Dynamic modeling method and system for biomass power plant peak capability analysis

Abstract

The invention discloses a biomass power plant peak capacity analysis-oriented dynamic modeling method and system. The method comprises the following steps of dividing a process from a biomass unit feeding amount to a unit power into three processes of feeding-heat, heat-main steam pressure and main steam pressure-power; designing static parameters and dynamic parameters, and respectively establishing a feeding-heat dynamic model, a heat-main steam pressure dynamic model and a main steam pressure-power dynamic model; combining the three established dynamic models to form a preliminary dynamic model containing unknown static parameters and unknown dynamic parameters; the static parameters and the dynamic parameters are solved, and the final dynamic model for biomass power plant peak capacity analysis is obtained. According to the method, the influence of the in-furnace combustion process and the unit output on the peak capacity of the biomass unit is considered at the same time, a mechanism model can be provided for analyzing the influence of the fuel heat value on the unit power, and support is provided for fuel management and scheduling strategies in the peak period of the biomass unit.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass power plant modeling, and in particular to a dynamic modeling method and system for peak capacity analysis of a biomass power plant. Background Art

[0002] The characteristics of the "winter and summer" double peaks of my country's power load are becoming increasingly obvious, with the proportion of summer cooling and winter heating loads increasing. In some provinces, the proportion of summer cooling loads to the highest power load has reached 40%-50%, or even exceeded 50%, and the power supply and demand are in a tight balance. Taking Henan Province as an example, the power supply situation in Henan Province during the peak summer period is relatively severe and complex. It is estimated that the maximum load of Henan power grid this year will be 83.5 million kilowatts, an increase of 5.58 million kilowatts year-on-year, an increase of 7.2%. This year's peak gap is 7.5 million kilowatts, and the supply situation is severe. Due to the randomness, uncertainty and transient nature of renewable energy such as wind power and photovoltaics, which depends on the weather, the severe situation of power supply has been further increased. Due to its environmental protection advantages and short-term peak capacity advantages, biomass units have gradually played a certain role in the peak summer and winter.

[0003] In order to analyze the peak output capacity of biomass units, it is necessary to establish a dynamic model from biomass fuel to unit load, so as to provide theoretical support for analyzing the peak capacity of biomass units. At present, there are few studies on biomass combustion boilers. Since the combustion mechanism of biomass is different from that of coal, the unit load and fuel composition have a great impact on the power generation efficiency of the entire power plant. Dynamic modeling research needs to consider the differences in combustion characteristics. At present, most of the modeling research on biomass units only analyzes the combustion process in the furnace, and lacks research on unit output, which is not conducive to analyzing the peak capacity of biomass units. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a dynamic modeling method and system for peak capacity analysis of biomass power plants, which simultaneously considers the impact of the combustion process in the furnace and the unit output on the peak capacity of the biomass unit, and can provide a mechanism model for analyzing the impact of the calorific value of the fuel on the unit power, which is beneficial to provide support for the fuel management and scheduling strategies during the peak period of the biomass unit.

[0005] The present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a dynamic modeling method for peak capacity analysis of a biomass power plant, comprising the following steps: dividing the process from the feed amount of a biomass unit to the unit power into three processes: feed-heat, heat-main steam pressure and main steam pressure-power; designing static parameters and dynamic parameters, and establishing a dynamic model of feed-heat, a dynamic model of heat-main steam pressure and a dynamic model of main steam pressure-power respectively; combining the established dynamic model of feed-heat, the dynamic model of heat-main steam pressure and the dynamic model of main steam pressure-power to form a preliminary dynamic model for peak capacity analysis of a biomass power plant containing unknown static parameters and unknown dynamic parameters; collecting the operating data of the biomass unit, and solving the static parameters and dynamic parameters based on the operating data; substituting the calculated static parameters and dynamic parameters into the preliminary dynamic model to obtain the final dynamic model for peak capacity analysis of a biomass power plant.

[0007] Preferably, the collecting of the operating data of the biomass unit and solving the static parameters based on the operating data include the following steps: collecting the operating data of the biomass unit, and selecting the steady-state operating data of the biomass unit from the collected operating data; setting each differential in the established preliminary dynamic model for the peak capacity analysis of the biomass power plant containing unknown static parameters and unknown dynamic parameters to zero, and calculating the static parameters.

[0008] Preferably, the operating data include: the feed amount entering the furnace, the opening of the steam turbine valve, the main steam pressure, the boiler drum pressure, the regulating stage pressure, the unit load, the total heat released inside the furnace within a set time, the heat absorbed by water and water vapor, the furnace temperature, the current total amount of fuel in the grate, and the steam flow rate of the turbine; after collecting the operating data, the feed amount data entering the furnace is filtered.

[0009] Preferably, the operation data of the biomass unit is collected, and the dynamic parameters are solved based on the operation data, including the following steps: substituting the solved static parameters into the established preliminary dynamic model; inputting the feed rate entering the furnace and the opening of the steam turbine valve as input quantities into the preliminary dynamic model, and outputting the unit power and main steam pressure using the preliminary dynamic model; comparing the output unit power and main steam pressure with the actual operating unit power and main steam pressure; adjusting the unknown dynamic parameters and delay time so that the error between the output unit power and main steam pressure and the actual operating unit power and main steam pressure is reduced; taking the dynamic parameters and delay time with the smallest error as the dynamic parameters and delay time in the final dynamic model for peak capacity analysis of biomass power plants.

[0010] Preferably, the root mean square error, the absolute value integral of the error, and the absolute value time integral of the error are used to calculate the error between the output unit power and main steam pressure and the actual operating unit power and main steam pressure; the root mean square error, the absolute value integral of the error, and the absolute value time integral of the error are minimized to obtain dynamic parameters and delay time.

[0011] Preferably, the root mean square error expression used is:

[0012]

[0013] Where: y i The actual value of the main steam pressure or turbine output power data; i * is the output value of the model main steam pressure or turbine output power; i represents the i-th collected data, and n is the sample length of the collected data.

[0014] Preferably, the expression of the absolute value integral of the error used is:

[0015]

[0016] Preferably, the expression of the time integral of the absolute value of the error used is:

[0017]

[0018] Preferably, the dynamic model of the feed-heat module is established as follows:

[0019]

[0020]

[0021]

[0022]

[0023] In the formula, c1, c2, c3, c4 are four dynamic parameters to be identified; K1, K2, K3, K4 are four static parameters to be identified; μ B is the feed rate entering the furnace at a certain moment (kg / s), t is the time, τ is the delay time, B(t) is the current total amount of fuel in the grate, kg; N e (t) is the unit load, MW; A ar is the received ash mass fraction of biomass fuel, %; Q r t is the total heat released inside the furnace at time t, MJ; T(t) is the furnace temperature; Q(t) is the heat absorbed by water and water vapor, MJ.

[0024] Preferably, the dynamic model of heat-main steam pressure is established as follows:

[0025]

[0026]

[0027] Where, Q(t) is the heat absorbed by water and water vapor, MJ; P b (t) is the boiler drum pressure, MPa; P T (t) Main steam pressure, MPa; D T (t) is the steam flow rate of the steam turbine, kg / s; c5 and c6 are two dynamic parameters to be identified; K6 and K7 are two static parameters to be identified.

[0028] Preferably, the established main steam pressure-power dynamic model is:

[0029]

[0030]

[0031] Where, P1 is the regulating stage pressure, MPa; μ T (t) is the opening of the turbine valve, %; P T (t) Main steam pressure, MPa; D T (t) is the steam flow rate of the steam turbine, kg / s; c7 and c8 are two dynamic parameters to be identified; K8 and K9 are two static parameters to be identified.

[0032] In a second aspect, the present invention provides a system utilizing the aforementioned dynamic modeling method for peak capacity analysis of biomass power plants, the system comprising: a data acquisition module, a model building module, an input module and an output module; the data acquisition module is used to collect operating data of the biomass unit; the model building module is used to establish a dynamic model of feed-heat, a dynamic model of heat-main steam pressure and a dynamic model of main steam pressure-power, and combine them to form a dynamic model for peak capacity analysis of biomass power plants; the input module is used to input the collected feed amount and steam turbine valve opening data entering the furnace as input quantities into the dynamic model established using the model building module; the output module is used to output the unit power and main steam pressure output by the dynamic model based on the collected operating data of the biomass unit.

[0033] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention divides the process from the feed amount of the biomass unit to the unit power into three processes: feed-heat, heat-main steam pressure and main steam pressure-power, and establishes dynamic models for the three processes respectively, and combines the three established dynamic models to form a dynamic model for the peak capacity analysis of the biomass power plant, while considering the influence of the combustion process in the furnace and the unit output on the peak capacity of the biomass unit, and can provide a mechanism model for analyzing the influence of the calorific value of the fuel on the unit power, which is beneficial to provide support for the fuel management and scheduling strategies during the peak period of the biomass unit.

[0034] Furthermore, the dynamic model for peak capacity analysis of biomass power plants established by the present invention takes feed rate and turbine valve opening as inputs, and main steam pressure and turbine output power as outputs, forming a "two-input and two-output" dynamic model.

[0035] Furthermore, the preliminary dynamic model for peak capacity analysis of biomass power plants established by the present invention contains unknown static parameters and dynamic parameters. By adjusting the static parameters and dynamic parameters, it can be applied to different biomass power plants and has universality. In addition, the present invention can reduce the error of the established dynamic model by designing dynamic parameters and improve the accuracy of dynamic model prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic flow chart of the dynamic modeling method for peak capacity analysis of biomass power plants in the present invention.

[0037] Figure 2 The figure is a comparison diagram between the output unit power and main steam pressure output by the dynamic model established by the present invention and the actual operation data.

[0038] Figure 3 It is the error distribution diagram of the power circuit and main steam pressure circuit identification conditions in the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0040] A dynamic modeling method and device for peak capacity analysis of a biomass power plant proposed in Example 1 of the present invention has a process as follows: Figure 1 As shown, the following steps are included:

[0041] Step 1, dividing the process from the feed amount of the biomass unit to the unit power into a feed-heat module, a heat-main steam pressure module and a main steam pressure-power module;

[0042] Step 2: Design static parameters, dynamic parameters and delay time, and establish dynamic models according to the feed-heat module, heat-main steam pressure module and main steam pressure-power module;

[0043] Step 2 includes the following steps:

[0044] Step 201: Based on the energy balance principle, a feed-heat dynamic model is established.

[0045] Specifically, assuming that the type and density of the fuel remain unchanged, the amount of fuel entering the boiler is K1μ B (t-τ), K1 is the static parameter to be identified; μ B is the feed rate entering the furnace at a certain moment (kg / s), t is the time, τ is the delay time, and the delay time τ is related to the belt structure.

[0046] Based on the balance between the newly added fuel and the fuel consumed by combustion, and ignoring the influence of the slag discharge amount and the influence of the ash layer diffusion mass transfer resistance coefficient on the combustion reaction rate coefficient, the following is obtained based on the mass conservation theorem:

[0047]

[0048] Where K2 is the static parameter 2 to be identified; c1 is the dynamic parameter 1 to be identified; B(t) is the current total amount of fuel in the grate, kg; N e (t) is the unit load, MW; A ar is the received ash mass fraction of biomass fuel, %, generally A ar ∈0.001,0.9, preferably 0.1373 in the embodiment of the present invention.

[0049] The heat source inside the furnace is mainly the heat released by the combustion of biomass fuel. Ignoring the convection heat transfer inside the furnace, according to the energy conservation theorem, we can get:

[0050]

[0051] In the formula, Q r t is the total heat released inside the furnace at time t, MJ, c2 is the second dynamic parameter to be identified; K3 is the third static parameter to be identified.

[0052] The bed material temperature in the dense phase area of ​​the furnace is regarded as the furnace temperature. According to the energy balance between the furnace temperature and the heat taken away by the flue gas flow and the slag discharge flow, we can obtain:

[0053]

[0054] Wherein, T(t) is the furnace temperature, K4 is the fourth static parameter to be identified, K4Ne(t) represents the heat absorbed by the water-cooled wall; K5 is the fifth static parameter to be identified; c3 is the third dynamic parameter to be identified.

[0055] According to the relationship between the total heat released inside the furnace and the heat absorbed by the water-cooled wall, the heat absorbed by water and water vapor Q(t) is obtained as follows:

[0056]

[0057] Where Q(t) is the heat absorbed by water and water vapor, MJ, and c4 is the dynamic parameter four to be identified.

[0058] Formulas (1), (2), (3) and (4) are combined to form a dynamic model of feed-heat.

[0059] Step 202: Based on the energy balance principle and convection heat transfer factors, a dynamic model of heat-main steam pressure is established.

[0060] Specifically, assuming that the water supply control system of the biomass unit can keep the water level in the drum constant, and the steam temperature and feed water temperature remain stable, and the temperature change on the steam side of the drum and the temperature change on the superheated pipe are ignored, the calculation method of the drum boiler is simplified as follows:

[0061]

[0062] Where P b (t) is the boiler drum pressure, MPa; P T (t) Main steam pressure, MPa. D T (t) is the steam flow rate of the steam turbine, kg / s, c5 is the dynamic parameter five to be identified; c6 is the dynamic parameter six to be identified; K6 is the static parameter six to be identified; K7 is the static parameter seven to be identified.

[0063] The above formulas (5) and (6) are combined to form a dynamic model of heat-main steam pressure.

[0064] Step 203: Establish a dynamic model of main steam pressure-power.

[0065] Specifically, assuming that the regulating stage pressure of the steam turbine is proportional to the product of the main steam pressure and the turbine valve opening, the dynamic expression of the regulating stage pressure of the steam turbine is obtained as follows:

[0066]

[0067] Where, P1 is the regulating stage pressure, MPa; μ T(t) is the opening of the steam turbine valve, %; c7 is the dynamic parameter seven to be identified; K8 is the static parameter eight to be identified.

[0068] Considering the steam flow rate of the steam turbine and the regulating stage pressure as a linear relationship, the dynamic expression is established as follows:

[0069]

[0070] Wherein, c8 is the dynamic parameter eight to be identified; K9 is the static parameter nine to be identified.

[0071] In normal operation, the steam flow rate of the steam turbine is D T (t) determines the efficiency of the steam turbine and the generator power, so the unit load is expressed as:

[0072] N e (t) = K 10 D T (t) (9)

[0073] In the formula, K 10 is the static parameter ten to be identified.

[0074] The above formulas (7), (8) and (9) are combined to form a dynamic model of main steam pressure-power.

[0075] Step 3: Combine the established feed-heat dynamic model, heat-main steam pressure dynamic model and main steam pressure-power dynamic model to form a preliminary dynamic model for biomass power plant peak capacity analysis containing unknown static parameters, unknown dynamic parameters and unknown delay time, as follows:

[0076]

[0077] and

[0078]

[0079] Step 4: Collect the operation data of the biomass unit for a certain period of time. The operation data mainly includes the feed volume μ B , Turbine valve opening μ T , Main steam pressure P T , Unit load N e , the total heat Q released inside the furnace during this time r , water and steam absorption heat Q, furnace temperature T, grate current total fuel B, turbine steam flow D T ; Then use the median filter method to filter the feed amount data μ B to be processed.

[0080] In this embodiment, historical data of a 30MW biomass unit is collected, and the feed rate data is processed by a median filtering method.

[0081] Step 5: Select the steady-state operation data of the biomass unit from the collected operation data, and calculate the static parameters to be identified K1, K2, K3, K4, K5, K6, K7, K8, K9 and K 10 The numerical value of .

[0082] Specifically, by setting each differential in the above formulas (1)-(8) to zero, the numerical value of each static parameter can be solved.

[0083] In this embodiment, K1=1, K2=6.8143×10 -6 K3=7.1550×10 -5 , K4=1.1500, K5=0.0615, K6=63.6531, K7=39.7832, K8=0.0082, K9=5.6358 and K 10 =0.8758.

[0084] Step 6: Substitute the static parameters calculated in step 5 into the dynamic model for peak capacity analysis of biomass power plants established in step 3, and input the feed rate μ into the furnace into the dynamic model. B , Turbine valve opening μ T , use the dynamic model to output the unit power and main steam pressure, and compare the output unit power and main steam pressure with the actual operating unit power and main steam pressure, adjust the unknown dynamic parameters and delay time, so that the error between the output unit power and main steam pressure and the actual operating unit power and main steam pressure is reduced; take the dynamic parameters and delay time with the smallest error as the dynamic parameters and delay time in the final dynamic model for biomass power plant peak capacity analysis.

[0085] Furthermore, in an embodiment of the present invention, indicators such as root mean square error, absolute value integral of error, and absolute value time integral of error are used to measure the prediction accuracy of the dynamic model for peak capacity analysis of biomass power plants built in step 3 for the main steam pressure loop and unit output power.

[0086] The calculation formulas used for the root mean square error, absolute value integral of error, and absolute value time integral of error are as follows:

[0087] Root Mean Square Error:

[0088] Where: y i The actual value of the main steam pressure or turbine output power data; i* is the output value of the model main steam pressure or turbine output power. i represents the i-th collected data, and n is the sample length of the collected data.

[0089] Absolute value integral of error:

[0090] Absolute value of error time integral: By minimizing the above indicators, the values ​​of the dynamic parameters c1, c2, c3, c4, c5, c6, c7, c8 and τ to be identified are obtained.

[0091] In this embodiment, the dynamic parameters to be identified are c1=80.0000, c2=0.4100, c3=148.5761, c4=0.4523, c5=31.9593, c6=37.5900, c7=1.8463, c8=1.0325 and τ=10.0000.

[0092] Substitute the obtained static and dynamic parameters into formulas (10), (11) and (12) to obtain the dynamic model for peak capacity analysis of biomass power plants.

[0093] In order to analyze the effectiveness of the method of the present invention in this embodiment, based on the obtained dynamic model, a comparison between the model output and the actual operation data under the same excitation can be obtained. Figure 2 As shown, the distribution of model error over time is also Figure 3 As shown in the figure, the power output change trajectory of the model established in this paper is almost consistent with the actual operating data, accurately reflecting the dynamic change trend in actual operation. However, the prediction effect of the main steam pressure loop is slightly inferior to that of the power output loop, but it can still be seen from the figure that its change trend has significant similarity with the actual operating data in the key decline interval, which verifies that the model established in this paper can well reflect the operating condition change trend of the biomass unit. It can be seen from the error distribution diagram that within a range of nearly 2 hours, the dynamic error of the power loop is within ±5%, and the dynamic error of the main steam pressure loop is within ±10%. The average dynamic error of the power loop of the established dynamic model is 3.93%, and the maximum value is less than 5%. The error is within the ideal acceptable range, and the established model has a high accuracy.

[0094] Embodiment 2 of the present invention provides a system for the dynamic modeling method for peak capacity analysis of biomass power plants in Embodiment 1, and the system includes: a data acquisition module, a model building module, an input module and an output module.

[0095] The data acquisition module is used to collect the operation data of the biomass unit;

[0096] The model building module is used to establish a dynamic model of feed-heat, a dynamic model of heat-main steam pressure, and a dynamic model of main steam pressure-power, and combine them to form a dynamic model for peak capacity analysis of biomass power plants;

[0097] The input module is used to input the collected data of the feed amount entering the furnace and the opening degree of the steam turbine valve as input quantities into the dynamic model established by the model building module;

[0098] The output module is used to output the unit power and main steam pressure output by the dynamic model based on the collected operation data of the biomass unit.

[0099] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention divides the process from the feed amount of the biomass unit to the unit power into three processes: feed-heat, heat-main steam pressure and main steam pressure-power, and establishes dynamic models for the three processes respectively, and combines the three established dynamic models to form a dynamic model for the peak capacity analysis of the biomass power plant, while considering the influence of the combustion process in the furnace and the unit output on the peak capacity of the biomass unit, and can provide a mechanism model for analyzing the influence of the calorific value of the fuel on the unit power, which is beneficial to provide support for the fuel management and scheduling strategies during the peak period of the biomass unit.

[0100] Furthermore, the dynamic model for peak capacity analysis of biomass power plants established by the present invention takes feed rate and turbine valve opening as inputs, and main steam pressure and turbine output power as outputs, forming a "two-input and two-output" dynamic model.

[0101] Furthermore, the preliminary dynamic model for peak capacity analysis of biomass power plants established by the present invention contains unknown static parameters and dynamic parameters. By adjusting the static parameters and dynamic parameters, it can be applied to different biomass power plants and has universality. In addition, the present invention can reduce the error of the established dynamic model by designing dynamic parameters and improve the accuracy of dynamic model prediction.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic modeling method for peak capacity analysis of biomass power plants, characterized in that: The following steps are involved: The process from the feed quantity to the power of the biomass unit is divided into three processes: feed-heat, heat-main steam pressure and main steam pressure-power. Design static parameters and dynamic parameters, and establish dynamic models of feed-heat, heat-main steam pressure, and main steam pressure-power respectively; The established dynamic model of feed-heat, dynamic model of heat-main steam pressure and dynamic model of main steam pressure-power are combined to form a preliminary dynamic model for peak capacity analysis of biomass power plants containing unknown static parameters and unknown dynamic parameters; Collect the operation data of the biomass unit and solve the static and dynamic parameters based on the operation data; The calculated static and dynamic parameters are substituted into the preliminary dynamic model to obtain the final dynamic model for peak capacity analysis of biomass power plants.

2. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 1 is characterized by: The collecting of the operation data of the biomass unit and solving the static parameters based on the operation data include the following steps: Collecting biomass unit operation data, and selecting biomass unit steady-state operation data from the collected operation data; Each differential in the preliminary dynamic model for peak capacity analysis of biomass power plants containing unknown static parameters and unknown dynamic parameters is set to zero, and the static parameters are calculated.

3. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 2 is characterized by: The operation data include: the amount of feed entering the furnace, the opening of the steam turbine valve, the main steam pressure, the boiler drum pressure, the regulating stage pressure, the unit load, the total heat released in the furnace within a set time, the heat absorbed by water and water vapor, the furnace temperature, the current total amount of fuel on the grate, and the steam flow rate of the steam turbine; After collecting the operating data, the feed quantity data entering the furnace is filtered.

4. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 2 is characterized by: The collecting of the operation data of the biomass unit and solving the dynamic parameters based on the operation data include the following steps: Substitute the solved static parameters into the established preliminary dynamic model; The feed rate into the furnace and the opening of the turbine valve are input into the preliminary dynamic model as input quantities, and the preliminary dynamic model is used to output the unit power and main steam pressure; Compare the output unit power and main steam pressure with the actual operating unit power and main steam pressure; Adjust unknown dynamic parameters and delay time to reduce the error between the output unit power and main steam pressure and the actual operating unit power and main steam pressure; The dynamic parameters and delay time with the smallest error are taken as the dynamic parameters and delay time in the final dynamic model for peak capacity analysis of biomass power plants.

5. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 4 is characterized by: The error between the output unit power and main steam pressure and the actual operating unit power and main steam pressure is calculated by using the root mean square error, the absolute value integral of the error, and the absolute value time integral of the error; The minimum value of the root mean square error, the absolute value integral of the error, and the absolute value time integral of the error is taken to obtain the dynamic parameters and the delay time.

6. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 5 is characterized by: The root mean square error expression used is: Where: y i The actual value of the main steam pressure or turbine output power data; i * is the output value of the model main steam pressure or turbine output power; i represents the i-th collected data, and n is the sample length of the collected data.

7. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 6 is characterized by: The expression used for the absolute value integral of the error is:

8. The dynamic modeling method for peak capacity analysis of biomass power plants according to claim 7 is characterized by: The expression used for the time integral of the absolute value of the error is:

9. The dynamic modeling method for peak capacity analysis of a biomass power plant according to any one of claims 1 to 8, characterized in that: The dynamic model of the feed-heat module is established as follows: In the formula, c1, c2, c3, c4 are four dynamic parameters to be identified; K1, K2, K3, K4 are four static parameters to be identified; μ B is the feed rate entering the furnace at a certain moment (kg / s), t is the time, τ is the delay time, B(t) is the current total amount of fuel in the grate, kg; N e (t) is the unit load, MW; A ar is the received ash mass fraction of biomass fuel, %; Q r t is the total heat released inside the furnace at time t, MJ; T(t) is the furnace temperature; Q(t) is the heat absorbed by water and water vapor, MJ.

10. The dynamic modeling method for peak capacity analysis of a biomass power plant according to any one of claims 1 to 8, characterized in that: The dynamic model of heat-main steam pressure is established as follows: Where, Q(t) is the heat absorbed by water and water vapor, MJ; P b (t) is the boiler drum pressure, MPa; P T (t) Main steam pressure, MPa; D T (t) is the steam flow rate of the steam turbine, kg / s; c5 and c6 are two dynamic parameters to be identified; K6 and K7 are two static parameters to be identified.

11. The dynamic modeling method for peak capacity analysis of a biomass power plant according to any one of claims 1 to 8, characterized in that: The established main steam pressure-power dynamic model is: Where, P1 is the regulating stage pressure, MPa; μ T (t) is the opening of the turbine valve, %; P T (t) Main steam pressure, MPa; D T (t) is the steam flow rate of the steam turbine, kg / s; c7 and c8 are two dynamic parameters to be identified; K8 and K9 are two static parameters to be identified.

12. A system using the dynamic modeling method for peak capacity analysis of a biomass power plant according to any one of claims 1 to 11, characterized in that: The system comprises: a data acquisition module, a model building module, an input module and an output module; The data acquisition module is used to collect the operation data of the biomass unit; The model building module is used to establish a dynamic model of feed-heat, a dynamic model of heat-main steam pressure and a dynamic model of main steam pressure-power, and combine them to form a dynamic model for peak capacity analysis of biomass power plants; The input module is used to input the collected data of the feed amount entering the furnace and the opening degree of the steam turbine valve as input quantities into the dynamic model established by the model building module; The output module is used to output the unit power and main steam pressure output by the dynamic model based on the collected operation data of the biomass unit.

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