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The Effects of Spinning Reserve Uncertainty and Demand Response Programs on Transmission-Constrained Bidding Strategy | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 8، دوره 56، شماره 2، 2024، صفحه 245-268 اصل مقاله (1 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2023.22502.5546 | ||
| نویسندگان | ||
| Mohammad Esmaeil Nazari* 1؛ Marzieh Roodsarabi2؛ Ehsan Modaressi Ghazvini3؛ Ahmad Moddaresi Ghazvini3 | ||
| 1Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, 87717- 67498, Iran | ||
| 2Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده | ||
| In the electricity market, generation company attempts to maximize their profit in a bidding strategy approach. As the transactions of power and spinning reserve are done in a transmission network, consideration of transmission constraints and spinning reserve uncertainties becomes necessary. In the bidding strategy problem, there are various demand uncertainties. Usually, electricity markets consider a fixed spinning reserve with fixed request probability to ensure that demand is met. However, the actual spinning reserve is stochastic in quantity and requests hours that should be modeled and simulated. Another demand uncertainty is demand response programs include various stochastic types. One of the most famous demand response programs is electric vehicle parking with stochastic charging/discharging amounts and hours. The objection of this study is solving the bidding strategy problem considering transmission constraints, spinning reserve uncertainty, and electric vehicle parking as a demand response program based on a heuristic approach. An actual spinning reserve model using normal distribution is proposed and three case studies are presented. In the first case, improvement in profit of the generation company by 4.15-47.95% and 20.84-31.30% under single and double-sided auctions are reached, respectively. Where transmission constraints and spinning reserve uncertainty are considered, the optimal bidding strategy problem is solved in the energy and spinning reserve market for three-generation companies in the IEEE 6-bus network where transmission constraints are satisfied at all scenarios of spinning reserve requests. When electric vehicle parking is considered, it is shown that demand response programs have direct effects of bidding parameters such as market clearing price, generation companies power awarded and profits. | ||
| کلیدواژهها | ||
| Bidding strategy؛ Demand response؛ Heuristic optimization؛ Spinning reserve uncertainty؛ Transmission constraints | ||
| مراجع | ||
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[1] Attaviriyanupap, P., Kita, H., Tanaka E., Hasegawa, J. New bidding strategy formulation for day-ahead energy and reserve markets based on evolutionary programming, Electr. Power Energy Syst., 2005, 27, pp. 157–167.
[2] Wen, F., David, A.K. A genetic algorithm based method for bidding strategy coordination in energy and spinning reserve markets, Art. Int. Eng., 2001, 15, (1), pp. 71-79.
[3] Badri, A. GenCos’ optimal bidding strategy considering market power and transmission constraints: a cournot-based model, World Acad. Scie. Eng. Techn., 2011, 5, pp. 06-21.
[4] Badri, A., Rashidinejad, M. Security constrained optimal bidding strategy of GenCos in day ahead oligopolistic power markets: a Cournot-based model, Electr. Eng., 2013, 95, pp. 63–72.
[5] Li, T., Shahidehpour, M. Strategic bidding of transmission-constrained GENCOs with incomplete information, IEEE Trans. Power Syst., 2005, 20, (1), pp. 437-447.
[6] Soleymani, S., Ranjbar, A.M., Shirani, A.R. Strategic bidding of generating units in competitive electricity market with considering their reliability, Electr. Power Energy Syst., 2008, 30, pp. 193–201.
[7] Soleymani, S. Bidding strategy of generation companies using PSO combined with SA method in the pay as bid markets, Electr. Power Energy Syst., 2011, 33, pp. 1272–1278.
[8] Badri, A., Jadid, S., Rashidinejad, M., Moghaddam, M.P. Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints, Electr. Power Syst. Res., 2008, 78, pp. 1089–1098.
[9] Kardakos, E.G., Simoglou, C.K., Vagropoulos, S.I., Bakirtzis, A.G. Bidding Strategy for Risk-Averse Producers in Transmission-Constrained Electricity Markets, Euro. Energy Market (EEM) Int. Con., 2015, pp. 1-5.
[10] Kardakos, E.G., Simoglou, C.K., Bakirtzis, A.G. Optimal bidding strategy in transmission-constrained electricity markets, Electr. Power Syst. Res., 2014, 109, pp. 141– 149.
[11] Nazari M.E., Ardehali M.M. Optimal bidding strategy for a GENCO in day-ahead energy and spinning reserve markets with considerations for coordinated wind-pumped storage-thermal system and CO2 emission, Energy Strat. Review, 2019, 26, pp. 1-18.
[12] Zolfaghari Moghaddam S., Akbari T. Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach, Energy, 2018, 151, pp. 478-489.
[13] Rayati M., Goodarzi H., Ranjbar A. Optimal bidding strategy of coordinated wind power and gas turbine units in real-time market using conditional value at risk, Int. Trans. Electr. Energy Syst., 2019, 29, (1), pp. 1-16.
[14] Liu Y., Guo L., Wang C., Li X. Strategic Bidding Optimization of Microgrids in Electricity Distribution Market, IEEE Power Energy Soci. Gen. Meeting. 2017, pp. 1-5.
[15] Li B., Wang X., Shahidehpour M., Jiang C., Li Z. DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market, IEEE Trans. Smart Grid, 2019, 10, (6), pp. 6756 - 6767.
[16] Moiseeva E., Hesamzadeh M.R. Strategic Bidding of a Hydropower Producer under Uncertainty: Modified Benders Approach, IEEE Trans. Power Syst., 2018, 33, (1), pp. 861-873.
[17] Renani Y.K., Ehsan M., Shahidehpour M. Day-ahead Self-Scheduling of a Transmission- Constrained GenCo with Variable Generation Units using the Incomplete Market Information, IEEE Trans Sust Energy, 2017, 8, (3), pp. 1260-1268.
[18] Karimi M., Kheradmandi M., Pirayesh A. Risk-Constrained Transmission Investing of Generation Companies, IEEE Trans. Power Syst., 2019, 34, (2), pp. 1043-1053.
[19] Nazari M.E., Ardehali M.M. Optimal Bidding Strategies of GENCOs in Day-Ahead Energy and Spinning Reserve Markets Based on Heuristic Optimization Algorithm, 32th Int. Power syst. Conf. (PSC 2017), 2017, pp. 1-6.
[20] Nazari M.E., Ardehali M.M. Optimal Bidding Strategies of GENCOs in Day-Ahead Energy and Spinning Reserve Markets Based on Hybrid GA-Heuristic Optimization Algorithm, Intl J Smart Electr Eng, 2017, 6, pp. 79-86.
[21] Lee K.C., Yang H.T., Tang W. Data-driven online interactive bidding strategy for demand response. Appl. Energy, 2022, 319, 119082.
[22] Nguyen H.T., Le L.B., Wang Z. A bidding strategy for virtual power plants with the intraday demand response exchange market using the stochastic programming. IEEE Trans. Indust, 2018, 54(4), pp. 3044-55.
[23] Lu X., Ge X., Li K., Wang F., Shen H., Tao P., Hu J., Lai J., Zhen Z., Shafie-khah M., Catalão JP. Optimal bidding strategy of demand response aggregator based on Customers’ responsiveness behaviors modeling under different incentives. IEEE Trans. Indust. Appl, 2021, 57(4), pp. 3329-40.
[24] Wang Y., Ai X., Tan Z., Yan L., Liu S. Interactive dispatch modes and bidding strategy of multiple virtual power plants based on demand response and game theory. IEEE Trans. Smart Grid. 2015, 7(1), pp. 510-9.
[25] Wang F., Ge X., Li K., Mi Z. Day-ahead market optimal bidding strategy and quantitative compensation mechanism design for load aggregator engaging demand response. IEEE Trans. Indust. Appl. 2019, 55(6), pp. 5564-73.
[26] Abapour S., Mohammadi-Ivatloo B., Hagh MT. Robust bidding strategy for demand response aggregators in electricity market based on game theory. J. Clean. Prod. 2020, 243, 118393.
[27] Nojavan S., Mohammadi‐Ivatloo B., Zare K. Optimal bidding strategy of electricity retailers using robust optimisation approach considering time‐of‐use rate demand response programs under market price uncertainties. IET Gen. Trans. Distri. 2015, 9(4), pp. 328-38.
[28] Nezamabadi H., Vahidinasab V. Market bidding strategy of the microgrids considering demand response and energy storage potential flexibilities. IET Gen. Trans. Distri. 2019, 13(8), pp. 1346-57.
[29] Tang W., Yang H.T. Optimal operation and bidding strategy of a virtual power plant integrated with energy storage systems and elasticity demand response. IEEE Access. 2019, 7, pp. 79798-809.
[30] Asensio M., Contreras J. Risk-constrained optimal bidding strategy for pairing of wind and demand response resources. IEEE Trans. Smart Grid. 2015, 8(1), pp. 200-8.
[31] Zhang X., Hug G. Bidding strategy in energy and spinning reserve markets for aluminum smelters' demand response. IEEE power energy soci. Innov. Smart Grid Tech. Conf. (ISGT), 2015, pp. 1-5.
[32] Zishan F, Akbari E, Montoya OD, Giral-Ramírez DA, Nivia-Vargas AM. Electricity retail market and accountability-based strategic bidding model with short-term energy storage considering the uncertainty of consumer demand response. Results in Engineering. 2022 Dec 1;16:100679.
[33] Li Y, Deng Y, Wang Y, Jiang L, Shahidehpour M. Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties. International Journal of Electrical Power & Energy Systems. 2023 Sep 1;151:109101.
[34] Maneesha A, Swarup KS. Optimal Double Auction Bidding Strategy Considering Ancillary Services and Demand Response. In2022 22nd National Power Systems Conference (NPSC) 2022 Dec 17 (pp. 554-559). IEEE.
[35] Khaloie H, Abdollahi A, Shafie-Khah M, Siano P, Nojavan S, Anvari-Moghaddam A, Catalão JP. Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties. Journal of Cleaner Production. 2020 Jan 1;242:118434.
[36] Khaji M, Amiri M, Taghavifard MT. Co-optimized bidding strategy of an integrated wind-thermal system in electricity day ahead and reserve market under uncertainties. Journal of Modeling in Engineering. 2023 Jun 6.
[37] Dimitriadis CN, Tsimopoulos EG, Georgiadis MC. Strategic bidding of an energy storage agent in a joint energy and reserve market under stochastic generation. Energy. 2022 Mar 1;242:123026.
[38] Zhang Q, Wu X, Deng X, Huang Y, Li C, Wu J. Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market. Applied Energy. 2023 Jul 1;341:121063.
[39] Lee KC, Yang HT, Tang W. Data-driven online interactive bidding strategy for demand response. Applied Energy. 2022 Aug 1;319:119082.
[40] Chen D, Jing Z, Li Z, Xu H, Ji T. Exact relaxation of complementary constraints for optimal bidding strategy for electric vehicle aggregators. IET Renewable Power Generation. 2022 Sep;16(12):2493-507.
[41] Yamin, H.Y., El-Dwairi, Q., Shahidehpour, S.M. A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty, Electr. Power Energy Syst., 2007, 29, pp. 609–616.
[42] Tseng, C.L., Zhu, W. Optimal self-scheduling and bidding strategy of a thermal unit subject to ramp constraints and price uncertainty, IET Gener. Transm. Distrib., 2009, 4, (2), pp. 125–137.
[43] Gountis, V.P., Bakirtzis, A.G. Bidding strategies for electricity producers in a competitive electricity marketplace, IEEE Trans. Power Syst., 2004, 19, (1), pp. 356-365.
[44] Rashedi, N., Tajeddini, M.A., Kebriaei, H. Markov game approach for multi-agent competitive bidding strategies in electricity market, IET Gener. Transm. Distrib., 2016, 10, (15), pp. 3756–3763.
[45] Mallick, R.K., Agrawal, R., Hota, P.K. Bidding strategies of Gencos and large consumers in competitive electricity market based on TLBO, IEEE Power Syst. Int. Conf., 2016, pp. 1-6.
[46] Jamalzadeh, R., Ardehali, M.M., Rashidinejad, M. Development of modified rational buyer auction for procurement of ancillary services utilizing participation matrix, Energy Policy, 2008, 36, pp. 900-909
[47] Wen, F.S., David, A.K. Optimal Bidding Strategy in Spinning Reserve Market, Electr. Power Comp. Syst., 2001, 29, pp. 835–848.
[48] Nazari, M.E., Ardehali, M.M. Optimal coordination of renewable wind and pumped storage with thermal power generation for maximizing economic profit with considerations for environmental emission based on newly developed heuristic optimization algorithm, Renew. Sustainable Energy, 2016, 8, pp. 1-30.
[49] Nazari, M.E., Ardehali, M.M., Jafari, S. Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints, Energy, 2010, 35, pp. 4092-4101.
[50] Nazari M.E., Farahmand M.Z. A new operation strategy of PEV parking and hydro storage in smart grid environment, Int. J. Energy Sector Manage. 2021, 15, pp. 1-24.
[51] Jafari S., Abdolmohammadi H.R., Nazari M.E., Shayanfar H.A. A new approach for global optimization in high dimension, Power Energy Socie. General Meet. Convers. Deliv. Electr. Energy, 2008, pp. 1-7.
[52] Nazari M.E., Ardehali M.M., Jafari S., Abdolmohammadi H.R. Development of new approaches for applying genetic algorithm to unit commitment and economic dispatch problems, Iranian Conf. Fuzzy Intelli. syst., 2008, pp. 1-7.
[53] Abdolmohammadi H.R., Jafari S., Nazari M.E., Shayanfar H.A. A bio-inspired evolutionary algorithm for combined heat and power economic dispatch problems, Iranian Conf. Fuzzy Intelli. syst., 2008, pp. 1-7.
[54] Abdolmohammadi H.R., Jafari S., Rajati M.R., Nazari M.E. A bio-inspired Genetic algorithm applied to a constrained optimization problem in power systems, Iranian Conf. Fuzzy Intelli. Syst., 2007, pp. 1-6.
[55] Nazari M.E., Bahravar S., Olamaei J., Effect of storage options on price-based scheduling for a hybrid trigeneration system, Int. J. Energy Res., 2020, pp. 1-15.
[56] Nazari M.E., Ardehali M.M., Profit-based unit commitment of integrated CHP-thermal-heat only units in energy and spinning reserve markets with considerations for environmental CO2 emission cost and valve-point effects, Energy, 2017, 133, pp. 621-635.
[57] Grey, A., Sekar, A. Unified solution of security-constrained unit commitment problem using a linear programming methodology, IET Gener. Transm. Distrib., 2008, 2, (6), pp. 856–867. | ||
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