Abstract
In the restructured power system, a large consumer can provide its required demand by using multiple options. Pool market, bilateral contracts, and self-generating units are some of the available options for power procurement. The main goal of large consumers from participation in the power market is procuring power at the minimum cost. The power price in the pool market has uncertainty, and to cope with this problem, bilateral contracts with predetermined prices can be considered by a large consumer. To model uncertainty in the pool market, different methods such as stochastic programming, robust optimization approach, and information gap decision method can be used. Renewable energy sources can be used as self-generating units to meet some part of the required demand of the large consumer. Also, energy storage systems can be implemented to store energy during off-peak demand periods and use the stored energy during peak demand periods. Different types of storage systems such as electrical storage systems, thermal storage systems, and cooling storage systems are used to store electricity, heating, and cooling energies, respectively. In this chapter, reviews of considered methods to solve the problem are presented and the obtained results are discussed.
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H.A. Bagal, Y.N. Soltanabad, M. Dadjuo, K. Wakil, N. Ghadimi, Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Sol. Energy 169, 343–352 (2018)
M. Carrion, A.B. Philpott, A.J. Conejo, J.M. Arroyo, A stochastic programming approach to electric energy procurement for large consumers. IEEE Trans. Power Syst. 22(2), 744–754 (2007)
A.J. Conejo, M. Carrion, Risk-constrained electricity procurement for a large consumer. IEE Proc. Gener. Transm. Distrib. 153(4), 407 (2006)
A.J. Conejo, J.J. Fernandez-Gonzalez, N. Alguacil, Energy procurement for large consumers in electricity markets. IEE Proc. Gener. Transm. Distrib. 152(3), 357 (2005)
K. Zare, M.P. Moghaddam, M.K. Sheikh El Eslami, Electricity procurement for large consumers based on information gap decision theory. Energy Policy 38(1), 234–242 (2010)
S. Nojavan, H. Ghesmati, K. Zare, Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2016)
K. Zare, A.J. Conejo, M. Carrión, M.P. Moghaddam, Multi-market energy procurement for a large consumer using a risk-aversion procedure. Electr. Power Syst. Res. 80(1), 63–70 (2010)
S.J. Kazempour, A.J. Conejo, C. Ruiz, Strategic bidding for a large consumer. IEEE Trans. Power Syst. 30(2), 848–856 (2015)
D. Fang, J. Wu, D. Tang, A double auction model for competitive generators and large consumers considering power transmission cost. Int. J. Electr. Power Energy Syst. 43(1), 880–888 (2012)
R.K. Mallick, R. Agrawal, P.K. Hota, Bidding strategies of Gencos and large consumers in competitive electricity market based on TLBO, in 2016 IEEE 6th International Conference on Power Systems (ICPS), (2016), pp. 1–6
M. Zarif, M.H. Javidi, M.S. Ghazizadeh, Self-scheduling approach for large consumers in competitive electricity markets based on a probabilistic fuzzy system. IET Gener. Transm. Distrib. 6(1), 50 (2012)
M. Zarif, M.H. Javidi, M.S. Ghazizadeh, Self-scheduling of large consumers with second-order stochastic dominance constraints. IEEE Trans. Power Syst. 28(1), 289–299 (2013)
Q. Zhang, A.M. Bremen, I.E. Grossmann, J.M. Pinto, Long-term electricity procurement for large industrial consumers under uncertainty. Ind. Eng. Chem. Res. 57(9), 3333–3347 (2018)
K. Zare, M.P. Moghaddam, M.K. Sheikh-El-Eslami, Risk-based electricity procurement for large consumers. IEEE Trans. Power Syst. 26(4), 1826–1835 (2011)
S. Nojavan, H. allah Aalami, Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program. Energy Convers. Manag. 103, 1008–1018 (2015)
S. Nojavan, H. Qesmati, K. Zare, H. Seyyedi, Large consumer electricity acquisition considering time-of-use rates demand response programs. Arab. J. Sci. Eng. 39(12), 8913–8923 (2014)
B.M. Soudmand, N.N. Esfetanaj, S. Mehdipour, R. Rezaeipour, Heating hub and power hub models for optimal performance of an industrial consumer. Energy Convers. Manag. 150, 425–432 (2017)
X. Chen, G. Gong, Z. Wan, C. Zhang, Z. Tu, Performance study of a dual power source residential CCHP system based on PEMFC and PTSC. Energy Convers. Manag. 119, 163–176 (2016)
F.A. Boyaghchi, M. Chavoshi, Monthly assessments of exergetic, economic and environmental criteria and optimization of a solar micro-CCHP based on DORC. Sol. Energy 166, 351–370 (2018)
J. Contreras, R. Espinola, F.J. Nogales, A.J. Conejo, ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)
H. Zareipour, C.A. Cañizares, K. Bhattacharya, J. Thomson, Application of public-domain market information to forecast Ontario’s wholesale electricity prices. IEEE Trans. Power Syst. 21(4), 1707–1717 (2006)
C. Garcia-Martos, J. Rodriguez, M.J. Sanchez, Mixed models for short-run forecasting of electricity prices: application for the Spanish market. IEEE Trans. Power Syst. 22(2), 544–552 (2007)
A.J. Conejo, M.A. Plazas, R. Espinola, A.B. Molina, Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst. 20(2), 1035–1042 (2005)
N. Amjady, M. Hemmati, Day-ahead price forecasting of electricity markets by a hybrid intelligent system. Eur. T. Electr. Power 19(1), 89–102 (2009)
N. Amjady, F. Keynia, Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm. IEEE Trans. Power Syst. 24(1), 306–318 (2009)
C.P. Rodriguez, G.J. Anders, Energy price forecasting in the Ontario competitive power system market. IEEE Trans. Power Syst. 19(1), 366–374 (2004)
J.P.S. Catalão, S.J.P.S. Mariano, V.M.F. Mendes, L.A.F.M. Ferreira, Short-term electricity prices forecasting in a competitive market: a neural network approach. Electr. Power Syst. Res. 77(10), 1297–1304 (2007)
A.T. Lora, J.M.R. Santos, A.G. Exposito, J.L.M. Ramos, J.C.R. Santos, Electricity market price forecasting based on weighted nearest neighbors techniques. IEEE Trans. Power Syst. 22(3), 1294–1301 (2007)
N. Amjady, Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans. Power Syst. 21(2), 887–896 (2006)
N.M. Pindoriya, S.N. Singh, S.K. Singh, An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans. Power Syst. 23(3), 1423–1432 (2008)
M.H. Albadi, E.F. El-Saadany, A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008)
H.A. Aalami, H. Pashaei-Didani, S. Nojavan, Deriving nonlinear models for incentive-based demand response programs. International Journal of Electrical Power & Energy Systems. 2019 Mar 1;106:223–31
R.S. Ferreira, L.A. Barroso, M.M. Carvalho, Demand response models with correlated price data: a robust optimization approach. Appl. Energy 96, 133–149 (2012)
R. Aazami, K. Aflaki, M.R. Haghifam, A demand response based solution for LMP management in power markets. Int. J. Electr. Power Energy Syst. 33(5), 1125–1132 (2011)
D. Kathan, R. Aldina, M.P. Lee, L. Medearis, P. Sporborg, M. Tita, D. Wight, A. Wilkerson, F. Kreger, V. Richardson, W. Gifford, Assessment of demand response and advanced metering. Rapport technique. Federal Energy Regulatory Commission, USA. 2012 Dec.
H.A. Aalami, M. Parsa Moghaddam, G.R. Yousefi, Modeling and prioritizing demand response programs in power markets. Electr. Power Syst. Res. 80(4), 426–435 (2010)
Y. Tang, H. Song, F. Hu, Y. Zou, Investigation on TOU pricing principles, in IEEE/PES Transmission and Distribution Conference and Exposition: Asia and Pacific, (2005), pp. 1–9
C.W. Gellings, J.H. Chamberlin, Demand-side Management: Concepts and Methods (The Fairmont Press, Lilburn, 1987)
P. Jazayeri et al., A survey of load control programs for price and system stability. IEEE Trans. Power Syst. 20(3), 1504–1509 (2005)
C. River, Primer on demand-side management with an emphasis on price-responsive programs. Prepared for The World Bank by Charles River Associates. Tech Rep. 2005
J. Edward, P. Policy, Assessment of customer response to real time pricing. New Jersey: Edward J, Bloustein School of Planning and Public Policy, State University of New Jersey. 2005 Jun 30
H. Leng, X. Li, J. Zhu, H. Tang, Z. Zhang, N. Ghadimi, A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Adv. Eng. Inform 36, 20–30 (2018)
M. Abbaspour, M. Satkin, B. Mohammadi-Ivatloo, F. Hoseinzadeh Lotfi, Y. Noorollahi, Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES). Renew. Energy 51, 53–59 (2013)
O. Abedinia, N. Amjady, N. Ghadimi, Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34(1), 241–260 (2018)
D.T. Nguyen, L.B. Le, Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics. IEEE Trans. Smart Grid 5(4), 1608–1620 (2014)
F. Mirzapour, M. Lakzaei, G. Varamini, M. Teimourian, N. Ghadimi, A new prediction model of battery and wind-solar output in hybrid power system. J. Ambient. Intell. Humaniz. Comput., 1–11 (2017)
D. Aydin, S.P. Casey, S. Riffat, The latest advancements on thermochemical heat storage systems. Renew. Sust. Energ. Rev. 41, 356–367 (2015)
X. Song, L. Liu, T. Zhu, S. Chen, Z. Cao, Study of economic feasibility of a compound cool thermal storage system combining chilled water storage and ice storage. Appl. Therm. Eng. 133, 613–621 (2018)
J.M. Morales, J. Perez-Ruiz, Point estimate schemes to solve the probabilistic power flow. IEEE Trans. Power Syst. 22(4), 1594–1601 (2007)
C.-L. Su, Probabilistic load-flow computation using point estimate method. IEEE Trans. Power Syst. 20(4), 1843–1851 (2005)
S. Nojavan, A. Najafi-Ghalelou, M. Majidi, K. Zare, Optimal bidding and offering strategies of merchant compressed air energy storage in deregulated electricity market using robust optimization approach. Energy 142, 250–257 (2018)
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Teimourian, M., Ghadimi, N., Nojavan, S., Abedinia, O. (2019). The Concept of Large Consumer. In: Nojavan, S., Shafieezadeh, M., Ghadimi, N. (eds) Robust Energy Procurement of Large Electricity Consumers . Springer, Cham. https://doi.org/10.1007/978-3-030-03229-6_1
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DOI: https://doi.org/10.1007/978-3-030-03229-6_1
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