Distribution system loss minimization with network reconfiguration and cost-benefit price based demand reduction modeling
Abstract
The developments in distribution automation technologies and optimization algorithms have made realization of highly reconfigurable and flexible distribution system viable. This paper proposes Grey Wolf Optimization algorithm (GWO) to generate best reconfigured topology of the system when grid is under stress condition. The solution is used in Demand Response Program based on cost-benefit price models. The reconfiguration scheme integrated with presented game theoretic demand response program is intended at getting peak load reductions to mitigate the distribution network power losses. Simultaneously this research is aimed at determining the optimal utility and customer profit and load shedding customers have to do to facilitate power grid during on-peak hours. The proposed technique is tested on IEEE 69 bus test system with residential customers. The results show 56.46% reduction in power losses and economic benefits a utility or a consumer can get with the proposed model.
References
REFERENCES
(2015). Projected Costs of Generating Electricity. Electricity generating costs. France, Organisation for Economic Co-operation and Development/International Energy Agency 9, rue de la Fédération, 75739 Paris Cedex 15, France: 1-11.
Ababei, C. and K. R. (2011). "Efficient network reconfiguration using minimum cost maximum flow-based branch exchanges and random walks-based loss estimations." Power Syst IEEE Trans: 30-37.
Abdelaziz A Y, Mohamed F M, Mekhamer S F and B. M. A. L. (2010). "Distribution system reconfiguration using a modified Tabu Search algorithm." Electric Power Systems Research 80(8): 943-953.
Arif A, Javed F and A. N (2014). "Integrating renewables economic dispatch with demand side management in micro-grids: a genetic algorithm-based approach." Energ Effi (7): 271-284.
Baran M. E and Wu F. F (1989). "Network reconfiguration in distribution systems for loss reduction and load balancing." Power Delivery, IEEE Transactions on 4(2): 1401-1407.
Cebrian, J. C. and N. Kagan (2010). "Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms." Electric Power Systems Research 80(1): 53-62.
Civanlar, S., J. J. Grainger, H. Yin and S. S. H. Lee (1988). "Distribution feeder reconfiguration for loss reduction." Power Delivery, IEEE Transactions on 3(3): 1217-1223.
Coroama, I., G. Chicco, M. Gavrilas and A. Russo (2013). "Distribution system optimisation with intra-day network reconfiguration and demand reduction procurement." Electric Power Systems Research 98(0): 29-38.
Duan, D. L., X. D. Ling, X. Y. Wu and B. Zhong (2015). "Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm." International Journal of Electrical Power & Energy Systems 64(0): 88-95.
Esmaeilian, H. R. F., R.Attari, S. M. (2013). Distribution network reconfiguration to reduce losses and enhance reliability using binary gravitational search algorithm. Electricity Distribution (CIRED 2013), 22nd International conference and Exhibition on.
F. V. Gomes, S. C., Jr, J. L. R. Pereira, M. P. Vinagre, P. A. N. and a. L. A. R. Garcia (2005). "A new heuristic reconfiguration algorithm for large distribution systems." IEEE Trans. Power Syst 20(3): 1373-1378.
Fahrioglu, M. and F. L. Alvarado (1999). Designing cost effective demand management contracts using game theory. Power Engineering Society 1999 Winter Meeting, IEEE.
Fahrioglu, M. and F. L. Alvarado (2000). "Designing incentive compatible contracts for effective demand management." Power Systems, IEEE Transactions on 15(4): 1255-1260.
Fahrioglu, M. and F. L. Alvarado (2002). Using utility information to calibrate customer demand management behavior models. Power Engineering Society Winter Meeting, 2002. IEEE.
Faria, P., Z. A. Vale, J. Soares and J. Ferreira (2011). Particle swarm optimization applied to integrated demand response resources scheduling. Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on.
H.A. Aalami, M.P. Moghaddam and G. R. Yousefi (2010). "Modeling and prioritizing demand response programs in power markets." Electric Power Systems Research 426–435.
Hamed, A. and M. J. R (2015). "Minimum-loss network reconfiguration: A minimum spanning tree problem." Sustainable Energy, Grids and Networks 1(0): 1-9.
J.-H. Kim and A. Shcherbakova (2011). "Common failures of demand response." Energy (36): 873–880.
J. Torriti, M.G. Hassan and M. Leach (2010). "Demand response experience in Europe: policies, programmes and implementation." Energy: 1575–1583.
Li, Chen, Yu K and S. L. H (2008). "A hybrid particle swarm optimization approach for distribution network reconfiguration problem." In: Power and energy society general meeting-conversion and delivery of electrical energy in the 21st century. 2008 IEEE: p. 1–7.
Liu B S, Xie K G and Z. JQ (2005). "Electrical distribution networks reconfiguration using dynamic programming." Proc. Chin Soc Electr Eng 25-29.
Logenthiran T, Srinivasan D and S. TZ (2012). "Demand side management in smart grid using heuristic optimization." IEEE Trans Smart Grid 1244-1252.
López, M. A., S. de la Torre, S. Martín and J. A. Aguado (2015). "Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support." International Journal of Electrical Power & Energy Systems 64(0): 689-698.
M.H. Albadi and E. F. El-Saadany (2008). "A summary of demand response in electricity markets." Electric Power Systems Research: 1989–1996.
Mazidi M, Zakariazadeh A, Jadid S and S. P. (2014). "Integrated scheduling of renewable generation and demand response programs in a microgrid." Energy Convers Manage (86): 1118-1127.
Mendoza, J., R. Lopez, D. Morales, E. Lopez, P. Dessante and R. Moraga (2006). "Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application." Power Systems, IEEE Transactions on 21(2): 948-954.
Mirjalili., S., S. M. Mirjalili. and A.Lewis (2014). "Grey Wolf Optimizer." Advances in Engineering Software 6: 46-61.
Mohamed Imran, A. and M. Kowsalya (2014). "A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm." International Journal of Electrical Power & Energy Systems 62(0): 312-322.
Moradzadeh, B. (2013). Optimal Distribution Reconfiguration, University of Tennessee, Knoxville.
N. Zareen, M.W. Mustafa, Saleh Y. I. AbuJarad and M. Moradi (2015). "Optimal Strategies Modeling in Electricity Market for Electric Vehicles Integration in Presence of Intermittent Resources." Arab J Sci Eng: 1607–1621.
Nguyen Thuan Thanh and T. A. Viet (2015). "Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm." International Journal of Electrical Power & Energy Systems 68: 233-242.
Palensky P and D. D (2011). "Demand side management: demand response, intelligent energy systems, and smart loads." IEEE Trans Ind Inf 381–388.
R. Dashti and S. Afsharnia (2011). "Demand response regulation modeling based on distribution system asset efficiency." Electric Power Systems Research: 667–676.
Reza Baghipour and S. M. Hosseini (2014). "A Hybrid Algorithm for Optimal Location and Sizing of Capacitors in the presence of Different Load Models in Distribution Network." International Journal of Mechatronics, Electrical and Computer Technology 4: 1084-1111.
Sarma N and P. R. K. (1995). "A new 0–1 integer programming method of feeder reconfiguration for loss minimization in distribution systems." Electr Power Syst Res 125–131.
Shareef, H., A. A. Ibrahim, N. Salman, A. Mohamed and W. Ling Ai (2014). "Power quality and reliability enhancement in distribution systems via optimum network reconfiguration by using quantum firefly algorithm." International Journal of Electrical Power & Energy Systems 58(0): 160-169.
Shengnan, S., M. Pipattanasomporn and S. Rahman (2011). An approach for demand response to alleviate power system stress conditions. Power and Energy Society General Meeting, 2011 IEEE.
Shirmohammadi and H. H. W (1989). "Reconfiguration of electric distribution networks for resistive line losses reduction." Power Delivery, IEEE Transactions on 4(2): 1492-1498.
Sudha Rani, D., N. Subrahmanyam and M. Sydulu (2014). Self Adaptive Harmony Search algorithm for Optimal Network Reconfiguration. Power and Energy Conference at Illinois (PECI), 2014.
Swarnkar, A., N. Gupta and K. R. Niazi (2011). "Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization." Swarm and Evolutionary Computation 1(3): 129-137.
Wagner T, C. A. and H. R. (1991). "Feeder reconfiguration for loss reduction: an application of distribution automation." Power Delivery IEEE Trans 6: 1922–1933.
- Distribution system loss minimization with network reconfiguration and cost-benefit price based demand reduction modeling
- Distribution system loss minimization with network reconfiguration and cost-benefit price based demand reduction modeling
- Distribution system loss minimization with network reconfiguration and cost-benefit price based demand reduction modeling
- Distribution system loss minimization with network reconfiguration and cost-benefit price based demand reduction modeling
- ithentication report