Source load distributed scheduling system based on carbon flow tracking of medium and low voltage power distribution network

Abstract

The invention discloses a source load distributed scheduling system based on medium and low voltage power distribution network carbon flow tracking, and relates to the technical field of power distribution network scheduling. The source-load distributed scheduling system comprises a data acquisition module, a carbon flow tracking module, a load prediction module, a distributed power source scheduling module, a source-load matching module, a collaborative scheduling module and a carbon emission monitoring module, and all the modules are connected through electric signals. The data acquisition module is used for collecting operation data of each node and branch in the middle and low voltage power distribution network. By monitoring the carbon emission condition of each distributed power supply and combining load prediction and a source-load matching strategy, a low-carbon scheduling scheme is accurately formulated, output adjustment of the power supply is considered, load reduction and transfer are also considered, collaborative optimization of the source side and the load side is realized, and the scheduling efficiency of the distributed power supply can be improved according to the carbon emission intensity of each power supply. And the overall carbon emission of the power distribution network is effectively reduced while the load requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching, and particularly to a source-load distributed dispatching system based on carbon flow tracking in medium- and low-voltage distribution networks. Background Art

[0002] With the growth of the global economy and the acceleration of the industrialization process, the total carbon emissions show an upward trend. Carbon emissions mainly come from the combustion of fossil fuels such as coal, oil, and natural gas. At present, the global carbon emissions are in the stage of transformation from high-carbon to low-carbon. In China, with the proposal of the dual-carbon goal and the implementation of a series of low-carbon policies, carbon emissions are gradually decreasing, the energy structure is being optimized, and the proportion of renewable energy is continuously increasing. With the proposal of the dual-carbon goal and the development of the low-carbon economy, the low-carbon economic operation of medium- and low-voltage distribution networks has become an important issue. In order to reduce carbon emissions and improve energy efficiency, a more intelligent and efficient power grid dispatching system needs to be established.

[0003] For example, a green dispatching method for a distribution network considering the distribution of network carbon losses, disclosed in Chinese Patent Publication No. CN117913922A, relates to the technical field of carbon emissions in distribution networks, models the carbon emissions in the whole life cycle of coal-fired power plants, photovoltaic power plants, and wind power plants, and obtains a carbon emission factor model for the energy side of the distribution network.

[0004] In the prior art, according to the final optimal reconstruction result of the distribution network, the network topology information is obtained, the power flow calculation is carried out to determine the power flow of the power system, a carbon emission responsibility sharing model is established based on the proportional sharing principle, the carbon emission flow is calculated based on the active power and other node matrices under the system power flow, and the carbon emission property rights quota of each part is obtained. It solves the problem of the lack of an integrated optimization method considering multiple objectives and constraints such as the carbon loss cost caused by the line losses of each branch of the distribution network, and cannot cope with the actual complex green and low-carbon optimization problem of the distribution network. However, the medium- and low-voltage distribution network involves a large number of nodes and branches, which will affect the accuracy of obtaining carbon emission data by carbon flow tracking. Moreover, due to the load fluctuation and the access of distributed power sources, the complexity of the distribution network is further increased, increasing the difficulty of carbon flow analysis, and thus affecting the efficiency of carbon flow tracking in the distribution network and the balance of coordinated control. Therefore, a source-load distributed dispatching system based on carbon flow tracking in medium- and low-voltage distribution networks is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a source-load distributed dispatching system based on carbon flow tracking in medium- and low-voltage distribution networks to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A source-load distributed scheduling system based on carbon flow tracking in medium- and low-voltage distribution networks. The source-load distributed scheduling system includes a data acquisition module, a carbon flow tracking module, a load forecasting module, a distributed power source scheduling module, a source-load matching module, a collaborative scheduling module, and a carbon emission monitoring module. Among them, the modules are electrically connected to each other;

[0008] The data acquisition module is used to collect the operation data of each node and branch in the medium- and low-voltage distribution network, including the current and voltage information of each branch, the power data, carbon emission data of each node, and the basic data of the output power and access status of distributed power sources, providing accurate data support for carbon flow tracking, ensuring real-time mastery of the load changes and carbon emission situations of each node and branch, and providing a basis for subsequent analysis;

[0009] Based on the collected data, the carbon flow tracking module combines the topological structure of the distribution network, the load distribution, and the energy exchange relationship between the power generation and load points to analyze the carbon emission situations generated during the flow of electricity between each branch and node in the medium- and low-voltage distribution network, accurately calculating the carbon emission costs of each entity in the distribution network, and providing an important basis for source-load distributed scheduling;

[0010] The load forecasting module collects historical load data and conducts analysis. Combining with the meteorological factor data related to the load, it analyzes the power demand of each load point and the fluctuation situation of the load, providing a prediction basis for the load change in the distribution network scheduling;

[0011] The distributed power source scheduling module conducts real-time monitoring and control of various distributed power sources connected to the medium- and low-voltage distribution network, and optimizes the output power plan of the distributed power sources according to the carbon flow tracking results and the load forecasting results. By optimizing the output of the distributed power sources, it reduces carbon emissions, balances the relationship between the load and the power source, and improves the flexibility of the system and the utilization rate of renewable energy;

[0012] The source-load matching module conducts dynamic matching of the source (distributed power source) and the load according to the carbon emission situation obtained from the carbon flow tracking, the load forecasting results, and the real-time output and adjustable capacity of the distributed power sources, obtains the best scheduling plan, realizes the optimal scheduling of the source and the load, enables the distributed power sources to meet the load demand, and at the same time realizes the minimization of carbon emissions, ensuring the power balance and economic operation of the distribution network;

[0013] The collaborative scheduling module generates specific source-load distributed scheduling strategies according to the best scheduling plan output by the source-load matching module, clarifies the output adjustment amounts of each distributed power source and the control strategies of each load, and conducts real-time scheduling of the medium- and low-voltage distribution network to ensure that the distribution network operates in a low-carbon, efficient, and safe state;

[0014] The carbon emission monitoring module is used to monitor the carbon emission situation of each node in the medium and low-voltage power grid in real time, analyze the rationality and effectiveness of the dispatching strategy, and generate a dispatching analysis report for decision-makers to refer to, ensuring that the implementation effect of the dispatching strategy is timely feedback, facilitating dynamic adjustment of the system, and promoting continuous carbon emission control and optimization.

[0015] A further improvement of the technical solution of the present invention lies in that: in the data acquisition module, the process of collecting the operation data of each node and branch includes:

[0016] Deploy sensors at each node and branch in the medium and low-voltage distribution network to collect the operation data of each node and branch. Among them, the operation data includes the current and voltage information of each branch, the power data, carbon emission data of each node, and the basic data of the output power and access status of distributed power sources. The sensors include current transformers, voltage transformers, power sensors, carbon emission monitors, and sensors for monitoring the output power and access status of distributed power sources, etc.;

[0017] Transmit the collected operation data to the data processing center through the sensor network, and preprocess the collected operation data of each node and branch, including operations such as filtering, denoising, and data cleaning, to ensure the accuracy and reliability of the data, and perform format conversion on the preprocessed data;

[0018] Perform time alignment processing on the preprocessed data through the timestamp synchronization method to ensure that the data is within the unified time scale range, and construct a data warehouse to store the processed operation data in the data warehouse for further processing and analysis.

[0019] A further improvement of the technical solution of the present invention lies in that: in the carbon flow tracking module, the analysis process of the carbon emission situation includes:

[0020] Call the operation data of each node and branch in the medium and low-voltage power grid collected from the data warehouse, and construct a distribution network model according to the topological structure of the distribution network;

[0021] Use the distribution network model to clarify the connection relationship of each node and branch, and then analyze the load distribution. By analyzing the load data of each node, identify the load distribution situation, including the size, change trend, and peak and valley periods of the load;

[0022] Combine the energy exchange relationship between power generation and load points to analyze the flow of electricity between each branch and node in the medium and low-voltage distribution network. According to the known node power and branch parameters, calculate the current, voltage, and power distribution of each branch;

[0023] Using the carbon flow tracking algorithm, according to the power flow situation and the carbon emission data of each node, calculate the total carbon emissions of the entire distribution network during the power flow process, analyze the carbon emission intensities of different nodes and branches, that is, the carbon emissions generated per unit of power consumption, and identify the hot spots and key paths of carbon emissions by comparing the carbon emission intensities of different nodes and branches;

[0024] According to the relationship between carbon emissions and carbon emission factors, calculate the total carbon emission costs of each entity in the distribution network. Among them, the carbon emission cost reflects the environmental responsibility and economic burden of each entity in the process of power production and consumption, and is obtained by multiplying the carbon emissions by the corresponding carbon emission factors. According to the domestic carbon emission standards, the carbon emission factors of different energy types can be determined.

[0025] A further improvement of the technical solution of the present invention lies in that: the calculation formula for the total carbon emissions of the entire distribution network during the power flow process is:

[0026]

[0027] wherein, E t is the total carbon emissions of the entire distribution network, N is the total number of nodes in the distribution network, P i is the active power loss of node i, D i is the load distance of node i, which is the reciprocal of reactance and represents the resistance of power flow. α is the carbon emission coefficient related to the power flow distance, reflecting the impact of power flow loss on carbon emissions, Q i is the reactive power loss of node i, and β is the carbon emission coefficient related to reactive power;

[0028] The calculation formula for the total carbon emission costs of each entity in the distribution network is:

[0029]

[0030] E m =P m ·EF e，m ;

[0031] wherein, C t is the total carbon emission costs of each entity in the distribution network, M is the total number of entities in the distribution network, E m is the carbon emissions of entity m, EF m is the carbon emission factor of entity m, reflecting the economic cost of carbon emissions, P m is the energy consumption of entity m, and EF e，m is the carbon emission factor per unit energy consumption of entity m.

[0032] A further improvement of the technical solution of the present invention lies in that: in the load prediction module, the analysis process of the load fluctuation situation includes:

[0033] Obtain historical load data from the database of the power system, and obtain meteorological factor data that matches the load data in terms of time and space from the meteorological department. Among them, the meteorological factor data includes temperature, humidity, and precipitation;

[0034] Using the regression analysis method, analyze the correlation between meteorological factors and load, and construct a meteorological load prediction model according to the characteristics of historical load data and meteorological factor data. Use historical data to train the prediction model, select the best model parameters through cross-validation, evaluate the performance of the model, and calculate indicators such as root mean square error and mean absolute error to optimize the model;

[0035] Combining meteorological factor data and historical load data, use the trained meteorological load prediction model to predict the power demand of each load point, and analyze the load fluctuation situation, including the fluctuation amplitude and fluctuation period;

[0036] Based on the analysis results of the load fluctuation situation, calculate the load trend index, conduct a trend analysis of the load and meteorological factors, and clarify the law of load change with meteorological factors;

[0037] The calculation formula of the load trend index is:

[0038]

[0039] Where, T index is the load trend index, which reflects the law of load change with meteorological factors, K is the total number of data points, F k is the load value of the kth data point, W k is the temperature value of the kth data point, H k is the humidity value of the kth data point, R k is the precipitation of the kth data point.

[0040] A further improvement of the technical solution of the present invention lies in: in the distributed power source scheduling module, the process of optimizing the distributed power source output plan includes:

[0041] Real-time monitor various distributed power sources connected to the medium and low voltage distribution network, obtain the operation status and output information of the distributed power sources, including the active power output and carbon emission factors of each distributed power source, and analyze the grid load situation including load size, change trend, and load spatio-temporal distribution, and obtain grid power flow information, including power losses and carbon flow density of each branch;

[0042] Combining the real-time monitoring data of various distributed power sources and the power grid flow information, conducting carbon flow tracking and load forecasting, calculating the contribution degree of each distributed power source to the power grid carbon emissions, and optimizing the output plan of the distributed power sources by integrating the real-time monitoring data, carbon flow tracking results and load forecasting results;

[0043] Based on the optimized output plan of the distributed power sources, coordinating and optimizing the dispatching strategy, and balancing the relationship between the load and the power sources, including increasing the output of the distributed power sources during peak load periods and reducing the output or performing energy storage operations during low load periods, and then implementing the optimized output plan into the power grid.

[0044] A further improvement of the technical solution of the present invention lies in that: the calculation formula for the contribution degree of each distributed power source to the power grid carbon emissions is:

[0045]

[0046] where C co , x is the contribution degree of the xth distributed power source to the power grid carbon emissions, P G , x is the active power output of the xth distributed power source, I G , x is the carbon emission factor of the xth distributed power source, Z is the total number of distributed power sources in the power grid, D loss , l is the power loss of the lth branch, F line , l is the carbon flow density of the lth branch, and L is the number of branches in the power grid.

[0047] A further improvement of the technical solution of the present invention lies in that: in the source-load matching module, the process of obtaining the optimal dispatching plan includes:

[0048] Extracting the real-time output data and adjustable capacity information of the distributed power sources from the real-time monitoring data of each distributed power source, formulating a source-load dynamic matching strategy by combining the load forecasting results and the carbon emission situation data obtained from the carbon flow tracking, and conducting dynamic matching analysis of the source and load, where the real-time output data reflects the current power generation capacity of each power source, and the adjustable capacity information provides the adjustment space of the power source in the future period of time;

[0049] Based on the formulated source-load dynamic matching strategy, setting the optimization objectives of carbon emission minimization, load demand satisfaction and power balance;

[0050] Using the optimization algorithm of linear programming, combining the dynamic matching analysis results of the source and load, generating multiple optimized dispatching plans, and evaluating each plan in terms of carbon emission, load demand satisfaction degree and power balance based on the optimization objectives;

[0051] Set the total time section for evaluating each plan, extract the actual output of distributed power sources and load demands at a single moment within a fixed time interval range, calculate the sum of squares of the source-load output change rates, analyze the consistency of source-load changes, calculate the sum of absolute values of source-load output differences, and analyze the deviation of source-load matching;

[0052] Based on the comprehensive evaluation results, compare the performances of different plans in various evaluation indicators, calculate the source-load matching degree of each optimal dispatching plan, and then select the best optimal dispatching plan to ensure that while meeting the load demand, it minimizes carbon emissions and guarantees the power balance and economic operation of the distribution network. Among them, the source-load matching degree reflects the comprehensive ability of the plan in meeting the load demand, minimizing carbon emissions, and achieving power balance.

[0053] A further improvement of the technical solution of the present invention lies in: the calculation formula for the source-load matching degree of each optimal dispatching plan is:

[0054]

[0055] where M SL is the source-load matching degree, reflecting the comprehensive ability of the plan in meeting the load demand, minimizing carbon emissions, and achieving power balance. λ is the weight coefficient used to balance the importance of the two indicators. P re,t is the actual output of the distributed power source at time t, P ld , t is the load demand at time t. T is the total time section, and Δt is the time interval, taking 1 hour. is the sum of squares of the source-load output change rates, reflecting the consistency of source-load changes. is the sum of absolute values of the source-load output differences.

[0056] A further improvement of the technical solution of the present invention lies in: in the collaborative dispatching module, the generation process of the source-load distributed dispatching strategy includes:

[0057] Receive the best dispatching plan output by the source-load matching module, parse the received dispatching plan, clarify the output adjustment amounts of each distributed power source and the control strategies of each load, including determining the upper limit, lower limit, and adjustment rate of the output of each power source, as well as the reduction amount or transfer strategy of each load, to ensure the matching of load demand and power source output;

[0058] Generate specific dispatching instructions according to the parsed dispatching plan, including output adjustment instructions for each distributed power source and load control instructions, to ensure the real-time matching of load demand and power source output while maintaining the power balance of the distribution network;

[0059] The generated dispatching instructions are sent to each distributed power source and load control center in real time through the communication network. After the dispatching instructions are sent, the output power changes of each distributed power source in the medium and low voltage distribution network, the changes in load demand, and the power balance situation of the distribution network are monitored. If any abnormal situation or deviation from the expected target is found, the dispatching strategy is adjusted and the dispatching instructions are regenerated.

[0060] Due to the adoption of the above technical solution, the technical progress achieved by the present invention compared with the prior art is as follows:

[0061] 1. The present invention provides a source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks. By monitoring the carbon emissions of each distributed power source, combining load forecasting and source-load matching strategies, a low-carbon dispatching plan is accurately formulated. It not only considers the output power adjustment of power sources but also takes into account the reduction and transfer of loads, achieving collaborative optimization on both the source and load sides. It can effectively reduce the overall carbon emissions of the distribution network while meeting the load demand according to the carbon emission intensity of each power source.

[0062] 2. The present invention provides a source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks. Through dynamic source-load matching, the efficient utilization of energy is realized. According to the load demand and the output power of power sources, the output power of each distributed power source is adjusted in real time to ensure an accurate match between energy supply and demand, avoiding energy waste and improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0064] Figure 1 is a module diagram of the present invention;

[0065] Figure 2 is an analysis flowchart of the load fluctuation situation of the present invention;

[0066] Figure 3 is a flowchart for obtaining the optimal dispatching plan of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Embodiment 1, as Figure 1 、 Figure 2 shown, the present invention provides a source-load distributed scheduling system based on carbon flow tracking in medium- and low-voltage distribution networks. The source-load distributed scheduling system includes a data acquisition module, a carbon flow tracking module, a load forecasting module, a distributed power source scheduling module, a source-load matching module, a collaborative scheduling module, and a carbon emission monitoring module. Among them, the modules are electrically connected to each other;

[0069] The data acquisition module is used to collect the operation data of each node and branch in the medium- and low-voltage distribution network, including the current and voltage information of each branch, the power data, carbon emission data of each node, and the basic data of the output power and access status of distributed power sources, providing accurate data support for carbon flow tracking, ensuring real-time grasp of the load changes and carbon emission situations of each node and branch, providing a basis for subsequent analysis. Deploy sensors at each node and branch in the medium- and low-voltage distribution network to collect the operation data of each node and branch. Among them, the operation data includes the current and voltage information of each branch, the power data, carbon emission data of each node, and the basic data of the output power and access status of distributed power sources. The sensors include current transformers, voltage transformers, power sensors, carbon emission monitors, and sensors for monitoring the output power and access status of distributed power sources, etc. Transmit the collected operation data to the data processing center through the sensor network, and preprocess the collected operation data of each node and branch, including operations such as filtering, denoising, and data cleaning, to ensure the accuracy and reliability of the data, and perform format conversion on the preprocessed data to meet the requirements of subsequent analysis or storage. Among them, filter the collected original data to remove high-frequency noise and interference, clean the data to remove outliers, duplicate values, and missing values, etc. For missing values, use methods such as interpolation method, mean filling method, or adjacent value filling method to complete the filling. Perform time alignment processing on the preprocessed data through the timestamp synchronization method to ensure that the data is within the unified time scale range, and build a data warehouse to store the processed operation data in the data warehouse for further processing and analysis;

[0070] The carbon flow tracking module, based on the collected data, combines the topological structure of the distribution network, the load distribution, and the energy exchange relationship between the power generation and the load points to analyze the carbon emissions generated during the flow of electricity among the branches and nodes of the medium- and low-voltage distribution network, accurately calculate the carbon emission costs of each entity in the distribution network, provide an important basis for the distributed scheduling of the source and load, call the operation data of each node and branch in the medium- and low-voltage distribution network collected from the data warehouse, and construct a distribution network model according to the topological structure of the distribution network. Use the distribution network model to clarify the connection relationship of each node and branch, and then analyze the load distribution. By analyzing the load data of each node, identify the load distribution, including the magnitude, change trend, and peak and valley periods of the load. Combine the energy exchange relationship between the power generation and the load points to analyze the flow of electricity among the branches and nodes of the medium- and low-voltage distribution network. According to the known node power and branch parameters, calculate the current, voltage, and power distribution of each branch;

[0071] The calculation formula for the current of each branch is:

[0072]

[0073] Where, I ip is the current of branch ip, P ip is the active power of branch ip, Q ip is the reactive power of branch ip, R ip is the resistance of branch ip, j is the imaginary unit, X ip is the reactance of branch ip, θ i is the voltage phase angle of node i, θ p is the voltage phase angle of node p. The magnitude and phase of the current will change with the changes in the active power and reactive power, and are also affected by the voltage phase angle difference;

[0074] The calculation formula for the voltage of each branch is:

[0075]

[0076] Where, U i is the voltage amplitude of node i, U0 is the reference voltage, P i is the active power loss of node i, G ii is the self-conductance of node i. The voltage amplitude will decrease with the increase in the active power loss and is affected by the self-conductance of the node;

[0077] The calculation formula for the power distribution of each branch is:

[0078]

[0079] Where, S ip is the complex power of branch ip, Pip is the active power of branch ip, Q ip is the reactive power of branch ip. The magnitude of the complex power changes with the active and reactive powers, and its phase is determined by the ratio of the reactive power to the active power;

[0080] Using the carbon flow tracking algorithm, based on the power flow situation and the carbon emission data of each node, calculate the total carbon emissions of the entire distribution network during the power flow process, analyze the carbon emission intensities of different nodes and branches, that is, the carbon emissions generated per unit of power consumption. By comparing the carbon emission intensities of different nodes and branches, identify the hot spots and key paths of carbon emissions. According to the relationship between carbon emissions and carbon emission factors, calculate the total carbon emission costs of each entity in the distribution network. Among them, the carbon emission cost reflects the environmental responsibility and economic burden of each entity during the power production and consumption processes, and is obtained by multiplying the carbon emissions by the corresponding carbon emission factors. According to the domestic carbon emission standards, the carbon emission factors of different energy types can be determined;

[0081] Furthermore, the calculation formula for the total carbon emissions of the entire distribution network during the power flow process is:

[0082]

[0083] where E t is the total carbon emissions of the entire distribution network, N is the total number of nodes in the distribution network, P i is the active power loss of node i, D i is the load distance of node i, which is the reciprocal of the reactance and represents the resistance of power flow. α is the carbon emission coefficient related to the power flow distance, reflecting the impact of power flow losses on carbon emissions, Q i is the reactive power loss of node i, β is the carbon emission coefficient related to reactive power, reflecting the impact of reactive power on carbon emissions. Considering the impacts of both active power and reactive power on carbon emissions, represents the impact of power flow losses on carbon emissions during the power flow process. The greater the loss, the higher the carbon emissions. log(1 + β·Q i ) represents the impact of reactive power on carbon emissions. The increase in reactive power will lead to an increase in carbon emissions;

[0084] The calculation formula for the total carbon emission costs of each entity in the distribution network is:

[0085]

[0086] E m =P m ·EF e，m ;

[0087] where C tis the total carbon emission cost of each entity in the distribution network, M is the total number of entities in the distribution network, and E m is the carbon emission of entity m, and EF m is the carbon emission factor of entity m, which reflects the economic cost of carbon emissions, and P m is the energy consumption of entity m, and EF e，m is the carbon emission factor per unit energy consumption of entity m, and P m The value of EF changes with time and energy demand e，m The value of EF depends on the energy type and combustion efficiency, and the carbon emission factors of different energy types are different;

[0088] Load forecasting module: Collect historical load data and conduct analysis. Combine meteorological factor data related to the load to analyze the power demand of each load point, analyze the load fluctuation situation, and provide a prediction basis for the load change in the distribution network dispatching. Obtain historical load data from the database of the power system and obtain meteorological factor data that matches the load data in time and space from the meteorological department. Among them, the meteorological factor data includes temperature, humidity, and precipitation. Use the regression analysis method to analyze the correlation between meteorological factors and the load, and construct a meteorological load forecasting model according to the characteristics of historical load data and meteorological factor data. Use historical data to train the forecasting model, select the best model parameters through cross-validation, evaluate the performance of the model, calculate indicators such as root mean square error and mean absolute error to optimize the model. Combine meteorological factor data and historical load data, and use the trained meteorological load forecasting model to predict the power demand of each load point and analyze the load fluctuation situation, including the fluctuation amplitude and fluctuation period. Based on the analysis results of the load fluctuation situation, calculate the load trend index, conduct a trend analysis of the load and meteorological factors, and clarify the law of load change with meteorological factors;

[0089] Furthermore, the calculation formula for the load trend index is:

[0090]

[0091] where T index is the load trend index, which reflects the law of load change with meteorological factors, K is the total number of data points, and F k is the load value of the kth data point, and W k is the temperature value of the kth data point, and H k is the humidity value of the kth data point, and R k is the precipitation of the kth data point, and F k The value of T depends on the specific load situation, and there will be different load values at different times of the day indexThe value will change with the variation of meteorological factors. When meteorological conditions are extreme (high temperature, high humidity, heavy rain), there are significant changes in the load trend index;

[0092] The distributed power generation scheduling module monitors and controls various types of distributed power generations connected to the medium and low voltage distribution network in real time, and optimizes the output plan of distributed power generations according to the results of carbon flow tracking and load forecasting. By optimizing the output of distributed power generations, carbon emissions are reduced, the relationship between load and power is balanced, and the flexibility of the system and the utilization rate of renewable energy are improved;

[0093] The source-load matching module conducts dynamic matching of the source (distributed power generation) and the load according to the carbon emission situation obtained from carbon flow tracking, the load forecasting results, and the real-time output and adjustable capacity of distributed power generations, obtains the optimal scheduling plan, realizes the optimal scheduling of the source and load, enables the distributed power generation to meet the load demand, and at the same time minimizes carbon emissions, ensuring the power balance and economic operation of the distribution network;

[0094] The collaborative scheduling module generates specific source-load distributed scheduling strategies according to the optimal scheduling plan output by the source-load matching module, clarifies the output adjustment amount of each distributed power generation and the control strategy of each load, and conducts real-time scheduling of the medium and low voltage distribution network to ensure that the distribution network operates in a low-carbon, efficient, and safe state;

[0095] The carbon emission monitoring module is used to monitor the carbon emission situation of each node in the medium and low voltage distribution network in real time, analyze the rationality and effectiveness of the scheduling strategy, and generate a scheduling analysis report for the reference of decision-makers, ensuring that the implementation effect of the scheduling strategy is timely feedback, facilitating dynamic adjustment of the system, and promoting continuous carbon emission control and optimization.

[0096] Embodiment 2, as Figure 3 shown, on the basis of Embodiment 1, the present invention provides a technical solution: Preferably, in the distributed power generation scheduling module, the process of optimizing the output plan of distributed power generations includes:

[0097] Real-time monitoring is carried out on various types of distributed power sources connected to the medium- and low-voltage distribution network to obtain the operation status and output information of the distributed power sources, including the active power output and carbon emission factors of each distributed power source, and the grid load conditions including the load size, change trend, and spatial-temporal distribution of the load are analyzed to obtain the grid power flow information, including the power loss and carbon flow density of each branch. Combining the real-time monitoring data of various distributed power sources and the grid power flow information, carbon flow tracking and load forecasting are carried out, the contribution degree of each distributed power source to the grid carbon emission is calculated, and based on the real-time monitoring data, the carbon flow tracking results, and the load forecasting results, the output plan of the distributed power source is optimized to ensure that the output plan can not only meet the power consumption needs of the grid but also achieve the goal of energy conservation and emission reduction. Based on the optimized output plan of the distributed power source, the dispatching strategy is coordinated and optimized, and the relationship between the load and the power source is balanced, including increasing the output of the distributed power source during peak load periods and reducing the output or performing energy storage operations during low load periods, and then the optimized output plan is implemented into the grid;

[0098] Furthermore, the calculation formula for the contribution degree of each distributed power source to the grid carbon emission is as follows:

[0099]

[0100] where C co , x is the contribution degree of the x-th distributed power source to the grid carbon emission, P G , x is the active power output of the x-th distributed power source, I G , x is the carbon emission factor of the x-th distributed power source, Z is the total number of distributed power sources in the grid, D loss , l is the power loss of the l-th branch, F line , l is the carbon flow density of the l-th branch, L is the number of branches in the grid, P G , x The value of depends on the specific output situation of the distributed power source and will have different output values at different times of the day. I G , x The value of depends on the type of distributed power source (solar energy, wind energy, etc.) and the power generation efficiency, and the carbon emission factors of different energy types are different. D loss , l The value of depends on the transmission efficiency of the grid and the line loss situation. F line , lThe value depends on the carbon emission factor of the branch head node, that is, the indirect carbon emission corresponding to the consumption of unit electric energy at this node. By calculating the product of the active power output of each distributed power source and its carbon emission factor, and then dividing by the sum of all distributed power sources, the carbon emission contribution degree of each power source is obtained. Analyze the power loss in the power grid and the carbon flow density of the branch to evaluate the impact of distributed power sources on the carbon emissions of the power grid, help identify distributed power sources with greater carbon emission contributions, and thus optimize the output plan of the power sources to achieve the goal of energy conservation and emission reduction;

[0101] In the source-load matching module, the process of obtaining the optimal scheduling plan includes:

[0102] Extract the real-time output data and adjustable capacity information of the distributed power sources from the real-time monitoring data of each distributed power source. Combine the load forecasting results and the carbon emission situation data obtained from carbon flow tracking to formulate a source-load dynamic matching strategy and conduct a dynamic matching analysis of the source and load. Among them, the real-time output data reflects the current power generation capacity of each power source, and the adjustable capacity information provides the adjustment space of the power source in the future period. Based on the formulated source-load dynamic matching strategy, set the optimization goals of minimizing carbon emissions, meeting load demand, and power balance. Use the optimization algorithm of linear programming, combine the dynamic matching analysis results of the source and load, generate multiple optimized scheduling plans, and based on the optimization goals, evaluate each plan in terms of carbon emissions, load demand satisfaction, and power balance. Set the total time section for evaluating each plan, and extract the actual output of the distributed power source and the load demand at a single moment within a fixed time interval range. Calculate the sum of the squares of the source-load output change rates to analyze the consistency of the source-load changes, calculate the sum of the absolute values of the source-load output differences to analyze the deviation of the source-load matching, comprehensively evaluate the results, compare the performances of different plans in various evaluation indicators, calculate the source-load matching degree of each optimized scheduling plan, and then select the best optimized scheduling plan to ensure that the plan minimizes carbon emissions while meeting the load demand, and ensure the power balance and economic operation of the distribution network. Among them, the source-load matching degree reflects the comprehensive ability of the plan in meeting load demand, minimizing carbon emissions, and power balance;

[0103] Furthermore, the calculation formula for the source-load matching degree of each optimized scheduling plan is:

[0104]

[0105] where M SL is the source-load matching degree, which reflects the comprehensive ability of the plan in meeting load demand, minimizing carbon emissions, and power balance. λ is the weight coefficient used to balance the importance of the two indicators. P re,t is the actual output of the distributed power source at time t, P ld , tis the load demand at time t, T is the total time section, and Δt is the time interval, taking 1 hour. is the sum of the squares of the source-load output change rates, reflecting the consistency of the source-load changes. is the sum of the absolute values of the source-load output differences, reflecting the deviation of the source-load matching. M SL The value range of M depends on the specific source-load matching situation. Ideally, when the source-load is perfectly matched, M SL is close to 0.

[0106] In the coordinated scheduling module, the generation process of the source-load distributed scheduling strategy includes:

[0107] Receiving the optimal scheduling plan output by the source-load matching module, and parsing the received scheduling plan to clarify the output adjustment amount of each distributed power source and the control strategy of each load, including determining the upper limit, lower limit, and adjustment rate of the output of each power source, as well as the reduction amount or transfer strategy of each load, to ensure that the load demand matches the power source output. According to the parsed scheduling plan, specific scheduling instructions are generated, including the output adjustment instructions and load control instructions of each distributed power source, to ensure real-time matching of the load demand and the power source output, while maintaining the power balance of the distribution network. The generated scheduling instructions are sent to each distributed power source and load control center in real time through the communication network. After the scheduling instructions are sent, the output changes of each distributed power source in the medium and low voltage distribution network, the changes in load demand, and the power balance situation of the distribution network are monitored. If abnormal situations or deviations from the expected goals are found, the scheduling strategy is adjusted and new scheduling instructions are generated to ensure the stable operation of the distribution network.

[0108] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks, characterized by: The source-load distributed dispatching system includes a data acquisition module, a carbon flow tracking module, a load prediction module, a distributed power dispatching module, a source-load matching module, a collaborative dispatching module, and a carbon emission monitoring module, wherein the electrical signals between the modules are connected; The data acquisition module is used to collect the operation data of each node and branch in the medium and low voltage distribution network; The carbon flow tracking module analyzes the carbon emissions generated during the flow of electricity between branches and nodes of the medium and low voltage distribution network based on the collected data; The load forecasting module collects and analyzes historical load data, combines the meteorological factor data related to the load, analyzes the power demand of each load point, and analyzes the load fluctuation; The distributed power dispatching module monitors and controls various distributed power sources connected to the medium and low voltage distribution networks in real time, and optimizes the output plan of distributed power sources; The source-load matching module dynamically matches the source and the load according to the carbon emission situation obtained by carbon flow tracking, the load forecast results, and the real-time output and adjustable capacity of the distributed power source, obtains the best scheduling plan, and realizes the optimized scheduling of the source and the load; The collaborative scheduling module generates a specific source-load distributed scheduling strategy based on the optimal scheduling solution output by the source-load matching module, and performs real-time scheduling of the medium and low voltage distribution networks; The carbon emission monitoring module is used to monitor the carbon emission of each node in the medium and low distribution network in real time and generate a scheduling analysis report for reference by decision makers.

2. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 1 is characterized in that: In the data acquisition module, the process of collecting the operation data of each node and branch includes: Deploy sensors at each node and branch in the medium and low voltage distribution network to collect the operation data of each node and branch. The operation data includes the current and voltage information of each branch, the power data and carbon emission data of each node, and the basic data of the output power and access status of the distributed power source. The sensors include current transformers, voltage transformers, power sensors, carbon emission monitors, and distributed power source output power and access status monitoring sensors. The collected operation data is transmitted to the data processing center through the sensor network, and the collected operation data of each node and branch are pre-processed, including filtering, denoising and data cleaning operations, and the pre-processed data is formatted; The preprocessed data is time-aligned using the timestamp synchronization method, and a data warehouse is built to store the processed operating data.

3. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 2 is characterized in that: In the carbon flow tracking module, the carbon emission analysis process includes: The operation data of each node and branch in the medium and low distribution network are collected from the data warehouse, and the distribution network model is constructed according to the topological structure of the distribution network; The distribution network model is used to clarify the connection relationship between each node and branch, and then analyze the load distribution. By analyzing the load data of each node, the distribution of the load can be identified, including the load size, change trend and peak and valley time periods; Combined with the energy exchange relationship between power generation and load points, the flow of electricity between branches and nodes in medium and low voltage distribution networks is analyzed, and the current, voltage and power distribution of each branch are calculated based on the known node power and branch parameters; Using the carbon flow tracking algorithm, based on the power flow and carbon emission data of each node, the total carbon emissions of the entire distribution network during the power flow process are calculated, and the carbon emission intensity of different nodes and branches is analyzed; Based on the relationship between carbon emissions and carbon emission factors, the total carbon emission cost of each entity in the distribution network is calculated.

4. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 3 is characterized in that: The calculation formula for the total carbon emissions of the entire distribution network during the power flow process is: Among them, E t is the total carbon emissions of the entire distribution network, N is the total number of nodes in the distribution network, and P i is the active power loss of node i, D i is the load distance of node i, α is the carbon emission coefficient related to the power flow distance, Q i is the reactive power loss of node i, β is the carbon emission coefficient related to reactive power; The calculation formula for the total carbon emission cost of each entity in the distribution network is: E m =P m ·EF e，m ; Among them, C t is the total carbon emission cost of each entity in the distribution network, M is the total number of entities in the distribution network, E m is the carbon emissions of entity m, EF m is the carbon emission factor of entity m, P m is the energy consumption of agent m, EF e，m is the carbon emission factor per unit energy consumption of entity m.

5. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 4 is characterized in that: In the load forecasting module, the analysis process of load fluctuation includes: Obtain historical load data from the power system database, and obtain meteorological factor data that matches the load data in time and space from the meteorological department, where the meteorological factor data includes temperature, humidity and precipitation; Use regression analysis methods to analyze the correlation between meteorological factors and load, and build a meteorological load forecasting model based on the characteristics of historical load data and meteorological factor data. Use historical data to train the forecasting model and evaluate the model performance to optimize the model. Combined with meteorological factor data and historical load data, the trained meteorological load forecasting model is used to forecast the power demand of each load point and analyze the load fluctuation, including the fluctuation amplitude and fluctuation period; Based on the analysis results of load fluctuation, calculate the load trend index, conduct trend analysis of load and meteorological factors, and clarify the law of load changes with meteorological factors; The calculation formula of the load tendency index is: Among them, T index is the load trend index, K is the total number of data points, F k is the load value of the kth data point, W k is the temperature value of the kth data point, H k is the humidity value of the kth data point, R k is the precipitation at the kth data point.

6. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 5 is characterized in that: In the distributed power dispatching module, the process of optimizing the distributed power output plan includes: Real-time monitoring of various distributed power sources connected to medium and low voltage distribution networks, obtaining information on the operating status and output of distributed power sources, including the active power output and carbon emission factor of each distributed power source, and analyzing the load conditions of the power grid, including load size, change trend, and temporal and spatial distribution of load, to obtain power grid flow information, including power loss and carbon flow density of each branch; Combine the real-time monitoring data of various distributed power sources and the power grid flow information to conduct carbon flow tracking and load forecasting, calculate the contribution of each distributed power source to the carbon emissions of the power grid, and integrate the real-time monitoring data, carbon flow tracking results and load forecasting results to optimize the output plan of distributed power sources; Based on the optimized distributed power output plan, coordinate and optimize the scheduling strategy and balance the relationship between load and power supply, including increasing the output of distributed power during peak load periods and reducing the output or performing energy storage operations during low load periods, and then implement the optimized output plan into the power grid.

7. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 6 is characterized in that: The calculation formula for the contribution of each distributed power source to the carbon emissions of the power grid is: Among them, C co , x is the contribution of the xth distributed generation to the carbon emissions of the grid, P G , x is the active power output of the xth distributed generation, I G , x is the carbon emission factor of the xth distributed generation, Z is the total number of distributed generation in the grid, D loss , l is the power loss of the lth branch, F line , l is the carbon flow density of the λth branch, and L is the number of branches in the power grid.

8. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 7 is characterized in that: In the source-load matching module, the process of obtaining the best scheduling solution includes: Extract the real-time output data and adjustable capacity information of distributed power sources from the real-time monitoring data of each distributed power source, combine the load forecast results and the carbon emission data obtained by carbon flow tracking, formulate a source-load dynamic matching strategy, and conduct a source-load dynamic matching analysis; Based on the formulated source-load dynamic matching strategy, set the optimization goals of minimizing carbon emissions, meeting load demand, and balancing power; Using the linear programming optimization algorithm and combining the dynamic matching analysis results of sources and loads, multiple optimization scheduling schemes are generated. Based on the optimization objectives, each scheme is evaluated in terms of carbon emissions, load demand satisfaction, and power balance. Set the total time section for evaluating each scheme, and extract the actual output and load demand of distributed power sources at a single moment within a fixed time interval, calculate the sum of squares of the source-load output change rate, analyze the consistency of source-load changes, calculate the sum of the absolute values ​​of the source-load output difference, and analyze the deviation of source-load matching; Comprehensively evaluate the results, compare the performance of different schemes in various evaluation indicators, calculate the source-load matching degree of each optimized scheduling scheme, and then select the best optimized scheduling scheme.

9. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 8 is characterized in that: The calculation formula of the source-load matching degree of each optimization scheduling scheme is: Among them, M SL is the source-charge matching degree, λ is the weight coefficient, P re,t is the actual output of distributed generation at time t, P ld , t is the load demand at time t, T is the total time section, Δt is the time interval, which is 1 hour. is the sum of the squares of the source and load output change rates, It is the sum of the absolute values ​​of the source-load output difference.

10. The source-load distributed dispatching system based on carbon flow tracking in medium and low voltage distribution networks according to claim 9 is characterized in that: In the collaborative scheduling module, the generation process of the source-load distributed scheduling strategy includes: Receive the best dispatching plan output by the source-load matching module, analyze the dispatching plan received, clarify the output adjustment of each distributed power source and the control strategy of each load, including determining the output upper limit, lower limit and adjustment rate of each power source, as well as the reduction or transfer strategy of each load; Generate specific dispatch instructions based on the parsed dispatch plan, including output adjustment instructions and load control instructions for each distributed power source; The generated dispatching instructions are sent to each distributed power source and load control center in real time through the communication network. After the dispatching instructions are sent, the output changes of each distributed power source in the medium and low voltage distribution network, the changes in load demand and the power balance of the distribution network are monitored. If any abnormal situation or deviation from the expected target is found, the dispatching strategy is adjusted and the dispatching instructions are regenerated.

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