Skip to main content

Electric Distribution Network Planning Under Uncertainty

  • Chapter
  • First Online:
Handbook of Optimization in Electric Power Distribution Systems

Part of the book series: Energy Systems ((ENERGY))

Abstract

This chapter presents a deterministic and an adaptive robust model for the short-term network expansion planning in electric distribution networks, considering siting and sizing of voltage regulators, capacitor banks, renewable energy generation, energy storage systems, and existing overloaded feeders reinforcement. The objective function to be minimized consists of investment and operation costs. Conventional expansion models in distribution networks are stated as a mixed-integer non-linear mathematical programs. In this chapter, we introduce the standard formulation and transform it into a mixed-integer linear programming form. This formulation is used to solve a deterministic short-term electric distribution network expansion planning case. Based on the deterministic formulation, we expand the formulation to a two-stage tri-level adaptive robust problem for considering load consumption and renewable-based DG uncertainties. By using Karush–Kuhn–Tucker conditions, this model is transformed into a two-stage bi-level adaptive robust optimization problem. A column and constraint generation framework is used to solve the problem. Computational results are obtained from a 123-node distribution system under different conditions to assess the performance of the proposed approach. Results show the effectiveness of the proposed methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdelouadoud, S., Girard, R., Neirac, F., Guiot, T.: Optimal power flow of a distribution system based on increasingly tight cutting planes added to a second order cone relaxation. Int. J. Electr. Power Energy Syst. 69, 9–17 (2015)

    Article  Google Scholar 

  2. Agalgaonkar, Y.P., Pal, B.C., Jabr, R.A.: Stochastic distribution system operation considering voltage regulation risks in the presence of PV generation. IEEE Trans. Sustain. Energy 6(4), 1315–1324 (2015)

    Article  Google Scholar 

  3. Ahmadigorji, M., Amjady, N., Dehghan, S.: A robust model for multiyear distribution network reinforcement planning based on information-gap decision theory. IEEE Trans. Power Syst. 33(2), 1339–1351 (2018)

    Article  Google Scholar 

  4. Alsaidan, I., Khodaei, A., Gao, W.: A comprehensive battery energy storage optimal sizing model for microgrid applications. IEEE Trans. Power Syst. 33(4), 3968–3980 (2018)

    Article  Google Scholar 

  5. Amjady, N., Attarha, A., Dehghan, S., Conejo, A.J.: Adaptive robust expansion planning for a distribution network with DERs. IEEE Trans. Power Syst. 33(2), 1698–1715 (2018)

    Article  Google Scholar 

  6. Baharvandi, A., Aghaei, J., Niknam, T., Shafie-Khah, M., Godina, R., Catalão, J.P.S.: Bundled generation and transmission planning under demand and wind generation uncertainty based on a combination of robust and stochastic optimization. IEEE Trans. Sustain. Energy 9(3), 1477–1486 (2018)

    Article  Google Scholar 

  7. Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Delivery 4(2), 1401–1407 (1989)

    Article  Google Scholar 

  8. Ben-Tal, A., Nemirovski, A.: Robust convex optimization. Math. Oper. Res. 23(4), 769–805 (1998)

    Article  Google Scholar 

  9. Ben-Tal, A., Nemirovski, A.: Robust solutions of uncertain linear programs. Oper. Res. Lett. 25(1), 1–13 (1999)

    Article  Google Scholar 

  10. Ben-Tal, A., Nemirovski, A.: Robust solutions of linear programming problems contaminated with uncertain data. Math. Program. 88(3), 411–424 (2000)

    Article  Google Scholar 

  11. Ben-Tal, A., Nemirovski, A.: Robust optimization - methodology and applications. Math. Program. 92(3), 453–480 (2002)

    Article  Google Scholar 

  12. Ben-Tal, A., Goryashko, A., Guslitzer, E., Nemirovski, A.: Adjustable robust solutions of uncertain linear programs. Math. Program. 99(2), 351–376 (2004)

    Article  Google Scholar 

  13. Bertsimas, D., Sim, M.: Robust discrete optimization and network flows. Math. Program. 98(1–3), 49–71 (2003)

    Article  Google Scholar 

  14. Bertsimas, D., Sim, M.: The price of robustness. Oper. Res. 52(1), 35–53 (2004)

    Article  Google Scholar 

  15. Bertsimas, D., Brown, D.B., Caramanis, C.: Theory and applications of robust optimization. SIAM Rev. 53(3), 464–501 (2011)

    Article  Google Scholar 

  16. Bertsimas, D., Litvinov, E., Sun, X.A., Zhao, J., Zheng, T.: Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans. Power Syst. 28(1), 52–63 (2013)

    Article  Google Scholar 

  17. Chiradeja, P., Ramakumar, R.: An approach to quantify the technical benefits of distributed generation. IEEE Trans. Energy Convers. 19(4), 764–773 (2004)

    Article  Google Scholar 

  18. Conejo, A.J., Baringo, L., Kazempour, S.J., Siddiqui, A.S.: Investment in Electricity Generation and Transmission. Springer, Berlin (2016). https://doi.org/10.1007/978-3-319-29501-5

    Book  Google Scholar 

  19. Dehghan, S., Amjady, N.: Robust transmission and energy storage expansion planning in wind farm-integrated power systems considering transmission switching. IEEE Trans. Sustain. Energy 7(2), 765–774 (2016)

    Article  Google Scholar 

  20. Dell, R., Rand, D.: Energy storage – a key technology for global energy sustainability. J. Power Sources 100(1), 2–17 (2001). https://doi.org/10.1016/S0378-7753(01)00894-1. Journal of Power Sources Volume 100

    Article  Google Scholar 

  21. Denholm, P., Hand, M.: Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy 39(3), 1817–1830 (2011). https://doi.org/10.1016/j.enpol.2011.01.019

    Article  Google Scholar 

  22. Ding, T., Liu, S., Yuan, W., Bie, Z., Zeng, B.: A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks. IEEE Trans. Sustain. Energy 7(1), 301–311 (2016)

    Article  Google Scholar 

  23. Evangelopoulos, V.A., Georgilakis, P.S., Hatziargyriou, N.D.: Optimal operation of smart distribution networks: a review of models, methods and future research. Electr. Power Syst. Res. 140, 95–106 (2016)

    Article  Google Scholar 

  24. Falugi, P., Konstantelos, I., Strbac, G.: Planning with multiple transmission and storage investment options under uncertainty: a nested decomposition approach. IEEE Trans. Power Syst. 33(4), 3559–3572 (2018)

    Article  Google Scholar 

  25. Fanzeres, B., Street, A., Barroso, L.A.: Contracting strategies for renewable generators: a hybrid stochastic and robust optimization approach. IEEE Trans. Power Syst. 30(4), 1825–1837 (2015)

    Article  Google Scholar 

  26. Farahani, V., Sadeghi, S.H.H., Abyaneh, H.A., Agah, S.M.M., Mazlumi, K.: Energy loss reduction by conductor replacement and capacitor placement in distribution systems. IEEE Trans. Power Syst. 28(3), 2077–2085 (2013)

    Article  Google Scholar 

  27. Florez, H.A.R., Carreno, E.M., Rider, M.J., Mantovani, J.R.S.: Distflow based state estimation for power distribution networks. Energy Syst. 9(4), 1055–1070 (2018)

    Article  Google Scholar 

  28. Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language for Mathematical Programming. Boston, Duxbury Press (2002)

    Google Scholar 

  29. Franco, J.F., Rider, M.J., Lavorato, M., Romero, R.: A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems. Int. J. Electr. Power Energy Syst. 48, 123–130 (2013)

    Article  Google Scholar 

  30. Frank, S., Rebennack, S.: An introduction to optimal power flow: theory, formulation, and examples. IIE Trans. 48(12), 1172–1197 (2016)

    Article  Google Scholar 

  31. Ghaoui, L.E., Oustry, F., Lebret, H.: Robust solutions to uncertain semidefinite programs. SIAM J. Optim. 9(1), 33–52 (1998)

    Article  Google Scholar 

  32. IBM: IBM ILOG CPLEX V12.1 Users Manual for CPLEX (2009)

    Google Scholar 

  33. Jabr, R.A., Džafić, I., Pal, C.B.: Robust optimization of storage investment on transmission networks. IEEE Trans. Power Syst. 30(1), 531–539 (2015)

    Article  Google Scholar 

  34. Ji, H., Wang, C., Li, P., Ding, F., Wu, J.: Robust operation of soft open points in active distribution networks with high penetration of photovoltaic integration. IEEE Trans. Sustain. Energy 10(1), 280–289 (2019)

    Article  Google Scholar 

  35. Jiang, R., Zhang, M., Li, G., Guan, Y.: Benders’ decomposition for the two-stage security constrained robust unit commitment problem. In: IIE Annual Conference, Proceedings, pp. 1–10 (2012)

    Google Scholar 

  36. Levron, Y., Shmilovitz, D.: Optimal power management in fueled systems with finite storage capacity. IEEE Trans. Circuits Syst. I Regul. Pap. 57(8), 2221–2231 (2010). https://doi.org/10.1109/TCSI.2009.2037405

    Article  Google Scholar 

  37. López, J., Pozo, D., Contreras, J.: Static and Dynamic Convex Distribution Network Expansion Planning, pp. 41–63. Singapore, Springer (2018). https://doi.org/10.1007/978-981-10-7056-3_2

    Chapter  Google Scholar 

  38. Macedo, L.H., Franco, J.F., Rider, M.J., Romero, R.: Optimal operation of distribution networks considering energy storage devices. IEEE Trans. Smart Grid 6(6), 2825–2836 (2015)

    Article  Google Scholar 

  39. Melgar-Dominguez, O.D., Pourakbari-Kasmaei, M., Mantovani, J.R.S.: Adaptive robust short-term planning of electrical distribution systems considering siting and sizing of renewable energy based DG units. IEEE Trans. Sustain. Energy 10(1), 158–169 (2019)

    Article  Google Scholar 

  40. Montoya-Bueno, S., Muñoz-Hernández, J., Contreras, J.: Uncertainty management of renewable distributed generation. J. Cleaner Prod. 138, 103–118 (2016)

    Article  Google Scholar 

  41. Muñoz-Delgado, G., Contreras, J., Arroyo, J.M.: Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability. IEEE Trans. Power Syst. 31(5), 3715–3728 (2016)

    Article  Google Scholar 

  42. Mwakabuta, N., Sekar, A.: Study of the application of evolutionary algorithms for the solution of capacitor deployment problem in distribution systems. In: 2008 40th Southeastern Symposium on System Theory (SSST), pp. 178–182 (2008)

    Google Scholar 

  43. Nikmehr, N., Najafi-Ravadanegh, S.: Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm. IET Renew. Power Gener. 9(8), 982–990 (2015)

    Article  Google Scholar 

  44. Available: https://ewh.ieee.org/soc/pes/dsacom/testfeeders/. Accessed Dec 3, 2018

  45. Ortiz, J.M.H., Pourakbari-Kasmaei, M., López, J., Mantovani, J.R.S.: A stochastic mixed-integer conic programming model for distribution system expansion planning considering wind generation. Energy Syst. 9(3), 551–571 (2018)

    Article  Google Scholar 

  46. Pereira Junior, B.R., Cossi, A.M., Mantovani, J.R.S.: Multiobjective short-term planning of electric power distribution systems using NSGA-II. J. Control Autom. Electr. Syst. 24(3), 286–299 (2013)

    Article  Google Scholar 

  47. Pozo, D., Contreras, J., Sauma, E.E.: Unit commitment with ideal and generic energy storage units. IEEE Trans. Power Syst. 29(6), 2974–2984 (2014)

    Article  Google Scholar 

  48. Ravichandran, A., Sirouspour, S., Malysz, P., Emadi, A.: A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles. IEEE Trans. Smart Grid 9(1), 346–359 (2018)

    Article  Google Scholar 

  49. Santos, S.F., Fitiwi, D.Z., Bizuayehu, A.W., Shafie-khah, M., Asensio, M., Contreras, J., Cabrita, C.M.P., João, P.S.C.: Novel multi-stage stochastic DG investment planning with recourse. IEEE Trans. Sustain. Energy 8(1), 164–178 (2017)

    Article  Google Scholar 

  50. Tan, S., Xu, J., Panda, S.K.: Optimization of distribution network incorporating distributed generators: an integrated approach. IEEE Trans. Power Syst. 28(3), 2421–2432 (2013)

    Article  Google Scholar 

  51. Thiele, A., Terry, T., Epelman, M.: Robust linear optimization with recourse. Rapport technique, pp. 4–37 (2009)

    Google Scholar 

  52. Wang, Z., Chen, B., Wang, J., Kim, J., Begovic, M.M.: Robust optimization based optimal DG placement in microgrids. IEEE Trans. Smart Grid 5(5), 2173–2182 (2014)

    Article  Google Scholar 

  53. Wang, R., Wang, P., Xiao, G.: A robust optimization approach for energy generation scheduling in microgrids. Energy Convers. Manag. 106, 597–607 (2015)

    Article  Google Scholar 

  54. Xiang, Y., Liu, J., Liu, Y.: Robust energy management of microgrid with uncertain renewable generation and load. IEEE Trans. Smart Grid 7(2), 1034–1043 (2016)

    Google Scholar 

  55. Xing, H., Cheng, H., Zhang, Y., Zeng, P.: Active distribution network expansion planning integrating dispersed energy storage systems. IET Gener. Transm. Distrib. 10(3), 638–644 (2016)

    Article  Google Scholar 

  56. Yanıkoǧlu, I., Gorissen, B.L., Hertog, D.D.: A survey of adjustable robust optimization. Eur. J. Oper. Res. 27, 799–813 (2019)

    Google Scholar 

  57. Zeng, B., Zhang, J., Ouyang, S., Yang, X., Dong, J., Zeng, M.: Two-stage combinatory planning method for efficient wind power integration in smart distribution systems considering uncertainties. Electr. Power Compon. Syst. 42(15), 1661–1672 (2014). https://doi.org/10.1080/15325008.2014.913735

    Article  Google Scholar 

  58. Zhao, L., Zeng, B.: Robust unit commitment problem with demand response and wind energy. In: 2012 IEEE Power and Energy Society General Meeting, pp. 1–8 (2012)

    Google Scholar 

  59. Zeng, B., Zhao, L.: Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 41(5), 457–461 (2013)

    Article  Google Scholar 

  60. Zheng, Y., Zhao, J., Song, Y., Luo, F., Meng, K., Qiu, J., Hill, D.J.: Optimal operation of battery energy storage system considering distribution system uncertainty. IEEE Trans. Sustain. Energy 9, 1051–1060 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

J. López would like to thank DIUC - University of Cuenca for the economic support in the development of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julio López .

Editor information

Editors and Affiliations

Appendix

Appendix

The notations used throughout this chapter are listed below.

Acronym

AMPL:

A Modeling Language for Mathematical Programming.

Indexes

b :

Index of installed CB sizes.

c :

Index of conductor types.

k :

Index of buses of the system.

km :

Index of branches of the system.

t :

Index of periods of planning.

μ :

Index of uncertain parameters.

Sets

B :

Set of branches of the system.

C :

Set of conductor types.

CB :

Set of CBs.

E :

Set of candidate buses to install ESSs.

N :

Set of buses of the system.

P :

Set of periods.

R :

Set of candidate buses to install CBs.

SE :

Set of substations of the system.

U :

Set of uncertainties.

V :

Set of candidate buses to install PV plants.

VR :

Set of voltage regulators.

W :

Set of candidate buses to install WP plants.

Z :

Set of integer numbers.

Constants

ES k,0 , ES k,T :

Initial/final stored energy in the storage unit at node k.

\(ES_{k}^{\min }, ES_{k}^{\max }\) :

Minimum/maximum storage capacity of the unit at node k.

L km :

Length of circuit km.

N ES :

Maximum number of storage units to be installed in the EDN.

N PV , N WT :

Maximum number of PV/WT plants to be installed in the EDN.

\(PV_v^{\max }, WT_w^{\max }\) :

Active power capacity of PV module/WT unit.

\(Pc^{\min }_{k}, Pc^{\max }_{k}\) :

Minimum/maximum charging power rate of the storage unit at node k.

\(P^{\max }_{c}, Q^{\max }_{c}\) :

Minimum/maximum active/reactive power through conductor c.

\(Pd^{\min }_{k}, Pd^{\max }_{k}\) :

Minimum/maximum discharging power rate of the storage unit at node k.

P D k, Q D k :

Active/reactive load demand in bus k.

\(Q_b^{sp}\) :

Specified reactive power capacity of CB b.

R c , X c :

Resistance/reactance of conductor c.

\(R^{\%}_{km}\) :

Regulation % of the VR to be installed in km.

r :

Discount rate.

\(V_k^{\min }, V_k^{\max }\) :

Minimum/maximum voltage magnitude limit in bus k.

ηc k , ηd k :

Charging/discharging storage efficiency of the storage unit at node k.

Δ t :

Time slot.

\(\varGamma _{km}^{vr}\) :

Installation cost of VR at node km.

\(\varGamma _{b}^{{cb}^{fx/sw}}\) :

Installation cost of FCB/SCB with capacity type b.

\(\varGamma _{v/w}^{pv/wt}\) :

Installation cost of PV modules/WT units.

\(\varGamma _{k}^{es}\) :

Installation cost of storage unit at node k.

\(\varGamma _{km,c}^{cr}\) :

Replacement cost of overloaded conductor in branch km by a conductor c.

\(\varPi _{k}^{es}\) :

Operation cost of storage unit at node k.

\(\varPi _{v/w}^{pv/wt}\) :

Operation cost of PV modules/WT units.

\(\varPi _{k}^{s}\) :

Cost of energy supply by the substation at node k.

\( \underline {\phi }^S, \overline {\phi }^S\) :

Minimum/maximum power factor at substation.

\( \underline {\varUpsilon }^D, \overline {\varUpsilon }^D\) :

Load demand uncertainty budget.

\( \underline {\varUpsilon }^{pv}, \overline {\varUpsilon }^{pv}\) :

PVG uncertainty budget.

\( \underline {\varUpsilon }^{wt}, \overline {\varUpsilon }^{wt}\) :

WTG uncertainty budget.

\( \underline {\mu }^D_t, \overline {\mu }^D_t\) :

Minimum/maximum limits for load demand factor in period t.

\( \underline {\mu }^{pv}_t, \overline {\mu }^{pv}_t\) :

Minimum/maximum limits for PVG factor in period t.

\( \underline {\mu }^{wt}_t, \overline {\mu }^{wt}_t\) :

Minimum/maximum limits for WTG factor in period t.

Continuous Variables

p km,c,t , q km,c,t :

Active/reactive power flow through replaced circuit c in branch km in period t.

\(p_{k,t}^S, q_{k,}^S\) :

Active/reactive power in substation k in period t.

\(v_{k,t}, \tilde {v}_{k,t}\) :

Non regulated/regulated voltage magnitude in bus k in period t.

\(q_{k,t}^{CB}\) :

Reactive capacitive power injected by the CB in bus k in period t.

\(p_{k,t}^{ESS}\) :

Active power injected or absorbed to/from the system by storage unit k in period t.

pd k,t , pc k,t :

Charging/discharging active power of storage unit k in period t.

es k,t :

Stored energy in storage unit k in period t.

\(p_{k,t}^{PV}, p_{k,t}^{PV}\) :

PV/WT active power generation at node k in period t.

\(\mu _t^D, \mu _t^{pv}, \mu _t^{wt}\) :

Uncertainty parameters for load demand, PV and WT power output in period t.

Binary and Integer Variables

\(\alpha _{km}^{vr}\) :

Binary variable, \(\alpha _{km}^{vr} = 1\) if the VR is installed in branch km in period t, \(\alpha _{km}^{vr} = 0\) otherwise.

\(\alpha _{k,b,t}^{fx/sw}\) :

Binary variable, \(\alpha _{k,b,t}^{fx/sw} = 1\) if the FCB/SCB of capacity b is installed at node k in period t, \(\alpha _{k,b,t}^{fx/sw} = 0\) otherwise.

αc k,t :

Binary variable equal to 1 if the storage installed unit at node k is being charged in period t, and equal to 0 otherwise.

αd k,t :

Binary variable equal to 1 if the storage installed unit at node k is being discharged in period t, and equal to 0 otherwise.

\(\alpha _{k,t}^{es}\) :

Binary variable equal to 1 if storage unit is installed at node k in period t, and equal to 0 otherwise.

\(\alpha _{k,v,t}^{pv}\) :

Binary variable equal to 1 if PV module v is installed at node k in period t, and equal to 0 otherwise.

\(\alpha _{k,w,t}^{wt}\) :

Binary variable equal to 1 if WT unit w is installed at node k in period t, and equal to 0 otherwise.

\(\alpha _{km,c,t}^{cr}\) :

Binary variable equal to 1 if conductor in branch km is replaced by conductor c in period t, and equal to 0 otherwise.

\(n_{k,t}^{cb}\) :

Integer variable that define the CB modules installed at node k in period t.

\(n_{k}^{\max }\) :

Integer variable that define the maximum CB modules to be installed at node k.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

López, J., Rider, M.J., Contreras, J. (2020). Electric Distribution Network Planning Under Uncertainty. In: Resener, M., Rebennack, S., Pardalos, P., Haffner, S. (eds) Handbook of Optimization in Electric Power Distribution Systems. Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-36115-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36115-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36114-3

  • Online ISBN: 978-3-030-36115-0

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics