New reward and penalty scheme for electric distribution utilities employing load-based reliability indices

New reward and penalty scheme for electric distribution utilities employing load-based reliability indices

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Electric distribution utilities are required to continuously deliver reliable electric power to their customers. Regulatory utility commissions often practise reward and penalty schemes to regulate reliability performance of utility companies annually with respect to a desired performance targets. However, the conventional regulation procedures are commonly found based on the customer-based standard reliability indices, which are not able to discern the service characteristics behind the electric meters and, hence, fail to holistically characterise the actual impact of electricity interruption. This study proposes a new method to evaluate the load-based reliability indices in power distribution systems using advanced metering infrastructure data. Furthermore, the authors introduce a reward/penalty regulation scheme for utility regulators to provide a reliability oversight using the proposed load-based reliability metrics. The new load-based reliability metric and the reward/penalty scheme proposed bring about superior advantages as the distribution grids become further complex with a high penetration of distributed energy resources and enabled microgrid flexibilities. Numerical analyses on different settings with and without microgrid considerations reveal the applicability and effectiveness of the proposed approach in real-world scenarios.


    1. 1)
      • 1. ‘The commission's investigation into modernizing the energy delivery structure for increased sustainability’, 2015. Available at
    2. 2)
      • 2. Yi, Z., Etemadi, A.H.: ‘Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine’, IEEE Trans. Ind. Electron., 2017, 64, (11), pp. 85468556.
    3. 3)
      • 3. Luan, S.W., Teng, J.H., Chan, S.Y., et al: ‘Development of a smart power meter for AMI based on Zigbee communication’. 2009 Int. Conf. Power Electronics and Drive Systems (PEDS), Taipei, 2009, pp. 661665.
    4. 4)
      • 4. ‘Next generation smart meters and AMI communications’, 2015. Available at
    5. 5)
      • 5. Liu, D., Yuan, X., Li, Q., et al: ‘Design of a hierarchical infrastructure for regional energy internet’. 2015 Fifth Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, 2015, pp. 26032607.
    6. 6)
      • 6. Peppanen, J., Reno, M.J., Thakkar, M., et al: ‘Leveraging AMI data for distribution system model calibration and situational awareness’, IEEE Trans. Smart Grid, 2015, 6, (4), pp. 20502059.
    7. 7)
      • 7. Peternel, B., Lovrencic, T., Gamulin, N., et al: ‘Methodology and key performance indicators for resilient dense prosumer oriented DEG smart grid energy and communications network’, 2016. Available at
    8. 8)
      • 8. Dehghanian, P., Aslan, S., Dehghanian, P.: ‘Quantifying power system resiliency improvement using network reconfiguration’. 2017 IEEE 60th International Midwest Symposium on Circuit and Systems (MWSCAS), Boston, MA, USA, August 2017, pp. 13641367.
    9. 9)
      • 9. Dehghanian, P., Fotuhi-Firuzabad, M., Aminifar, F., et al: ‘A comprehensive scheme for reliability centered maintenance in power distribution systems – part i: methodology’, IEEE Trans. Power Deliv., 2013, 28, (2), pp. 761770.
    10. 10)
      • 10. Simab, M., Alvehag, K., Soder, L., et al: ‘Designing reward and penalty scheme in performance based regulation for electric distribution companies’, IET Gener. Transm. Distrib., 2012, 6, (9), pp. 893901.
    11. 11)
      • 11. Mohammadnezhad-Shourkaei, H., Fotuhi-Firuzabad, M.: ‘Impact of penalty-reward mechanism on the performance of electric distribution systems and regulator budget’, IET Gener. Transm. Distrib., 2010, 4, (7), pp. 770779.
    12. 12)
      • 12. Mohammadnezhad-Shourkaei, H., Abiri-Jahromi, A., Fotuhi-Firuzabad, M.: ‘Incorporating service quality regulation in distribution system maintenance strategy’, IEEE Trans. Power Deliv., 2011, 26, (4), pp. 24952504.
    13. 13)
      • 13. Mohammadnezhad-Shourkaei, H., Fotuhi-Firuzabad, M., Billinton, R.: ‘Integration of clustering analysis and reward/penalty mechanisms for regulating service reliability in distribution systems’, IET Gener. Transm. Distrib., 2011, 5, (11), pp. 11921200.
    14. 14)
      • 14. ‘Jamaica public service company limited tariff review for period 2014–2019’, 2015. Available at
    15. 15)
      • 15. Luan, S.W., Teng, J.H., Chan, S.Y., et al: ‘Development of an automatic reliability calculation system for advanced metering infrastructure’. 2010 Eighth IEEE Int. Conf. Industrial Informatics, Osaka, 2010, pp. 342347.
    16. 16)
      • 16. Alvehag, K., Awodele, K.: ‘Impact of reward and penalty scheme on the incentives for distribution system reliability’, IEEE Trans. Power Syst., 2014, 29, (1), pp. 386394.
    17. 17)
      • 17. Moshari, A., Ebrahimi, A.: inKarki, R., Billinton, R., Verma, A.K. (Eds.): ‘A load management perspective of the smart grid: simple and effective tools to enhance reliability’ (Springer India, New Delhi, 2014), pp. 133146.
    18. 18)
      • 18. Wang, S., Li, Z., Wu, L., et al: ‘New metrics for assessing the reliability and economics of microgrids in distribution system’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 28522861.
    19. 19)
      • 19. Tarnate, W.R.D., Cruz, I.B.N.C., del Mundo, R.D., et al: ‘Maximizing service restoration in reliability optimization of radial distribution systems’. TENCON 2012 IEEE Region 10 Conf., Cebu, 2012, pp. 16.
    20. 20)
      • 20. Al-Muhaini, M., Heydt, G.T.: ‘Evaluating future power distribution system reliability including distributed generation’, IEEE Trans. Power Deliv., 2013, 28, (4), pp. 22642272.
    21. 21)
      • 21. Chowdhury, A., Koval, D.: ‘Power distribution system reliability: practical methods and applications’ (John Wiley & Sons, Hoboken, NJ, USA, 2011), pp. 317374.
    22. 22)
      • 22. Ge, S., Xu, L., Liu, H., et al: ‘Reliability assessment of active distribution system using Monte Carlo simulation method’, J. Appl. Math., 2014, 2014, pp. 110.
    23. 23)
      • 23. Hernandez, L., Baladron, C., Aguiar, J.M.: ‘A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants’, IEEE Commun. Mag., 2013, 51, (1), pp. 106113.
    24. 24)
      • 24. Quilumba, F.L., Lee, W.J., Huang, H., et al: ‘Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 911918.
    25. 25)
      • 25. Sevlian, R.A., Rajagopal, R.: ‘A model for the effect of aggregation on short term load forecasting’. 2014 IEEE PES General Meeting Conf. Exposition, National Harbor, MD, 2014, pp. 15.
    26. 26)
      • 26. Silva, P.G.D., Ilic, D., Karnouskos, S.: ‘The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 402410.
    27. 27)
      • 27. Hayes, B., Gruber, J., Prodanovic, M.: ‘Short-term load forecasting at the local level using smart meter data’. 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 16.
    28. 28)
      • 28. Ziekow, H., Goebel, C., Struker, J., et al: ‘The potential of smart home sensors in forecasting household electricity demand’. 2013 IEEE Int. Conf. Smart Grid Communications (SmartGridComm), Vancouver, BC, 2013, pp. 229234.
    29. 29)
      • 29. Deoras, A.: ‘Electricity load and price forecasting with MATLAB’, 2010. Available at
    30. 30)
      • 30. ‘Quality controlled local climatological data’. Available at
    31. 31)
      • 31. ‘IEEE guide for electric power distribution reliability indices’, IEEE Std. 1366-2012 (Revision of IEEE Std. 1366-2003), 2012, pp. 143.
    32. 32)
      • 32. Elmitwally, A., Elsaid, M., Elgamal, M., et al: ‘A fuzzy-multiagent self-healing scheme for a distribution system with distributed generations’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 26122622.
    33. 33)
      • 33. Sullivan, M.J., Schellenberg, J., Blundell, M.: ‘Updated value of service reliability estimates for electric utility customers in the United States’. Lawrence Berkeley National Laboratory, 2015. Available at
    34. 34)
      • 34. Cheng, L., Chang, Y., Lin, J., et al: ‘Power system reliability assessment with electric vehicle integration using battery exchange mode’, IEEE Trans. Sustain. Energy, 2013, 4, (4), pp. 10341042.
    35. 35)
      • 35. ‘Daily real-time lmp’. Available at

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