Unit commitment and dispatch with coordination of wind and pumped storage hydro units by using cuckoo search algorithm


  • R. Jayashree
  • R. Soundarapandian






AUC, PS, CS, ANN, wind power.


This paper proposes a multi objective model for Advanced Unit Commitment (AUC) with wind power and Pumped Storage (PS) units using Cuckoo Search (CS) algorithm. The novelty of the proposed method is improved levy flight searching ability, random reduction and ability to adapt complex optimization problems. Here, the CS algorithm to accommodate wind output uncertainty, with the multi-objective of providing an optimal AUC schedule for the thermal generators in the day-ahead market that minimizes the total cost under the different wind power output scenario. The proposed method is more reliable for AUC because it considering the wind power uncertainty using the Artificial Neural Network (ANN) and PS units, which are significantly reduces the total cost. Then the proposed method is implemented in the MATLAB/simulink platform and tested under IEEE standard bench mark system. The proposed method performance has been verified through the comparison analysis with the existing techniques. The comparison results were proved the superiority of the proposed method.


[1] Saber AY & Venayagamoorthy GK, “Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehiclesâ€, IEEE systems journal, Vol.6, No.1, (2012), pp.103-109.

[2] Liu G & Tomsovic K, “Quantifying spinning reserve in systems with significant wind power penetrationâ€, IEEE Transactions on Power Systems, Vol.27, No.4, (2012), pp.2385-2393.

[3] Xu Y, Hu Q & Li F, “Probabilistic model of payment cost minimization considering wind power and its uncertainty†IEEE Transactions on Sustainable Energy, Vol.4, No.3, (2013), pp.716-724

[4] Chen CL, “Optimal Wind-Thermal Generating Unit Commitmentâ€, IEEE Transactions on Energy Conversion, Vol.23, No.1, (2008), pp.273-280.

[5] Billinton R, Karki B, Karki R & Ramakrishna G, “Unit commitment risk analysis of wind integrated power systemsâ€, IEEE Transactions on Power Systems, Vol.24, No.2, (2009), pp.930-939.

[6] Delarue E & D’haeseleer W, “Adaptive mixed-integer programming unit commitment strategy for determining the value of forecastingâ€, Applied Energy, Vol.85, No.4, (2008), pp.171-181.

[7] Hargreaves JJ & Hobbs BF, “Commitment and dispatch with uncertain wind generation by dynamic programmingâ€, IEEE Transactions on sustainable energy, Vol.3, No.4, (2012), pp.724-734.

[8] Venkatesh B, Yu P, Gooi HB & Choling D, “Fuzzy MILP unit commitment incorporating wind generatorsâ€, IEEE transactions on power systems, Vol.23, No.4, (2008), pp.1738-1746.

[9] Lowery C & O'Malley M, “Impact of wind forecast error statistics upon unit commitmentâ€, IEEE Transactions on Sustainable Energy, Vol.3, No.4, (2012), pp.760-768.

[10] Khodayar ME, Wu L & Shahidehpour M, “Hourly coordination of electric vehicle operation and volatile wind power generation in SCUCâ€, IEEE Transactions on Smart Grid, Vol.3, No.3, (2012), pp.1271-1279.

[11] Methaprayoon K, Yingvivatanapong C, Lee WJ & Liao JR, “An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty†IEEE Transactions on Industry Applications, Vol.43, No.6, (2007), pp.1441-1448.

[12] Zhang N, Kang C, Kirschen DS, Xia Q, Xi W, Huang J & Zhang, Q, “Planning pumped storage capacity for wind power integrationâ€, IEEE Transactions on Sustainable Energy, Vol.4, No.2, (2013), pp.393-401.

[13] Khodayar ME, Shahidehpour M & Wu L, “Enhancing the dispatchability of variable wind generation by coordination with pumped-storage hydro units in stochastic power systems†IEEE Transactions on Power Systems, Vol.28, No.3, (2013), pp.2808-2818.

[14] Zhao C, Wang J, Watson JP & Guan Y, “Multi-stage robust unit commitment considering wind and demand response uncertaintiesâ€, IEEE Transactions on Power Systems, Vol.28, No.3, (2013), pp.2708-2717.

[15] Mohan MR, Kuppusam K & Abdullah Khan M, “Short-term hydrothermal scheduling of power systems with a pumped hydro plant using the local variation approachâ€, Electric Power Systems Research, Vol.27, (1993), pp.153-159.

[16] Nazaria ME, Ardehali MM & Jafari S, “Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraintsâ€, Energy, Vol.35, (2010), pp.4092-4101.

[17] Micusik D, Stopjakova V & Benuskova L, “Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuitsâ€, Neural Computing and Applications, Vol.11, (2002), pp.71-79.

[18] Bai X & Wei H, “Semi-Definite Programming-Based Method For Security-Constrained Unit Commitment with Operational and Optimal Power Flow Constraintsâ€, IET Generation, Transmission and Distribution, Vol.3, No.2, (2009), pp.182–197.

[19] Chandrasekaran K, Hemamalini S, Simon SP & Padhy NP, “Thermal unit commitment using binary/real coded artificial bee colony algorithmâ€, Electrical Power System Research, Vol.84, (2009), pp.109–119.

[20] Chandrasekaran K & Simon SP, “Optimal Deviation Based Firefly Algorithm Tuned Fuzzy Design for Multi-Objective UCPâ€, IEEE Transactions on Power Systems, Vol.28, No.1, (2013).

View Full Article: