Heuristic optimisation for automated distribution system planning in network integration studies

Heuristic optimisation for automated distribution system planning in network integration studies

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(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.

Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.


    1. 1)
      • 1. Deutsche Energie-Agentur GmbH (dena): ‘Ausbau- und Innovationsbedarf der Stromverteilnetze in Deutschland bis 2030’, 2012.
    2. 2)
      • 2. Bundesministerium für Wirtschaft und Energie (BMWi): ‘Moderne Verteilernetze für Deutschland (Verteilernetzstudie)’, September 2014.
    3. 3)
      • 3. Witzmann, R., Altschäffl, S., Esslinger, P., et al: ‘Verteilnetzstudie Bayern 2013 (Zwischenbericht)’, August 2013.
    4. 4)
      • 4. Ackermann, T., Martensen, N., Brown, T., et al: ‘Verteilnetzstudie Rheinland-Pfalz’, January 2014.
    5. 5)
      • 5. Rehtanz, C., Moser, A., Kays, J.: ‘Leistungsfähigkeit und Ausbaubedarf der Verteilnetze in Nordrhein-Westfalen (Gutachten)’, August 2014.
    6. 6)
      • 6. Rehtanz, C., Greve, M., Häger, U.: ‘Verteilnetzstudie für das Land Baden-Württemberg’, April 2017.
    7. 7)
      • 7. Scheidler, A., Thurner, L., Kraiczy, M., et al: ‘Automated grid planning for distribution grids with increasing pv penetration’. 6th Int. Workshop on Integration of Solar Power into Power Systems, Vienna, Austria, 2016.
    8. 8)
      • 8. Thurner, L., Scheidler, A., Schäfer, F., et al: ‘pandapower – an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems’. 2017, Available at:
    9. 9)
      • 9. Thurner, L., Scheidler, A., Probst, A., et al: ‘Heuristic optimization for network restoration and expansion in compliance with the single contingency policy’, IET Gener. Transm. Distrib., 2017, 11, (17), pp. 42644273.
    10. 10)
      • 10. Georgilakis, P.S., Hatziargyriou, N.D.: ‘A review of power distribution planning in the modern power systems era: models, methods and future research’, Electr. Power Syst. Res., 2015, 121, pp. 89100. Available at:
    11. 11)
      • 11. Jordehi, A.R.: ‘Optimisation of electric distribution systems: a review’, Renew. Sust. Energy Rev., 2015, 51, pp. 10881100. Available at:
    12. 12)
      • 12. Sedghi, M., Ahmadian, A., Aliakbar-Golkar, M.: ‘Assessment of optimization algorithms capability in distribution network planning: review, comparison and modification techniques’, Renew. Sust. Energy Rev., 2016, 66, pp. 415434. Available at:
    13. 13)
      • 13. Zubo, R.H.A., Mokryani, G., Rajamani, H.S., et al: ‘Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review’, Renew. Sust. Energy Rev., 2017, 72, pp. 11771198. Available at:
    14. 14)
      • 14. Ganguly, S., Sahoo, N.C., Das, D.: ‘Recent advances on power distribution system planning: a state-of-the-art survey’, Energy Syst., 2013, 4, (2), pp. 165193. Available at:
    15. 15)
      • 15. Franco, J.F., Rider, M.J., Romero, R.: ‘A mixed-integer quadratically-constrained programming model for the distribution system expansion planning’, Int. J. Electr. Power Energy Syst., 2014, 62, pp. 265272. Available at:
    16. 16)
      • 16. Miranda, V., Ranito, J.V., Proenca, L.M.: ‘Genetic algorithms in optimal multistage distribution network planning’, IEEE Trans. Power Syst., 1994, 9, (4), pp. 19271933.
    17. 17)
      • 17. Chen, T.H., Cherng, J.T.: ‘Optimal phase arrangement of distribution transformers connected to a primary feeder for system unbalance improvement and loss reduction using a genetic algorithm’, IEEE Trans. Power Syst., 2000, 15, (3), pp. 9941000.
    18. 18)
      • 18. Camargo, V., Lavorato, M., Romero, R.: ‘Specialized genetic algorithm to solve the electrical distribution system expansion planning’. IEEE Power Energy Society General Meeting, Vancouver, Canada, 2013, pp. 15.
    19. 19)
      • 19. Fletcher, J., Fernando, T., Iu, H., et al: ‘A case study on optimizing an electrical distribution network using a genetic algorithm’. IEEE 24th Int. Symp. on Industrial Electronics (ISIE), 2015, pp. 2025.
    20. 20)
      • 20. Falaghi, H., Singh, C., Haghifam, M.R., et al: ‘Dg integrated multistage distribution system expansion planning’, Int. J. Electr. Power Energy Syst., 2011, 33, (8), pp. 14891497.
    21. 21)
      • 21. Kong, T., Cheng, H., Hu, Z., et al: ‘Multiobjective planning of open-loop mv distribution networks using ComGIS network analysis and MOGA’, Electr. Power Syst. Res., 2009, 79, (2), pp. 390398. Available at:
    22. 22)
      • 22. Sahoo, N.C., Ganguly, S., Das, D.: ‘Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization’, Swarm Evol. Comput., 2012, 3, pp. 1532.
    23. 23)
      • 23. Ganguly, S., Sahoo, N., Das, D.: ‘A novel multi-objective pso for electrical distribution system planning incorporating distributed generation’, Energy Syst., 2010, 1, (3), pp. 291337.
    24. 24)
      • 24. Cossi, A.M., Silva, L.G.W.D., Lazaro, R.A.R., et al: ‘Primary power distribution systems planning taking into account reliability, operation and expansion costs’, IET Gener. Transm. Distrib., 2012, 6, (3), pp. 274284.
    25. 25)
      • 25. Navarro, A., Rudnick, H.: ‘Large-scale distribution planning - Part ii: macrooptimization with Voronoi's diagram and tabu search’, IEEE Trans. Power Syst., 2009, 24, (2), pp. 752758.
    26. 26)
      • 26. Keko, H., Skok, M., Skrlec, D.: ‘Solving the distribution network routing problem with artificial immune systems’. Proc. of the 12th IEEE Mediterranean Electrotechnical Conf. (MELECON), Dubrovnik, Croatia, 2004, vol. 3, pp. 959962.
    27. 27)
      • 27. Lin, C.H., Chen, C.S., Huang, M.Y., et al: ‘Optimal phase arrangement of distribution feeders using immune algorithm’. Int. Conf. Intelligent Systems Applications to Power Systems, 2007, pp. 16.
    28. 28)
      • 28. Zmijarević, Z., Skok, M., Keko, H., et al: ‘A comprehensive methodology for long-term planning of distribution networks with intrinsic contingency support’. 18th Int. Conf. and Exhibition on Electricity Distribution, CIRED, Turin, Italy, 2005, pp. 15.
    29. 29)
      • 29. Cossi, A.M., Romero, R., Mantovani, J.R.S.: ‘Planning of secondary distribution circuits through evolutionary algorithms’, IEEE Trans. Power Deliv., 2005, 20, (1), pp. 205213.
    30. 30)
      • 30. Gitizadeh, M., Vahed, A.A., Aghaei, J.: ‘Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms’, Appl. Energy, 2013, 101, (Supplement C), pp. 655666, sustainable Development of Energy, Water and Environment Systems. Available at:
    31. 31)
      • 31. Domingo, C.M., Sanchez-Miralles, A., Roman, T.G.S., et al: ‘A reference network model for large-scale distribution planning with automatic street map generation’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 190197.
    32. 32)
      • 32. Lourenço, H.R., Martin, O.C., Stützle, T.: ‘Iterated Local Search’, In Glover, F., Kochenberger, G.A., (Eds): ‘Handbook of metaheuristics’ (Springer US, Boston, USA, 2003), pp. 320353, Available at:
    33. 33)
      • 33. Burke, E.K., Bykov, Y.: ‘The Late Acceptance Hill-Climbing heuristic’, Eur. J. Oper. Res., 2017, 258, (1), pp. 7078, Available at:
    34. 34)
      • 34. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: ‘Optimization by simulated annealing’, Science, 1983, 220, (4598), pp. 671680.

Related content

This is a required field
Please enter a valid email address