Optimal Allocation of Different Types of Distributed Generators in Distribution System
Optimal Allocation of Different Types of Distributed Generators in Distribution System
In this paper, an effective methodology is proposed for the optimal allocation of conventional(Gas turbines) and renewable based distributed generators (solar, wind) in the distribution system(DS) are presented. The objectives are to minimize real, reactive power losses and emissionproduced by the sources. Initially, the best locations for placement of DGs are identified byvoltage stability factor (VSF) concept. The number and size of solar, wind based DGs and gasturbines corresponding to these locations are determined by applying search-based dragonflyalgorithm (DFA). The generation uncertainties associated with wind and solar based DGs iseffectively modeled by Weibull and beta probability distribution functions (PDF) to determinethe exact output power. Two different scenarios, i.e. optimal allocation and the combination ofdifferent types DERs in the distribution system is considered in this analysis. The developedmethod is tested on IEEE 33 and 69 bus distribution systems. The results show its effectivenessin terms of solving respective objective function.
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- Outlook, A. E.. US Energy Information Administration. US Department of Energy, United States
Government Printing Office: Washington, DC. (2013).
- Ackermann, T., Andersson, G.,and Söder, L., “Distributed generation: a definition”, Electric power
systems research, 57(3): 195-204, (2001).
- Hung, D. Q., Mithulananthan, N.,and Bansal, R. C., “ An optimal investment planning framework for
multiple distributed generation units in industrial distribution systems”, Applied Energy, 124: 62-72,
(2014).
- Wang, C., and Nehrir, M. H., “Analytical approaches for optimal placement of distributed generation
sources in power systems”, IEEE Transactions on Power systems, 19(4): 2068-2076, (2004).
- Acharya, N., Mahat, P., and Mithulananthan, N., “An analytical approach for DG allocation in primary
distribution network”, International Journal of Electrical Power & Energy Systems, 28(10): 669-678,
(2006).
- Hung, D. Q., Mithulananthan, N., and Bansal, R. C., “Analytical expressions for DG allocation in
primary distribution networks”, IEEE Transactions on energy conversion, 25(3): 814-820, (2010).
- Hung, D. Q., and Mithulananthan, N., “Multiple distributed generator placement in primary
distribution networks for loss reduction”, IEEE Transactions on industrial electronics, 60(4): 1700-
1708, (2013).
- Al Abri, R. S., El-Saadany, E. F., and Atwa, Y. M., “Optimal placement and sizing method to improve
the voltage stability margin in a distribution system using distributed generation”, IEEE transactions
on power systems, 28(1): 326-334, (2013).
- Atwa, Y. M., El-Saadany, E. F., Salama, M. M. A., and Seethapathy, R., “Optimal renewable resources
mix for distribution system energy loss minimization”, IEEE Transactions on Power Systems, 25(1):
360-370, (2010).
- Khatod, D. K., Pant, V., and Sharma, J., “Evolutionary programming based optimal placement of
renewable distributed generators”, IEEE Transactions on Power systems, 28(2): 683-695, (2013).
- Kayal, P., & Chanda, C. K., “Placement of wind and solar based DGs in distribution system for power
loss minimization and voltage stability improvement”, International Journal of Electrical Power &
Energy Systems, 53: 795-809, (2013).
- Safaei, A., Vahidi, B., Askarian-Abyaneh, H., Azad-Farsani, E., and Ahadi, S. M., “A two-step
optimization algorithm for wind turbine generator placement considering maximum allowable
capacity”, Renewable Energy, 92: 75-82, (2016).
- Sultana, S., and Roy, P. K., “Oppositional krill herd algorithm for optimal location of distributed
generator in radial distribution system”, International Journal of Electrical Power & Energy Systems,
73: 182-191, (2015).
- Kansal, S., Kumar, V., and Tyagi, B., “Optimal placement of different type of DG sources in
distribution networks”, International Journal of Electrical Power & Energy Systems, 53: 752-760,
(2013).
- El-Fergany, A., “Optimal allocation of multi-type distributed generators using backtracking search
optimization algorithm”, International Journal of Electrical Power & Energy Systems, 64: 1197-1205,
(2015).
- Injeti, S. K., and Kumar, N. P., “A novel approach to identify optimal access point and capacity of
multiple DGs in a small, medium and large-scale radial distribution systems”, International Journal of
Electrical Power & Energy Systems, 45(1):142-151, (2013).
- Kowsalya, M., “Optimal size and siting of multiple distributed generators in distribution system using
bacterial foraging optimization”, Swarm and Evolutionary computation, 15: 58-65, (2014).
- Yammani, C., Maheswarapu, S., and Matam, S. K., “A Multi-objective Shuffled Bat algorithm for
optimal placement and sizing of multi distributed generations with different load models”,
International Journal of Electrical Power & Energy Systems, 79: 120-131, (2016).
- Kansal, S., Kumar, V., and Tyagi, B., “Hybrid approach for optimal placement of multiple DGs of
multiple types in distribution networks”, International Journal of Electrical Power & Energy Systems,
75: 226-235, (2016).
- Vatani, M., Alkaran, D. S., Sanjari, M. J., and Gharehpetian, G. B., “Multiple distributed generation
units allocation in distribution network for loss reduction based on a combination of analytical and
genetic algorithm methods”, IET Generation, Transmission & Distribution, 10(1): 66-72, (2016).
- Vinothkumar, K., and Selvan, M. P., “Fuzzy embedded genetic algorithm method for distributed
generation planning”, Electric Power Components and Systems, 39(4): 346-366, (2011).
- Mohammadi, M., and Nafar, M., “Optimal placement of multitypes DG as independent private sector
under pool/hybrid power market using GA-based Tabu Search method”, International Journal of
Electrical Power & Energy Systems, 51: 43-53, (2013).
- Tan, W. S., Hassan, M. Y., Majid, M. S., and Rahman, H. A., “Optimal distributed renewable
generation planning: A review of different approaches”, Renewable and Sustainable Energy Reviews,
18: 626-645, (2013).
- Salameh, Z. M., Borowy, B. S., and Amin, A. R., “Photovoltaic module-site matching based on the
capacity factors”, IEEE transactions on Energy conversion, 10(2): 326-332, (1995).
- Hung, D. Q., Mithulananthan, N., and Lee, K. Y., Determining PV penetration for distribution systems
with time-varying load models”, IEEE Transactions on Power Systems, 29(6): 3048-3057, (2014).
- Kayal, P., & Chanda, C. K.. “Optimal mix of solar and wind distributed generations considering
performance improvement of electrical distribution network” Renewable energy, 75: 173-186, (2015).
- Teng, J. H., Luan, S. W., Lee, D. J., and Huang, Y. Q., “Optimal charging/discharging scheduling of
battery storage systems for distribution systems interconnected with sizeable PV generation systems”,
IEEE Transactions on Power Systems, 28(2):1425-1433, (2013).
- El-Zonkoly, A. M.. “Optimal placement of multi-distributed generation units including different load
models using particle swarm optimization”, IET generation, transmission & distribution, 5(7): 760-
771, (2011).
- Kefayat, M., Ara, A. L., and Niaki, S. N., “A hybrid of ant colony optimization and artificial bee
colony algorithm for probabilistic optimal placement and sizing of distributed energy resources”,
Energy Conversion and Management, 92: 149-161, (2015).
- Jain, N., Singh, S. N., AND Srivastava, S. C., “PSO based placement of multiple wind DGs and
capacitors utilizing probabilistic load flow model”, Swarm and Evolutionary Computation, 19: 15-24,
(2014).
- Kayal, P.,AND Chanda, C. K., “Placement of wind and solar based DGs in distribution system for
power loss minimization and voltage stability improvement”, International Journal of Electrical Power
& Energy Systems, 53: 795-809, (2013).
- Mirjalili, S.. “Dragonfly algorithm: a new meta-heuristic optimization technique for solving singleobjective,
discrete, and multi-objective problems”, Neural Computing and Applications, 27(4): 1053-
1073, (2016).
- Teng, J. H.. “A direct approach for distribution system load flow solutions”, IEEE Transactions on
power delivery, 18(3): 882-887, (2003).
- Sahoo, N. C.,ND Prasad, K.. “A fuzzy genetic approach for network reconfiguration to enhance
voltage stability in radial distribution systems”, Energy conversion and management, 47(18-19): 3288-
3306, (2006)