Abstract
Recently, the integration of Renewable Energies Sources (RES) into Smart Distribution System (SDS) has become one of the best solutions to ensure balance between production and consumption of electric energy. This is attributed to the numerous advantages of this integration such as minimizing power losses, improving the voltage profile as well as minimization of CO2 emissions. In this paper, a new algorithm known as Slime Mould Algorithm (SMA) is used to solve the problem of integrating Distributed Generators (DGs) into the SDS based on photovoltaic solar sources and wind turbine generators. This is performed while considering the uncertainty of energy delivered by the DG as well as the variation of load demand in 24 h. The proposed SMA algorithm is applied to obtain the optimal location and size of DGs units, where a unit or two are considered. The objective is to optimize Active Power Loss (PLoss), Voltage Stability Index (VSI), Short Circuit Level (SCL), and Annual Losses Cost (ALC). The efficiency of the algorithm is validated using a test case which is IEEE 33-bus SDS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmadi, M., Lotfy, M.E., Shigenobu, R., Yona, A., Senjyu, T.: Optimal sizing and placement of rooftop solar photovoltaic at Kabul city real distribution network. IET Gener. Transm. Distrib. 12(2), 303–309 (2018)
Poornazaryan, B., Karimyan, P., Gharehpetian, G.B., Abedi, M.: Optimal allocation and sizing of DG units considering voltage stability, losses and load variations. Int. J. Electr. Power Energy Syst. 79, 42–52 (2016)
Pecas Lopes, A., Hatziargyriou, N., Mutale, J., Djapic, P., Jenkins, N.: Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr. Power Syst. Res. 77(9), 1189–1203 (2007)
Ganguly, S., Samajpati, D.: Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm. IEEE Trans. Sustain. Energy 6(3), 688–697 (2015)
Ehsanab, A., Yanga, Q.: State-of-the-art techniques for modelling of uncertainties in active distribution network planning: a review. Appl. Energy 239, 1509–1523 (2019)
Notton, G., Nivet, M.L., Voyant, C., Paoli, C., Darras, C., Motte, F., Fouilloy, A.: Intermittent and stochastic character of renewable energy sources: consequences, cost of intermittence and benefit of forecasting. Renew. Sustain. Energy Rev. 87, 96–105 (2018)
Ghanegaonkar, S.P., Pande, V.N.: Optimal hourly scheduling of distributed generation and capacitors for minimisation of energy loss and reduction in capacitors switching operations. IET Gener. Transm. Distrib. 11(9), 2244–2250 (2017)
Gholami, K., Dehnavi, E.: A modified particle swarm optimization algorithm for scheduling renewable generation in a micro-grid under load uncertainty. Appl. Soft Comput. 78, 496–514 (2019)
Barik, S., Das, D.: Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed-discrete particle swarm-based point estimation method. IET Renew. Power Gener. 13(9), 1431–1445 (2019)
Abou El-Ela, A.A., El-Sehiemy, R.A., Ali, E.S., Kinawy, A.M.: Minimisation of voltage fluctuation resulted from renewable energy sources uncertainty in distribution systems. IET Gener. Transm. Distrib. 13(12), 2339–2351 (2019)
Souza, S.S.F., Romero, R., Pereira, J., Saraiva, J.T.: Artificial immune algorithm applied to distribution system reconfiguration with variable demand. Int. J. Electr. Power Energy Syst. 82, 561–568 (2016)
Yong, C., Kong, X., Chen, Y., Xu, Q., Yu, L.: An optimization method of active distribution network considering uncertainties of renewable DGs. Energy Procedia 158, 934–939 (2019)
Murty, V.S.N., Kumar, A.: Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimization. IET Smart Grid 2(2), 192–202 (2019)
Elsakaan, A.A., El-Sehiemy, R.A., Kaddah, S.S., Elsaid, M.I.: Optimal economic-emission power scheduling of RERs in MGs with uncertainty. IET Gener. Transm. Distrib. 14(1), 37–52 (2020)
Selim, A., Kamel, S., Jurado, F.: Efficient optimization technique for multiple DG allocation in distribution networks. Appl. Soft Comput. 86, 1–20 (2020)
Li, S., Chen, H., Wang, M., Heidari, A.A., Mirjalili, S.: Slime mould algorithm: a new method for stochastic optimization. Future Gener. Comput. Syst. 111, 300–323 (2020)
Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., Seethapathy, R.: Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)
Soroudi, A., Aien, M., Ehsan, M.: A probabilistic modelling of photo voltaic modules and wind power generation impact on distribution networks. IEEE Syst. J. 6(2), 254–259 (2012)
Haque, M.H.: Evaluation of power flow solutions with fixed speed wind turbine generating systems. Energy Convers. Manag. 79, 511–518 (2014)
Lokman, M.H., Musirin, I., Suliman, S.I., Suyono, H., Hasanah, R.N., Mustafa, S.A.S., Zellagui, M.: Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme. IAES Int. J. Artif. Intell. 8(3), 292–298 (2019)
Settoul, S., Chenni, R., Hassan, H.A., Zellagui, M., Kraimia, M.N.: MFO algorithm for optimal location and sizing of multiple photovoltaic distributed generations units for loss reduction in distribution systems. In: Proceedings of the 7th International Renewable and Sustainable Energy Conference (IRSEC), Agadir, Morocco, 27–30 November 2019 (2019)
Parizad, A., Baghaee, H.R., Yazdani, A., Gharehpetian, G.B.: Optimal distribution systems reconfiguration for short circuit level reduction using PSO algorithm. In: Proceedings of the IEEE Power and Energy Conference at Illinois (PECI), Illinois, USA, 22–23 February 2018 (2018)
Zellagui, M., Hassan, H.A.: Modeling the effects of PWMSC and fault resistance on ground fault system in MV distribution line. WSEAS Trans. Syst. Control 12, 114–122 (2017)
Hassan, H.A., Zellagui, M.: MVO algorithm for optimal simultaneous integration of DG and DSTATCOM in standard radial distribution systems based on technical-economic indices. In: Proceedings of the 21st IEEE International Middle East Power Systems Conference (MEPCON), Tanta, Egypt, 17–19 December 2019 (2019)
Lasmari, A., Zellagui, M., Chenni, R., Semaoui, S., El-Bayeh, C.Z., Hassan, H.A.: Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units. Energetika 66(1), 1–14 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zellagui, M., Settoul, S., Lasmari, A., El-Bayeh, C.Z., Chenni, R., Hassan, H.A. (2021). Optimal Allocation of Renewable Energy Source Integrated-Smart Distribution Systems Based on Technical-Economic Analysis Considering Load Demand and DG Uncertainties. In: Hatti, M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-63846-7_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63845-0
Online ISBN: 978-3-030-63846-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)