182
Views
6
CrossRef citations to date
0
Altmetric
Original Articles

Comparative Analysis of Economic Viability with Distributed Energy Resources on Unit Commitment

&
Pages 1588-1607 | Received 15 Apr 2014, Accepted 12 Mar 2016, Published online: 15 Aug 2016

References

  • Yousefi, A., Iu, H.H.-C., Fernado, T., and Trinh, H., “An approach for wind power integration using demand side resources,” IEEE Trans. Sustain. Energy, Vol. 4, pp. 917–924, October 2013.
  • Yao, W., Zhao, J., Wen, F., Xue, Y., and Gerard, L., “A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles,” IEEE Trans. Power Syst., Vol. 28, pp. 2768–2778, August 2013.
  • Federal Energy Regulatory Commission (FERC), “Assessment of demand response and advanced metering,” Docket AD-06-2-000, 2006.
  • Albadi M., and El-saadany, E., “A summary of demand response in electricity markets,” Electr. Power Syst. Res., Vol. 78, pp. 1989–1996, November 2008.
  • Wang, C., Lu, Z., and Qiao, Y., “A consideration of the wind power benefits in day-ahead scheduling of wind-coal intensive power systems,” IEEE Trans. Power Syst., Vol. 28, pp. 236–245, February 2013.
  • Botterude, A., Zhou, Z., Wang, J., Sumaili, J., Keko, H., Mendes, J., Bessa, R., and Miranda, V., “Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of Illinois,” IEEE Trans. Sustain. Energy, Vol. 4, pp. 250–261, January2013.
  • Lowery, C., and O’Malley, M., “Impact of wind forecast error statistics upon unit commitment,” IEEE Trans. Sustain. Energy, Vol. 3, pp. 760–768, October 2012.
  • Ahmed, M.H., Bhattacharya, K., and Salama, M.M. A., “Stochastic unit commitment with wind generation penetration,” Electr. Power Compon. Syst., Vol. 40, pp. 1405–1422, May 2012.
  • Saber, A., and Venayagamoorthy, G., “Intelligent unit commitment with vehicle-to-grid a cost-emission optimization,” J. Power Sources, Vol. 195, pp. 898–911, February 2010.
  • Saber, A., and Venayagamoorthy, G., “Plug-in vehicles and renewable energy sources for cost and emission reduction,” IEEE Trans. Ind. Electron., Vol. 58, pp. 1229–1238, April 2011.
  • Saber, A., and Venayagamoorthy, G., “Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles,” IEEE Syst. J., Vol. 6, pp. 103–109, March 2012.
  • Talebizadeh, E., Rashidinejad, M., and Abdollahi, A., “Evaluation of plug-in electric vehicles impact on cost-base unit commitment,” J. Power Sources, Vol. 248, pp. 545–552, February 2014.
  • Sortomme, E., and El-Sharkawi, M., “Optimal scheduling of vehicle-to-grid energy and ancillary services,” IEEE Trans. Smart Grid, Vol. 3, pp. 351–359, March 2012.
  • Khodayar, M., Wu, L., and Li, Z., “Electric vehicle mobility in transmission-constrained hourly power generation scheduling,” IEEE Trans. Smart Grid, Vol. 4, pp. 779–788, June 2013.
  • Liu, C., Wang, J., Botterude, A., Zhou, Y., and Vyas, A., “Assessments of impacts PHEV charging patterns on wind-thermal scheduling by stochastic unit commitment,” IEEE Trans. Smart Grid, Vol. 3, pp. 675–683, June 2012.
  • Sahebi, M., Duki, E., Kia, M., Soroudi, A., and Ehsan, M., “Simultaneous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts,” IET Gener. Transm. Distrib., Vol. 6, pp. 605–611, July 2012.
  • Abdollahi, A., Moghaddam, M., Rashidinejad, M., and Sheikh-El-Eslami, M., “Investigation of economic and environmental-driven demand response measures incorporating Uc,” IEEE Trans. Smart Grid, Vol. 3, pp. 12–25, March 2012.
  • Aghaei, J., and Alizadeh, M., “Critical peak pricing with load control demand response program in unit commitment problem,” IET Gener. Transm. Distrib., Vol. 7, pp. 681–690, August 2013.
  • Rahmani-andebili, M., “Investigating effects of responsive loads models on unit commitment collaborated with demand-side resources,” IET Gener. Transm. Distrib., Vol. 7, pp. 420–430, July 2013.
  • Arasteh, H., Parsa Moghaddam, M., Sheikh-El-Eslami, and Abdolahi, A., “Integrating commercial demand response resources with unit commitment,” Int. J. Electr. Power Energy Syst., Vol. 51, pp. 153–161, October 2013.
  • Wu, H., Shahidehpour, M., and Khodayar, M., “Hourly demand response in day-ahead scheduling considering generating unit ramping cost,” IEEE Trans. Power Syst., Vol. 28, pp. 2446–2454, August 2013.
  • Soares, J., Morais, H., Sousa, T., and Faria, P., “Day-ahead resource scheduling including demand response for electric vehicles,” IEEE Trans. Smart Grid, Vol. 4, pp. 596–605, February 2013.
  • Sousa, T., Morais, H., Soares, J., and Vale, Z., “Day-ahead resource scheduling in smart grid considering vehicle-to-grid and network constraints,” Appl. Energy, Vol. 96, pp. 183–193, May 2012.
  • Zhao, C., Wang, J., Watson, J., and Guan, Y., “Multi-stage robust unit commitment considering wind and demand response uncertainties,” IEEE Trans. Power Syst., Vol. 28, pp. 2708–2717, April 2013.
  • Falsafi, H., Zakariazadeh, A., and Jadid, S., “The role of demand response in single multi-objective wind thermal generation scheduling: A stochastic programming,” Energy, Vol. 64, pp. 853–867, January2014.
  • De Jonghe, C., Hobbs, B., and Belmans, R., “Value of price responsive load for wind integration in unit commitment,” IEEE Trans. Power Syst., Vol. 29, pp. 675–685, March 2014.
  • Sousa, T., Morais, H., Vale, Z., Faria, P., and Soares, J., “Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach,” IEEE Trans. Smart Grid, Vol. 3, pp. 535–542, March 2012.
  • Goleijani, S., Ghanbarzadeh, T., Sadeghi Nikoo, F., and Parsa Moghaddam, M. “Reliability constrained unit commitment in smart grid environment,” Electr. Power Syst. Res., Vol. 97, pp. 100–108, April2013.
  • Saravanan, B., Das, S., Sikri, S., and Kothari, D., “A solution to the unit commitment problem—a review,” Front. Energy, pp. 223–236, 2013.
  • Rao, R., and More, K., “Advance optimal tolerance design of machine elements using teaching–learning-based optimization algorithm,” Product. Manufact. Res., Vol. 2, pp. 71–94, February 2014.
  • Rao, R., Savsani, V., and Vakharia, D., “Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems,” Info. Sci., Vol. 183, pp. 1–15, January 2012.
  • Krishnanand, K., Panigrahi, B., Rout, P., and Mohapatra, A., “Application of multi-objective teaching–learning based algorithm to an economic load dispatch with incommensurable objectives,” Swarm Evolut. Memetic Comput. LNCS, Vol. 7076, pp. 697–705, December 2011.
  • Sultana, S., and Roy, P., “Optimal capacitor placement in radial distribution systems using teaching–learning based optimization,” Int. J. Electr. Power Energy Syst., Vol. 54, pp. 387–393, January2014.
  • Niknam, T., Azizipanah-Abarghooee, R., and Rasoul Narimani, M., “A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems,” Eng. Appl. Art. Intell., Vol. 25, pp. 1577–1588, December 2012.
  • Nayak, M., Nayak, C., and Rout, P., “Application of multi objective teaching–learning based optimization algorithm to optimal power flow problem,” Proc. Technol., Vol. 6, pp. 255–264, 2012.
  • Rao, R., and Patel, V., “Multi-objective optimization of heat exchangers using a modified teaching–learning-based optimization algorithm,” Appl. Math. Model., Vol. 37, pp. 1147–1162, February 2013.
  • Hetzer, J., Yu, D., and Bhattarai, K., “An economic dispatch model incorporating wind power,” IEEE Trans. Energy Convers., Vol. 23, pp. 603–611, May 2008.
  • Jadhav, H., and Roy, R., “Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power,” Expert Syst. Appl., Vol. 40, pp. 6385–6399, November2013.
  • Seguro, J., and Lambert, T., “Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis,” Wind Eng. Ind. Aerodynam., Vol. 85, pp. 75–84, March 2000.
  • Aalami, H., Moghaddam, M., and Yousefi, G., “Demand response modeling considering interruptible/curtailable loads and capacity market programs,” Appl. Energy, Vol. 87, pp. 243–250, January2010.
  • Aalami, H., Moghaddam, M., and Yousefi, G., “Modeling and prioritizing demand response programs in power markets,” Electric Power Syst. Res., Vol. 80, pp. 426–435, April 2010.
  • Hu, W., Su, C., Chen, Z., and Bak-Jensen, B., “Optimal operation of plug-in electric vehicles in power systems with high wind power penetrations,” IEEE Trans. Sustain. Energy, Vol. 4, pp. 577–585, July2013.
  • Deb, K., “An efficient constarint handling methods for genetic algorithms,” Comput. Meth. Appl. Mechanics Eng., Vol. 186, pp. 311–338, June2000.
  • Dieu, VoN., and Ongsakul, W., “Ramp rate constrained unit commitment by improved priority list and augmented Largange hopfield network,” Electr. Power Syst. Res., Vol. 78, pp. 291–301, March2008.
  • Yuan, B., Zhou, M., Zhang, X., and Gengyin, L., “A joint smart generation scheduling approach for wind thermal pumped storage systems,” Electr. Power Compon. Syst., Vol. 42, No. 3–4, pp. 372–385, February 2014.
  • Rao, R., Savsani, V., and Vakharia, D., “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems,” Comput. Aided Design, Vol. 43, pp. 303–315, March 2011.
  • Swarup, K., and Yamashiro, S., “Unit commitment solution methodology using genetic algorithm,” IEEE Trans. Power Syst., Vol. 17, pp. 87–91, February 2002.
  • Chandram, K., Subrahmanyam, N., and Sydulu, M., “New approach with muller method for profit based unit commitment,” Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in 21st Century, pp. 1–8, Pittsburgh, PA, 20–24 July 2008.
  • Wong, P., Albrecht, P., Allan, R., Billinton, R., Chen, Q., Fong, C., Haddad, S., Li, W., Mukerji, R., Patton, D., and Schneider, A., “The IEEE reliability test system—1996, A report prepared by the reliability test sytem task force of the application of probability methods subcommittee, “ IEEE Trans. Power Syst., Vol. 14, pp. 1010–1020, August 1999.
  • Wang, C., and Shahidehpour, M., “Effects of ramp-rate limits on unit commitment and economic dispatch,” IEEE Trans. Power Syst., Vol. 8, pp. 1341–1350, August1993.
  • Chandrasekaran, K., and Simon, S., “Network and reliability constrained unit commitment problem using binary real coded firefly algorithm,” Int. J. Electr. Power Energy Syst., Vol. 43, pp. 921–932, July 2012.
  • Albadi, M., and El-Saadany, E., “Overview of wind power intermittency impacts on power systems,” Electr. Power Syst. Res., Vol. 80, pp. 627–632, June 2010.
  • Damousis, I., Bakirtzis, A., and Dokopoulos, P., “A solution to the unit commitment problem using integer coded genetic algorithm,” IEEE Trans. Power Syst., Vol. 19, pp. 1165–1172, May 2004.
  • Eslamian, M., Hosseinian, S., and Vahidi, B., “Bacterial foraging-based solution to the unit-commitment problem,” IEEE Trans. Power Syst., Vol. 24, pp. 1478–1488, August 2009.
  • Balci, H., and Valenzuela, J., “Scheduling electric power generators using particle swarm optimization combined with Lagrangian relaxation method,” J. Appl. Math. Comp. Sci., Vol. 14, pp. 411–421, September2004.
  • Simopoulos, D., Kavatza, S., and Vournas, C., “Unit commitment by an enhanced simulated annealing algorithm,” IEEE Trans. Power Syst., Vol. 21, pp. 68–76, February 2006.
  • Jorge, V., and Smith, A., “A seeded memetic algorithm for large unit commitment problems,” J. Heuristics, Vol. 8, pp. 173–195, March2002.
  • Pourjamal, Y., and Ravadanegh, S., “HAS based solution to the UC problem,” Int. J. Electr. Power Energy Syst., Vol. 46, pp. 211–220, March 2013.
  • Kazarlis, S., Bakirtzis, A., and Petridis, V., “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. Power Syst., Vol. 11, pp. 83–92, February 1996.
  • Patra, S., Goswami, S., and Goswami, B., “Differential evolution algorithm for solving unit commitment with ramp constarints,” Electr. Power Compon. Syst., Vol. 36, pp. 771–787, January 2008.
  • Ongsakul, W., and Petcharaks, N., “Unit commitment by enhanced adaptive Lagrangian relaxation,” IEEE Trans. Power Syst., Vol. 19, pp. 620–628, February2004.
  • Juste, K., Kita, H., Tanaka, E., and Hasegawa, J., “An evolutionary algorithm to solve large scale unit commitment problem,” IEEE Conference on PowerCon., pp. 1746–1751, Singapore, 21–24 November 2004.
  • Senjyu, T., Miyagi, T., Saber, A., Urasaki, N., and Funabashi, T., “Emerging solution of large-scale unit commitment problem by stochastic priority list,” Electr. Power Syst. Res., Vol. 76, pp. 283–292, March 2006.
  • Xie, Y., and Chiang, H., “A novel solution methodology for solving large-scale thermal unit commitment problems,” Electr. Power Compon. Syst., Vol. 38, pp. 1615–1634, December 2010.
  • Chandrasekaran, K., Hemamalini, S., Simon, S., and Padhy, N., “Thermal unit commitment using binary/real coded artificial bee colony algorithm,” Electr. Power Syst. Res., Vol. 84, pp. 109–119, March 2012.
  • Amiri, M., and Khanmohammadi, S., “ A primary unit commitment approach with a modification process,” Appl. Soft Comput., Vol. 13, pp. 1007–1015, February 2013.
  • Roy, P., “ Solution of unit commitment problem using gravitational search algorithm,” Int. J. Electr. Power Energy Syst., Vol. 53, pp. 85–94, December 2013.
  • Yuan, X., Ji, Bin., Zhang, S., Tian, H., and Hou, Y., “A new approach for unit commitment problem via binary gravitational search algorithm,” Appl. Soft Comput., Vol. 22, pp. 249–260, June 2014.
  • Yu, X., and Zhang, X., “Unit commitment using Lagrangian relaxation and particle swarm optimization,” Int. J. Electr. Power Energy Syst., Vol. 61, pp. 510–522, May2014.
  • Roy, P., and Sarkar, R., “Solution of unit commitment problem using quasi-oppositional teaching–learning based algorithm,” Int. J. Electr. Power Energy Syst., Vol. 60, pp. 96–106, March2014.
  • Simopoulos, D., Kavatza, S., and Vournas, C., “Reliability constrained unit commitment using simulated annealing,” IEEE Trans. Power Syst., Vol. 21, pp. 1699–1706, November 2006.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.