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Coordination of wind generation and demand response to minimise operation cost in day-ahead electricity markets using bi-level optimisation framework

Coordination of wind generation and demand response to minimise operation cost in day-ahead electricity markets using bi-level optimisation framework

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Demand response (DR) can play an important role when dealing with the increasing variability of renewables in power system. This study proposes a bi-level market model for wind-integrated electricity market, where the DR requirement is paired with the wind profile to deal with wind variability. At the upper level, an electricity market operator aims to minimise the day-ahead operation cost considering plausible wind generation scenarios. At the lower level, the DR exchange operator aims to maximise social welfare by trading aggregated DR among several aggregators. The solution at this level determines the optimal DR amount and price setting for each aggregator. The DR from the flexible loads is modelled from the end-users' perspective considering their willingness parameter. The market model is formulated as a bi-level optimisation problem using Lagrangian relaxation with Karush–Kuhn–Tucker optimality conditions. The effectiveness of the proposed scheme is demonstrated on sample 4-bus and IEEE 24-bus systems. Different scenarios such as high, medium and low levels of wind and DR are investigated. The high-wind low-DR scenario leads to minimum operation cost and is least inconvenient for end users as it sets the DR at a minimum level while keeping the higher levels of wind generation.

References

    1. 1)
      • 1. Wiser, R., Lantz, E., Mai, T.: ‘Wind vision: a new era for wind power in the United States’, Electr. J., 2014, 28, (9), p. 384.
    2. 2)
      • 2. Bird, L., Milligan, M., Lew, D.: ‘Integrating variable renewable energy: challenges and solutions’, NREL/TP-6A20–60451, 2013, p. 14.
    3. 3)
      • 3. Golden, R., Paulos, B.: ‘Curtailment of renewable energy in California and beyond’, Electr. J., 2015, 28, (6), pp. 3650.
    4. 4)
      • 4. Vivekananthan, C., Mishra, Y., Ledwich, G., et al: ‘Demand response for residential appliances via customer reward scheme’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 809820.
    5. 5)
      • 5. Chen, Z., Wu, L., Fu, Y.: ‘Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 18221831.
    6. 6)
      • 6. Wu, H., Shahidehpour, M., Alabdulwahab, A.: ‘Demand response exchange in the stochastic day-ahead scheduling with variable renewable generation’, IEEE Trans. Sustain. Energy, 2015, 6, (2), pp. 516525.
    7. 7)
      • 7. Torriti, J., Hassan, M.G., Leach, M.: ‘Demand response experience in Europe: policies, programmes and implementation’, Energy, 2010, 35, (4), pp. 15751583.
    8. 8)
      • 8. Vardakas, J.S., Zorba, N., Verikoukis, C.V.: ‘A survey on demand response programs in smart grids: pricing methods and optimization algorithms’, IEEE Commun. Surv. Tutor., 2015, 17, (1), pp. 152178.
    9. 9)
      • 9. Nguyen, T., Negnevitsky, M., De Groot, M.: ‘Pool-based demand response exchange: concept and modeling’, IEEE Trans. Power Syst., 2011, 26, (3), p. 1.
    10. 10)
      • 10. Wu, H., Shahidehpour, M.: ‘Stochastic SCUC solution with variable wind energy using constrained ordinal optimization’, IEEE Trans. Sustain. Energy, 2014, 5, (2), pp. 379388.
    11. 11)
      • 11. Fang, X., Hu, Q., Li, F., et al: ‘Coupon-based demand response considering wind power uncertainty: a strategic bidding model for load serving entities’, IEEE Trans. Power Syst., 2015, 31, (2), pp. 10251037.
    12. 12)
      • 12. Kazempour, S.J., Conejo, A.J., Ruiz, C.: ‘Strategic bidding for a large consumer’, IEEE Trans. Power Syst., 2015, 30, (2), pp. 848856.
    13. 13)
      • 13. Daraeepour, A., Kazempour, S.J., Patino-Echeverri, D., et al: ‘Strategic demand-side response to wind power integration’, IEEE Trans. Power Syst., 2016, 31, (5), pp. 34953505.
    14. 14)
      • 14. Abbaspourtorbati, F., Conejo, A.J., Wang, J., et al: ‘Is being flexible advantageous for demands?’, IEEE Trans. Power Syst., 2017, 32, (3), pp. 23372345.
    15. 15)
      • 15. Zhang, Y., Giannakis, G.B.: ‘Distributed stochastic market clearing with high-penetration wind power’, IEEE Trans. Power Syst., 2016, 31, (2), pp. 895906.
    16. 16)
      • 16. Kazempour, S.J., Pinson, P.: ‘Effects of risk aversion on market outcomes: a stochastic two-stage equilibrium model’. 2016 Int. Conf. Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China, October 2016.
    17. 17)
      • 17. Mahmoudi, N., Saha, T.K., Eghbal, M.: ‘Wind power offering strategy in day-ahead markets: employing demand response in a two-stage plan’, IEEE Trans. Power Syst., 2015, 30, (4), pp. 18881896.
    18. 18)
      • 18. Reddy, S.S., Abhyankar, A.R., Bijwe, P.R.: ‘Market clearing for a wind-thermal power system incorporating wind generation and load forecast uncertainties’, IEEE Power Energy Soc. Gen. Meet., San Diego, CA, USA, July 2012, pp. 18.
    19. 19)
      • 19. Reddy, S.S., Bijwe, P.R., Abhyankar, A.R.: ‘Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties’, IEEE Syst. J., 2015, 9, (1), pp. 152164.
    20. 20)
      • 20. Reddy, S.S., Momoh, J.A.: ‘Realistic and transparent optimum scheduling strategy for hybrid power system’, IEEE Trans. Smart Grid, 2015, 6, (6), pp. 31143125.
    21. 21)
      • 21. Haring, T., Mathieu, J.L., Andersson, G.: ‘Decentralized contract design for demand response’. Int. Conf. European Energy Market (EEM), Stockholm, Sweden, May 2013.
    22. 22)
      • 22. Colson, B., Marcotte, P., Savard, G.: ‘An overview of bilevel optimization’, Ann. Oper. Res., 2007, 153, (1), pp. 235256.
    23. 23)
      • 23. Nasrolahpour, E., Kazempour, S.J., Zareipour, H., et al: ‘Strategic sizing of energy storage facilities in electricity markets’, IEEE Trans. Sustain. Energy, 2016, 4, (c), p. 1.
    24. 24)
      • 24. Kazempour, S.J., Member, S., Conejo, A.J., et al: ‘Generation investment equilibria with strategic producers — part II: case studies’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 26232631.
    25. 25)
      • 25. Mohammad, N., Mishra, Y.: ‘Competition driven bi-level supply offer strategies in day ahead electricity market’. Australasian Universities Power Engineering Conf., Brisbane, QLD, AU, September 2016, pp. 16.
    26. 26)
      • 26. Mohammad, N., Mishra, Y.: ‘Transactive market clearing model with coordinated integration of large-scale solar PV farms and demand response capable loads’. Proc. 2017 Australasian Universities Power Engineering Conf. (AUPEC), Melbourne, VIC, Australia, November 2017, pp. 16.
    27. 27)
      • 27. Baringo, L., Conejo, A.J.: ‘Strategic wind power investment’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 12501260.
    28. 28)
      • 28. Mnatsakanyan, A., Kennedy, S.: ‘A novel demand response model with an application for a virtual power plant’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 23023.
    29. 29)
      • 29. Asimakopoulou, G.E., Dimeas, A.L., Hatziargyriou, N.D.: ‘Leader–follower strategies for energy management of multi-microgrids’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 19091916.
    30. 30)
      • 30. Wang, Z., Chen, B., Wang, J., et al: ‘Coordinated energy management of networked microgrids in distribution systems’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 4553.
    31. 31)
      • 31. Kim, H., Thottan, M.: ‘A two-stage market model for microgrid power transactions via aggregators’, Bell Labs Technol. J., 2011, 16, (3), pp. 101107.
    32. 32)
      • 32. Gkatzikis, L., Koutsopoulos, I., Salonidis, T.: ‘The role of aggregators in smart grid demand response markets’, IEEE J. Sel. Areas Commun., 2013, 31, (7), pp. 12471257.
    33. 33)
      • 33. Kozanidis, G., Saharidis, G.K.D., Conejo, A.J.: ‘Exact solution methodologies for linear and (mixed) integer bilevel programming’, in Talbi, E.G., (Ed.): ‘Metaheuristics for Bi-level Optimization. Studies in Computational Intelligence’ (Springer, Berlin & Heidelberg, 2013, Vol.482).
    34. 34)
      • 34. Xu, Y., Li, N., Low, S.H.: ‘Demand response with capacity constrained supply function bidding’, IEEE Trans. Power Syst., 2015, 31, (2), pp. 112.
    35. 35)
      • 35. Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R. J.: ‘MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 1219.
    36. 36)
      • 36. Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., et al: ‘Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid’, IEEE Trans. Smart Grid, 2010, 1, (3), pp. 320331.
    37. 37)
      • 37. Singh, R., Pal, B.C., Jabr, R.A: ‘Statistical representation of distribution system loads using Gaussian mixture model’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 2937.
    38. 38)
      • 38. Ayón, X., Gruber, J.K., Hayes, B.P., et al: ‘An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands’, Appl. Energy, 2017, 198, pp. 111.
    39. 39)
      • 39. Setlhaolo, D., Xia, X.: ‘Optimal scheduling of household appliances with a battery storage system and coordination’, Energy Build., 2015, 94, pp. 6170.
    40. 40)
      • 40. Kardakos, E.G., Simoglou, C.K., Bakirtzis, A.G.: ‘Optimal offering strategy of a virtual power plant: a stochastic bi-level approach’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 794806.
    41. 41)
      • 41. Arroyo, J.M.: ‘Bilevel programming applied to power system vulnerability analysis under multiple contingencies’, IET Gener. Transm. Distrib., 2010, 4, (2), p. 178.
    42. 42)
      • 42. Bertsekas, D.: ‘Network optimization: continuous and discrete models’ (Athena Scientific, Belmont, MA, USA, 1998).
    43. 43)
      • 43. Kazempour, S.J., Conejo, A.J., Ruiz, C.: ‘Strategic generation investment using a complementarity approach’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 940948.
    44. 44)
      • 44. Siddiqui, S., Gabriel, S.A.: ‘An SOS1-based approach for solving MPECs with a natural gas market application’, Netw. Spat. Econ., 2013, 13, (2), pp. 205227.
    45. 45)
      • 45. Fortuny-amat, A.J., Mccarl, B., Fortuny-amat, J., et al: ‘A representation and economic interpretation of a two-level programming problem’, J. Oper. Res. Soc., 1981, 32, (9), pp. 783792.
    46. 46)
      • 46. Zhao, Q., Shen, Y., Li, M.: ‘Control and bidding strategy for virtual power plants with renewable generation and inelastic demand in electricity markets’, IEEE Trans. Sustain. Energy, 2016, 7, (2), pp. 562575.
    47. 47)
      • 47. Wang, G., Zhao, J., Wen, F., et al: ‘Dispatch strategy of PHEVs to mitigate selected patterns of seasonally varying outputs from renewable generation’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 627639.
    48. 48)
      • 48. Liu, G., Tomsovic, K.: ‘Quantifying spinning reserve in systems with significant wind power penetration’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 23852393.
    49. 49)
      • 49. Tastu, J., Pinson, P., Trombe, P.J., et al: ‘Probabilistic forecasts of wind power generation accounting for geographically dispersed information’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 480489.
    50. 50)
      • 50. Papavasiliou, A., Oren, S.S., Neill, R.P.O.: ‘Reserve requirements for wind power integration: a stochastic programming framework’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 21972206.
    51. 51)
      • 51. Nguyen, D.T., Le, L.B.: ‘Risk-constrained profit maximization for microgrid aggregators with demand response’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 135146.
    52. 52)
      • 52. Zhang, Y., Giannakis, G.B.: ‘Efficient decentralized economic dispatch for microgrids with wind power integration’, 2014 Sixth Annual IEEE Green Technol. Conference, Corpus Christi, TX, USA, April 2014, pp. 712.
    53. 53)
      • 53. He, M., Yang, L., Zhang, J., et al: ‘A spatio-temporal analysis approach for short-term forecast of wind farm generation’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 16111622.
    54. 54)
      • 54. Van Donk, S.J., Wagner, L.E., Skidmore, E.L., et al: ‘Comparison of the Weibull model with measured wind speed distributions for stochastic wind generation’, Am. Soc. Agric. Eng., 2005, 48, (2), pp. 503510.
    55. 55)
      • 55. Xie, K., Jiang, Z., Li, W.: ‘Effect of wind speed on wind turbine power’, IEEE Trans. Energy Convers., 2012, 27, (1), pp. 96104.
    56. 56)
      • 56. Mohammadi, J., Rahimi-Kian, A., Ghazizadeh, M.-S.: ‘Aggregated wind power and flexible load offering strategy’, IET Renew. Power Gener., 2011, 5, (6), p. 439.
    57. 57)
      • 57. Fleten, S.E., Pettersen, E.: ‘Constructing bidding curves for a price-taking retailer in the Norwegian electricity market’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 701708.
    58. 58)
      • 58. Grigg, C., Wong, P.: ‘The IEEE reliability test system – 1996 a report prepared by the reliability test system task force of the application of probability methods subcommittee’, IEEE Trans. Power Syst., 1999, 14, (3), pp. 10101020.
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