Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks

Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks

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 Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi-period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage-dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on-load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two-stage stochastic programming formulation of the multiobjective problem. The result is a set of non-inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.


    1. 1)
      • 1. Hallberg, P.: ‘Active distribution system management a key tool for the smooth integration of distributed generation’ (Eurelectric, 2013).
    2. 2)
      • 2. Dent, C.J., Ochoa, L.F., Harrison, G.P.: ‘Network distributed generation capacity analysis using OPF with voltage step constraints’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 296304.
    3. 3)
      • 3. Vovos, P.N., Bialek, J.W.: ‘Direct incorporation of fault level constraints in optimal power flow as a tool for network capacity analysis’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 21252134.
    4. 4)
      • 4. Keane, A., O'Malley, M.: ‘Optimal utilization of distribution networks for energy harvesting’, IEEE Trans. Power Syst., 2007, 22, (1), pp. 467475.
    5. 5)
      • 5. AlKaabi, S.S., Khadkikar, V., Zeineldin, H.H.: ‘Incorporating PV inverter control schemes for planning active distribution networks’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 12241233.
    6. 6)
      • 6. Siano, P., Chen, P., Chen, Z., et al: ‘Evaluating maximum wind energy exploitation in active distribution networks’, IET Gener. Transm. Distrib., 2010, 4, (5), pp. 598608.
    7. 7)
      • 7. Ochoa, L.F., Dent, C.J., Harrison, G.P.: ‘Distribution network capacity assessment: variable DG and active networks’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 8795.
    8. 8)
      • 8. Capitanescu, F., Ochoa, L.F., Margossian, H., et al: ‘Assessing the potential of network reconfiguration to improve distributed generation hosting capacity in active distribution systems’, IEEE Trans. Power Syst., 2015, 30, (1), pp. 346356.
    9. 9)
      • 9. Wang, S., Chen, S., Ge, L., et al: ‘Distributed generation hosting capacity evaluation for distribution systems considering the robust optimal operation of OLTC and SVC’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 11111123.
    10. 10)
      • 10. Zio, E., Delfanti, M., Giorgi, L., et al: ‘Monte carlo simulation-based probabilistic assessment of DG penetration in medium voltage distribution networks’, Int. J. Electr. Power Energy Syst., 2015, 64, pp. 852860. Available at
    11. 11)
      • 11. Cohon, J.L.: ‘Multiobjective programming and planning’ (Academic Press, USA, 1978).
    12. 12)
      • 12. Rizy, D.T., Li, H., Li, F., et al: ‘Impacts of varying penetration of distributed resources with and without volt/var control: case study of varying load types’. 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 17.
    13. 13)
      • 13. Quijano, D.A., Feltrin, A.P.: ‘Assessment of conservation voltage reduction effects in networks with distributed generators’. 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), 2015, pp. 393398.
    14. 14)
      • 14. Padilha Feltrin, A., Rodezno, D.A.Q., Mantovani, J.R.S.: ‘Volt-var multiobjective optimization to peak-load relief and energy efficiency in distribution networks’, IEEE Trans. Power Deliv., 2015, 30, (2), pp. 618626.
    15. 15)
      • 15. Wang, Z., Chen, B., Wang, J., et al: ‘Stochastic DG placement for conservation voltage reduction based on multiple replications procedure’, IEEE Trans. Power Deliv., 2015, 30, (3), pp. 10391047.
    16. 16)
      • 16. Singh, D., Misra, R.K., Singh, D.: ‘Effect of load models in distributed generation planning’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 22042212.
    17. 17)
      • 17. Wang, Z., Wang, J., Chen, B., et al: ‘MPC-based voltage/var optimization for distribution circuits with distributed generators and exponential load models’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 24122420.
    18. 18)
      • 18. Singh, R., Tuffner, F., Fuller, J., et al: ‘Effects of distributed energy resources on conservation voltage reduction (CVR)’. In: 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 17.
    19. 19)
      • 19. Ellis, A., Nelson, R., Engeln, E.V., et al: ‘Reactive power interconnection requirements for PV and wind plants – recommendations to NERC’ (Albuquerque, NM, Sandia National Laboratories, 2012). SAND2012-1098.
    20. 20)
      • 20. IEEE SCC 21: ‘IEEE standard conformance test procedures for equipment interconnecting distributed resources with electric power systems – amendment 1’, IEEE Std 15471a-2015 (amendment to IEEE Std 15471-2005), 2015, pp. 127.
    21. 21)
      • 21. Atwa, Y.M., El Saadany, E.F., Salama, M.M.A., et al: ‘Optimal renewable resources mix for distribution system energy loss minimization’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 360370.
    22. 22)
      • 22. Soroudi, A., Amraee, T.: ‘Decision making under uncertainty in energy systems: state of the art’, Renew. Sustain. Energy Rev., 2013, 28, pp. 376384. Available at
    23. 23)
      • 23. Birge, J.R., Louveaux, F.: ‘Introduction to stochastic programming’ (Springer, USA, 1978, 2nd edn.).
    24. 24)
      • 24. Baran, M.E., Wu, F.F.: ‘Network reconfiguration in distribution systems for loss reduction and load balancing’, IEEE Trans. Power Deliv., 1989, 4, (2), pp. 14011407.
    25. 25)
      • 25. Korunović, L.M., Sterpu, S., Djokić, S., et al: ‘Processing of load parameters based on existing load models’. 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), 2012, pp. 16.
    26. 26)
      • 26. Heitsch, H., Römisch, W.: ‘Scenario reduction algorithms in stochastic programming’, Comput. Optim. Appl., 2003, 24, (2), pp. 187206. Available at
    27. 27)
      • 27. Slyke, R.M.V., Wets, R.: ‘L-shaped linear programs with applications to optimal control and stochastic programming’, SIAM J. Appl. Math., 1969, 17, (4), pp. 638663.
    28. 28)
      • 28. Birge, J.R., Louveaux, F.V.: ‘A multicut algorithm for two-stage stochastic linear programs’, Eur. J. Oper. Res., 1988, 34, (3), pp. 384392. Available at
    29. 29)
      • 29. Crainic, T.G., Hewitt, M., Rei, W.: ‘Partial Benders decomposition strategies for two-stage stochastic integer programs’ (Publication, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, Université de Montréal, Montréal, QC, Canada, 2016).
    30. 30)
      • 30. IBM: ‘IBM ILOG AMPL – User's guide’, 2010. Available at
    31. 31)
      • 31. IEEE PES: ‘Distribution test feeders’. Available at

Related content

This is a required field
Please enter a valid email address