Microgrid energy and reserve management incorporating prosumer behind-the-meter resources

Microgrid energy and reserve management incorporating prosumer behind-the-meter resources

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The unpredictable nature of renewable power, coupled with the influx of prosumers has made accurate power forecasts in microgrids (MGs) more difficult to obtain. A direct consequence of this is the need for additional spinning reserve (SR) capacity to compensate for resulting power imbalances. Due to the economic and environmental concerns, increasing conventional generation to meet this additional SR capacity is undesirable. The aggregation of prosumer behind-the-meter resources for the provision of SR is proposed in this study, and a mathematical model for the proposed scheme is developed. This scheme is formulated as a constrained optimisation problem, whose solution maintains power supply and demand balance whilst reserving a virtual spinning capacity. The formulation is linearised and solved using the CPLEX 12.6.3 solver in the Advanced Interactive Multidimensional Modelling System environment. A 14-bus MG test system is used to validate the proposed scheme, and results show the benefits of using prosumer behind-the-meter resources to provide ancillary services like SR.


    1. 1)
      • 1. Europe, S.P.: ‘Global market outlook for solar power/2016–2020’ (Solar Power Europe, Bruxelles, Belgium, 2016), p. 32.
    2. 2)
      • 2. Ghofrani, M., Arabali, A., Etezadi-Amoli, M., et al: ‘Operating reserve requirements in a power system with dispersed wind generation'. 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, WA, 16 January 2012, pp. 18.
    3. 3)
      • 3. Motalleb, M., Thornton, M., Reihani, E., et al: ‘A nascent market for contingency reserve services using demand response’, Appl. Energy, 2016, 179, pp. 985995.
    4. 4)
      • 4. Rejc, Ž.B., Čepin, M.: ‘Estimating the additional operating reserve in power systems with installed renewable energy sources’, Int. J. Electr. Power Energy Syst., 2014, 62, pp. 654664.
    5. 5)
      • 5. Cappers, P., MacDonald, J., Goldman, C., et al: ‘An assessment of market and policy barriers for demand response providing ancillary services in US electricity markets’, Energy Policy, 2013, 62, pp. 10311039.
    6. 6)
      • 6. Energy, G.E.: ‘Western wind and solar integration study' (National Renewable Energy Laboratory (NREL), Golden, CO, 2010).
    7. 7)
      • 7. Zhang, Y., Gatsis, N., Giannakis, G.B.: ‘Robust energy management for microgrids with high-penetration renewables’, IEEE Trans. Sustain. Energy, 2013, 4, (4), pp. 944953.
    8. 8)
      • 8. Kottick, D., Blau, M., Edelstein, D.: Battery energy storage for frequency regulation in an island power system. IEEE Trans. Energy Convers., 1993, 8, (3), pp. 455459.
    9. 9)
      • 9. Fleer, J., Stenzel, P.: ‘Impact analysis of different operation strategies for battery energy storage systems providing primary control reserve’, J. Energy Storage, 2016, 8, pp. 320338.
    10. 10)
      • 10. Chen, Y., Keyser, M., Tackett, M.H., et al: ‘Incorporating short-term stored energy resource into Midwest ISO energy and ancillary service market’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 829838.
    11. 11)
      • 11. Thien, T., Schweer, D., vom Stein, D., et al: ‘Real-world operating strategy and sensitivity analysis of frequency containment reserve provision with battery energy storage systems in the German market’, J. Energy Storage, 2017, 13, pp. 143163.
    12. 12)
      • 12. Kazemi, M., Zareipour, H., Amjady, N., et al: ‘Operation scheduling of battery storage systems in joint energy and ancillary services markets’, IEEE Trans. Sustain. Energy, 2017, 8, (4), pp. 17261735.
    13. 13)
      • 13. Hollinger, R., Diazgranados, L.M., Braam, F., et al: ‘Distributed solar battery systems providing primary control reserve’, IET Renew. Power Gener., 2016, 10, (1), pp. 6370.
    14. 14)
      • 14. Sortomme, E., El-Sharkawi, M.A.: ‘Optimal scheduling of vehicle-to-grid energy and ancillary services’, IEEE Trans. Smart Grid, 2012, 3, (1), pp. 351359.
    15. 15)
      • 15. Alipour, M., Mohammadi-Ivatloo, B., Moradi-Dalvand, M., et al: ‘Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets’, Energy, 2017, 118, pp. 11681179.
    16. 16)
      • 16. Pavić, I., Capuder, T., Kuzle, I.: ‘Value of flexible electric vehicles in providing spinning reserve services’, Appl. Energy, 2015, 157, pp. 6074.
    17. 17)
      • 17. Juul, F., Negrete-Pincetic, M., MacDonald, J., et al: ‘Real-time scheduling of electric vehicles for ancillary services’. 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 26 July 2015, pp. 15.
    18. 18)
      • 18. Brooks, A., Lu, E., Reicher, D., et al: ‘Demand dispatch’, IEEE Power Energy Mag., 2010, 8, (3), pp. 2029.
    19. 19)
      • 19. Pourmousavi, S.A., Nehrir, M.H.: ‘Real-time central demand response for primary frequency regulation in microgrids’, IEEE Trans. Smart Grid., 2012, 3, (4), pp. 19881996.
    20. 20)
      • 20. Clausen, A., Ghatikar, G.: ‘Load management of data centers as regulation capacity in Denmark’. IEEE 2014 Int. Green Computing Conf. (IGCC), Dallas, TX, 3 November 2014, pp. 110.
    21. 21)
      • 21. Arnone, D., Barberi, A., La Cascia, D., et al: ‘Smart grid integrated green data centres as ancillary service providers’. 2015 IEEE Int. Conf. on Clean Electrical Power (ICCEP), Taormina, 16 June 2015, pp. 170177.
    22. 22)
      • 22. Nikolic, D., Negnevitsky, M., De Groot, M.: ‘Fast demand response as spinning reserve in microgrids’. IET Mediterranean Conf. on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016), Belgrade, 6 November 2016, pp. 15.
    23. 23)
      • 23. Borlase, S. (Ed.): ʻSmart grids: infrastructure, technology, and solutions' (CRC Press, Boca Raton, FL, 2016).
    24. 24)
      • 24. Zakariazadeh, A., Jadid, S.: ‘Smart microgrid operational planning considering multiple demand response programs’, J. Renew. Sustain. Energy, 2014, 6, (1), p. 013134.
    25. 25)
      • 25. Zakariazadeh, A., Jadid, S., Siano, P.: ‘Smart microgrid energy and reserve scheduling with demand response using stochastic optimization’, Int. J. Electr. Power Energy Syst., 2014, 63, pp. 523533.
    26. 26)
      • 26. Zakariazadeh, A., Jadid, S., Siano, P.: ‘Economic-environmental energy and reserve scheduling of smart distribution systems: a multiobjective mathematical programming approach’, Energy Convers. Manage., 2014, 78, pp. 151164.
    27. 27)
      • 27. Mohan, V., Singh, J.G., Ongsakul, W.: ‘An efficient two stage stochastic optimal energy and reserve management in a microgrid’, Appl. Energy, 2015, 160, pp. 2838.
    28. 28)
      • 28. Cecati, C., Citro, C., Siano, P.: ‘Combined operations of renewable energy systems and responsive demand in a smart grid’, IEEE Trans. Sustain. Energy, 2011, 2, (4), pp. 468476.
    29. 29)
      • 29. Tazvinga, H., Xia, X., Zhang, J.: ‘Minimum cost solution of photovoltaic–diesel–battery hybrid power systems for remote consumers’, Sol. Energy, 2013, 96, pp. 292299.
    30. 30)
      • 30. Nwulu, N.I., Xia, X.: ‘Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs’, Energy, 2015, 91, pp. 404419.
    31. 31)
      • 31. Nwulu, N.I., Xia, X.: ‘Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs’, Energy Convers. Manage., 2015, 89, pp. 963974.
    32. 32)
      • 32. Pal, R., Chelmis, C., Frincu, M., et al: ‘MATCH for the prosumer smart grid the algorithmics of real-time power balance’, IEEE Trans. Parallel Distrib. Syst., 2016, 27, (12), pp. 35323546.
    33. 33)
      • 33. Hussain, A., Bui, V.H., Kim, H.M.: ‘Optimal operation of hybrid microgrids for enhancing resiliency considering feasible islanding and survivability’, IET Renew. Power Gener., 2017, 11, (6), pp. 846857.

Related content

This is a required field
Please enter a valid email address