Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free User centric economic demand response management in a secondary distribution system in India

This study presents a demand response (DR) model to curtail the load during peak hours in a secondary (230/440 V) distribution system of Tamil Nadu Generation and Distribution Corporation (TANGEDCO), a state-owned enterprise, India. TANGEDCO penalises utilities if they violate their permitted contractual limit of demand. Conventional demand control usually curtails a specific region of the secondary distribution network. The limitation of the complete blackout of the particular region in the distribution network increases the loss of load probability. Hence, this project implements an economic DR model in real time using demand side forecasting and by scheduling air-conditioning loads based on their priority. The pilot project is executed and is monitored at the National Institute of Technology, Tiruchirappalli, India campus. A standard back propagation neural network is used for forecasting a 15 min interval ahead maximum demand in kVA. This economic model comprises of a communication network that uses ON/OFF switching wirelessly controlled relay modules. Finally, the benefits and the strategy involved in the project are presented. It is found that the proposed scheme prevents the electrical demand from exceeding the contractual limit, whereby the penalty due to the violation is zeroed when compared to the previous year.

References

    1. 1)
      • 3. International Energy Agency: ‘The power to choose – demand response in liberalized electricity markets’ (International Energy Agency, Paris, 2003), pp. 1011.
    2. 2)
      • 27. Central Electricity Authority (CEA): ‘Load generation balance report 2016–17’ (CEA, New Delhi, 2016), p. 2.
    3. 3)
      • 25. Wenzel, G., Negrete-Pincetic, M., Olivares, D.E., et al: ‘Real-Time charging strategies for an electric vehicle aggregator to provide ancillary services’, IEEE Trans. Smart Grid(Early Access), 2017, 9, (5), pp. 51415151.
    4. 4)
      • 21. Mohseni, A., Mortazavi, S.S., Ghasemi, A., et al: ‘The application of household appliances’ flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid’, Energy, 2017, 139, pp. 315328.
    5. 5)
      • 28. Central Electricity Authority (CEA): ‘Load generation balance report 2017–18’ (CEA, New Delhi, 2017), p. 2.
    6. 6)
      • 38. National Institute of Technology, Tiruchirappalli, Tamil Nadu. Available at https://www.google.com/maps/@10.7606271,78.8156856,16.75z, accessed 27 April 2018.
    7. 7)
      • 14. Ali, M., Jokisalo, J., Siren, K., et al: ‘Combining the demand response of direct electric space heating and partial thermal storage using LP optimization’, Electr. Power Syst. Res., 2014, 106, pp. 160167.
    8. 8)
      • 18. Chen, C., Wang, J., Kishore, S.: ‘A distributed direct load control approach for large-scale residential demand response’, IEEE Trans. Power Syst., 2014, 29, (5), pp. 22192228.
    9. 9)
      • 31. Charytoniuk, W., Chen, M.-S.: ‘Very short-term load forecasting using artificial neural networks’, IEEE Trans. Power Syst., 2000, 15, (1), pp. 263268.
    10. 10)
      • 8. Li, S., Zhang, D., Roget, A.B., et al: ‘Integrating home energy simulation and dynamic electricity price for demand response study’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 779788.
    11. 11)
      • 11. Wen, Z., O'Neill, D., Maei, H.: ‘Optimal demand response using device-based reinforcement learning’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 23122324.
    12. 12)
      • 35. Taylor, J.W., de Menezes, L.M., McSharry, P.E.: ‘A comparison of univariate methods for forecasting electricity demand up to a day ahead’, Int. J. Forecast., 2005, 22, (1), pp. 116.
    13. 13)
      • 2. Fraser, H.: ‘The importance of an active demand side in the electricity industry’, Electr. J., 2001, 14, (9), pp. 5273.
    14. 14)
      • 10. Muratori, M., Rizzoni, G.: ‘Residential demand response: dynamic energy management and time-varying electricity pricing’, IEEE Trans. Power. Syst., 2016, 31, (2), pp. 11081117.
    15. 15)
      • 4. Yoon, J.H., Baldick, R., Novoselac, A.: ‘Dynamic demand response controller based on real-time retail price for residential buildings’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 121129.
    16. 16)
      • 30. Bakirtzis, A.G., Petridis, V., Kiartzis, S.J., et al: ‘A neural network short term load forecasting model for the Greek power system’, IEEE Trans. Power Syst., 1996, 11, (2), pp. 858863.
    17. 17)
      • 29. ‘A system to determine a day-ahead loading pattern of heavy machineries in industries and proactive control of peak load overshoot’. Available at http://www.ipindia.nic.in/writereaddata/Portal/IPOJournal/1_1554_1/Part-3.pdf, accessed 29 April, 2018.
    18. 18)
      • 12. Huang, Y., Tian, H., Wang, L.: ‘Demand response for home energy management system’,  IEEE. Trans. Power Syst., 2015, 73, pp. 448455.
    19. 19)
      • 17. Oh, E., Kwon, Y., Son, S.-Y.: ‘A new method for cost-effective demand response strategy for apartment-type factory buildings’, Energy Build., 2017, 151, pp. 275282.
    20. 20)
      • 34. Mandal, P., Senjyu, T., Urasaki, N., et al: ‘A neural network based several-hour-ahead electric load forecasting using similar days approach’, Int. J. Electr. Power Energy Syst., 2006, 28, (6), pp. 367373.
    21. 21)
      • 37. Hyndman, R.J., Koehler, A.B.: ‘Another look at measures of forecast accuracy’, Int. J. Forecast., 2006, 22, (4), pp. 679688.
    22. 22)
      • 19. Ye, Z., Kim, M.K.: ‘Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: case study of a shopping mall in China’, Sustain. Cities Soc.(Early Access), 2018, 42, pp. 176183.
    23. 23)
      • 16. Xue, X., Wang, S., Yan, C., et al: ‘A fast chiller power demand response control strategy for buildings connected to smart grid’, Appl. Energy, 2015, 137, pp. 7787.
    24. 24)
      • 32. Taylor, J.W., Buizza, R.: ‘Neural network load forecasting with weather ensemble predictions’, IEEE Trans. Power Syst., 2002, 17, (3), pp. 626632.
    25. 25)
      • 24. Wang, G., Zhang, Q., Li, H., et al: ‘Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis’, Appl. Energy, 2017, 185, (2), pp. 18691878.
    26. 26)
      • 9. Pipattanasomporn, M., Kuzlu, M., Rahmanand, S., et al: ‘Load profiles of selected major household appliances and their demand response opportunities’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 742750.
    27. 27)
      • 36. Li, J.-Y., Chow, T.W.S., Yu, Y.-L.: ‘The estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network’. Proc. ICNN'95 – Int. Conf. on Neural Networks, Perth, Australia, November–December 1995, 3, pp. 12291233.
    28. 28)
      • 6. Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M.: ‘A distributed algorithm for managing residential demand response in smart grids’, IEEE Trans. Ind. Inf., 2014, 10, (4), pp. 23852393.
    29. 29)
      • 23. Salah, F., Henriquez, R., Wenzel, G., et al: ‘Portfolio design of a demand response aggregator with satisficing consumers’, IEEE Trans. Smart Grid(Early Access), 2018, pp. 1.
    30. 30)
      • 33. Khosravi, A., Nahavandi, S., Creighton, D., et al: ‘Comprehensive review of neural network-based prediction intervals and new advances’, IEEE Trans. Neural Netw., 2011, 22, (9), pp. 13411356.
    31. 31)
      • 26. Zhang, H., Hu, Z., Xu, Z., et al: ‘Evaluation of achievable vehicle-to-grid capacity using Aggregate PEV model’, IEEE Trans. Power Syst., 2017, 32, (1), pp. 784794.
    32. 32)
      • 1. Albadi, M.H., El-Saadany, E.F.: ‘A summary of demand response in electricity markets’, Electr. Power Syst. Res., 2008, 78, (11), pp. 19891996.
    33. 33)
      • 13. Elghitani, F., Zhuang, W.: ‘Aggregating a large number of residential appliances for demand response applications’, IEEE Trans. Smart Grid(Early Access), 2017, 9, (5), pp. 50925100.
    34. 34)
      • 5. Tan, Z., Yang, P., Nehorai, A.: ‘An optimal and distributed demand response strategy with electric vehicles in the smart grid’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 861869.
    35. 35)
      • 20. Hao, H., Wu, D., Lian, J., et al: ‘Optimal coordination of building loads and energy storage for power grid and End user services’, IEEE Trans. Smart Grid(Early Access), 2017, 9, (5), pp. 43354345.
    36. 36)
      • 7. 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.
    37. 37)
      • 22. Muller, F.L., Szabo, J., Sundstrom, O., et al: ‘Aggregation and disaggregation of energetic flexibility from distributed energy resources’, IEEE Trans. Smart Grid(Early Access), 2017, pp. 1.
    38. 38)
      • 15. Mnatsakanyan, A., Kennedy, S.W.: ‘A novel demand response model with an application for a virtual power plant’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 230237.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5194
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5194
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address