage systems’, Energies, 2018, 11, (1), p. 125.
    7. 7)
      • 10. Han, S., Han, S., Aki, H.: ‘A practical battery wear model for electric vehicle charging applications’, Appl. Energy, 2014, 113, pp. 11001108.
    8. 8)
      • 26. Liu, C., Wang, X., Wu, X., et al: ‘Economic scheduling model of microgrid considering the lifetime of batteries’, IET. Gener. Transm. Distrib., 2017, 11, (3), pp. 759767.
    9. 9)
      • 12. Linssen, J., Stenzel, P., Fleer, J.: ‘Techno-economic analysis of photovoltaic battery systems and the influence of different consumer load profiles’, Appl. Energy, 2017, 185, pp. 20192025.
    10. 10)
      • 32. Lorca, A., Sun, X.A.: ‘The adaptive robust multi-period alternating current optimal power flow problem’, IEEE Trans. Power Syst., 2017, 33, (2), pp. 19932003.
    11. 11)
      • 20. Qiu, H., Gu, W., Xu, Y., et al: ‘Multi-time-scale rolling optimal dispatch for ac/dc hybrid microgrids with day-ahead distributionally robust scheduling’, IEEE Trans. Sustain. Energy, 2019, 10, (4), pp. 16531663.
    12. 12)
      • 5. Zidar, M., Hatziargyriou, N.D., krlec, D., et al: ‘Review of energy storage allocation in power distribution networks: applications, methods and future research’, IET. Gener. Transm. Distrib., 2016, 10, (3), pp. 645652.
    13. 13)
      • 11. Erdinc, O., Paterakis, N.G., Pappi, I.N., et al: ‘A new perspective for sizing of distributed generation and energy storage for smart households under demand response’, Appl. Energy, 2015, 143, pp. 2637.
    14. 14)
      • 23. Akel, N., Bowker, T., Goncalves, V.: ‘Dual-purposing telecom backup systems for cloud energy storage and grid ancillary services’. 2014 IEEE 36th Int. Telecommunications Energy Conf. (INTELEC), Vancouver, BC, 2014, pp. 14.
    15. 15)
      • 17. Rappaport, R.D., Miles, J.: ‘Cloud energy storage for grid scale applications in the uk’, Energy Policy, 2017, 109, pp. 609622.
    16. 16)
      • 33. Pan, G., Gu, W., Zhou, S., et al: ‘Synchronously decentralized adaptive robust planning method for multi-stakeholder integrated energy systems’, IEEE Trans. Sustain. Energy, 2019, early access.
    17. 17)
      • 3. Kim, W.-W., Shin, J.-S., Kim, J.-O.: ‘Operation strategy of multi-energy storage system for ancillary services’, IEEE Trans. Power Syst., 2017, 32, (6), pp. 44094417.
    18. 18)
      • 30. Lorca, A., Sun, X.A.: ‘Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind’, IEEE Trans. Power Syst., 2014, 30, (4), pp. 17021713.
    19. 19)
      • 7. Mu, , Gao, , Yang, J., et al: ‘Design of power supply package for electricity sales companies considering user side energy storage configuration’. Energies, 2019, 12, (17), p. 3219.
    20. 20)
      • 28. Wang, Z., Gu, C., Li, F.: ‘Active demand response using shared energy storage for household energy management’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 18881897.
    21. 21)
      • 16. Lombardi, P., Schwabe, F.: ‘Sharing economy as a new business model for energy storage systems’, Appl. Energy, 2017, 188, pp. 485496.
    22. 22)
      • 8. Javadi, M.S., Anvari-Moghaddam, A., Guerrero, J.M.: ‘Optimal scheduling of a multi-carrier energy hub supplemented by battery energy storage systems’. 2017 IEEE Int. Conf. on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, 2017, pp. 16.
    23. 23)
      • 29. Huang, L., Walrand, J., Ramchandran, K.: ‘Optimal demand response with energy storage management’. IEEE Third Int. Conf. on Smart Grid Communications, Tainan, 2012, pp. 6166.
    24. 24)
      • 25. Billaud, J., Bouville, F., Magrini, T., et al: ‘Magnetically aligned graphite electrodes for high-rate performance Li-ion batteries’, Nature Energy, 2016, 1, (8), pp. 16.
    25. 25)
      • 21. Zhao, B., Qiu, H., Qin, R., et al: ‘Robust optimal dispatch of ac/dc hybrid microgrids considering generation and load uncertainties and energy storage loss’, IEEE Trans. Power Syst., 2018, 33, (6), pp. 59455957.
    26. 26)
      • 1. Jayasekara, N., Masoum, M.A.S., Wolfs, P.J.: ‘Optimal operation of distributed energy storage systems to improve distribution network load and generation hosting capability’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 250261.
    27. 27)
      • 24. Rong, P., Pedram, M.: ‘An analytical model for predicting the remaining battery capacity of lithium-ion batteries’, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 2006, 14, (5), pp. 441451.
    28. 28)
      • 35. Dong, M., Meira, P.C., Xu, W., et al: ‘Non-intrusive signature extraction for major residential loads’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 14211430.
    29. 29)
      • 18. Qiu, H., Zhao, B., Gu, W., et al: ‘Bi-level two-stage robust optimal scheduling for ac/dc hybrid multi-microgrids’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 54555466.
    30. 30)
      • 31. Lorca, A., Sun, X.A.: ‘Multistage robust unit commitment with dynamic uncertainty sets and energy storage’, IEEE Trans. Power Syst., 2016, 32, (3), pp. 16781688.
    31. 31)
      • 9. Han, S., Han, S.: ‘Economic feasibility of v2g frequency regulation in consideration of battery wear’, Energies, 2013, 6, (2), pp. 748765.
    32. 32)
      • 4. Fossati, J.P., Galarza, A., Martín-Villate, A., et al: ‘A method for optimal sizing energy storage systems for microgrids’, Renew. Energy, 2015, 77, pp. 539549.
    33. 33)
      • 14. Liu, J., Zhang, N., Kang, C., et al: ‘Decision-making models for the participants in cloud energy storage’, IEEE Trans. Smart Grid, 2018, 9, (6), pp. 55125521.
    34. 34)
      • 34. Qiu, H., Gu, W., Pan, J., et al: ‘Multi-interval-uncertainty constrained robust dispatch for ac/dc hybrid microgrids with dynamic energy storage degradation’, Appl. Energy, 2018, 228, pp. 205214.
    35. 35)
      • 22. Kang, C., Liu, J., Zhang, N.: ‘A new form of energy storage in future power system: cloud energy storage’, Autom. Electr. Power Syst., 2017, 41, (21), pp. 28.
    36. 36)
      • 6. Acar, C.: ‘A comprehensive evaluation of energy storage options for better sustainability’, Int. J. Energy Res., 2018, 42, (12), pp. 37323746.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2019.1832
Loading

Related content

content/journals/10.1049/iet-gtd.2019.1832
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading