# Consideration of a Combined Cycle Gas Turbine’s Operating Modes in Pricing Mechanisms: A Korean Electricity Market Study

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## Abstract

This study proposes a modeling technique for applying combined cycle gas turbines (CCGTs) to the unit commitment problem. The proposed method does not specify the turbine operation order in advance, and uses a simple and basic formula structured as mixed integer linear programming (MILP). In addition, we propose a system marginal price (SMP) calculation considering combination modeling of CCGTs, and we analyze market price change and its effect. After applying the proposed method to the unit commitment problem and calculating the market price from January 2017, although average market price and volatility increased, there was no rapid change, such as inversion of the dispatching rank of fuel sources or excessive fuel costs. And the profitability of the CCGTs participating in the day-ahead energy market improve.

## Keywords

Unit commitment Optimal dispatch Market clearing Day-ahead market SMP CCGT modeling methodology## List of Symbols

## Abbreviations and acronyms

- CCGT
Combined cycle gas turbine

- CFBM
Configuration-based model

- CPBM
Component-based model

- GT
Gas turbine

- ST
Steam turbine

- KRW
Korean won

- KPX
Korea power exchange (the independent system operator in South Korea)

## Sets and Indices

- \({\mathcal{G}}\)
Set of generators

- \({\mathcal{G}}_{ccgt}\)\(\subset {\mathcal{G}}\)
Set of CCGT generators

- \({ \mathcal{K}}\)
Set of buses

- \({\mathcal{T}}\)
Set of hourly periods

- \({\mathcal{L}}\)
Set of output blocks

- \({\mathcal{Y}}_{g}\)
Set of configurations within each CCGT generator \(g\)

- \({\mathcal{X}}_{g}\)
Set of individual turbines within each CCGT generator \(g\)

- \({\mathcal{X}}_{g}^{GT}\)\(\subset {\mathcal{X}}_{g}\)
Set of GTs within each CCGT generator \(g\)

- \({\mathcal{X}}_{g}^{ST}\)\(\subset {\mathcal{X}}_{g}\)
Set of STs within each CCGT generator \(g\)

- \({\mathcal{F}}{\mathcal{S}}_{g}^{yy'}\)
Set of feasible state transitions within CCGT generator \(g\), where \(y\) and \(y^{\prime}\) represent

*from*and*to*configurations- \({\mathcal{I}}{\mathcal{F}}{\mathcal{S}}_{g}^{{yy^{\prime}}}\)
Set of infeasible state transitions within CCGT generator \(g\), where \(y\) and \(y^{\prime}\) represent

*from*and*to*configurations- \(g \in {\mathcal{G}}\)
Index for generators

- \(i,j \in {\mathcal{K}}\)
Indices for buses

- \(t,\tau \in {\mathcal{T}}\)
Indices for hourly time steps

- \(l\in L\)
Index for blocks

- \(y \in {\mathcal{Y}}_{g}\)
Index for configurations within each CCGT generator \(g\)

- \({\mathcal{X}}\)\(\in {\mathcal{X}}_{g}\)
Index for individual turbines within each CCGT generator \(g\)

## Parameters

- \(C_{g}^{SU}\)
Unit start-up cost of generator \(g\)

- \(F_{g}^{(l)}\)
Slope of block \(l\) of generator \(g\)

- \(\text{P}_{g}^{{\rm min} }\), \(\text{P}_{g}^{{\rm max} }\)
Minimum and maximum output level of generator \(g\)

- \(\text{RD}_{g}\), \(\text{RU}_{g}\)
Ramp-down and -up rate of generator \(g\)

- \(\text{UT}_{g}\), \(\text{DT}_{g}\)
Minimum up- and down-time of generator \(g\)

- \(X_{ij}\)
Reactance of transmission line between bus \(i\) and \(j\)

- \(D_{it}\)
Active power demand of bus \(i\) at hour \(t\)

- \(a_{g}\), \(b_{g}\), \(c_{g}\)
Quadratic, linear, and no-load price coefficients of generator \(g\)

## Continuous Variables

- \(p_{gt}\)
Power output of generator \(g\) at hour \(t\)

- \(\delta_{gt}^{(l)}\)
Piecewise power output in block \(l\) of generator \(g\) at hour \(t\)

- \(suc_{gt}\)
Start-up cost of generator \(g\) at hour \(t\)

- \(\theta_{it}\)
Voltage angle of bus \(i\) at hour \(t\)

## Binary variables

- \(i_{gt}\)
Binary variable for operational status of generator \(g\) at hour \(t\) (1: Operational, 0: Otherwise)

- \(v_{gt}\)
Binary variable for start-up status of generator \(g\) at hour \(t\) (1: Start-up, 0: Otherwise)

- \(w_{gt}\)
Binary variable for shutdown status of generator \(g\) at hour \(t\) (1: Under shutdown, 0: Otherwise)

## Notes

### Funding

This work was supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence, and also was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A3A01059931).

## References

- 1.Lu B, Shahidehpour M (2004) Short-term scheduling of combined cycle units. IEEE Trans Power Syst 19(3):1616–1625CrossRefGoogle Scholar
- 2.Liu Cong et al (2009) Component and Mode Models for the short-term scheduling of combined-cycle units. IEEE Trans Power Syst 24(2):976–990CrossRefGoogle Scholar
- 3.Hui H et al (2011) Combined cycle resource scheduling in ERCOT nodal market, in July, pp 1–8Google Scholar
- 4.Chen Y, Wang F (2017) MIP formulation improvement for large scale security constrained unit commitment with configuration based combined cycle modeling. Electric Power Syst Res 148:147–154CrossRefGoogle Scholar
- 5.Anders G, Morched A (2005) Commitment techniques for combined-cycle units. Montreal, QuebecGoogle Scholar
- 6.Dai C et al (2019) A configuration-component-based hybrid model for combined-cycle units in MISO day-ahead market. IEEE Trans Power Syst 34(2):883–896CrossRefGoogle Scholar
- 7.Tamayo M et al (2013) Configuration based combined cycle model in market resource commitment. In: Proceedings of 2013 IEEE Power & Energy Society General Meeting. IEEE, Vancouver, Canada, pp 1–5Google Scholar
- 8.Alemany J et al (2013) Short-term scheduling of combined cycle units using mixed integer linear programming solution. Energy Power Eng 5(2):161–170MathSciNetCrossRefGoogle Scholar
- 9.Ammari S, Cheung KW (2013) Advanced combined-cycle modeling. In: Proceedings of 2013 IEEE grenoble conference, IEEE, Grenoble, France, pp 1–5Google Scholar
- 10.Fang X et al (2018) Hybrid component and configuration model for combined-cycle units in unit commitment problem. J Mod Power Syst Clean Energy 6(6):1332–1337MathSciNetCrossRefGoogle Scholar
- 11.Hermans M, Bruninx K, Delarue E (2018) Impact of CCGT start-up flexibility and cycling costs toward renewables integration. IEEE Trans Sustain Energy 9(3):1468–1476CrossRefGoogle Scholar
- 12.Campos FA, Reneses J (2014) Energy and reserve co-optimization of a combined cycle plant using mixed integer linear programming. J Eng Gas Turbines Power 136(10):101702CrossRefGoogle Scholar
- 13.Kim Hyoungtae et al (2018) An analysis on the adequate level of capacity price from a long-term generation expansion planning perspective. J Electr Eng Technol 13(6):2203–2211Google Scholar
- 14.Lee J et al (2018) A study on the cost function based on operating modes for combined cycle power plant. Trans Korean Inst Electr Eng 67(3):358–364Google Scholar
- 15.Ostrowski J, Anjos MF, Vannelli A (2012) Tight mixed integer linear programming formulations for the unit commitment problem. IEEE Trans Power Syst 27(1):39–46CrossRefGoogle Scholar
- 16.Carrion M, Arroyo JM (2006) A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans Power Syst 21(3):1371–1378CrossRefGoogle Scholar
- 17.Morales-Espana G, Latorre JM, Ramos A (2013) Tight and compact MILP formulation for the thermal unit commitment problem. IEEE Trans Power Syst 28(4):4897–4908CrossRefGoogle Scholar
- 18.Frangioni A, Gentile C, Lacalandra F (2009) Tighter approximated MILP formulations for unit commitment problems. IEEE Trans Power Syst 24(1):105–113CrossRefGoogle Scholar
- 19.Jufri F, Oh S, Jung J (2019) Day-ahead system marginal price forecasting using artificial neural network and similar-days information. J Electr Eng Technol 14(2):561–568CrossRefGoogle Scholar
- 20.Korea 2017 Electricity market statistics report (in Korean). https://kpx.or.kr/www/selectBbsNttView.do?key=100&bbsNo=8&nttNo=17525&searchCtgry=&searchCnd=SJ&searchKrwd=2017&pageIndex=1&integrDeptCode=/. Accessed 1 May 2018
- 21.Brook A, Kendrick D, Meeraus A (1988) GAMS, a user’s guide. ACM Signum Newsl 23(3–4):10–11CrossRefGoogle Scholar
- 22.I. I. Cplex (2009) V12. 1: user’s manual for CPLEX. Int Bus Mach Corporation 46(53):157Google Scholar
- 23.Lee S, Kim W, Kim BH (2015) Performance comparison of optimal power flow algorithms for LMP calculations of the full scale Korean power system. J Electr Eng Technol 10(1):109–117CrossRefGoogle Scholar
- 24.Wang C et al (2012) A study of commitment cost in approximate extended locational marginal prices. In: Proceedings of 2012 IEEE Power and Energy Society General Meeting, San Diego, USA, pp 1–7Google Scholar