Thermal power generating unit power supply-heat supply-steam supply distribution optimization method and system

Abstract

The application discloses a power generation-heat supply-steam supply distribution optimization method and system for a thermal power generating unit, and proposes a dynamic optimization solution for the problems of low thermal power distribution benefit and lack of quantitative guidance for deep adjustment working conditions under the spot market.Through real-time collection of parameters such as power generation load 71.1%, urban heat supply 5.8 million gigajoules per year, industrial steam supply and real-time electricity price 0.38 yuan / kWh, a comprehensive benefit model is constructed: real-time benefit = (power generation quantity * electricity price) + (heat supply quantity * heat price * correction coefficient) - (fuel cost + carbon cost). The fuel cost is accurately calculated according to the standard coal unit price 700 yuan / ton and the urban heat supply correction coefficient 0.92 and the industrial steam supply coefficient 0.85.

Description

Technical Field

[0001] This invention belongs to the field of thermal power unit optimization technology, specifically involving a method and system for optimizing the power generation, heating, and steam supply distribution of thermal power units. Background Technology

[0002] Against the backdrop of the deepening development of the electricity spot market, thermal power generating units face the dual challenges of drastic electricity price fluctuations and the coupled supply of multiple energy sources. In 2024, with thermal power accounting for 63% of the energy structure, thermal power units had a deep-shifting capacity of 30% of the load factor. However, traditional operating models exposed three major technical deficiencies: the spot market frequently experienced periods of zero or even negative electricity prices (e.g., the Hohhot-Baotou West node saw 12% of its electricity price period in 2024), while combined heat and power (CHP) companies still used a coal consumption analysis model that prioritized electricity sales for benefits. Furthermore, shift supervisors lacked real-time benefit calculation tools, making it impossible to dynamically allocate the revenue weights of power generation, heating (5.8 million GJ / year), and steam supply. A report from the Northern Power Regulatory Bureau showed that the annual benefit loss due to improper allocation of CHP exceeded 3 million yuan. For example, the industrial steam supply price (120 yuan / GJ) was 1.33 times that of urban heating (90 yuan / GJ), but the traditional DCS system did not establish a priority response mechanism for heat prices. During the 20%-50% deep peak shaving period, the generating units lack design parameter guidance, forcing monitoring personnel to rely on experience. When the load factor drops to 50%, every 5% increase in heating supply requires adjusting the power generation coal consumption coefficient by 0.92 (urban heating) and 0.85 (industrial steam supply), but the existing system only supports monthly calculations. In 2024, the Baotou No. 1 Thermal Power Plant experienced three unplanned shutdowns due to inaccurate control under deep peak shaving conditions, resulting in direct economic losses of 1.8 million yuan. Traditional monitoring and adjustments focus on parameter compliance rates, neglecting cost-benefit linkages. Under a real-time electricity price of 0.38 yuan / kWh, every 1g / kWh increase in power supply coal consumption will erode marginal revenue by 0.25 yuan, but operators lack cost visualization tools. Electricity market meter reading data (day-ahead clearing price and real-time clearing price) is not integrated into the DCS system, making it impossible for shift supervisors to predict the impact of a 5% load increase or decrease on profitability. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for optimizing the power generation, heating and steam supply distribution of thermal power units.

[0004] To achieve the above objectives, this invention employs the following technical solution: a method and system for optimizing the power generation-heating-steam supply distribution of thermal power units. This involves acquiring parameters such as a power generation load factor of 71.1%, urban heating supply of 5.8 million GJ / year, real-time industrial steam supply, real-time electricity market clearing price of 0.38 yuan / kWh, and medium- and long-term contract electricity prices through a DCS system. Simultaneously, it reads 10 types of electricity market meter reading data, including fitted electricity, day-ahead clearing electricity, and the unified clearing price across the entire grid. The data collection covers 50 key measuring points, including auxiliary equipment parameters such as high-load transformers, standby transformers, and excitation transformers, ensuring data comprehensiveness. A comprehensive benefit calculation formula is established.

[0005] Real-time benefits = (Electricity generation × Real-time electricity price) + (Urban heating supply × Average heat price × Correction factor 0.92) + (Industrial steam supply × Industrial heat price × Correction factor 0.85) - (Fuel cost + Carbon cost)

[0006] Fuel costs are calculated based on a comprehensive standard coal price of 700 yuan / ton, while carbon costs are precisely calculated using the formula: fuel consumption × received base element carbon content × 44 / 12 × carbon unit price. The model integrates real-time management data such as spot node electricity prices and settlement reference point electricity prices, updating the benefit value every minute. Heat and power distribution optimization: Dynamic response is achieved using heat-power micro-increase distribution technology.

[0007] During periods of zero electricity price: the power generation load will be automatically reduced to 50% of the approved lower limit, industrial steam supply will be increased to the design upper limit, and urban heating supply will remain at the benchmark level.

[0008] During normal periods: Automatic allocation ratio based on marginal contribution (industrial steam supply > urban heating > power generation)

[0009] During periods of high electricity prices (>0.5 yuan / kWh): The power generation load is increased to 75% of the declared limit. The allocation strategy displays the electricity-heat-steam ratio (e.g., 50:30:20) in real time through the monitoring system and compares the unit energy revenue using a three-dimensional bar chart.

[0010] Manual simulation function: An interactive interface has been developed for simulating load factor fluctuations of ±5% (66.1%-76.1%) and heating capacity fluctuations of ±5% (5.51-6.09 million GJ / year). After the shift supervisor inputs the spot electricity price, the system generates a 24-hour daily benefit curve, marking hourly benefit values ​​and heat-power allocation ratios, and supports dragging the time axis to view details. Command generation and execution: The optimal allocation scheme is pushed to the shift supervisor's decision-making interface, including:

[0011] Real-time comparison of marginal contributions of power generation / heating / steam supply

[0012] Predicted impact of a 5% increase or decrease in load (e.g., a 5% increase in steam supply could improve hourly profits by 58 yuan).

[0013] The real-time converted value of carbon emission reduction is synchronized to the unit control system via API interface, automatically adjusting the opening of heating valves and the power generation setting.

[0014] Real-time benefit allocation innovation breaks through the traditional monthly accounting model, dynamically optimizing the power generation-heating-steam supply ratio through a minute-level benefit refresh mechanism. During periods of zero electricity price, it automatically reduces power generation load to 50% and increases industrial steam supply to the maximum, avoiding a loss of 123,000 yuan in a single event. The precise application of a 0.92 correction coefficient for urban heating and a 0.85 correction coefficient for industrial steam supply solves the cost-sharing problem of combined heat and power units. Deep peak shaving safety control establishes a safety boundary model based on 15 years of unit operation data, under deep peak shaving conditions at a load factor of 50%:

[0015] The main steam pressure fluctuation is stable within ±0.2 MPa.

[0016] The risk of back pressure exceeding limits has decreased by 80%.

[0017] The number of unplanned shutdowns was reduced to zero through synergistic optimization that lowered back pressure by 1.11 kPa and increased furnace efficiency by 0.12%, resulting in an annual saving of 4,800 tons of standard coal.

[0018] The daily benefit curve of the decision support system upgrade achieves three major breakthroughs:

[0019] 1. The horizontal axis marks the peaks and troughs of electricity prices over a 24-hour period.

[0020] 2. The difference between real-time metered electricity and fitted electricity is correlated with the vertical axis benefit value.

[0021] 3. The heat and power distribution ratio is displayed in color-coded layers (red for industrial steam supply > yellow for urban heating > blue for power generation). The value is calculated by simulating load rate changes of ±5% to predict the range of benefit fluctuations and optimize dispatch instructions.

[0022] Annual operating data proves that both environmental protection and economic benefits are achieved:

[0023] Economic benefits increased by 3.36 million yuan: including 820,000 yuan in losses avoided during periods of zero electricity price and 2.54 million yuan in increased efficiency in industrial steam supply.

[0024] SO2 emissions reduced by 41 tons: Sulfur release reduced through optimized combustion.

[0025] NOx emissions reduced by 36 tons: The emission reduction is achieved by controlling the air-to-coal ratio to suppress the formation of nitrogen oxides. The monitoring interface displays the emission reduction equivalent in real time (e.g., 41 tons of SO2 is equivalent to neutralizing the sulfur pollution of 2,400 tons of coal).

[0026] The industrial adaptability advantage system is seamlessly integrated with the real-time production platform of Northern Company:

[0027] The benefit calculation module automatically reads electricity price meter reading data.

[0028] The output of the optimization algorithm module directly drives the heating valve controller.

[0029] The decision-making interface conforms to the shift leader's operating habits (drag-and-drop parameter adjustment). The software copyright "Thermal Power Unit Heat and Power Distribution Optimization System V1.0" (registration number 2024SR1115680) ensures technical reliability.

[0030] This invention provides a method and system for optimizing the power generation, heating, and steam supply distribution of thermal power units, which has the following beneficial effects:

[0031] Construct a comprehensive benefit model to calculate the marginal contributions of power generation, heating, and steam supply in real time:

[0032] Revenue from electricity generation = Real-time metered electricity × (Real-time clearing price - Base cost of electricity generation)

[0033] Heating revenue = Heat supply × (Heating price - Coal consumption for heating × Standard coal price) × Correction factor

[0034] Carbon cost = fuel quantity × elemental carbon content × 44 / 12 × carbon unit price. The system reads 10 types of market data, including fitted electricity and day-ahead clearing electricity prices, and combines them with urban heating correction factors of 0.92 and industrial steam supply correction factors of 0.85 to achieve minute-level benefit updates. Compared with the traditional monthly accounting model, the timeliness of benefit allocation is improved by 99%.

[0035] Breakthrough in intelligent allocation strategy: Thermal-electric micro-increase allocation technology establishes a three-level response mechanism.

[0036] 1. High electricity price period (>0.5 yuan / kWh): Power generation load is increased to 75% of the approved upper limit, and steam supply is maintained at the benchmark level.

[0037] 2. Zero-price period: Power generation load reduced to 50% of the minimum, industrial steam supply increased by 5%.

[0038] 3. During normal periods: Based on the marginal contribution ranking (industrial steam supply > urban heating > power generation), the system automatically allocated electricity during the zero-price event in January 2025. This system helped Baotou Power Plant avoid a loss of 123,000 yuan and compensated for the revenue of 86,000 yuan through the increase in industrial steam supply.

[0039] Decision support system upgrade: Develop a load rate ±5% variation simulation function.

[0040] The simulated power generation load range is 66.1%-76.1%.

[0041] Urban heating supply varies from 5.51 million to 6.09 million gigajoules per year.

[0042] After inputting the ±5% benchmark value of industrial steam supply into the spot electricity price, the system generates a daily benefit curve, marking the 24-hour benefit fluctuation and the heat-to-power ratio. Under a 71.1% load factor, the simulation shows that a 5% increase in steam supply can improve hourly benefits by 58 yuan.

[0043] Closed-loop control and benefit verification: Real-time display through the monitoring system:

[0044] Power generation / heat supply / steam supply ratio (e.g., electricity:heat:steam = 50:30:20)

[0045] Marginal contribution comparison bar chart (marginal revenue from industrial steam supply > marginal revenue from power generation)

[0046] The annual operating data shows that the difference in benefits before and after optimization (an increase of 420 yuan / h) proves that the system achieves an annual economic benefit of 3.36 million yuan and reduces SO2 emissions by 41 tons and NOx emissions by 36 tons through synergistic optimization such as a 1.11 kPa reduction in back pressure and a 0.12% increase in furnace efficiency. Detailed Implementation

[0047] How to use:

[0048] System initialization and data access

[0049] Operators log into the power plant's real-time production system and enter the "Heat and Power Distribution Optimization" module. The system automatically connects to the DCS interface to collect real-time parameters.

[0050] The power generation load factor was 71.1%.

[0051] Urban heating supply: 5.8 million gigajoules / year

[0052] Real-time value of industrial steam supply

[0053] Manually enter management parameters for electricity market data (real-time clearing price 0.38 yuan / kWh, day-ahead clearing price, and unified clearing price for Hohhot and Baotou). These parameters include: comprehensive standard coal price 700 yuan / ton, urban heating correction factor 0.92, industrial steam supply correction factor 0.85, and carbon price. The benefit calculation model will be automatically activated after system initialization.

[0054] Real-time benefit monitoring and strategy triggering

[0055] The system refreshes the overall benefit value every minute:

[0056] Real-time benefits = (Electricity generation × Real-time electricity price) + (Heat supply × Heat price × Correction factor) - (Fuel cost + Carbon cost)

[0057] Fuel costs are calculated based on standard coal price per unit area × coal consumption × correction factor, while carbon costs are calculated based on fuel carbon content × 44 / 12 × carbon price per unit area. When the real-time electricity price is detected to be less than 0.3 yuan / kWh, a zero-price response strategy is automatically triggered.

[0058] The power generation load has been reduced to 50% of the approved lower limit.

[0059] Industrial steam supply increased to design limit

[0060] A red warning pop-up window appeared on the urban heating supply baseline monitoring interface, accompanied by a buzzer notification.

[0061] Manual simulation and decision support

[0062] The shift leader executes the command through the "Simulation Analysis" function:

[0063] 1. Load factor variation simulation: Adjust within the range of 66.1%-76.1% (±5% benchmark), and generate a benefit variation curve after inputting the spot nodal price.

[0064] 2. Heating Supply Variation Simulation: The urban heating supply is adjusted within the range of 5.51-6.09 million GJ / year, and the industrial steam supply varies by ±5%. The system outputs a 24-hour daily benefit curve, with the horizontal axis marking the time point and the vertical axis displaying the benefit value (yuan). The curve is layered to indicate the heat and power distribution ratio (industrial steam supply in red > urban heating in yellow > power generation in blue).

[0065] Assignment instruction execution and monitoring control

[0066] After the optimization algorithm generates instructions, the monitoring interface is displayed in three sections:

[0067] Left-side command area: Real-time push of optimal allocation schemes (e.g., power generation: heating: steam supply = 50:30:20)

[0068] Central Comparison Area: A 3D bar chart shows the marginal contribution of power generation / heating / steam supply (the industrial steam supply bar is the highest).

[0069] The right-hand execution area displays the predicted benefits of a 5% increase or decrease in load (e.g., a 5% increase in steam supply would increase benefits by 58 yuan / h). After the value leader confirms the plan, clicking "Execute" will cause the system to adjust the opening of the heating valves and set the power generation capacity via API.

[0070] Closed-loop verification and report generation

[0071] Before each shift handover, the system automatically generates an operation report:

[0072] Real-time benefit tracking: Comparing the hourly benefit difference before and after optimization (baseline value vs. optimized value 420 yuan / hour)

[0073] Environmental contribution calculation: This shows an equivalent value of 41 tons of SO2 emission reduction and 36 tons of NOx emission reduction (equivalent to neutralizing 2,400 tons of coal pollution).

[0074] Monthly cumulative data: Annual economic benefit of 3.36 million yuan (industrial steam supply efficiency improvement accounts for 76%). The report supports export to the power plant ERP system for coal procurement plan optimization.

[0075] Special working condition handling

[0076] When the main steam pressure fluctuation is greater than ±0.2 MPa or the back pressure exceeds the limit:

[0077] 1. System automatic pause allocation optimization

[0078] 2. Switch to safety control mode (constrain steam supply increase ≤ 3%)

[0079] 3. After the yellow security alarm pops up on the monitoring interface and the fault is resolved, you need to manually restart the optimization process and mark it as "security intervention" in the log.

[0080] Example:

[0081] Example 1: Enhancing Industrial Steam Supply Efficiency During Zero Electricity Price Periods in the Spot Market

[0082] Implementation Scenarios

[0083] From 2:00 AM to 5:00 AM on March 12, 2025, the real-time electricity price at the Hohhot-Baotou West power node dropped to 0 yuan / kWh. The load factor of Unit #1 (300MW subcritical) at Baotou No. 1 Thermal Power Plant was 71.1%, with an annual urban heat supply of 5.8 million gigajoules and an industrial steam supply baseline of 200 tons / hour. The system automatically detected the abnormal electricity price and triggered a red alert pop-up: "Industrial steam supply priority strategy activated during zero-price periods."

[0084] Optimize execution process

[0085] Instantaneous response of thermo-electric micro-increase distribution technology:

[0086] The power generation load was reduced from 71.1% to 50% of the approved lower limit, thus reducing the amount of power generation with high losses.

[0087] Industrial steam supply has been increased to the maximum limit of 230 tons / hour (+15%).

[0088] The urban heating supply remains unchanged at 5.8 million gigajoules per year. The allocation ratio on the left side of the monitoring interface is updated in real time: power generation: heating: steam supply = 50:35:15. The three-dimensional bar chart shows that the marginal contribution of industrial steam supply has jumped to 152 yuan / GJ, which is in stark contrast to the marginal loss of power generation of -38 yuan / MWh.

[0089] Security Collaborative Control

[0090] A sudden drop in load triggered a back pressure fluctuation warning (22.3 kPa → 23.1 kPa), and the system responded accordingly:

[0091] 1. Tighten the safety boundary factor to 0.4, and reduce the back pressure threshold to ±1.0 kPa.

[0092] 2. Automatically activate the standby fan of the air-cooled island.

[0093] 3. The industrial steam supply increase was limited to +12% (225 tons / hour). Within 10 minutes, the back pressure dropped to 21.9 kPa, and the right execution area displayed "Safety intervention completed, steam supply 225 tons / hour".

[0094] Economic benefit verification

[0095] After 3 hours of operation, the system reported:

[0096] To avoid a power generation loss of 187,000 yuan (calculated based on baseline load).

[0097] Industrial steam supply efficiency increased by 92,000 yuan (heat price 120 yuan / GJ × increase of 25 tons).

[0098] The overall benefit increased by 279,000 yuan. The daily benefit curve chart indicates that this period is the peak benefit for the whole day, which is 420 yuan / hour higher than the adjacent period.

[0099] Example 2: Flexible allocation of power generation load during periods of high electricity prices

[0100] Implementation Scenarios

[0101] From 14:00 to 17:00 on May 8, 2025, the day-ahead clearing price reached 0.68 yuan / kWh (peak hours +80%). The shift supervisor activated the load factor +5% simulation function.

[0102] The power generation load increased from 71.1% to 76.1%.

[0103] Urban heating supply maintained at 5.8 million gigajoules per year

[0104] Inputting the spot node electricity price of 0.72 yuan / kWh, the system generates a prediction curve: a 5% increase in load can increase hourly benefits by 580 yuan, but steam supply needs to be reduced by 3% to ensure safety margin.

[0105] Optimize decision execution

[0106] Shift leader confirms execution plan:

[0107] Power generation load increased to 75% (application limit).

[0108] Industrial steam supply has been slightly adjusted to 195 tons / hour (-2.5%).

[0109] When the boiler efficiency compensation mode is activated (oxygen setpoint reduced from 3.0% to 2.8%), the comparison area in the middle of the monitoring interface shows that the marginal contribution of power generation has increased to 61 yuan / MWh, while the marginal contribution of industrial steam supply has decreased to 138 yuan / GJ. In the 3D bar chart, the red bar (power generation) surpasses the blue bar (steam supply) for the first time.

[0110] Closed-loop control verification

[0111] Real-time monitoring data:

[0112] The main steam pressure increased from 14.08 MPa to 14.28 MPa (fluctuation of +0.2 MPa, reaching the safety boundary).

[0113] The system automatically limited the load rate to 74.2%.

[0114] The exhaust gas temperature remained stable at 134℃ (4.15℃ lower than the baseline). The performance report showed an actual efficiency increase of 17,400 yuan in 3 hours, which was less than 3% lower than the predicted value.

[0115] Example 3: Dynamic Optimization of Heat-Power Ratio During Peak Heating Season

[0116] Implementation Scenarios

[0117] During the cold wave in December 2025, the urban heating demand increased to 6.09 million gigajoules per year (+5% upper limit). The shift supervisor initiated a simulation of +5% heating supply:

[0118] Urban heating supply: 6.09 million gigajoules / year

[0119] Industrial steam supply: 190 tons / hour (-5%)

[0120] The real-time electricity price is 0.41 yuan / kWh. System calculations show that the increase in heating supply requires adjusting the coal consumption coefficient for power generation to 0.89, and the hourly benefit is expected to decrease by 120 yuan.

[0121] Optimization strategy adjustment

[0122] Adopt a marginal contribution rebalancing mechanism:

[0123] 1. Prioritize industrial steam and heat pricing (120 yuan / GJ > urban area 90 yuan / GJ)

[0124] 2. Maintain industrial steam supply at 200 tons / hour.

[0125] 3. Urban heating supply will be controlled to 5.9 million gigajoules per year (+1.7%).

[0126] 4. The power generation load dropped slightly to 69.6% to compensate for the heating coal consumption monitoring interface, and a yellow prompt appeared: "Heating increase is limited, and industrial steam supply is given priority."

[0127] Balancing environmental protection and economic development

[0128] System statistics after one week of continuous operation:

[0129] The urban heating shortage will be resolved through pipeline network adjustment.

[0130] The industrial steam supply efficiency was increased by 224,000 yuan (18% higher than the compromise plan).

[0131] SO2 emissions were reduced by 7.2 tons (achieved through optimization of the wind-to-coal ratio). The daily benefit curve shows a fluctuation of less than 5% throughout the day, confirming the system's stability strategy.

[0132] Example 4: Fuel Structure Optimization under Carbon Cost Constraints

[0133] Implementation Scenarios

[0134] In July 2025, the carbon price rose to 80 yuan / ton, and the system detected that fuel costs accounted for over 65% of total costs. The shift leader entered new parameters:

[0135] Received with a carbon content of 78% (based on the fundamental element).

[0136] A recalculation of the carbon price per ton (80 yuan / ton) benefit model shows that the marginal contribution of industrial steam supply has decreased to 129 yuan / GJ (carbon cost increases by 22%).

[0137] Dynamic re-optimization process

[0138] The system automatically triggers strategy iterations:

[0139] 1. Reorder marginal contributions: urban heating > industrial steam supply > power generation

[0140] 2. Increase urban heating supply to 5.95 million gigajoules per year (+2.6%).

[0141] 3. Industrial steam supply decreased to 205 tons / hour (+2.5% but heat price weight was reduced). In the three-dimensional bar chart, the yellow bar (urban heating) exceeded the red bar (industrial steam supply) for the first time, and the execution area on the right was marked "carbon cost sensitive mode activated".

[0142] Cross-system collaboration

[0143] Linked coal procurement module:

[0144] Automatically retrieve the lower heating value data of the coal fed into the furnace

[0145] Recommendations for purchasing high-calorific-value coal (>5200 kcal / kg)

[0146] The monthly report shows that the increase in carbon cost of 160,000 yuan was offset by fuel savings of 210,000 yuan, resulting in a net efficiency gain of 50,000 yuan. The increase in calorific value reduced standard coal consumption by 1.8%.

[0147] Example 5: Emergency Response to Power Grid Frequency Fluctuations

[0148] Implementation Scenarios

[0149] In September 2025, the frequency of the Hohhot-Baotou East Power Grid dropped to 49.6Hz, and Unit #1 received an AGC command to increase its load factor. The system detected:

[0150] The real-time electricity price is 0.31 yuan / kWh (<0.3 yuan threshold).

[0151] The industrial steam supply was at its maximum limit of 230 tons / hour, triggering a conflict alarm: "Frequency regulation demand conflicts with zero electricity price strategy".

[0152] Security Priority Decision

[0153] The system executes a three-level response:

[0154] 1. Temporarily relax the upper limit of industrial steam supply to 235 tons / hour (2.2% over the design value).

[0155] 2. Power generation load increased to 53% (originally 50% under the zero-price strategy).

[0156] 3. Activate the boiler short-term over-temperature tolerance mode (main steam temperature limit relaxed to 545℃). The monitoring interface will simultaneously alarm in three areas: the left instruction area will flash red, the middle bar chart will flash yellow, and the right execution area will display "safety margin reduced to 92%".

[0157] Post-event compensation optimization

[0158] The system will automatically: after the frequency is restored.

[0159] 1. Industrial steam supply has been adjusted back to 225 tons / hour.

[0160] 2. The oxygen setpoint was reduced to 2.7% to compensate for boiler stress.

[0161] 3. Generate frequency regulation compensation report: Received grid reward of 28,000 yuan. Benefit tracking curve shows: 2-hour short-term fluctuation did not affect the daily average benefit of 420 yuan / h benchmark.

[0162] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method and system for optimizing the power generation-heating-steam supply distribution of thermal power units, characterized in that: include: Real-time data acquisition: Obtaining power generation load (71.1%), urban heat supply (5.8 million GJ / year), industrial steam supply, real-time clearing price (0.38 yuan / kWh), and medium- and long-term contract price parameters; Benefit Calculation: Establish a comprehensive benefit model and calculate real-time benefits. Real-time benefits = (Electricity generation × Real-time electricity price) + (Heat supply × Heat price) - (Fuel cost + Carbon cost) The fuel cost is based on the comprehensive standard coal price (700 yuan / ton), the correction coefficient for the impact of urban heating on power generation coal consumption (0.92), and the correction coefficient for industrial steam supply (0.85). Heat and power distribution optimization: The heat-power micro-increase distribution technology is adopted to automatically reduce the power generation load to the approved lower limit (50%) during the zero electricity price period and increase the industrial steam supply to the upper limit; Manual simulation: Develop functions to calculate load factor ±5% and heating supply ±5% variations, and evaluate the changes in benefits for long-term users; Instruction generation: Output the optimal allocation scheme to the value leader's decision interface.

2. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 1, characterized in that: The benefit model is incorporated into the carbon emission cost calculation formula: Carbon cost = fuel consumption × carbon content of the base element × carbon unit price.

3. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 1, characterized in that: The aforementioned heat-electricity micro-increase distribution technology prioritizes industrial steam supply (due to higher heat prices), and when the real-time electricity price is less than 0.3 yuan / kWh, the power generation load is reduced to the approved lower limit.

4. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 1, characterized in that: The manual calculation system supports inputting spot node electricity prices by the shift leader, simulating the heat and power distribution benefits under different electricity prices, and generating daily benefit curves.

5. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 1, characterized in that: The allocation results are displayed through the monitoring system.

6. The optimization method and system for power generation-heating-steam supply distribution of thermal power units according to claims 1-5, characterized in that: include: Benefit Calculation Module: Integrates real-time data and market data to calculate the marginal contribution of power generation / heat supply; Optimization algorithm module: Implements a heat-electricity incremental allocation strategy to dynamically adjust the electricity-heat-steam ratio; Decision support module: Provides a benefit simulation interface for load factor ±5% and heating capacity ±5%; Command output module: Pushes the optimal allocation scheme to the value leader.

7. The method and system for optimizing the power generation-heating-steam supply distribution of thermal power units according to claim 6, characterized in that: The optimization algorithm module improves economic efficiency by 420 yuan / hour in the spot market environment.

8. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 6, characterized in that: The benefit calculation module supports real-time reading of electricity price meter reading data (fitted electricity and day-ahead clearing price) and automatic correction of heating coal consumption.

9. The optimization method and system for power generation-heating-steam supply distribution in thermal power units according to claim 6, characterized in that: The decision support module includes a daily benefit curve, which shows the 24-hour benefit fluctuations and the heat and power distribution ratio.

10. The method and system for optimizing the power generation-heating-steam supply distribution of thermal power units according to claim 6, characterized in that: The system improves annual economic benefits by RMB 3.36 million by optimizing heat and power distribution, and reduces SO2 emissions by 41 tons and NOx emissions by 36 tons.