A New Strategic Bidding Model for the Retail Market for Maximum Profit by Demand Response Programs

Farhad Zishan, Saeid Khademifard, Saber Fooladfar, Amin Hajati

Abstract


This research aims to plan the retailer’s profit and the amount of electricity exchanged in the presence of both batteries and demand response programs(DRP) for load-serving entities via a mixed-integer linear programming (MILP) model. For power system operators, various DRPs have been proposed as potential resources for balancing supply and demand, reducing peak load hours, and increasing generation efficiency. The two main sources of uncertainty are the issue of power pool prices and customer demand. The uncertainty of these parameters affects the amount of electricity exchanged, the retailer's profit, and decision-making variables. A retail store that only uses batteries or a DRP maximizes its profit by reducing the cost of purchasing energy from the market. Thus, if DRP is used, by encouraging customers to reduce demand, it is not necessary to buy more from the spot market, and, if batteries are used, by buying during non-peak hours while charging the battery for later use, the income increases during peak time. In this capacity planning with 200 MW and expected profit considering DR and without DR, the value of the local limit price is determined. In the expected profit of the retailer's market in different uncertainty states, the trading power of the retailer's planning considering the coupon-based DRP and battery allocation (taking into account the uncertainty of the customer's behavior). The goal of the electricity retailer is to manage futures contracts and determine the selling price offered to its consumers. In purchasing electrical energy and selling to consumers, the retailer faces two important tasks. First, in purchasing electrical energy, it must deal with market price uncertainty and conclude futures contracts at higher prices. Second, in selling electricity, it must consider consumers’ uncertainty and take into account the fact that consumers may choose another retailer if the selling price is not competitive enough. Therefore, in this paper, the financial risk associated with market price uncertainty is modeled using the expected probability, which is used explicitly as a constraint in a stochastic optimization problem with MILP.


Keywords


Electricity Exchanged, Retailer Market, Demand Response Programs , Mixed Integer Linear Programming

Full Text:

PDF

References


A.-H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid,” IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 320–331, Dec. 2010, doi: https://doi.org/10.1109/TSG.2010.2089069.

M. H. Albadi and E. F. El-Saadany, “demand response in electricity markets: an overview,” 2007 IEEE Power Engineering Society General Meeting, Jun. 2007, doi: https://doi.org/10.1109/pes.2007.385728.

Y. Ding, Seung Bong Hong, and H. Zhang, “A Demand Response Energy Management Scheme for Industrial Facilities in Smart Grid,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2257–2269, Jun. 2014, doi: https://doi.org/10.1109/tii.2014.2330995.

B. Yu, F. Sun, C. Chen, G. Fu, and L. Hu, “Power demand response in the context of smart home application,” Energy, vol. 240, p. 122774, Feb. 2022, doi: https://doi.org/10.1016/j.energy.2021.122774.

S. A. Mansouri, A. Ahmarinejad, F. Sheidaei, M. S. Javadi, A. R. Jordehi, A. E. Nezhad, J. P. S. Catalao, “A multi-stage joint planning and operation model for energy hubs considering integrated demand response programs,” vol. 140, pp. 108103–108103, Sep. 2022, doi: https://doi.org/10.1016/j.ijepes.2022.108103.

S. Kharrati, M. Kazemi, and M. Ehsan, “Medium-term retailer’s planning and participation strategy considering electricity market uncertainties,” International Transactions on Electrical Energy Systems, vol. 26, no. 5, pp. 920–933, Jul. 2015, doi: https://doi.org/10.1002/etep.2113.

Z. Wang and Y. He, “Two-stage optimal demand response with battery energy storage systems,” IET Generation, Transmission & Distribution, vol. 10, no. 5, pp. 1286–1293, Apr. 2016, doi: https://doi.org/10.1049/iet-gtd.2015.0401.

F. Zishan, E. Akbari, O. D. Montoya, D. A. Giral-Ramírez, and A. M. Nivia-Vargas, “Electricity retail market and accountability-based strategic bidding model with short-term energy storage considering the uncertainty of consumer demand response,” Results in Engineering, vol. 16, p. 100679, Dec. 2022, doi: https://doi.org/10.1016/j.rineng.2022.100679.

S. Borenstein, “The long-run efficiency of real-time electricity pricing,” The Energy Journal, vol. 26, no. 3, Jul. 2005, doi: https://doi.org/10.5547/issn0195-6574-ej-vol26-no3-5.

Sandeep Chawda, Rohit Bhakar, and Parul Mathuria, “Uncertainty and risk management in electricity market: Challenges and opportunities,” Dec. 2016, doi: https://doi.org/10.1109/npsc.2016.7858971.

A. Ahmadi, M. Charwand, and J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 704–713, Dec. 2013, doi: https://doi.org/10.1016/j.ijepes.2013.05.051.

N. Mahmoudi, M. Eghbal, and T. K. Saha, “Employing demand response in energy procurement plans of electricity retailers,” International Journal of Electrical Power & Energy Systems, vol. 63, pp. 455–460, Dec. 2014, doi: https://doi.org/10.1016/j.ijepes.2014.06.018.

Pavel Matrenin, Murodbek Safaraliev, S. A. Dmitriev, Sergey Kokin, Bahtiyor Eshchanov, and A. G. Rusina, “Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change,” Energy Reports, vol. 8, pp. 439–447, Apr. 2022, doi: https://doi.org/10.1016/j.egyr.2021.11.112.

Y. Qin, H. Lin, M. Zhang, X. Ai, G. De, and J. Li, “Two-stage flexible power sales optimization for electricity retailers considering demand response strategies of multi-type users,” International Journal of Electrical Power & Energy Systems, vol. 137, pp. 107031–107031, May 2022, doi: https://doi.org/10.1016/j.ijepes.2021.107031.

S. Zeynali, N. Rostami, A. Ahmadian, and A. Elkamel, “Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model,” Sustainable Cities and Society, vol. 74, p. 103154, Nov. 2021, doi: https://doi.org/10.1016/j.scs.2021.103154.

S. Kharrati, M. Kazemi, and M. Ehsan, “Equilibria in the competitive retail electricity market considering uncertainty and risk management,” Energy, vol. 106, pp. 315–328, Jul. 2016, doi: https://doi.org/10.1016/j.energy.2016.03.069.

M. Khorasany, Y. Mishra, and G. Ledwich, “A decentralised bilateral energy trading system for peer-to-peer electricity markets,” IEEE Transactions on Industrial Electronics, pp. 1–1, 2019, doi: https://doi.org/10.1109/tie.2019.2931229.

X. Kong, C. Yong, C. Wang, P. Li, L. Yu, and Y. Chen, “Multi-objective power supply capacity evaluation method for active distribution network in power market environment,” International Journal of Electrical Power & Energy Systems, vol. 115, p. 105467, Feb. 2020, doi: https://doi.org/10.1016/j.ijepes.2019.105467.

B. Masoud, N. M. Mohammad, and K. Ahad, “Optimal price and quantity determination of retailer electric contract and maximizing social welfare in retail electrical power markets with DG,” arXiv.org, Jul. 27, 2016. https://arxiv.org/abs/1607.08217

H. Xu, H. Sun, D. Nikovski, S. Kitamura, K. Mori, and H. Hashimoto, “Deep reinforcement learning for joint bidding and pricing of load serving entity,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6366–6375, Nov. 2019, doi: https://doi.org/10.1109/tsg.2019.2903756.

H. Rashidizadeh?Kermani, M. Vahedipour?Dahraie, A. Anvari?Moghaddam, and J. M. Guerrero, “A stochastic bi?level decision?making framework for a load?serving entity in day?ahead and balancing markets,” International Transactions on Electrical Energy Systems, vol. 29, no. 11, Jul. 2019, doi: https://doi.org/10.1002/2050-7038.12109.

P.-Y. Liu, Y. Yang, Z. Zou, and Y. Yang, “Integrated demand response for a load serving entity in multi-energy market considering network constraints,” vol. 250, pp. 512–529, Sep. 2019, doi: https://doi.org/10.1016/j.apenergy.2019.05.003.

Dean Holland Clift, K. N. Hasan, and G. Rosengarten, “Peer-to-peer energy trading for demand response of residential smart electric storage water heaters,” Applied Energy, vol. 353, pp. 122182–122182, Jan. 2024, doi: https://doi.org/10.1016/j.apenergy.2023.122182.

Y. Wan, J. Qin, Y. Shi, W. Fu, and F. Xiao, “Stackelberg–Nash game approach for price-based demand response in retail electricity trading,” International Journal of Electrical Power & Energy Systems, vol. 155, p. 109577, Jan. 2024, doi: https://doi.org/10.1016/j.ijepes.2023.109577.

Z. Liao, X. Liao, and A. Khakichi, “RETRACTED:Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm,” Energy, vol. 286, p. 129587, Jan. 2024, doi: https://doi.org/10.1016/j.energy.2023.129587.

H. Khazaei, H. Aghamohammadloo, M. Habibi, M. Mehdinejad, and A. Mohammadpour Shotorbani, “Novel decentralized peer-to-peer gas and electricity transaction market between prosumers and retailers considering integrated demand response programs,” Sustainability, vol. 15, no. 7, p. 6165, Jan. 2023, doi: https://doi.org/10.3390/su15076165.

A. Ciarreta, M. P. Espinosa, and C. Pizarro-Irizar, “Pricing policies for efficient demand side management in liberalized electricity markets,” Economic Modelling, vol. 121, p. 106215, Apr. 2023, doi: https://doi.org/10.1016/j.econmod.2023.106215.

E. Akbari, A. R. Sheikholeslami, and F. Zishan, “Participation of renewable energy in providing demand response in presence of energy storage,” renewable energy research and applications, vol. 4, no. 2, pp. 225–234, Jul. 2023, doi: https://doi.org/10.22044/rera.2022.11818.1115.

D. Nevius, “The History of The North American Electric Reliability Corporation Helping Owners, Operators, And Users of The Bulk Power System Assure Reliability And Security For More Than 50 Years.” Available: https://www.nerc.com/AboutNERC/Resource%20Documents/NERCHistoryBook.pdf

Atle Øglend, F. Asche, and Hans?Martin Straume, “Estimating pricing rigidities in bilateral transactions markets,” American Journal of Agricultural Economics, vol. 104, no. 1, pp. 209–227, May 2021, doi: https://doi.org/10.1111/ajae.12230.

J. Ke, H. Yang, X. Li, H. Wang, and J. Ye, “Pricing and equilibrium in on-demand ride-pooling markets,” Transportation Research Part B: Methodological, vol. 139, pp. 411–431, Sep. 2020, doi: https://doi.org/10.1016/j.trb.2020.07.001.

Ç. B. Nalan, Ö. Murat, and Ö. Nuri, “Renewable energy market conditions and barriers in Turkey,” Renewable and Sustainable Energy Reviews, vol. 13, no. 6–7, pp. 1428–1436, Aug. 2009, doi: https://doi.org/10.1016/j.rser.2008.09.001.

Hogan, W. W, Transmission Market Design. Available at SSRN 453483. https://doi.org/10.7208/9780226308586-010

D. Jay and K. S. Swarup, “Game theoretical approach to novel reactive power ancillary service market mechanism,” IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1298–1308, Mar. 2021, doi: https://doi.org/10.1109/tpwrs.2020.3019786.

N. Stauff, G. Maronati, R. Ponciroli, F. Ganda, T. Kim, T. Taiwo, A. Cuadra, M. Todosow, P. Talbot, C. Rabiti, B. Dixon, S. Kim, “Daily Market Analysis Capability and Results,” www.osti.gov, Apr. 30, 2019. https://www.osti.gov/biblio/1511150.

L. Bai, Y. Wei, G. Wei, X. Li, and S. Zhang, “Infectious disease pandemic and permanent volatility of international stock markets: a long-term perspective,” Finance Research Letters, vol. 40, p. 101709, Jul. 2020, doi: https://doi.org/10.1016/j.frl.2020.101709.

J. Iria, F. Soares, and M. Matos, “Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets,” Applied Energy, vol. 238, pp. 1361–1372, Mar. 2019, doi: https://doi.org/10.1016/j.apenergy.2019.01.191.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v9i2.468.g383

Refbacks

  • There are currently no refbacks.


www.ijsmartgrid.com; www.ijsmartgrid.org

ilhcol@gmail.com; icolak@gazi.edu.tr

Online ISSN: 2602-439X

Publisher: ilhami COLAK

https://www.ilhamicolak.org/english.htm

Cited in SCOPUS Q1, SCIMAGO, Google Scholar and CrossRef

 

 

LinkedIn Logo Follow us on LinkedIn

Google Scholar Statistics of ijSmartGrid

https://scholar.google.com/citations?user=qP9ZUKAAAAAJ&hl=tr&authuser=1 

 

SCIMAGO information:

https://www.scimagojr.com/journalsearch.php?q=21101271431&tip=sid&clean=0#google_vignette

Q2, h-index:13