AN ADVANCED STRATEGY FOR OPTIMAL SITING AND SIZING OF DISTRIBUTED GENERATORS THROUGH GENETIC ALGORITHM AND D-STATCOM INTEGRATION FOR SUSTAINABLE POWER SYSTEM OPERATIONS
Optimal DG Siting & Sizing via GA and D-STATCOM Integration
DOI:
https://doi.org/10.55766/sujst1455Keywords:
Distributed Generators, D-STATCOM, Genetic Algorithm, Least Power Deprivation, Voltage Stability IndexAbstract
This research article suggests a unique technique for the best placement and size of distributed generators (DGs) which may in the form of solar or wind in a power system network. The objective is to identify the optimum positions and sizes of the DGs that minimize the power losses in addition to fulfill increased load demand in the system. The proposed methodology combines two methods: the Genetic Algorithm (GA) and the Least Power Deprivation (LPL) method. The LPL method is used to determine the optimum positions of the DGs, while the GA is used to determine their optimum sizes. Potentially unstable voltage spots in a power system can be identified by computing the Voltage Stability Index (VSI) of each bus. Installing a Distribution Static Compensators (D-STATCOM) at these sites reduces voltage fluctuations, improving overall system stability and dependability, which is essential for preserving operational effectiveness and averting power network cascade failures. The methodology is validated through extensive simulations on the 30-bus IEEE power network using simulink. The study provides valuable insights into the best positioning and sizing of DGs and D-STATCOMs in power systems, which can help power system operators and planners in enhancing the overall system performance and reliability including reduction in carbon emissions and supplying clean energy. The proposed methodology is also compared with other intelligent techniques reported in literature to determine its versatility and applicability like harmony search algorithm, fire fly algorithm, coyote optimizers and particle swarm optimization; is generic and can be applied to other power systems as well for optimum location and sizing of DGs.
References
Almabsout, E.A., El-Sehiemy, R.A., An, O.N.U., and Bayat, O. (2020). A hybrid local search-genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems. IEEE Access, 8:54465-54481. https://doi.org/10.1109/ACCESS.2020.2981406
Amanifar, O. (2011). Optimal distributed generation placement and sizing for loss and THD reduction and voltage profile improvement in distribution systems using Particle Swarm Optimization and sensitivity analysis. 16th Electrical Power Distribution Conference, Bandar Abbas, Iran, 1-7.
Ameli, A., Bahrami, S., Khazaeli, F., and Haghifam, M.-R. (2014). A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner’s and distribution company’s viewpoints. IEEE Transactions on Power Delivery, 29(4):1831-1840. https://doi.org/10.1109/TPWRD.2014.2300845
Anuradha, K.B.J., Jayatunga, U., and Perera, H.Y.R. (2019). Voltage-loss sensitivity based approach for optimal DG placement in distribution networks. 2019 14th Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, p. 553-558. https://doi.org/10.1109/ICIIS47346.2019.9063298
Arya, S.R., Singh, B., Niwas, R., Chandra, A., and Al-Haddad, K. (2016). Power quality enhancement using DSTATCOM in distributed power generation system. IEEE Transactions on Industry Applications, 52(6):5203-5212.https://doi.org/10.1109/TIA.2016.2600644
Bagheri Tolabi, H., Ali, M.H., and Rizwan, M. (2015). Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzy-ACO approach. IEEE Transactions on Sustainable Energy, 6(1):210-218. https://doi.org/10.1109/TSTE.2014.2364230
Bajaj, M., Singh, A.K., Alowaidi, M., Sharma, N.K., Sharma, S.K., and Mishra, S. (2020). Power quality assessment of distorted distribution networks incorporating renewable distributed generation systems based on the analytic hierarchy process. IEEE Access, 8:145713-145737. https://doi.org/10.1109/ACCESS.2020.3014288
Ćetković, D., and Komen, V. (2023). Optimal distributed generation and capacitor bank allocation and sizing at two voltage levels. IEEE Systems Journal, 17(4):5831-5841. https://doi.org/10.1109/JSYST.2023.3280673
Devineni, G.K., Ganesh, A., Naga Malleswara Rao, D.S., and Saravanan, S. (2021). Optimal sizing and placement of DGs to reduce the fuel cost and T&D losses by using GA & PSO optimization algorithms. 2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, p. 1-6. https://doi.org/10.1109/SeFet48154.2021.9375701
Elattar, E.E., and Elsayed, S.K. (2020). Optimal location and sizing of distributed generators based on renewable energy sources using modified moth flame optimization technique. IEEE Access, 8:109625-109638. https://doi.org/10.1109/ACCESS.2020.3001758
El-Ela, A.A.A., El-Sehiemy, R.A., and Abbas, A.S. (2018). Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm. IEEE Systems Journal, 12(4):3629-3636. https://doi.org/10.1109/JSYST.2018.2796847
El-Zonkoly, A.M. (2011). Optimal placement of multi-distributed generation units including different load models using particle swarm optimization. Swarm and Evolutionary Computation, 1(1):50-59. https://doi.org/10.1016/j.swevo.2011.02.003
Ettehadi, M., Ghasemi, H., and Vaez-Zadeh, S. (2013). Voltage stability-based DG placement in distribution networks. IEEE Transactions on Power Delivery, 28(1):171-178. https://doi.org/10.1109/TPWRD.2012.2214241
Gupta, Y., Doolla, S., Chatterjee, K., and Pal, B.C. (2021). Optimal DG allocation and volt-var dispatch for a droop-based microgrid. IEEE Transactions on Smart Grid, 12(1):169-181. https://doi.org/10.1109/TSG.2020.3017952
Hussain, S.M.S., and Subbaramiah, M. (2013). An analytical approach for optimal location of DSTATCOM in radial distribution system. 2013 International Conference on Energy Efficient Technologies for Sustainability, Nagercoil, India, p. 1365-1369. https://doi.org/10.1109/ICEETS.2013.6533586
Kamel, S., Amin, A., Selim, A., and Ahmed, M. H. (2019). Application of coyote optimizer for optimal DG placement in radial distribution systems. 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 1-6. https://doi.org/10.1109/ICCCEEE46830.2019.9070817
Kaur, M., and Ghosh, S. (2016). Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm. Applied Soft Computing, 49:868-886. https://doi.org/10.1016/j.asoc.2016.09.019
Lee, S.H., and Park, J.-W. (2013). Optimal placement and sizing of multiple DGs in a practical distribution system by considering power loss. IEEE Transactions on Industry Applications, 49(5):2262-2270. https://doi.org/10.1109/TIA.2013.2260117
Mahdavi, M., Jurado, F., Schmitt, K., and Verdú Ramos, R.A. (2023). Consumption manner effect on renewable DG allocation in reconfigurable distribution systems. 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT), 1-6. https://doi.org/10.1109/GlobConHT56829.2023.10087685
Moayedi, H., and Mosavi, A. (2021). Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings. Sustainability, 13(6):3198. https://doi.org/10.3390/su13063198
Mota, A., Mota, L., and Galiana, F. (2011). An analytical approach to the economical assessment of wind distributed generators penetration in electric power systems with centralized thermal generation. IEEE Latin America Transactions, 9(5):726-731. https://doi.org/10.1109/TLA.2011.6030982
Prashant, Sarwar, M., Siddiqui, A.S., Ghoneim, S.S.M., Mahmoud, K., and Darwish, M.M.F. (2022). Effective transmission congestion management via optimal DG capacity using hybrid swarm optimization for contemporary power system operations. IEEE Access (SCIE), 10:71091-71106. https://doi.org/10.1109/ACCESS.2022.3187723
Prashant, Siddiqui, A.S., and Saxena, A. (2021). Optimal intelligent strategic LMP solution and effect of DG in deregulated system for congestion management. International Transactions on Electrical Energy Systems (SCIE), 31(11):e13040. https://doi.org/10.1002/2050-7038.13040
Purlu, M., and Turkay, B.E. (2022). Optimal allocation of renewable distributed generations using heuristic methods to minimize annual energy losses and voltage deviation index. IEEE Access, 10:21455-21474. https://doi.org/10.1109/ACCESS.2022.3153042
Pushkarna, M., Ashfaq, H., Singh, R., and Kumar, R. (2022a). A new analytical method for optimal sizing and sitting of Type-IV DG in an unbalanced distribution system considering power loss minimization. Journal of Electrical Engineering & Technology, 17(5):2579-2590. https://doi.org/10.1007/s42835-022-01064-9
Pushkarna, M., Ashfaq, H., Singh, R., and Kumar, R. (2022b). An optimal placement and sizing of type-IV DG with reactive power support using UPQC in an unbalanced distribution system using particle swarm optimization. Energy Systems, 15(2):353-370. https://doi.org/10.1007/s12667-022-00551-2
Raza, A., Zahid, M., Chen, J., Qaisar, S.M., Ilahi, T., Waqar, A., and Alzahrani, A. (2023). Dynamic gesture recognition based on three-stream coordinate attention network and knowledge distillation. IEEE Access, 11:123610-123624. https://doi.org/10.1109/ACCESS.2023.3329704
Shivarudraswamy, R., Gaonkar, D.N., and Jayalakshmi, N.S. (2016). GA based optimal location and size of the distributed generators in distribution system for different load conditions. 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, p. 1-4. https://doi.org/10.1109/ICPEICES.2016.7853256
Siddiqui, A.S., and Prashant. (2022). Optimal location and sizing of conglomerate DG-FACTS using an artificial neural network and heuristic probability distribution methodology for modern power system operation. Protection and Control of Modern Power Systems, 7(9):1-25. https://doi.org/10.1186/s41601-022-00230-5
Singh, D., Singh, D., and Verma, K.S. (2009). Multiobjective optimization for DG planning with load models. IEEE Transactions on Power Systems, 24(1):427-436. https://doi.org/10.1109/TPWRS.2008.2009483
Sowmya, M., Sheela, A., and Shankar, V.G. (2017). Optimal placement and sizing of renewable energy generation considering uncertainties using intelligent water drops algorithm. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, p. 1-4. https://doi.org/10.1109/ICIIECS.2017.8276018
Uniyal, A., and Kumar, A. (2016). Comparison of optimal DG placement using CSA, GSA, PSO, and GA for minimum real power loss in radial distribution system. 2016 IEEE 6th International Conference on Power Systems (ICPS), New Delhi, India, p. 1-6. https://doi.org/10.1109/ICPES.2016.7584027
Wang, Z., Chen, B., Wang, J., and Begovic, M.M. (2015). Stochastic DG placement for conservation voltage reduction based on multiple replications procedure. IEEE Transactions on Power Delivery, 30(3):1039-1047. https://doi.org/10.1109/TPWRD.2014.2331275
Yammani, C., Sydulu, M., and Matam, S.K. (2015). Optimal placement and sizing of DGs at various load conditions using shuffled bat algorithm. 2015 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA, p. 1-5. https://doi.org/10.1109/PECI.2015.7064926
Yang, H., Zhang, J., Qiu, J., Zhang, S., Lai, M., and Dong, Z.Y. (2018). A practical pricing approach to smart grid demand response based on load classification. IEEE Transactions on Smart Grid, 9(1):179-190. https://doi.org/10.1109/TSG.2016.2547883
Yuvaraj, T., Devabalaji, K., and Ravi, K. (2015). Optimal placement and sizing of DSTATCOM using harmony search algorithm. Energy Procedia, 79:759-765. https://doi.org/10.1016/j.egypro.2015.11.563
Zeinalzadeh, A., Estebsari, A., and Bahmanyar, A. (2019). Simultaneous optimal placement and sizing of DSTATCOM and parallel capacitors in distribution networks using multi-objective PSO. 2019 IEEE Milan PowerTech, Milan, Italy, p. 1-6. https://doi.org/10.1109/PTC.2019.8810577
