Peak shaving auxiliary service benefit optimization method based on power generation change cost analysis

Abstract

The invention relates to a peak regulation auxiliary service benefit optimization method based on power generation change cost analysis, and the method comprises the steps: obtaining real-time power generation change cost data, including fire coal, fuel oil and other material consumption data, and resource environment tax data, calculating the real-time power generation change cost, and combining the power generation income data, including power production, market price and tax data, to obtain a peak regulation auxiliary service benefit optimization result. Calculating electricity selling income and deep peak regulation income; the total marginal contribution amount is calculated based on the data, whether power generation continues or not is judged, and power generation and peak regulation strategies are optimized through deep peak regulation quotation and amplitude optimization, so that the optimal benefit is achieved. According to the method, the power generation and peak regulation strategy is optimized in real time through the steps, and the economic benefit and the cost control capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and mainly to a method for optimizing the benefits of peak load auxiliary services based on power generation variable cost analysis. Background Art

[0002] With the rapid development of renewable energy power generation and the gradual overcapacity of coal-fired power generation in my country, the demand for flexible transformation of coal-fired power generation is becoming increasingly urgent. The proportion of renewable energy in the power grid is gradually increasing, resulting in an increase in the demand for peak-shaving power sources. Compared with renewable energy power generation, coal-fired power has a stronger peak-shaving capacity. Therefore, in countries where coal is the main energy source, using coal-fired power plants for peak-shaving has become a realistic and feasible option.

[0003] At present, my country's electricity market is still in a state of "overall surplus and partial oversupply", which is mainly due to the slowdown in the growth of electricity consumption, the excessive commissioning of coal-fired power units, and the deep peak-shaving and standby functions of coal-fired power units in non-fossil energy power generation. In the next few years, thermal power units, especially coal-fired power units, will continue to face the challenges of low-load operation or deep peak-shaving.

[0004] Deep peak shaving means that due to the large difference between peak and valley loads in the power grid, power plants need to reduce the load of the units to a level beyond the basic peak shaving range. Under the deep peak shaving conditions of the units, their coal consumption for power supply increases, and the cost per kilowatt-hour of electricity increases. In addition, due to the low load rate, the power generation decreases, and the profit of power generation decreases. But on the other hand, the peak shaving of the units will bring additional peak shaving subsidies. The electricity generated by the units during the deep peak shaving operation will be compensated during the normal operation of the units. This part of the electricity will also generate profits. However, whether the peak shaving income can offset the equipment loss costs and operating costs brought by the peak shaving process to bring additional income is an urgent issue for thermal power companies to participate in peak shaving.

[0005] For example, CN109524958B "A method for optimizing the flexibility of a power system considering deep peak shaving and demand response" discloses "a method for optimizing the flexibility of a power system considering deep peak shaving and demand response. At present, research on the flexibility of the power system is still in its infancy. The technical solution of the present invention includes: step 1, considering the deep peak shaving and demand response of the unit, and establishing a flexibility resource model; step 2, from the perspective of the power fluctuation range allowed by the studied node, establishing a power system flexibility interval evaluation index; step 3, considering the regulation capacity of the system flexibility resources, and establishing a unit output day-ahead optimization scheduling model considering the flexibility of the power system; step 4, by transforming the constraints, converting the NP-hard problem into a linear programming and quadratic programming problem for solution. Based on the power system flexibility interval index, the present invention considers the deep peak shaving capability of the unit and the regulation capability of the demand response resources on the load side, and optimizes the output scheduling plan of the unit in the day-ahead scheduling stage to achieve the purpose of improving the flexibility of the system", but the above invention does not take into account the additional cost of deep peak shaving and the compensation mechanism of the ancillary service market, and the peak shaving method needs to be further improved. Summary of the invention

[0006] In order to solve the above-mentioned problems existing in the prior art, the present application provides a method for optimizing the benefits of peak-shaving auxiliary services based on analysis of power generation variable costs.

[0007] The technical solution of this application is as follows:

[0008] A peak load ancillary service benefit optimization method based on power generation variable cost analysis, the method comprising:

[0009] Acquire real-time power generation variable cost related data, including real-time coal burning data, real-time fuel oil data, real-time consumption data of other materials and resource and environmental tax data; calculate and obtain the corresponding real-time power generation variable cost according to the real-time power generation variable cost related data;

[0010] Obtaining data related to power generation revenue, including power production data, power market data, and power tax data; calculating power sales revenue and deep peak regulation revenue based on the power generation revenue related data;

[0011] The total marginal contribution is calculated based on the real-time variable cost of power generation, power sales revenue and deep peak-shaving revenue. Whether to continue power generation is determined based on the total marginal contribution. If power generation continues, the optimal power generation and peak-shaving strategy is obtained by optimizing the deep peak-shaving quotation and the deep peak-shaving amplitude, and the optimal revenue is obtained based on the optimal power generation and peak-shaving strategy.

[0012] Preferably, the real-time coal burning data includes the real-time coal consumption for power supply and the standard coal unit price of the coal entering the furnace, and the real-time coal burning cost is calculated according to the real-time coal burning data as follows:

[0013] Obtain the raw coal quantity and coal type data of the coal entering the furnace in the raw coal bunker of the coal-fired power plant, wherein the coal type data includes the blending ratio, low calorific value and corresponding standard coal unit price of each coal type; weight the coal quantity of each coal type in real time according to the blending ratio of each coal type, and calculate the average calorific value and standard coal unit price of the coal entering the furnace according to the weighted coal quantity and low calorific value;

[0014] The standard coal quantity of the coal entering the furnace is calculated based on the raw coal quantity, average calorific value of the coal entering the furnace and the lower calorific value of the standard coal, which can be expressed as follows:

[0015] Standard coal quantity of coal fed into the furnace = raw coal quantity of coal fed into the furnace × (average calorific value of coal fed into the furnace / lower calorific value of standard coal);

[0016] Get the total heat demand Q of the coal-fired power plant heating system 热 The standard coal consumption required for heating is calculated by using the efficiency η, and the standard coal consumption for power generation is obtained by deducting the standard coal consumption required for heating from the standard coal consumption of the coal fed into the furnace, which is expressed as follows:

[0017] Standard coal consumption for power generation = standard coal quantity of coal entering the furnace - (Q 热 / (η×average calorific value of coal fed into the furnace));

[0018] Obtain the high-voltage transformer capacity and power generation of the coal-fired power plant, calculate the power consumption rate of the coal-fired power plant, and express it in the formula:

[0019] Coal-fired power plant power consumption rate = high-power plant power generation / power generation;

[0020] According to the power consumption rate of coal-fired power plants, the standard coal consumption for power generation is adjusted, and then the real-time power supply coal consumption is calculated, which is expressed as:

[0021] Real-time power supply coal consumption = standard coal consumption for power generation / (1-power consumption rate of coal-fired power plants).

[0022] Preferably, the real-time fuel data includes the instantaneous fuel usage flow and the fuel purchase price. The real-time fuel cost is calculated based on the real-time fuel data and is expressed as follows:

[0023] Real-time fuel cost = instantaneous fuel usage flow × fuel purchase price.

[0024] Preferably, the real-time consumption data of other materials include the real-time consumption and corresponding price of desulfurizer limestone, the real-time consumption and corresponding price of urea, and the real-time consumption and corresponding price of raw water. The real-time consumption cost of other materials is calculated based on the real-time consumption data of other materials. Specifically, the calculation method is to multiply the real-time consumption by the corresponding price.

[0025] The resource and environmental tax data include environmental protection tax data and water resource tax data. The environmental protection tax data include sulfur dioxide environmental protection tax data, nitrogen oxide environmental protection tax data and dust environmental protection tax data. Among them, the cost calculated based on the sulfur dioxide environmental protection tax data is the product of the sulfur dioxide emission concentration, flue gas volume and the tax standard per ton of sulfur dioxide on the net flue gas side; the cost calculated based on the nitrogen oxide environmental protection tax data is the product of the nitrogen oxide emission concentration, flue gas volume and the tax standard per ton of nitrogen oxide on the net flue gas side; the cost calculated based on the dust environmental protection tax data is the product of the dust emission concentration, flue gas volume and the tax standard per ton of dust on the net flue gas side; the cost calculated based on the water resource tax data is the product of the real-time raw water consumption and the tax standard per ton of water.

[0026] Preferably, the power production data includes the power consumption rate of coal-fired power plants, the real-time load of the units and the rated capacity of the units; the power market data includes the electricity price in the power market, the price of deep peak regulation and its changes; the power tax data includes the tax rate and other related taxes and fees; the power sales revenue is calculated based on the power generation revenue related data, expressed in the formula:

[0027] Electricity sales revenue = real-time load of the unit × electricity price × (1-electricity consumption rate of coal-fired power plant) × 1000 / (1+tax rate);

[0028] Preferably, the deep peak load regulation benefit is calculated based on the power generation benefit related data as follows:

[0029] Determine the paid peak-shaving benchmark according to the rated capacity of the unit, divide the deep peak-shaving grading interval according to the paid peak-shaving benchmark, and if the real-time load of the unit is within the deep peak-shaving grading interval, take the difference between the real-time load of the unit and the paid peak-shaving benchmark as the deep peak-shaving electricity;

[0030] A deep peak-shaving transaction tiered quotation table is preset, and the deep peak-shaving transaction tiered quotation table includes the real-time load segment of the unit and the corresponding gears and the upper and lower limits of the quotation. The unit with the lowest quotation lower limit in the real-time load segment of the unit is selected for deep peak-shaving, and the deep peak-shaving clearing price corresponding to the real-time load segment of the unit on that day is obtained, and the product of the deep peak-shaving clearing price and the deep peak-shaving electricity is used as the deep peak-shaving income.

[0031] Preferably, the total marginal contribution is calculated based on the real-time variable cost of power generation, the revenue from power sales and the revenue from deep peak regulation, and whether to continue power generation is determined based on the total marginal contribution, specifically:

[0032] Total marginal contribution = electricity sales revenue + deep peak load regulation revenue - real-time power generation variable cost;

[0033] If the total marginal contribution is positive, continue to generate electricity; otherwise, stop generating electricity.

[0034] Preferably, the goal of deep peak load shaving quotation optimization is to determine the optimal deep peak load shaving quotation in each load segment, specifically:

[0035] According to the deep peak-shaving transaction tiered quotation table, determine the corresponding gear in the load segment where the unit is located, and calculate the deep peak-shaving gear balance point quotation for the gear, which is expressed in the formula:

[0036] Deep peak load adjustment balance point quotation = (benchmark income - (total marginal contribution - deep peak load adjustment compensation)) / deep peak load adjustment electricity × (1 / corresponding discount coefficient of deep peak load adjustment gear of the unit);

[0037] In the formula, the benchmark income is the income from power generation when there is no peak load regulation;

[0038] If the balance point quotation of the deep-shaving gear is less than the lower limit of the quotation for that gear in the step-by-step quotation table of deep peak-shaving transactions, the optimal deep peak-shaving quotation is the lower limit of the quotation; if the balance point quotation of the deep-shaving gear exceeds the upper limit of the quotation, the peak-shaving depth is increased to the next gear, and the shortfall of the corresponding gear in the load segment where the unit is located is amortized through the revenue of the next gear. At this time, the optimal deep peak-shaving quotation of the previous gear is the upper limit of the quotation; if the balance point quotation of the deep-shaving gear is between the upper limit and the lower limit of the quotation for that gear, the balance point quotation of the deep-shaving gear is the optimal deep peak-shaving quotation.

[0039] Preferably, the optimization of the deep peak-shaving amplitude is specifically to set different loads and discount factors, and calculate the total marginal contribution of the computer group under each load and discount factor; analyze the total marginal contribution to determine that the load segment that is not lower than the non-peak-shaving under different loads and different discount factors is the optimal peak-shaving depth.

[0040] The present application also provides a peak-shaving auxiliary service benefit optimization system based on power generation variable cost analysis, the system includes a furnace coal blending analysis module, a power generation variable cost analysis module, a power generation revenue analysis module, a power generation marginal contribution analysis module, a deep peak-shaving amplitude optimization module, a deep peak-shaving quotation optimization module and an output module, wherein:

[0041] The coal blending analysis module for the incoming coal is used to obtain data on the incoming coal of the coal-fired power plant in real time, including real-time coal consumption for power supply, coal blending ratio, low calorific value of each type of coal and standard coal unit price, and calculate the standard coal quantity and standard coal purchase unit price of the incoming coal through real-time coal data; obtain the total heat demand and efficiency of the heating system of the coal-fired power plant, calculate the standard coal quantity required for heating, and deduct it from the standard coal quantity of the incoming coal to obtain the standard coal consumption for power generation, adjust the standard coal consumption for power generation according to the power consumption rate of the coal-fired power plant, and calculate the real-time coal consumption for power supply;

[0042] The power generation variable cost analysis module is used to calculate the real-time coal burning cost according to the real-time power supply coal consumption and the standard coal unit price of the coal entering the furnace; obtain and calculate the real-time fuel oil data and cost, including the instantaneous fuel oil usage flow and the fuel oil purchase price; obtain and calculate the real-time consumption data and cost of other materials, including the real-time consumption and corresponding prices of desulfurization agent limestone, urea, and raw water; obtain and calculate the resource and environmental tax data and cost, including environmental protection tax and water resource tax data; and integrate the above costs into the power generation variable cost;

[0043] The power generation revenue analysis module is used to obtain power production data, power market data and power tax data, and calculate power sales revenue and deep peak regulation revenue;

[0044] The power generation marginal contribution analysis module is used to calculate the total marginal contribution of power generation based on the variable cost of power generation, power sales revenue and deep peak-shaving revenue, and use this as the basis for judging whether to continue power generation. If power generation continues, the deep peak-shaving amplitude optimization module and the deep peak-shaving quotation optimization module are triggered to optimize and obtain the best peak-shaving amplitude and quotation;

[0045] The deep peak-shaving amplitude optimization module is used to set different loads and discount coefficients, calculate the total marginal contribution under different loads and discount coefficients, analyze the total marginal contribution, and determine the optimal peak-shaving depth under different loads and discount coefficients;

[0046] The deep peak-shaving quotation optimization module is used to calculate the balance point of the deep peak-shaving quotation according to the deep peak-shaving transaction ladder quotation table, determine the optimal peak-shaving quotation for each load segment, and maximize the economic benefits of peak-shaving;

[0047] The output module is used to output the best power generation and peak load regulation strategy, as well as the corresponding peak load regulation quotation and amplitude.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention provides a method for optimizing the benefits of peak-shaving auxiliary services based on analysis of power generation variable costs. By analyzing the power generation variable costs in real time, the power sales revenue and deep peak-shaving revenue are comprehensively considered, and the marginal contribution of power generation is analyzed based on this. Scientific analysis reduces human intervention, and through optimization of deep peak-shaving quotations and amplitudes, the power generation strategy can be effectively adjusted when the power demand fluctuates to achieve maximum peak-shaving revenue. In the deep peak-shaving quotation process, the optimal quotation is determined by utilizing the deep peak-shaving transaction ladder quotation table, and in the deep amplitude optimization process, the peak-shaving depth is optimized by setting different loads and discount factors, thereby ensuring the maximization of the peak-shaving benefit. The above-mentioned peak-shaving strategy can be quickly adjusted to adapt to market changes, and the real-time response capability can effectively cope with price fluctuations and demand changes in the power market, thereby improving the flexibility of power generation and peak-shaving. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a method flow chart of an embodiment of the present invention;

[0051] Figure 2 It is a diagram of the total marginal contribution of deep peak load regulation under different loads and discounts in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0053] The present invention provides the following technical solution: a method for optimizing the benefits of peak load auxiliary services based on analysis of power generation variable costs.

[0054] Example 1

[0055] This embodiment provides a method for optimizing the benefits of peak load ancillary services based on power generation variable cost analysis, the method comprising:

[0056] S1. Obtaining real-time power generation variable cost related data, including real-time coal burning data, real-time oil burning data, real-time consumption data of other materials and resource and environmental tax data; calculating and obtaining the corresponding real-time power generation variable cost according to the real-time power generation variable cost related data;

[0057] S11, real-time coal data and real-time coal cost;

[0058] S111, real-time coal burning data;

[0059] The real-time coal burning data includes real-time power supply coal consumption and standard coal unit price of coal entering the furnace;

[0060] The coal fed into the raw coal bunker of a coal-fired power plant is a mixture of different types of coal. The selection of the blending ratio of each type of coal affects the overall combustion efficiency and cost. Some types of coal may have a higher calorific value but a higher price, while some types of coal may have a slightly lower calorific value but a lower cost. The raw coal bunker of a coal-fired power plant optimizes the use of coal by reasonably blending different types of coal to ensure that the calorific value and economy of the fuel are optimal, and obtains the raw coal quantity and coal type data of the coal burned into the furnace at this time. The coal type data includes the blending ratio, low calorific value and corresponding standard coal purchase unit price of each type of coal, wherein the low calorific value of coal refers to the lowest amount of heat released by coal when it is burned. When calculating and comparing the economy of coal, the low calorific value is usually used to measure and compare the calorific value of coal. Therefore, in this implementation, the low calorific value of the coal fed into the furnace is the calorific value of the coal fed into the furnace;

[0061] The coal quantity of each type of coal is weighted in real time according to the blending ratio of each type of coal, and the average calorific value of the coal entering the furnace and the purchase price of standard coal are calculated based on the weighted coal quantity and low calorific value;

[0062] Standard coal quantity is a standardized unit of calculation used to compare coals with different calorific values. Because different types of coal have different calorific values, it is not accurate to compare them directly by weight. This embodiment more accurately evaluates coal consumption and cost by converting the actual amount of coal used into standard coal quantity. The specific conversion steps are to calculate the standard coal quantity of the coal fed into the furnace based on the raw coal quantity, average calorific value of the coal fed into the furnace, and the lower calorific value of the standard coal, which is expressed as follows:

[0063] Standard coal quantity of coal fed into the furnace = raw coal quantity of coal fed into the furnace × (average calorific value of coal fed into the furnace / lower calorific value of standard coal);

[0064] The lower calorific value of standard coal in the formula is 29.31MJ / kg (7000kcal / kg);

[0065] Obtain the total heat demand and efficiency of the heating system of the coal-fired power plant, wherein the total heat demand is expressed in heat units (such as kilocalories (kcal) or megajoules (MJ)). In this embodiment, the total heat demand is Q 热 kcal, the efficiency of the heating system is η;

[0066] The standard coal consumption required for heating is calculated based on the total heat demand and efficiency of the heating system, and the standard coal consumption for power generation is obtained by deducting the standard coal consumption required for heating from the standard coal consumption of the coal fed into the furnace, which is expressed as follows:

[0067] Standard coal consumption for power generation = standard coal quantity of coal entering the furnace - (Q 热 / (η×average calorific value of coal fed into the furnace));

[0068] Obtain the high-voltage transformer capacity and power generation of the coal-fired power plant, where high-voltage transformer capacity refers to the power consumption within the coal-fired power plant to support its own operation. Calculate the ratio of the coal-fired power plant's self-consumption to the total power generation based on the high-voltage transformer capacity and power generation of the coal-fired power plant, that is, the power consumption rate of the coal-fired power plant, which can be expressed as follows:

[0069] Coal-fired power plant power consumption rate = high-power plant power generation / power generation;

[0070] According to the power consumption rate of coal-fired power plants, the standard coal consumption for power generation is adjusted, and then the real-time power supply coal consumption is calculated, which is expressed as:

[0071] Real-time power supply coal consumption = standard coal consumption for power generation / (1-power consumption rate of coal-fired power plants).

[0072] S112, real-time coal cost;

[0073] The real-time coal burning cost is calculated based on the real-time coal consumption for power supply and the standard coal unit price of the coal entering the furnace, and is expressed as follows:

[0074] Real-time coal burning cost = real-time power supply coal consumption × standard coal unit price of coal entering the furnace;

[0075] S12, real-time fuel data and real-time fuel cost;

[0076] S121, real-time fuel data;

[0077] The fuel data includes instantaneous fuel usage flow and fuel purchase price, where:

[0078] The boiler load of a coal-fired power plant affects the demand for fuel and the combustion conditions. When the boiler load drops below 40%, the cross-sectional heat load and volumetric heat load in the furnace decrease, the temperature in the furnace drops, the combustion stability weakens, and the ignition distance increases. In order to ensure the safe and stable operation of the boiler, an oil gun needs to be used for stable combustion. The instantaneous fuel usage flow is generated through the deviation of the oil flow inlet and outlet.

[0079] S122, real-time fuel cost;

[0080] Get real-time fuel data and calculate the real-time fuel cost based on the real-time fuel data, expressed as:

[0081] Real-time fuel cost = instantaneous fuel usage flow × fuel purchase price;

[0082] S13, real-time consumption data of other materials and real-time consumption costs of other materials;

[0083] S131, real-time consumption data of other materials;

[0084] Real-time consumption data of other materials include real-time consumption and corresponding price of desulfurization agent limestone, real-time consumption and corresponding price of urea and real-time consumption and corresponding price of raw water;

[0085] S1311, real-time consumption of desulfurizing agent limestone;

[0086] The real-time supply volume and supply density of limestone slurry in the absorption tower are obtained, and the real-time consumption of desulfurizer limestone is obtained by multiplying the real-time supply volume and the supply density. The unit of the real-time supply volume is cubic meters per hour (m 3 / h) or tons per hour (t / h), the unit of the slurry density is tons per cubic meter (t / m 3 ) or kilograms per cubic meter (kg / m 3 );

[0087] S1312, real-time urea consumption;

[0088] Get the real-time flow rate of ammonia injection in the denitrification system. If the real-time flow rate of ammonia injection is expressed in tons per hour (t / h), use it directly. If the real-time flow rate of ammonia injection is expressed in cubic meters per hour (m 3 / h), then convert it into tons per hour;

[0089] The real-time urea consumption is calculated based on the molar ratio of urea to ammonia (1:2) and the real-time flow rate of ammonia injection, and is expressed as follows:

[0090] Real-time urea consumption = real-time ammonia injection flow / 2;

[0091] S1313, real-time consumption of raw water;

[0092] When calculating the real-time consumption of raw water, since the actual water used is treated water, and the raw water enters the pool or water tank after treatment, there is a certain loss. It is not accurate to directly use the raw water to calculate the raw water consumption. It is necessary to consider the consumption of desalted water and industrial water supplemented by the condenser. Specifically:

[0093] Calculate the consumption of desalted water to supplement the condenser, expressed as:

[0094] The consumption of desalted water for condenser supplement = condenser supplement water / desalted water recovery rate;

[0095] Calculate the industrial water consumption, expressed as:

[0096] Industrial water consumption = industrial water volume / industrial water recovery rate;

[0097] The desalted water recovery rate and industrial water recovery rate are obtained from the design parameters or long-term operation data of the system;

[0098] S132, real-time consumption costs of other materials;

[0099] The real-time consumption cost of other materials is calculated based on the real-time consumption data of other materials. Specifically, the calculation method is to multiply the real-time consumption by the corresponding price, which can be expressed as:

[0100] Real-time consumption cost of desulfurizer limestone = real-time consumption of desulfurizer limestone × corresponding price of desulfurizer limestone;

[0101] Real-time urea consumption cost = real-time urea consumption × corresponding urea price;

[0102] Real-time consumption cost of raw water = real-time consumption of raw water × corresponding price of raw water;

[0103] S14. Resource and environmental tax data and resource and environmental tax costs;

[0104] S141, resource and environmental tax data;

[0105] Resource and environmental tax data include environmental protection tax data and water resource tax data;

[0106] S1411, environmental tax data;

[0107] The environmental protection tax data include sulfur dioxide environmental protection data, nitrogen oxide environmental protection data and dust environmental protection data, among which:

[0108] Sulfur dioxide environmental protection data include sulfur dioxide emission concentration on the net flue gas side, flue gas volume and sulfur dioxide tax standard per ton;

[0109] The environmental tax data on nitrogen oxides include the nitrogen oxide emission concentration on the net flue gas side, the flue gas volume and the tax standard for tons of nitrogen oxides;

[0110] The dust environmental tax data include the dust emission concentration on the net flue gas side, the flue gas volume and the dust tax standard per ton;

[0111] S1412, water resource tax data;

[0112] Water resource tax data include real-time raw water consumption and the tax rate per ton of water;

[0113] S142, resource and environmental tax costs;

[0114] The resource and environmental tax cost is calculated based on the resource and environmental tax data. Specifically, the calculation method is the product of the data, which can be expressed as:

[0115] Cost corresponding to sulfur dioxide environmental tax data = sulfur dioxide emission concentration on the net flue gas side × flue gas volume × sulfur dioxide tax standard per ton;

[0116] Cost corresponding to nitrogen oxide environmental tax data = nitrogen oxide emission concentration on the net flue gas side × flue gas volume × nitrogen oxide tax standard per ton;

[0117] The cost corresponding to the dust environmental protection tax data = dust emission concentration on the net flue gas side × flue gas volume × dust tax standard per ton;

[0118] The cost corresponding to the water resource tax data = real-time raw water consumption × ton water tax standard;

[0119] S2. Obtaining power generation revenue-related data, including power production data, power market data, and power tax data; calculating power sales revenue and deep peak regulation revenue based on the power generation revenue-related data;

[0120] Power production data includes the real-time power generation and load of the units; power market data includes the electricity price in the power market, the price of deep peak regulation and its changes; power tax data includes tax rates and other related taxes and fees;

[0121] S21, income from electricity sales;

[0122] The electricity sales revenue is calculated based on the data related to power generation revenue, expressed in the formula:

[0123] Electricity sales revenue = real-time load of the unit × electricity price × (1-electricity consumption rate of coal-fired power plant) × 1000 / (1+tax rate);

[0124] S22, deep peak regulation benefits;

[0125] When a coal-fired power plant needs to actively reduce its output, in order to maintain the stability of the power grid, the unit reduces its electricity and obtains certain compensation, which is called deep peak-shaving compensation. Generally speaking, the paid peak-shaving benchmark is 50% of the rated capacity of the unit. When a coal-fired power plant needs the unit to actively reduce its output to a load rate less than the paid peak-shaving benchmark, the transaction with the unit's reduced output as the subject is called the unit's deep peak-shaving transaction. If the unit's average load rate is lower than the paid peak-shaving benchmark, deep peak-shaving electricity will be formed. That is, deep peak-shaving electricity refers to the amount of ungenerated electricity caused by the unit's average load rate being lower than the benchmark value within the deep peak-shaving grading interval;

[0126] The specific calculation of deep peak regulation benefits based on power generation revenue data is:

[0127] Determine the paid peak-shaving benchmark according to the rated capacity of the unit, divide the deep peak-shaving grading interval according to the paid peak-shaving benchmark, and if the real-time load of the unit is within the deep peak-shaving grading interval, take the difference between the real-time load of the unit and the paid peak-shaving benchmark as the deep peak-shaving electricity;

[0128] A deep peak-shaving transaction tiered quotation table as shown in Table 1 is preset, and the deep peak-shaving transaction tiered quotation table includes gears, and each gear corresponds to a real-time load segment of the unit and an upper and lower limit of the quotation. The unit with the lowest quotation lower limit in the real-time load segment of the unit is selected for deep peak-shaving, and the deep peak-shaving clearing price corresponding to the real-time load segment of the unit on that day is obtained. The product of the deep peak-shaving clearing price and the deep peak-shaving electricity is used as the deep peak-shaving compensation amount, which is the deep peak-shaving income.

[0129] Table 1 Tiered quotation table for deep peak shaving transactions

[0130]

[0131] The daily deep peak-shaving electricity volume in each gear is multiplied by the deep peak-shaving clearing price of that gear on that day to obtain the daily deep peak-shaving compensation quota. The daily deep peak-shaving compensation quota is accumulated to obtain the discounted deep peak-shaving compensation quota for each gear in the month.

[0132] S3. Calculate the total marginal contribution based on the real-time variable cost of power generation, power sales revenue and deep peak-shaving revenue, and determine whether to continue power generation based on the total marginal contribution. If power generation continues, obtain the best power generation and peak-shaving strategy through deep peak-shaving quotation optimization and deep peak-shaving amplitude optimization, and obtain the best revenue based on the best power generation and peak-shaving strategy;

[0133] S31, total marginal contribution;

[0134] Marginal contribution refers to the balance after deducting variable costs from sales revenue (excluding value-added tax), which is a very important indicator for making production decisions by applying the profit and loss analysis principle. For thermal power companies, marginal contribution is the balance after deducting fuel and other variable costs from power generation revenue (excluding value-added tax), which is an important reference indicator for determining power generation strategies. The calculation formula of marginal contribution is:

[0135] Marginal contribution per kWh = electricity price - variable cost per kWh = electricity price including value-added tax / 1.13 - variable cost per kWh; wherein 1.13 is the inverse of the value-added tax rate (13%), which is used to remove the value-added tax from the tax-inclusive electricity price; specifically in this embodiment, the calculation of the total marginal contribution is expressed by the formula:

[0136] Total marginal contribution = total electricity sales revenue excluding VAT - total variable costs = electricity sales revenue + deep peak regulation revenue - real-time power generation variable costs;

[0137] When the total marginal contribution is positive, it means that for every additional kilowatt-hour of electricity generated, the company can obtain additional profits. The company should increase profits by continuously increasing electricity sales and strive to increase electricity generation;

[0138] When the marginal contribution is negative, it means that the enterprise has lost its hematopoietic function, and not only can it not generate profits, but even normal production and operation activities will not be able to be maintained. At this time, thermal power companies should stop generating electricity, or generate as little electricity as possible on the basis of fulfilling necessary social responsibilities.

[0139] S32, deep peak load regulation quotation optimization;

[0140] The goal of deep peak-shaving quotation optimization is to determine the optimal peak-shaving quotation for each load segment to maximize the economic benefits of peak-shaving. The benefits of some peak-shaving load segments cannot cover the impact of reduced power generation and the cost of increased coal consumption for power supply. The peak-shaving depth can be increased and the next level of benefits can be used to amortize the shortfall of the previous level. Specifically:

[0141] According to the deep peak-shaving transaction tiered quotation table, determine the corresponding gear of the load segment where the unit is located, and calculate the deep peak-shaving quotation of the gear, which is expressed as follows:

[0142] Deep peak load adjustment balance point quotation = (benchmark income - (total marginal contribution - deep peak load adjustment compensation)) / deep peak load adjustment electricity × (1 / corresponding discount coefficient of deep peak load adjustment gear of the unit);

[0143] In the formula, the benchmark income is the income from power generation when there is no peak load regulation;

[0144] If the balance point quotation of the deep adjustment gear is less than the lower limit of the quotation of the gear in the deep peak-shaving transaction tiered quotation table, the optimal deep peak-shaving quotation is the lower limit of the quotation; if the balance point quotation of the deep adjustment gear exceeds the upper limit of the quotation, the peak-shaving depth is increased to the next gear, and the insufficient part of the corresponding gear in the load section where the unit is located is amortized through the income of the next gear. At this time, the optimal deep peak-shaving quotation of the previous gear is the upper limit of the quotation, reducing the peak-shaving time of this gear (dispatching gives priority to the output of units with low quotations, and the higher the quotation, the shorter the peak-shaving time), thereby reducing the loss of income of the next gear; if the balance point quotation of the deep adjustment gear is between the upper limit and the lower limit of the quotation of the gear, then the balance point quotation of the deep adjustment gear is the optimal deep peak-shaving quotation.

[0145] In this embodiment, the data in Table 1 is used as an example to illustrate:

[0146] When the peak load range is in the first gear (40%-50% load), find a balance point quotation that makes the peak load benefit equal to the marginal contribution when there is no peak load (50% rated load), which can be expressed as:

[0147] The first-tier balance point quotation of deep regulation = (benchmark income - (total marginal contribution - first-tier peak regulation compensation)) / first-tier electricity × (1 / first-tier discount coefficient);

[0148] In the formula, the base income is the income when there is no peak load regulation; the total marginal contribution is the marginal contribution of peak load regulation; the first-level peak load regulation compensation is the compensation amount under the first-level peak load regulation; the first-level electricity is the electricity of the first-level load segment; the first-level discount coefficient is the coefficient used to adjust the peak load regulation quotation;

[0149] If the first-tier balance point quotation is less than 0, it means that as long as peak shaving is carried out, the benefits will be greater than the benefits without peak shaving; if the first-tier balance point quotation is greater than 0.30 yuan / kWh, it means that the benefits of peak shaving are always less than the benefits without peak shaving. At this time, 0.30 yuan / kWh is selected to increase the depth of peak shaving;

[0150] When the peak load range is in the second gear (35%-40% load), find the balance point quotation that makes the peak load benefit equal to the marginal contribution when there is no peak load, expressed as:

[0151] Second-tier balance point quotation = (benchmark income - (total marginal contribution - first-tier peak load compensation)) / second-tier electricity × (1 / second-tier discount coefficient);

[0152] If the second-tier balance point quotation is less than 0.30 yuan / kWh, it means that the revenue from peak shaving to the second tier is greater than the revenue from no peak shaving; if the second-tier balance point quotation is greater than 0.50 yuan / kWh, it means that the revenue from peak shaving to the second tier is always less than the revenue from no peak shaving.

[0153] S33, optimizing the depth peak regulation amplitude;

[0154] Set different loads and discount factors, such as 30.0%, 35.0%, 40.0%, 45.0% and 50.0% load rates and a 50% discount factor; calculate the total marginal contribution of the computer group under each load and discount factor, analyze the total marginal contribution, and determine that the load segment that is not lower than the load segment without peak regulation under different loads and different discount factors is the optimal peak regulation depth, such as Figure 2 As shown, when the deep peak-shaving compensation is not discounted, the marginal contribution of deep peak-shaving (below 50% of rated load) is higher than the marginal contribution of 50% of rated load; when the deep peak-shaving compensation is discounted by 50%, the marginal contribution of 32% of rated load is the same as the marginal contribution of 50% of rated load, and the marginal contribution below 32% of rated load is higher than the marginal contribution of 50% of rated load, so the operating time of 32%-50% rated load should be reduced.

[0155] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0156] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0157] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for optimizing the benefits of peak load auxiliary services based on power generation variable cost analysis, characterized in that: The method comprises: Acquire real-time power generation variable cost related data, including real-time coal burning data, real-time fuel oil data, real-time consumption data of other materials and resource and environmental tax data; calculate and obtain the corresponding real-time power generation variable cost according to the real-time power generation variable cost related data; Obtaining data related to power generation revenue, including power production data, power market data, and power tax data; calculating power sales revenue and deep peak regulation revenue based on the power generation revenue related data; The total marginal contribution is calculated based on the real-time variable cost of power generation, power sales revenue and deep peak-shaving revenue. Whether to continue power generation is determined based on the total marginal contribution. If power generation continues, the optimal power generation and peak-shaving strategy is obtained by optimizing the deep peak-shaving quotation and the deep peak-shaving amplitude, and the optimal revenue is obtained based on the optimal power generation and peak-shaving strategy.

2. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 1 is characterized in that: The real-time coal burning data includes the real-time coal consumption for power supply and the standard coal unit price of coal entering the furnace. The real-time coal burning cost is calculated based on the real-time coal burning data as follows: Obtain the raw coal quantity and coal type data of the coal entering the furnace in the raw coal bunker of the coal-fired power plant, wherein the coal type data includes the blending ratio, low calorific value and corresponding standard coal unit price of each coal type; weight the coal quantity of each coal type in real time according to the blending ratio of each coal type, and calculate the average calorific value and standard coal unit price of the coal entering the furnace according to the weighted coal quantity and low calorific value; The standard coal quantity of the coal entering the furnace is calculated based on the raw coal quantity, average calorific value of the coal entering the furnace and the lower calorific value of the standard coal, which can be expressed as follows: Standard coal quantity of coal fed into the furnace = raw coal quantity of coal fed into the furnace × (average calorific value of coal fed into the furnace / lower calorific value of standard coal); Get the total heat demand Q of the coal-fired power plant heating system 热 The standard coal consumption required for heating is calculated by using the efficiency η, and the standard coal consumption for power generation is obtained by deducting the standard coal consumption required for heating from the standard coal consumption of the coal fed into the furnace, which is expressed as follows: Standard coal consumption for power generation = standard coal quantity of coal entering the furnace - (Q 热 / (η×average calorific value of coal fed into the furnace)); Obtain the high-voltage transformer capacity and power generation of the coal-fired power plant, calculate the power consumption rate of the coal-fired power plant, and express it in the formula: Coal-fired power plant power consumption rate = high-power plant power generation / power generation; According to the power consumption rate of coal-fired power plants, the standard coal consumption for power generation is adjusted, and then the real-time power supply coal consumption is calculated, which is expressed as: Real-time power supply coal consumption = standard coal consumption for power generation / (1-power consumption rate of coal-fired power plants).

3. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 2 is characterized in that: Real-time fuel data includes instantaneous fuel usage flow and fuel purchase price. The real-time fuel cost is calculated based on the real-time fuel data and expressed as: Real-time fuel cost = instantaneous fuel usage flow × fuel purchase price.

4. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 3 is characterized in that: The real-time consumption data of other materials include the real-time consumption and corresponding price of desulfurization agent limestone, the real-time consumption and corresponding price of urea, and the real-time consumption and corresponding price of raw water. The real-time consumption cost of other materials is calculated based on the real-time consumption data of other materials. Specifically, the calculation method is to multiply the real-time consumption by the corresponding price. The resource and environmental tax data include environmental protection tax data and water resource tax data. The environmental protection tax data include sulfur dioxide environmental protection tax data, nitrogen oxide environmental protection tax data and dust environmental protection tax data. Among them, the cost calculated based on the sulfur dioxide environmental protection tax data is the product of the sulfur dioxide emission concentration, flue gas volume and the tax standard per ton of sulfur dioxide on the net flue gas side; the cost calculated based on the nitrogen oxide environmental protection tax data is the product of the nitrogen oxide emission concentration, flue gas volume and the tax standard per ton of nitrogen oxide on the net flue gas side; the cost calculated based on the dust environmental protection tax data is the product of the dust emission concentration, flue gas volume and the tax standard per ton of dust on the net flue gas side; the cost calculated based on the water resource tax data is the product of the real-time raw water consumption and the tax standard per ton of water.

5. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 4 is characterized in that: Electricity production data include the electricity consumption rate of coal-fired power plants, real-time load of units and rated capacity of units; electricity market data include electricity prices in the electricity market, prices of deep peak regulation and their changes; electricity tax data include tax rates and other related taxes and fees; electricity sales revenue is calculated based on power generation revenue related data, expressed in the formula: Electricity sales revenue = real-time load of the unit × electricity price × (1-electricity consumption rate of coal-fired power plant) × 1000 / (1+tax rate).

6. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 5 is characterized in that: The specific calculation of deep peak regulation benefits based on power generation revenue data is: Determine the paid peak-shaving benchmark according to the rated capacity of the unit, divide the deep peak-shaving grading interval according to the paid peak-shaving benchmark, and if the real-time load of the unit is within the deep peak-shaving grading interval, take the difference between the real-time load of the unit and the paid peak-shaving benchmark as the deep peak-shaving electricity; A deep peak-shaving transaction tiered quotation table is preset, and the deep peak-shaving transaction tiered quotation table includes the real-time load segment of the unit and the corresponding gears and the upper and lower limits of the quotation. The unit with the lowest quotation lower limit in the real-time load segment of the unit is selected for deep peak-shaving, and the deep peak-shaving clearing price corresponding to the real-time load segment of the unit on that day is obtained, and the product of the deep peak-shaving clearing price and the deep peak-shaving electricity is used as the deep peak-shaving income.

7. The method for optimizing the benefits of peak load auxiliary services based on power generation variable cost analysis according to claim 6 is characterized in that: The total marginal contribution is calculated based on the real-time variable cost of power generation, power sales revenue and deep peak regulation revenue, and whether to continue power generation is determined based on the total marginal contribution. Specifically: Total marginal contribution = electricity sales revenue + deep peak load regulation revenue - real-time power generation variable cost; If the total marginal contribution is positive, continue to generate electricity; otherwise, stop generating electricity.

8. The method for optimizing the benefits of peak load auxiliary services based on power generation variable cost analysis according to claim 7 is characterized in that: The goal of deep peak load shaving quotation optimization is to determine the optimal deep peak load shaving quotation in each load segment. Specifically: According to the deep peak-shaving transaction tiered quotation table, determine the corresponding gear in the load segment where the unit is located, and calculate the deep peak-shaving gear balance point quotation for the gear, which is expressed as: Deep peak load regulation balance point quotation = (benchmark income - (total marginal contribution - deep peak load regulation compensation)) / deep peak load regulation electricity × (1 / corresponding discount coefficient of deep peak load regulation gear of the unit); In the formula, the benchmark income is the income from power generation when there is no peak load regulation; If the balance point quotation of the deep-shaving gear is less than the lower limit of the quotation for that gear in the step-by-step quotation table of deep peak-shaving transactions, the optimal deep peak-shaving quotation is the lower limit of the quotation; if the balance point quotation of the deep-shaving gear exceeds the upper limit of the quotation, the peak-shaving depth is increased to the next gear, and the shortfall of the corresponding gear in the load segment where the unit is located is amortized through the revenue of the next gear. At this time, the optimal deep peak-shaving quotation of the previous gear is the upper limit of the quotation; if the balance point quotation of the deep-shaving gear is between the upper limit and the lower limit of the quotation for that gear, the balance point quotation of the deep-shaving gear is the optimal deep peak-shaving quotation.

9. The peak load auxiliary service benefit optimization method based on power generation variable cost analysis according to claim 8 is characterized in that: The optimization of deep peak-shaving amplitude is specifically to set different loads and discount factors, and calculate the total marginal contribution of the computer group under each load and discount factor; analyze the total marginal contribution to determine that the load segment that is not lower than no peak-shaving under different loads and different discount factors is the optimal peak-shaving depth.

10. A peak load auxiliary service benefit optimization system based on power generation variable cost analysis, characterized in that: The system includes a coal blending analysis module, a power generation variable cost analysis module, a power generation revenue analysis module, a power generation marginal contribution analysis module, a deep peak regulation amplitude optimization module, a deep peak regulation quotation optimization module and an output module, wherein: The coal blending analysis module for the incoming coal is used to obtain data on the incoming coal of the coal-fired power plant in real time, including real-time coal consumption for power supply, coal blending ratio, low calorific value of each type of coal and standard coal unit price, and calculate the standard coal quantity and standard coal purchase unit price of the incoming coal through real-time coal data; obtain the total heat demand and efficiency of the heating system of the coal-fired power plant, calculate the standard coal quantity required for heating, and deduct it from the standard coal quantity of the incoming coal to obtain the standard coal consumption for power generation, adjust the standard coal consumption for power generation according to the power consumption rate of the coal-fired power plant, and calculate the real-time coal consumption for power supply; The power generation variable cost analysis module is used to calculate the real-time coal burning cost according to the real-time power supply coal consumption and the standard coal unit price of the coal entering the furnace; obtain and calculate the real-time fuel oil data and cost, including the instantaneous fuel oil usage flow and the fuel oil purchase price; obtain and calculate the real-time consumption data and cost of other materials, including the real-time consumption and corresponding prices of desulfurization agent limestone, urea, and raw water; obtain and calculate the resource and environmental tax data and cost, including environmental protection tax and water resource tax data; and integrate the above costs into the power generation variable cost; The power generation revenue analysis module is used to obtain power production data, power market data and power tax data, and calculate power sales revenue and deep peak regulation revenue; The power generation marginal contribution analysis module is used to calculate the total marginal contribution of power generation based on the variable cost of power generation, power sales revenue and deep peak-shaving revenue, and use this as the basis for judging whether to continue power generation. If power generation continues, the deep peak-shaving amplitude optimization module and the deep peak-shaving quotation optimization module are triggered to optimize and obtain the best peak-shaving amplitude and quotation; The deep peak-shaving amplitude optimization module is used to set different loads and discount coefficients, calculate the total marginal contribution under different loads and discount coefficients, analyze the total marginal contribution, and determine the optimal peak-shaving depth under different loads and discount coefficients; The deep peak-shaving quotation optimization module is used to calculate the balance point of the deep peak-shaving quotation according to the deep peak-shaving transaction ladder quotation table, determine the optimal peak-shaving quotation for each load segment, and maximize the economic benefits of peak-shaving; The output module is used to output the best power generation and peak load regulation strategy, as well as the corresponding peak load regulation quotation and amplitude.

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