Construction method of thermal power load optimization model for multi-source and multi-pressure industrial heating network under all working conditions

Abstract

The present invention relates to a method for constructing a full-operation thermal power load optimization model of a multi-source and multi-pressure industrial heating network, the method comprising: 101, establishing a thermal performance calculation model of a heat source unit; 102, establishing a calculation method for the variable cost and production income of the heat source unit, and a calculation method for the shared expenses generated by participating in the deep peak-shaving market, and establishing a production profit calculation model for the heat source unit as the objective function of thermal power load optimization; 103, establishing the constraints of the optimization variables, giving the equality constraints and the inequality constraints, using the setting method to decouple the coupling constraints between the heat supply and the power generation, and the coupling constraints between the heat supplies of different pressure levels, and establishing the mathematical expression of the inequality constraints; 104, performing optimization calculations through the thermal power load optimization model, and obtaining an operation plan with the best production profit. Compared with the prior art, the method constructed by the present invention considers the constraints more comprehensively, and has the advantages of high calculation model accuracy and strong guidance of optimization results.

Description

Technical Field

[0001] The present invention relates to a method for constructing a thermal power load optimization model, and in particular to a method for constructing a thermal power load optimization model for a multi-source and multi-pressure industrial heating network under all operating conditions. Background Art

[0002] With the operation of the deep peak-shaving market mechanism and the advancement of the electricity market reform, the power supply side will face more and more challenges, and the profit model and profitability of the cogeneration unit will also change. After the deep peak-shaving market mechanism is launched, the unit can obtain compensation fees by reducing the load to provide deep peak-shaving auxiliary services, or reduce the shared costs borne in the deep peak-shaving market, but the unit's load reduction will also reduce the unit's efficiency and heating capacity. When the primary energy price (coal price or natural gas price) and the on-grid electricity price change, the thermal load distribution of the cogeneration unit cannot follow the traditional approach, only considering the lowest coal or gas consumption of the unit, but should take the unit's production profit as the objective function, comprehensively consider the impact of changes in primary energy prices and on-grid electricity prices, and reasonably select the unit's operation plan to obtain the best economy.

[0003] Industrial heat networks are characterized by high heating parameters and multiple pressure levels. In order to ensure heating safety, there are usually multiple heat sources in the heat network. The heat sources include gas turbine combined heat and power combined cycle units, gas boilers, extraction condensing heating coal-fired units and back pressure heating coal-fired units. Combined cycle units can also be divided into extraction condensing and back pressure types according to the heating method. Among them, extraction condensing units can provide heating steam of different pressures according to different extraction points. When the electric power of the gas turbine is constant, the exhaust energy of the gas turbine is relatively fixed, that is, the energy entering the waste heat boiler is basically unchanged. The waste heat boiler uses this part of energy to generate electricity and heat. There is a coupling relationship between this part of the power generation and the heating capacity of the unit; for steam turbine units with multi-stage extraction steam heating, there is also a coupling relationship between the extraction volume of each stage. When establishing the thermal power load optimization model of the combined cycle unit, these coupling relationships are constraints that must be considered. When constructing a traditional thermal power load optimization model, the heat supply constraint is usually constructed as an inequality constraint, and the upper and lower limits of the constraint are constants, without considering the thermal power coupling and the coupling relationship between each level of steam extraction.

[0004] The calculation method of the variable operating condition thermal performance of the combined cycle unit is the key and difficulty of the construction of the thermal power load optimization model. Due to the confidentiality of the manufacturer's technology, the thermal performance calculation of the gas turbine combined cycle unit usually adopts the operation history data fitting or thermal system simulation model to obtain the fitting polynomial for the calculation of the thermal performance index. Since the operating load of the gas turbine is relatively high, the data of the low-load stable operating condition is usually less. After the deep peak-shaving market starts, the unit may run to a lower load, so the calculation model obtained by historical data is difficult to cover the full load; and the calculation model obtained by the thermal system simulation model has limited accuracy. Therefore, in order to obtain high-precision thermal performance of the unit under all operating conditions, other means are needed.

[0005] After searching the Chinese patent publication CN110070460A, a thermal power load optimization scheduling system for multiple gas-steam combined cycle units is disclosed. Specifically, the system obtains the operating parameters of the gas-steam combined cycle units measured and uploaded by the field measuring instruments from the DCS system of the power plant, and analyzes the operating parameters to obtain the actual operating trend curve of the equipment, and then combines the thermal demand and the power generation load demand as the total energy demand of the unit, with the goal of minimizing the total natural gas consumption. Through the optimization method, the thermal and power generation load demands of the thermal power plant are optimally allocated among each unit, so that the thermal power plant can achieve the lowest comprehensive energy consumption for power generation and heating and the best energy efficiency level under the same thermal and power generation load conditions. However, the existing patent obtains the operating parameters of the unit from the DCS system of the power plant, and fits the thermal performance curve of the unit according to the operating parameters. Only the ambient temperature is considered in the calculation, and only the minimum natural gas consumption is used as the objective function. At the same time, the upper and lower limits of the electric load and the heat supply are proposed, but no coupling constraints are given.

[0006] Therefore, when constructing a thermal power load optimization model, the prior art considers more from the perspective of minimizing the production cost of the unit. Even if the goal is to maximize production profit, the production cost considered is mainly the fuel cost, which is relatively simple. At the same time, when the prior art constructs a thermal power load optimization model for a gas-steam combined cycle cogeneration unit, the constraints are not considered comprehensively. For an industrial heating network with multiple pressure levels for heating, it is necessary to consider the thermal-electric coupling constraints and the coupling constraints between different pressure levels. Usually, when constructing the model, only the upper and lower limits of the heating supply and the power generation are considered, and the upper and lower limits of the constraints are fixed as constants, rather than a mathematical expression related to the optimization variable. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method for constructing a high-precision multi-source and multi-pressure industrial heating network full-operating condition thermal power load optimization model.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to one aspect of the present invention, a method for constructing a full-operating-condition thermal power load optimization model for a multi-source and multi-pressure industrial heating network is provided, the method comprising the following steps:

[0010] Step 101, based on the performance test data of the combined cycle unit under all operating conditions, a thermal performance technical model of the heat source unit is established, including a calculation model of the heat rate of the gas turbine and the power of the steam turbine;

[0011] Step 102, establishing the variable cost of the heat source unit and the calculation method of the production income of the heat source unit, and according to the deep peak-shaving market rules, reasonably assuming the boundary parameters of the deep peak-shaving market, establishing the shared costs of the units after the deep peak-shaving market is started, and establishing the production profit calculation model of the heat source unit as the objective function of thermal power load optimization;

[0012] Step 103, establishing the constraints of the optimization variables, giving the equality constraints and the inequality constraints, using the setting method to decouple the coupling constraints between the heat supply and the power generation, and the coupling constraints between the heat supplies of different pressure levels, and establishing the mathematical expression of the inequality constraints;

[0013] Step 104, performing optimization calculations through a thermal power load optimization model to obtain an operation plan with the best production profit.

[0014] As a preferred technical solution, the calculation model of the heat rate of the gas turbine and the steam turbine power in step 101 is established as follows:

[0015] The input variables of the gas turbine heat rate calculation model are gas turbine power, ambient temperature, atmospheric pressure and relative humidity; the input variables of the steam turbine power calculation model are gas turbine power, ambient temperature, atmospheric pressure, relative humidity, high-reduction heat supply, high-extraction heat supply and medium-extraction heat supply, which can be expressed as:

[0016]

[0017]

[0018] in is the heat rate of the jth combined cycle unit gas turbine, g j () is the calculation model of the heat rate of the gas turbine of the jth combined cycle unit, is the electrical power of the jth combined cycle unit gas turbine, T is the ambient temperature, P is the atmospheric pressure, and θ is the ambient humidity;

[0019] is the steam turbine power of the jth combined cycle unit, f j () is the power calculation model of the steam turbine of the jth combined cycle unit, Provide heat for the jth combined cycle unit. Extract heat from the jth combined cycle unit, Provides high-pressure heat for the j-th combined cycle unit.

[0020] As a preferred technical solution, the unit production profit calculation model in step 102 includes a revenue calculation sub-model of the thermal power unit, a cost calculation sub-model of the thermal power unit, a deep peak-shaving market cost sharing calculation sub-model and a profit sub-model.

[0021] As a preferred technical solution, the income calculation sub-model of the thermal power unit is specifically:

[0022] The income of the power generation unit includes power supply income and heat supply income. The power supply income is calculated by the income generated by the gas turbine power supply and steam turbine power supply of the combined cycle unit. The unit price of heat supply is different according to different heat sources. Different heat sources should be distinguished and calculated separately. The mathematical expressions for each calculation are:

[0023] I=I E +I H

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Among them, I is the total income, I E is the power supply income, I H is the heating income, r1 is the power consumption rate of the plant, a i is the on-grid electricity price or heating unit price, Heating income for combined cycle units, Income from heating by gas boilers, Income from heating for coal-fired units, Provide high-pressure heat for coal-fired units. Medium pressure heat supply for coal-fired units, D F Provide heat for gas boilers, is the high-pressure return flow of the heating network, is the medium pressure return flow of the heating network, and r2 is the loss rate of the heating network.

[0030] As a preferred technical solution, the cost calculation sub-model of the thermal power unit is specifically:

[0031] The thermal power unit includes fuel cost, purchase of recycled steam cost, purchase of heat supply cost of coal-fired units and water production cost. The mathematical expression of each calculation is:

[0032] C=C f +C B +C R +C W

[0033]

[0034]

[0035]

[0036]

[0037] Among them, C is the total cost, C f is the fuel cost, including the natural gas cost consumed by each gas turbine and the natural gas cost consumed by the gas boiler, C B is the cost of purchasing heat from coal-fired units, C R C is the cost of purchasing recycled steam. W is the water production cost, a5 is the natural gas price, a6 is the unit price of steam supply from coal-fired units, a7: the unit price of steam return, a8: the unit price of water production, D F Provide heat for the fast furnace, α F It is the heating gas consumption rate of the fast furnace.

[0038] As a preferred technical solution, the deep peak load regulation market sharing cost calculation sub-model is specifically as follows:

[0039] The mathematical expression is as follows

[0040]

[0041] ξ j =U j / (U+U j )

[0042]

[0043] in, To share the cost of deep peak load regulation market, j is the apportionment ratio, P j is the unit capacity, is the average load rate of the power grid, the shared cost is a function of the gas turbine power and the steam turbine power, a1 is the on-grid electricity price, a9 is the deep peak load regulation market clearing price, U j is the excess power generation of unit j, U is the excess power generation of other units, is the average load of the power grid.

[0044] As a preferred technical solution, the profit sub-model is specifically:

[0045]

[0046] Among them; I is the total income, I E is the power supply income, I H is the heating income, C is the total cost, C f is the fuel cost, C B is the cost of purchasing heat from coal-fired units, C R C is the cost of purchasing recycled steam. W The cost of water production, Share the costs for the deep peak-shaving market.

[0047] As a preferred technical solution, the objective function of electric load optimization is MAX(P), and the optimization goal is to maximize the profit of the unit. The optimized variables include

[0048] Electric power of the gas turbine of the jth combined cycle unit

[0049] High-reduction heating supply of the jth combined cycle unit

[0050] High extraction heat supply of j-th combined cycle unit

[0051] Heat extraction from the jth combined cycle unit

[0052] Gas boiler heating capacity D F ;

[0053] High-pressure heating capacity of coal-fired units

[0054] Medium-pressure heating capacity of coal-fired units

[0055] As a preferred technical solution, the constraint conditions for establishing the optimization variables in step 103 are specifically:

[0056] The equality constraint is the high and medium pressure heating demand of the heating network. The sum of the high pressure heating supply and medium pressure heating supply of each heat source point is equal to the high and medium pressure demand of the heating network. The mathematical expression is:

[0057]

[0058]

[0059] in Provide high-pressure heat for coal-fired units. The high extraction heat supply of the jth combined cycle unit, The high-temperature reduction heating of the jth combined cycle unit, D F Gas boiler heat supply, Medium pressure heating capacity of coal-fired units, The heat extracted from the jth combined cycle unit, To meet the needs of high-voltage heating network, To meet the needs of medium-pressure heating network;

[0060] The inequality constraints include upper and lower limit constraints of electric load and heat load. For the optimized variable gas turbine electric load, there are minimum load and maximum load for stable operation of the unit; for the optimized variable coal-fired unit high and medium pressure heating, there are minimum heating supply and maximum heating supply constraints.

[0061] For the optimized variable combined cycle unit high-pressure extraction and medium-pressure extraction heat supply, there are minimum and maximum values ​​at different gas turbine loads. At the same time, the maximum and minimum values ​​of high-pressure reduction, high-pressure extraction and medium-pressure extraction are coupled with the steam turbine power. The mathematical expression of the inequality constraint is:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] in is the electrical power of the jth combined cycle unit gas turbine, are the upper and lower limits of the jth gas turbine power, The upper and lower limits of the medium-pressure heating capacity of coal-fired units. The upper and lower limits of high-pressure heat supply for coal-fired units. is the upper limit of high-pressure heat supply of the jth combined cycle unit, The upper and lower limits of the medium-pressure heating capacity of the j-th combined cycle unit.

[0068] As a preferred technical solution, the coupling constraints between the heating amounts at different pressure levels in step 103 are decoupled as follows:

[0069] According to the unit operation adjustment method, the lower limit of the unit's high extraction heat supply is adjusted to 0 through the extraction valve, so that the minimum value of the high extraction heat supply is 0. When the extraction valve is adjusted to the maximum, the maximum value of the high extraction heat supply is related to the gas turbine power and the high reduction heat supply. Through the full-condition test data analysis, the relationship between the maximum value of the high extraction heat supply and the gas turbine power and the high reduction heat supply is fitted. The mathematical expression of the maximum value of the high extraction heat supply is expressed as

[0070]

[0071] in is the electrical power of the jth combined cycle unit gas turbine, is the minimum load of high extraction heating for combined cycle units, γ i are the polynomial coefficients;

[0072] Using the full-condition performance test data, a calculation model for medium-pressure extraction heat supply is established to fit the boundary value of medium-pressure extraction heat supply. The mathematical expression is:

[0073]

[0074]

[0075] is the maximum and minimum load of the combined cycle unit steam turbine, χ i , i are the polynomial coefficients.

[0076] Compared with the prior art, the present invention has the following advantages:

[0077] 1) The decoupling method of the thermoelectric coupling constraints and the coupling constraints of different heating pressure levels proposed in the present invention is also based on the full-condition thermal performance test. At the same time, combined with the production process of the combined cycle unit, the important characteristic variables are grasped in the decoupling process, and reasonable simplification is performed to give mathematical expressions of the upper and lower limits of the constraints related to the optimization variables, which are used to establish the thermoelectric load optimization model and limit the optimization results within the operating area of ​​the unit.

[0078] 2) In the process of using profit as the objective function, the present invention uses profit as the objective function. Since the thermal power load optimization is a dynamic process carried out during the operation of the unit, the fixed cost does not change much after the unit operation mode changes. The variable cost in the unit production process is given priority and the influence of fixed cost is ignored, which simplifies the model. When calculating the heating income, the heating prices of different heat sources are different, and the heating price changes according to the fuel price, etc., which makes the optimization model more in line with the actual situation.

[0079] 3) The thermal power load optimization model proposed in the present invention takes into account factors of electricity marketization such as on-grid electricity price, fuel price, heating price and deep peak-shaving market. With the deepening of electricity marketization, the optimization results given by using the model to optimize the thermal power load of the heating network can provide decision-making basis for heating network operation managers to deal with the adverse factors brought about by electricity marketization, and bring greater economic benefits.

[0080] 4) The thermal performance calculation model proposed in the present invention is based on the thermal performance test data of the unit under all operating conditions. The data volume is large and the selected operating conditions are relatively comprehensive. Therefore, the thermal performance calculation model has high accuracy and can be used as a basis for technical upgrading and transformation of the heat source side. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 Flow chart for implementing the present invention;

[0082] Figure 2 It is a schematic diagram of the production system of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0084] The present invention combines the full-operation thermal performance test data of the unit and proposes a method for constructing a thermal power load optimization model for a multi-source and multi-pressure industrial heating network. The thermal power load optimization model established by this method introduces factors related to the deep peak-shaving market, energy prices, and on-grid electricity prices. Under the deep peak-shaving market mechanism and the power market environment, combined with the thermal power load allocation algorithm, it provides guidance for the operation decision-making of the multi-source and multi-pressure industrial heating network. The thermal power load optimization model established by the present invention provides an effective method for decoupling the thermal power coupling constraints and the multi-pressure heat supply coupling constraints when constructing the optimization model of the gas turbine combined cycle cogeneration unit, ensuring that the obtained optimization solution result is within the operable area of ​​the unit.

[0085] like Figure 1 As shown, the specific process of the present invention is as follows:

[0086] Firstly, according to the performance test data of the combined cycle unit under all operating conditions, the calculation model of gas turbine heat rate and steam turbine power is established.

[0087] Secondly, establish the variable costs of the unit, including fuel costs, water production costs and heat purchase costs, as well as the calculation method of the unit production income, including power supply profit and steam supply profit. Then, based on the rules of the deep peak-shaving market, reasonably assume the boundary parameters of the deep peak-shaving market, and establish the unit's shared costs after the deep peak-shaving market is started. Based on this, the unit production profit calculation model is used as the objective function of thermal power load optimization.

[0088] Then, the constraints of the optimization variables are established, equality constraints and inequality constraints are given, the setting method is used to decouple the coupling constraints between the heat supply and the power generation, and the coupling constraints between the heat supplies of different pressure levels, and the mathematical expression of the inequality constraints is established.

[0089] Finally, the optimization calculation is performed through the thermal power load optimization algorithm to obtain the operation plan with the best production profit.

[0090] like Figure 2 As shown in the figure, for a certain industrial heat network, there are two heat sources, including two gas turbine combined heat and power combined cycle units, two extraction condensing heating coal-fired units and gas boilers, with two pressure levels of high and medium pressure. In this industrial heat network, the combined cycle unit is the main heat source, the coal-fired unit is a supplementary heat source, and its heat supply accounts for a small proportion of the boiler evaporation capacity, so its electrical load is not allocated, and the gas boiler is a backup heat source, which is activated when other units are under maintenance. In addition, users in the industrial heat network can also supply part of the steam back. The combined cycle unit is a double-axis arrangement, and the heating mode includes the new steam of the waste heat boiler through temperature reduction and pressure reduction heating (referred to as "high-pressure heating"), high-pressure extraction steam heating (referred to as "high-pressure heating") and medium-pressure extraction steam heating (referred to as "medium-pressure heating"). Then, considering the fuel price, the on-grid electricity price, the shared expenses generated by the deep peak-shaving market and other factors, the specific steps of the thermal power load optimization model are as follows.

[0091] 1. Establishment of thermal performance model of combined cycle unit. Based on the full-operating thermal performance test data of two combined cycle units in different seasons, different gas turbine loads and different heating combinations, the gas turbine heat rate and steam turbine power calculation model are constructed. The input variables of the gas turbine heat rate calculation model are gas turbine power, ambient temperature, atmospheric pressure and relative humidity; the input variables of the steam turbine power calculation model are gas turbine power, ambient temperature, atmospheric pressure, relative humidity, high-reduction heating supply, high-extraction heating supply and medium-extraction heating supply, which can be expressed as:

[0092]

[0093]

[0094] 2. Calculation of income of thermal power units. The income of thermal power units includes power supply income and heat supply income. The power supply income is calculated from the income generated by the gas turbine power supply and steam turbine power supply of the combined cycle unit. Usually, the heating unit price is different according to different heat sources. Different heat sources should be distinguished and calculated separately. The mathematical expressions for each calculation are:

[0095] I=I E +I H

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] Among them, I is the total income, I E is the power supply income, I H is the heating income, r1 is the power consumption rate of the plant, a i is the on-grid electricity price or heating unit price, Heating income for combined cycle units, Income from heating by gas boilers, Income from providing heating for coal-fired units.

[0102] 3. Cost calculation of thermal power units. The cost of thermal power units includes fuel cost, cost of purchasing recycled steam, cost of purchasing heat supply from coal-fired units and water production cost. The mathematical expressions for each calculation are:

[0103] C=C f +C B +C R +C W

[0104]

[0105]

[0106]

[0107]

[0108] Among them, C is the total cost, C f is the fuel cost, including the natural gas cost consumed by each gas turbine and the natural gas cost consumed by the gas boiler, C B is the cost of purchasing heat from coal-fired units, C R C is the cost of purchasing recycled steam. W The cost of water production.

[0109] 4. Calculation of deep peak load regulation market sharing costs. This cost is generated after the deep peak load regulation market mechanism is activated and is related to the load rate of the unit. Its mathematical expression is as follows

[0110]

[0111] ξ j =U j / (U+U j )

[0112]

[0113] in, To share the cost of deep peak load regulation market, j is the apportionment ratio, P j is the unit capacity, is the average load rate of the power grid. It can be seen that the shared cost is a function of the gas turbine power and the steam turbine power.

[0114] 5. Establish a profit model. The mathematical expression of the profit model is:

[0115]

[0116] Therefore, the objective function of thermal power load optimization is MAX(P), and the optimization goal is to maximize the profit of the unit.

[0117] The optimized variables include

[0118] The electrical power of the gas turbine of the jth combined cycle unit, here 2, can be N;

[0119] The high-reduction heating capacity of the jth combined cycle unit is 2 here, and can be N;

[0120] The jth combined cycle unit provides high-pressure heat, here 2 units, it can be N units;

[0121] The heat extracted from the jth combined cycle unit is 2 here, but can be N;

[0122] D F The gas boiler supplies heat, here 1, can be N;

[0123] High-pressure heating capacity of coal-fired units, here 1 unit, can be N units;

[0124] The medium-pressure heating capacity of the coal-fired units, here 1 unit, can be N units.

[0125] 6. Constraints are established. The equality constraint is the high and medium pressure heating demand of the heating network. The sum of the high pressure heating and medium pressure heating of each heat source point is equal to the high and medium pressure demand of the heating network. The mathematical expression is

[0126]

[0127]

[0128] The inequality constraints include upper and lower limit constraints of electric load and thermal load. For the optimized variable gas turbine electric load, there are minimum load and maximum load at which the unit can operate stably; for the optimized variable coal-fired unit high and medium pressure heating capacity, there are minimum heating capacity and maximum heating capacity constraints. The upper and lower limit constants of the above two types of inequality constraints.

[0129] For the optimized variable combined cycle unit high-pressure extraction and medium-pressure extraction heat supply, there are minimum and maximum values ​​at different gas turbine loads. At the same time, the maximum and minimum values ​​of high-pressure reduction, high-pressure extraction and medium-pressure extraction are coupled with the steam turbine power. The mathematical expression of the inequality constraint is:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] 7. Decoupling of heat supply inequality constraints. As mentioned above, there is a coupling relationship between the upper and lower limits of the heat supply of the combined cycle cogeneration unit and the gas turbine power and steam turbine load. There is a coupling relationship between the upper and lower limits of the heat supply of each pressure level. The gas turbine power and the heat supply of each heat source are optimization variables. In order to optimize the thermal power load within the unit's operating area, these coupling relationships need to be decoupled. The specific method is as follows.

[0136] According to the unit operation adjustment mode, the lower limit of the unit's high extraction heat supply can be adjusted to 0 through the extraction valve, so that the minimum value of the high extraction heat supply is 0. When the extraction valve is adjusted to the maximum, the maximum value of the high extraction heat supply is related to the gas turbine power and the high reduction heat supply. Through the full-condition test data analysis, the relationship between the maximum value of the high extraction heat supply and the gas turbine power and the high reduction heat supply can be fitted. The mathematical expression of the maximum value of the high extraction heat supply is expressed as

[0137]

[0138] Theoretically, when the combined cycle unit's gas turbine power, high-pressure reduction, and high-pressure extraction heat supply remain unchanged, the turbine power depends on the medium-pressure extraction heat supply. When the medium-pressure extraction heat supply increases, the turbine power decreases. For the turbine, there is a minimum steam intake requirement to ensure stable operation of the turbine; an increase in the turbine steam intake or exhaust volume increases the condenser load, which will lead to an increase in exhaust back pressure. Therefore, the maximum and minimum values ​​of the turbine power correspond to the minimum and maximum values ​​of the steam extraction volume.

[0139] Using the full-condition performance test data, a calculation model for medium-pressure extraction heat supply is established to fit the boundary value of medium-pressure extraction heat supply. The mathematical expression is:

[0140]

[0141]

[0142] 8. The constructed thermal power load optimization model is used in combination with thermal power load optimization algorithms such as linear regression, particle swarm algorithm or genetic algorithm to solve the problem. When processing the heat supply constraint in the algorithm, the high-voltage heat supply equation constraint is satisfied first, and then the medium-voltage heat supply equation constraint is satisfied, so as to find the operating condition with the best profit and assist the production and operation decision-making of the power plant. Specific embodiments

[0144] The thermal power load optimization model construction method proposed in the present invention is applied to the above-mentioned industrial heating network, and combined with the particle swarm algorithm, the thermal power load optimization calculation is performed. According to the annual average load of the unit operation, a benchmark operating condition is selected, and the profit difference between the benchmark operating condition and the optimized operating condition under different combinations of grid-connected electricity prices and gas prices is compared. Among them, the grid-connected electricity price of optimized operating condition 1 is high and the gas price is low; the grid-connected electricity price of optimized operating condition 2 is low and the gas price is low; the grid-connected electricity price of optimized operating condition 3 is low and the gas price is high.

[0145]

[0146] It can be seen from the calculation results that the optimized operating conditions have higher profits than the benchmark operating conditions; after using profit as the objective function, the optimized operating conditions are different under different on-grid electricity prices and fuel prices.

[0147] For the coupling processing of constraints, it can be seen from the calculation results of "Optimization Condition 2" that when the load of No. 1 gas turbine decreases, the high-extraction heating capacity also decreases, and the optimization result is 46t / h. At the same time, after the high-extraction heating of No. 2 combined cycle unit is reduced, its high-extraction heating capacity is affected, and the optimization results are high-extraction heating capacity of 94t / h and high-extraction heating capacity of 0t / h.

[0148] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for constructing a full-condition thermal power load optimization model for a multi-source and multi-pressure industrial heating network, characterized in that: The method comprises the following steps: Step 101, based on the performance test data of the combined cycle unit under all operating conditions, a thermal performance technical model of the heat source unit is established, including a calculation model of the heat rate of the gas turbine and the power of the steam turbine; Step 102, establishing the variable cost of the heat source unit and the calculation method of the production income of the heat source unit, and according to the deep peak-shaving market rules, reasonably assuming the boundary parameters of the deep peak-shaving market, establishing the shared costs of the units after the deep peak-shaving market is started, and establishing the production profit calculation model of the heat source unit as the objective function of thermal power load optimization; Step 103, establishing the constraints of the optimization variables, giving the equality constraints and the inequality constraints, using the setting method to decouple the coupling constraints between the heat supply and the power generation, and the coupling constraints between the heat supplies of different pressure levels, and establishing the mathematical expression of the inequality constraints; Step 104, performing optimization calculations through a thermal power load optimization model to obtain an operation plan with the best production profit; The calculation model of the heat rate of the gas turbine and the power of the steam turbine in step 101 is established as follows: The input variables of the gas turbine heat rate calculation model are gas turbine power, ambient temperature, atmospheric pressure and relative humidity; the input variables of the steam turbine power calculation model are gas turbine power, ambient temperature, atmospheric pressure, relative humidity, high-reduction heat supply, high-extraction heat supply and medium-extraction heat supply, which can be expressed as: in is the heat rate of the jth combined cycle unit gas turbine, g j () is the calculation model of the heat rate of the gas turbine of the jth combined cycle unit, is the electrical power of the jth combined cycle unit gas turbine, T is the ambient temperature, P is the atmospheric pressure, and θ is the ambient humidity; is the steam turbine power of the jth combined cycle unit, f j () is the power calculation model of the steam turbine of the jth combined cycle unit, Provide heat for the jth combined cycle unit. Extract heat from the jth combined cycle unit, Provide high-pressure heating for the jth combined cycle unit; The constraint conditions for establishing the optimization variables in step 103 are specifically: The equality constraint is the high and medium pressure heating demand of the heating network. The sum of the high pressure heating supply and medium pressure heating supply of each heat source point is equal to the high and medium pressure demand of the heating network. The mathematical expression is: in Provide high-pressure heat for coal-fired units. The high extraction heat supply of the jth combined cycle unit, The high-temperature reduction heating of the jth combined cycle unit, D F Gas boiler heat supply, Medium pressure heating capacity of coal-fired units, The heat extracted from the jth combined cycle unit, To meet the needs of high-voltage heating network, To meet the needs of medium-pressure heating network; The inequality constraints include upper and lower limit constraints of electric load and heat load. For the optimized variable gas turbine electric load, there are minimum load and maximum load for stable operation of the unit; for the optimized variable coal-fired unit high and medium pressure heating, there are minimum heating supply and maximum heating supply constraints. For the optimized variable combined cycle unit high-pressure extraction and medium-pressure extraction heat supply, there are minimum and maximum values ​​at different gas turbine loads. At the same time, the maximum and minimum values ​​of high-pressure reduction, high-pressure extraction and medium-pressure extraction are coupled with the steam turbine power. The mathematical expression of the inequality constraint is: in is the electrical power of the jth combined cycle unit gas turbine, are the upper and lower limits of the jth gas turbine power, The upper and lower limits of the medium-pressure heating capacity of coal-fired units. The upper and lower limits of high-pressure heat supply for coal-fired units. is the upper limit of high-pressure heat supply of the jth combined cycle unit, is the upper and lower limits of the medium-pressure heat supply of the jth combined cycle unit, Provide high-pressure heat for coal-fired units. Provide medium-pressure heat for coal-fired units. For the high-pressure return flow of the heat network, It is the medium pressure return flow of the heating network.

2. The construction method according to claim 1, characterized in that: The unit production profit calculation model in step 102 includes a revenue calculation sub-model of the thermal power unit, a cost calculation sub-model of the thermal power unit, a deep peak-shaving market cost sharing calculation sub-model and a profit sub-model.

3. The construction method according to claim 2, characterized in that: The income calculation sub-model of the thermal power unit is specifically as follows: The income of the power generation unit includes power supply income and heat supply income. The power supply income is calculated by the income generated by the gas turbine power supply and steam turbine power supply of the combined cycle unit. The unit price of heat supply is different according to different heat sources. Different heat sources should be distinguished and calculated separately. The mathematical expressions for each calculation are: Among them, I is the total income, I E is the power supply income, I H is the heating income, r1 is the power consumption rate of the plant, a i is the on-grid electricity price or heating unit price, Heating income for combined cycle units, Income from heating with gas boilers, Income from heating for coal-fired units, Provide high-pressure heat for coal-fired units. Medium pressure heat supply for coal-fired units, D F Provide heat for gas boilers, is the high-pressure return flow of the heat network, is the medium pressure return flow of the heating network, and r2 is the loss rate of the heating network.

4. The construction method according to claim 2, characterized in that: The cost calculation sub-model of the thermal power unit is specifically as follows: The thermal power unit includes fuel cost, purchase of recycled steam cost, purchase of heat supply cost of coal-fired units and water production cost. The mathematical expression of each calculation is: C=C f +C B +C R +C W Among them, C is the total cost, C f is the fuel cost, including the natural gas cost consumed by each gas turbine and the natural gas cost consumed by the gas boiler, C B is the cost of purchasing heat from coal-fired units, C R C is the cost of purchasing recycled steam. W is the water production cost, a5 is the natural gas price, a6 is the unit price of steam supply from coal-fired units, a7: the unit price of steam return, a8: the unit price of water production, D F Provide heat for the fast furnace, α F is the heating gas consumption rate of the fast furnace, Provide high-pressure heat for coal-fired units. Provide medium-pressure heat for coal-fired units. For the high-pressure return flow of the heat network, It is the medium pressure return flow of the heating network.

5. The construction method according to claim 2, characterized in that: The sub-model for calculating the deep peak-shaving market cost sharing is as follows: The mathematical expression is as follows ξ j =U j / (U+U j ) in, To share the cost of deep peak load regulation market, j is the apportionment ratio, P j is the unit capacity, is the average load rate of the power grid, the shared cost is a function of the gas turbine power and the steam turbine power, a1 is the on-grid electricity price, a9 is the deep peak load regulation market clearing price, U j is the excess power generation of unit j, U is the excess power generation of other units, is the average load of the power grid.

6. The construction method according to claim 2, characterized in that: The profit sub-model is specifically: in; I is the total income, I E is the power supply income, I H is the heating income, C is the total cost, C f is the fuel cost, C B is the cost of purchasing heat from coal-fired units, C R C is the cost of purchasing recycled steam. W The cost of water production, Share the costs for the deep peak-shaving market.

7. The construction method according to claim 6, characterized in that: The objective function of the electric load optimization is MAX(P). The optimization goal is to maximize the profit of the unit. The optimized variables include Electric power of the gas turbine of the jth combined cycle unit High-reduction heating supply of the jth combined cycle unit High extraction heat supply of j-th combined cycle unit Heat extraction from the jth combined cycle unit Gas boiler heating capacity D F ; High-pressure heating capacity of coal-fired units Medium-pressure heating capacity of coal-fired units 8. The construction method according to claim 1, characterized in that: The decoupling of the coupling constraints between the heating amounts at different pressure levels in step 103 is specifically as follows: According to the unit operation adjustment method, the lower limit of the unit's high extraction heat supply is adjusted to 0 through the extraction valve, so that the minimum value of the high extraction heat supply is 0. When the extraction valve is adjusted to the maximum, the maximum value of the high extraction heat supply is related to the gas turbine power and the high reduction heat supply. Through the full-condition test data analysis, the relationship between the maximum value of the high extraction heat supply and the gas turbine power and the high reduction heat supply is fitted. The mathematical expression of the maximum value of the high extraction heat supply is expressed as in is the electrical power of the gas turbine of the jth combined cycle unit, is the minimum load of high extraction heating for combined cycle units, γ i are the polynomial coefficients; Using the full-condition performance test data, a calculation model for medium-pressure extraction heat supply is established to fit the boundary value of medium-pressure extraction heat supply. The mathematical expression is: is the maximum and minimum load of the combined cycle unit steam turbine, χ i , i are the polynomial coefficients, The high extraction heat supply of the jth combined cycle unit, The high-reduction heating capacity of the jth combined cycle unit.

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