TRANSIENT SIMULATION OF WIND ENERGY PRODUCTION FOR ELECTRIC MARKET STABILITY

Year 2024, Volume: 25 Issue: 2, 250 - 261, 28.06.2024

Huseyin Gunhan Ozcan

https://doi.org/10.18038/estubtda.1394484

Abstract

Today, energy sustainability, which is one of the most significant concerns in the energy industry, is of utmost importance. In this context, investments and interest in renewable energy sources are growing. As a nation with vast wind energy potential, Türkiye is at the forefront of expanding investments in this sector. This study highlights the significance of wind power plants in electricity market and the relevance of wind energy forecasts, as well as the significance of ensuring the imbalance in energy supply and enhancing electricity market stability. Parallel to this, the transient system simulation (TRNSYS) model was used to determine annual energy generation of a wind power plant in Izmir with a capacity of 18 MW, and the obtained results were compared with the real-time generation data from EPİAŞ transparency platform. The model had two approaches, one based on standard data from the second generation of a typical meteorological year (Plan (1)), and the other on actual field data collected in the plant (Plan (2)).

The numerical findings indicate that the annual energy generation values for Plan (1) and Plan (2) are 24,018.1 MWh and 61,699.1 MWh, respectively. Additionally, the real-time production yields a total of 60,176.2 MWh. In a meantime, Plan (1) generated a positive imbalance value of 45,726.7 MWh, whereas Plan (2) has 6,651.3 MWh over the course of one year. In contrast, the annual sum of negative imbalance values was determined to be 9,475.9 MWh for Plan (1) and 8,368.6 MWh for Plan (2). The analysis yielded annual figures of 2,379,110.4 TL and 351,318.3 TL for positive and negative imbalance penalties, respectively, for Plan (1). For Plan (2), the corresponding amounts were 310,875.9 TL and 337,186.4 TL. Consequently, the total penalty payments for Plan (1) amounted to 2,730,428.8 TL, while for Plan (2) it reached 648,062.3 TL.

Keywords

Energy sustainability, Wind power, Electricity market stability, Numerical study

TRANSIENT SIMULATION OF WIND ENERGY PRODUCTION FOR ELECTRIC MARKET STABILITY

Abstract

TToday, energy sustainability, which is one of the most significant concerns in the energy industry, is of utmost importance. In this context, investments and interest in renewable energy sources are growing. As a nation with vast wind energy potential, Türkiye is at the forefront of expanding investments in this sector. This study highlights the significance of wind power plants in electricity market and the relevance of wind energy forecasts, as well as the significance of ensuring the imbalance in energy supply and enhancing electricity market stability. Parallel to this, the transient system simulation (TRNSYS) model was used to determine annual energy generation of a wind power plant in Izmir with a capacity of 18 MW, and the obtained results were compared with the real-time generation data from EPİAŞ transparency platform. The model had two approaches, one based on standard data from the second generation of a typical meteorological year (Plan (1)), and the other on actual field data collected in the plant (Plan (2)).

The numerical findings indicate that the annual energy generation values for Plan (1) and Plan (2) are 24,018.1 MWh and 61,699.1 MWh, respectively. Additionally, the real-time production yields a total of 60,176.2 MWh. In a meantime, Plan (1) generated a positive imbalance value of 45,726.7 MWh, whereas Plan (2) has 6,651.3 MWh over the course of one year. In contrast, the annual sum of negative imbalance values was determined to be 9,475.9 MWh for Plan (1) and 8,368.6 MWh for Plan (2). The analysis yielded annual figures of 2,379,110.4 TL and 351,318.3 TL for positive and negative imbalance penalties, respectively, for Plan (1). For Plan (2), the corresponding amounts were 310,875.9 TL and 337,186.4 TL. Consequently, the total penalty payments for Plan (1) amounted to 2,730,428.8 TL, while for Plan (2) it reached 648,062.3 TL.

Keywords

Energy sustainability, Wind power, Electricity market stability, Numerical study

References

• [1] Gönül Ö, Duman AC, Deveci K, Güler Ö. An assessment of wind energy status, incentive mechanisms and market in Turkey. Eng Sci Technol Int J 2021; 24: 1383-1395.
• [2] Başaran HH, Tarhan İ. Investigation of offshore wind characteristics for the northwest of Türkiye region by using multi-criteria decision-making method (MOORA)☆. Results Eng 2022; 16: 100757.
• [3] Arslan H, Baltaci H, Akkoyunlu BO, Karanfil S, Tayanc M. Wind speed variability and wind power potential over Turkey: Case studies for Çanakkale and İstanbul. Renew Energy 2020; 145: 1020-1032.
• [4] Kılıç B. Determination of wind dissipation maps and wind energy potential in Burdur province of Turkey using geographic information system (GIS). Sustain Energy Technol Assess 2019; 36: 100555.
• [5] Kaytez F. Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis. Renew Energy 2022; 196: 1281-1293.
• [6] Ma Z, Mei G. A hybrid attention-based deep learning approach for wind power prediction. Appl Energy 2022; 323: 119608.
• [7] Yaniktepe B, Koroglu T, Savrun MM. Investigation of wind characteristics and wind energy potential in Osmaniye, Turkey. Renew Sustain Energy Rev 2013; 21: 703-711.
• [8] Akpınar A. Evaluation of wind energy potentiality at coastal locations along the north eastern coasts of Turkey. Energy 2013; 50: 395-405.
• [9] Bilir L, İmir M, Devrim Y, Albostan A. An investigation on wind energy potential and small scale wind turbine performance at İncek region–Ankara, Turkey. Energy Convers Manag 2015; 103: 910-923.
• [10] Qian GW, Ishihara T. A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain. Energy 2022; 261 (Part A): 125171.
• [11] Kim DY, Kim BS. Differences in wind farm energy production based on the atmospheric stability dissipation rate: Case study of a 30 MW onshore wind farm. Energy 2022; 239: 122380.
• [12] Hassanian R, Helgadóttir Á, Riedel M. Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine. Cleaner Energy 2023; 4: 100058.
• [13] Cuevas-Figueroa G, Stansby PK, Stallard T. Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production. Energy 2022; 254: 124362.
• [14] Moradian S, Olbert AI, Gharbia S, Iglesias G. Copula-based projections of wind power: Ireland as a case study. Renew Sustain Energy Rev 2023; 175: 113147.
• [15] Paraschiv S, Paraschiv LS, Serban A, Cristea AG. Assessment of onshore wind energy potential under temperate continental climate conditions. Energy Rep 2022; 8: 251-258.
• [16] Daoudi M, Mou AAS, Naceur LA. Analysis of the first onshore wind farm installation near the Morocco-United Kingdom green energy export project. Sci Afr 2022; 17: e01388.
• [17] Ağbulut Ü. A novel stochastic model for very short-term wind speed forecasting in the determination of wind energy potential of a region: A case study from Turkey. Sustain Energy Technol Assess 2022; 51: 101853.
• [18] Dinler A. Reducing balancing cost of a wind power plant by deep learning in market data: A case study for Turkey. Appl Energy 2021; 298: 116728.
• [19] Sirin MS, Yilmaz BN. The impact of variable renewable energy technologies on electricity markets: An analysis of the Turkish balancing market. Energy Policy 2021; 151: 112093.
• [20] Quint D, Dahlke S. The impact of wind generation on wholesale electricity market prices in the midcontinent independent system operator energy market: An empirical investigation. Energy 2019; 169: 456-466.
• [21] Hu X, Jaraitė J, Kažukauskas A. The effects of wind power on electricity markets: A case study of the Swedish intraday market. Energy Econ 2021; 96: 105159.
• [22] Liu T, Xu J. Equilibrium strategy based policy shifts towards the integration of wind power in spot electricity markets: A perspective from China. Energy Policy 2021; 157: 112482.
• [23] Sirin SM, Uz D, Sevindik I. How do variable renewable energy technologies affect firm-level day-ahead output decisions: Evidence from the Turkish wholesale electricity market. Energy Econ 2022; 112: 106169.
• [24] Sirin SM, Yilmaz BN. Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect. Energy Policy 2020; 144: 111660.
• [25] https://www.epias.com.tr/en/
• [26] Bakić V, Pezo M, Stevanović Ž, Živković M, Grubor, B. Dynamical simulation of PV/Wind hybrid energy conversion system. Energy 2012; 45(1): 324-328.
• [27] Żołądek M, Figaj R, Kafetzis A, Panopoulos K. Energy-economic assessment of self-sufficient microgrid based on wind turbine, photovoltaic field, wood gasifier, battery, and hydrogen energy storage. Int J Hydrogen Energy 2024; 52: 728-744.
• [28] Yesilyurt MS, Ozcan HG, Yavasoglu HA. Co-simulation-based conventional exergy evaluation of a hybrid energy generation-vanadium redox flow battery-air source heat pump system. Energy 2023; 281: 128301.
• [29] Anoune K, Laknizi A, Bouya M, Astito A, Abdellah AB. Sizing a PV-Wind based hybrid system using deterministic approach. Energy Convers Manag 2018; 169: 137-148.
• [30] Panayiotou G, Kalogirou S, Tassou S. Design and simulation of a PV and a PV–Wind standalone energy system to power a household application. Renew Energy 2012; 37(1): 355-363.