Multi-energy coupled micro-energy network optimization scheduling method and system

Abstract

The invention belongs to the technical field of electric power construction, and particularly relates to a multi-energy coupled micro-energy network optimization scheduling method and system, which realize dynamic optimization scheduling and remarkably reduce the total carbon emission level of the system by periodically updating a comprehensive scheduling scheme and combining the latest energy use condition and environmental change. Meanwhile, all optimized path information is integrated, a comprehensive comprehensive scheduling scheme is constructed, and it is ensured that the whole micro-energy network meets the requirements and achieves global optimization at the same time. Besides, all unprocessed paths are circularly optimized, and the working state of the energy supply source is adjusted according to real-time data, so that the operation efficiency of the system is remarkably improved, and unnecessary energy loss is reduced. In conclusion, the limitation of a static scheduling strategy in the prior art is overcome, a more flexible and efficient optimization method is provided, and the overall performance and environmental protection benefits of the system are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power construction, and in particular relates to a multi-energy coupled micro energy network optimization scheduling method and system. Background Art

[0002] At present, the optimization scheduling method of micro-energy grid with multi-energy coupling has been widely used in many fields. The existing technical solutions usually include the following aspects: Energy network modeling: By establishing a micro-energy network connection diagram that includes multiple energy supply sources (such as solar energy, wind energy, fossil fuels, etc.), the conversion paths between various energy sources are defined.

[0003] Carbon emissions assessment: Identify all available energy flow directions and label the initial carbon emissions in each direction to assess the overall carbon emissions of the system.

[0004] Path selection and optimization: Select the optimal path based on carbon emissions and optimize system operation by adjusting the working status of energy supply sources.

[0005] Monitoring and feedback: Monitor system operation status, compare data before and after adjustment, calculate carbon emission reduction, and verify the effectiveness of optimization measures.

[0006] Although existing technologies can achieve efficient energy utilization and reduce carbon emissions to a certain extent, they often face the following challenges: Static scheduling strategy: Many existing scheduling methods use static scheduling strategies, which cannot be dynamically adjusted according to real-time changes in energy usage and environmental conditions, resulting in limited optimization effects.

[0007] Lack of comprehensive scheduling solutions: Although some systems can optimize a single path or local area, they lack a comprehensive scheduling solution, making it difficult to achieve global optimization in the entire micro-energy network.

[0008] In view of the above-mentioned deficiencies in the prior art, a major technical problem that the present invention aims to solve is: how to continuously optimize the scheduling strategy of the entire micro-energy network under dynamically changing energy usage and environmental conditions to minimize total carbon emissions and ensure efficient operation of the system. Summary of the invention

[0009] The purpose of the present invention is to provide a multi-energy coupled micro-energy grid optimization scheduling method and system, which not only solves the limitations of the static scheduling strategy in the prior art, but also provides a more flexible and efficient multi-energy coupled micro-energy grid optimization scheduling method, thereby effectively reducing the total carbon emissions of the system and improving the overall operating efficiency.

[0010] To achieve the above objectives, the present invention proposes a multi-energy coupled micro energy grid optimization scheduling method, comprising the following steps: Step 1: Establish a micro-energy network connection diagram containing at least two different types of energy supply sources and define the conversion paths between each energy source; Step 2: Based on the micro-energy network connection diagram, identify all available energy flow directions and mark the initial carbon emissions in each direction; Step 3: According to the marked initial carbon emissions, a path with the lowest carbon emissions is selected as the starting operation path, and real-time data on the starting operation path is recorded; Step 4: using the recorded real-time data, adjusting the working state of the energy supply source associated with the initial operation path; Step 5: Monitor the system operation status after adjustment, compare the data before adjustment, and calculate the reduction in carbon emissions; Step 6: Repeat steps 3 to 5 to perform cyclic optimization on the remaining unoptimized paths until all paths have been evaluated and adjusted; Step 7: Integrate all optimized route information and build a comprehensive dispatching plan to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions; Step 8: Regularly update the comprehensive scheduling plan described in Step 7, taking into account the latest energy usage and environmental changes, and continuously reduce carbon emissions.

[0011] Preferably, the establishment of a micro-energy network connection diagram including at least two different types of energy supply sources and defining conversion paths between energy sources includes: In the established micro energy network connection diagram, a unique identifier is assigned to each energy supply source, and basic parameters of each energy supply source are recorded; Based on the identifier and the basic parameters, a conversion path between each two energy supply sources is defined, and the energy conversion amount on the path is calculated; Using the calculated energy conversion amount, all possible energy supply source combinations are sorted to form a priority list to ensure that the path with high conversion efficiency is selected first; According to the generated priority list, the micro-energy network connection diagram is updated, and the actual connection relationship between each energy supply source is adjusted so that the energy conversion amount of all paths in the final micro-energy network connection diagram meets the minimum total conversion amount threshold set by the system.

[0012] Preferably, the method of identifying all available energy flow directions based on the micro energy network connection diagram and marking the initial carbon emissions in each direction includes: For the constructed micro-energy network connection diagram, a direction is assigned to each conversion path, and the initial carbon emissions in each direction are marked; Based on the directions and their corresponding initial carbon emissions, the cumulative carbon emissions in each direction are calculated, and all directions are sorted to form a list sorted in ascending order of carbon emissions; Using the calculated cumulative carbon emissions, the micro-energy network connection diagram is updated to mark the final carbon emissions in each direction, and the actual connection relationship between each energy supply source is adjusted to ensure that the carbon emissions in all directions meet the maximum total carbon emissions threshold set by the system.

[0013] Preferably, selecting a path with the lowest carbon emissions as a starting running path according to the marked initial carbon emissions, and recording real-time data on the starting running path, includes: Based on the generated list sorted in ascending order of carbon emissions, a path with the lowest carbon emissions is selected as the starting running path, and real-time data on the path is recorded; On the selected starting operation route, real-time data is recorded, including current power output, received power, and actual carbon emissions on the route; Using the recorded real-time data, calculate the energy utilization of the path and check whether it meets the minimum energy utilization threshold set by the system; According to the calculated energy utilization rate, the working state of the starting operation path is adjusted to ensure that it meets the minimum energy utilization rate threshold set by the system.

[0014] Preferably, the using the recorded real-time data to adjust the working state of the energy supply source related to the initial operation path includes: Based on the recorded real-time data, including the actual carbon emissions of the route, the power output at the starting point, and the received power at the end point, the current carbon emission intensity of the route is calculated; Using the calculated carbon emission intensity, determine the working status of the energy supply source that needs to be adjusted, and measure the goal of reducing carbon emissions through adjustment factors; According to the determined adjustment factor, the working state of the energy supply source related to the path is adjusted; Based on the adjusted power output, the energy utilization of the path is recalculated and checked whether it meets the minimum energy utilization threshold set by the system.

[0015] Preferably, the monitoring of the system operating status after adjustment, comparing the data before adjustment, and calculating the carbon emission reduction includes: Under the adjusted system operation state, record the new real-time data of the route, including the new starting power output, the end point receiving power and the new actual carbon emissions; Based on the new real-time data recorded, calculate the carbon emission difference between the paths before and after the adjustment; Using the calculated carbon emission differences, evaluate the effects of adjustment measures and define effect coefficients to measure the effects of adjustments; According to the effect coefficient, the overall performance improvement of the system is analyzed, and the minimum energy utilization threshold and minimum carbon emission intensity threshold set by the system are dynamically adjusted.

[0016] Preferably, repeating step 3 to step 5 to perform cyclic optimization on the remaining unoptimized paths until all paths are evaluated and adjusted includes: Select a path with the smallest cumulative carbon emissions from the remaining unoptimized paths, and repeat steps 3 to 5; Based on the selected route, the real-time data of the route is recorded and the new carbon emission intensity is calculated; Using the recorded data, the working status of energy supply sources related to the path is adjusted, and the goal of reducing carbon emissions is measured by the adjustment factor; According to the adjustment factor, the starting power output of the path is updated, and the energy utilization of the path is recalculated to check whether it meets the minimum energy utilization threshold set by the system.

[0017] Preferably, the integration of all optimized path information to construct a comprehensive scheduling solution to ensure that the entire micro-energy network minimizes total carbon emissions while meeting demand, includes: Collect all optimized path information, including the starting power output, terminal receiving power, actual carbon emissions and energy utilization of each path, and build a comprehensive scheduling plan; Based on all the collected path information, the total carbon emissions of the entire micro-energy network are calculated; Using the calculated total carbon emissions, the overall performance of the system is evaluated, and the overall performance coefficient is defined to measure the system performance; According to the overall performance coefficient, the power allocation of each path in the comprehensive scheduling plan is adjusted to ensure that the total carbon emissions are minimized while meeting the demand.

[0018] Preferably, the comprehensive scheduling scheme described in step 7 is regularly updated, combined with the latest energy usage and environmental changes, including: Set up regular update cycles and collect the latest energy usage data and environmental change information at the beginning of each update cycle; Recalculate the carbon emissions of each route based on the latest data collected and update the comprehensive dispatch plan; Using the new calculated carbon emissions, define an update factor to measure the extent of the required adjustment; According to the update factor, the power allocation of each path in the comprehensive scheduling scheme is adjusted to ensure that the total carbon emissions of the entire system are minimized under the new conditions.

[0019] On the other hand, the present invention proposes a multi-energy coupled micro energy grid optimization scheduling system, comprising: A network construction and path definition module is used to establish a micro-energy network connection diagram including at least two different types of energy supply sources and define the conversion paths between the energy sources; A carbon emission identification module, used to identify all available energy flow directions based on the micro energy network connection diagram, and mark the initial carbon emission in each direction; A starting operation path selection module, used to select a path with the lowest carbon emissions as a starting operation path according to the marked initial carbon emissions, and record real-time data on the starting operation path; An energy supply source adjustment module, used to adjust the working state of the energy supply source related to the starting operation path by using the recorded real-time data; System performance evaluation module, used to monitor the system operation status after adjustment, compare the data before adjustment, and calculate the carbon emission reduction; A loop optimization module, used for repeating steps 3 to 5 to perform loop optimization on the remaining unoptimized paths until all paths are evaluated and adjusted; The comprehensive dispatching scheme formulation module is used to integrate all optimized route information and build a comprehensive dispatching scheme to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions; The continuous improvement module is used to regularly update the comprehensive scheduling plan described in step 7, combining the latest energy usage and environmental changes to continuously reduce carbon emissions.

[0020] Technical effects and advantages of the present invention: Compared with the prior art, the multi-energy coupled micro-energy grid optimization scheduling method and system proposed by the present invention has the following advantages: The present invention can continuously optimize the scheduling strategy of the entire micro-energy network under dynamically changing energy usage and environmental conditions. The present invention realizes dynamic optimization scheduling by regularly updating the comprehensive scheduling scheme, combining the latest energy usage and environmental changes, and significantly reduces the total carbon emission level of the system. At the same time, the present invention integrates all optimized path information and constructs a comprehensive comprehensive scheduling scheme to ensure that the entire micro-energy network achieves global optimization while meeting demand. In addition, by cyclically optimizing all unprocessed paths and adjusting the working status of the energy supply source according to real-time data, the present invention significantly improves the operating efficiency of the system and reduces unnecessary energy loss. In summary, the present invention not only overcomes the limitations of the static scheduling strategy in the prior art, but also provides a more flexible and efficient optimization method, which effectively improves the overall performance and environmental benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a multi-energy coupled micro energy network optimization scheduling method of the present invention; Figure 2 The block diagram of the multi-energy coupled micro energy grid optimization scheduling system of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the accompanying 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 only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] The present invention provides Figure 1 The present invention is a multi-energy coupled micro energy network optimization scheduling method, which can continuously optimize the scheduling strategy of the entire micro energy network under dynamically changing energy usage and environmental conditions, and specifically includes the following steps: Step 1: Establish a micro-energy network connection diagram containing at least two different types of energy supply sources and define the conversion paths between the energy sources; specifically, the following sub-process steps are included:

[0024] In the established micro energy network connection diagram, each energy supply source is assigned a unique identifier A, B, C..., and the basic parameters V(A), V(B), V(C)... of each energy supply source are recorded, where V represents power; by assigning a unique identifier (such as A, B, C, etc.) to each energy supply source and recording its power value V(A), V(B), V(C), the capacity of each energy supply source can be clearly identified and quantified.

[0025] Based on the identifiers and basic parameters, define the conversion path between every two energy supply sources, and use the formula Q(X,Y)=k*(V(X)-V(Y)) to calculate the energy conversion amount on the path, where X and Y represent any two different energy supply source identifiers, and k is the conversion efficiency coefficient; this step defines the energy conversion amount between any two energy supply sources X and Y. The conversion efficiency coefficient k reflects the loss when converting energy from one energy supply source to another. V(X) and V(Y) in the formula represent the power of the two energy supply sources respectively, and the difference (V(X)-V(Y)) represents the potential energy difference. Multiplying it by the conversion efficiency coefficient k gives the actual convertible energy Q(X,Y). If V(X)>V(Y), energy can flow from X to Y; and vice versa.

[0026] Using the calculated energy conversion amount Q(X,Y), all possible energy supply source combinations are sorted to form a priority list L. According to the rule of L=[(X,Y)|Q(X,Y)>0], the high conversion efficiency path is selected first; according to the calculated energy conversion amount Q(X,Y), all forward conversion possibilities are sorted in descending order to ensure that the high conversion efficiency path is selected first. Only when Q(X,Y)>0, it indicates that there is an effective energy conversion path, which ensures the effective use of resources and the stability of the system.

[0027] According to the generated priority list L, the micro-energy network connection diagram is updated, and the actual connection relationship between each energy supply source is adjusted so that the energy conversion amount of all paths in the final micro-energy network connection diagram meets the condition sum(Q(X,Y))forall(X,Y)inL>=T, where T is the minimum total conversion amount threshold set by the system; based on the generated priority list L, the connection between energy supply sources is reconfigured so that the total energy conversion amount of the entire network reaches or exceeds the set threshold T. By adjusting the connection relationship, the energy flow of the entire micro-energy network is optimized to ensure that the total output of the system meets the minimum requirements.

[0028] Assume that there are three energy supply sources A, B, and C, with powers of V(A)=100W, V(B)=80W, and V(C)=90W, respectively, and conversion efficiency coefficient k=0.8.

[0029] Calculate Q(A,B)=0.8*(100-80)=16W; Calculate Q(A,C)=0.8*(100-90)=8W; Calculate Q(B,C)=0.8*(80-90)=-8W (not considered); The priority list L is sorted according to the Q value as follows: [(A,B),(A,C)].

[0030] If the minimum total conversion threshold T is set to 20W, you need to check whether the conditions are met: sum(Q(X,Y))forall(X,Y)inL=16+8=24W>=T; Therefore, the current configuration meets the conditions and no further adjustment is required. In the final micro-energy network connection diagram, the energy conversion paths between A and B and between A and C are confirmed to be valid.

[0031] Step 2: Based on the micro-energy network connection diagram, identify all available energy flow directions and mark the initial carbon emissions in each direction; specifically, the following sub-process steps are included: For the constructed micro-energy network connection diagram, a direction D(X,Y) is assigned to each conversion path, where X and Y represent the energy supply source identifiers of energy outflow and inflow, respectively; the initial carbon emissions E_i(D) in the defined direction are the direct emissions associated with the path; by specifying the direction D(X,Y) for each conversion path, the direction of energy flow can be clarified. At the same time, the initial carbon emissions E_i(D) in the defined direction are used as the starting point for evaluation. The direction D(X,Y) indicates that energy flows from X to Y, and E_i(D) represents the carbon emissions directly generated by the path, which helps to understand the basic environmental impact of each path.

[0032] Based on the direction D(X,Y) and its corresponding initial carbon emissions E_i(D), the cumulative carbon emissions C_e(X,Y) in each direction are calculated using the formula C_e(X,Y)=E_i(D)+α*V(Y), where α is the new carbon emission coefficient in the conversion process and V(Y) is the basic parameter of the receiving energy supply source; this step takes into account the new carbon emissions in the conversion process, and calculates the cumulative carbon emissions C_e(X,Y) by multiplying α (the new carbon emission coefficient in the conversion process) by the basic parameter V(Y) of the receiving energy supply source. E_i(D) in the formula is the direct emission value, while α*V(Y) takes into account the additional carbon emissions during energy conversion and transmission. This provides a comprehensive assessment of the environmental impact of the entire conversion path.

[0033] Using the calculated cumulative carbon emissions C_e(X,Y), sort all directions to form a list S in ascending order of carbon emissions, following the rule S=[(X,Y)|C_e(X,Y)] and ensuring that they are sorted in ascending order of cumulative carbon emissions; sort all directions according to cumulative carbon emissions to ensure that paths with lower carbon emissions are given priority to promote the use of green energy. By sorting the carbon emissions of the paths, it can help optimize the network configuration and reduce the overall carbon footprint.

[0034] According to the generated list S, the micro-energy network connection diagram is updated, the final carbon emissions in each direction are marked, and the actual connection relationship between each energy supply source is adjusted so that the carbon emissions in all directions in the final micro-energy network connection diagram meet the condition sum(C_e(X,Y))forall(X,Y)inS<=Z, where Z is the maximum total carbon emissions threshold set by the system; based on the generated list S, the connection between energy supply sources is reconfigured so that the total carbon emissions of the entire network do not exceed the set maximum threshold Z. By adjusting the connection relationship, the total carbon emissions of the system are ensured to be within an acceptable range, supporting the realization of environmental protection goals.

[0035] Assume that there are three energy supply sources A, B, and C, with powers of V(A)=100W, V(B)=80W, and V(C)=90W, respectively, and conversion efficiency coefficient k=0.8 (for reference). The initial carbon emissions E_i(D) are E_i(D(A,B))=5kgCO2 and E_i(D(A,C))=3kgCO2, respectively. The additional carbon emission coefficient α during the conversion process is 0.02kgCO2 / W.

[0036] Calculate C_e(A,B)=5+0.02*80=6.6kgCO2; Calculate C_e(A,C)=3+0.02*90=4.8kgCO2; The cumulative carbon emissions list S is arranged in ascending order as follows: [(A,C),(A,B)], and the corresponding cumulative carbon emissions are 4.8kgCO2 and 6.6kgCO2 respectively.

[0037] If the maximum total carbon emission threshold Z=12kgCO2 is set, it is necessary to check whether the conditions are met: sum(C_e(X,Y))forall(X,Y)inS=4.8+6.6=11.4kgCO2<=Z; Therefore, the current configuration meets the conditions and no further adjustment is required. In the final micro-energy network connection diagram, the energy conversion paths between A and C and between A and B are confirmed to be valid, and the respective final carbon emissions are marked as 4.8kgCO2 and 6.6kgCO2, ensuring that the total carbon emissions are controlled within the target range.

[0038] Step 3: According to the marked initial carbon emissions, a path with the lowest carbon emissions is selected as the starting operation path, and real-time data on the starting operation path is recorded; specifically, the following sub-process steps are included: Based on the generated list S sorted in ascending order by carbon emissions, a path with the smallest cumulative carbon emissions C_e(X,Y) is selected as the starting operation path, and the path is defined as R(A,B), where A and B are the energy supply source identifiers of energy outflow and inflow respectively; selecting a path with the smallest C_e(X,Y) from the list S sorted in ascending order by carbon emissions as the starting operation path R(A,B) helps to minimize environmental impact while meeting energy needs. Giving priority to paths with lower carbon emissions can reduce the carbon footprint of the entire system and support the use of green energy.

[0039] On the selected initial operating path R(A,B), record real-time data, including the current power output V(A), the received power V(B), and the actual carbon emissions E_a(R) on the path; use the formula E_a(R)=C_e(A,B) to represent the actual carbon emissions of the path; record the current power output V(A), the received power V(B), and the actual carbon emissions E_a(R) on the path, which are crucial for monitoring and optimizing system performance. By recording real-time data, the actual performance of the path can be accurately evaluated and provide a basis for subsequent adjustments. E_a(R)=C_e(A,B) means that the actual carbon emissions of the path are equal to its cumulative carbon emissions.

[0040] Using the recorded real-time data, calculate the energy utilization rate U_r(R) of the path R(A,B), using the formula U_r(R)=(V(B) / V(A))*100%, where V(A) is the power output at the start of the path and V(B) is the received power at the end of the path; calculating the energy utilization rate U_r(R) can help understand the path transmission efficiency and ensure efficient use of resources. U_r(R) reflects the proportion of the energy received at the end of the path to the energy emitted at the start, and the percentage form is convenient for intuitive understanding.

[0041] According to the calculated energy utilization rate U_r(R), adjust the working state of the initial operating path R(A,B) to ensure that it meets the minimum energy utilization rate threshold W_u set by the system; the specific operation is that if U_r(R)<W_u, then adjust by increasing V(A) or decreasing V(B) until U_r(R)>=W_u; ensure that the energy utilization rate of the path is not lower than the minimum standard W_u set by the system, thereby improving the overall energy efficiency. By adjusting V(A) or V(B), the energy conversion efficiency of the path can be optimized, making U_r(R) reach or exceed the preset threshold and ensuring system performance.

[0042] Suppose there are the following parameters: The initial operating path R(A,B), where A and B are the identifiers of the energy supply sources for energy outflow and inflow respectively; The cumulative carbon emissions C_e(A,B)=4.8kgCO2 (from the calculation result in step two); The current power output V(A)=100W, and the received power V(B)=75W; The minimum energy utilization rate threshold W_u=75%.

[0043] Calculate the actual carbon emissions E_a(R)=C_e(A,B)=4.8kgCO2; Calculate the energy utilization rate U_r(R)=(V(B) / V(A))*100%=(75 / 100)*100%=75%; Since U_r(R)=75%, it is exactly equal to W_u, so no adjustment is required. If U_r(R) is lower than 75%, for example, U_r(R)=70%, then adjustment is required. Assuming that you choose to increase V(A) to about 107W to increase U_r(R) to at least 75% (the specific value needs to be fine-tuned according to the actual situation), the calculation is as follows: U_r(R)=(75 / 107)*100%≈70.09% This suggests that directly increasing V(A) may not be enough to achieve the goal, and reducing V(B) may be more effective, or taking both measures to ensure U_r(R)>=W_u. For example, reducing V(B) to 70W: U_r(R)=(70 / 100)*100%=70%; Further fine-tuning is performed until a suitable combination of V(A) and V(B) is found so that U_r(R)>=75%. This process emphasizes the importance of dynamic adjustment to maintain system performance and meet environmental protection requirements.

[0044] Step 4: Using the recorded real-time data, adjust the working state of the energy supply source related to the initial operation path; specifically including the following sub-process steps: Based on the recorded real-time data, including the actual carbon emissions E_a(R) of the path R(A,B), the starting power output V(A) and the end point received power V(B), the current carbon emission intensity I_c(R) of the path is calculated using the formula I_c(R)=E_a(R) / V(A); by calculating the carbon emission intensity I_c(R), the carbon emissions generated per unit power output can be quantified, providing a basis for subsequent optimization. The carbon emission intensity I_c(R) reflects the energy conversion efficiency and environmental impact of the path R(A,B). The lower the value, the more environmentally friendly the path is.

[0045] The calculated carbon emission intensity I_c(R) is used to determine the working state of the energy supply source that needs to be adjusted; the adjustment factor F_a is defined as the target coefficient for reducing carbon emissions, and is calculated using the formula F_a=(I_c(R)-I_min) / I_min, where I_min is the minimum carbon emission intensity threshold set by the system; by calculating the adjustment factor F_a, the gap between the current path and the minimum carbon emission intensity threshold I_min can be clarified, thereby guiding specific adjustment measures. The adjustment factor F_a measures the difference between the current carbon emission intensity and the ideal state. F_a>0 indicates that optimization is needed to reduce carbon emissions.

[0046] According to the determined adjustment factor \(F_a\), adjust the operating state of the energy supply source \(A\) associated with the path \(R(A,B)\); if \(F_a > 0\), increase the efficiency of the energy supply source \(A\) or reduce its output power, and use the formula \(V'(A)=V(A)*(1 - F_a)\) to update \(V(A)\); update the starting power output \(V(A)\) according to the adjustment factor \(F_a\) to reduce carbon emissions and improve energy efficiency. Reduce carbon emissions by reducing the output power or increasing the efficiency, and the specific operation depends on the magnitude of \(F_a\). If \(F_a > 0\), it indicates that adjustments are needed.

[0047] Based on the adjusted power output \(V'(A)\), recalculate the energy utilization rate \(U'_r(R)\) of the path \(R(A,B)\) using the formula \(U'_r(R)=(V(B) / V'(A))*100\%\), and check whether it meets the minimum energy utilization rate threshold \(W_u\) set by the system; if \(U'_r(R)<W_u\), adjust \(V'(A)\) or \(V(B)\) until \(U'_r(R)\geq W_u\); by recalculating the energy utilization rate \(U'_r(R)\) of the path, ensure that the adjusted system can still meet the minimum energy utilization rate requirement \(W_u\). If \(U'_r(R)<W_u\), then further adjust \(V'(A)\) or \(V(B)\) until the condition is met. This ensures that the system operates efficiently while reducing carbon emissions.

[0048] Suppose there are the following parameters: Path \(R(A,B)\), where \(A\) and \(B\) are the identifiers of the energy supply sources for energy outflow and inflow respectively; Actual carbon emissions \(E_a(R)=4.8\ kgCO_2\); Current power output \(V(A)=100\ W\), received power \(V(B)=75\ W\); The minimum carbon emission intensity threshold \(I_{min}=0.04\ kgCO_2 / W\) set by the system; Minimum energy utilization rate threshold \(W_u = 75\%\).

[0049] Calculate the carbon emission intensity \(I_c(R)\): \(I_c(R)=E_a(R) / V(A)=4.8 / 100 = 0.048\ kgCO_2 / W\); Determine the adjustment factor \(F_a\): \(F_a=(I_c(R)-I_{min}) / I_{min}=(0.048 - 0.04) / 0.04 = 0.2\); Adjust the operating state of the energy supply source \(A\): If \(F_a > 0\), then \(V(A)\) needs to be adjusted: \(V'(A)=V(A)*(1 - F_a)=100*(1 - 0.2)=80\ W\); Recalculate the energy utilization rate of the path U'_r(R): U'_r(R)=(V(B) / V'(A))*100%=(75 / 80)*100%=93.75%; Since U'_r(R)=93.75%, which is much higher than the minimum energy utilization threshold W_u=75%, no further adjustment is required. If U'_r(R) is lower than 75%, for example, U'_r(R)=70%, V'(A) or V(B) needs to be further adjusted.

[0050] Assume U'_r(R)=70%, that is: (75 / V'(A))*100%=70%; The solution is: V'(A)=75 / 0.7≈107.14W; At this point, V'(A) needs to be adjusted to about 107.14W so that U'_r(R)>=75%. This shows that in actual operation, it may be necessary to fine-tune the specific values ​​of V'(A) and V(B) to ensure that the system meets the minimum energy utilization requirements while minimizing carbon emissions. In this way, the best balance can be found between environmental protection and energy efficiency.

[0051] Step 5: Monitor the system operation status after adjustment, compare the data before adjustment, and calculate the carbon emission reduction; including the following sub-process steps: In the adjusted system operation state, record the new real-time data of path R(A,B), including the new starting power output V'(A), the end point received power V(B), and the new actual carbon emissions E'_a(R); use the formula E'_a(R)=I_c(R)*V'(A) to calculate the new carbon emissions; record the new starting power output V'(A), the end point received power V(B), and the new actual carbon emissions E'_a(R) to evaluate the effect of the adjustment measures. By comparing the data before and after the adjustment, the impact of the adjustment measures on the system performance can be quantified. E'_a(R)=I_c(R)*V'(A) represents the carbon emissions recalculated based on the adjusted power output.

[0052] Based on the new real-time data recorded, calculate the carbon emission difference ΔE of path R(A,B) before and after the adjustment; use the formula ΔE=E_a(R)-E'_a(R), where E_a(R) is the actual carbon emission before the adjustment, and E'_a(R) is the new actual carbon emission after the adjustment; by calculating the carbon emission difference ΔE, the carbon emission reduction brought about by the adjustment measures can be clearly identified, providing a basis for subsequent optimization. ΔE reflects the change in carbon emissions before and after the adjustment, a positive value indicates a reduction in carbon emissions, and a negative value indicates an increase.

[0053] The calculated carbon emission difference ΔE is used to evaluate the effect of the adjustment measures; the effect coefficient K_e is defined as an indicator to measure the effect of the adjustment, and is calculated by the formula K_e=ΔE / E_a(R), which expresses the proportion of carbon emission reduction in percentage form; by calculating the effect coefficient K_e, the effectiveness of the adjustment measures can be measured, and the proportion of carbon emission reduction can be intuitively displayed in percentage form. K_e=ΔE / E_a(R) provides a quantitative evaluation of the effect of the adjustment measures, and the larger the value, the more significant the improvement.

[0054] According to the effect coefficient K_e, analyze the overall performance improvement of the system; if K_e>0, confirm that the adjustment measures are effective, and update the minimum energy utilization rate threshold W_u and the minimum carbon emission intensity threshold I_min set by the system; the specific operation is to use the formula W_u_new=W_u+δW and I_min_new=I_min-δI, where δW and δI are the changes in the energy utilization rate and carbon emission intensity thresholds dynamically adjusted based on the effect coefficient K_e; dynamically adjust the energy utilization rate and carbon emission intensity thresholds to ensure that the system maintains efficient operation while meeting environmental protection requirements. δW and δI are based on the changes in the effect coefficient K_e, and are used to gradually improve the performance standards of the system.

[0055] Assume the following parameters: The actual carbon emissions before adjustment are E_a(R)=4.8kgCO2; Carbon emission intensity I_c(R) = 0.048 kgCO2 / W (calculated in step 4); The adjusted starting power output V'(A)=80W (from the calculation result in step 4), the end point received power V(B)=75W; The minimum energy utilization threshold W_u=75%; The minimum carbon emission intensity threshold I_min=0.04kgCO2 / W.

[0056] Calculate the new actual carbon emissions E'_a(R): E'_a(R)=I_c(R)*V'(A)=0.048*80=3.84kgCO2; Calculate the carbon emission difference ΔE: ΔE=E_a(R)-E'_a(R)=4.8-3.84=0.96kgCO2; Evaluate the effectiveness of adjustment measures: K_e=ΔE / E_a(R)=0.96 / 4.8=0.2 or 20%; Analyze the overall performance improvement of the system and update the set thresholds: If K_e>0, the adjustment measures are confirmed to be effective. Assume that the energy utilization rate threshold Wu and the carbon emission intensity threshold I_min are increased and decreased by a certain percentage respectively, for example: δW = 0.05 (i.e. 5%); δI = 0.002 (i.e. 0.2%); Updated thresholds: W_u_new=W_u+δW=75%+5%=80%; I_min_new=I_min-δI=0.04-0.002=0.038kgCO2 / W; Summary of formula application principles and meanings: E'_a(R)=I_c(R)*V'(A): Calculates adjusted actual carbon emissions and re-evaluates carbon emissions levels based on adjusted power output.

[0057] ΔE=E_a(R)-E'_a(R): Calculate the difference in carbon emissions before and after adjustment to reflect the carbon reduction effect brought about by the adjustment measures.

[0058] K_e=ΔE / E_a(R): Measure the effectiveness of adjustment measures and show the proportion of carbon emissions reduction in percentage form to help decide whether further optimization is needed.

[0059] W_u_new=W_u+δW and I_min_new=I_min-δI: The energy utilization rate and carbon emission intensity thresholds set by the system are dynamically updated according to the adjustment effects, and the environmental protection and energy efficiency standards of the system are gradually improved.

[0060] Through the above steps and formulas, the operating status of the micro-energy network can be systematically evaluated and optimized to ensure efficient energy utilization while reducing carbon emissions.

[0061] Step 6: Repeat steps 3 to 5 to perform cyclic optimization on the remaining unoptimized paths until all paths have been evaluated and adjusted. This includes the following sub-process steps: Select a path R'(X,Y) with the smallest cumulative carbon emissions from the remaining unoptimized paths, where X and Y are the energy supply source identifiers for energy outflow and inflow, respectively; determine the path using the formula C'_e(X,Y)=min(C_e(X,Y)), where C_e(X,Y) is the cumulative carbon emissions of each path; select a path R'(X,Y) with the smallest cumulative carbon emissions from the remaining unoptimized paths, giving priority to high-potential optimization paths. By selecting the path with the smallest cumulative carbon emissions, the total carbon emissions of the entire system can be gradually reduced, ensuring that each adjustment step can bring significant environmental benefits.

[0062] Based on the selected path R'(X,Y), repeat the operations from step 3 to step 5; first record the real-time data of the path R'(X,Y), including the starting power output V(X), the end point received power V(Y), and the actual carbon emissions E'_a(R'), and calculate the new carbon emission intensity I'_c(R'), using the formula I'_c(R')=E'_a(R') / V(X); record the starting power output V(X), the end point received power V(Y), and the actual carbon emissions E'_a(R') of the path R'(X,Y), and calculate the new carbon emission intensity I'_c(R'). Real-time data recording and carbon emission intensity calculation help understand the performance of the current path and provide a basis for subsequent adjustments.

[0063] Using the recorded data, adjust the working status of the energy supply source related to the path R'(X,Y); define the adjustment factor F'_a as the target coefficient for reducing carbon emissions, and calculate it through the formula F'_a=(I'_c(R')-I_min_new) / I_min_new, where I_min_new is the updated minimum carbon emission intensity threshold; calculate the adjustment factor F'_a based on the new minimum carbon emission intensity threshold I_min_new to guide how to adjust the energy supply source on the path to reduce carbon emissions. The adjustment factor F'_a reflects the gap between the current carbon emission intensity and the ideal state, and helps to formulate specific adjustment strategies.

[0064] Update the starting power output V''(X) of path R'(X,Y) according to the adjustment factor F'_a, using the formula V''(X)=V(X)*(1 - F'_a); then recalculate the energy utilization rate U'_r(R') of path R'(X,Y), using the formula U'_r(R')=(V(Y) / V''(X))*100%, and check whether it meets the minimum energy utilization rate threshold W_u_new set by the system; if U'_r(R') < W_u_new, adjust V''(X) or V(Y) until U'_r(R') >= W_u_new; update the starting power output V''(X) of path R'(X,Y) according to the adjustment factor F'_a, and recalculate the energy utilization rate U'_r(R') to ensure that the minimum energy utilization rate threshold W_u_new set by the system is met. By dynamically adjusting the starting power output, high-efficiency energy utilization can be maintained while reducing carbon emissions.

[0065] Suppose there are the following parameters (based on the examples in the previous steps): The path R'(X,Y) with the minimum cumulative carbon emissions in the remaining unoptimized paths, where X and Y are the identifiers of the energy supply sources for energy outflow and inflow, respectively; The cumulative carbon emissions C_e(X,Y)=6.6kgCO2 (from the calculation result in step two); The current power output V(X)=90W, and the received power V(Y)=70W; The actual carbon emissions E'_a(R')=6.6kgCO2 (assuming the data before adjustment); The updated minimum carbon emission intensity threshold I_min_new=0.038kgCO2 / W; The updated minimum energy utilization rate threshold W_u_new=80%.

[0066] Select the path with the minimum cumulative carbon emissions: C'_e(X,Y)=min(C_e(X,Y))=6.6kgCO2;

[0067] Record the real-time data of the selected path and calculate the new carbon emission intensity: I'_c(R')=E'_a(R') / V(X)=6.6 / 90≈0.0733kgCO2 / W; Determine the adjustment factor and update the working status of the energy supply source: F'_a=(I'_c(R') - I_min_new) / I_min_new=(0.0733 - 0.038) / 0.038≈0.93; Update the starting power output of the path and recalculate the energy utilization rate: V''(X)=V(X)*(1-F'_a)=90*(1-0.93)≈6.3W; U'_r(R')=(V(Y) / V''(X))*100%=(70 / 6.3)*100%≈1111.11%; Since U'_r(R') is much higher than the minimum energy utilization threshold W_u_new=80%, the adjustment strategy needs to be re-evaluated. Assuming that the power output after adjustment cannot be too low, the value of F'_a can be slightly adjusted, for example, set F'_a=0.5: V''(X)=V(X)*(1-F'_a)=90*(1-0.5)=45W; U'_r(R')=(V(Y) / V''(X))*100%=(70 / 45)*100%≈155.56%; At this point, U'_r(R') is still high, but more reasonable. To further optimize, you can try adjusting V(Y): Assume that V(Y) is adjusted to 60W: U'_r(R')=(60 / 45)*100%≈133.33%; Ultimately, ensure that U'_r(R')≥80% and reduce carbon emissions. The specific operations are as follows: Confirm that the adjustment measures are effective: The new actual carbon emissions E'_a(R')=I'_c(R')*V''(X)=0.0733*45≈3.3kgCO2; ΔE=E_a(R')-E'_a(R')=6.6-3.3=3.3kgCO2; K_e=ΔE / E_a(R')=3.3 / 6.6=0.5 or 50%; Analyze the overall performance improvement of the system and update the set thresholds: If K_e>0, the adjustment measures are confirmed to be effective. Assume that the energy utilization rate threshold Wu and the carbon emission intensity threshold I_min are increased and decreased by a certain percentage respectively, for example: δW=0.05 (i.e. 5%); δI = 0.002 (i.e. 0.2%); Updated thresholds: W_u_new=W_u+δW=80%+5%=85%; I_min_new=I_min-δI=0.038-0.002=0.036kgCO2 / W; Through the above steps and formulas, all paths can be systematically evaluated and optimized to ensure that each path maintains efficient energy utilization while reducing carbon emissions. This process can be continuously optimized through iteration until all paths have been evaluated and adjusted.

[0068] Step 7: Integrate all optimized route information and build a comprehensive scheduling plan to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions; specifically, it includes the following sub-process steps: Collect all optimized path information, including the starting power output V''(X), the terminal receiving power V(Y), the actual carbon emissions E'_a(R'), and the energy utilization rate U'_r(R') of each path; define a comprehensive scheduling scheme D_s, which contains the information of all paths; collect the starting power output V''(X), the terminal receiving power V(Y), the actual carbon emissions E'_a(R'), and the energy utilization rate U'_r(R') of each path, so as to construct a comprehensive comprehensive scheduling scheme D_s. By summarizing the information of all paths, we can fully understand the operating status of the system and provide data support for the subsequent overall performance evaluation.

[0069] Based on all the collected path information, the total carbon emissions T_c of the entire micro energy network are calculated; the formula T_c=sum(E'_a(R'))forallpaths is used, where E'_a(R') is the actual carbon emissions of each path; by accumulating the actual carbon emissions E'_a(R') of all paths, the total carbon emissions T_c of the entire micro energy network can be obtained to help evaluate the environmental performance of the system. The total carbon emissions T_c reflects the environmental impact of the system under the current configuration. The lower the value, the more environmentally friendly the system is.

[0070] The calculated total carbon emissions T_c are used to evaluate the overall performance of the system; the overall performance coefficient G_p is defined as an indicator to measure the system performance, and is calculated using the formula G_p=(sum(U'_r(R'))forallpaths) / n, where n is the total number of paths and U'_r(R') is the energy utilization rate of each path; by calculating the overall performance coefficient G_p, the energy utilization efficiency of each path in the system can be measured to ensure high-efficiency operation. G_p is the average value of the energy utilization rate U'_r(R') of all paths, reflecting the overall energy conversion efficiency of the system. A higher G_p value indicates a higher overall performance.

[0071] Adjust the comprehensive scheduling plan \(D_s\) according to the overall performance coefficient \(G_p\) to ensure minimizing the total carbon emissions \(T_c\) while meeting the demand; if \(G_p < G_{min}\), reallocate the power output of each path, and adjust the starting power output of each path using the formula \(V'''(X)=V''(X)*(G_{min} / G_p)\) until \(G_p\geq G_{min}\), where \(G_{min}\) is the minimum overall performance threshold set by the system; dynamically adjust the power output of each path according to the overall performance coefficient \(G_p\) to ensure minimizing the total carbon emissions \(T_c\) of the system while meeting the demand. By adjusting the starting power output, carbon emissions can be reduced while maintaining high efficiency, gradually improving the system performance.

[0072] Suppose there are the following parameters (based on the examples in the previous steps): There are two optimized paths in total: \(R'(A,B)\) and \(R'(C,D)\): For path \(R'(A,B)\), the starting power output \(V''(A) = 45W\), the received power at the end \(V(B)=70W\), the actual carbon emissions \(E'_a(R'(A,B)) = 3.3kgCO_2\), and the energy utilization rate \(U'_r(R'(A,B)) = 155.56\%\); For path \(R'(C,D)\), the starting power output \(V''(C) = 80W\), the received power at the end \(V(D)=60W\), the actual carbon emissions \(E'_a(R'(C,D)) = 3.84kgCO_2\), and the energy utilization rate \(U'_r(R'(C,D)) = 75\%\); Collect all the information of the optimized paths: Path \(R'(A,B)\): \(V''(A)=45W\), \(V(B)=70W\), \(E'_a(R'(A,B))=3.3kgCO_2\), \(U'_r(R'(A,B))=155.56\%\); Path \(R'(C,D)\): \(V''(C)=80W\), \(V(D)=60W\), \(E'_a(R'(C,D))=3.84kgCO_2\), \(U'_r(R'(C,D))=75\%\); Calculate the total carbon emissions of the entire micro - energy network: \(T_c = E'_a(R'(A,B))+E'_a(R'(C,D))=3.3 + 3.84 = 7.14kgCO_2\); Evaluate the overall performance of the system: The total number of paths \(n = 2\); \(G_p=(U'_r(R'(A,B))+U'_r(R'(C,D))) / n=(155.56\%+75\%) / 2 = 115.28\%\); Adjust the comprehensive scheduling plan to minimize the total carbon emissions: Assume that the minimum overall performance threshold G_min set by the system is 90%; If G_p>G_min (i.e. 115.28%>90%), no further adjustment is required.

[0073] But if we assume G_min=120%, we need to make adjustments: Calculate the new starting power output V'''(X): For the path R'(A,B): V''''(A)=V''(A)*(G_min / G_p)=45*(120% / 115.28%)≈46.8W; For the path R'(C,D): V''''(C)=V''(C)*(G_min / G_p)=80*(120% / 115.28%)≈83.2W; Recalculate the energy utilization rate U'_r(R'): For the path R'(A,B): U'_r(R'(A,B))=(V(B) / V'''(A))*100%=(70 / 46.8)*100%≈149.57% For the path R'(C,D): U'_r(R'(C,D))=(V(D) / V'''(C))*100%=(60 / 83.2)*100%≈72.12% Recalculate the overall performance coefficient G_p: G_p=(149.57%+72.12%) / 2≈110.85%; At this point, G_p is still less than G_min (120%), so further adjustment is needed: Suppose G_min is adjusted to 110% again: For the path R'(A,B): V''''(A)=45*(110% / 110.85%)≈44.6W; For the path R'(C,D): V''''(C)=80*(110% / 110.85%)≈79.4W; Recalculate the energy utilization rate U'_r(R'): For the path R'(A,B): U'_r(R'(A,B))=(70 / 44.6)*100%≈156.95%; For the path R'(C,D): U'_r(R'(C,D))=(60 / 79.4)*100%≈75.57%; Recalculate the overall performance coefficient G_p: G_p=(156.95%+75.57%) / 2≈116.26%; At this point, G_p is already greater than or equal to G_min (110%), so no further adjustment is required.

[0074] Summary of formula application principles and meanings: T_c=sum(E'_a(R'))forallpaths: The actual carbon emissions of all paths are accumulated to obtain the total carbon emissions of the entire system, which is used to evaluate the environmental performance of the system.

[0075] G_p=(sum(U'_r(R'))forallpaths) / n: Calculating the average value of energy utilization of all paths reflects the overall energy conversion efficiency of the system and helps evaluate the overall performance.

[0076] V'''(X)=V''(X)*(G_min / G_p): According to the gap between the overall performance coefficient G_p and the minimum overall performance threshold G_min, the starting power output of each path is dynamically adjusted to ensure that the system minimizes total carbon emissions while meeting demand.

[0077] Through the above steps and formulas, the operation status of the entire micro-energy network can be systematically evaluated and optimized to ensure efficient energy utilization while reducing carbon emissions.

[0078] Step 8: Regularly update the comprehensive scheduling plan described in step 7, combining the latest energy usage and environmental changes to continuously reduce carbon emissions; specifically, the following sub-process steps are included: Set a regular update cycle Tu, for example, every 7 days or once a month; at the beginning of each update cycle, collect the latest energy usage data and environmental change information, including the actual power output P''(X), received power P(Y), carbon emissions E''(R') and external environmental parameters (such as temperature T_e, light intensity L_s, etc.) of each path; by regularly collecting the latest data, it can be ensured that the system can adapt to changing energy demands and environmental conditions, thereby optimizing the operating status.

[0079] Based on the latest data collected, recalculate the carbon emissions E''(R') of each path; use the formula E''(R')=I(R')*P''(X), where I(R') is the carbon emission intensity of the path and P''(X) is the adjusted starting power output; based on the latest power output P''(X) and the carbon emission intensity of the path I(R'), recalculate the carbon emissions E''(R') of each path to reflect the actual operating status of the current system. The carbon emissions E''(R') are calculated based on the latest starting power output P''(X) and carbon emission intensity I(R'), which helps to accurately assess the environmental impact of the current path.

[0080] Use the calculated new carbon emissions E''(R') to update the comprehensive scheduling plan D; define an update factor U_f to measure the degree of adjustment required, and calculate it through the formula U_f=(sum(E'(R'))-sum(E''(R'))) / sum(E'(R')), where E'(R') is the carbon emissions after the last optimization, and E''(R') is the newly calculated carbon emissions; use the newly calculated carbon emissions E''(R') to update the comprehensive scheduling plan D, and define the update factor U_f to measure the degree of adjustment required. U_f reflects the changes in carbon emissions and helps decide whether further optimization is needed. U_f represents the difference ratio between the new and old carbon emissions. A positive value indicates a decrease in carbon emissions, while a negative value indicates an increase. This provides a basis for subsequent adjustments.

[0081] According to the update factor U_f, adjust the power allocation of each path in the comprehensive scheduling scheme D; if U_f>0, it means that carbon emissions have been reduced and the working state of the path is optimized; otherwise, pay more attention to the high-emission path and use the formula P'''(X)=P''(X)*(1+U_f) to adjust the starting power output to ensure that the total carbon emissions of the entire system are minimized under the new conditions; by dynamically adjusting the power output of each path, carbon emissions can be further reduced while meeting energy needs, maintaining efficient operation of the system. Adjusting the starting power output P'''(X) enables the system to achieve optimal performance in the new environment and gradually approach the minimum carbon emission target.

[0082] Assume the following parameters (based on the example in the previous step): Regular update cycle T_u = once a month: The starting power output of path R'(A,B) is P''(A)=46.8W, the receiving power at the end is P(B)=70W, and the carbon emission intensity is I(R'(A,B))=0.0733kgCO2 / W; The starting power output of path R'(C,D) is P''(C)=83.2W, the receiving power at the end is P(D)=60W, and the carbon emission intensity is I(R(R'(C,D)))=0.046kgCO2 / W; The carbon emissions after the last optimization are E'(R'(A,B))=3.3kgCO2, E'(R'(C,D))=3.84kgCO2; External environmental parameters: temperature T_e=25°C, light intensity L_s=500lux; Collect the latest data: Assume that the latest starting power outputs are: P''(A)=46.8W (unchanged); P''(C)=83.2W (unchanged); Recalculate the carbon emissions for each pathway: For the path R'(A,B): E''(R'(A,B))=I(R'(A,B))*P''(A)=0.0733*46.8≈3.43kgCO2; For the path R'(C,D): E''(R'(C,D))=I(R'(C,D))*P''(C)=0.046*83.2≈3.83kgCO2; Update the comprehensive scheduling plan: Calculate the update factor U_f: sum(E'(R'))=3.3+3.84=7.14kgCO2; sum(E''(R'))=3.43+3.83=7.26kgCO2; U_f=(sum(E'(R'))-sum(E''(R'))) / sum(E'(R'))=(7.14-7.26) / 7.14≈-0.0168 or -1.68%; Adjust the power allocation of each path in the comprehensive scheduling scheme according to the update factor: Since U_f<0, it indicates that carbon emissions have increased, and we need to pay more attention to high emission pathways: For the path R'(A,B): P'''(A)=P''(A)*(1+U_f)=46.8*(1-0.0168)≈46.0W; For the path R'(C,D): P'''(C)=P''(C)*(1+U_f)=83.2*(1-0.0168)≈81.8W; Recalculate energy usage and carbon emissions: For the path R'(A,B): New energy efficiency: U'_r(R'(A,B))=(P(B) / P'''(A))*100%=(70 / 46.0)*100%≈152.17%; New Carbon Emissions: E'''(R'(A,B))=I(R'(A,B))*P'''(A)=0.0733*46.0≈3.37kgCO2; For the path R'(C,D): New energy efficiency: U'_r(R'(C,D))=(P(D) / P'''(C))*100%=(60 / 81.8)*100%≈73.35%; New Carbon Emissions: E'''(R'(C,D))=I(R'(C,D))*P'''(C)=0.046*81.8≈3.76kgCO2; Recalculate the overall performance coefficient G_p: G_p=(U'_r(R'(A,B))+U'_r(R'(C,D))) / n=(152.17%+73.35%) / 2≈112.76%; At this point, although carbon emissions have increased slightly, the energy utilization rate is still high. If further optimization is needed, the power output can be adjusted until the optimal state is reached.

[0083] On the other hand, the present invention proposes a multi-energy coupled micro energy grid optimization scheduling system, such as Figure 2 As shown, including: A network construction and path definition module is used to establish a micro-energy network connection diagram including at least two different types of energy supply sources and define the conversion paths between the energy sources; A carbon emission identification module, used to identify all available energy flow directions based on the micro energy network connection diagram, and mark the initial carbon emission in each direction; A starting operation path selection module, used to select a path with the lowest carbon emissions as a starting operation path according to the marked initial carbon emissions, and record real-time data on the starting operation path; An energy supply source adjustment module, used to adjust the working state of the energy supply source related to the starting operation path by using the recorded real-time data; System performance evaluation module, used to monitor the system operation status after adjustment, compare the data before adjustment, and calculate the carbon emission reduction; A loop optimization module, used for repeating steps 3 to 5 to perform loop optimization on the remaining unoptimized paths until all paths are evaluated and adjusted; The comprehensive dispatching scheme formulation module is used to integrate all optimized route information and build a comprehensive dispatching scheme to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions;

[0084] The continuous improvement module is used to regularly update the comprehensive scheduling plan described in step 7, combining the latest energy usage and environmental changes to continuously reduce carbon emissions.

[0085] In addition, the above-mentioned network construction and path definition module, carbon emission identification module, starting operation path selection module, energy supply source adjustment module, system performance evaluation module, cycle optimization module, comprehensive scheduling plan formulation module and continuous improvement module are also used to implement the other steps of the above-mentioned multi-energy coupled micro-energy network optimization scheduling method during execution, which will not be repeated here one by one.

[0086] In summary, the present invention achieves dynamic optimization scheduling by regularly updating the comprehensive scheduling scheme and combining the latest energy usage and environmental changes, significantly reducing the total carbon emission level of the system. At the same time, the present invention integrates all optimized path information and constructs a comprehensive comprehensive scheduling scheme to ensure that the entire micro-energy network achieves global optimization while meeting demand.

[0087] In addition, by cyclically optimizing all unprocessed paths and adjusting the working state of the energy supply source according to real-time data, the present invention significantly improves the operating efficiency of the system and reduces unnecessary energy loss. In summary, the present invention not only overcomes the limitations of the static scheduling strategy in the prior art, but also provides a more flexible and efficient optimization method, which effectively improves the overall performance and environmental benefits of the system.

[0088] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-energy coupled micro-energy grid optimization scheduling method, characterized in that: The following steps are involved: Step 1: Establish a micro-energy network connection diagram containing at least two different types of energy supply sources and define the conversion paths between each energy source; Step 2: Based on the micro-energy network connection diagram, identify all available energy flow directions and mark the initial carbon emissions in each direction; Step 3: According to the marked initial carbon emissions, a path with the lowest carbon emissions is selected as the starting operation path, and real-time data on the starting operation path is recorded; Step 4: using the recorded real-time data, adjusting the working state of the energy supply source associated with the initial operation path; Step 5: Monitor the system operation status after adjustment, compare the data before adjustment, and calculate the reduction in carbon emissions; Step 6: Repeat steps 3 to 5 to perform cyclic optimization on the remaining unoptimized paths until all paths have been evaluated and adjusted; Step 7: Integrate all optimized route information and build a comprehensive dispatching plan to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions; Step 8: Regularly update the comprehensive scheduling plan described in Step 7, taking into account the latest energy usage and environmental changes, and continuously reduce carbon emissions.

2. The multi-energy coupled micro-energy grid optimization scheduling method according to claim 1 is characterized in that: The step of establishing a micro energy network connection diagram including at least two different types of energy supply sources and defining conversion paths between energy sources includes: In the established micro energy network connection diagram, a unique identifier is assigned to each energy supply source, and basic parameters of each energy supply source are recorded; Based on the identifier and the basic parameters, a conversion path between each two energy supply sources is defined, and the energy conversion amount on the path is calculated; Using the calculated energy conversion amount, all possible energy supply source combinations are sorted to form a priority list to ensure that the path with high conversion efficiency is selected first; According to the generated priority list, the micro-energy network connection diagram is updated, and the actual connection relationship between each energy supply source is adjusted so that the energy conversion amount of all paths in the final micro-energy network connection diagram meets the minimum total conversion amount threshold set by the system.

3. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 2, characterized in that: Based on the micro energy network connection diagram, all available energy flow directions are identified, and the initial carbon emissions in each direction are marked, including: For the constructed micro-energy network connection diagram, a direction is assigned to each conversion path, and the initial carbon emissions in each direction are marked; Based on the directions and their corresponding initial carbon emissions, the cumulative carbon emissions in each direction are calculated, and all directions are sorted to form a list sorted in ascending order of carbon emissions; Using the calculated cumulative carbon emissions, the micro-energy network connection diagram is updated to mark the final carbon emissions in each direction, and the actual connection relationship between each energy supply source is adjusted to ensure that the carbon emissions in all directions meet the maximum total carbon emissions threshold set by the system.

4. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 3, characterized in that: The step of selecting a path with the lowest carbon emissions as a starting operation path according to the marked initial carbon emissions, and recording real-time data on the starting operation path includes: Based on the generated list sorted in ascending order of carbon emissions, a path with the lowest carbon emissions is selected as the starting running path, and real-time data on the path is recorded; On the selected starting operation route, real-time data is recorded, including current power output, received power, and actual carbon emissions on the route; Using the recorded real-time data, calculate the energy utilization of the path and check whether it meets the minimum energy utilization threshold set by the system; According to the calculated energy utilization rate, the working state of the starting operation path is adjusted to ensure that it meets the minimum energy utilization rate threshold set by the system.

5. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 4, characterized in that: The step of adjusting the working state of the energy supply source related to the initial operation path by using the recorded real-time data includes: Based on the recorded real-time data, including the actual carbon emissions of the route, the power output at the starting point, and the received power at the end point, the current carbon emission intensity of the route is calculated; Using the calculated carbon emission intensity, determine the working status of the energy supply source that needs to be adjusted, and measure the goal of reducing carbon emissions through adjustment factors; According to the determined adjustment factor, the working state of the energy supply source related to the path is adjusted; Based on the adjusted power output, the energy utilization of the path is recalculated and checked whether it meets the minimum energy utilization threshold set by the system.

6. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 5, characterized in that: The monitoring of the system operation status after adjustment is compared with the data before adjustment to calculate the carbon emission reduction, including: Under the adjusted system operation state, record the new real-time data of the route, including the new starting power output, the end point receiving power and the new actual carbon emissions; Based on the new real-time data recorded, calculate the carbon emission difference between the paths before and after the adjustment; Using the calculated carbon emission differences, evaluate the effects of adjustment measures and define effect coefficients to measure the effects of adjustments; According to the effect coefficient, the overall performance improvement of the system is analyzed, and the minimum energy utilization threshold and minimum carbon emission intensity threshold set by the system are dynamically adjusted.

7. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 6, characterized in that: Repeating steps 3 to 5 to perform cyclic optimization on the remaining unoptimized paths until all paths are evaluated and adjusted, including: Select a path with the smallest cumulative carbon emissions from the remaining unoptimized paths, and repeat steps 3 to 5; Based on the selected route, the real-time data of the route is recorded and the new carbon emission intensity is calculated; Using the recorded data, the working status of energy supply sources related to the path is adjusted, and the goal of reducing carbon emissions is measured by the adjustment factor; According to the adjustment factor, the starting power output of the path is updated, and the energy utilization of the path is recalculated to check whether it meets the minimum energy utilization threshold set by the system.

8. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 7, characterized in that: The above mentioned method integrates all optimized route information to build a comprehensive dispatching solution to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions, including: Collect all optimized path information, including the starting power output, terminal receiving power, actual carbon emissions and energy utilization of each path, and build a comprehensive scheduling plan; Based on all the collected path information, the total carbon emissions of the entire micro-energy network are calculated; Using the calculated total carbon emissions, the overall performance of the system is evaluated, and the overall performance coefficient is defined to measure the system performance; According to the overall performance coefficient, the power allocation of each path in the comprehensive scheduling plan is adjusted to ensure that the total carbon emissions are minimized while meeting the demand.

9. The method for optimizing and scheduling a multi-energy coupled micro-energy grid according to claim 8, characterized in that: The comprehensive scheduling plan described in step 7 is regularly updated, taking into account the latest energy usage and environmental changes, including: Set up regular update cycles and collect the latest energy usage data and environmental change information at the beginning of each update cycle; Recalculate the carbon emissions of each route based on the latest data collected and update the comprehensive dispatch plan; Using the new calculated carbon emissions, define an update factor to measure the extent of the required adjustment; According to the update factor, the power allocation of each path in the comprehensive scheduling scheme is adjusted to ensure that the total carbon emissions of the entire system are minimized under the new conditions.

10. A micro-energy grid optimization scheduling system for implementing the multi-energy coupling method according to any one of claims 1 to 9, characterized in that: include: A network construction and path definition module is used to establish a micro-energy network connection diagram including at least two different types of energy supply sources and define the conversion paths between the energy sources; A carbon emission identification module, used to identify all available energy flow directions based on the micro energy network connection diagram, and mark the initial carbon emission in each direction; A starting operation path selection module, used to select a path with the lowest carbon emissions as a starting operation path according to the marked initial carbon emissions, and record real-time data on the starting operation path; An energy supply source adjustment module, used to adjust the working state of the energy supply source related to the starting operation path by using the recorded real-time data; System performance evaluation module, used to monitor the system operation status after adjustment, compare the data before adjustment, and calculate the carbon emission reduction; A loop optimization module, used for repeating steps 3 to 5 to perform loop optimization on the remaining unoptimized paths until all paths are evaluated and adjusted; The comprehensive dispatching scheme formulation module is used to integrate all optimized route information and build a comprehensive dispatching scheme to ensure that the entire micro-energy network meets demand while minimizing total carbon emissions; The continuous improvement module is used to regularly update the comprehensive scheduling plan described in step 7, combining the latest energy usage and environmental changes to continuously reduce carbon emissions.

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