Abstract
An increased use of variable generation technologies such as wind power generation can have important effects on system frequency performance during normal operation as well as contingencies. This has led to new challenges for system operators in terms of improving frequency characteristics during contingencies. This subject is stated within the framework of frequency-constrained unit commitment problem (FCUCP) in this study by augmenting the frequency characteristic in the multi-thermal units. The ramp rate is proposed as dynamic constraint, and FCUCP is solved efficiently by binary particle swarm optimization (BPSO) based heuristic optimization algorithms. The influence of the proposed ramping on FCUCP is simulated, and it can be shown that the costs of the system under considering practical limitations that are solved by BPSO are minimized.
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Doherty R, Mullane R, Nolan G, Burke DJ, Bryson A, O’Malley M (2010) An assessment of the impact of wind generation on system frequency control. IEEE Trans Power Syst 25:452–460. https://doi.org/10.1109/TPWRS.2009.2030348
Brouwer AS, van den Broek M, Özdemir Ö, Koutstaal P, Faaij A (2016) Business case uncertainty of power plants in future energy systems with wind power. Energy Policy 29:237–256. https://doi.org/10.1016/j.enpol.2015.11.022
Gautam D, Goel L, Ayyanar R, Vittal V, Harbour T (2011) Control strategy to mitigate the impact of reduced inertia due to doubly fed induction generators on large power systems. IEEE Trans Power Syst 26:214–224. https://doi.org/10.1109/TPWRS.2010.2051690
Abad G, Lopez J, Rodriguez M, Marroyo L, Iwanski G (2011) Doubly fed induction machine: modeling and control for wind energy generation. Wiley, Hoboken. https://doi.org/10.1002/9781118104965
Ahmadi H, Ghasemi H (2011) Probabilistic optimal power flow incorporating wind power using point estimate methods. In Proceedings 10th Internatonal Conference on Environment Electrical Engineering, Rome, Italy, pp 1–5. https://doi.org/10.1109/eeeic.2011.5874815
Ela E, Gevorgian V, Tuohy A, Kirby B, Milligan M, O’Malley M (2014) Market designs for the primary frequency response ancillary service part I: motivation and design. IEEE Trans Power Syst 29:421–431. https://doi.org/10.1109/TPWRS.2013.2264942
Ela E, Gevorgian V, Tuohy A, Kirby B, Milligan M, O’Malley M (2014) Market designs for the primary frequency response ancillary service part II: case studies. IEEE Trans Power Syst 29:432–440. https://doi.org/10.1109/TPWRS.2013.2264951
Yuan X, Nie H, Su A, Wang L, Yuan Y (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36:8049–8055. https://doi.org/10.1016/j.eswa.2008.10.047
Menhas MI, Wang L, Fei M, Pan H (2012) Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Syst Appl 39:4390–4401. https://doi.org/10.1016/j.eswa.2011.09.152
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings Conference on Systems, Man and Cybernetics, Piscataway, pp 4104–4108. https://doi.org/10.1109/icsmc.1997.637339
Doherty R, Lalor G, O’Malley M (2005) Frequency control in competitive electricity market dispatch. IEEE Trans Power Syst 20:1588–1596. https://doi.org/10.1109/TPWRS.2005.852146
Lotfalian M, Schlueter R, Idizior D, Rusche P, Tedeschi S, Shu L, Yazdankhah A (1985) Inertial, governor and AGC/economic dispatch load flow simulations of loss of generation contingencies. IEEE Trans Power Appl Syst 104:3020–3028. https://doi.org/10.1109/TPAS.1985.318943
Ceja-Gomez F, Qadri S, Galiana F (2012) Under-frequency load shedding via integer programming. IEEE Trans Power Syst 27:1387–1394. https://doi.org/10.1109/TPWRS.2012.2186156
Restrepo J, Galiana F (2005) Unit commitment with primary frequency regulation constraints. IEEE Trans Power Syst 20:1836–1842. https://doi.org/10.1109/TPWRS.2005.857011
Anderson PM, Mirheydar M (1990) A low-order system frequency response model. IEEE Trans Power Syst 5:720–729. https://doi.org/10.1109/59.65898
Ahmadi H, Ghasemi H (2014) Security-constrained unit commitment with linearized system frequency limits constraints. IEEE Trans Power Syst 29:1536–1545. https://doi.org/10.1109/TPWRS.2014.2297997
Soder L (1993) Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans Power Syst 8:564–571. https://doi.org/10.1109/59.260826
Ortega-Vazquez MA, Kirschen DS (2009) Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 24:114–124. https://doi.org/10.1109/TPWRS.2008.2004745
Morales JM, Conejo AJ, Perez-Ruiz J (2009) Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans Power Syst 24:900–910. https://doi.org/10.1109/TPWRS.2009.2016598
Lee TY (2007) Optimal spinning reserve for a wind-thermal power system using EIPSO. IEEE Trans Power Syst 22:1612–1621. https://doi.org/10.1109/TPWRS.2007.907519
Wang J, Shahidehpour M, Li Z (2008) Security-constrained unit commitment with volatile wind power generation. IEEE Trans Power Syst 23:1319–1327. https://doi.org/10.1109/TPWRS.2008.926719
Muhammad T, Halim Z (2016) Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization technique. Appl Soft Comput 49:365–384. https://doi.org/10.1016/j.asoc.2016.08.039
Vagropoulos SI, Kardakos EG, Simoglou CK, Bakirtzis AG, Catalão JP (2016) ANN-based scenario generation methodology for stochastic variables of electric power systems. Electr Power Syst Res 134:9–18. https://doi.org/10.1016/j.epsr.2015.12.020
Halim Z, Muhammad T (2017) Quantifying and optimizing visualization: an evolutionary computing-based approach. Inform Sci 385:284–313. https://doi.org/10.1016/j.ins.2016.12.035
Farrokhabadi M, Canizares C, Bhattacharya K (2016) Unit commitment for isolated microgrids considering frequency control. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2016.2629982
Cardozo C, Capely L, Dessante P (2017) Frequency constrained unit commitment. Energy Syst 8:31–56. https://doi.org/10.1007/s12667-015-0166-4
Khazaei P, Dabbaghjamanesh M, Kalantarzadeh A, Mousavi H (2016) Applying the modified TLBO algorithm to solve the unit commitment problem. In: World automation congress, pp 1–6. https://doi.org/10.1109/wac.2016.7583026
Wen Y, Li W, Huang G, Liu X (2016) Frequency dynamics constrained unit commitment with battery energy storage. IEEE Trans Power Syst 31:5115–5125. https://doi.org/10.1109/TPWRS.2016.2521882
Fu Y, Shahidehpour M, Li Z (2005) Security-constrained unit commitment with AC constraints. IEEE Trans Power Syst 20:1538–1550. https://doi.org/10.1109/TPWRS.2005.846076
Tuohy A, Meibom P, Denny E, O’Malley M (2009) Unit commitment for systems with significant wind penetration. IEEE Trans Power Syst 24:592–601. https://doi.org/10.1109/TPWRS.2009.2016470
Ahmadi H, Ghasemi H (2012) Maximum penetration level of wind generation considering power system security limits. IET Gener Transm Distrib 6:1164–1170. https://doi.org/10.1049/iet-gtd.2012.0015
Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control. McGraw-Hill, NewYork
Aik DLH (2006) A general-order system frequency response model incorporating load shedding: analytic modeling and applications. IEEE Trans Power Syst 21:709–717. https://doi.org/10.1109/TPWRS.2006.873123
Carrion M, Arroyo JM (2006) A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans Power Syst 21:1371–1378. https://doi.org/10.1109/TPWRS.2006.876672
Li T, Shahidehpour M (2007) Dynamic ramping in unit commitment. IEEE Trans Power Syst 22:1379–1381. https://doi.org/10.1109/TPWRS.2007.901453
Datta D (2013) Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm. Appl Soft Comput 13:3873–3883. https://doi.org/10.1016/j.asoc.2013.05.002
Acknowledgements
This work is supported by the Azarbaijan Shahid Madani University through the Grant: ASMU/1093-17/18947.
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Appendices
Appendix 1
Min up/down
where TUp and TDn are the generator minimum up/down time, respectively, and T0 is the generator initial time.
Appendix 2
IEEE 30 Bus Test System Data
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Safari, A., Shahsavari, H. Frequency-constrained unit commitment problem with considering dynamic ramp rate limits in the presence of wind power generation. Neural Comput & Applic 31, 5241–5254 (2019). https://doi.org/10.1007/s00521-018-3363-y
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DOI: https://doi.org/10.1007/s00521-018-3363-y