A mixed integer linear programming based approach for unit commitment in smart grid environment



The future of power systems known as smart grids is expected to involve an increasing level ofintelligence and incorporation of new information and communication technologies in everyaspect of the power grid. Demand response resources and gridable vehicle are two interestingprograms which can be utilized in the smart grid environment. Demand response resources canbe used as a demand side virtual power plant (resource) to enhance the security and reliabilityof utility and have the potential to offer substantial benefits in the form of improved economicefficiency in wholesale electricity markets. An economic model of incentive responsive loadsis modelled based on price elasticity of demand and customers’ benefit function. On the otherhand, a gridable vehicle can be used as a small portable power plant to improve the reliabilityas well as security of the power system.This paper formulates a mixed-integer programming approach to solve the unit commitmentproblem with demand response resources and gridable vehicles. The objective function of theunit commitment problem has been modified to incorporate demand response resources andgridable vehicles. The proposed method is conducted on the conventional 10-unit test systemto illustrate the impacts of smart grid environment on the unit commitment problem. Moreoverthe benefits of implementing demand response resources and gridable vehicle in electricitymarkets are demonstrated.



Demand response; mixed-integer programming; smart grid; unit commitment.

Full Text:



Afkousi-Paqaleh, M., Rashidi-Nejad, M. & Pourakbari, M. 2010. An implementation of harmony

search algorithm to unit commitment problem. Electrical Engineering (Springer) 92(6): 215-225.

Aalami, H., Yousefi, G. R. & Parsa Moghaddam, M. 2008. A MADM-based Support System for DR

Programs. 43rd Int. Univ. Power Eng. Conf. (UPEC). Italy.

Aalami, H., Yousefi, G. R. & Parsa Moghaddam, M. 2008. Demand Response Model Considering

EDRP and TOU Programs. IEEE PES T & D Conf. Chicago. USA: 1-6.

Aalami, H., Parsa Moghaddam, M. & Yousefi, G. R. 2010. Demand Response Modeling Considering

Interruptible/Curtailable Loads and Capacity Market Programs. Applied Energy 87(1): 243-250.

Aalami, H., Parsa Moghaddam, M. & Yousefi, G. R. 2010. Modeling and Prioritizing Demand Response

Programs in Power Markets. Electr. Power Syst. Res. (EPSR). 80(4): 426-435.

Annakkage, U. D., Nummonda, T. & Pahalawatha, N. C. 1995. Unit commitment by parallel simulated

annealing. IEEE Proc. Gen. Tansm. Dist. 142(6): 595-600.

Al-Alawi, B. M. & Bradley, T. H. 2013. Total cost of ownership, payback, and consumer preference

modeling of plug-in hybrid electric vehicles, Applied Energy, 103: 488–506.

Al-Alawi, B. M. & Bradley, T. H. 2013. Review of Hybrid and Electric Vehicle Market Modeling Studies,

Renewable and Sustainable Energy Reviews, 21: 190-203.

Al-Alawi, B. M. & Bradley, T. H. 2014. Analysis of Corporate Average Fuel Economy Regulation

Compliance Scenarios Inclusive of Plug in Hybrid Vehicles, Applied Energy, 113: 1323-1337.

Battaglini, A., Lilliestam, J., Haas, A. & Patt, A. 2009. Development of SuperSmart Grids for a more

efficient utilisation of electricity from renewable sources. Journal of Cleaner Production 17: 911–

Bompard, E., Ma, Y., Napoli, R. & Abrate, G. 2007. The Demand Elasticity Impacts on the Strategic

Bidding Behavior of the Electricity Producers. IEEE Trans. on Power Syst. 22(1): 188-197.

Bradley, T. H. & Frank, A. A. 2009. Design, demonstrations and sustainability impact assessments for

plug-in hybrid electric vehicles. Renewable and Sustainable Energy Reviews. 13: 115–128.

Carrión, M. & Arroyo, J. M. 2006. A Computationally Efficient Mixed-Integer Linear Formulation for

the Thermal Unit Commitment Problem. IEEE Trans. on Power Syst. 21(6): 1371-1378.

Chaoyue, Z., Jianhui, W., Watson, J.-P. & Yongpei, G. 2013. Multi-Stage Robust Unit Commitment

Considering Wind and Demand Response Uncertainties. IEEE Transactions on Power Systems.

(3): 2708-2717.

Cheng, C.-P., Liu, C.-W. & Liu, C.-C. 2002. Unit commitment by annealing-genetic algorithm.

International Journal of Electrical Power & Energy Systems 24(2): 149-158.

FERC. Staff Report. 2006. Assessment of Demand Response and Advanced Metering. www.FERC.gov.

FERC. Staff Report. 2008. Assessment of Demand Response and Advanced Metering.www.FERC.gov.

Galus, M. D., Zima, M. & Andersson, G. 2010. On integration of plug-in hybrid electric vehicles into

existing power system structures/ Energy Policy 38: 6736–6745.

Goel, L., Wu, Q. & Wang, P. 2006. Reliability Enhancement of a Deregulated Power System Considering

Demand Response. IEEE PES General Meeting: 1-6.

A mixed integer linear programming based approach for unit commitment in smart grid environment 156

Goel, L., Wu, Q. & Wang, P. 2007. Reliability Enhancement and Nodal Price Volatility Reduction of

Restructured Power Systems with Stochastic Demand Side Load Shift. IEEE General Meeting: 1-8

Hosseini, S. H., Khodaei, A. & Aminifar, F. 2007. A Novel Straightforward Unit Commitment method

for Large-Scale Power Systems. IEEE Trans. Power Syst. 22(4): 2134 - 2143.

Huang, S. J. 2001. Enhancement of hydroelectric generation scheduling using ant colony system based

optimization approaches. IEEE Trans. Energy convers. 16(3): 296-301.

IEA.2010. Strategic Plan for the IEA Demand-Side Management Program 2004-2009. www.iea.org.

Kazarlis, S. A., Bakirtzis, A. G. & Petridis, V. 1996. A genetic algorithm solution to the unit commitment

problem. IEEE Trans. Power Syst. 11(1): 83-92.

Kempton, W., Tomic, J., Letendre, S., Brooks, A. & Lipman, T. 2005. Vehicle-to-grid power: battery,

hybrid and fuel cell vehicles as resources for distributed electric power in California, Davis, CA.

Institute of Transportation Studies. Report #IUCD-ITSRR: 01-03.

Kempton, W. & Tomic, J. 2005. Vehicle-to-grid power fundamentals: Calculating capacity and net

revenue. J. Power Sources. 144 (1): 268–279.

Kempton, W. & Tomic, J. 2005. Vehicle-to-grid power implementation: From stabilizing the grid to

supporting large-scale renewable energy. J. Power Sources. 144(1): 280–294.

Kirschen, D. S., Strbac, G., Cumperayot, P. & Mendes, D. 2000. Factoring the Elasticity of Demand in

Electricity Prices. IEEE Trans. on Power Syst. 15(2): 612-617.

Kirschen, D. S. & Strbac, G. 2005. Fundamentals of Power System Economics. John Wiley & Sons

Ltd, 2004C. R. Associates, Primer on demand side management. Report for the World Bank: 6–9.

Li, C. A., Johnson, R. B. & Svoboda, A. J. 1997. A new unit commitment method. IEEE Trans. power

syst. 12(1): 113-119.

Ma, O., Alkadi, N., Cappers, P., Denholm, P., Dudley, J., Goli, S., Hummon, M., Kiliccote,

S., MacDonald, J., Matson, N., Olsen, D., Rose, C., Sohn, M. D., Starke, M., Kirby, B. &

O’Malley, M. 2013. Demand Response for Ancillary Services. IEEE Transactions on Smart Grid.

(4): 1988-1995.

Mantawy, A. H., Soliman, S. A. & El-Hawari, M. E. 2002. A new Tabu search algorithm for the longterm

hydro scheduling problem. In Proc. Large Eng. Syst. Conf. Power Eng.: 29-34.

Li, T. & Shahidehpour, M. 2005. Price-Based Unit Commitment: A Case of Lagrangian Relaxation

Versus Mixed Integer Programming, Prices. IEEE Trans. on Power Syst. 20(4): 2015-2025.

Ouyang, Z. & Shahidehpour, M. 1991. An Intelligent Dynamic Programming for Unit Commitment

Application. IEEE Trans. Power Syst. 6(3): 1203-1209.

Padhy, N. P. 2004. Unit commitment-A bibliographical Survey. IEEE Trans. Power Syst. 19(2): 1196-1205.

Pourmousavi, S. A. & Nehrir, M. H. 2014. Introducing Dynamic Demand Response in the LFC Model.

IEEE Trans. Power Syst. (99): 1-11.

Quinn, C., Zimmerle, D. & Bradley, T. H. 2010. The effect of communication architecture on the

availability, reliability, and economics of plug-in hybrid electric vehicle-to-grid ancillary services.

Journal of Power Sources 195: 1500–1509.

Ruiwei, J., Jianhui, W., Muhong, Z. & Yongpei, G. 2013. Two-Stage Minimax Regret Robust Unit

Commitment. IEEE Transactions on Power Systems. 28(3): 2271-2282.

Saber, A. Y. & Venayagamoorthy, G. K. 2010a. Intelligent unit commitment with vehicle-to-grid -A

cost-emission optimization. Journal of Power Sources 195: 898–911.

Saber, A. Y. & Venayagamoorthy, G. K. 2010b. Plug-in Vehicles and Renewable Energy Sources for

Cost and Emission Reductions. IEEE Trans. on Industrial Elec. 21(6): 1371-1378.

Sarjiya, Mulyawan, A. B., Setiawan, A. & Sudiarso, A. 2013. Thermal unit commitment solution using

genetic algorithm combined with the principle of Tabu search and priority list method. International

Conference on Information Technology and Electrical Engineering (ICITEE). 414-419.

R. Ghadiri Anari M. Rashidinejad and M. Fotuhi-Firuzabad

Schweppe, F. C., Caramanis, M. C., Tabors, R. D. & Bohn, R. E. 1989. Spot Pricing of Electricity.

Kluwer Academic Publishers. Appendix E.

Su, C. L. & Kirschen, D. 2009. Quantifying the Effect of Demand Response on Electricity Markets.

IEEE Trans. on Power Syst. 24(3): 1199-1207.

Swarup, K. S. & Yamashiro, S. 2003. A genetic algorithm approach to generator unit commitment. Int.

J. Electr. Power Energy Syst. 25: 679-687.

Tomic, J. & Kempton, W. 2007. Using fleets of electric-drive vehicles for grid support. J. Power Sources.

(2): 45-468.

Tseng, C. L., Li, C. A. & Oren, S. S. 2000. Solving the Unit commitment Problem by a Unit Decommitment

Method. Journal of Optimization Theory and Applications. 105(3): 707-730.

Williams, B. D. & Kurani, K. S. 2006. Estimating the early household market for light-duty hydrogenfuel-

cell vehicles and other “Mobile Energy” innovations in California: A constraints analysis. J.

Power Sources 160(1): 446-453.

Yu, N. & Yu, J. 2006. Optimal TOU Decision Considering Demand Response Model. IEEE, Int. Conf. on

Power Syst. Techno. Chongqing: 1-5.

Zhanle, W., Paranjape, R., Sadanand, A. & Zhikun C. 2013. Residential demand response: An

overview of recent simulation and modeling applications. 26th Annual IEEE Canadian Conference

on Electrical and Computer Engineering (CCECE). 1-6.

Zhao, B., Guo, C. X., Bai, B. R. & Cao, Y. J. 2006. An improved particle swarm optimization algorithm

for unit commitment. Int. J. Elect. Power Energy Syst., 28(7): 482-490.


  • There are currently no refbacks.