Microgrid scheduling method, system and equipment based on time-sharing carbon flow and storage medium

Abstract

The invention relates to the technical field of micro-grid scheduling, and discloses a time-sharing carbon flow-based micro-grid scheduling method, system and device and a storage medium, and the method comprises the steps: inputting power load data and characteristic data into a carbon flow calculation model, and calculating the carbon flow of a micro-grid; according to the energy consumption of the enterprise, calculating other energy carbon flow of the enterprise, and according to the power consumption period, adding the micro-grid carbon flow and the other energy carbon flow to obtain a time-sharing carbon flow data sequence; and performing carbon flow analysis on the time-sharing carbon flow data sequence, determining a scheduling period, constructing an optimal scheduling model of the micro-grid according to the scheduling period and the carbon flow calculation model, and solving the optimal scheduling model to obtain an optimal scheduling strategy. Optimized scheduling is carried out according to carbon emission conditions of different time periods, organic unification of operation of the micro-grid and operation of an enterprise is achieved under the low-carbon target, and carbon emission of the enterprise is indirectly reduced by improving the participation degree of new energy elements at the specific moment and optimizing the energy structure.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid scheduling, and in particular to a microgrid scheduling method, system, device and storage medium based on time-sharing carbon flow. Background Art

[0002] With the large-scale grid connection of intermittent renewable energy, the power system is facing more and more power disturbance problems, and traditional centralized control resources are difficult to maintain the safety and stability of the system. Therefore, it is necessary to guide the user side to participate in the grid-side regulation work, and jointly complete the task of maintaining the safety and stability of the system through demand response. The park microgrid is a micro system on the load side of the power system, which integrates photovoltaic, energy storage and load and other equipment. Through the reasonable coordinated operation optimization of the park microgrid, it can not only reduce the electricity cost of the park, but also coordinate with the grid side to assist the grid side in absorbing large-capacity new energy such as photovoltaic power stations, so as to improve the safe operation of the power system.

[0003] At present, the main dispatching method to reduce carbon emissions through park microgrids is to dispatch for power production. More park microgrid service providers rely on reducing the proportion of municipal electricity and the peak-shaving and valley-filling capabilities of energy storage equipment to generate dispatching strategies. However, this method is mostly based on price incentives and does not take into account the different carbon emission characteristics of the park's production work in different time periods, resulting in poor dispatching effects. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a microgrid scheduling method, system, equipment and storage medium based on time-sharing carbon flow. By combining the operation of the microgrid in the park with the time-sharing carbon flow calculation of the enterprise's production, the enterprise's power load, energy supply characteristics and carbon emission factors are comprehensively considered to generate the optimal microgrid scheduling strategy, so as to achieve the technical effects of low carbon, stability and high efficiency of the operation of the park microgrid.

[0005] In a first aspect, the present invention provides a microgrid scheduling method based on time-sharing carbon flow, the method comprising:

[0006] Collect the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divide the power consumption day into multiple power consumption periods according to the production characteristics and power consumption patterns of the enterprises in the industrial park;

[0007] Input the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period, wherein the carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the flow direction of electric energy as the core;

[0008] According to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each electricity consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the electricity consumption period, and obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple electricity consumption periods;

[0009] Performing carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determining a scheduling cycle according to the power consumption periods to be scheduled, and constructing an optimal scheduling model of the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimal scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as an objective function, and is constructed with power balance constraints, energy storage equipment constraints, and energy supply equipment operation constraints as constraint conditions;

[0010] The optimal scheduling model is solved by using a preset optimization algorithm to obtain the optimal scheduling strategy, and the optimal scheduling strategy is sent to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle.

[0011] Furthermore, the step of inputting the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period includes:

[0012] According to the power load data and the characteristic data, the electric energy transmitted between two device nodes in the power usage period is obtained, and according to the electric energy and the corresponding carbon emission factor, the branch carbon flow of the branch corresponding to the device node in the power usage period is calculated;

[0013] The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

[0014] Furthermore, the step of constructing an optimization dispatching model for a microgrid includes:

[0015] The objective function is established based on the minimization of the total carbon emissions of the industrial park within the dispatch period, the power load demand shortfall of the enterprise, and the instability of the microgrid operation;

[0016] Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load;

[0017] Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid;

[0018] According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

[0019] Furthermore, the objective function is expressed by the following formula:

[0020]

[0021] In the formula, t represents the electricity consumption period, C total,t represents the carbon flow of the microgrid in the industrial park during the electricity consumption period t, E load,unsatisfied Indicates the shortfall of the power demanded by the power load, V instabiliyt Indicates the instability of microgrid operation, λ1 represents the first weight coefficient, and λ2 represents the second weight coefficient;

[0022] The power balance constraint is expressed by the following formula:

[0023]

[0024] In the formula, represents the power generation of the i-th energy supply device in the power consumption period t, represents the discharge power of the jth energy storage device in the power consumption period t, represents the power of the kth power load in power consumption period t, represents the charging power of the lth energy storage device in the power consumption period t;

[0025] The energy storage device constraint is expressed by the following formula:

[0026] S min ≤S T ≤S max

[0027] 0≤P ch ≤P ch,max

[0028] 0≤P dis ≤P dis,max

[0029] In the formula, S T Indicates the power of the energy storage device, S min Represents the minimum capacity of the energy storage device, S max Indicates the maximum capacity of the energy storage device, P ch Represents the charging power of the energy storage device, P ch,max Indicates the maximum charging power of the energy storage device, P dis Represents the discharge power of the energy storage device, P dis,max Indicates the maximum discharge power of the energy storage device.

[0030] Furthermore, after the step of obtaining the optimal scheduling strategy, the method further includes:

[0031] Monitor the operating status, power load and total carbon emissions of each device in the industrial park in real time, and determine whether an abnormal event occurs based on changes in the operating status, power load and total carbon emissions;

[0032] In response to an abnormal event, data collection and carbon flow calculation are performed on the industrial park again, and the currently executed scheduling strategy is adjusted according to the optimized scheduling model.

[0033] In a second aspect, the present invention provides a microgrid dispatching system based on time-sharing carbon flow, the system comprising:

[0034] The data acquisition module is used to collect the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divide the power consumption day into multiple power consumption periods according to the production characteristics and power consumption patterns of the enterprises in the industrial park;

[0035] A carbon flow calculation module is used to input the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period. The carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the flow direction of electric energy as the core;

[0036] According to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each electricity consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the electricity consumption period, and obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple electricity consumption periods;

[0037] An optimization scheduling module is used to perform carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determine a scheduling cycle according to the power consumption periods to be scheduled, and construct an optimization scheduling model for the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimization scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as an objective function and is constructed with power balance constraints, energy storage equipment constraints, and energy supply equipment operation constraints as constraints;

[0038] The optimal scheduling model is solved by using a preset optimization algorithm to obtain the optimal scheduling strategy, and the optimal scheduling strategy is sent to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle.

[0039] Furthermore, the carbon flow calculation module is further used to obtain the electric energy transmitted between two device nodes in the power consumption period according to the power load data and the characteristic data, and calculate the branch carbon flow of the branch corresponding to the device node in the power consumption period according to the electric energy and the corresponding carbon emission factor;

[0040] The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

[0041] Furthermore, the optimization scheduling module also includes a scheduling model building module;

[0042] The scheduling model building module is used to establish an objective function according to the minimization of the total carbon emissions of the industrial park within the scheduling period, the power load demand shortfall of the enterprise and the instability of the microgrid operation;

[0043] Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load;

[0044] Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid;

[0045] According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

[0046] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0047] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0048] The present invention provides a microgrid scheduling method, system, device and storage medium based on time-sharing carbon flow. The present invention combines the operation of the park microgrid with the carbon flow calculation of each production period of high-energy-consuming enterprises, can accurately grasp the carbon emissions in different time periods, and optimize the scheduling according to the actual carbon emission needs, so that the operation of the park microgrid and the high-energy-consuming enterprises can be organically unified under the low-carbon goal. The present invention optimizes the energy structure by increasing the participation of new energy elements at specific times, reduces the dependence of enterprises on other energy sources such as fossil energy, and indirectly reduces the carbon emissions of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of a microgrid scheduling method based on time-sharing carbon flow in an embodiment of the present invention;

[0050] Figure 2 is a structural schematic diagram of a microgrid dispatching system based on time-sharing carbon flow in an embodiment of the present invention;

[0051] Figure 3 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 The first embodiment of the present invention proposes a microgrid scheduling method based on time-sharing carbon flow, which includes steps S10 to S50:

[0054] Step S10, collecting the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and dividing the power consumption day according to the production characteristics and power consumption rules of the enterprises in the industrial park to obtain multiple power consumption time periods;

[0055] Step S20, inputting the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period, wherein the carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the power flow direction as the core;

[0056] Step S30, according to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each power consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the power consumption period, obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple power consumption periods;

[0057] Step S40, performing carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determining a scheduling cycle according to the power consumption periods to be scheduled, and constructing an optimal scheduling model of the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimal scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as an objective function, and is constructed with power balance constraints, energy storage equipment constraints, and energy supply equipment operation constraints as constraint conditions;

[0058] Step S50, using a preset optimization algorithm to solve the optimization scheduling model to obtain an optimal scheduling strategy, and sending the optimal scheduling strategy to each energy supply device and energy storage device through the control system of the microgrid to achieve optimized scheduling of the microgrid within the scheduling cycle.

[0059] The present invention provides a microgrid dispatching method based on time-sharing carbon flow for a microgrid in an industrial park containing high-energy-consuming enterprises. First, the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid are collected. The power load data refers to the power load data of different power units such as production equipment and office areas in each enterprise in the park, especially in high-energy-consuming enterprises. These power load data can be obtained by installing power monitoring equipment such as smart meters in each power branch, and the data can be collected at a continuous time period as the collection frequency, such as one time period every 15 minutes, and a total of 96 time periods a day. The data collection of microgrid equipment mainly collects the characteristic data of various energy supply equipment (such as distributed photovoltaic, adjustable load, charging pile, energy storage device, etc.) in the microgrid, that is, remote signaling data and telemetry data. For example, for photovoltaic power generation equipment, real-time light intensity, photovoltaic panel temperature, power generation, etc. are collected; for energy storage devices, information such as current power, charge and discharge power limit, maximum and minimum capacity are obtained. In addition, for the carbon emission factors used in carbon flow calculation, in this embodiment, the carbon emission factors of different energy sources in the production, conversion and use process are determined based on the sources and characteristics of various energy sources, combined with the authoritative carbon emission database and relevant industry standards. For example, the carbon dioxide emission equivalent generated by coal-fired power generation per kilowatt-hour, the carbon emission value corresponding to natural gas power generation per cubic meter, etc., are clarified. The data collection steps can refer to the conventional steps and will not be repeated here.

[0060] The present invention is an optimized scheduling of microgrids based on time-sharing carbon flow. Therefore, before optimizing scheduling, time periods are first divided. In this embodiment, time sharing, that is, time period division, refers to dividing the electricity consumption day into multiple different electricity consumption periods according to the production characteristics and electricity consumption patterns of the enterprise. The electricity consumption day here can be represented by 24 hours. Preferably, a day can be divided into a peak electricity consumption period (usually the peak production period of the enterprise, such as 8 am to 12 pm, 2 pm to 6 pm), a valley electricity consumption period (such as 11 pm to 6 am the next day) and a flat electricity consumption period (other times except peak and valley periods). The division of different time periods can be flexibly adjusted according to the actual situation of the enterprise, and no excessive restrictions are made here.

[0061] In this embodiment, the carbon flow of the industrial park is divided into two parts, namely, the microgrid carbon flow generated by the operation of the microgrid and the other energy carbon flow generated by the production of the enterprise. The flow calculation of the microgrid carbon flow is realized through a pre-built carbon flow calculation model, wherein the carbon flow calculation model is a mathematical model for carbon flow calculation based on energy flow and carbon emission factors. The model is based on the device nodes and transmission branches in the microgrid, with the flow of electric energy as the core. The model represents the entire process of electric energy starting from the energy supply equipment, passing through the transmission links such as transformers and transmission lines, and finally reaching the power load. Based on this model, the calculation steps of the microgrid carbon flow of the microgrid include:

[0062] According to the power load data and the characteristic data, the electric energy transmitted between two device nodes in the power usage period is obtained, and according to the electric energy and the corresponding carbon emission factor, the branch carbon flow of the branch corresponding to the device node in the power usage period is calculated;

[0063] The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

[0064] In this embodiment, the carbon flow calculation model is used to analyze and calculate the power flow direction of the power load data of the production equipment and the characteristic data of the energy supply equipment, and the power P transmitted from the device node i to the device node j in a certain power consumption period t can be obtained. ij,t , obtain the carbon emission factor CF corresponding to the electricity ij , we can get the carbon flow C of the branch between the two device nodes in this period ij,t , that is: C ij,t =P ij,t ×CF ij By using the carbon flow calculation model, the carbon flow of all branches in the microgrid is accumulated, and the carbon flow of the entire microgrid in the power consumption period t can be obtained. total,t , that is: C total,t =∑C ij,t .

[0065] For the carbon flow of non-electric energy, in this embodiment, the daily energy consumption of enterprises that use non-electric energy as the main energy source, such as steel enterprises that use petrochemical energy as the main energy source, is counted, and the energy consumption is divided into each time period according to the divided time periods, and the carbon flow of other energy sources is calculated based on the energy consumption of each time period.

[0066] After obtaining the two parts of carbon flow through the above steps, the carbon flow is added according to the time period to get the carbon emissions of the entire industrial park. Since the above carbon flow is calculated based on the time period, the time-sharing carbon emissions of the industrial park in each time period are obtained. The time-sharing carbon flow of each time period is composed according to the time series to obtain the time-sharing carbon flow data sequence of the industrial park.

[0067] The present invention can be divided into three steps for the optimization scheduling of microgrids. The first step is the data collection and processing part mentioned above, the second step is the construction part of the optimization scheduling model, and the third step is the solution and execution part of the optimal scheduling strategy based on the data and model. In a preferred embodiment, the construction step of the optimization scheduling model includes:

[0068] The objective function is established based on the minimization of the total carbon emissions of the industrial park within the dispatch period, the power load demand shortfall of the enterprise, and the instability of the microgrid operation;

[0069] Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load;

[0070] Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid;

[0071] According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

[0072] In this embodiment, firstly, carbon flow analysis is performed based on the time-sharing carbon flow data sequence obtained by real-time calculation in the park, and the power consumption period with carbon emissions higher than expected and requiring optimized scheduling is selected. The scheduling period is determined according to the power consumption period. A scheduling period may include one or more power consumption periods, which shall be specifically determined according to the actual carbon flow emission situation. After determining the scheduling period, the setting of the objective function of the model takes minimizing the total carbon emissions of the industrial park in a scheduling period as the main goal, while considering meeting the power load demand of the enterprise in each period and ensuring the stable operation of the microgrid. Since the present invention is aimed at the optimized scheduling of the microgrid, the microgrid carbon flow of the microgrid can be realized in the total carbon emissions used in the objective function. Therefore, when constructing the objective function, the microgrid carbon flow can be used to characterize the total carbon emissions, and whether the power load demand of the enterprise is met can be determined by analyzing the power load data of the enterprise to determine the shortage of power load demand. The stable operation of the microgrid is characterized by the instability of the microgrid operation, and the instability can be measured by indicators such as voltage fluctuation and frequency deviation of the microgrid.

[0073] Based on the above description, the objective function can be expressed as:

[0074]

[0075] In the formula, t represents the electricity consumption period, C total,t represents the carbon flow of the microgrid during the power consumption period t, It represents the carbon emissions of the microgrid during the scheduling period.

[0076] E load,unsatisfied Indicates the shortfall of the power demanded by the power load, V instabiliyt It indicates the instability of microgrid operation, λ1 indicates the first weight coefficient, and λ2 indicates the second weight coefficient. The weight coefficient is mainly used to balance the relationship between carbon emissions, power load guarantee and microgrid stability. The weight coefficient can be pre-set by analyzing historical data.

[0077] At the same time, the optimization scheduling model also needs to meet the constraints between the operation relationship of production equipment and energy supply equipment. The specific constraints include:

[0078] ① Power balance constraints

[0079] In each period, the sum of the power generation of all energy supply equipment and the discharge power of the energy storage equipment in the microgrid should be equal to the sum of the power of the power load and the charging power of the energy storage equipment, that is:

[0080]

[0081] In the formula, represents the power generation of the i-th energy supply device in the power consumption period t, represents the discharge power of the jth energy storage device in the power consumption period t, represents the power of the kth power load in power consumption period t, Represents the charging power of the lth energy storage device in power consumption period t.

[0082] ② Energy storage equipment constraints

[0083] Energy storage device constraints include capacity constraints and charge and discharge power constraints. The capacity constraint refers to the amount of electricity S of the energy storage device at any time. T Must meet its minimum capacity S min and the maximum capacity S max The restriction is: S min ≤S T ≤S max .

[0084] The charging and discharging power constraint refers to the charging power P of the energy storage device. ch and discharge power P dis The maximum charging power P ch,maxand the maximum discharge power P dis,max , that is, 0≤P ch ≤P ch,max And 0≤P dis ≤P dis,max .

[0085] ③Energy supply equipment operation constraints

[0086] Energy supply equipment operation constraints refer to the fact that various energy supply equipment (such as photovoltaics) have their own operating power range, start and stop times and other operating constraints, which are set according to the technical specifications and actual operating requirements of the equipment to ensure that the equipment operates within a reasonable and safe range. The specific constraint formula can be set according to the actual operation of the park, and will not be repeated here.

[0087] After constructing the above-mentioned optimization scheduling model, the third step is to solve the objective function with constraints through a preset optimization algorithm. In a preferred embodiment, the present invention uses a particle swarm optimization algorithm to solve the model, and encodes the scheduling strategy (including decision variables such as the power generation power of each energy supply device in each time period, the charging and discharging power of the energy storage device, etc.) as a particle position vector, and each particle represents a possible scheduling scheme. By initializing a group of particles, the objective function value corresponding to each particle is calculated (i.e., the total carbon emissions, the electricity load satisfaction, and the comprehensive evaluation index of the stability of the microgrid). According to the speed update formula and position update formula of the particle swarm optimization algorithm, the position and speed of the particles are continuously iterated and updated to guide the particles to search in the direction of the global optimal solution. After multiple iterations (the number of iterations can be set according to the convergence situation and the calculation time requirements), a scheduling strategy that meets the constraints and optimizes the objective function value is found.

[0088] The generated optimal scheduling strategy is then issued to each energy supply device and energy storage device through the control system of the microgrid, so that the inverter of the photovoltaic power generation system outputs according to the set power, and the charge and discharge controller of the energy storage battery system performs the corresponding charge and discharge operations, etc., to achieve the optimal scheduling of the microgrid within the scheduling cycle.

[0089] Furthermore, in a preferred embodiment, the present invention also provides a method for adjusting the scheduling strategy feedback, the specific steps of which include:

[0090] Monitor the operating status, power load and total carbon emissions of each device in the industrial park in real time, and determine whether an abnormal event occurs based on changes in the operating status, power load and total carbon emissions;

[0091] In response to an abnormal event, data collection and carbon flow calculation are performed on the industrial park again, and the currently executed scheduling strategy is adjusted according to the optimized scheduling model.

[0092] In this embodiment, the operating status, power load changes and carbon flow of each device in the industrial park are monitored in real time, and whether an abnormal event has occurred is determined based on the changes in the monitoring data. For example, at 3 pm, a certain iron-making blast furnace suddenly malfunctions and shuts down, causing a significant drop in power load. When this abnormal situation is monitored, data collection, carbon flow calculation, and scheduling strategy generation steps are re-performed to adjust the scheduling strategy, such as appropriately reducing the power generation power of the diesel generator and increasing the charging power of the energy storage battery, to ensure that the microgrid continues to operate stably and in a low-carbon manner.

[0093] This embodiment provides a microgrid scheduling method based on time-sharing carbon flow. The present invention combines the operation of the park microgrid with the carbon flow calculation of each production period of high-energy-consuming enterprises. By deeply analyzing the production process and energy consumption pattern of the enterprise, the carbon emissions in different time periods are accurately grasped, so that the output arrangement of new energy elements is no longer limited to price factors, but is optimized and scheduled according to actual carbon emission needs, so that the operation of the park microgrid and high-energy-consuming enterprises can be organically unified under the low-carbon goal. At the same time, by increasing the participation of new energy elements at specific times, optimizing the energy structure, and reducing the dependence of enterprises on other energy sources such as fossil energy, the carbon emissions of enterprises are indirectly reduced, providing strong support for the low-carbon development of high-energy-consuming enterprises such as metallurgy and chemical industry.

[0094] See also Figure 2 Based on the same inventive concept, a microgrid dispatching system based on time-sharing carbon flow is proposed in the second embodiment of the present invention, including:

[0095] The data collection module 10 is used to collect the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divide the power consumption day according to the production characteristics and power consumption rules of the enterprises in the industrial park to obtain multiple power consumption time periods;

[0096] The carbon flow calculation module 20 is used to input the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period. The carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the flow direction of electric energy as the core;

[0097] According to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each electricity consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the electricity consumption period, and obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple electricity consumption periods;

[0098] The optimization scheduling module 30 is used to perform carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determine the scheduling cycle according to the power consumption periods to be scheduled, and construct an optimization scheduling model of the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimization scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as the objective function, and is constructed with power balance constraints, energy storage equipment constraints and energy supply equipment operation constraints as constraints;

[0099] The optimal scheduling model is solved by using a preset optimization algorithm to obtain the optimal scheduling strategy, and the optimal scheduling strategy is sent to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle.

[0100] Furthermore, the carbon flow calculation module 20 is further used to obtain the electric energy transmitted between two device nodes in the power consumption period according to the power load data and the characteristic data, and calculate the branch carbon flow of the branch corresponding to the device node in the power consumption period according to the electric energy and the corresponding carbon emission factor;

[0101] The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

[0102] The optimization scheduling module 30 also includes a scheduling model building module;

[0103] The scheduling model building module is used to establish an objective function according to the minimization of the total carbon emissions of the industrial park within the scheduling period, the power load demand shortfall of the enterprise and the instability of the microgrid operation;

[0104] Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load;

[0105] Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid;

[0106] According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

[0107] The technical features and technical effects of the microgrid dispatching system based on time-sharing carbon flow proposed in the embodiment of the present invention are the same as the method proposed in the embodiment of the present invention, and will not be repeated here. Each module in the above-mentioned microgrid dispatching system based on time-sharing carbon flow can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0108] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0109] See also Figure 3 , an internal structure diagram of a computer device in one embodiment, the computer device can specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a microgrid scheduling method based on time-sharing carbon flow is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0110] It can be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computing device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0111] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0112] In summary, the embodiments of the present invention propose a microgrid scheduling method, system, device and storage medium based on time-sharing carbon flow. The method collects the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divides the power consumption day according to the production characteristics and power consumption rules of the enterprises in the industrial park to obtain multiple power consumption time periods; the power load data and the characteristic data are input into a preset carbon flow calculation model to calculate the microgrid carbon flow of the microgrid in each power consumption time period, and the carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park, and is constructed with the flow direction of electric energy as the core; according to the energy consumption of the enterprise, the other energy carbon flow of the enterprise in each power consumption time period is calculated, and according to the power consumption time period, the microgrid carbon flow and the corresponding other energy carbon flow are added to obtain the industrial park carbon flow. The time-sharing carbon flow of the industrial park is calculated, and a time-sharing carbon flow data sequence is obtained according to the time-sharing carbon flow of multiple power consumption periods; the time-sharing carbon flow data sequence is subjected to carbon flow analysis to obtain a number of power consumption periods to be scheduled, and a scheduling cycle is determined according to the power consumption periods to be scheduled, and an optimal scheduling model of the microgrid is constructed according to the scheduling cycle and the carbon flow calculation model, wherein the optimal scheduling model takes the minimization of the total carbon emissions of the industrial park within the scheduling cycle as the objective function, and is constructed with power balance constraints, energy storage equipment constraints and energy supply equipment operation constraints as constraints; a preset optimization algorithm is used to solve the optimal scheduling model to obtain an optimal scheduling strategy, and the optimal scheduling strategy is issued to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle. The present invention combines the operation of the park microgrid with the carbon flow calculation of each production period of high-energy-consuming enterprises. By deeply analyzing the production process and energy consumption pattern of the enterprise, the carbon emissions in different time periods can be accurately grasped, so that the output arrangement of new energy elements is no longer limited to price factors, but is optimized and scheduled according to actual carbon emission needs, so that the operation of the park microgrid and high-energy-consuming enterprises can be organically unified under the low-carbon goal. At the same time, by increasing the participation of new energy elements at specific times, optimizing the energy structure, and reducing the dependence of enterprises on other energy sources such as fossil energy, the carbon emissions of enterprises can be indirectly reduced, providing strong support for the low-carbon development of high-energy-consuming enterprises such as metallurgy and chemical industry.

[0113] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A microgrid scheduling method based on time-sharing carbon flow, characterized in that: include: Collect the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divide the power consumption day into multiple power consumption periods according to the production characteristics and power consumption patterns of the enterprises in the industrial park; Input the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period, wherein the carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the flow direction of electric energy as the core; According to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each electricity consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the electricity consumption period, and obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple electricity consumption periods; Performing carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determining a scheduling cycle according to the power consumption periods to be scheduled, and constructing an optimal scheduling model of the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimal scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as an objective function, and is constructed with power balance constraints, energy storage equipment constraints, and energy supply equipment operation constraints as constraint conditions; The optimal scheduling model is solved by using a preset optimization algorithm to obtain the optimal scheduling strategy, and the optimal scheduling strategy is sent to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle.

2. The microgrid scheduling method based on time-sharing carbon flow according to claim 1 is characterized in that: The step of inputting the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period includes: According to the power load data and the characteristic data, the electric energy transmitted between two device nodes in the power usage period is obtained, and according to the electric energy and the corresponding carbon emission factor, the branch carbon flow of the branch corresponding to the device node in the power usage period is calculated; The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

3. The microgrid scheduling method based on time-sharing carbon flow according to claim 1 is characterized in that: The steps of constructing the optimization dispatch model of the microgrid include: The objective function is established based on the minimization of the total carbon emissions of the industrial park within the dispatch period, the power load demand shortfall of the enterprise, and the instability of the microgrid operation; Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load; Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid; According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

4. The microgrid scheduling method based on time-sharing carbon flow according to claim 3 is characterized in that: The objective function is expressed by the following formula: In the formula, t represents the electricity consumption period, C total,t represents the carbon flow of the microgrid in the industrial park during the electricity consumption period t, E load,unsatisfied Indicates the shortfall of the power demanded by the power load, V instabiliyt Indicates the instability of microgrid operation, λ1 represents the first weight coefficient, and λ2 represents the second weight coefficient; The power balance constraint is expressed by the following formula: In the formula, represents the power generation of the i-th energy supply device in the power consumption period t, represents the discharge power of the jth energy storage device in the power consumption period t, represents the power of the kth power load in power consumption period t, represents the charging power of the lth energy storage device in the power consumption period t; The energy storage device constraint is expressed by the following formula: S min ≤S T ≤S max 0≤P ch ≤P ch,max 0≤P dis ≤P dis,max In the formula, S T Indicates the power of the energy storage device, S min Represents the minimum capacity of the energy storage device, S max Indicates the maximum capacity of the energy storage device, P ch Represents the charging power of the energy storage device, P ch,max Indicates the maximum charging power of the energy storage device, P dis Represents the discharge power of the energy storage device, P dis,max Indicates the maximum discharge power of the energy storage device.

5. The microgrid scheduling method based on time-sharing carbon flow according to claim 1 is characterized in that: After the step of obtaining the optimal scheduling strategy, the method further includes: Monitor the operating status, power load and total carbon emissions of each device in the industrial park in real time, and determine whether an abnormal event occurs based on changes in the operating status, power load and total carbon emissions; In response to an abnormal event, data collection and carbon flow calculation are performed on the industrial park again, and the currently executed scheduling strategy is adjusted according to the optimized scheduling model.

6. A microgrid dispatching system based on time-sharing carbon flow, characterized in that: include: The data acquisition module is used to collect the power load data of the production equipment of each enterprise in the industrial park and the characteristic data of the energy supply equipment in the microgrid in real time, and divide the power consumption day into multiple power consumption periods according to the production characteristics and power consumption patterns of the enterprises in the industrial park; A carbon flow calculation module is used to input the power load data and the characteristic data into a preset carbon flow calculation model to calculate the carbon flow of the microgrid in each power consumption period. The carbon flow calculation model is based on the equipment nodes and transmission branches of the microgrid in the industrial park and is constructed with the flow direction of electric energy as the core; According to the energy consumption of the enterprise, calculate the carbon flow of other energy sources of the enterprise in each electricity consumption period, add the carbon flow of the microgrid and the corresponding carbon flow of other energy sources according to the electricity consumption period, and obtain the time-sharing carbon flow of the industrial park, and obtain the time-sharing carbon flow data sequence according to the time-sharing carbon flow of multiple electricity consumption periods; An optimization scheduling module is used to perform carbon flow analysis on the time-sharing carbon flow data sequence to obtain a number of power consumption periods to be scheduled, determine a scheduling cycle according to the power consumption periods to be scheduled, and construct an optimization scheduling model for the microgrid according to the scheduling cycle and the carbon flow calculation model, wherein the optimization scheduling model takes minimization of the total carbon emissions of the industrial park within the scheduling cycle as an objective function and is constructed with power balance constraints, energy storage equipment constraints, and energy supply equipment operation constraints as constraints; The optimal scheduling model is solved by using a preset optimization algorithm to obtain the optimal scheduling strategy, and the optimal scheduling strategy is sent to each energy supply device and energy storage device through the control system of the microgrid to achieve optimal scheduling of the microgrid within the scheduling cycle.

7. The microgrid dispatching system based on time-sharing carbon flow according to claim 6 is characterized in that: The carbon flow calculation module is further used to obtain the electric energy transmitted between two device nodes during the power consumption period according to the power load data and the characteristic data, and calculate the branch carbon flow of the branch corresponding to the device node during the power consumption period according to the electric energy and the corresponding carbon emission factor; The branch carbon flow corresponding to each branch in the microgrid is added together to obtain the microgrid carbon flow in each power consumption period.

8. The microgrid dispatching system based on time-sharing carbon flow according to claim 6 is characterized in that: The optimization scheduling module also includes a scheduling model building module; The scheduling model building module is used to establish an objective function according to the minimization of the total carbon emissions of the industrial park within the scheduling period, the power load demand shortfall of the enterprise and the instability of the microgrid operation; Establish power balance constraints based on the power generation of each energy supply device in the microgrid, the charging and discharging power of the energy storage device, and the power of the power load; Establish energy storage device constraints based on the capacity and charging and discharging power of energy storage devices in the microgrid; According to the operating conditions of each energy supply device in the microgrid, establish the operating constraints of the energy supply equipment.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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