Formulation of the Optimization Problem for Unit Commitment with Price Elasticity-Based Demand Response
DOI:
https://doi.org/10.62146/ijecbe.v2i3.78Keywords:
Unit Commitment, market demandAbstract
Integrating Demand Response (DR) programs into the Unit Commitment (UC) problem is a promising method to enhance the efficiency and reliability of power systems. This work introduces a new formulation that incorporates price elasticity into the UC problem using a relaxed optimization approach. Our objective is to maximize overall system performance by reducing generation costs and maximizing the utility function while accounting for how demand changes in response to electricity prices, i.e., price elasticity-based DR. The proposed model employs Mixed-Integer Linear Programming (MILP) techniques to efficiently solve the UC problem, using a linear function to model price elasticity-based DR. Our approach has demonstrated its effectiveness in achieving substantial cost reductions and improved load management, as shown through numerical simulations.
References
R. Dalimi, Menuju Era Energi Terbarukan, Penerbit Departemen Teknik Elektro FTUI, Depok, 2021.
C. Arun, R. Aswinraj, M. Bijoy, M. Nidheesh, R. R. Micky, Day ahead demand response using load shifting technique in presence of increased renewable penetration, in: 2022 IEEE 7th International conference for Convergence in Technology (I2CT), IEEE, 2022, pp. 1–6.
C. W. Gellings, J. H. Chamberlin, Demand-side management: concepts and methods (1987).
A. J. Wood, B. F. Wollenberg, G. B. Shebl´e, Power generation, operation, and control, John Wiley & Sons, 2013.
L. L. Garver, Power generation scheduling by integer programming-development of theory, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems 81 (3) (1962) 730–734.
T. Koch, T. Achterberg, E. Andersen, O. Bastert, T. Berthold, R. E. Bixby, E. Danna, G. Gamrath, A. M. Gleixner, S. Heinz, et al., Miplib 2010: mixed integer programming library version 5, Mathematical Programming Computation 3 (2011) 103–163.
M. Carri´on, J. M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem, IEEE Transactions on power systems 21 (3) (2006) 1371–1378.
S. Atakan, G. Lulli, S. Sen, A state transition mip formulation for the unit commitment problem, IEEE Transactions on Power Systems 33 (1) (2017) 736–748.
B. Knueven, J. Ostrowski, J.-P. Watson, On mixed-integer programming formulations for the unit commitment problem, INFORMS Journal on Computing 32 (4) (2020) 857–876.
J. Wang, S. Kennedy, J. Kirtley, A new wholesale bidding mechanism for enhanced demand response in smart grids, in: 2010 Innovative Smart Grid Technologies (ISGT), IEEE, 2010, pp. 1–8.
M. P. Moghaddam, A. Abdollahi, M. Rashidinejad, Flexible demand response programs modeling in competitive electricity markets, Applied Energy 88 (9) (2011) 3257–3269.
H. Aalami, M. P. Moghaddam, G. R. Yousefi, Modeling and prioritizing demand response programs in power markets, Electric Power Systems Research 80 (4) (2010) 426–435.
M. Parvania, M. Fotuhi-Firuzabad, M. Shahidehpour, Iso’s optimal strategies for scheduling the hourly demand response in day-ahead markets, IEEE Transactions on Power Systems 29 (6) (2014) 2636–2645.
R. R. Gaddam, Optimal unit commitment using swarm intelligence for secure operation of solar energy integrated smart grid, Power Systems Research Center (2013).
F. Magnago, J. Alemany, J. Lin, Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market, International Journal of Electrical Power & Energy Systems 68 (2015) 142–149.
P. Pinson, H. Madsen, et al., Benefits and challenges of electrical demand response: A critical review, Renewable and Sustainable Energy Reviews 39 (2014) 686–699.
M. Hummon, D. Palchak, P. Denholm, J. Jorgenson, D. J. Olsen, S. Kiliccote, N. Matson, M. Sohn, C. Rose, J. Dudley, et al., Grid integration of aggregated demand response, part 2: modeling demand response in a production cost model, Tech. rep., National Renewable Energy Lab.(NREL), Golden, CO (United States) (2013).
G. Morales-Espa˜na, R. Mart´ınez-Gord´on, J. Sijm, Classifying and modelling demand response in power systems, Energy 242 (2022) 122544.
A. K. David, Y. Li, Consumer rationality assumptions in the real-time pricing of electricity, in: IEE Proceedings C (Generation, Transmission and Distribution), Vol. 139, IET, 1992, pp. 315–322.
V. K. Tumuluru, Z. Huang, D. H. Tsang, Integrating price responsive demand into the unit commitment problem, IEEE Transactions on Smart Grid 5 (6) (2014) 2757–2765.
J. Lofberg, Yalmip : a toolbox for modeling and optimization in matlab, in: 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508), 2004, pp. 284–289. doi:10.1109/CACSD.2004.1393890.
Gurobi Optimization, LLC, Gurobi Optimizer Reference Manual (2024). URL https://www.gurobi.com
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