Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter August 11, 2021

Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs

Saurabh Kumar ORCID logo, Bharti Dwivedi ORCID logo and Nitin Anand Shrivastava ORCID logo


Under the deregulated market scenario, the electric power industry is rapidly getting transformed due to integration of increasing renewable generation. Profit maximization has always been of highest priority for all the stakeholders. The generation planning problem is an important problem in the operation and planning of power systems which is required to be solved to accomplish the maximum profit. The individual GENeration COmpanies (GENCOs) are envisioning their most profitable Unit Commitment (UC) in the light of the available forecasts of availability of solar and wind power, energy prices and reserve prices during a given scheduling period. This paper presents a UC strategy for GENCOs’ profit with a generation portfolio comprising thermal generation, wind generation, and solar PV generation in different combinations. Priority List Method (PLM) and Modified Particle Swarm Optimization (PSO) with Time-Varying Acceleration Coefficients (PSO-TVAC) solution technique is deployed on the MATLAB platform. The aim is to maximize the profit while satisfying the load demand together with maintaining sufficient reserve at each hour. Sensitivity analysis of profit has also been carried out to showcase the impact of different pricing techniques, different parameters chosen and the different bidding prices for the power dispatched as well as for the reserve power maintained. The observations that emerged through the presented solution model may help develop an insight to the GENCOs to gain maximum profits.

Corresponding author: Saurabh Kumar, Department of Electrical Engineering, Institute of Engineering and Technology, Lucknow, Uttar Pradesh, 226021, India, E-mail:

Funding source: Dr. APJ Abdul Kalam Technical University, Lucknow

Funding source: Institute of Engineering and Technology, Lucknow


The authors would like to acknowledge Homi Bhabha Teaching Assistant Fellowship of Dr. APJ Abdul Kalam Technical University, Lucknow and Institute of Engineering and Technology, Lucknow for supporting this research.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The study was supported by the Homi Bhabha Teaching Assistant Fellowship of Dr. APJ Abdul Kalam Technical University, Lucknow and Institute of Engineering and Technology, Lucknow.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.


1. Kumar, S, Dwivedi, B. Techno-economical study of power system market- A game theory approach. In: International Conference on Automation, Computational and Technology Management (ICACTM); 2019. pp. 84–8. in Google Scholar

2. Nosratabadi, SM, Hooshmand, R-A. Stochastic electrical energy management of industrial Virtual Power Plant considering time-based and incentive-based Demand Response programs option in contingency condition. Int J Emerg Elec Power Syst 2020;21. in Google Scholar

3. Jabari, F, Shamizadeh, M, Mohammadi‐Ivatloo, B. Risk‐constrained day‐ahead economic and environmental dispatch of thermal units using information gap decision theory. Int Trans Electr Mach Energy Convers Syst 2018;29:1–18. in Google Scholar

4. Gumpu, S, Pamulaparthy, B, Sharma, A. Review of congestion management methods from conventional to smart grid scenario. Int J Emerg Elec Power Syst 2020;20. in Google Scholar

5. Sriyanyong, P. Application of particle swarm optimization technique to a profit-based unit commitment problem. In: 2nd International Conference on Education Technology and Computer (ICETC); 2010. pp. 1–6. in Google Scholar

6. Abdelzaher, MM, Abdelaziz, AY, Mahmoud, HM, Mekhamer, SF, Ali, SG, Alhelou, HH. Generation expansion planning with high shares of variable renewable. AIMS Energy 2020;8:272–98. in Google Scholar

7. Sharma, D, Srinivasan, D, Trivedi, A. Multi-agent approach for profit based unit commitment. IEEE Congr Evol Comput 2011;2527–33. in Google Scholar

8. Yu, J, Zhou, J, Hua, B, Liao, R. Optimal short-term generation scheduling with multi-agent system under a deregulated power market. Int J Comput Cognit 2005;3:61–5.Search in Google Scholar

9. Kumar, S, Dwivedi, B, Shrivastava, NA. A game theory strategy-based bidding evaluation for power generation market. IEEE Can J Electr Comput Eng 2021;44:283–8. in Google Scholar

10. Dayalan, S, Rathinam, R. Energy management of a microgrid using demand response strategy including renewable uncertainties. Int J Emerg Elec Power Syst 2021;22:85–100. in Google Scholar

11. Xiaohui, Y, Yanbin, Y, Cheng, W, Xiaopan, Z. An improved PSO approach for profit-based unit commitment in electricity market. In: IEEE/PES Transmission and Distribution Conference & Exhibition; 2005. pp. 1–4.Search in Google Scholar

12. Longenthiran, T, Srinivasan, D. LRGA for solving profit based generation scheduling problem in competitive environment. IEEE Congr Evol Comput 2011:1148–54.Search in Google Scholar

13. Pluta, M, Wyrwa, A, Suwała, W, Zyśk, J, Raczyński, M, Tokarski Hasegawa, S. A generalized unit commitment and economic dispatch approach for analysing the polish power system under high renewable penetration. Energies 2020;13:1–18. in Google Scholar

14. Chandram, K, Subrahmanyam, N, Sydulu, M. Improved preprepared power demand table with Muller method for solving profit based unit commitment. In: IEEE Region 10 Conference TENCON; 2008. pp. 1–6. in Google Scholar

15. Li, T, Shahidehpour, M. Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming. IEEE Trans Power Syst 2005;20:2015–25. in Google Scholar

16. Kelley, CT. Iterative methods for linear and nonlinear equations. Soc Ind Appl Math 1995;1–169. in Google Scholar

17. Gaing, ZL. Discrete particle swarm optimization algorithm for unit commitment. In: 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), Toronto, Ont., 2003, vol 1. pp. 418–24. in Google Scholar

18. Ting, TO, Rao, M, Loo, C, Ngu, SS. Solving unit commitment problem using hybrid particle swarm optimization. J Heuristics 2003;9:507–20. in Google Scholar

19. Padhy, NP. Discussion on "A fast technique for unit commitment problem by extended priority list. IEEE Trans Power Syst 2004;19:2119. in Google Scholar

20. Merlin, A, Sandrin, P. A new method for unit commitment at electricite de France. IEEE Trans Power Apparatus Syst 1983;PAS-102:1218–25. in Google Scholar

21. Tiwari, S, Dwivedi, B, Dave, MP. Economic load dispatch using particle swarm optimization, 1st ed.. Germany: Lap Lambert Academic Publishing; 2017.Search in Google Scholar

22. Attaviriyanupap, P, Kita, H, Tanaka, E, Hasegawa, J. A hybrid LR-EP for solving new profit-based UC problem under competitive environment. IEEE Trans Power Syst 2003;18:229–37. in Google Scholar

23. Shukla, A, Lal, VN, Singh, SN. Profit-based unit commitment problem using PSO with modified dynamic programming. In: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), Porto, 2015. pp. 1–6. in Google Scholar

24. Sharma, D, Trivedi, A, Srinivasan, D, Thillainathan, L. Multi-agent modeling for solving profit based unit commitment problem. Appl Soft Comput 2013;13:3751–61. in Google Scholar

25. Jain, AK, Srivastava, SC. Price responsive demand management of an industrial buyer in day-ahead electricity market. Int J Emerg Elec Power Syst 2017;18. in Google Scholar

26. Tiwari, S, Dwivedi, B, Dave, MP. A two stage solution methodology for deterministic unit commitment problem. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), Varanasi, 2016. pp. 317–22. in Google Scholar

27. Sudhakar, AVV, Karri, C, Laxmi, AJ. Profit based unit commitment for GENCOs using Lagrange relaxation–differential evolution. Eng Sci Technol Int J 2017;20:738–47. in Google Scholar

28. Wood, AJ, Wollenberg, BF, Sheble, GB. Power generation, operation and control, 3rd ed.. New York: John Wiley & Sons; 2014.Search in Google Scholar

29. Chakraborty, S, Senjyu, T, Saber, AY, Yona, A, Funabashi, T. Optimal thermal unit commitment integrated with renewable energy sources using advanced particle swarm optimization. IEEJ Trans Electr Electron Eng 2009;4:609–17. in Google Scholar

30. Khanmohammadi, S, Amir, M, Haque, MT. A new three stage method for solving unit commitment method. Energy 2010;35:3072–80. in Google Scholar

31. Thota, MS, Alla, S. Convex economic emission dispatch using particle swarm optimization with time varying acceleration coefficients. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017. pp. 1830–5. in Google Scholar

32. Chaturvedi, KT, Pandit, M, Srivastava, L. Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Trans Power Syst 2008;23:1079–87. in Google Scholar

Received: 2021-02-15
Accepted: 2021-07-16
Published Online: 2021-08-11

© 2021 Walter de Gruyter GmbH, Berlin/Boston