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.
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References
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)
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)
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)
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)
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)
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)
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)
Ben-Tal, A., Nemirovski, A.: Robust convex optimization. Math. Oper. Res. 23(4), 769–805 (1998)
Ben-Tal, A., Nemirovski, A.: Robust solutions of uncertain linear programs. Oper. Res. Lett. 25(1), 1–13 (1999)
Ben-Tal, A., Nemirovski, A.: Robust solutions of linear programming problems contaminated with uncertain data. Math. Program. 88(3), 411–424 (2000)
Ben-Tal, A., Nemirovski, A.: Robust optimization - methodology and applications. Math. Program. 92(3), 453–480 (2002)
Ben-Tal, A., Goryashko, A., Guslitzer, E., Nemirovski, A.: Adjustable robust solutions of uncertain linear programs. Math. Program. 99(2), 351–376 (2004)
Bertsimas, D., Sim, M.: Robust discrete optimization and network flows. Math. Program. 98(1–3), 49–71 (2003)
Bertsimas, D., Sim, M.: The price of robustness. Oper. Res. 52(1), 35–53 (2004)
Bertsimas, D., Brown, D.B., Caramanis, C.: Theory and applications of robust optimization. SIAM Rev. 53(3), 464–501 (2011)
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)
Chiradeja, P., Ramakumar, R.: An approach to quantify the technical benefits of distributed generation. IEEE Trans. Energy Convers. 19(4), 764–773 (2004)
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
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)
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
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
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)
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)
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)
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)
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)
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)
Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language for Mathematical Programming. Boston, Duxbury Press (2002)
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)
Frank, S., Rebennack, S.: An introduction to optimal power flow: theory, formulation, and examples. IIE Trans. 48(12), 1172–1197 (2016)
Ghaoui, L.E., Oustry, F., Lebret, H.: Robust solutions to uncertain semidefinite programs. SIAM J. Optim. 9(1), 33–52 (1998)
IBM: IBM ILOG CPLEX V12.1 Users Manual for CPLEX (2009)
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)
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)
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)
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
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
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)
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)
Montoya-Bueno, S., Muñoz-Hernández, J., Contreras, J.: Uncertainty management of renewable distributed generation. J. Cleaner Prod. 138, 103–118 (2016)
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)
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)
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)
Available: https://ewh.ieee.org/soc/pes/dsacom/testfeeders/. Accessed Dec 3, 2018
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)
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)
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)
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)
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)
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)
Thiele, A., Terry, T., Epelman, M.: Robust linear optimization with recourse. Rapport technique, pp. 4–37 (2009)
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)
Wang, R., Wang, P., Xiao, G.: A robust optimization approach for energy generation scheduling in microgrids. Energy Convers. Manag. 106, 597–607 (2015)
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)
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)
Yanıkoǧlu, I., Gorissen, B.L., Hertog, D.D.: A survey of adjustable robust optimization. Eur. J. Oper. Res. 27, 799–813 (2019)
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
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)
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)
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)
Acknowledgements
J. López would like to thank DIUC - University of Cuenca for the economic support in the development of this work.
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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.
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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
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