Skip to main content

Advertisement

SpringerLink
Log in

Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Due to unforeseen variations in wind speed profiles, wind farm integrations are recognized as intermittent and uncertain energy contributors. More specifically, integration of such renewable energy resources aligned with the conventional thermal units although reduces the emissions and brings about a clean environment, it introduces serious problems in assigning optimal and reliable level of these units in load supplying and spinning reserve provision. This situation is more intensified considering the uncertainties arisen by the power system loading demand. To facilitate such operational hurdles, the ongoing study puts forward an efficient model for assigning the optimal spinning reserve which accommodates the uncertainties in both the wind speed and load profiles. Stochastic behavior of these parameters is simulated by generating a proper number of scenarios through the Monte Carlo simulation (MCS) approach. Then, each of these scenarios is evaluated based on the established linear mixed integer approach in a deterministic fashion. Accordingly, a computationally efficient approach is obtained paving the way for real-world implementations and assuring the global optimum results. The proposed approach is applied to a 12-unit test system including 10 thermal units and 2 wind farms. Results are reflected in terms of the commitment status, energy dispatches, and reserve contributions of each committed unit. A comprehensive discussion is conducted to disclose the possible improvements.

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
Fig. 10

Similar content being viewed by others

Abbreviations

\(g\) :

Index of generating units.

\(t,T\) :

Index and set of time intervals

\(s\) :

Index of scenarios

\(d\) :

Index of load points

\(w\) :

Index of wind farms

\(P_{d}^{{}}\left( t \right)\) :

Active power demand at load point d at time t

\({v_i}\left( t \right)\) :

Wind speed at wind farm i at time t

\(A(g)\) :

Coefficient of the piecewise linear production cost function of unit g

\({a_g},{b_g},{c_g}\) :

Coefficients of the quadratic production cost function of unit g

\({\alpha _g},{\beta _g},{\gamma _g}\) :

Coefficients of the quadratic emission function of unit g

\(f(l,g)\) :

Slope of block l of the piecewise linear production cost function of unit g

\(NL\) :

Number of segments in piecewise linearization approach

\(OFC\left( g \right)\) :

Operation and maintenance fixed cost of thermal unit g

\(OFC\left( w \right)\) :

Operation and maintenance fixed cost of wind farm w

\(OVC\left( g \right)\) :

Operation and maintenance variable cost of thermal unit g

\(OVC\left( w \right)\) :

Operation and maintenance variable cost of wind farm w

\(P_{G}^{{\hbox{max} }}\left( g \right)\) :

Maximum power generating capacity of thermal unit g

\(P_{G}^{{\hbox{min} }}\left( g \right)\) :

Minimum power generating limit of thermal unit g

\(P{r^S}\) :

Probability of scenario s

\(RW\) :

A fraction of total wind power considered as the reserve requirement due to wind power prediction errors

\({T_g}(l)\) :

Upper limit in each segment of the linearized cost function of thermal unit g

\(n\left( t \right)\) :

Number of hours at time interval t

\(P_{{GD}}^{S}\left( {g,t} \right)\) :

Load contribution of thermal unit g at time t in scenario s

\(P_{{GR}}^{S}\left( {g,t} \right)\) :

Reserve contribution of thermal unit g at time t in scenario s

\(P_{R}^{S}\left( t \right)\) :

Fraction of total system load as the reserve requirement at time t in scenario s

\(P_{W}^{S}\left( {w,t} \right)\) :

Generation of wind farm w at time t in scenario s

\({\rho ^S}\) :

Probability of scenario s

\(u_{{i,t}}^{S}\) :

Decision variables of unit i at time t in scenario s. (on = 1, off = 0)

\(x_{{i,t}}^{S}\) :

State variables of unit i at time t in scenario s

\({\xi ^S}\) :

Vector of scenario s

References

  • Afkousi-Paqaleh M, Rashidinejad M, Pourakbari-Kasmaei M (2010) An implementation of harmony search algorithm to unit commitment problem. Electr Eng 92(6):215–225

    Article  Google Scholar 

  • Aien M, Hajebrahimi A, Fotuhi-Firuzabad M (2016) A comprehensive review on uncertainty modeling techniques in power system studies. Renew Sustain Energy Rev 57:1077–1089

    Article  Google Scholar 

  • Billinton R, Allan RN (1992) Reliability evaluation of engineering systems. Plenum press, New York

    Book  Google Scholar 

  • Carrión M, Arroyo JM (2006) A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on power systems 21(3):1371–1378

    Article  Google Scholar 

  • Dieu VN, Ongsakul W (2007) Improved merit order and augmented Lagrange Hopfield network for unit commitment. IET Gen Transm Distrib 1(4):548–556

    Article  Google Scholar 

  • Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2017) A new prediction model based on multi-block forecast engine in smart grid. J Ambient Intell Hum Comput 9(6):1873–1888

    Article  Google Scholar 

  • Handschin E, Slomski H (1990) Unit commitment in thermal power systems with long-term energy constraints. IEEE Trans Power Syst 5(4):1470–1477

    Article  Google Scholar 

  • Huang Y, Zheng QP, Wang J (2014) Two-stage stochastic unit commitment model including non-generation resources with conditional value-at-risk constraints. Electr Power Syst Res 116:427–438

    Article  Google Scholar 

  • Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92

    Article  Google Scholar 

  • Kazemi M, Siano P, Sarno D, Goudarzi A (2016) Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators. Energy 113:338–354

    Article  Google Scholar 

  • Kjeldsen NH, Chiarandini M (2012) Heuristic solutions to the long-term unit commitment problem with cogeneration plants. Comput Oper Res 39(2):269–282

    Article  Google Scholar 

  • Kumar SS, Palanisamy V (2007) A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch. Electr Power Syst Res 77(8):917–925

    Article  Google Scholar 

  • Lange M (2005) On the uncertainty of wind power predictions—analysis of the forecast accuracy and statistical distribution of errors. Trans ASME-N-J Solar Energy Eng 127(2):177–184

    Article  Google Scholar 

  • Li T, Shahidehpour M (2005) Price-based unit commitment: A case of Lagrangian relaxation versus mixed integer programming. IEEE Trans Power Syst 20(4):2015–2025

    Article  Google Scholar 

  • Li X, Li T, Wei J, Wang G, Yeh WW (2014) Hydro unit commitment via mixed integer linear programming: a case study of the three gorges project, China. IEEE Trans Power Syst 29(3):1232–1241

    Article  Google Scholar 

  • Liang H, Tamang AK, Zhuang W, Shen XS (2014) Stochastic information management in smart grid. IEEE Commun Surveys Tutor 16(3):1746–1770

    Article  Google Scholar 

  • Liu G, Tomsovic K (2015) Robust unit commitment considering uncertain demand response. Electr Power Syst Res 119:126–137

    Article  Google Scholar 

  • Liu Z, Hajiali M, Torabi A, Ahmadi B, Simoes R (2018) Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting. J Ambient Intell Hum Comput ‎9(6):1919–1931‎

    Article  Google Scholar 

  • Reddy SS, Bijwe PR, Abhyankar AR (2015) Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Syst J 9(1):152–164

    Article  Google Scholar 

  • Saber AY, Venayagamoorthy GK (2010) Intelligent unit commitment with vehicle-to-grid—a cost-emission optimization. J Power Sources 195(3):898–911

    Article  Google Scholar 

  • Saber AY, Venayagamoorthy GK (2012) Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. IEEE Syst J 6(1):103–109

    Article  Google Scholar 

  • Siahkali H, Vakilian M (2010) Stochastic unit commitment of wind farms integrated in power system. Electr Power Syst Res 80(9):1006–1017

    Article  Google Scholar 

  • Vieira B, Viana A, Matos M, Pedroso JP (2016) A multiple criteria utility-based approach for unit commitment with wind power and pumped storage hydro. Electr Power Syst Res 131:244–254

    Article  Google Scholar 

  • Yang Y, Wangb J, Guan X, Zhai Q (2012) Subhourly unit commitment with feasible energy delivery constraints. Appl Energy 96:245–252

    Article  Google Scholar 

  • Yu X, Zhang X (2014) Unit commitment using Lagrangian relaxation and particle swarm optimization. Int J Electr Power Energy Syst 61:510–522

    Article  Google Scholar 

  • Zhang J, Tang Q, Chen Y, Lin S (2016) A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem. Energy 109:765–780

    Article  Google Scholar 

  • Zou X, Li M, Wang W, Niu Y, Liu J (2015) Dispatch of wind-thermal power system containing heat storage units with fast ramping capabilities. In Power Tech IEEE 2015, Eindhoven, pp 1–6

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer and Industrial Engineering, Birjand University of Technology, Birjand, Iran

    Mostafa Esmaeeli

  2. Electrical Engineering Department, Urmia University, Urmia, Iran

    Sajjad Golshannavaz

  3. Department of Management and Innovation Systems, University of Salerno, Fisciano, Italy

    Pierluigi Siano

Authors
  1. Mostafa Esmaeeli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sajjad Golshannavaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierluigi Siano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mostafa Esmaeeli.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esmaeeli, M., Golshannavaz, S. & Siano, P. Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty. J Ambient Intell Human Comput 11, 1565–1576 (2020). https://doi.org/10.1007/s12652-019-01231-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01231-3

Keywords

Search

Navigation