Document Type : Original Article

Author

Member of Department of Science and Engineering, Yildirim Beyazit University, Ankara, Turkey

Abstract

Several methods have been proposed for sale and payment mechanisms in electricity markets; but, appropriate evaluation of this mechanisms is so difficult. The offer cost minimization (OCM) has been presented previously for solving this problem which minimizes the total offer cost through the evaluation by locational marginal prices (LMPs). In recent years, payment cost minimization (PCM) method is suggested which directly minimizes the consumer payments and is more complicated than OCM in terms of framework and converting to single-level linearized optimization problem as well as computational burden. In the current study, a new meta-heuristic optimizer has been proposed for to PCM through solving the joint energy-reserve PCM problem. The achievements are put in comparison with conventional model based offer cost minimization through various studied cases.  

Keywords

Mobayen S. Payment cost minimization solution based on an intelligent algorithm in power system. J. Journal of Smart Energy and Sustainability, 2022; 1(2): 166-182.

[1] Schulze, Tim, and Ken McKinnon. “The value of stochastic programming in day-ahead and intra-day generation unit commitment.” Energy 101 (2016): 592-605.
[2] Wang, Bo, et al. “Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties.” Energy 111 (2016): 18-31. 
[3] Aghaei, Jamshid, et al. “Exploring the reliability effects on the short term AC security-constrained unit commitment: A stochastic evaluation.” Energy 114 (2016): 1016-1032.
[4] Zhang, Jingrui, et al. “A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem.” Energy 109 (2016): 765-780.
[5] Quan, Hao, Dipti Srinivasan, and Abbas Khosravi. “Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study.” Energy 103 (2016): 735-745.
[6] Shukla, Anup, and S. N. Singh. “Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem.” Energy 96 (2016): 23-36.
 [7] Saber, Navid Abdolhoseyni, Mahdi Salimi, and Davar Mirabbasi. “A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm.” Energy 117 (2016): 272-280.
[8] Ji, Bin, et al. “Improved gravitational search algorithm for unit commitment considering uncertainty of wind power.” Energy 67 (2014): 52-62.
[9] Zheng, J. H., et al. “Reliability constrained unit commitment with combined hydro and thermal generation embedded using self-learning group search optimizer.” Energy 81 (2015): 245-254.
[10] Amjady, N., Rabiee, A., Shayanfar, HA., 2010. Multiobjective clearing of coupled active and reactive power market considering power system security. European Transactions on Electrical Power, 20,1190–1208.
[11] Noruzi, Alireza, et al. “A new method for probabilistic assessments in power systems, combining monte carlo and stochastic‐algebraic methods.” Complexity 21.2 (2015): 100-110.
[12] Ahmadian, Iraj, Oveis Abedinia, and Noradin Ghadimi. “Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization.” Frontiers in Energy 8.4 (2014): 412-425.
[13] Abedinia, Oveis, Nima Amjady, and Noradin Ghadimi. “Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm.” Computational Intelligence (2017).
[14] Mohammadi, M., Talebpour, F., Safaee, E., Ghadimi, N., & Abedinia, O. (2017). Small-scale building load forecast based on hybrid forecast engine. Neural Processing Letters, 1-23.
[15] Eskandari Nasab, Mohammad, et al. “A new multiobjective allocator of capacitor banks and distributed generations using a new investigated differential evolution.” Complexity 19.5 (2014): 40-54.
[16] Abedinia, Oveis, Masoud Bekravi, and Noradin Ghadimi. “Intelligent controller based wide-area control in power system.” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25.01 (2017): 1-30.
[17] Abedinia, Oveis, and Noradin Ghadimi. “Modified harmony search algorithm based unit commitment with plug-in hybrid electric vehicles.” Journal of Artificial Intelligence in Electrical Engineering 2.6 (2013): 49-62.
[18] Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2017). Application of a new hybrid forecast engine with feature selection algorithm in a power system. International Journal of Ambient Energy, 1-10.
[19] Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2017). A new prediction model based on multi-block forecast engine in smart grid. Journal of Ambient Intelligence and Humanized Computing, 1-16.
[20] Liu, Yang, Wei Wang, and Noradin Ghadimi. “Electricity load forecasting by an improved forecast engine for building level consumers.” Energy 139 (2017): 18-30.
[21] Gollou, Abbas Rahimi, and Noradin Ghadimi. “A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets.” Journal of Intelligent & Fuzzy Systems 32.6 (2017): 4031-4045.
[22] Ghadimi, Noradin, and Mansour Hosseini Firouz. “Short-term management of hydro-power systems based on uncertainty model in electricity markets.” Journal of Power Technologies 95.4 (2015): 265. 
[23] Fernandez-Blanco, R., Arroyo, J.M., Alguacil, N., 2012. A unified bilevel programming framework for price-based market clearing under marginal pricing. IEEE Trans. Power Syst., 27 (1), 517,525.
[24] Fernandez-Blanco, R., Arroyo, J.M, Alguacil, N., 2017. On the solution of revenue- and network-constrained day-ahead market clearing under marginal pricing—Part I. IEEE Trans. Power Syst., 32 (1), 208-219.
[25] Fernandez-Blanco, R., Arroyo, J.M, Alguacil, N., 2017. On the solution of revenue- and network-constrained day-ahead market clearing under marginal pricing— Part II: Case Studies. IEEE Trans. Power Syst., 32 (1), 220-227.
[26] Han, X., Luh, P.B., Yan, J.H., Stern, G.A., 2010. Payment cost minimization with transmission capacity constraints and losses using the objective switching method,” IEEE Power and Energy Society General Meeting.
[27] Sharma, S., Bhakar, R., Padhy, N.P., Gupta, H.O., 2011. Payment cost minimization auction in electricity markets. 2011 IEEE Power and Energy Society General Meeting, San Diego, CA.
[28] Xu, Y., Hu, Q., Li, F., 2013. Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty. IEEE Trans. Sustainable Energy, 4 (3), 716-724.
[29] Han, X., Luh, P.B., Bragin, M.A., Yan, J.H., Yu, N., Stern, G.A., 2012. Solving payment cost co-optimization problems. IEEE PES General Meeting, San Diego.
[30] Han, X., Luh, P.B., Yan, J.H., Stern, G.A., 2011. “Energy and spinning reserve payment cost co-optimization. IEEE PES General Meeting, Detroit.
[31] Bragin, M.A., Luh, P.B., Yan, J.H., Yu, N., Stern, G.A., 2013. Efficient surrogate optimization for payment cost co-optimization with transmission capacity constraints. IEEE PES General Meeting, Vancouver.
[32] Carvalho, C., Cuervo, P., 2013. A mixed-integer linear approach for assessing the impact of bilateral contracts in a combined energy market operating under payment minimization. J. Control Autom. Electr. Syst., 24 (5) 649–660.
[33] Carvalho, C., Cuervo, P., 2011. Payment minimization in a combined energy market through a bilevel linear model. 17th Power Systems Computation Conf., Stockholm, Sweden.
[34] Finn, P., et al. “Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market.” Energy 36.5 (2011): 2952-2960.
[35] O. Abedinia, N. Amjady, A. Ghasemi, A New Meta-heuristic Algorithm Based on Shark Smell Optimization, Complexity Journal, vol. 21, no. 5, pp. 97–116, 2016. 
[36] O. Abedinia, N. Amjady, Short-Term Wind Power Prediction based on Hybrid Neural Network and Chaotic Shark Smell Optimization, International Journal of Precision Engineering and Manufacturing-Green Technology (Springer), vol. 2, no. 3, pp. 245-254, July 2015.
[37] T.T. Anilkumar, Sishaj P. Simon, N. Prasad Padhy, Residential electricity cost minimization model through open well-pico turbine pumped storage system, Applied Energy, vol. 195, pp. 23-35, 2017.
[38] Germán Morales-España, Laura Ramírez-Elizondo, Benjamin F. Hobbs, Hidden power system inflexibilities imposed by traditional unit commitment formulations, Applied Energy, vol. 191, pp. 223-238, 2017. 
[39] Hongyang Jin, Zhengshuo Li, Hongbin Sun, Qinglai Guo, Bin Wang, A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment, Applied Energy, vol. 206, pp. 1364-1378, 2017.
[40] Nouri, A., Hosseini, S.H., 2017. Payment Minimization Auction with Security Constraints. IET Gener. Transm. Distrib., 11(6), 1370-1380.
[41] Nouri, A., Afkousi-Paqaleh, M., Hosseini, S.H., 2013. Probabilistic assessment and sensitivity analysis of marginal price of different services in power markets. IEEE Syst. J., 7 (4), 873-880.
[42] Wenxiao Wang, Chaoshun Li, Xiang Liao, Hui Qin, Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm, Applied Energy, vol. 187, pp. 612-626, 2017. 
[43] Amjady, N., J. Aghaei, and H. A. Shayanfar. “Market clearing of joint energy and reserves auctions using augmented payment minimization.” Energy 34.10 (2009): 1552-1559.
[44] Voorspools KR, D’Haeseleer WD. Long-term unit commitment optimisation for large power systems: Unit decommitment versus advanced priority listing, Appl Energy 2003;76:157–67.
[45] Delarue E, D’Haeseleer W. Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting. Appl Energy, 2008;85:171–81.
[46] Mousavi, Y., Alfia, A., 2015. A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers. Applied Soft Computing, 36, 599-617.
[47] Wood, A.J., Wollenberg, B.F., 1984. Power Generation Operation and Control. John Wiley, New York.