Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches
Source abstract
Abstract
In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Optimal Power Flow Distribution · Smart Grid Energy Management · Microgrid Control and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran
L@bISEN, ISEN Yncréa Ouest, Brest Campus, 20, Rue Cuirassé Bretagne, 29200 Brest, France
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
References (45)
Show source-provided references
1. Gil-González, W., Garces, A., Montoya, O.D., and Hernández, J.C. (2021). A Mixed-Integer Convex Model for the Optimal Placement and Sizing of Distributed Generators in Power Distribution Networks. Appl. Sci., 11.10.3390/app11020627 ↗
2. Two-Stage Optimization Method for Energy Loss Minimization in Microgrid Based on Smart Power Management Scheme of PHEVs10.1109/TSG.2015.2480999 ↗
3. Optimal daily scheduling of reconfiguration based on mini-misation of the cost of energy losses and switching operations in microgrids10.1049/iet-gtd.2014.0612 ↗
4. Optimal Placement of Distributed Generators with Regard to Reliability Assessment using Virus Colony Search Algorithm
5. Optimizing Configuration of Cyber Network Considering Graph Theory Structure and Teaching-Learning-Based Optimization (GT-TLBO)10.1109/TII.2018.2860984 ↗
6. Towards a Unified Operational Value Index of Energy Storage in Smart Grid Environment10.1109/TSG.2012.2190114 ↗
7. Assessing the benefits and economics of bulk energy storage technologies in the power grid10.1016/j.apenergy.2014.11.017 ↗
8. Rahiminejad, A., Aranizadeh, A., and Vahidi, B. (2014). Simultaneous distributed generation and capacitor placement and sizing in radial distribution system considering reactive power market. J. Renew. Sustain. Energy, 6.10.1063/1.4893431 ↗
9. Moazzami, M., Gharehpetian, G.B., Shahinzadeh, H., and Hosseinian, S.H. (2017, January 7–9). Optimal locating and sizing of DG and D-STATCOM using Modified Shuffled Frog Leaping Algorithm. Proceedings of the 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Kerman, Iran.10.1109/CSIEC.2017.7940157 ↗
10. Karunarathne, E., Pasupuleti, J., Ekanayake, J., and Almeida, D. (2020). Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm. Energies, 13.10.3390/en13236185 ↗
11. A Selecting Method of Optimal Load on Time Varying Distribution System for Network Reconfiguration considering DG10.5370/JICEE.2012.2.2.166 ↗
12. A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm10.1016/j.energy.2013.12.037 ↗
13. A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner’s and distribution company’s viewpoints10.1109/TPWRD.2014.2300845 ↗
14. Optimal DG Placement in a Smart Distribution Grid Considering Economic As-pects10.5370/JEET.2014.9.4.1240 ↗
15. Robust Optimization Based Optimal DG Placement in Microgrids10.1109/TSG.2014.2321748 ↗
16. Alotaibi, M., Almutairi, A., and Salama, M. (2016, January 12–14). Effect of wind turbine parameters on optimal DG placement in power dis-tribution systems. Proceedings of the 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada.10.1109/EPEC.2016.7771708 ↗
17. Optimal sizing of photovoltaic distributed generators in a dis-tribution system with consideration of solar radiation and harmonic distortion10.1016/j.ijepes.2012.01.002 ↗
18. Hybrid wind-diesel stand-alone system sizing accounting for component expected life and fuel price uncertainty10.1016/j.epsr.2012.02.003 ↗
19. Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran10.1016/j.renene.2008.11.022 ↗
20. A feasibility study of a stand-alone hybrid solar-wind-battery system for a remote island10.1016/j.apenergy.2014.01.090 ↗
21. Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA10.1016/j.egyr.2020.06.013 ↗
22. Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems10.1016/j.jesit.2017.06.001 ↗
23. Alzahrani, A., Alharthi, H., and Khalid, M. (2020). Minimization of power losses through optimal battery placement in a dis-tributed network with high penetration of photovoltaics. Energies, 13.10.3390/en13010140 ↗
24. A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation10.1109/ACCESS.2020.2968462 ↗
25. Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth10.1016/j.ijepes.2018.03.038 ↗
26. Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO)10.1016/j.asej.2020.07.009 ↗
27. Optimal Sizing and Sitting of Distributed Generations in Power Distribution Networks Using Firefly Algorithm10.1007/s40866-020-00081-9 ↗
28. Optimal placement and sizing of energy storage systems to improve the reliability of hybrid power distribution network with renewable energy sources
29. Mukhopadhyay, B., and Das, D. (2020). Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system. Renew. Sustain. Energy Rev., 124.10.1016/j.rser.2020.109777 ↗
30. Shahinzadeh, H., Moradi, J., Gharehpetian, G.B., Nafisi, H., and Abedi, M. (2019, January 8–9). IoT Architecture for Smart Grids. Proceedings of the 2019 International Conference on Protection and Automation of Power System (IPAPS), Tehran, Iran.10.1109/IPAPS.2019.8641944 ↗
31. Shahinzadeh, H., Moradi, J., Gharehpetian, G.B., Nafisi, H., and Abedi, M. (2019, January 28–29). Internet of Energy (IoE) in Smart Power Systems. Proceedings of the 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), Tehran, Iran.10.1109/KBEI.2019.8735086 ↗
32. An Improved Lightning Attachment Procedure Optimizer for Optimal Reactive Power Dispatch With Uncertainty in Renewable Energy Resources10.1109/ACCESS.2020.3022846 ↗
33. Youssef, H., Kamel, S., and Ebeed, M. (2018, January 18–20). Optimal Power Flow Considering Loading Margin Stability Using Lightning Attachment Optimization Technique. Proceedings of the 2018 Twentieth International Middle East Power Systems Conference (MEPCON, Nasr City, Egypt.10.1109/MEPCON.2018.8635110 ↗
34. Ramadan, A., Ebeed, M., Kamel, S., and Nasrat, L. (2019, January 23–25). Optimal Allocation of Renewable Energy Resources Considering Uncertainty in Load Demand and Generation. Proceedings of the 2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE), Aswan, Egypt.10.1109/CPERE45374.2019.8980092 ↗
35. Ebeed, M., Alhejji, A., Kamel, S., and Jurado, F. (2020). Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems. Energies, 13.10.3390/en13174316 ↗
36. Ramadan, A., Ebeed, M., Kamel, S., and Nasrat, L. (2021). Optimal power flow for distribution systems with uncertainty. Uncertainties in Modern Power Systems, Elsevier BV.10.1016/B978-0-12-820491-7.00005-0 ↗
37. Ebeed, M., and Aleem, S.H.E.A. (2021). Overview of uncertainties in modern power systems: Uncertainty models and methods. Uncertainties in Modern Power Systems, Elsevier BV.10.1016/B978-0-12-820491-7.00001-3 ↗
38. Power load probability density forecasting using Gaussian process quantile regres-sion10.1016/j.apenergy.2017.11.035 ↗
39. Sirat, A.P., Mehdipourpicha, H., Zendehdel, N., and Mozafari, H. (2020, January 20–21). Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms. Proceedings of the 2020 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA.10.1109/PECI48348.2020.9064636 ↗
40. A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization10.1016/j.asoc.2017.06.033 ↗
41. Nematollahi, A.F., Rahiminejad, A., Vahidi, B., Askarian, H., and Safaei, A. (2018). A new evolutionary-analytical two-step optimization method for optimal wind turbine allocation considering maximum capacity. J. Renew. Sustain. Energy, 10.10.1063/1.5043403 ↗
42. A novel multi-objective optimization algorithm based on Lightning Attachment Procedure Optimization algorithm10.1016/j.asoc.2018.11.032 ↗
43. Nematollahi, A.F., Dadkhah, A., Gashteroodkhani, O.A., and Vahidi, B. (2016). Optimal sizing and siting of DGs for loss reduction using an iterative-analytical method. J. Renew. Sustain. Energy, 8.10.1063/1.4966230 ↗
44. Optimal placement of multi-distributed generation units including different load models using particle swarm optimization10.1016/j.swevo.2011.02.003 ↗
45. Optimal placement and schedule of multiple grid connected hybrid energy systems10.1016/j.ijepes.2014.03.040 ↗