A self-circulating carbon reduction system and its optimization method

Abstract

The present invention discloses a self-circulating carbon reduction system and an optimization method thereof, wherein the self-circulating carbon reduction system comprises a cost input unit and an economic value self-circulating control unit, wherein the cost input comprises planting shelterbelts to reduce carbon emissions, building wind turbines to generate electricity, and controlling dust and improving soil to plant cash crops; the economic value self-circulating control unit adopts a wild boar optimization algorithm to perform local optimization and global optimization, so that the self-circulating carbon reduction system can generate higher economic value; the economic value self-circulating control unit is used to control cost input, wind turbine maintenance, cash crop value and carbon reduction economic value; the economic value generated by the self-circulating carbon reduction system includes wind turbine power generation, shelterbelt carbon reduction economic value and cash crop value. The present invention can solve the desertification problem at a relatively low cost and help green and low-carbon transformation.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy conservation, emission reduction and environmental protection, and specifically relates to a self-circulating carbon reduction system and an optimization method thereof. Background Art

[0002] At present, the Northwest region is facing problems such as land degradation and desertification. In order to achieve the goals of carbon peak and carbon neutrality, it is necessary to transform the development mode from extensive to green and low-carbon, protect the ecological environment, achieve carbon reduction, and take into account economic value. In order to solve the desertification problem in the Northwest region and achieve the goal of carbon reduction, some forest ecosystems and grassland ecosystems have been proposed. However, the economic value realization paths of these ecosystems are relatively single and fail to build a self-circulation mechanism. Summary of the invention

[0003] Purpose of the invention: The first purpose of the present invention is to provide a self-circulating carbon reduction system, which can generate economic value and improve economic benefits by building wind turbines to generate electricity and planting shelterbelts and cash crops, and can solve the desertification problem at a lower cost and help green and low-carbon transformation. The second purpose of the present invention is to provide an optimization method for the self-circulating carbon reduction system, so as to achieve the purpose of obtaining the highest economic value with the lowest cost investment.

[0004] Technical solution: The self-circulating carbon reduction system of the present invention comprises:

[0005] Cost input units are used to plant shelterbelts to reduce carbon emissions, build wind turbines to generate electricity, and control dust and improve soil for growing cash crops;

[0006] A collection unit, used for collecting the power generation of the wind turbine; and

[0007] The economic value self-circulation regulation unit is used to regulate cost input, wind turbine maintenance, economic crop value and carbon reduction economic value, so that the self-circulation carbon reduction system can obtain the optimal economic value; the economic value generated by the self-circulation carbon reduction system includes wind turbine power generation, shelterbelt carbon reduction economic value and economic crop value.

[0008] Further, cash crops grown include Cistanche deserticola, forage grass, peanuts and walnuts.

[0009] The optimization method of the self-circulating carbon reduction system described in the present invention includes: the economic value self-circulating control unit adopts the wild boar optimization algorithm to optimize to obtain the optimal economic value.

[0010] Furthermore, the wild boar optimization algorithm includes local optimization and global optimization. The wild boar algorithm is used to optimize the soil locally, and the optimized soil is comprehensively judged through monitoring indicators to determine whether the soil has reached its exploitable value. After the soil is locally optimized to reach its exploitable value, the wild boar algorithm is used to globally optimize the route with the optimal economic value to maximize the economic value.

[0011] Furthermore, the self-circulating carbon reduction system also includes a soil monitoring unit for obtaining soil monitoring parameters, and there are eight soil monitoring parameters, namely water content, bulk density, organic matter, pH value, total potassium, total nitrogen, total sulfur and total phosphorus;

[0012] The local optimization of the wild boar algorithm includes:

[0013] (1) Construct the objective function:

[0014]

[0015]

[0016] Among them, M represents the overall target soil value, k represents the soil optimization coefficient, Indicates the location of the soil; Represents eight monitoring indicators, α1, α2, α3, α4, α5, α6, α7, α8 represent the weights of the monitoring indicators; U represents the cumulative value of the monitoring indicator multiplied by the corresponding weight;

[0017] (2) Initialize soil monitoring indicators;

[0018]

[0019] Among them, gb i Indicates the upper boundary of each soil monitoring index, fb i Indicates the lower boundary of each soil monitoring index, rand i is a random number between 0 and 1; i∈[1,8], indicating that there are eight monitoring indicators; Indicates initializing the value of each monitoring indicator and limiting it to the upper and lower boundaries;

[0020] (3) Determine whether the soil monitoring indicators are qualified; if yes, implement a group attack on the soil monitoring indicators, which includes a group attack strategy and a persecution attack strategy; if no, implement a cleaning strategy on the soil monitoring indicators;

[0021] (4) updating the current soil planting position to the position obtained by optimizing in step (3);

[0022] (5) Determine whether the plantable area is optimal, that is, whether the target value M is within the available range [10, 20]. If not, repeat steps (1) to (4) to continue to optimize.

[0023] Further, step (3) comprises:

[0024] Group attack strategy:

[0025]

[0026] in, is the new location with suitable soil for planting, na is a random integer, It is the unfavorable weather conditions that limit soil cultivation, is the current overall soil position, It is the qualified soil found in the previous iteration;

[0027] Persecution attack strategy:

[0028]

[0029] Among them, Q1, Q2 are random optimization coefficients between -1 and 1. It means that Q2 is limited to [e -1 ,e], f1 is a random number from 1 to the maximum search space, It represents the soil position obtained by exploration;

[0030] Cleaning strategy:

[0031]

[0032] Among them, λ represents the odd-even threshold of the current soil position. If it is an even number, the current soil has no usable value for cleaning; otherwise, it has usable value.

[0033] Further, step (4) comprises:

[0034]

[0035] Furthermore, in step (3), the conditions for qualified soil monitoring indicators include: soil moisture content of 20% to 30%; bulk density of 1.2 to 1.8 g / cm 3 ; Organic matter content 0.8%~4.5%; pH value 5.2 to 6.8; total potassium content 1%~2%; total nitrogen content greater than 1.5g / kg; effective sulfur content 6.6~265.1mg / kg, average (36.8±3.1)mg / kg; total phosphorus content 0.10-0.15%.

[0036] Further,

[0037]

[0038] The frequency of soil monitoring is shown in the table above.

[0039] Furthermore, the global optimization of the wild boar algorithm includes:

[0040] Minimize costs:

[0041] S1=N1*L1

[0042] S2=N2*W*L2

[0043] Cost=c1*x1+c2*S1+c3*S2+c4*x4

[0044] Assume that the width of each sand control belt is consistent, and the width of the sand control belts is inconsistent;

[0045] Among them, S1 represents the sand control area, N1 represents the number of sand control belts, and L1 represents the length of each sand control belt;

[0046] S2 represents the planting area, N2 represents the number of planting belts, W represents the width of each planting belt, and L2 represents the length of each planting belt;

[0047] Cost represents cost, c1 represents generator cost, x1 represents the number of generators, c2 represents sand control cost, c3 represents planting cost, c4 represents wind turbine maintenance cost, and x4 represents the number of wind turbines;

[0048] Maximize Profits:

[0049] Profit=p1*S2+α*x1

[0050] Among them, Profit represents profit, p1 represents the economic crop harvest gain, and α represents the carbon reduction economic gain;

[0051] Constraints:

[0052]

[0053] Among them, e1, e2, e3, γ, and f are all constant coefficients, representing the power generation of the generator, sand control capacity coefficient, planting coefficient, carbon emission coefficient, and carbon reduction coefficient, respectively; E max Indicates the maximum power generation capacity, A max Indicates the maximum sand control, B max Indicates planting capacity.

[0054] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0055] The present invention can solve the desertification problem in the northwest region to a great extent, shorten the payback time, and obtain higher economic value. The present invention uses the wild boar optimization algorithm to perform multi-objective optimization, which can obtain the optimal economic value and efficiency, so that the more money invested in the cost, the less carbon dioxide emissions, forming an optimal ratio of investment and carbon reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a structural schematic diagram of a self-circulating carbon reduction system provided in an embodiment of the present application;

[0057] Figure 2 This is a schematic diagram of the control object of the economic value self-circulation control unit in the embodiment of the present application;

[0058] Figure 3 This is a flowchart of an optimization method for a self-circulating carbon reduction system provided in an embodiment of the present application;

[0059] Figure 4 It is the optimal ratio diagram of input cost and carbon emission reduction in the embodiment of the present application;

[0060] Figure 5 This is a comparison chart of the economic benefits before and after using the self-circulating carbon reduction system in the embodiments of the present application. DETAILED DESCRIPTION

[0061] The present invention will be further described below in conjunction with the accompanying drawings.

[0062] The present invention constructs a self-circulating carbon reduction system that can be applied to the northwest desert area, which aims to obtain the highest economic value with the least investment, thereby forming a self-circulating system. Specifically,

[0063] like Figure 1 and Figure 2 A self-circulating carbon reduction system shown includes:

[0064] Cost input units are used to plant shelterbelts to reduce carbon emissions, build wind turbines to generate electricity, and control dust and improve soil for growing cash crops;

[0065] A collection unit, used for collecting the power generation of the wind turbine;

[0066] Soil monitoring unit, used to obtain soil monitoring parameters. There are eight soil monitoring parameters, namely water content, bulk density, organic matter, pH value, total potassium, total nitrogen, total sulfur and total phosphorus; and,

[0067] The economic value self-circulation regulation unit is used to regulate cost input, wind turbine maintenance, economic crop value and carbon reduction economic value, so that the self-circulation carbon reduction system can obtain the optimal economic value; the economic value generated by the self-circulation carbon reduction system includes wind turbine power generation, shelterbelt carbon reduction economic value and economic crop value.

[0068] Wind turbines can fully utilize the advantage of relatively high wind speeds in the northwest region, reduce energy consumption and alleviate environmental burden.

[0069] The desertification trend in the Northwest is quite serious. Planting large areas of shelter forests can reduce carbon dioxide emissions. The more shelter forests are planted, the less carbon dioxide emissions there are. Cash crops are planted by controlling sand and dust and maintaining good soil. In this embodiment, the cash crops planted include Cistanche deserticola, forage grass, peanuts and walnuts.

[0070] The frequency of soil monitoring is shown in Table 1.

[0071] Table 1

[0072]

[0073] The embodiment of the present application also provides an optimization method for a self-circulating carbon reduction system. In the self-circulating carbon reduction system, the economic value self-circulating regulation unit is the core unit, which is affected by factors such as cost input, wind turbine maintenance, economic crop value and carbon reduction economic value.

[0074] In this embodiment, the economic value self-circulation control unit uses the wild boar optimization algorithm for optimization to obtain the optimal economic value. The wild boar algorithm includes local optimization and global optimization. The wild boar algorithm is used for local optimization to optimize the soil, and the optimized soil is comprehensively judged by monitoring indicators to determine whether the soil has reached the usable value. After the soil is locally optimized to reach the usable value, the wild boar algorithm is used for global optimization to obtain the route with the optimal economic value to maximize the economic value.

[0075] Combination Figure 3 , the optimization algorithm specifically includes the following steps:

[0076] (1) Determine the objective function:

[0077]

[0078]

[0079] Among them, M represents the overall target soil value, k represents the soil optimization coefficient, Indicates the location of the soil; Represents eight monitoring indicators, α1, α2, α3, α4, α5, α6, α7, and α8 represent the weights of the monitoring indicators; U represents the cumulative value of the monitoring indicator multiplied by the corresponding weight.

[0080] (2) Initialize soil monitoring indicators;

[0081] The performance of random initialization within the search boundary of the wild boar optimization algorithm is used to initialize the soil monitoring indicators. The formula is as follows:

[0082]

[0083] Among them, gb i Indicates the upper boundary of each soil monitoring index, fb i Indicates the lower boundary of each soil monitoring index, rand i is a random number between 0 and 1; i∈[1,8], indicating that there are eight monitoring indicators; Indicates initializing the value of each monitoring indicator and limiting it to the upper and lower boundaries.

[0084] (3) Due to the serious desertification in the Northwest region, some areas have no usable value at all, so it is particularly important to find a suitable soil location.

[0085] In order to find a suitable soil location, step (3) first determines whether the soil monitoring indicators are qualified; if yes, a group attack is implemented on the soil monitoring indicators, and the group attack includes a group attack strategy and a persecution attack strategy; if no, a cleaning strategy is implemented on the soil monitoring indicators.

[0086] The conditions for qualified soil monitoring indicators include: soil moisture content 20% to 30%; bulk density 1.2 to 1.8 g / cm 3 ; Organic matter content 0.8%~4.5%; pH value 5.2 to 6.8; total potassium content 1%~2%; total nitrogen content greater than 1.5g / kg; effective sulfur content 6.6~265.1mg / kg, average (36.8±3.1)mg / kg; total phosphorus content 0.10-0.15%.

[0087] Group attack strategy:

[0088] Wild boar groups search for prey in large areas; the Northwest region as a whole finds suitable soil locations to locate them in this place, and the behavior is expressed as follows:

[0089]

[0090] in, is the new location with suitable soil for planting, na is a random integer, It is the unfavorable weather conditions that limit soil cultivation, is the current overall soil position, It is the qualified soil found in the previous iteration;

[0091] Persecution attack strategy:

[0092] When a group of wild boars capture prey individually and in a targeted manner, they will continue to do so until they are captured; when applied to finding soil positions, they will optimize from the overall position to the local position;

[0093]

[0094] Among them, Q1, Q2 are random optimization coefficients between -1 and 1. It means that Q2 is limited to [e -1 ,e], f1 is a random number from 1 to the maximum search space, It represents the soil position obtained by exploration.

[0095] Cleaning strategy:

[0096] The behavior of wild boars in a group to find food aimlessly; applied to finding soil locations, they can find plantable areas without any purpose over a large area;

[0097]

[0098] Among them, λ represents the odd-even threshold of the current soil position. If it is an even number, the current soil has no usable value for cleaning; otherwise, it has usable value.

[0099] (4) updating the current soil planting position to the position obtained by optimizing in step (3);

[0100] Update the new location of the current soil planting:

[0101]

[0102] (5) Determine whether the plantable area is optimal, that is, whether the target value M is within the available range [10, 20]. If not, repeat steps (1) to (4) to continue to optimize.

[0103] (6) In the above steps (1) to (5), local optimization is performed on the soil to achieve the usable value. In step (6), the global optimization of the wild boar optimization algorithm is used to obtain the optimal economic value route, including:

[0104] Minimize costs:

[0105] S1=N1*L1

[0106] S2=N2*W*L2

[0107] Cost=c1*x1+c2*S1+c3*S2+c4*x4

[0108] Assume that the width of each sand control belt is consistent, and the width of the sand control belts is inconsistent;

[0109] Among them, S1 represents the sand control area, N1 represents the number of sand control belts, and L1 represents the length of each sand control belt;

[0110] S2 represents the planting area, N2 represents the number of planting belts, W represents the width of each planting belt, and L2 represents the length of each planting belt;

[0111] Cost represents cost, c1 represents generator cost, x1 represents the number of generators, c2 represents sand control cost, c3 represents planting cost, c4 represents wind turbine maintenance cost, and x4 represents the number of wind turbines;

[0112] Maximize Profits:

[0113] Profit=p1*S2+α*x1

[0114] Among them, Profit represents profit, p1 represents the economic crop harvest gain, and α represents the carbon reduction economic gain;

[0115] Constraints:

[0116]

[0117] Among them, e1, e2, e3, γ, and f are all constant coefficients, representing the power generation of the generator, sand control capacity coefficient, planting coefficient, carbon emission coefficient, and carbon reduction coefficient, respectively; E max Indicates the maximum power generation capacity, A max Indicates the maximum sand control, B max Indicates planting capacity.

[0118] like Figure 4 As shown, as the input cost increases, the carbon dioxide emissions decrease. This data just illustrates the optimal ratio between input cost and carbon dioxide.

[0119] like Figure 5 As shown, it can be seen that after using the self-circulating carbon reduction system, the economic value is obviously much higher than before.

Claims

1. A method for optimizing a self-circulating carbon reduction system, characterized in that: The self-circulating carbon reduction system comprises: Cost input units are used to plant shelterbelts to reduce carbon emissions, build wind turbines to generate electricity, and control dust and improve soil for growing cash crops; A collection unit, used for collecting the power generation of the wind turbine; and An economic value self-circulation control unit is used to control cost input, wind turbine maintenance, economic crop value and carbon reduction economic value, so that the self-circulation carbon reduction system obtains the optimal economic value; the economic value generated by the self-circulation carbon reduction system includes wind turbine power generation, shelterbelt carbon reduction economic value and economic crop value; The self-circulating carbon reduction system also includes a soil monitoring unit for obtaining soil monitoring parameters, and there are eight soil monitoring parameters, namely water content, bulk density, organic matter, pH value, total potassium, total nitrogen, total sulfur and total phosphorus; The optimization method includes: the economic value self-circulation control unit is optimized using a wild boar optimization algorithm to obtain the optimal economic value; The wild boar optimization algorithm includes local optimization and global optimization. The wild boar algorithm is used to optimize the soil locally. The optimized soil is comprehensively judged through monitoring indicators to see whether the soil has reached its usable value. After the soil is locally optimized to reach its usable value, the wild boar algorithm is used to globally optimize the route with the best economic value to maximize the economic value. The local optimization of the wild boar algorithm includes: (1) Construct the objective function: Among them, M represents the overall target soil value, k represents the soil optimization coefficient, Indicates the location of the soil; Represents eight monitoring indicators, α1, α2, α3, α4, α5, α6, α7, α8 represent the weights of the monitoring indicators; U represents the cumulative value of the monitoring indicator multiplied by the corresponding weight; (2) Initialize soil monitoring indicators; Among them, gb i Indicates the upper boundary of each soil monitoring index, fb i Indicates the lower boundary of each soil monitoring index, rand i is a random number between 0 and 1; i∈[1,8], indicating that there are eight monitoring indicators; Indicates initializing the value of each monitoring indicator and limiting it to the upper and lower boundaries; (3) Determine whether the soil monitoring indicators are qualified; if yes, implement a group attack on the soil monitoring indicators, which includes a group attack strategy and a persecution attack strategy; if no, implement a cleaning strategy on the soil monitoring indicators; (4) updating the current soil planting position to the position obtained by optimizing in step (3); (5) Determine whether the plantable area is optimal, that is, whether the target value M is within the available range [10, 20]. If not, repeat the above steps (1) to (4) to continue to search for the optimal value. Step (3) includes: Group attack strategy: in, is the new location with suitable soil for planting, na is a random integer, It is the unfavorable weather conditions that limit soil cultivation, is the current overall soil position, It is the qualified soil found in the previous iteration; Persecution attack strategy: Among them, Q1, Q2 are random optimization coefficients between -1 and 1. It means that Q2 is limited to [e -1 ,e], f1 is a random number from 1 to the maximum search space, It represents the soil position obtained by exploration; Cleaning strategy: Among them, λ represents the odd-even threshold of the current soil position. If it is an even number, the current soil has no usable value for cleaning; otherwise, it has usable value; Step (4) comprises: The global optimization of the wild boar algorithm includes: Minimize costs: S1=N1*L1 S2=N2*W*L2 Cost=c1*x1+c2*S1+c3*S2+c4*x4 Among them, S1 represents the sand control area, N1 represents the number of sand control belts, and L1 represents the length of each sand control belt; S2 represents the planting area, N2 represents the number of planting belts, W represents the width of each planting belt, and L2 represents the length of each planting belt; Cost represents cost, c1 represents generator cost, x1 represents the number of generators, c2 represents sand control cost, c3 represents planting cost, c4 represents wind turbine maintenance cost, and x4 represents the number of wind turbines; Maximize Profits: Profit=p1*S2+α*x1 Among them, Profit represents profit, p1 represents the economic crop harvest gain, and α represents the carbon reduction economic gain; Constraints: Among them, e1, e2, e3, γ, and f are all constant coefficients, representing the power generation of the generator, sand control capacity coefficient, planting coefficient, carbon emission coefficient, and carbon reduction coefficient, respectively; E max Indicates the maximum power generation capacity, A max Indicates the maximum sand control, B max Indicates planting capacity.

2. The optimization method according to claim 1, characterized in that: Cash crops grown include Cistanche deserticola, forage grass, peanuts and walnuts.

3. The optimization method according to claim 1, characterized in that: In step (3), the conditions for qualified soil monitoring indicators include: soil moisture content of 20% to 30%; bulk density of 1.2 to 1.8 g / cm 3 ; Organic matter content 0.8%~4.5%; pH value 5.2 to 6.8; total potassium content 1%~2%; total nitrogen content greater than 1.5g / kg; effective sulfur content 6.6~265.1mg / kg, average (36.8±3.1)mg / kg; total phosphorus content 0.10-0.15%.

4. The optimization method according to claim 1, characterized in that: The frequency of soil monitoring is shown in the table above.

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