The Effect of Operating Load Changes on Turbine Thermal Performance

Study Case The Effect of Operating Load Changes on Turbine Thermal Performance and Fuel Costs in a 1,000 MW Steam Power Plant Michael Kurniawan (2018123456) University of Cincinnati, 2600 Clifton Ave, Cincinnati, OH 45221, United States kurniaml@mail.uc.edu Abstract Changes in operating load at a 1,000 MW coal-fired power plant operated by Duke Energy in the United States often trigger fluctuations in steam turbine performance, which directly affect generation efficiency and fuel consumption. This study aims to evaluate the effect of load variations on turbine heat rate values, examine changes in turbine thermal efficiency, and analyze the implications for fuel consumption and operating costs. A correlational quantitative approach was applied using secondary data from December 2023 covering generator load, steam flow rate, turbine inlet and outlet enthalpy, and generator output power. Heat rate and thermal efficiency calculations were performed based on standard thermodynamic equations, while fuel consumption and costs were analyzed by comparing values per kWh. The results show that an increase in operating load consistently reduces the turbine heat rate and increases thermal efficiency, indicating more optimal utilization of steam energy. In addition, fuel consumption and fuel costs tend to decrease when the load approaches optimum conditions. These findings emphasize the importance of regulating the operating load within the ideal range to maximize energy efficiency, reduce fuel consumption, and lower electricity production costs. Keywords: Generator Load, Turbine Heat Rate, Thermal Efficiency, Fuel Consumption MICHAEL KURNIAWAN 1 Study Case INTRODUCTION The growth of the industrial, technology, and information systems sectors in the United States has driven an increase in energy demand from year to year, especially in the electricity sector, which has shown a significant increase in demand [1]. Electricity load is influenced by various factors, including weather conditions—such as increased use of air conditioning in summer or heating in winter—schedules of large-scale events that trigger spikes in consumption, and the intensity of regional economic and business activities that determine the level of industrial and commercial operations to achieve production efficiency [2][3]. Coal-fired steam power plants still play an important role in the United States' national energy mix. One such plant is a 1,000 MW ultra-supercritical unit managed by Duke Energy. The dynamics of electricity demand mean that this power plant must operate at various load levels that are not always optimal [4]. Daily load fluctuations—from early morning to midday peaks—cause changes in operating parameters such as steam flow rate, pressure, and temperature, which directly affect turbine heat rate, thermal efficiency, and coal consumption. Several previous studies have examined the relationship between load variations and steam turbine performance. Johnson [5] reported an improvement in heat rate after overhaul at a load of 175 MW, while Smith et al. [6] found an increase in efficiency at high loads in a regional power plant. However, specific studies on large-scale units (≥ 1,000 MW) in the United States, especially those highlighting daily partial load characteristics, are still relatively limited. Detailed analyses related to specific coal consumption and operating costs have also not been widely published. Brown [7] showed that an increase in load caused a decrease in both Gross Specific Fuel Consumption (SFC) and Net SFC, indicating an increase in plant efficiency with lower fuel requirements per kWh at high loads. Davis [8] also reports that electricity production costs tend to decrease as load increases, with maximum load resulting in the lowest cost per kWh. These findings confirm that load changes directly affect turbine heat rate, thermal efficiency, specific fuel consumption, and fuel costs. However, verification of this trend in 1,000 MW ultra-supercritical units still requires further study. This study fills this gap through a correlational analysis between load and turbine performance at six daily operating intervals (02:00, 06:00, 10:00, 14:00, 18:00, and MICHAEL KURNIAWAN 2 Study Case 22:00). Quantitative methods were used to calculate turbine heat rate, thermal efficiency, specific fuel consumption, and fuel costs based on secondary data from December 2023. This period was selected based on the availability of complete operating records from the power plant operator. Using standard thermodynamic equations and descriptive statistical analysis (mean values and standard deviations), this study evaluates the extent to which load variations affect the technical efficiency and economic aspects of power plant operations. The main objectives of this study are to identify the effect of load changes on turbine heat rate, assess variations in turbine thermal efficiency at various load levels, and analyze the impact on specific coal consumption and electricity generation costs. The results obtained are expected to form the basis for recommendations on operational load management strategies to improve energy efficiency, reduce coal consumption, and lower electricity production costs at large-scale steam power plants in the United States. RESEARCH METHOD This study uses a correlational quantitative approach that aims to evaluate the extent to which generator load variations as independent variables affect turbine heat rate, turbine thermal efficiency, specific fuel consumption, and fuel costs as dependent variables. The data used is secondary data obtained from the historical operation reports of Duke Energy's 1,000 MW ultra-supercritical steam power plant in the United States during the period of December 2023. The parameters analyzed include electrical load, steam pressure and temperature, steam mass flow rate, and coal consumption at six daily operational time intervals (02:00, 06:00, 10:00, 14:00, 18:00, and 22:00). The historical operating data was then processed using Microsoft Excel to perform direct calculations on each data point, interpolate thermodynamic properties (steam enthalpy and feed water), and create graphs of the relationship between load and turbine performance. Literature on the Rankine cycle and related scientific journals were used as the basis for formulating the turbine heat rate, thermal efficiency, specific fuel consumption, and fuel cost estimates. The main specifications of the steam turbine in the analyzed power plant unit are shown in Table 1. MICHAEL KURNIAWAN 3 Study Case Table 1. Steam Turbine Specifications Based on the operating parameters listed in Table 2 (December 2023 data), calculations were performed to obtain the turbine heat rate, thermal efficiency, specific fuel consumption, and fuel cost values. Next, the relationship between operating time and load changes on these four performance indicators was analyzed. Turbine Heat Rate Turbine heat rate is defined as the amount of heat energy required to generate 1 kWh of electrical energy, which represents the total rate of heat energy entering the steam cycle. This quantity is generally expressed in kJ/kWh or Btu/kWh [9]. The turbine heat rate is calculated using the following equation [10]: 𝑇𝐻𝑅 = 𝑚̇(ℎ1 − ℎ2 ) 𝐺𝑟𝑜𝑠𝑠 𝑂 𝑢𝑡𝑝𝑢𝑡 (1) Explanation: THR : Turbine Heat Rate (kJ/kWh) m ̇: Steam mass flow rate (kg/h) h1: Enthalpy of steam leaving the boiler (kJ/kg) h2: Enthalpy of water entering the boiler (kJ/kg) Gross Output: Generator output power (kW) Turbine Thermal Efficiency Thermal efficiency indicates the turbine's ability to convert high-pressure steam heat energy into electrical energy. The calculation of this efficiency is based on the Rankine cycle concept and can be evaluated from the comparison of electrical output energy to heat energy input [11]: MICHAEL KURNIAWAN 4 Study Case 𝜂𝑇 Electricity x 100% = Energy Output (2) Heat input Since the heat input energy is usually expressed in terms of heat rate (kJ/kWh), and 1 kWh is equivalent to 3,600 kJ, the thermal efficiency of the turbine can also be expressed as follows [11]: 𝜂𝑇 = 3600 x 100% Heat rate turbin (3) Explanation: ηT: Turbine thermal efficiency (%) Heat energy 1 kWh : 3,600 kJ Specific Fuel Consumption Specific Fuel Consumption (SFC) is an indicator that states the amount of fuel (kg) needed to produce 1 kWh of electrical energy under certain load conditions [12]. The SFC value is calculated using the following equation [13]: 𝑆𝐹𝐶 = 𝑄𝑓 𝐺𝑟𝑜𝑠𝑠 𝑂 𝑢𝑡𝑝𝑢𝑡 (4) Explanation: SFC: Specific fuel consumption (kg/kWh) Qf: Fuel consumption rate (kg/h) Gross Output: Generator output power (kW) The gross output value is obtained directly from the power plant's historical operation report for December 2023. Fuel Costs Fuel costs are a dominant component in the operating cost structure of coal-fired power plants. Estimated electricity generation costs are calculated based on specific fuel consumption and the price of coal per kilogram as follows [8]: Fuel Costs = SFC × Coal Price (5) Explanation: Fuel Cost: Fuel cost (USD/kWh) SFC: Specific fuel consumption (kg/kWh) Coal price: $0.04/kg (average price in December 2023 on the US market) MICHAEL KURNIAWAN 5 Study Case RESULTS AND DISCUSSION This section presents the main operational parameters used as the basis for analyzing the gross output of the generator, main steam, feedwater, and coal flow rate at six daily operational time intervals (02:00, 06:00, 10:00, 14:00, 18:00, and 22:00). Each parameter—including temperature, pressure, flow rate, and enthalpy—is an important variable in calculating turbine heat rate, thermal efficiency, specific fuel consumption, and fuel cost estimates at Duke Energy's 1,000 MW ultra-supercritical coal-fired power plant in the United States. Table 2. Average Operational Parameter Values for December 2023 MICHAEL KURNIAWAN 6 Study Case Table 3. Standard Deviation of Operational Parameters for December 2023 Based on these average values and standard deviations, calculations were performed for turbine heat rate, thermal efficiency, specific fuel consumption, and fuel costs. The results were then analyzed to examine the relationship between operating time and load changes across all performance parameters. Operating Time Figure 1. Graph of the Relationship between Load and Operating Time Figure 1 shows that turbine operation was relatively stable at all load levels during December 2023, with a power range from 625 MW to 906 MW. This condition indicates that the generating unit operated without significant disruption despite load fluctuations. The lowest load was recorded at 02:00 (625 MW), reflecting low electricity consumption activity in the early hours of the morning. As household, office, MICHAEL KURNIAWAN 7 Study Case and industrial sector activity increased, the load peaked at around 14:00 at 906 MW, then gradually decreased as daily activity decreased. Heat Rate Turbine heat rate is calculated using Equation (1). Figure 2. Graph of Load vs. Turbine Heat Rate Figure 2 shows a downward trend in heat rate as load increases. At a minimum load of 625 MW, the heat rate was recorded at 10.279 kJ/kWh, then decreased to 9.136 kJ/kWh (705 MW), 8.092 kJ/kWh (802 MW), 7.764 kJ/kWh (842 MW), and 7.596 kJ/kWh (874 MW). At the peak load of 906 MW, the heat rate reached its lowest value of 7.420 kJ/kWh, or about 27.8% lower than the minimum load condition. This decrease indicates an increase in the efficiency of steam energy utilization at high loads. Turbine Thermal Efficiency Turbine thermal efficiency is calculated using Equation (3). Figure 3. Graph of the Relationship between Load and Turbine Thermal Efficiency Figure 3 shows that thermal efficiency increases as the load increases. At a load of MICHAEL KURNIAWAN 8 Study Case 625 MW, the efficiency was recorded at 35.02%, then increased to 39.40% (705 MW), 44.49% (802 MW), 46.37% (842 MW), and 47.38% (874 MW). The maximum value of 48.52% was achieved at a load of 906 MW. These results show that the greater the load, the more effective the conversion of heat energy into electrical energy. Specific Fuel Consumption Specific fuel consumption is calculated using Equation (4). Figure 4. Graph of the Relationship between Load and Fuel Consumption Figure 4 shows that coal consumption per kWh decreases as the load increases. At 625 MW, SFC was recorded at 0.585 kg/kWh and gradually decreased to 0.581 kg/kWh (705 MW), 0.577 kg/kWh (802 MW), 0.574 kg/kWh (842 MW), 0.571 kg/kWh (874 MW), to 0.568 kg/kWh at a peak load of 906 MW. This decrease of around 2.9% indicates that the combustion process becomes more optimal at high loads. Fuel Costs Fuel costs are calculated using Equation (5). Figure 5. Graph of the Relationship between Load and Fuel Consumption Costs MICHAEL KURNIAWAN 9 Study Case Figure 5 shows that the cost of generating electricity per kWh decreases as the load increases. At a load of 625 MW, fuel costs were recorded at around $0.023/kWh, then decreased to $0.0228/kWh (705 MW), $0.0226/kWh (802 MW), $0.0224/kWh (842 MW), $0.0223/kWh (874 MW), and reached the lowest value of $0.0222/kWh at a load of 906 MW, or a decrease of around 3.04%. This condition shows that the power plant reaches its most economical operating point at high loads. The average coal price of $0.04/kg was obtained from data from the United States Energy Information Administration (EIA) for December 2023 [14]. Next, statistical testing was performed using the single-factor analysis of variance (ANOVA) method to test the significance of the effect of load changes on performance parameters [15]. Table 4. Test Results Using Single-Factor ANOVA The one-way ANOVA results show that variations in operational load have a very significant effect on all performance indicators, namely Total Heat Rate (THR) (F(1,10)=277.36; p≈1.27×10⁻⁷), thermal efficiency ηT (F(1,10)=291.02; p≈1.01×10⁻⁷), Specific Fuel Consumption (SFC) (F(1,10)=326.14; p≈8.7×10⁻⁸), and Fuel Consumption Cost (BPL) (F(1,10)=114.26; p≈8.6×10⁻⁷). All F values far exceed F₍crit₎(α=0.05)=4.96, with p-values much smaller than 0.05. This confirms that an increase in load statistically reduces the heat rate, increases thermal efficiency, reduces fuel consumption per kWh, and lowers fuel costs, thereby supporting the strategy of operating the plant at optimal load to maximize energy efficiency and cost savings. CONCLUSION Based on the results of the analysis of the effect of operating load variations on Duke Energy's 1,000 MW ultra-supercritical steam power plant in the United States during December 2023, it can be concluded that increasing the generator load from 625 MW to 906 MW significantly reduces the turbine heat rate and specific fuel consumption, while increasing the thermal efficiency of the turbine. The decrease in MICHAEL KURNIAWAN 10 Study Case heat rate from 10,279 kJ/kWh to 7,420 kJ/kWh and the increase in thermal efficiency from 35.02% to 48.52% indicate that operating near maximum capacity can optimize the conversion of steam energy into electrical energy. In addition, coal consumption per kWh decreased, followed by a reduction in fuel costs from approximately $0.023/kWh to $0.0222/kWh. These results confirm that highload operation not only improves the thermal performance of the system but also provides economic benefits through fuel savings. These findings are in line with the research objective of evaluating the technical and financial aspects of coal-based power generation. As a recommendation for future development, further research is recommended to include monitoring daily partial loads over a longer period of time to capture more representative load fluctuation patterns in the field. In addition, coal quality analysis needs to be conducted to evaluate its impact on generation efficiency and emissions, and steam control strategies under ultra-supercritical conditions can be further optimized to improve overall system performance. With this approach, recommendations for optimal operating loads and best practices for plant management can be formulated more precisely and sustainably for large-scale power plants in the United States. REFERENCE MICHAEL KURNIAWAN 11 Study Case MICHAEL KURNIAWAN 12