A multi-energy coupling micro-energy network optimization scheduling method and system

Abstract

The present application belongs to the technical field of power construction, and specifically relates to a multi-energy coupling micro-energy network optimization scheduling method and system. The present application periodically updates the comprehensive scheduling scheme, combines the latest energy usage and environmental changes, realizes dynamic optimization scheduling, and significantly reduces the total carbon emission level of the system. At the same time, the present application integrates all the optimized path information, constructs a comprehensive comprehensive scheduling scheme, and ensures that the entire micro-energy network meets the demand while realizing global optimization. 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 application significantly improves the operation efficiency of the system and reduces unnecessary energy loss. In summary, the present application not only overcomes the limitations of the static scheduling strategy in the prior art, but also provides a more flexible and efficient optimization method, effectively improving the overall performance and environmental benefits of the system.

Description

Technical Field

[0001] This invention belongs to the field of power construction technology, specifically relating to a method and system for optimizing the scheduling of microgrids with multi-energy coupling. Background Technology

[0002] Currently, multi-energy coupled microgrid optimal scheduling methods have been widely applied in various fields. Existing technical solutions typically include the following aspects:

[0003] 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.

[0004] Carbon emission 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.

[0005] Route selection and optimization: Select the optimal route based on carbon emissions and optimize system operation by adjusting the operating status of energy supply sources.

[0006] Monitoring and feedback: Monitor the system's operation, compare data before and after adjustments, calculate carbon emission reductions, and verify the effectiveness of optimization measures.

[0007] While existing technologies can achieve energy efficiency and carbon emission reduction to some extent, they often face the following challenges:

[0008] 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.

[0009] Lack of comprehensive scheduling scheme: Although some systems can optimize individual paths or local areas, they lack a comprehensive scheduling scheme, making it difficult to achieve global optimization in the entire micro energy network.

[0010] To address the shortcomings of the existing technologies, the present invention aims to solve a key technical problem: how to continuously optimize the scheduling strategy of the entire micro-energy network under dynamically changing energy usage and environmental conditions in order to minimize total carbon emissions and ensure the efficient operation of the system. Summary of the Invention

[0011] The purpose of this invention is to provide a method and system for optimizing the scheduling of multi-energy coupled microgrids. This not only solves the limitations of static scheduling strategies in the prior art, but also provides a more flexible and efficient method for optimizing the scheduling of multi-energy coupled microgrids, thereby effectively reducing the total carbon emissions of the system and improving the overall operating efficiency.

[0012] To achieve the above objectives, this invention proposes an optimized scheduling method for multi-energy coupled microgrids, comprising the following steps:

[0013] Step 1: Establish a micro-energy network connection diagram that includes at least two different types of energy supply sources, and define the conversion paths between each energy source;

[0014] 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;

[0015] Step 3: Based on the marked initial carbon emissions, select a path with the lowest carbon emissions as the starting operating path, and record the real-time data on the starting operating path;

[0016] Step 4: Using the recorded real-time data, adjust the operating status of the energy supply sources related to the initial operating path;

[0017] Step 5: Monitor the system's operation after the adjustment, compare the data with the data before the adjustment, and calculate the reduction in carbon emissions;

[0018] Step Six: Repeat Steps Three through Five to iterate and optimize the remaining unoptimized paths until all paths have been evaluated and adjusted.

[0019] Step 7: Integrate all optimized path information to construct a comprehensive scheduling scheme to ensure that the entire micro-energy network minimizes total carbon emissions while meeting demand;

[0020] Step 8: Regularly update the comprehensive scheduling plan described in Step 7, and continuously reduce carbon emission levels by taking into account the latest energy usage and environmental changes.

[0021] Preferably, the establishment of a micro-energy network connection diagram containing at least two different types of energy supply sources, and the definition of conversion paths between energy sources, includes:

[0022] In the established micro-energy network connection diagram, a unique identifier is assigned to each energy supply source, and the basic parameters of each energy supply source are recorded;

[0023] Based on identifiers and basic parameters, define the conversion path between every two energy supply sources and calculate the amount of energy conversion along the path;

[0024] Using the calculated energy conversion amount, all possible combinations of energy supply sources are sorted to form a priority list, ensuring that high conversion efficiency paths are selected first;

[0025] Based on the generated priority list, the micro-energy network connection diagram is updated, and the actual connection relationships between each energy supply source are adjusted so that the energy conversion of all paths in the final micro-energy network connection diagram meets the minimum total conversion threshold set by the system.

[0026] Preferably, the step 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:

[0027] 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;

[0028] Based on the direction and its corresponding initial carbon emissions, the cumulative carbon emissions in each direction are calculated, and all directions are sorted to form a list arranged in ascending order of carbon emissions.

[0029] Using the calculated cumulative carbon emissions, the micro-energy network connection diagram is updated, the final carbon emissions in each direction are marked, and the actual connection relationships between each energy supply source are adjusted to ensure that the carbon emissions in all directions meet the maximum total carbon emission threshold set by the system.

[0030] Preferably, the step of selecting a path with the lowest carbon emissions as the starting operating path based on the marked initial carbon emissions, and recording real-time data on the starting operating path, includes:

[0031] Based on the generated list sorted in ascending order of carbon emissions, select the path with the lowest carbon emissions as the starting path and record the real-time data on that path.

[0032] Record real-time data along the selected initial operating path, including current power output, received power, and actual carbon emissions along the path.

[0033] Using the recorded real-time data, calculate the energy utilization rate of the path and check whether it meets the minimum energy utilization rate threshold set by the system.

[0034] Based on the calculated energy utilization rate, adjust the working state of the initial operating path to ensure that it meets the minimum energy utilization rate threshold set by the system.

[0035] Preferably, adjusting the operating status of the energy supply source related to the initial operating path using the recorded real-time data includes:

[0036] Based on recorded real-time data, including the actual carbon emissions of the path, the starting power output, and the receiving power at the end point, the current carbon intensity of the path is calculated.

[0037] Using the calculated carbon emission intensity, the operating status of energy supply sources that need to be adjusted is determined, and the carbon emission reduction target is measured by the adjustment factor.

[0038] Adjust the operational status of path-related energy supply sources based on the determined adjustment factors;

[0039] Based on the adjusted power output, the energy utilization rate of the path is recalculated, and it is checked whether the minimum energy utilization rate threshold set by the system is met.

[0040] Preferably, the process of monitoring the adjusted system operation status, comparing the data with the data before the adjustment, and calculating the carbon emission reduction includes:

[0041] Under the adjusted system operating conditions, record new real-time data of the path, including new starting point power output, ending point received power, and new actual carbon emissions;

[0042] Based on newly recorded real-time data, calculate the difference in carbon emissions before and after the adjustment;

[0043] The calculated carbon emission differences are used to assess the effectiveness of adjustment measures, and an effectiveness coefficient is defined to measure the effectiveness of the adjustments.

[0044] Based on the effect coefficient, analyze the overall performance improvement of the system and dynamically adjust the minimum energy utilization threshold and minimum carbon emission intensity threshold set by the system.

[0045] Preferably, the repeated steps three to five, which iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted, include:

[0046] Select the path with the minimum cumulative carbon emissions from the remaining unoptimized paths, and repeat steps three through five.

[0047] Based on the selected route, record real-time data of the route and calculate the new carbon emission intensity;

[0048] By using recorded data, the operating status of path-related energy supply sources is adjusted, and carbon emission reduction targets are measured through adjustment factors.

[0049] Based on the adjustment factor, update the starting power output of the path and recalculate the energy utilization rate of the path, checking whether it meets the minimum energy utilization rate threshold set by the system.

[0050] Preferably, the integration of all optimized path information to construct a comprehensive scheduling scheme, ensuring that the entire micro-energy network minimizes total carbon emissions while meeting demand, includes:

[0051] Collect all optimized path information, including the starting power output, ending power received, actual carbon emissions, and energy utilization rate of each path, and construct a comprehensive scheduling scheme.

[0052] Based on all collected path information, calculate the total carbon emissions of the entire micro energy network;

[0053] The overall performance of the system is evaluated using the calculated total carbon emissions, and an overall performance coefficient is defined to measure the system performance.

[0054] Based on the overall performance coefficient, adjust the power allocation of each path in the integrated scheduling scheme to ensure that the total carbon emissions are minimized while meeting the demand.

[0055] Preferably, the periodic update of the comprehensive scheduling scheme described in step seven, taking into account the latest energy usage and environmental changes, includes:

[0056] Establish a regular update cycle, and at the beginning of each update cycle, collect the latest energy usage data and environmental change information;

[0057] Based on the latest collected data, the carbon emissions for each path are recalculated, and the integrated scheduling scheme is updated.

[0058] Using the calculated new carbon emissions, an update factor is defined to measure the degree of adjustment needed;

[0059] Based on the update factor, the power allocation of each path in the integrated scheduling scheme is adjusted to ensure that the entire system minimizes total carbon emissions under the new conditions.

[0060] On the other hand, this invention proposes a multi-energy coupled microgrid optimized scheduling system, comprising:

[0061] The network construction and path definition module is used to 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;

[0062] The carbon emission labeling module is used to identify all available energy flow directions based on the micro-energy network connection diagram and mark the initial carbon emissions in each direction;

[0063] The starting operation path selection module is used to select a path with the lowest carbon emissions as the starting operation path based on the marked initial carbon emissions, and to record real-time data on the starting operation path.

[0064] An energy supply source adjustment module is used to adjust the working status of the energy supply source related to the initial running path using the recorded real-time data;

[0065] The system performance evaluation module is used to monitor the adjusted system operation status, compare the data with the data before the adjustment, and calculate the carbon emission reduction.

[0066] The iterative optimization module is used to repeat steps three through five to iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted.

[0067] The integrated scheduling scheme formulation module is used to integrate all optimized path information to construct an integrated scheduling scheme, ensuring that the entire micro energy network meets demand while minimizing total carbon emissions.

[0068] The continuous improvement module is used to periodically update the comprehensive scheduling scheme described in step seven, and to continuously reduce carbon emission levels by taking into account the latest energy usage and environmental changes.

[0069] Technical effects and advantages of the present invention: The multi-energy coupled microgrid optimization scheduling method and system proposed in this invention have the following advantages compared with the prior art:

[0070] This invention continuously optimizes the scheduling strategy of the entire micro-energy network under dynamically changing energy usage and environmental conditions. By periodically updating the comprehensive scheduling scheme and incorporating the latest energy usage and environmental changes, this invention achieves dynamic optimization scheduling, significantly reducing the system's total carbon emissions. Simultaneously, this invention integrates all optimized path information to construct a comprehensive scheduling scheme, ensuring that the entire micro-energy network achieves global optimization while meeting demand. Furthermore, by iteratively optimizing all unprocessed paths and adjusting the operating status of energy supply sources based on real-time data, this invention significantly improves system operating efficiency and reduces unnecessary energy loss. In summary, this invention not only overcomes the limitations of static scheduling strategies in existing technologies but also provides a more flexible and efficient optimization method, effectively improving the overall system performance and environmental benefits. Attached Figure Description

[0071] Figure 1 This is a flowchart of a multi-energy coupled microgrid optimization scheduling method according to the present invention;

[0072] Figure 2 This is a block diagram of a multi-energy coupled microgrid optimized scheduling system according to the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] This invention provides, for example Figure 1 The invention presents a multi-energy coupled microgrid optimization scheduling method. This invention can continuously optimize the scheduling strategy of the entire microgrid under dynamically changing energy usage and environmental conditions. Specifically, it includes the following steps:

[0075] 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; specifically including the following sub-process steps:

[0076] 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 values ​​V(A), V(B), V(C), the capacity of each energy supply source can be clearly identified and quantified.

[0077] Based on identifiers and basic parameters, a conversion path is defined between any two energy supply sources. The energy conversion amount along the path is calculated using the formula Q(X,Y)=k*(V(X)-V(Y)), 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 losses during energy conversion from one energy supply source to another. In the formula, V(X) and V(Y) represent the power of the two energy supply sources, and the difference (V(X)-V(Y)) represents the potential energy difference. Multiplying this by the conversion efficiency coefficient k yields the actual convertible energy Q(X,Y). If V(X)>V(Y), energy can flow from X to Y; conversely, if V(X)>V(Y), energy can flow from X to Y.

[0078] Using the calculated energy conversion quantity Q(X,Y), all possible combinations of energy supply sources are ranked to form a priority list L. Following the rule L=[(X,Y)|Q(X,Y)>0], high-efficiency conversion paths are prioritized. Based on the calculated energy conversion quantity Q(X,Y), all forward conversion possibilities are arranged in descending order to ensure high-efficiency conversion paths are prioritized. Only when Q(X,Y)>0 is an effective energy conversion path considered to exist, thus guaranteeing efficient resource utilization and system stability.

[0079] Based on the generated priority list L, the micro-energy network connection diagram is updated, and the actual connection relationships between each energy supply source are adjusted so that the energy conversion amount of all paths in the final micro-energy network connection diagram satisfies the condition sum(Q(X,Y))forall(X,Y)inL>=T, where T is the minimum total conversion threshold set by the system. Based on the generated priority list L, the connections between energy supply sources are reconfigured so that the total energy conversion of the entire network reaches or exceeds the set threshold T. By adjusting the connection relationships, the energy flow of the entire micro-energy network is optimized, ensuring that the total system output meets the minimum requirements.

[0080] Suppose there are three energy sources A, B, and C, with power outputs of V(A) = 100W, V(B) = 80W, and V(C) = 90W, respectively, and a conversion efficiency coefficient k = 0.8.

[0081] Calculate Q(A,B) = 0.8 * (100 - 80) = 16W;

[0082] Calculate Q(A,C)=0.8*(100-90)=8W;

[0083] Calculate Q(B,C)=0.8*(80-90)=-8W (ignoring it);

[0084] The priority list L is sorted by Q value as: [(A,B),(A,C)].

[0085] If the minimum total conversion threshold T = 20W is set, then it is necessary to check whether the condition is met:

[0086] sum(Q(X,Y))forall(X,Y)inL=16+8=24W>=T;

[0087] Therefore, the current configuration meets the requirements and no further adjustments are needed. In the final micro-energy network connection diagram, the energy conversion paths between A and B, and between A and C, have been confirmed as valid.

[0088] 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 including the following sub-process steps:

[0089] 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 for energy outflow and inflow, respectively. The initial carbon emissions E_i(D) in the direction are defined as the direct emissions value associated with that path. By assigning a direction D(X,Y) to each conversion path, the directionality of energy flow can be clearly defined. Simultaneously, the initial carbon emissions E_i(D) in the direction are defined 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 that path, which helps to understand the basic environmental impact of each path.

[0090] 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 carbon emission factor added during the conversion process, and V(Y) is the basic parameter of the receiving energy supply source. This step considers the additional carbon emissions during the conversion process by multiplying α (the carbon emission factor added during the conversion process) by the basic parameter V(Y) of the receiving energy supply source to calculate the cumulative carbon emissions C_e(X,Y). E_i(D) in the formula represents direct emissions, while α*V(Y) considers additional carbon emissions during energy conversion and transmission. This provides a comprehensive assessment of the environmental impact of the entire conversion path.

[0091] Using the calculated cumulative carbon emissions C_e(X,Y), all directions are sorted to form a list S ordered by carbon emissions in ascending order, following the rule S=[(X,Y)|C_e(X,Y)] and ensuring that the paths are arranged in ascending order of cumulative carbon emissions. Sort all directions based on cumulative carbon emissions to ensure that paths with lower carbon emissions are prioritized, promoting the use of green energy. By sorting the carbon emissions of the paths, network configuration can be optimized, reducing the overall carbon footprint.

[0092] Based on the generated list S, the micro-energy network connection graph is updated, marking the final carbon emissions in each direction. The actual connection relationships between energy supply sources are adjusted so that the carbon emissions in all directions of the final micro-energy network connection graph satisfy the condition sum(C_e(X,Y))forall(X,Y)inS<=Z, where Z is the maximum total carbon emission threshold set by the system. Based on the generated list S, the connections between energy supply sources are reconfigured so that the total carbon emissions of the entire network do not exceed the set maximum threshold Z. By adjusting the connection relationships, the total carbon emissions of the system are ensured to be within an acceptable range, supporting the achievement of environmental protection goals.

[0093] Assume there are three energy sources A, B, and C, with power outputs of V(A) = 100W, V(B) = 80W, and V(C) = 90W, respectively. The 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 during the conversion process is α = 0.02kgCO2 / W.

[0094] Calculate C_e(A,B) = 5 + 0.02 * 80 = 6.6 kg CO2;

[0095] Calculate C_e(A,C) = 3 + 0.02 * 90 = 4.8 kg CO2;

[0096] The cumulative carbon emissions list S is arranged in ascending order as: [(A,C),(A,B)], with corresponding cumulative carbon emissions of 4.8kgCO2 and 6.6kgCO2, respectively.

[0097] If the maximum total carbon emission threshold Z = 12 kg CO2 is set, then it is necessary to check whether the condition is met:

[0098] sum(C_e(X,Y))forall(X,Y)inS=4.8+6.6=11.4kgCO2<=Z;

[0099] Therefore, the current configuration meets the requirements and no further adjustments are needed. In the final micro-energy network connection diagram, the energy conversion paths between A and C, and between A and B, have been confirmed as valid, and their respective final carbon emissions have been marked as 4.8 kg CO2 and 6.6 kg CO2, ensuring that the total carbon emissions are controlled within the target range.

[0100] Step 3: Based on the marked initial carbon emissions, select a path with the lowest carbon emissions as the starting operating path, and record real-time data on the starting operating path; specifically including the following sub-process steps:

[0101] Based on the generated list S sorted in ascending order of carbon emissions, the path with the smallest cumulative carbon emissions C_e(X,Y) is selected as the starting operating path, defined as R(A,B), where A and B are the energy supply source identifiers for energy outflow and inflow, respectively. Selecting the path with the smallest C_e(X,Y) from the list S sorted in ascending order of carbon emissions as the starting operating path R(A,B) helps to minimize environmental impact while meeting energy needs. Prioritizing paths with lower carbon emissions can reduce the overall carbon footprint of the system and support the use of green energy.

[0102] 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 a basis for subsequent adjustments can be provided. E_a(R)=C_e(A,B) means that the actual carbon emissions of the path are equal to its cumulative carbon emissions.

[0103] 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.

[0104] According to the calculated energy utilization rate U_r(R), adjust the operating 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, 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 to make U_r(R) reach or exceed the preset threshold and ensure system performance.

[0105] Suppose there are the following parameters:

[0106] 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;

[0107] The cumulative carbon emissions C_e(A,B)=4.8kgCO2 (from the calculation result in step two);

[0108] The current power output V(A)=100W and the received power V(B)=75W;

[0109] The minimum energy utilization rate threshold W_u=75%.

[0110] Calculate the actual carbon emissions E_a(R)=C_e(A,B)=4.8kgCO2;

[0111] The energy utilization rate U_r(R) is calculated as follows: U_r(R) = (V(B) / V(A)) * 100% = (75 / 100) * 100% = 75%;

[0112] Since U_r(R) = 75%, which is exactly equal to W_u, no adjustment is needed. If U_r(R) is lower than 75%, for example, U_r(R) = 70%, then adjustment is required. Assuming we choose to increase V(A) to approximately 107W to raise U_r(R) to at least 75% (the specific value needs to be fine-tuned based on the actual situation), the calculation is as follows:

[0113] U_r(R) = (75 / 107) * 100% ≈ 70.09%

[0114] This suggests that directly increasing V(A) may not be sufficient to achieve the goal, while decreasing V(B) may be more effective, or both measures can be taken simultaneously to ensure that U_r(R) >= W_u. For example, reducing V(B) to 70W:

[0115] U_r(R) = (70 / 100) * 100% = 70%;

[0116] Further fine-tuning is performed until a suitable combination of V(A) and V(B) is found, such that U_r(R) >= 75%. This process emphasizes the importance of dynamic adjustment to maintain system performance and meet environmental requirements.

[0117] Step 4: Using the recorded real-time data, adjust the operating status of the energy supply sources related to the initial operating path; specifically including the following sub-process steps:

[0118] Based on recorded real-time data, including the actual carbon emissions E_a(R) of path R(A,B), the starting power output V(A), and the ending received power V(B), the current carbon emission intensity I_c(R) of this 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 of 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 path R(A,B); a lower value indicates a more environmentally friendly path.

[0119] Using the calculated carbon emission intensity \(I_c(R)\), determine the operating state of the energy supply source that needs to be adjusted; define the adjustment factor \(F_a\) as the target coefficient for reducing carbon emissions, and calculate it through the formula \(F_a=(I_c(R)-I_{min}) / I_{min}\), 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.

[0120] According to the determined adjustment factor \(F_a\), adjust the operating state of the energy supply source \(A\) related to 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 adjustment is needed.

[0121] 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 still meets 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.

[0122] Suppose there are the following parameters:

[0123] Path \(R(A,B)\), where \(A\) and \(B\) are the identifiers of the energy supply sources for energy outflow and inflow respectively;

[0124] Actual carbon emissions \(E_a(R)=4.8kgCO2\);

[0125] Current power output \(V(A)=100W\), received power \(V(B)=75W\);

[0126] The minimum carbon emission intensity threshold \(I_{min}=0.04kgCO2 / W\) set by the system;

[0127] The minimum energy utilization rate threshold \(W_u = 75\%\).

[0128] Calculate carbon emission intensity I_c(R):

[0129] I_c(R)=E_a(R) / V(A)=4.8 / 100=0.048kgCO2 / W;

[0130] Determine the adjustment factor F_a:

[0131] F_a=(I_c(R)-I_min) / I_min=(0.048-0.04) / 0.04=0.2;

[0132] Adjust the operating status of energy supply source A:

[0133] If F_a > 0, then V(A) needs to be adjusted:

[0134] V'(A)=V(A)*(1-F_a)=100*(1-0.2)=80W;

[0135] Recalculate the energy efficiency of the path U'_r(R):

[0136] U'_r(R)=(V(B) / V'(A))*100%=(75 / 80)*100%=93.75%;

[0137] Since U'_r(R) = 93.75%, which is much higher than the minimum energy efficiency threshold W_u = 75%, no further adjustment is needed. If U'_r(R) is lower than 75%, for example, U'_r(R) = 70%, then V'(A) or V(B) needs further adjustment.

[0138] Assuming U'_r(R) = 70%, that is: (75 / V'(A)) * 100% = 70%;

[0139] Solving for V, we get: V'(A) = 75 / 0.7 ≈ 107.14 W;

[0140] At this point, V'(A) needs to be adjusted to approximately 107.14 W so that U'_r(R) >= 75%. This indicates 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 efficiency requirements while minimizing carbon emissions. In this way, the optimal balance between environmental protection and energy efficiency can be found.

[0141] Step 5: Monitor the adjusted system operation status, compare the data with the data before the adjustment, and calculate the carbon emission reduction; including the following sub-process steps:

[0142] Under the adjusted system operating conditions, record the new real-time data for path R(A,B), including the new starting power output V'(A), the ending received power V(B), and the new actual carbon emissions E'_a(R). Calculate the new carbon emissions using the formula E'_a(R)=I_c(R)*V'(A). Record the new starting power output V'(A), ending received power V(B), and new actual carbon emissions E'_a(R) to evaluate the effectiveness of the adjustment measures. By comparing the data before and after the adjustment, the impact of the adjustment measures on system performance can be quantified. E'_a(R)=I_c(R)*V'(A) represents the carbon emissions recalculated based on the adjusted power output.

[0143] Based on newly recorded real-time data, the carbon emission difference ΔE for path R(A,B) before and after adjustment is calculated. The formula ΔE = E_a(R) - E'_a(R) is used, where E_a(R) is the actual carbon emission before adjustment, and E'_a(R) is the new actual carbon emission after adjustment. By calculating the carbon emission difference ΔE, the amount of 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 adjustment; a positive value indicates a decrease in carbon emissions, while a negative value indicates an increase.

[0144] The effectiveness of adjustment measures is evaluated using the calculated carbon emission difference ΔE. An effectiveness coefficient K_e is defined as an indicator to measure the adjustment effect, calculated using the formula K_e=ΔE / E_a(R), and expressed as a percentage of carbon emission reduction. By calculating the effectiveness coefficient K_e, the effectiveness of the adjustment measures can be measured, and the percentage of carbon emission reduction is visually displayed. K_e=ΔE / E_a(R) provides a quantitative assessment of the effectiveness of the adjustment measures; a larger value indicates a more significant improvement.

[0145] Based on the effect coefficient K_e, analyze the overall performance improvement of the system. If K_e > 0, the adjustment measures are confirmed to be effective, and the minimum energy utilization rate threshold W_u and the minimum carbon emission intensity threshold I_min set by the system are updated. Specifically, the formulas W_u_new = W_u + δW and I_min_new = I_min - δI are used, where δW and δI are the changes in energy utilization rate and carbon emission intensity thresholds dynamically adjusted based on the effect coefficient K_e, respectively. Dynamically adjusting the energy utilization rate and carbon emission intensity thresholds ensures that the system maintains efficient operation while meeting environmental protection requirements. δW and δI are the changes based on the effect coefficient K_e, used to gradually improve the system's performance standards.

[0146] Assume the following parameters:

[0147] The actual carbon emissions before adjustment were E_a(R) = 4.8 kg CO2;

[0148] Carbon emission intensity I_c(R) = 0.048 kgCO2 / W (from the calculation result in step four);

[0149] The adjusted starting power output V'(A) = 80W (from the calculation result in step four), and the ending received power V(B) = 75W;

[0150] Minimum energy efficiency threshold W_u = 75%;

[0151] The minimum carbon emission intensity threshold I_min = 0.04 kgCO2 / W.

[0152] Calculate the new actual carbon emissions E'_a(R):

[0153] E'_a(R)=I_c(R)*V'(A)=0.048*80=3.84kgCO2;

[0154] Calculate the carbon emission difference ΔE:

[0155] ΔE=E_a(R)-E'_a(R)=4.8-3.84=0.96kgCO2;

[0156] Evaluate the effectiveness of the adjustment measures:

[0157] K_e=ΔE / E_a(R)=0.96 / 4.8=0.2 or 20%;

[0158] Analyze the overall performance improvement of the system and update the set thresholds:

[0159] If K_e > 0, then the adjustment measures are confirmed to be effective. Assume that the energy utilization rate threshold W_u and the carbon emission intensity threshold I_min are increased and decreased by a certain percentage, respectively, for example:

[0160] δW = 0.05 (i.e., 5%);

[0161] δI = 0.002 (i.e., 0.2%);

[0162] Updated threshold:

[0163] W_u_new=W_u+δW=75%+5%=80%;

[0164] I_min_new=I_min-δI=0.04-0.002=0.038kgCO2 / W;

[0165] Summary of the application principles and meanings of the formulas:

[0166] E'_a(R)=I_c(R)*V'(A):

[0167] Calculate the adjusted actual carbon emissions and reassess the carbon emission level based on the adjusted power output.

[0168] ΔE = E_a(R) - E'_a(R):

[0169] The difference in carbon emissions before and after the adjustment is calculated to reflect the carbon reduction effect of the adjustment measures.

[0170] K_e=ΔE / E_a(R):

[0171] To measure the effectiveness of the adjustment measures, present the percentage reduction in carbon emissions to help decide whether further optimization is needed.

[0172] W_u_new=W_u+δW and I_min_new=I_min-δI:

[0173] The system's energy utilization rate and carbon emission intensity thresholds are dynamically updated based on the adjustment results, gradually improving the system's environmental protection and energy efficiency standards.

[0174] By following the steps and formulas above, the operational status of micro-energy networks can be systematically evaluated and optimized, ensuring efficient energy utilization while reducing carbon emissions.

[0175] Step Six: Repeat Steps Three through Five to iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted; this includes the following sub-process steps:

[0176] From the remaining unoptimized paths, select the path R'(X,Y) with the minimum cumulative carbon emissions, where X and Y are the energy source identifiers for energy outflow and inflow, respectively. Determine this path using the formula C'_e(X,Y)=min(C_e(X,Y)), where C_e(X,Y) is the cumulative carbon emissions for each path. Prioritize high-potential optimization paths by selecting the path with the minimum cumulative carbon emissions from the remaining unoptimized paths. By selecting the path with the minimum cumulative carbon emissions, the total carbon emissions of the entire system can be gradually reduced, ensuring that each adjustment brings significant environmental benefits.

[0177] Based on the selected path R'(X,Y), repeat the operations from Step 3 to Step 5; first record the real-time data of path R'(X,Y), including the starting point power output V(X), the ending point received power V(Y), and the actual carbon emission 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 point power output V(X), the ending point received power V(Y), and the actual carbon emission E'_a(R') of path R'(X,Y), and calculate the new carbon emission intensity I'_c(R'). The real-time data recording and carbon emission intensity calculation help to understand the performance of the current path and provide a basis for subsequent adjustments.

[0178] Utilize the recorded data to adjust the working state of the energy supply source related to path R'(X,Y); define the adjustment factor F'_a as the target coefficient for carbon emission reduction, 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 according to 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, helping to formulate specific adjustment strategies.

[0179] According to the adjustment factor F'_a, update the starting point power output V''(X) of path R'(X,Y) 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, then adjust V''(X) or V(Y) until U'_r(R') >= W_u_new; update the starting point 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'), ensuring that it meets the minimum energy utilization rate threshold W_u_new set by the system. By dynamically adjusting the starting point power output, it is possible to reduce carbon emissions while maintaining high-efficiency energy utilization.

[0180] Suppose there are the following parameters (based on the examples in the previous steps):

[0181] The path R'(X,Y) with the minimum cumulative carbon emission in the remaining unoptimized paths, where X and Y are the identifiers of the energy supply sources for energy outflow and inflow respectively;

[0182] Cumulative carbon emissions C_e(X,Y) = 6.6 kg CO2 (from the calculation result in step two);

[0183] The current power output V(X) = 90W, and the received power V(Y) = 70W;

[0184] Actual carbon emissions E'_a(R') = 6.6 kg CO2 (assuming data before adjustment);

[0185] The updated minimum carbon emission intensity threshold I_min_new = 0.038 kgCO2 / W;

[0186] The updated minimum energy efficiency threshold W_u_new=80%.

[0187] Choose the path with the lowest cumulative carbon emissions:

[0188] C'_e(X,Y)=min(C_e(X,Y))=6.6kgCO2;

[0189] Record real-time data for the selected route and calculate the new carbon intensity:

[0190] I'_c(R')=E'_a(R') / V(X)=6.6 / 90≈0.0733kgCO2 / W;

[0191] Determine adjustment factors and update the operational status of energy supply sources:

[0192] F'_a=(I'_c(R')-I_min_new) / I_min_new=(0.0733-0.038) / 0.038≈0.93;

[0193] Update the starting power output of the path and recalculate the energy efficiency:

[0194] V''(X)=V(X)*(1-F'_a)=90*(1-0.93)≈6.3W;

[0195] U'_r(R')=(V(Y) / V''(X))*100%=(70 / 6.3)*100%≈1111.11%;

[0196] Since U'_r(R') is much higher than the minimum energy efficiency threshold W_u_new=80%, the adjustment strategy needs to be re-evaluated. Assuming the adjusted power output cannot be too low, the value of F'_a can be slightly adjusted, for example, setting F'_a=0.5:

[0197] V''(X)=V(X)*(1-F'_a)=90*(1-0.5)=45W;

[0198] U'_r(R')=(V(Y) / V''(X))*100%=(70 / 45)*100%≈155.56%;

[0199] At this point, U'_r(R') is still relatively high, but more reasonable. For further optimization, we can try adjusting V(Y):

[0200] Assuming V(Y) is adjusted to 60W:

[0201] U'_r(R')=(60 / 45)*100%≈133.33%;

[0202] Ultimately, ensure that U'_r(R') ≥ 80% and carbon emissions are reduced. The specific steps are as follows:

[0203] The adjustment measures have been confirmed to be effective.

[0204] The new actual carbon emissions E'_a(R') = I'_c(R') * V''(X) = 0.0733 * 45 ≈ 3.3 kg CO2;

[0205] ΔE=E_a(R')-E'_a(R')=6.6-3.3=3.3kgCO2;

[0206] K_e=ΔE / E_a(R')=3.3 / 6.6=0.5 or 50%;

[0207] Analyze the overall performance improvement of the system and update the set thresholds:

[0208] If K_e > 0, then the adjustment measures are confirmed to be effective. Assume that the energy utilization rate threshold W_u and the carbon emission intensity threshold I_min are increased and decreased by a certain percentage, respectively, for example:

[0209] δW = 0.05 (i.e., 5%);

[0210] δI = 0.002 (i.e., 0.2%);

[0211] Updated threshold:

[0212] W_u_new=W_u+δW=80%+5%=85%;

[0213] I_min_new=I_min-δI=0.038-0.002=0.036kgCO2 / W;

[0214] By following the steps and formulas described above, all pathways can be systematically evaluated and optimized, ensuring that each pathway maintains high energy efficiency while reducing carbon emissions. This process can be continuously optimized iteratively until all pathways have been evaluated and adjusted.

[0215] Step 7: Integrate all optimized path information to construct a comprehensive scheduling scheme, ensuring that the entire micro-energy network meets demand while minimizing total carbon emissions; this includes the following sub-process steps:

[0216] Collect all optimized path information, including the starting power output V''(X), ending received power V(Y), actual carbon emissions E'_a(R'), and energy utilization rate U'_r(R') for each path; define a comprehensive scheduling scheme D_s that includes information from all paths; collect the starting power output V''(X), ending received power V(Y), actual carbon emissions E'_a(R'), and energy utilization rate U'_r(R') for each path to construct a comprehensive scheduling scheme D_s. By aggregating information from all paths, a comprehensive understanding of the system's operating status can be obtained, providing data support for subsequent overall performance evaluation.

[0217] Based on all 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')) for all paths is used, where E'_a(R') is the actual carbon emissions for each path. By summing the actual carbon emissions E'_a(R') for all paths, the total carbon emissions T_c of the entire micro-energy network can be obtained, helping to assess the system's environmental performance. The total carbon emissions T_c reflects the system's environmental impact under the current configuration; a lower value indicates a more environmentally friendly system.

[0218] The overall performance of the system is evaluated using the calculated total carbon emissions T_c. An overall performance coefficient G_p is defined as an indicator of system performance, 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, ensuring high-efficiency operation. G_p is the average of the energy utilization rates U'_r(R') of all paths, reflecting the overall energy conversion efficiency of the system. A higher G_p value indicates higher overall performance.

[0219] Adjust the comprehensive scheduling plan \(D_s\) according to the overall performance coefficient \(G_p\) to ensure that the total carbon emissions \(T_c\) are minimized 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 lowest 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 that the total carbon emissions \(T_c\) are minimized while meeting the demand. By adjusting the starting power output, carbon emissions can be reduced while maintaining high efficiency, gradually improving the system performance.

[0220] Suppose there are the following parameters (based on the examples in the previous steps):

[0221] There are two optimized paths in total: \(R'(A,B)\) and \(R'(C,D)\):

[0222] The starting power output of path \(R'(A,B)\) is \(V''(A) = 45W\), the received power at the end is \(V(B)=70W\), the actual carbon emissions \(E'_a(R'(A,B)) = 3.3kgCO_2\), and the energy utilization rate \(U'_r(R'(A,B)) = 155.56\%\);

[0223] The starting power output of path \(R'(C,D)\) is \(V''(C) = 80W\), the received power at the end is \(V(D)=60W\), the actual carbon emissions \(E'_a(R'(C,D)) = 3.84kgCO_2\), and the energy utilization rate \(U'_r(R'(C,D)) = 75\%\);

[0224] Collect all the information of the optimized paths:

[0225] Path \(R'(A,B)\): \(V''(A)=45W\), \(V(B)=70W\), \(E'_a(R'(A,B)) = 3.3kgCO_2\), \(U'_r(R'(A,B)) = 155.56\%\);

[0226] Path \(R'(C,D)\): \(V''(C)=80W\), \(V(D)=60W\), \(E'_a(R'(C,D)) = 3.84kgCO_2\), \(U'_r(R'(C,D)) = 75\%\);

[0227] Calculate the total carbon emissions of the entire micro-energy network:

[0228] \(T_c = E'_a(R'(A,B))+E'_a(R'(C,D)) = 3.3 + 3.84 = 7.14kgCO_2\);

[0229] Evaluate the overall performance of the system:

[0230] Total number of paths n=2;

[0231] G_p=(U'_r(R'(A,B))+U'_r(R'(C,D))) / n=(155.56%+75%) / 2=115.28%;

[0232] Adjust the overall scheduling plan to minimize total carbon emissions:

[0233] Assume the system's minimum overall performance threshold G_min is set to 90%;

[0234] If G_p > G_min (i.e., 115.28% > 90%), then no further adjustment is needed.

[0235] However, if we assume G_min = 120%, then adjustments are needed:

[0236] Calculate the new starting power output V'''(X):

[0237] For path R'(A,B):

[0238] V''''(A)=V''(A)*(G_min / G_p)=45*(120% / 115.28%)≈46.8W;

[0239] For path R'(C,D):

[0240] V''''(C)=V''(C)*(G_min / G_p)=80*(120% / 115.28%)≈83.2W;

[0241] Recalculate the energy efficiency U'_r(R'):

[0242] For path R'(A,B):

[0243] U'_r(R'(A,B))=(V(B) / V'''(A))*100%=(70 / 46.8)*100%≈149.57%

[0244] For path R'(C,D):

[0245] U'_r(R'(C,D))=(V(D) / V'''(C))*100%=(60 / 83.2)*100%≈72.12%

[0246] Recalculate the overall performance coefficient G_p:

[0247] G_p=(149.57%+72.12%) / 2≈110.85%;

[0248] At this point, G_p is still less than G_min (120%), so further adjustments are needed:

[0249] Suppose we adjust G_min to 110% again:

[0250] For path R'(A,B):

[0251] V''''(A)=45*(110% / 110.85%)≈44.6W;

[0252] For path R'(C,D):

[0253] V''''(C)=80*(110% / 110.85%)≈79.4W;

[0254] Recalculate the energy efficiency U'_r(R'):

[0255] For path R'(A,B):

[0256] U'_r(R'(A,B))=(70 / 44.6)*100%≈156.95%;

[0257] For path R'(C,D):

[0258] U'_r(R'(C,D))=(60 / 79.4)*100%≈75.57%;

[0259] Recalculate the overall performance coefficient G_p:

[0260] G_p=(156.95%+75.57%) / 2≈116.26%;

[0261] At this point, G_p is already greater than or equal to G_min (110%), so no further adjustment is needed.

[0262] Summary of the application principles and meanings of the formulas:

[0263] T_c=sum(E'_a(R'))forallpaths:

[0264] The total carbon emissions of the entire system are obtained by summing up the actual carbon emissions of all pathways, which is used to evaluate the environmental performance of the system.

[0265] G_p=(sum(U'_r(R'))forallpaths) / n:

[0266] Calculate the average energy utilization rate of all paths to reflect the overall energy conversion efficiency of the system and help evaluate overall performance.

[0267] V'''(X)=V''(X)*(G_min / G_p):

[0268] Based on the difference 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 the requirements.

[0269] By following the steps and formulas above, the operation of the entire micro-energy network can be systematically evaluated and optimized, ensuring efficient energy utilization while reducing carbon emissions.

[0270] Step 8: Regularly update the comprehensive scheduling plan described in Step 7, taking into account the latest energy usage and environmental changes, to continuously reduce carbon emission levels; this specifically includes the following sub-process steps:

[0271] Set a regular update cycle T_u, such as once 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') of each path, and external environmental parameters (such as temperature T_e, light intensity L_s, etc.); by regularly collecting the latest data, it can be ensured that the system can adapt to constantly changing energy demands and environmental conditions, thereby optimizing its operating status.

[0272] Based on the latest collected data, the carbon emissions E''(R') for each path are recalculated using the formula E''(R') = I(R') * P''(X), where I(R') is the path's carbon emission intensity and P''(X) is the adjusted starting power output. The carbon emissions E''(R') for each path are recalculated based on the latest power output P''(X) and the path's carbon emission intensity I(R') to reflect the current actual operating status of the 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.

[0273] The integrated scheduling scheme D is updated using the calculated new carbon emissions E''(R'). An update factor U_f is defined to measure the degree of adjustment needed, calculated using the formula U_f = (sum(E'(R')) - sum(E''(R'))) / sum(E'(R')), where E'(R') is the carbon emissions after the previous optimization, and E''(R') is the newly calculated carbon emissions. The integrated scheduling scheme D is updated using the newly calculated carbon emissions E''(R'), and the update factor U_f is defined to measure the degree of adjustment needed. U_f reflects the change in carbon emissions, helping to decide whether further optimization is needed. U_f represents the proportion of the difference between the old and new carbon emissions; a positive value indicates a decrease in carbon emissions, and a negative value indicates an increase. This provides a basis for subsequent adjustments.

[0274] Based on the update factor U_f, adjust the power allocation of each path in the integrated scheduling scheme D. If U_f > 0, it indicates a reduction in carbon emissions, and the working state of the optimized path is optimized. Otherwise, increase attention to high-emission paths and adjust the starting power output using the formula P'''(X) = P''(X) * (1 + U_f) to ensure that the entire system minimizes total carbon emissions under the new conditions. By dynamically adjusting the power output of each path, carbon emissions can be further reduced while meeting energy demands, maintaining the efficient operation of the system. Adjusting the starting power output P'''(X) enables the system to achieve optimal performance in the new environment, gradually approaching the minimum carbon emission target.

[0275] Assume the following parameters (based on the example from the previous steps):

[0276] The update cycle T_u is once a month:

[0277] The starting power output of path R'(A,B) is P''(A)=46.8W, the ending power received is P(B)=70W, and the carbon emission intensity is I(R'(A,B))=0.0733kgCO2 / W;

[0278] The starting power output of path R'(C,D) is P''(C)=83.2W, the ending power received is P(D)=60W, and the carbon emission intensity is I(R(R'(C,D)))=0.046kgCO2 / W;

[0279] The carbon emissions after the last optimization were E'(R'(A,B)) = 3.3 kg CO2 and E'(R'(C,D)) = 3.84 kg CO2.

[0280] External environmental parameters: temperature T_e=25°C, light intensity L_s=500 lux;

[0281] Collect the latest data:

[0282] Assume the latest starting power outputs are as follows:

[0283] P''(A) = 46.8W (unchanged);

[0284] P''(C) = 83.2W (unchanged);

[0285] Recalculate the carbon emissions for each path:

[0286] For path R'(A,B):

[0287] E''(R'(A,B))=I(R'(A,B))*P''(A)=0.0733*46.8≈3.43kgCO2;

[0288] For path R'(C,D):

[0289] E''(R'(C,D))=I(R'(C,D))*P''(C)=0.046*83.2≈3.83kgCO2;

[0290] Update the integrated scheduling scheme:

[0291] Calculate the update factor U_f:

[0292] sum(E'(R'))=3.3+3.84=7.14kgCO2;

[0293] sum(E''(R'))=3.43+3.83=7.26kgCO2;

[0294] U_f=(sum(E'(R'))-sum(E''(R'))) / sum(E'(R'))=(7.14-7.26) / 7.14≈-0.0168 or -1.68%;

[0295] Adjust the power allocation of each path in the integrated scheduling scheme according to the update factor:

[0296] Since U_f < 0, it indicates an increase in carbon emissions, necessitating greater attention to high-emission pathways.

[0297] For path R'(A,B):

[0298] P'''(A)=P''(A)*(1+U_f)=46.8*(1-0.0168)≈46.0W;

[0299] For path R'(C,D):

[0300] P'''(C)=P''(C)*(1+U_f)=83.2*(1-0.0168)≈81.8W;

[0301] Recalculate energy efficiency and carbon emissions:

[0302] For path R'(A,B):

[0303] New energy efficiency:

[0304] U'_r(R'(A,B))=(P(B) / P'''(A))*100%=(70 / 46.0)*100%≈152.17%;

[0305] New carbon emissions:

[0306] E'''(R'(A,B))=I(R'(A,B))*P'''(A)=0.0733*46.0≈3.37kgCO2;

[0307] For path R'(C,D):

[0308] New energy efficiency:

[0309] U'_r(R'(C,D))=(P(D) / P'''(C))*100%=(60 / 81.8)*100%≈73.35%;

[0310] New carbon emissions:

[0311] E'''(R'(C,D))=I(R'(C,D))*P'''(C)=0.046*81.8≈3.76kgCO2;

[0312] Recalculate the overall performance coefficient G_p:

[0313] G_p=(U'_r(R'(A,B))+U'_r(R'(C,D))) / n=(152.17%+73.35%) / 2≈112.76%;

[0314] At this point, although carbon emissions have increased slightly, energy efficiency remains high. Further optimization can be achieved by adjusting the power output until the optimal state is reached.

[0315] On the other hand, this invention proposes a multi-energy coupled microgrid optimized scheduling system, such as... Figure 2 As shown, it includes:

[0316] The network construction and path definition module is used to 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;

[0317] The carbon emission labeling module is used to identify all available energy flow directions based on the micro-energy network connection diagram and mark the initial carbon emissions in each direction;

[0318] The starting operation path selection module is used to select a path with the lowest carbon emissions as the starting operation path based on the marked initial carbon emissions, and to record real-time data on the starting operation path.

[0319] An energy supply source adjustment module is used to adjust the working status of the energy supply source related to the initial running path using the recorded real-time data;

[0320] The system performance evaluation module is used to monitor the adjusted system operation status, compare the data with the data before the adjustment, and calculate the carbon emission reduction.

[0321] The iterative optimization module is used to repeat steps three through five to iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted.

[0322] The integrated scheduling scheme formulation module is used to integrate all optimized path information to construct an integrated scheduling scheme, ensuring that the entire micro energy network meets demand while minimizing total carbon emissions.

[0323] The continuous improvement module is used to periodically update the comprehensive scheduling scheme described in step seven, and to continuously reduce carbon emission levels by taking into account the latest energy usage and environmental changes.

[0324] In addition, the aforementioned network construction and path definition module, carbon emission identification module, initial operation path selection module, energy supply source adjustment module, system performance evaluation module, cycle optimization module, comprehensive scheduling scheme formulation module, and continuous improvement module are also used to implement other steps of the aforementioned multi-energy coupled microgrid optimization scheduling method during execution, which will not be elaborated here.

[0325] In summary, this invention achieves dynamic optimization scheduling by periodically updating the integrated scheduling scheme and incorporating the latest energy usage and environmental changes, significantly reducing the system's total carbon emissions. Furthermore, this invention integrates all optimized path information to construct a comprehensive integrated scheduling scheme, ensuring that the entire micro-energy network achieves global optimization while meeting demand.

[0326] Furthermore, by iteratively optimizing all unprocessed paths and adjusting the operating status of energy supply sources based on real-time data, this invention significantly improves system operating efficiency and reduces unnecessary energy consumption. In summary, this invention not only overcomes the limitations of static scheduling strategies in the prior art but also provides a more flexible and efficient optimization method, effectively improving the overall system performance and environmental benefits.

[0327] Finally, it should be noted that the above description 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 foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimal scheduling of a multi-energy coupled microgrid, characterized in that, Includes the following steps: Step 1: Establish a micro-energy network connection diagram that includes 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: Based on the marked initial carbon emissions, select a path with the lowest carbon emissions as the starting operating path, and record the real-time data on the starting operating path; Step 4: Using the recorded real-time data, adjust the operating status of the energy supply sources related to the initial operating path; Step 5: Monitor the system's operation after the adjustment, compare the data with the data before the adjustment, and calculate the reduction in carbon emissions; Step Six: Repeat Steps Three through Five to iterate and optimize the remaining unoptimized paths until all paths have been evaluated and adjusted. Step 7: Integrate all optimized path information to construct a comprehensive scheduling scheme to ensure that the entire micro-energy network minimizes total carbon emissions while meeting demand; Step 8: Regularly update the comprehensive scheduling plan described in Step 7, and continuously reduce carbon emission levels by taking into account the latest energy usage and environmental changes; The step of adjusting the operating status of energy supply sources related to the initial operating path using the recorded real-time data includes: calculating the current carbon emission intensity of the path based on the recorded real-time data, including the actual carbon emissions of the path, the starting power output, and the receiving power at the end point; determining the operating status of energy supply sources that need to be adjusted using the calculated carbon emission intensity, and measuring the carbon emission reduction target through an adjustment factor; adjusting the operating status of energy supply sources related to the path according to the determined adjustment factor; and recalculating the energy utilization rate of the path based on the adjusted power output, and checking whether it meets the minimum energy utilization rate threshold set by the system. The process of monitoring the adjusted system operation status, comparing the data with that before the adjustment, and calculating the carbon emission reduction includes: recording new real-time data of the path under the adjusted system operation status, including new starting point power output, ending point received power, and new actual carbon emissions; calculating the carbon emission difference of the path before and after the adjustment based on the recorded new real-time data; evaluating the effectiveness of the adjustment measures using the calculated carbon emission difference, and defining an effect coefficient to measure the adjustment effect; and analyzing the overall performance improvement of the system based on the effect coefficient, and dynamically adjusting the minimum energy utilization rate threshold and minimum carbon emission intensity threshold set by the system.

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

3. The multi-energy coupled microgrid optimization scheduling method according to claim 2, characterized in that, The process 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 direction and its corresponding initial carbon emissions, the cumulative carbon emissions in each direction are calculated, and all directions are sorted to form a list arranged in ascending order of carbon emissions. Using the calculated cumulative carbon emissions, the micro-energy network connection diagram is updated, the final carbon emissions in each direction are marked, and the actual connection relationships between each energy supply source are adjusted to ensure that the carbon emissions in all directions meet the maximum total carbon emission threshold set by the system.

4. The multi-energy coupled microgrid optimization scheduling method according to claim 3, characterized in that, The step of selecting a path with the lowest carbon emissions as the starting operating path based on the marked initial carbon emissions, and recording real-time data on the starting operating path, includes: Based on the generated list sorted in ascending order of carbon emissions, select the path with the lowest carbon emissions as the starting path and record the real-time data on that path. Record real-time data along the selected initial operating path, including current power output, received power, and actual carbon emissions along the path. Using the recorded real-time data, calculate the energy utilization rate of the path and check whether it meets the minimum energy utilization rate threshold set by the system. Based on the calculated energy utilization rate, adjust the working state of the initial operating path to ensure that it meets the minimum energy utilization rate threshold set by the system.

5. The multi-energy coupled microgrid optimization scheduling method according to claim 4, characterized in that, Steps three through five are repeated to iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted, including: Select the path with the minimum cumulative carbon emissions from the remaining unoptimized paths, and repeat steps three through five. Based on the selected route, record real-time data of the route and calculate the new carbon emission intensity; By using recorded data, the operating status of path-related energy supply sources is adjusted, and carbon emission reduction targets are measured through adjustment factors. Based on the adjustment factor, update the starting power output of the path and recalculate the energy utilization rate of the path, checking whether it meets the minimum energy utilization rate threshold set by the system.

6. The multi-energy coupled microgrid optimization scheduling method according to claim 5, characterized in that, The process of integrating all optimized path information to construct a comprehensive scheduling scheme ensures that the entire micro-energy network minimizes total carbon emissions while meeting demand, including: Collect all optimized path information, including the starting power output, ending power received, actual carbon emissions, and energy utilization rate of each path, and construct a comprehensive scheduling scheme. Based on all collected path information, calculate the total carbon emissions of the entire micro energy network; The overall performance of the system is evaluated using the calculated total carbon emissions, and an overall performance coefficient is defined to measure the system performance. Based on the overall performance coefficient, adjust the power allocation of each path in the integrated scheduling scheme to ensure that the total carbon emissions are minimized while meeting the demand.

7. The multi-energy coupled microgrid optimization scheduling method according to claim 6, characterized in that, The periodic update of the comprehensive scheduling scheme described in step seven, taking into account the latest energy usage and environmental changes, includes: Establish a regular update cycle, and at the beginning of each update cycle, collect the latest energy usage data and environmental change information; Based on the latest collected data, the carbon emissions for each path are recalculated, and the integrated scheduling scheme is updated. Using the calculated new carbon emissions, an update factor is defined to measure the degree of adjustment needed; Based on the update factor, the power allocation of each path in the integrated scheduling scheme is adjusted to ensure that the entire system minimizes total carbon emissions under the new conditions.

8. A microgrid optimization scheduling system for implementing the method of any one of claims 1-7, characterized in that, include: The network construction and path definition module is used to 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; The carbon emission labeling module is used to identify all available energy flow directions based on the micro-energy network connection diagram and mark the initial carbon emissions in each direction; The starting operation path selection module is used to select a path with the lowest carbon emissions as the starting operation path based on the marked initial carbon emissions, and to record real-time data on the starting operation path. An energy supply source adjustment module is used to adjust the working status of the energy supply source related to the initial running path using the recorded real-time data; Specifically, this includes: calculating the current carbon emission intensity of the path based on recorded real-time data, including the actual carbon emissions of the path, the starting power output, and the receiving power at the end point; using the calculated carbon emission intensity to determine the operating status of the energy supply sources that need to be adjusted, and measuring the carbon emission reduction target through adjustment factors; adjusting the operating status of the energy supply sources related to the path according to the determined adjustment factors; and recalculating the energy utilization rate of the path based on the adjusted power output, and checking whether it meets the minimum energy utilization rate threshold set by the system. The system performance evaluation module monitors the adjusted system operation status, compares the data with that before the adjustment, and calculates the carbon emission reduction. Specifically, it includes: recording new real-time data of the path under the adjusted system operating conditions, including new starting point power output, ending point received power, and new actual carbon emissions; calculating the carbon emission difference of the path before and after the adjustment based on the recorded new real-time data; evaluating the effectiveness of the adjustment measures using the calculated carbon emission difference and defining an effect coefficient to measure the adjustment effect; and analyzing the overall performance improvement of the system based on the effect coefficient, and dynamically adjusting the system's set minimum energy utilization rate threshold and minimum carbon emission intensity threshold. The iterative optimization module is used to repeat steps three through five to iteratively optimize the remaining unoptimized paths until all paths have been evaluated and adjusted. The integrated scheduling scheme formulation module is used to integrate all optimized path information to construct an integrated scheduling scheme, ensuring that the entire micro energy network meets demand while minimizing total carbon emissions. The continuous improvement module is used to periodically update the comprehensive scheduling scheme described in step seven, and to continuously reduce carbon emission levels by taking into account the latest energy usage and environmental changes.

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