New energy base coal and electricity deep adjustment transformation planning method and device and storage medium

Abstract

The invention discloses a new energy base coal and electricity deep adjustment transformation planning method and device and a storage medium, and the method comprises the steps: building an energy equipment model according to the actual wind, light and electricity resource conditions of a new energy base, and giving out the wind, light and fire storage and combustion constraint conditions according to the model; constructing a carbon dioxide emission model and a carbon emission cost model after deep adjustment and transformation of the coal power unit by using a particle swarm optimization algorithm, and constructing a capacity transformation scheme of coal power deep adjustment of the new energy base by taking the minimum sum of investment, operation and carbon emission cost of wind-light-fire-storage gas turbine equipment as a target and considering constraint conditions; according to the European Union carbon market development technology, future carbon policies are predicted, predicted values of the carbon quotas and the carbon prices are obtained, the predicted values are substituted into calculation of the carbon emission cost again, changes of capacity configuration of the new energy base under different carbon quotas and carbon prices are obtained, and transformation and optimization of coal power deep adjustment of the new energy base under different carbon policies are achieved. According to the method, the environmental protection property and the carbon dioxide emission cost of the new energy base can be calculated more accurately.

Description

Technical Field

[0001] The present invention belongs to the field of planning of new energy bases, and relates to a method and a device for optimizing deep adjustment and transformation of coal-fired power in new energy bases under the influence of long-term dynamic changes in carbon quotas. Background Art

[0002] The Northwest region is rich in wind and solar resources. In recent years, the construction of new energy bases has significantly increased the peak-shaving demand of the power system. In the context of a high proportion of thermal power installed capacity, deep regulation of thermal power can reduce the minimum output of the unit, increase the ramp rate of the unit, and reduce the start and stop time of the unit, so that thermal power can fully respond to the volatility of the power system and greatly improve the peak-shaving capacity. However, global control of carbon emissions is becoming increasingly stringent, and countries have set corresponding carbon emission targets. After deep regulation of thermal power, carbon emissions will increase significantly. Therefore, how to plan new energy bases while considering the carbon budget has become a problem worthy of study. Existing technologies, such as the author: Lin Li, Zhou Longwen, Zheng Xinyao. "Low-carbon economic dispatch of deep peak regulation of pumped storage assisted coal-fired units taking into account the carbon emission characteristics of the units" (Journal of North China Electric Power University (Natural Science Edition), 2024, 51(05): 1-14 pages), however, the existing technologies have the following problems: the existing technical solutions use the solver Cplex for solution, which requires piecewise linearization of carbon emissions, resulting in inaccurate calculation of carbon emissions and carbon emission costs. However, under the premise of the gradual tightening of carbon policies, the use of solvers to linearize the cost and carbon emissions after deep regulation of thermal power will cause a large deviation between cost calculation and actual situation, thus failing to accurately plan the capacity of wind, solar, thermal and storage. Summary of the invention

[0003] In order to solve the defects in the prior art, the present invention discloses a method, and its technical solution is as follows:

[0004] The planning method for deep adjustment and transformation of coal-fired power in new energy bases is characterized by:

[0005] According to the actual wind and solar power resource conditions of the new energy base, an energy equipment model is established, and the wind, solar, thermal, storage and combustion constraints are given based on the energy equipment model;

[0006] The particle swarm algorithm is used to construct a carbon emission cost model after deep adjustment and transformation of coal-fired power units. The goal is to minimize the sum of investment, operation and carbon emission costs of wind, solar, thermal and storage gas turbine equipment, and take into account the constraints of wind, solar, thermal and storage to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases.

[0007] Make forecasts for future carbon policies and obtain predicted values ​​of carbon quotas and carbon prices. Substitute the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep regulation of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep regulation under different carbon policies of new energy bases.

[0008] Preferably, the energy equipment model includes the following contents: the output power of the wind power generation equipment in a certain period of time It is represented by the following model:

[0009]

[0010] Where W represents the wind turbine, V t represents the actual wind speed at time t, V r Indicates rated wind speed, r means rated, P rW Represents the rated power of a single wind turbine, rW represents the rated value of the wind turbine unit, Indicates the maximum wind power output, V in Indicates the aerodynamic performance leading to the cut-in wind speed, V out Indicates the minimum wind speed requirement and cut-out wind speed for the unit to be connected to the grid for power generation; the photovoltaic power generation output power is related to the light intensity. The photovoltaic power generation output power in a certain period of time It can be expressed as:

[0011]

[0012] In the formula, PV represents photovoltaic unit, A PV represents the area of ​​photovoltaic panels, η PV Indicates the efficiency under standard test conditions, E t represents the average light intensity at time t, Represents the maximum output of photovoltaic power; the mathematical model expression of electric energy storage is:

[0013]

[0014] 0≤E BESS ≤1 (58)

[0015]

[0016] 0≤τ cha +τ dis ≤1 (61)

[0017] In the formula, E t and E t-1 They represent the amount of electricity stored in the energy storage device at time t and time t-1 respectively, E BESSrepresents the percentage of power, Δt represents the time from time t-1 to time t, cha represents energy storage charging, and dis represents energy storage discharging. Respectively represent the charging and discharging power of the energy storage at time t, η cha , η dis They represent the charging and discharging efficiency of the energy storage, They represent the maximum single charge and discharge power of energy storage, S represents the maximum capacity of electrical energy storage; τ cha represents the charging state of the energy storage, τ dis Indicates the discharge state of the energy storage, which is a 0-1 variable;

[0018] After deep regulation of coal-fired power, it is divided into three stages: conventional peak regulation, deep non-combustion peak regulation and deep combustion peak regulation. a is the minimum output of the unit during the conventional peak load regulation phase, P b is the minimum stable combustion output of the unit in the deep non-combustion peak-shaving stage, P c It is the stable combustion limit output of the unit in the deep combustion peak regulation stage; its mathematical model is as follows:

[0019]

[0020]

[0021] Where U i,t Indicates the start / stop status of the i-th unit at time t, which is a 0-1 variable. is the actual start-up and shutdown time of the unit, T ion is the minimum startup time of the unit, T loff is the minimum downtime of the unit, is the output of coal-fired power unit i at time t, α iup , α idown represents the maximum upward and downward climbing rate of the coal-fired power unit, α istart , α ishut They are the start-up and shutdown times of coal-fired power units, is the actual output, maximum output and minimum output of coal-fired power unit i at time t.

[0022] Preferably, the establishing of the energy equipment model includes the following contents:

[0023] The objective function is expressed as:

[0024]

[0025] Where, F is the daily operating cost of the new energy base, F con 、F operatingThey are the investment cost and operation and maintenance cost of wind power, photovoltaic power, coal power, energy storage, and gas turbines. Cost of CO2 emissions from coal-fired power plants and gas turbines;

[0026] The specific investment cost is calculated as follows:

[0027]

[0028] In the formula, w tx is the discount rate, w smi is the estimated service life of device i, N is the total number of devices, f is the annual value coefficient of the capital of the i-th device, C i is the capacity of the i-th device, G i is the unit purchase cost of the i-th equipment;

[0029] The specific operation and maintenance cost is calculated as follows:

[0030]

[0031] In the formula, F i,t , They are the total cost, coal consumption cost, wear cost, and oil input cost of coal-fired unit i at time t, specifically:

[0032]

[0033] In the formula, F grespair is the maintenance cost of thermal power units, F goperating Operation and maintenance costs of thermal power units;

[0034] Total operation and maintenance cost F operating as follows:

[0035] F operating =F goperating +F gasoperating +F woperating +F pvoperating +F batoperating (72)

[0036] In the formula, F gasoperating is the operation and maintenance cost of the gas turbine, F woperating is the operation and maintenance cost of the wind turbine, F pvoperating is the operation and maintenance cost of the photovoltaic unit, F batoperating is the operation and maintenance cost of energy storage;

[0037] The specific carbon emission cost is calculated as follows:

[0038] Specific carbon emissions from thermal power generation The calculation is as follows:

[0039]

[0040] Where M i,coal Represents carbon emissions mainly caused by coal combustion; M i,aux The increase in carbon emissions is caused by the decrease in boiler combustion efficiency, the decrease in turbine blade efficiency, the increase in mechanical friction loss, and the additional operation of auxiliary devices; M i,sn In order to consider the increased carbon emissions caused by desulfurization and denitrification, P i,t Indicates the actual output of the coal-fired unit, P i,max Indicates the maximum output of the thermal power unit, P i,a , P i,b , P i,c Indicates that the output of the coal-fired unit is at the minimum output of the 1st, 2nd and 3rd stages;

[0041] The specific carbon emission costs of thermal power and gas turbines are calculated as follows:

[0042]

[0043] In the formula, represents the carbon emission cost of thermal power units, the carbon emission cost of gas turbines, and the total carbon emission cost, k c is the carbon trading price, P gN , P gasN Indicates the rated output of thermal power units and gas turbines, ψ g is the carbon emission intensity quota value of thermal power units, ψ gas is the carbon emission intensity quota value of gas turbines, is the amount of carbon dioxide emitted by the gas turbine.

[0044] Preferably, the prediction of future carbon policies, obtaining predicted values ​​of carbon quotas and carbon prices, substituting the predicted values ​​into the calculation of carbon emission costs, and realizing the transformation and optimization of deep adjustment of coal-fired power in new energy bases include the following contents:

[0045] For carbon quota ψ and carbon price k c To make a prediction, the formula is as follows:

[0046] ψ=0.0001(year-2024) 3 -0.0004(year-2024) 2 -0.0860 (77)

[0047] k c =-0.0884(year-2024) 3 +2.1212(year-2024) 2 +35.202(year-2024)+77.8788 (78)

[0049] By updating the values ​​of carbon quotas and carbon prices, recalculating the cost of carbon dioxide emissions, and replanning the capacity configuration of new energy bases, a new energy base planning method that takes carbon budgets into consideration is obtained.

[0050] The present invention also discloses a non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above method.

[0051] The present invention also discloses a new energy base coal-fired power deep adjustment and transformation planning device, which is characterized by comprising:

[0052] Establish a module, establish an energy equipment model according to the actual wind and solar power resource conditions of the new energy base, and give wind, solar, thermal, storage and combustion constraints based on the energy equipment model;

[0053] The construction module uses the particle swarm algorithm to construct a carbon emission cost model for coal-fired power units after deep adjustment and transformation. The goal is to minimize the sum of wind, solar, thermal, and storage gas turbine equipment investment, operation, and carbon emission costs, and consider the wind, solar, thermal, and storage constraints to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases.

[0054] The optimization module predicts future carbon policies and obtains the predicted values ​​of carbon quotas and carbon prices. The predicted values ​​are substituted into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases.

[0055] Beneficial Effects

[0056] The present invention uses particle swarm algorithm to finely characterize the carbon emissions caused by deep adjustment of coal-fired power generation, so that the environmental protection of new energy bases and the cost of carbon dioxide emissions can be calculated more accurately. At the same time, the technology of the present invention predicts the capacity changes of various power generation equipment in the future of new energy bases based on the prediction of the future carbon market, and realizes the transformation and optimization of deep adjustment of coal-fired power generation in new energy bases. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Carbon emission curve of thermal power units;

[0058] Figure 2 Gas turbine carbon emissions graph;

[0059] Figure 3 Output bar graphs in different scenarios;

[0060] Figure 4 Histogram of total cost in different scenarios;

[0061] Figure 5Carbon price and quota forecast curve;

[0062] Figure 6 Histogram of the cost composition of thermal power generation under different scenarios;

[0063] Figure 7 Schematic diagram of capacity configuration changes under carbon policy changes. DETAILED DESCRIPTION

[0064] The purpose of the present invention is to configure the capacity of the new energy base according to the changes in carbon emission policies. The new energy base model includes constraints such as the ramp constraints of thermal power and the minimum on / off time constraints. On the basis of this model, taking into account the changes in the minimum output, ramp rate, and minimum start / stop time after deep regulation of thermal power, a carbon emission model after deep regulation of thermal power is proposed. In order to solve the limitation that the linearization of the carbon emission model after deep regulation of traditional thermal power leads to the deviation of the results from reality, the present invention uses a particle swarm algorithm to calculate the model indicators.

[0065] Example 1

[0066] The planning method for deep adjustment and transformation of coal-fired power in new energy bases includes the following steps:

[0067] According to the actual wind, solar and electricity resource conditions of the new energy base, an energy equipment model is established, and the wind, solar, thermal and storage combustion constraints are given based on the energy equipment model.

[0068] Wind turbines and photovoltaic power generation equipment are installed at new energy bases. Deep adjustment and transformation of coal-fired power is carried out to ensure that the minimum output and ramp rate are adapted to the fluctuations of new energy, and energy storage and gas turbines are installed for peak regulation.

[0069] Wind speed directly determines the size of wind power output. Considering that the wind power generation system can be connected to the grid, the cut-in wind speed V is derived from the aerodynamic performance. in ——Minimum wind speed requirement and cut-out wind speed V for the unit to be connected to the grid for power generation out ——The maximum wind speed limit at which the unit can be connected to the grid for power generation, so the output power of the wind power generation equipment in a certain period of time t It can be represented by the following model:

[0070]

[0071] Where W represents wind power equipment, V t Indicates the actual wind speed at the moment, V r represents the rated wind speed, r represents the rated state, P rW Indicates the rated power of a single wind turbine. (30) Indicates the maximum output limit of wind power. Indicates the maximum output of the wind turbine.

[0072] The output power of photovoltaic power generation is related to the light intensity. At a certain moment t, the output power of photovoltaic power generation is It can be expressed as:

[0073]

[0074] Where: PV represents photovoltaic equipment, A PV represents the area of ​​photovoltaic panels, η PV Indicates the efficiency under standard test conditions, E t represents the average light intensity at time t. (32) indicates that wind power cannot exceed the maximum output limit. Indicates the maximum output of wind power.

[0075] The commonly used mathematical model expression of electric energy storage is:

[0076]

[0077] 0≤τ cha +τ dis ≤1 (87)

[0078] In the formula, E t and E t-1 Respectively represent the amount of electricity stored in the energy storage device at time t and time t-1, represents the percentage of power, Δt represents the time from time t-1 to time t, cha represents energy storage charging, and dis represents energy storage discharging. Respectively represent the charging and discharging power of the energy storage at time t, η cha , η dis They represent the charging and discharging efficiency of the energy storage, They represent the maximum single charge and discharge power of energy storage, S represents the maximum capacity of electrical energy storage; τ cha represents the charging state of the energy storage, τ dis Indicates the discharge state of the energy storage, which is a 0-1 variable;

[0079] After deep regulation of coal-fired power, it is divided into three stages: conventional peak regulation, deep non-combustion peak regulation and deep combustion peak regulation. a is the minimum output of the unit during the conventional peak load regulation phase, P b is the minimum stable combustion output of the unit in the deep non-combustion peak-shaving stage, P c It is the stable combustion limit output of the unit in the deep combustion-supporting peak-shaving stage. Its mathematical model is as follows:

[0080]

[0081] Where U i,t Indicates the start / stop status of the i-th unit at time t, which is a 0-1 variable. is the actual start-up and shutdown time of the unit, Tion is the minimum startup time of the unit, T ioff is the minimum downtime of the unit, is the output of coal-fired power unit i at time t, α iup , α idown represents the maximum upward and downward climbing rate of the coal-fired power unit, α istart , α ishut They are the start-up and shutdown times of coal-fired power units, are the maximum and minimum outputs of coal-fired power unit i at time t. (38)-(42) represent the operating requirements of coal-fired power. The operating requirements of gas turbines are similar to those of coal-fired power.

[0082] The particle swarm algorithm is used to construct a carbon emission cost model after deep regulation and transformation of coal-fired power units. The goal is to minimize the sum of investment, operation and carbon emission costs of wind, solar, thermal and storage gas turbine equipment, and the constraints of wind, solar, thermal and storage are taken into account to construct a capacity transformation model for deep regulation of coal-fired power in new energy bases.

[0083] The specific objective function is expressed as:

[0084]

[0085] In the formula, F represents the daily operating cost of the new energy base, F con 、F operating They are the investment cost and operating cost of wind power, photovoltaic power, coal power, energy storage, and gas turbines. CO2 emission costs for coal-fired power plants and gas turbines.

[0086] The specific investment cost is calculated as follows:

[0087] Formula (44)~(45)

[0088]

[0089] In the formula, w tx is the discount rate, w smi is the expected service life of device i, f i is the annual capital value coefficient of the i-th equipment, C i is the capacity of the i-th device, G i is the unit purchase cost of the i-th device.

[0090] The specific operation and maintenance cost is calculated as follows:

[0091] Formula (46)~(50), Formula (51)~(54), Formula (55)~(58)

[0092] The operation and maintenance cost of thermal power is calculated as follows:

[0093] Formula (46)~(50)

[0094]

[0095] In the formula, F i,t , They are respectively the operating cost, coal consumption cost, wear cost, and oil input cost of coal-fired unit i at time t, specifically:

[0096]

[0097] Where a, b, c are the unit consumption characteristic function coefficients, S coal is the unit coal price of the season. gi,t ) is the rotor cracking cycle, δ is the rotor wear coefficient, S u is the purchase cost, P gi,t is the output of coal-fired unit i at time t, E cost is the amount of oil required for oil injection, S oil For oil prices.

[0098]

[0099] In the formula, F grespair is the maintenance cost of coal-fired units, F goperating It is the operation and maintenance cost of coal-fired units.

[0100] Similar to thermal power units, the peak load regulation process of gas units can be divided into two stages: basic peak load regulation and deep peak load regulation. The operation and maintenance cost of gas turbines is calculated as follows:

[0101] Formula (51)~(54)

[0102]

[0103] In the formula, is the operating cost of gas turbine i at time t, is the gas cost of gas turbine i at time t, C loss is the unit life loss cost caused by the unit participating in a deep regulation, respectively

[0104]

[0105] C loss =d gas S gasunit (103)

[0106] In the formula, a r 、b r 、c r is the coefficient of the unit gas consumption characteristic function, S gas is the price of natural gas in that season, d gasis the unit life loss rate caused by deep adjustment, S gasunit The purchase cost of the gas turbine.

[0107]

[0108] In the formula, F gasres F is the gas turbine maintenance cost; gasoperating Gas turbine operation and maintenance costs.

[0109] The operation and maintenance costs of wind power, photovoltaic power, and energy storage are as follows:

[0110] Formula (55)~(57)

[0111]

[0112] In the formula, F woperating is the operation and maintenance cost of the wind turbine, F pvoperating is the operation and maintenance cost of the photovoltaic unit, F batoperating is the operation and maintenance cost of energy storage, S w , S pv , S bat is the corresponding unit operation and maintenance cost, P w , P pv , P bat Indicates the output of wind power, photovoltaic power and energy storage.

[0113] The total operation and maintenance costs are as follows:

[0114] F operating =F goperating +F gasoperating +F woperating +F pvoperating +F batoperating (108)

[0115] The specific carbon emission cost is calculated as follows:

[0116] Formula (59)~(62), Formula (63)~(65), Formula (66)~(68)

[0117] The specific diagram of thermal power carbon emissions is as follows: Figure 1 As shown, the calculation is as follows:

[0118] Formula (59)~(62)

[0119]

[0120]

[0121] Where M i,coal Represents carbon emissions mainly caused by coal combustion; M i,auxThe increase in carbon emissions is caused by the decrease in boiler combustion efficiency, the decrease in turbine blade efficiency, the increase in mechanical friction loss, and the additional operation of auxiliary devices; M i,sn To consider the increased carbon emissions due to the effects of desulfurization and denitrification. i , O F (P i ) are the coal consumption of power generation unit i and the corrected carbon oxidation rate, respectively; C c is the statistical quantity of carbon content in coal combustion; M C 、M S is the corresponding molar mass of carbon dioxide, carbon, and sulfur; P i,N is the rated operating power of coal-fired unit i; α is the proportion of energy consumption of auxiliary equipment; C q is the CO2 produced per unit of electricity; ζ is the combustion efficiency of the boiler, ζ(P i ),ζ(P i,N ) are the actual combustion efficiency and rated combustion efficiency of the boiler respectively; ε is the absolute internal efficiency of the steam turbine, ε(P i ),ε(P i,N ) is the actual internal efficiency and rated internal efficiency of the steam turbine; ρ s is the sulfur content of coal; Δη s is the desulfurization efficiency; M s is the molar mass of sulfur; α s , γ N They are the energy consumption ratios of desulfurization and denitrification respectively.

[0122] The specific carbon emissions diagram of gas turbines is as follows: Figure 2 As shown, the calculation is as follows:

[0123] Formula (63)~(65)

[0124]

[0125] M i,gasaux =H gasi ×(1-ζ gas (P gasi ) / ζ gas (P gasi,N ))×C gasq (115)

[0126] In the formula, represents the total amount of carbon dioxide produced by the gas turbine, M i,gas Indicates the amount of carbon dioxide produced by the gas turbine due to combustion gas, M i,gasaux It represents the amount of carbon dioxide caused by deep peak load regulation of gas turbines, H gasi , O gas (P gasi) are respectively the power consumption of gas turbine i and the corrected carbon oxidation rate; C gasc Statistics of carbon content in gas; M C is the corresponding molar mass; P gasi,N is the rated operating power of the coal-fired unit; C gasq is the CO2 produced per unit of electricity; gas (P gasi ) / ζ gas (P gasi,N ) is used to approximately reflect the impact of changes in boiler combustion efficiency of gas turbines during the deep peak regulation phase on carbon emissions, and ζ gas is the boiler combustion efficiency, and P gasi Related.

[0127] The specific carbon emission costs of thermal power and gas turbines are calculated as follows:

[0128]

[0129]

[0130] In the formula, represents the carbon emission cost of thermal power units, the carbon emission cost of gas turbines, and the total carbon emission cost. M and N are the number of thermal power units and gas turbines, respectively. P gN , P gasN are the rated capacities of thermal power units and gas turbines, respectively, k c is the carbon trading price, ψ g is the carbon emission intensity quota value of thermal power units, ψ gas is the carbon emission intensity quota value of gas turbines.

[0131] Predict future carbon policies, obtain predicted values ​​of carbon quotas and carbon prices, substitute the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases, obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases

[0132] According to the EU's development history, the carbon quota ψ and carbon price k for China c Forecasting Figure 5 , the formula is as follows:

[0133] ψ=0.0001(year-2024) 3 -0.0004(year-2024) 2 -0.0860 (119)

[0134] k c =-0.0884(year-2024) 3+2.1212(year-2024) 2 +35.202(year-2024)+77.8788(120)

[0135] To accurately calculate the carbon emissions after deep peak-shaving transformation of thermal power, linearization using a solver will result in inaccurate calculation results. Therefore, it is crucial to use a particle swarm algorithm to deal with nonlinear problems. This method ensures that the accuracy of carbon dioxide emissions calculations is improved, and the cost of carbon emissions is calculated more accurately, so that the planning results of new energy bases are more in line with actual requirements in the context of gradually tightening carbon quotas. The particle swarm algorithm has more advantages in terms of accuracy.

[0136] Initialize particles, including setting the group size, number of particle iterations, and maximum allowed particle speed Assign random speed to the initial particles

[0137] According to the set objective function, the fitness value F(x i ), evaluate the fitness value of each particle, and update the global optimal position after initialization, and update the position of each particle after initialization to the historical optimal position.

[0138] Update the velocity of each particle according to formulas (41) and (42): and location

[0139] v i (t) = ωv i (t-1)+c1r1[pbest(t-1)-x i ]+c2r2[gebest(t-1)-x i ] (121)

[0141] x i (t) = x i (t-1)+v i (t) (122)

[0142] Where t is the number of iterations; c1 and c2 are learning factors, which are constants; r1 and r2 are random numbers between (0,1); and ω is the inertia weight.

[0143] Calculate the fitness value F(x i ), if the fitness value of the particle's new position is lower than the fitness value of the historical optimal position, then update the current position to the historical optimal position. If there is a particle whose fitness value at the new position is lower than the fitness value at the global optimal position, then update the global optimal position.

[0144] Determine whether the iteration stop condition is met. If so, stop optimizing.

[0145] Model solution process description.

[0146] The particle swarm-based optimization model of coal-fired power deep regulation and transformation for new energy bases considering carbon budget proposed in this invention has the following specific process:

[0147] After data initialization, set the initial data of wind power, photovoltaic power, thermal power and energy storage.

[0148] Using the particle swarm algorithm, with the goal of minimizing investment and operating costs, the optimal capacity configuration of wind power, photovoltaic power, thermal power, deep-regulation thermal power, energy storage, and gas turbines under the current carbon policy is calculated.

[0149] By predicting future changes in carbon policies and taking the lowest investment and operating costs as the goal, the optimal capacity configuration of wind power, photovoltaic power, thermal power, deep-regulation thermal power, energy storage, and gas turbines under future carbon policies is calculated.

[0150] The capacity configuration of wind, solar, thermal, storage and combustion in the new energy base has been completed and the planning is completed.

[0151] The specific implementation method is described in detail below with reference to an example.

[0152] A deep adjustment and transformation plan for coal-fired power plants in new energy bases was carried out taking into account the carbon budget. The calculation results of the example planning meet the current demand and can effectively predict the future capacity planning.

[0153] A city in western China is taken as the research object. The region has many sunny days throughout the year, sufficient sunshine, and is rich in wind resources due to the influence of circulation.

[0154] Carbon emissions from thermal power after deep adjustment and transformation Figure 1 As shown in Figure 2, carbon emissions from gas turbines are Figure 2 shown.

[0155] Wind turbines, photovoltaic units, thermal power, gas turbines and energy storage devices are set up as options in the system. Four scenarios are set up to demonstrate the construction of new energy bases under different unit type settings. S1 is no deep adjustment and transformation of thermal power, and no gas turbine is set up. S2 is deep adjustment and transformation of thermal power, and no gas turbine is set up. S3 is no deep adjustment and transformation of thermal power units, and gas turbines are set up. S4 is deep adjustment and transformation of thermal power, and gas turbines are set up.

[0156] Combined with the current carbon policy, the particle swarm algorithm is used to perform simulation and solve the problem.

[0157] The planning results are shown in Table 1. The output under different scenarios is as follows: Figure 3 shown.

[0158] As can be seen from Table 1, compared with S1, when S2, S3, and S4 are adopted, the energy storage capacity is reduced, while the new energy consumption rate is improved to a certain extent. The output changes and start-stop of thermal power and gas turbines are used to adapt to the output changes of wind power and photovoltaic power, making the flexibility of the entire system higher.

[0159] It can be seen from Table 2 that compared with S1, the thermal power costs of S2, S3 and S4 have increased, but other system costs have been greatly reduced.

[0160] from Figure 4 It can be seen that after deep regulation and transformation of thermal power, its economy is far better than that of gas turbines. Therefore, although the peak regulation effect of gas turbines is better, if there is a choice, we will still choose to build all thermal power and not gas turbines.

[0161] Future carbon policy forecasts Figure 5 As shown in Figure 2, C1, C2, C3, and C4 are set to represent the scenarios under the carbon policies in 2024, 2030, 2035, and 2040, respectively. The planning model is re-solved and the planning results are shown in Table 3. The planned capacity changes are shown in Table 3. Figure 7 shown.

[0162] exist Figure 7 It can be observed from Table 3 that from 2024 to 2030, the transformed thermal power is more economical in peak regulation than energy storage. Therefore, thermal power is gradually transformed, and peak regulation is mainly achieved through transformed thermal power and a small amount of energy storage.

[0163] From 2030 to 2035, as carbon policies tighten and carbon emission prices rise significantly, gas turbines are more economical than modified thermal power. Therefore, gas turbines will be selected to replace part of thermal power, and the construction of renewable energy will increase slightly.

[0164] From 2035 to 2040, due to the extremely strict carbon policy, gas turbines and converted thermal power will be much less economically viable than energy storage due to the costs associated with carbon emissions. Therefore, the construction of gas turbines and converted thermal power will be reduced, unconverted thermal power will no longer be built, and the construction of energy storage will be increased to mitigate the impact of carbon policy on costs.

[0165] In addition, due to the stronger peak-shaving capabilities of energy storage and gas turbines, the construction of new energy will increase after 2030, allowing more wind power and solar power to be consumed. And with the changes in carbon policies, the cost of thermal power will increase, such as Figure 6 shown.

[0166] In summary, the optimization method for deep regulation and transformation of coal-fired power in new energy bases considering the carbon budget takes into account the increase in carbon emissions after deep regulation of thermal power, which can reduce carbon emissions, reduce system costs, and improve the utilization rate of new energy.

[0167] Table 1 Comparison of construction capacity under different scenarios

[0168]

[0169]

[0170] Table 2 Comparison of economic performance under different scenarios

[0171]

[0172] Table 3 Comparison of construction capacity in different years under carbon policy changes

[0173]

[0174] Example 2

[0175] A non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the new energy base planning method described in Example 1, the planning method comprising: establishing an energy equipment model according to actual wind and solar power resource conditions of the new energy base, and providing wind, solar, thermal, storage and combustion constraints according to the energy equipment model;

[0176] The particle swarm algorithm is used to construct a carbon emission cost model after deep adjustment and transformation of coal-fired power units. The goal is to minimize the sum of investment, operation and carbon emission costs of wind, solar, thermal and storage gas turbine equipment, and take into account the constraints of wind, solar, thermal and storage to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases.

[0177] Make forecasts for future carbon policies and obtain predicted values ​​of carbon quotas and carbon prices. Substitute the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep regulation of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep regulation under different carbon policies of new energy bases.

[0178] Example 3

[0179] A new energy base coal-fired power deep adjustment and transformation planning device, characterized in that:

[0180] Establish a module, establish an energy equipment model according to the actual wind and solar power resource conditions of the new energy base, and give wind, solar, thermal, storage and combustion constraints based on the energy equipment model;

[0181] The construction module uses the particle swarm algorithm to construct a carbon emission cost model for coal-fired power units after deep adjustment and transformation. The goal is to minimize the sum of wind, solar, thermal, and storage gas turbine equipment investment, operation, and carbon emission costs, and consider the wind, solar, thermal, and storage constraints to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases.

[0182] The optimization module predicts future carbon policies and obtains the predicted values ​​of carbon quotas and carbon prices. The predicted values ​​are substituted into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases.

[0183] The technology of the present invention establishes an accurate carbon dioxide emission model for thermal power units and gas turbines through the use of particle swarm algorithm, and provides future capacity planning changes through the prediction of carbon policy, thereby solving the problems of calculation deviation caused by cplex piecewise linearization in the prior art and the failure of wind, solar, thermal and storage capacity configuration to meet the actual situation due to ignoring the changes in carbon policy, and achieving the beneficial effects to be achieved by the present invention, and accurately describing the additional carbon emissions caused by deep peak regulation of thermal power units and gas turbines.

[0184] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. The planning method for deep adjustment and transformation of coal-fired power in new energy bases is characterized by: According to the actual wind and solar power resource conditions of the new energy base, an energy equipment model is established, and the wind, solar, thermal, storage and combustion constraints are given based on the energy equipment model; The particle swarm algorithm is used to construct a carbon emission cost model after deep adjustment and transformation of coal-fired power units. The goal is to minimize the sum of investment, operation and carbon emission costs of wind, solar, thermal and storage gas turbine equipment, and take into account the constraints of wind, solar, thermal and storage to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases. Make forecasts for future carbon policies and obtain predicted values ​​of carbon quotas and carbon prices. Substitute the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep regulation of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep regulation under different carbon policies of new energy bases.

2. The method for planning deep adjustment and transformation of coal-fired power generation in a new energy base according to claim 1 is characterized by: The energy equipment model includes the following contents: the output power of wind power generation equipment in a certain period of time It is represented by the following model: Where W represents the wind turbine, V t represents the actual wind speed at time t, V r Indicates rated wind speed, r means rated, P rW Indicates the rated power of a single fan, r W Indicates the rated value of the wind turbine. Indicates the maximum wind power output, V in Indicates the aerodynamic performance leading to the cut-in wind speed, V out Indicates the minimum wind speed requirement and cut-out wind speed for the unit to be connected to the grid for power generation; the photovoltaic power generation output power is related to the light intensity. The photovoltaic power generation output power in a certain period of time It can be expressed as: In the formula, PV represents photovoltaic unit, A PV represents the area of ​​photovoltaic panels, η PV Indicates the efficiency under standard test conditions, E t represents the average light intensity at time t, Represents the maximum output of photovoltaic power; the mathematical model expression of electric energy storage is: 0≤E BESS ≤1 (6) 0≤τ cha +t dis ≤1 (9) In the formula, E t and E t-1 They represent the amount of electricity stored in the energy storage device at time t and time t-1 respectively, E BESS represents the percentage of power, Δt represents the time from time t-1 to time t, cha represents energy storage charging, and dis represents energy storage discharging. Respectively represent the charging and discharging power of the energy storage at time t, η cha , η dis They represent the charging and discharging efficiency of the energy storage, They represent the maximum single charge and discharge power of energy storage, S represents the maximum capacity of electrical energy storage; τ cha represents the charging state of the energy storage, τ dis Indicates the discharge state of the energy storage, which is a 0-1 variable; After deep regulation of coal-fired power, it is divided into three stages: conventional peak regulation, deep non-combustion peak regulation and deep combustion peak regulation. a is the minimum output of the unit during the conventional peak load regulation phase, P b is the minimum stable combustion output of the unit in the deep non-combustion peak-shaving stage, P c It is the stable combustion limit output of the unit in the deep combustion peak regulation stage; its mathematical model is as follows: Where U i,t Indicates the start / stop status of the i-th unit at time t, which is a 0-1 variable. is the actual start-up and shutdown time of the unit, T ion is the minimum startup time of the unit, T ioff is the minimum downtime of the unit, is the output of coal-fired power unit i at time t, α iup , α idown represents the maximum upward and downward climbing rate of the coal-fired power unit, α istart , α ishut They are the start-up and shutdown times of coal-fired power units, is the actual output, maximum output and minimum output of coal-fired power unit i at time t.

3. The method for planning deep adjustment and transformation of coal-fired power generation in a new energy base according to claim 1 is characterized in that the establishment of the energy equipment model includes the following contents: The objective function is expressed as: Where, F is the daily operating cost of the new energy base, F con 、F operating They are the investment cost and operation and maintenance cost of wind power, photovoltaic power, coal power, energy storage, and gas turbines. Cost of CO2 emissions from coal-fired power plants and gas turbines; The specific investment cost is calculated as follows: In the formula, w tx is the discount rate, w smi is the expected service life of device i, N is the total number of devices, f i is the annual value coefficient of the capital of the i-th equipment, C i is the capacity of the i-th device, G i is the unit purchase cost of the i-th equipment; The specific operation and maintenance cost is calculated as follows: In the formula, F i,t , They are the total cost, coal consumption cost, wear cost, and oil input cost of coal-fired unit i at time t, specifically: In the formula, F grespair is the maintenance cost of thermal power units, F goperating Operation and maintenance costs of thermal power units; Total operation and maintenance cost F operating as follows: F operating =F goperating +F gasoperating +F woperating +F pvoperating +F batoperating (20) In the formula, F gasoperating is the operation and maintenance cost of the gas turbine, F woperating is the operation and maintenance cost of the wind turbine, F pvoperating is the operation and maintenance cost of the photovoltaic unit, F batoperating is the operation and maintenance cost of energy storage; The specific carbon emission cost is calculated as follows: Specific carbon emissions from thermal power generation The calculation is as follows: Where M i,coal Represents carbon emissions mainly caused by coal combustion; M i,aux The increase in carbon emissions is caused by the decrease in boiler combustion efficiency, the decrease in turbine blade efficiency, the increase in mechanical friction loss, and the additional operation of auxiliary devices; M i,sn In order to consider the increased carbon emissions caused by desulfurization and denitrification, P i,t Indicates the actual output of the coal-fired unit, P i,max Indicates the maximum output of the thermal power unit, P i,a , P i,b , P i,c Indicates that the output of the coal-fired unit is at the minimum output of the 1st, 2nd and 3rd stages; The specific carbon emission costs of thermal power and gas turbines are calculated as follows: In the formula, represents the carbon emission cost of thermal power units, the carbon emission cost of gas turbines, and the total carbon emission cost, k c is the carbon trading price, P gN , P gasN Indicates the rated output of thermal power units and gas turbines, ψ g is the carbon emission intensity quota value of thermal power units, ψ gas is the carbon emission intensity quota value of gas turbines, is the amount of carbon dioxide emitted by the gas turbine.

4. The method for planning deep adjustment and transformation of coal-fired power generation in a new energy base according to claim 1 is characterized by: The above-mentioned prediction of future carbon policies, obtaining the predicted values ​​of carbon quotas and carbon prices, substituting the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases, obtaining the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realizing the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases include the following contents: For carbon quota ψ and carbon price k c To make a prediction, the formula is as follows: ψ=0.0001(year-2024) 3 -0.0004(year-2024) 2 -0.0860 (25) k c =-0.0884(year-2024) 3 +2.1212(year-2024) 2 +35.202(year-2024)+77.8788 (26) By updating the values ​​of carbon quotas and carbon prices, recalculating the cost of carbon dioxide emissions, and replanning the capacity configuration of new energy bases, a new energy base planning method that takes carbon budgets into consideration is obtained.

5. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the method for deep adjustment and transformation planning of coal-fired power generation in a new energy base as described in any one of claims 1 to 4.

6. A new energy base coal-fired power deep adjustment and transformation planning device, characterized in that: include: Establish a module, establish an energy equipment model according to the actual wind and solar power resource conditions of the new energy base, and give wind, solar, thermal, storage and combustion constraints based on the energy equipment model; The construction module uses the particle swarm algorithm to construct a carbon emission cost model for coal-fired power units after deep adjustment and transformation. The goal is to minimize the sum of wind, solar, thermal, and storage gas turbine equipment investment, operation, and carbon emission costs, and consider the wind, solar, thermal, and storage constraints to construct a capacity transformation model for deep adjustment of coal-fired power in new energy bases. The optimization module predicts future carbon policies and obtains the predicted values ​​of carbon quotas and carbon prices. The predicted values ​​are substituted into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases to obtain the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realize the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases.

7. The deep adjustment and transformation planning device for coal-fired power generation in a new energy base according to claim 6 is characterized by: The energy equipment model includes the following contents: the output power of wind power generation equipment in a certain period of time It is represented by the following model: Where W represents the wind turbine, V t represents the actual wind speed at time t, V r Indicates rated wind speed, r means rated, P rW Represents the rated power of a single wind turbine, rW represents the rated value of the wind turbine unit, Indicates the maximum wind power output, V in Indicates the aerodynamic performance leading to the cut-in wind speed, V out Indicates the minimum wind speed requirement and cut-out wind speed for the unit to be connected to the grid for power generation; the photovoltaic power generation output power is related to the light intensity. The photovoltaic power generation output power in a certain period of time It can be expressed as: In the formula, PV represents photovoltaic unit, A PV represents the area of ​​photovoltaic panels, η PV Indicates the efficiency under standard test conditions, E t represents the average light intensity at time t, Represents the maximum output of photovoltaic power; the mathematical model expression of electric energy storage is: 0≤E BESS ≤1 (32) 0≤τ cha +t dis ≤1 (35) In the formula, E t and E t-1 They represent the amount of electricity stored in the energy storage device at time t and time t-1 respectively, E BESS represents the percentage of power, Δt represents the time from time t-1 to time t, cha represents energy storage charging, and dis represents energy storage discharging. Respectively represent the charging and discharging power of the energy storage at time t, η cha , η dis They represent the charging and discharging efficiency of the energy storage, They represent the maximum single charge and discharge power of energy storage, S represents the maximum capacity of electrical energy storage; τ cha represents the charging state of the energy storage, τ dis Indicates the discharge state of the energy storage, which is a 0-1 variable; After deep regulation of coal-fired power, it is divided into three stages: conventional peak regulation, deep non-combustion peak regulation and deep combustion peak regulation. a is the minimum output of the unit during the conventional peak load regulation phase, P b is the minimum stable combustion output of the unit in the deep non-combustion peak-shaving stage, P c It is the stable combustion limit output of the unit in the deep combustion peak regulation stage; its mathematical model is as follows: Where U i,t Indicates the start / stop status of the i-th unit at time t, which is a 0-1 variable. is the actual start-up and shutdown time of the unit, T ion is the minimum startup time of the unit, T ioff is the minimum downtime of the unit, is the output of coal-fired power unit i at time t, α iup , α idown represents the maximum upward and downward climbing rate of the coal-fired power unit, α istart , α ishut They are the start-up and shutdown times of coal-fired power units, is the actual output, maximum output and minimum output of coal-fired power unit i at time t.

8. The deep adjustment and transformation planning device for coal-fired power generation in a new energy base according to claim 6 is characterized by: The energy equipment model establishment includes the following contents: The objective function is expressed as: Where, F is the daily operating cost of the new energy base, F con 、F operating They are the investment cost and operation and maintenance cost of wind power, photovoltaic power, coal power, energy storage, and gas turbines. Cost of CO2 emissions from coal-fired power plants and gas turbines; The specific investment cost is calculated as follows: In the formula, w tx is the discount rate, w smi is the expected service life of device i, N is the total number of devices, f i is the annual value coefficient of the capital of the i-th equipment, C i is the capacity of the i-th device, G i is the unit purchase cost of the i-th equipment; The specific operation and maintenance cost is calculated as follows: In the formula, F i,t , They are the total cost, coal consumption cost, wear cost, and oil input cost of coal-fired unit i at time t, specifically: In the formula, F grespair is the maintenance cost of thermal power units, F goperating Operation and maintenance costs of thermal power units; Total operation and maintenance cost F operating as follows: F operating =F goperating +F gasoperating +F woperating +F pvoperating +F batoperating (46) In the formula, F gasoperating is the operation and maintenance cost of the gas turbine, F woperating is the operation and maintenance cost of the wind turbine, F pvoperating is the operation and maintenance cost of the photovoltaic unit, F batoperating is the operation and maintenance cost of energy storage; The specific carbon emission cost is calculated as follows: Specific carbon emissions from thermal power generation The calculation is as follows: Where M i,coal Represents carbon emissions mainly caused by coal combustion; M i,aux The increase in carbon emissions is caused by the decrease in boiler combustion efficiency, the decrease in turbine blade efficiency, the increase in mechanical friction loss, and the additional operation of auxiliary devices; M i,sn In order to consider the increased carbon emissions caused by desulfurization and denitrification, P i,t Indicates the actual output of the coal-fired unit, P i,max Indicates the maximum output of the thermal power unit, P i,a , P i,b , P i,c Indicates that the output of the coal-fired unit is at the minimum output of the 1st, 2nd and 3rd stages; The specific carbon emission costs of thermal power and gas turbines are calculated as follows: In the formula, represents the carbon emission cost of thermal power units, the carbon emission cost of gas turbines, and the total carbon emission cost, k c is the carbon trading price, P gN , P gasN Indicates the rated output of thermal power units and gas turbines, ψ g is the carbon emission intensity quota value of thermal power units, ψ gas is the carbon emission intensity quota value of gas turbines, is the amount of carbon dioxide emitted by the gas turbine.

9. The deep adjustment and transformation planning device for coal-fired power generation in a new energy base according to claim 6 is characterized by: The above-mentioned prediction of future carbon policies, obtaining the predicted values ​​of carbon quotas and carbon prices, substituting the predicted values ​​into the carbon emission cost model and the capacity transformation model of coal-fired power deep adjustment of new energy bases, obtaining the capacity configuration of new energy bases under different carbon quotas and carbon prices, and realizing the transformation and optimization of coal-fired power deep adjustment under different carbon policies of new energy bases include the following contents: For carbon quota ψ and carbon price k c To make a prediction, the formula is as follows: ψ=0.0001(year-2024) 3 -0.0004(year-2024) 2 -0.0860 (51) k c =-0.0884(year-2024) 3 +2.1212(year-2024) 2 +35.202(year-2024)+77.8788 (52) By updating the values ​​of carbon quota and carbon price, recalculating the cost of carbon dioxide emission, and replanning the capacity configuration of the new energy base, a new energy base planning device that takes carbon budget into consideration is obtained.

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