Cost-based Risk Approach for Spinning Reserve Assessment in Bulk Power Systems

Document Type : Original Article


Faculty of Engineering, University of Hormozgan, Bandar-Abbas, Iran


Spinning reserve (SR) is one of the most prevalent methods for balancing grid uncertainties, such as generator faults, to maintain grid reliability. Literature review shows that several deterministic as well as probabilistic methos have been proposed for determining SR. It is always a challenge for a system operator to decide which approach better from security and reliability point of view. This is important because the allocated SR may provide in some cases a misleading sense of confidence with respect to safe, secure, reliable and economic operation of power systems. This paper presents a cost-based risk index approach for assessing the spinning reserve requirements in a power system. To that end, the performance of spinning reserve is classified in three types, namely, not-effective, partially-effective, and not-meeting-load. Then probability of each type and its consequences are subsequently computed and finally that the risk associated with any spinning reserve value is determined. It is shown that one might consider various spinning reserve values for an operating condition (randomly or using approaches proposed in literature), then calculate risks associated with each value, and finally use the calculated risk indices to determining the optimal level of spinning reserve. As an example, we have shown that in the studied network with 6600MW load, maintaining 240MW SR will increase cost of 1MW hour energy by $0.154 while the optimal value of 200MW SR will increase cost of 1MWhour energy by just $0.126. This paper initially focuses on providing a measure of the quality of an ex-ante specified spinning reserve, latter on the flowchart of using the proposed approach for determining optimal level of spinning reserve is presented. The proposed risk index can also be used for comparing different deterministic as well as probabilistic approaches presented in literature for spinning reserve requirements.


Main Subjects

  1. Ye, L., Yao, J., Ouyang, X., Zhu, X. and Yang, S., "Risk analysis and utility function-based decision-making model for spinning reserve allocations." IEEE Access, Vol. 9, (2021), 18752-18761, doi: 10.1109/ACCESS.2021.3054404.
  2. Nikolaidis, P. and Poullikkas, A., "A novel cluster-based spinning reserve dynamic model for wind and PV power reinforcement." Energy, Vol. 234, (2021), 121270, doi: 10.1016/
  3. Basiri, M.H., Sharifi, M.R. and Ostadi, B., "Reliability and risk assessment of electric cable shovel at chadormalu iron ore mine in iran", International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 1, (2020), 170-177, doi: 10.5829/ije.2020.33.01a.20.
  4. Malekshah, S., Alhelou, H.H. and Siano, P., "An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment." International Transactions on Electrical Energy Systems, Vol 31, No. 11, (2021), e13052,doi: 10.1002/2050-7038.13052.
  5. Asgari, S., Menhaj, M., Suratgar, A.A. and Kazemi, M., "A disturbance observer based fuzzy feedforward proportional integral load frequency control of microgrids", International Journal of Engineering, Transactions A: Basics, Vol. 34, No. 7, (2021), 1694-1702, doi: 10.5829/ije.2021.34.07a.13.
  6. Anstine, L., Burke, R., Casey, J., Holgate, R., John, R. and Stewart, H., "Application of probability methods to the determination of spinning reserve requirements for the pennsylvania-new jersey-maryland interconnection", IEEE Transactions on Power Apparatus and Systems, Vol. 82, No. 68, (1963), 726-735, doi: 10.1109/TPAS.1963.291390.
  7. Khoshjahan, M., Dehghanian, P., Moeini-Aghtaie, M. and Fotuhi-Firuzabad, M., "Harnessing ramp capability of spinning reserve services for enhanced power grid flexibility." IEEE Transactions on Industry Applications, Vol. 55, No. 6, (2019), 7103-7112, doi: 10.1109/TIA.2019.2921946.
  8. Ansari, J. and Malekshah, S., "A joint energy and reserve scheduling framework based on network reliability using smart grids applications", International Transactions on Electrical Energy Systems, Vol. 29, No. 11, (2019), e12096, doi: 10.1002/2050-7038.12096.
  9. Amirahmadi, M. and Akbari Foroud, A., "A new approach to locational pricing and settlement of day‐ahead spinning reserve market", International Transactions on Electrical Energy Systems, Vol. 26, No. 1, (2016), 155-174, doi: 10.1002/etep.2078.
  10. Bhattacharya, B., Chakraborty, N. and Mandal, K.K., "A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty", International Transactions on Electrical Energy Systems, Vol. 29, No. 7, (2019), e12104, doi: 10.1002/2050-7038.12104.
  11. Gupta, A., Verma, Y.P. and Chauhan, A., "Financial analysis of reactive power procurement in pool‐based deregulated power market integrated with dfig‐based wind farms", International Transactions on Electrical Energy Systems, Vol. 29, No. 3, (2019), e2739, doi: 10.1002/etep.2739.
  12. Zhang, L., Yuan, Y., Yuan, X., Chen, B., Su, D. and Li, Q., "Spinning reserve requirements optimization based on an improved multiscenario risk analysis method", Mathematical Problems in Engineering, Vol. 2017, (2017),doi: 10.1155/2017/6510213.
  13. Emarati, M., Keynia, F. and Rashidinejad, M., "A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant", International Transactions on Electrical Energy Systems, Vol. 30, No. 3, (2020), e12255, doi: 10.1002/2050-7038.12255.
  14. Datta, S. and Vittal, V., "Operational risk metric for dynamic security assessment of renewable generation", IEEE Transactions on Power Systems, Vol. 32, No. 2, (2016), 1389-1399, doi: 10.1109/TPWRS.2016.2577500.
  15. Wang, Y., Vittal, V., Abdi-Khorsand, M. and Singh, C., "Probabilistic reliability evaluation including adequacy and dynamic security assessment", IEEE Transactions on Power Systems, Vol. 35, No. 1, (2019), 551-559, doi: 10.1109/TPWRS.2019.2923844.
  16. Jabari, F., Shamizadeh, M. and Mohammadi‐Ivatloo, B., "Risk‐constrained day‐ahead economic and environmental dispatch of thermal units using information gap decision theory", International Transactions on Electrical Energy Systems, Vol. 29, No. 2, (2019), e2704, doi: 10.1002/etep.2704.
  17. De Caro, F., Vaccaro, A. and Villacci, D., "A markov chain-based model for wind power prediction in congested electrical grids", The Journal of Engineering, Vol. 2019, No. 18, (2019), 4961-4964, doi: 10.1049/joe.2018.9247.
  18. Wang, Y., "Probabilistic spinning reserve adequacy evaluation for generating systems using an markov chain monte carlo-integrated cross-entropy method", IET Generation, Transmission & Distribution, Vol. 9, No. 8, (2015), 719-726, doi: 10.1049/iet-gtd.2014.0763.
  19. Rajabdorri, M., Sigrist, L., Lobato, E., Prats, M.D.C. and Echavarren, F.M., "Viability of providing spinning reserves by RES in Spanish island power systems." IET Renewable Power Generation, Vol. 15, No. 13 (2021), 2878-2890,doi: 10.1049/rpg2.12216.
  20. Bento, M.E. and Ramos, R.A., "An approach for monitoring and updating the load margin of power systems in dynamic security assessment", Electric Power Systems Research, Vol. 198,, (2021), 107365, doi: 10.1016/j.epsr.2021.107365.
  21. Bento, M.E., "Monitoring of the power system load margin based on a machine learning technique", Electrical Engineering, Vol. 104, No. 1, (2022), 249-258, doi: 10.1007/978-0-387-32935-2