Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Time Series Forecasting
3.2. Optimization Model
3.2.1. Assumptions
- Even if construction is completed within the previous year, the new power plants are expected to be operational at the beginning of the following year;
- The availability factor determines the maximum working hours of power plants while taking maintenance and other resource requirements into account. Unplanned interruptions and plant failures are considered at the operational level;
- Due to the closing dates of the power plants are unknown due to the government’s information privacy policy, it is assumed that the existing facilities will be operational without interruption until the end of the planning period;
- Future costs are calculated using average escalation rates that are determined for each cost component, and future cash flows are calculated using an average interest rate;
- There is no significant variation or dramatic change in economic indicators and demand patterns, and they continue to follow the long-term trend;
- The potential energy resources in Turkey will not change significantly over the planning horizon;
- Power plant basic data, efficiency, initial investment costs, and CO2 emissions are assumed to be constant over time.
3.2.2. Set, Indices, Parameters, and Decision Variables
I: | Set of energy resources used for electricity production, indexed by i; I = {lignite, hard coal, imported coal, natural gas, uranium}; |
J: | Set of electrical generation power plant types, indexed by j; J = {Fluized Lignite, Elbistan Lignite, Hard Coal, Imported Coal, Natural Gas, Nuclear, Hydroelectric, Wind, solar, Geothermal}; |
K: | Set of power plant categories indexed by k; K = {Renewables (R), Fossil Fuels (F), Nuclear (N)}; |
Jk: | Set of power plants that are in resource category k; JR = {Hydroelectric, Wind, Solar, Geothermal}; JF = {Fluized Lignite, Elbistan Lignite, Hard Coal, Imported Coal, Natural Gas}; JN = {Nuclear}; |
T: | Set of years considered in the planning period, indexed by t; t = {2021,2023, …,T}. |
Cj,tinv: | Capital investment cost of type j power plant at year t ($); |
Cj,tOM: | Operation and maintenance cost of type j plant at year t; |
Cj,tfuel: | Fuel cost of type j power plan at year t ($); |
Ej,t: | Total energy generation of type j power plant at year t (MWh); |
Tj: | The operational lifetime of type j power plant (year); |
Tjconst: | Construction time of type j power plant (year); |
ICapj: | Installed capacity of type j power plant (MW); |
: | Availability percentage of type j power plant (%); |
Availability factor of type j power plant (h/year); | |
LCj: | Levelized cost of type j power plant ($/MWh); |
Ctimp: | Unit import cost in year t ($/MWh); |
Ctexp: | Export revenue in year t ($/MWh); |
explimit: | Annual export limit (MWh); |
implimit: | Annual import limit (MWh); |
AVLj: | Number of type j power plants that are operational before the planning horizon; |
PLNjt: | Number of type j power plants that are already planned to be opened before the planning horizon at year t |
Dt: | Electric Demand in Year t (MWh); |
NJRopr: | Maximum number of renewable power plants that can be in operation in a year (calculated based on resource potential); |
: | Maximum number of hydroelectric power plants that can be opened in year t (calculated based on construction capacity in Turkey); |
The CO2 emission factor of type j power plant (ton/MWh); | |
Emission limit of CO2 in year t (ton); | |
Yt: | Percentage of renewable power plant capacity in year t (%); |
M: | A sufficiently large number; |
r: | Interest rate (%); |
ef: | Escalation rate for fuel type f (%); |
Escalation rate for operation and maintenance costs (%). |
xjt: | Number of type j power plants opened in year t; |
wjt: | Number of type j power plants closed in year t; |
Njt: | Total number of type j power plants in year t; |
vj: | Binary variable, 1 if the capacity of type j power plants is increased, 0 otherwise; |
yjt: | The energy supply of type j power plant in year t (MWh); |
expt: | Electric energy exported in year t (MWh); |
impt: | Electric energy imported in year t (MWh); |
z: | Total levelized cost of power plants. |
3.2.3. Mathematical Model
4. Time Series Analysis and Application of the Model
4.1. Time Series Analysis
4.2. Application of the Mathematical Model
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- IEA. World Energy Outlook. Available online: https://www.iea.org/reports/world-energy-outlook-2019 (accessed on 1 December 2022).
- Zhou, P.; Ang, B.W.; Poh, K.L. Decision analysis in energy and environmental modeling: An update. Energy 2006, 31, 2604–2622. [Google Scholar] [CrossRef]
- Lazard. Lazard’s Annual Levelized Cost of Energy Analysis (LCOE 12.0). Available online: https://www.lazard.com/ (accessed on 1 December 2022).
- Baños, R.; Manzano-Agugliaro, F.; Montoya, F.G.; Gil, C.; Alcayde, A.; Gómez, J. Optimization methods applied to renewable and sustainable energy: A review. Renew. Sustain. Energy Rev. 2011, 15, 1753–1766. [Google Scholar] [CrossRef]
- Yörük, G. Strategic Energy Production Planning of Turkey Using Mixed Integer Programming Based on Electricity Demand Forecasting. Master’s Thesis, Atilim University, Ankara, Turkey, 2021. [Google Scholar]
- Ramanathan, R.; Ganesh, L.S. Energy alternatives for lighting in households: An evaluation using an integrated goal programming-AHP model. Energy 1995, 20, 63–72. [Google Scholar] [CrossRef]
- Pokharel, S.; Chandrashekar, M. A multiobjective approach to rural energy policy analysis. Energy 1998, 23, 325–336. [Google Scholar] [CrossRef]
- Mavrotas, G.; Diakoulaki, D.; Papayannakis, L. An energy planning approach based on mixed 0-1 multiple objective linear programming. Int. Trans. Oper. Res. 1999, 6, 231–244. [Google Scholar] [CrossRef]
- Agrawal, R.K.; Singh, S.P. Energy allocations for cooking in UP households (India): A fuzzy multi-objective analysis. Energy Convers. Manag. 2001, 42, 2139–2154. [Google Scholar] [CrossRef]
- Antunes, C.H.; Martins, A.G.; Brito, I.S. A multiple objective mixed integer linear programming model for power generation expansion planning. Energy 2004, 29, 613–627. [Google Scholar] [CrossRef] [Green Version]
- San Cristóbal, J.R. A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain. Renew. Sustain. Energy Rev. 2012, 16, 4461–4464. [Google Scholar] [CrossRef]
- Özcan, E.C.; Erol, S. A multi-objective mixed integer linear programming model for energy resource allocation problem: The case of Turkey. Gazi Univ. J. Sci. 2014, 27, 1157–1168. [Google Scholar]
- Şengül, Ü.; Eren, M.; Eslamian Shiraz, S.; Gezder, V.; Sengül, A.B. Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew. Energy 2015, 75, 617–625. [Google Scholar] [CrossRef]
- Cormio, C.; Dicorato, M.; Minoia, A.; Trovato, M. A regional energy planning methodology including renewable energy sources and environmental constraints. Renew. Sustain. Energy Rev. 2003, 7, 99–130. [Google Scholar] [CrossRef]
- Ai, Y.P.; Huang, G.H.; Yang, Z.F.; Lin, Q.G.; Bass, B.; Tan, Q. Development of an optimization model for energy systems planning in the Region of Waterloo. Int. J. Energy Res. 2008, 32, 988–1005. [Google Scholar]
- Huang, Y.H.; Wu, J.H. A portfolio risk analysis on electricity supply planning. Energy Policy 2008, 36, 627–641. [Google Scholar] [CrossRef]
- Daniel, J.; Dicorato, M.; Forte, G.; Iniyan, S.; Trovato, M. A methodology for the electrical energy system planning of Tamil Nadu state (India). Energy Policy 2009, 37, 904–914. [Google Scholar] [CrossRef]
- Arnette, A.; Zobel, C.W. An optimization model for regional renewable energy development. Renew. Sustain. Energy Rev. 2012, 16, 4606–4615. [Google Scholar] [CrossRef]
- Pina, A.; Silva, C.A.; Ferrão, P. High-resolution modeling framework for planning electricity systems with high penetration of renewables. Appl. Energy 2013, 112, 215–223. [Google Scholar] [CrossRef]
- Zhang, Q.; Mclellan, B.C.; Tezuka, T.; Ishihara, K.N. An integrated model for long-term power generation planning toward future smart electricity systems. Appl. Energy 2013, 112, 1424–1437. [Google Scholar] [CrossRef]
- Habib, M.A.; Chungpaibulpatana, S. Electricity generation expansion planning with environmental impact abatement: Case study of Bangladesh. Energy Procedia 2014, 52, 410–420. [Google Scholar] [CrossRef] [Green Version]
- Moret, S.; Bierlaire, M.; Maréchal, F. Strategic Energy Planning under Uncertainty: A Mixed-Integer Linear Programming Modeling Framework for Large-Scale Energy Systems. Comput. Aided Chem. Eng. 2016, 38, 1899–1904. [Google Scholar]
- Santos, M.J.; Ferreira, P.; Araújo, M. A methodology to incorporate risk and uncertainty in electricity power planning. Energy 2016, 115, 1400–1411. [Google Scholar] [CrossRef] [Green Version]
- Kachoee, M.S.; Salimi, M.; Amidpour, M. The long-term scenario and greenhouse gas effects cost-benefit analysis of Iran’s electricity sector. Energy 2018, 143, 585–596. [Google Scholar] [CrossRef]
- Shen, W.; Qiu, J.; Dong, Z. Electricity network planning targeting Low-Carbon energy transition. Glob. Energy Interconnect. 2018, 1, 487–499. [Google Scholar]
- Walmsley, M.R.W.; Walmsley, T.G.; Atkins, M.J. Linking greenhouse gas emissions footprint and energy return on investment in electricity generation planning. J. Clean. Prod. 2018, 200, 911–921. [Google Scholar] [CrossRef]
- Senatla, M.; Nchake, M.; Taele, B.M.; Hapazari, I. Electricity capacity expansion plan for Lesotho—Implications on energy policy. Energy Policy 2018, 120, 622–634. [Google Scholar] [CrossRef]
- Levin, T.; Kwon, J.; Botterud, A. The long-term impacts of carbon and variable renewable energy policies on electricity markets. Energy Policy 2019, 131, 53–71. [Google Scholar] [CrossRef]
- Anderson, D. Models for Determining Least-Cost Investments in Electricity Supply. Bell J. Econ. 2008, 3, 267–299. [Google Scholar] [CrossRef]
- Trotter, P.A.; McManus, M.C.; Maconachie, R. Electricity planning and implementation in sub-Saharan Africa: A systematic review. Renew. Sustain. Energy Rev. 2017, 74, 1189–1209. [Google Scholar] [CrossRef]
- Melikoglu, M. Vision 2023: Scrutinizing achievability of Turkey’s electricity capacity targets and generating scenario based nationwide electricity demand forecasts. Energy Strateg. Rev. 2018, 22, 188–195. [Google Scholar] [CrossRef]
- Calikoglu, U.; Koksal, M.A. Green electricity and Renewable Energy Guarantees of Origin demand analysis for Türkiye. Energy Policy 2022, 170, 113229. [Google Scholar] [CrossRef]
- Ünal, B.B.; Onaygil, S.; Acuner, E.; Cin, R. Application of energy efficiency obligation scheme for electricity distribution companies in Turkey. Energy Policy 2022, 163, 112851. [Google Scholar] [CrossRef]
- Selçuklu, S.B.; Coit, D.W.; Felder, F.A. Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty. Energy Policy 2023, 173, 113393. [Google Scholar] [CrossRef]
- Singh, A.K.; Khatoon, S. An Overview of Electricity Demand Forecasting Techniques. Natl. Conf. Emerg. Trends Electr. Instrum. Commun. Eng. 2013, 3, 38–48. [Google Scholar]
- Srinivasan, D.; Lee, M.A. Survey of hybrid fuzzy neural approaches to electric load forecasting. Proc. IEEE Int. Conf. Syst. Man Cybern. 1995, 5, 4004–4008. [Google Scholar]
- Jiang, W.; Wu, X.; Gong, Y.; Yu, W.; Zhong, X. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy 2020, 193, 116779. [Google Scholar] [CrossRef]
- Al-Musaylh, M.S.; Deo, R.C.; Adamowski, J.F.; Li, Y. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Adv. Eng. Inform. 2017, 35, 1–16. [Google Scholar] [CrossRef]
- Yukseltan, E.; Yucekaya, A.; Bilge, A.H. Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation. Appl. Energy 2017, 193, 287–296. [Google Scholar] [CrossRef]
- Abd Jalil, N.A.; Ahmad, M.H.; Mohamed, N. Electricity load demand forecasting using exponential smoothing methods. World Appl. Sci. J. 2013, 22, 1540–1543. [Google Scholar]
- Lee, C.M.; Ko, C.N. Short-term load forecasting using lifting scheme and ARIMA models. Expert Syst. Appl. 2011, 38, 5902–5911. [Google Scholar] [CrossRef]
- Dilaver, Z.; Hunt, L.C. Modelling and forecasting Turkish residential electricity demand. Energy Policy 2011, 39, 3117–3127. [Google Scholar] [CrossRef] [Green Version]
- Taylor, J.W. Triple seasonal methods for short-term electricity demand forecasting. Eur. J. Oper. Res. 2010, 204, 139–152. [Google Scholar] [CrossRef] [Green Version]
- Shin, S.-Y.; Woo, H.-G. Energy Consumption Forecasting in Korea Using Machine Learning Algorithms. Energies 2022, 15, 4880. [Google Scholar] [CrossRef]
- Leite Coelho da Silva, F.; da Costa, K.; Canas Rodrigues, P.; Salas, R.; López-Gonzales, J.L. Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector. Energies 2022, 15, 588. [Google Scholar] [CrossRef]
- Ünlü, K.D. A Data-Driven Model to Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load. Electronics 2022, 11, 1524. [Google Scholar] [CrossRef]
- Akbal, Y.; Ünlü, K.D. A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production. Renew. Energy 2022, 200, 832–844. [Google Scholar] [CrossRef]
- Akdi, Y.; Gölveren, E.; Okkaoğlu, Y. Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting. Energy 2020, 191, 116524. [Google Scholar] [CrossRef]
- Kazancık, L.B. Türkiye Elektrik Enerji Sektöründe Robust Optimizasyon Modeli. Ph.D. Thesis, Department of Statistics, Ankara Üniversitesi, Ankara, Turkey, 2016. [Google Scholar]
- İncekara, Ç.Ö. Türkiye’nin Sürdürülebilir Stratejik Enerji Politikalarının Oluşturulması için Optimizasyon Modellerinin Geliştirilmesi ve Uygulama Adımları. Master’s Thesis, Department of Ind. Eng., Çukurova Üniversitesi, Adana, Turkey, 2007. [Google Scholar]
- Box, G.; Jenkins, G. Time Series Analysis; Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1970. [Google Scholar]
- TEİAŞ. Electric Generation and Transfer Statistics 2018, Electric Statistics. 2020. Available online: https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri (accessed on 29 January 2021).
- Cosic, A.; Stadler, M.; Mansoor, M.; Zellinger, M. Mixed-integer linear programming based optimization strategies for renewable energy communities. Energy 2021, 237, 121559. [Google Scholar] [CrossRef]
- Meier, A.; Kramer, O. Recurrent neural network-predictions for PSO in dynamic optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; Volume 29, p. 36. [Google Scholar]
- Ordu, M.; Demir, E.; Tofallis, C.; Gunal, M.M. A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach. J. Oper. Res. Soc. 2021, 72, 485–500. [Google Scholar] [CrossRef]
Performance Metrics | Nonlinear Regression (Third Order Polynomial) | Exponential (Double) | Holt–Winters (Additive) | Holt–Winters (Multiplicative) | Holt–Winters (Linear) | ARIMA (2,1,2) |
---|---|---|---|---|---|---|
RMSE | 3984 | 4986 | 5600 | 5329 | 4771 | 3236 |
MAPE | 2.160% | 3.162% | 3.541% | 3.247% | 2.844% | 1.702% |
MAE | 2776 | 3689 | 4181 | 3842 | 3519 | 2322 |
R² | 99.82% | 99.69% | 99.62% | 99.65% | 99.72% | 99.87% |
Power Plant (j) | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 |
---|---|---|---|---|---|---|---|---|---|---|---|
Elbistan Lignite | 8 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fluized Lignite | 48 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Geothermal | 54 | 54 | 54 | 33 | 4 | 3 | 2 | 1 | 1 | 0 | 0 |
Hard Coal | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hydroelectric | 214 | 214 | 214 | 214 | 214 | 225 | 277 | 285 | 288 | 291 | 295 |
Imported Coal | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Natural Gas | 37 | 37 | 37 | 37 | 28 | 25 | 19 | 17 | 15 | 13 | 11 |
Nuclear | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Solar | 133 | 133 | 133 | 233 | 352 | 452 | 552 | 652 | 752 | 852 | 950 |
Wind | 221 | 221 | 321 | 464 | 564 | 664 | 764 | 864 | 964 | 1064 | 1164 |
Power Plant (j) | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 |
---|---|---|---|---|---|---|---|---|---|---|
Elbistan Lignite | 1080 | 1080 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fluidized Lignite | 0 | 0 | 0 | 0 | 0 | 150 | 0 | 0 | 0 | 0 |
Geothermal | 1620 | 1620 | 990 | 120 | 90 | 60 | 30 | 30 | 0 | 0 |
Hard Coal | 300 | 300 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hydroelectric | 28,676 | 28,676 | 28,676 | 28,676 | 30,150 | 37,118 | 38,190 | 38,592 | 38,994 | 39,530 |
Imported Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Natural Gas | 25,900 | 25,900 | 25,900 | 19,600 | 17,500 | 13,300 | 11,900 | 10,500 | 9100 | 7700 |
Nuclear | 0 | 0 | 0 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 |
Solar | 6650 | 6650 | 11,650 | 17,600 | 22,600 | 27,600 | 32,600 | 37,600 | 42,600 | 47,500 |
Wind | 8840 | 12,840 | 18,560 | 22,560 | 26,560 | 30,560 | 34,560 | 38,560 | 42,560 | 46,560 |
Power Plant (j) | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 |
---|---|---|---|---|---|---|---|---|---|---|---|
Elbistan Lignite | 8 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fluidized Lignite | 48 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Geothermal | 54 | 54 | 54 | 33 | 4 | 3 | 3 | 0 | 0 | 0 | 0 |
Hard Coal | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hydroelectric | 214 | 214 | 214 | 214 | 214 | 225 | 277 | 288 | 299 | 311 | 335 |
Imported Coal | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Natural Gas | 37 | 37 | 37 | 37 | 28 | 25 | 19 | 17 | 15 | 12 | 8 |
Nuclear | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Solar | 133 | 133 | 133 | 233 | 352 | 452 | 552 | 652 | 752 | 852 | 1052 |
Wind | 221 | 221 | 321 | 464 | 563 | 663 | 762 | 856 | 940 | 1040 | 1188 |
Power Plant (j) | 2031 | 2032 | 2033 | 2034 | 2035 | 2036 | 2037 | 2038 | 2039 | 2040 | |
Elbistan Lignite | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Fluized Lignite | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Geothermal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Hard Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Hydroelectric | 347 | 357 | 367 | 378 | 390 | 402 | 415 | 428 | 442 | 456 | |
Imported Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Natural Gas | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Nuclear | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
Solar | 1152 | 1252 | 1352 | 1452 | 1552 | 1652 | 1752 | 1852 | 1951 | 2050 | |
Wind | 1198 | 1198 | 1200 | 1200 | 1200 | 1200 | 1200 | 1200 | 1200 | 1200 |
Power Plant (j) | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 |
---|---|---|---|---|---|---|---|---|---|---|
Elbistan Lignite | 1080 | 1080 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fluidized Lignite | 0 | 0 | 0 | 0 | 0 | 150 | 0 | 0 | 0 | 0 |
Geothermal | 1620 | 1620 | 990 | 120 | 90 | 90 | 0 | 0 | 0 | 0 |
Hard Coal | 300 | 300 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hydroelectric | 28,676 | 28,676 | 28,676 | 28,676 | 30,150 | 37,118 | 38,592 | 40,066 | 41,674 | 43,282 |
Imported Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Natural Gas | 25,900 | 25,900 | 25,900 | 19,600 | 17,500 | 13,300 | 11,900 | 10,500 | 8400 | 7000 |
Nuclear | 0 | 0 | 0 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 |
Solar | 6650 | 6650 | 11,650 | 17,600 | 22,600 | 27,600 | 32,600 | 37,600 | 42,600 | 47,600 |
Wind | 8840 | 12,840 | 18,560 | 22,520 | 26,520 | 30,480 | 34,240 | 37,600 | 41,600 | 44,440 |
Power Plant (j) | 2031 | 2032 | 2033 | 2034 | 2035 | 2036 | 2037 | 2038 | 2039 | 2040 |
Elbistan Lignite | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fluidized Lignite | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Geothermal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hard Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hydroelectric | 44,890 | 46,498 | 47,838 | 49,178 | 50,652 | 52,260 | 53,868 | 55,610 | 57,352 | 59,228 |
Imported Coal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Natural Gas | 5600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Nuclear | 5000 | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 |
Solar | 52,600 | 57,600 | 62,600 | 67,600 | 72,600 | 77,600 | 82,600 | 87,600 | 92,600 | 97,550 |
Wind | 47,520 | 47,920 | 47,920 | 48,000 | 48,000 | 48,000 | 48,000 | 48,000 | 48,000 | 48,000 |
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Yörük, G.; Bac, U.; Yerlikaya-Özkurt, F.; Ünlü, K.D. Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting. Mathematics 2023, 11, 1865. https://doi.org/10.3390/math11081865
Yörük G, Bac U, Yerlikaya-Özkurt F, Ünlü KD. Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting. Mathematics. 2023; 11(8):1865. https://doi.org/10.3390/math11081865
Chicago/Turabian StyleYörük, Gökay, Ugur Bac, Fatma Yerlikaya-Özkurt, and Kamil Demirberk Ünlü. 2023. "Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting" Mathematics 11, no. 8: 1865. https://doi.org/10.3390/math11081865