Skip to main content
SpringerLink
Log in

Modified grey wolf optimization approach for power system transmission line congestion management based on the influence of solar photovoltaic system

  • Original Research
  • Published:
International Journal of Energy and Environmental Engineering Aims and scope Submit manuscript

Abstract

This research manuscript proposes a Modified Grey Wolf Optimization approach for the power system congestion cost problem based on real power rescheduling methodology with solar photovoltaic system integration. The Bus Sensitivity Factor is utilized to determine the optimal positioning of the solar photovoltaic system. The Bus Sensitivity Factor assists in identification of the most sensitive bus for the injection of real power that will influence power flow in congested lines. The Modified Grey Wolf Optimization has been proposed for the congestion management problem with improved convergence rate and the potency to avoiding getting trapped into local optima. This is accomplished by enhancing the equilibrium between exploration and exploitation stages in traditional Grey Wolf Optimization algorithm. Furthermore, in the proposed Modified Grey Wolf Optimization, incorporation of the weighted distance technique has assisted in rapid discovery of the global optima. The performance of the proposed Modified Grey Wolf Optimization has been evaluated based on the benchmark functions in comparison with recent optimization techniques. The efficacy of the proposed strategy has been evaluated and validated on IEEE-30 bus system. Simulation findings highlight that the congestion cost achieved with Modified Grey Wolf Optimization with the influence of solar photovoltaic system has been reduced by 19.23, 16.57, 12.58, 10.97, 6.76 and 1.86% in comparison with some of the recent optimization techniques. Comparative analysis with recent optimization techniques reveals that the Modified Grey Wolf Optimization method for congestion management with solar photovoltaic system is more effective and accurate in terms of congestion cost, system losses, bus voltage magnitudes, convergence characteristic and computational time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

\(T_{c}\) :

PV module temperature in °C

\(T_{a}\) :

Ambient temperature in °C

\(K_{ot}\) :

Nominal operating temperature in °C

\(P_{pv}\) :

Power output of solar PV system in MW

\(I\) :

Actual solar irradiation in W/m2

\(I_{\max }\) :

Maximum solar irradiation in W/m2

\(Q_{pv}\) :

Rating of PV system

\(l_{pv}\) :

Loss factor for PV system

\(R_{pv} (t)\) :

Hourly incident radiation

\(R_{pv}\) :

Standard incident radiation

\(\Gamma ( \bullet )\) :

Gamma factor

\(\kappa\)and \(\psi\) :

Shape factors for the beta distribution function

\(\mu\) :

Mean deviation

\(\sigma\) :

Standard deviation

\(CB_{SOC}^{T} (t)\) :

State of charge of the battery

\(\eta_{CB}\) :

Overall efficiency of the battery

\(\eta_{CB}^{c}\) :

Charging efficiency of the battery

\(\eta_{CB}^{d}\) :

Discharging efficiency of the battery

\(V_{bus}\) :

Bus voltage in volts

\(CB_{bank}^{T}\) :

Total capacity of battery bank in Ah

\(CB_{n} (Ah)\) :

Capacity of a single battery

\(N_{CB}^{s}\) :

Number of batteries connected in series

\(N_{CB}^{{}}\) :

Total number of batteries

\(V_{CB}\) :

Voltage of single battery in v

\(P_{CB}^{\max }\) :

Maximum power of the battery in MW

\(I_{CB}^{\max }\) :

Maximum battery current in Amp.

\(P_{CB}^{m}\) :

Power injected to the grid in MW

\(\eta_{inv}\) :

Inverter efficiency

\(P_{inv}^{0}\) :

Inverter power output in MW

\(n\) :

Number batteries connected in series

\(BSF_{k}^{n}\) :

Bus sensitivity factor

\(\Delta P_{ij}\) :

Change in congested line power flow in MW

\(\Delta P_{n}\) :

Real power injected at bus n

\(RC\) :

Rescheduling cost in $/h

\(R_{i}^{u}\) :

Generator incremental bid in $/MWh

\(R_{i}^{d}\) :

Generator decremental bid in $/MWh

\(R_{pv}^{{}}\) :

Price bid for solar PV system in $/MWh

\(\Delta P_{gi}^{u}\) :

Incremental rescheduled power in MW

\(\Delta P_{gi}^{d}\) :

Decremental rescheduled power in MW

\(F_{t}^{\max }\) :

Maximum power flowing limit in MVA

\(F_{t}\) :

Actual power flowing in the line in MVA

\(G_{ij}\) :

Conductance of the transmission system

\(B_{ij}^{{}}\) :

Susceptance of transmission system

\(N_{g}\) :

Total number of generator bus

\(N_{l}\) :

Total number of load bus

\(N_{t}\) :

Total number of transmission lines

\(VP_{j}\) :

Penalty term for bus voltage limit violation

\(QP_{j}\) :

Penalty term for reactive power violation

\(FP_{j}\) :

Penalty term for line limit violation

\(cz_{j}^{n}\) :

Chaotic variable

Dmin :

Dimension

\(r_{1} ,r_{2}\) :

Random vectors in the range [0,1]

References

  1. Paul, K., Dalapati, P., and Kumar, N.: Optimal rescheduling of generators to alleviate congestion in transmission system: A novel modified whale optimization approach. Arabian J. Sci. Eng. 1–25, (2021)

  2. Prajapati, V.K., Mahajan, V.: Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources. Energy 215, 119134 (2021)

    Article  Google Scholar 

  3. Paul, K., Kumar, N., Dalapati, P.: Bat algorithm for congestion alleviation in power system network. Technol. Econ. Smart Grids Sustain. Energy 6, 1–18 (2021)

    Article  Google Scholar 

  4. Dehghani, M., Ghiasi, M., Niknam, T., Kavousi-Fard, A., Shasadeghi, M., Ghadimi, N., et al.: Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13, 90 (2021)

    Article  Google Scholar 

  5. Narain, A., Srivastava, S., Singh, S.: Congestion management approaches in restructured power system: key issues and challenges. Electric. J. 33, 106715 (2020)

    Article  Google Scholar 

  6. Pillay, A., Karthikeyan, S.P., Kothari, D.: Congestion management in power systems–a review. Int. J. Electr. Power Energy Syst. 70, 83–90 (2015)

    Article  Google Scholar 

  7. Jaisisngpure, A., Chandrakar, V., Mohril, R.: Market based criteria for congestion management and transmission pricing. Int. J. Eng. Res. Appl. 4, 18–23 (2014)

    Google Scholar 

  8. Hosseini, S.A., Amjady, N., Shafie-khah, M., Catalao, J.P.: A new multi-objective solution approach to solve transmission congestion management problem of energy markets. Appl. Energy 165, 462–471 (2016)

    Article  Google Scholar 

  9. Siddiqui, A. S., and Saxena, A.: Optimal intelligent strategic LMP solution and effect of DG in deregulated system for congestion management. Int. Transactions Electric. Energy Syst. 31, 1–51 (2021)

  10. Verma, S., Mukherjee, V.: Firefly algorithm for congestion management in deregulated environment. Eng. Sci. Technol. Int. J. 19, 1254–1265 (2016)

    Google Scholar 

  11. Kumar, A., Mittapalli, R.K.: Congestion management with generic load model in hybrid electricity markets with FACTS devices. Int. J. Electr. Power Energy Syst. 57, 49–63 (2014)

    Article  Google Scholar 

  12. Stawska, A., Romero, N., de Weerdt, M., Verzijlbergh, R.: Demand response: For congestion management or for grid balancing? Energy Policy 148, 111920 (2021)

    Article  Google Scholar 

  13. Nappu, M.B., Arief, A., Bansal, R.C.: Transmission management for congested power system: a review of concepts, technical challenges and development of a new methodology. Renew. Sustain. Energy Rev. 38, 572–580 (2014)

    Article  Google Scholar 

  14. Talukdar, B., Sinha, A., Mukhopadhyay, S., Bose, A.: A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management. Int. J. Electr. Power Energy Syst. 27, 379–388 (2005)

    Article  Google Scholar 

  15. Sarwar, M., Siddiqui, A. S., Jaffery, Z. A., and Kothari, D.: Bid responsive generation rescheduling for congestion management in electricity market. Eng. Rep. 3, 1–15 (2021)

  16. Masood, A., Xin, A., Rizvi, S. A. A., Qazi, H. S., Qambrani, L. K., and Khan, S.: "Congestion management in local market considering the payback effect in 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE), pp 595–600 (2021)

  17. Dutta, S., Singh, S.: Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE Trans. Power Syst. 23, 1560–1569 (2008)

    Article  Google Scholar 

  18. Reddy, S.S.: Multi-objective based congestion management using generation rescheduling and load shedding. IEEE Trans. Power Syst. 32, 852–863 (2016)

    Google Scholar 

  19. Ramesh, G., and Ranjith Babu, V.: Combined FACTS and microgrid-based congestion management in transmission lines in Advances in Electrical and Computer Technologies: Select Proceedings of ICAECT 2020, pp 1063–1073 (2021)

  20. Kamaraj, N.: Transmission congestion management using particle swarm optimization. J. Electric. Syst. 7, 54–70 (2011)

    Google Scholar 

  21. Verma, S., Mukherjee, V.: Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser. IET Gener. Transm. Distrib. 10, 2548–2561 (2016)

    Article  Google Scholar 

  22. Paul, K., Kumar, N., Agrawal, S., Paul, K.: Optimal rescheduling of real power to mitigate congestion using gravitational search algorithm. Turk. J. Electr. Eng. Comput. Sci. 27, 2213–2225 (2019)

    Article  Google Scholar 

  23. Srivastava, J., Yadav, N.K., Sharma, A.K.: A novel hybrid algorithm for rescheduling-based congestion management scheme in power system. Electr. Eng. 102, 1993–2010 (2020)

    Article  Google Scholar 

  24. Patel, N.K., Suthar, B.N., Thakkar, J.: Transmission congestion management considering voltage stability margin. SN Appl. Sci. 3, 1–12 (2021)

    Article  Google Scholar 

  25. C. Saravanan and P. Anbalagan, "Multi objective dragonfly algorithm for congestion management in deregulated power systems," Journal of Ambient Intelligence and Humanized Computing, pp. 1–10, 2020.

  26. Elsheikh, A., Abd Elaziz, M.: Review on applications of particle swarm optimization in solar energy systems. Int. J. Environ. Sci. Technol. 16, 1159–1170 (2019)

    Article  Google Scholar 

  27. Mandal, S., Singh, P., Kumar, S., and Mishra, S.: Parametric investigation of CuO-doped charged nanofluid in solar water heater. Int. J. Environ. Sci. Technol. 18, 2855–2864, (2021)

  28. Massihi, N., Abdolvand, N., Harandi, S.R.: A business environment analysis model for renewable solar energy. Int. J. Environ. Sci. Technol. 18, 401–416 (2021)

    Article  Google Scholar 

  29. Mohammadi, Z., Mirdamadi, S., Hosseini, S.F., Lashgarara, F.: Qualitative analysis of effective factors on the feasibility of utilizing solar technology in the poultry industry. Int. J. Environ. Sci. Technol. 18, 703–710 (2021)

    Article  Google Scholar 

  30. Sood, Y.R., Singh, R.: Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources. Renew. Energy 35, 1828–1836 (2010)

    Article  Google Scholar 

  31. Namilakonda, S., Guduri, Y.: Chaotic darwinian particle swarm optimization for real-time hierarchical congestion management of power system integrated with renewable energy sources. Int. J. Electric. Power Energy Syst. 128, 106632 (2021)

    Article  Google Scholar 

  32. Suganthi, S., Devaraj, D., Ramar, K., Thilagar, S.H.: An improved differential evolution algorithm for congestion management in the presence of wind turbine generators. Renew. Sustain. Energy Rev. 81, 635–642 (2018)

    Article  Google Scholar 

  33. Vanin, A., Ellina, S., Nardelli, P. J., and Mashlakov, A.: Dispatch optimization of energy communities for collective provision of network congestion management. In: International Youth Conference on Radio Electronics, Electrical and Power Engineering, pp 1–5 (2021)

  34. Gope, S., Dawn, S., Mitra, R., Goswami, A. K., and Tiwari, P. K.: Transmission congestion relief with integration of photovoltaic power using lion optimization algorithm. In: Soft Computing for Problem Solving, pp 327–338 (2019)

  35. Sulaiman, M.H., Mustaffa, Z.: Solving optimal power flow problem with stochastic wind–solar–small hydro power using barnacles mating optimizer. Control Eng. Pract. 106, 104672 (2021)

    Article  Google Scholar 

  36. Gil-González, W., Garces, A., Montoya, O.D., Hernández, J.C.: A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks. Appl. Sci. 11, 627 (2021)

    Article  Google Scholar 

  37. Ramadan, A., Ebeed, M., Kamel, S., and Nasrat, L.: Optimal power flow for distribution systems with uncertainty. In: Uncertainties in Modern Power Systems, (eds.), pp 145–162. Elsevier, (2021)

  38. Biswas, P., Pal, B.B.: A fuzzy goal programming method to solve congestion management problem using genetic algorithm. Decision Mak Appl. Manag. Eng. 2, 36–53 (2019)

    Google Scholar 

  39. Singh, B., Mahanty, R., Singh, S.P.: Social welfare maximization for congestion management in multiutility market using improved PSO incorporating transmission loss cost allocation. Int Transactions Electric. Energy Syst. 28, e2593 (2018)

    Article  Google Scholar 

  40. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  41. Behera, S. K., and Mohanty, N. K.: Congestion management using thyristor controlled series compensator employing Improved Grey Wolf Optimization technique. Int. J. Electric. Eng. Edu. 58, 179–199 (2019)

  42. Wang, L., Li, Q., Zhang, B., Ding, R., Sun, M.: Robust multi-objective optimization for energy production scheduling in microgrids. Eng. Optim. 51, 332–351 (2019)

    Article  MathSciNet  Google Scholar 

  43. Velamuri, S., Sreejith, S.: Economic dispatch and cost analysis on a Power system network interconnected with Solar Farm. Int. J. Renew. Energy Res. (IJRER) 5, 1098–1105 (2015)

    Google Scholar 

  44. Ayan, K., Kılıç, U., Baraklı, B.: Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow. Int. J. Electr. Power Energy Syst. 64, 136–147 (2015)

    Article  Google Scholar 

  45. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, BIT Sindri, Dhanbad, Jharkhand, 828123, India

    Kaushik Paul

Authors
  1. Kaushik Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaushik Paul.

Ethics declarations

Conflict of interest

The corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

The price bids of the generators and the line rating are listed in Tables

Table 13 Price bids for the generators
Full size table

13 and

Table 14 IEEE 30 Bus maximum Line MVA Capability Thermal Rating
Full size table

14.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paul, K. Modified grey wolf optimization approach for power system transmission line congestion management based on the influence of solar photovoltaic system. Int J Energy Environ Eng 13, 751–767 (2022). https://doi.org/10.1007/s40095-021-00457-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40095-021-00457-2

Keywords

Search

Navigation