Optimized scheduling method, system and equipment based on active support of micro-grid, and medium

Abstract

The invention discloses an optimal scheduling method, system and device based on active support of a micro-grid, and a medium. The method comprises the following steps: sampling to obtain a plurality of target source load prediction error scenes, scene probabilities and net load scenes; calculating a power grid side active support power demand based on the power grid side frequency variation; constructing a first evaluation equation of the active support power demand of the micro-grid based on the scene probability, the active support power demand of the power grid side and the inertia support form of the micro-grid; taking the plurality of net load scenes as constraint conditions, and constructing a microgrid optimization scheduling model based on a scheduling cost equation and a risk cost equation constructed according to the loss cost, the cyclic energy cost and the first evaluation equation in the microgrid scheduling process; and obtaining a micro-grid active support scheduling scheme based on the micro-grid optimization scheduling model. The method provided by the invention fully considers and utilizes the active support frequency of the micro-grid, remarkably enhances the frequency response capability of the power distribution network, and improves the operation efficiency and stability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching management, and in particular to an optimization dispatching method, system, equipment and medium based on active support of a microgrid. Background Art

[0002] In recent years, with the vigorous promotion and application of renewable energy, especially the widespread access to distributed photovoltaic power generation systems, the structure and operating characteristics of distribution networks are undergoing profound changes. These distributed photovoltaic power sources have made important contributions to energy transformation with their clean and efficient characteristics. However, their large-scale access has also brought new challenges to the operation of distribution networks. Since photovoltaic power generation is significantly affected by weather conditions, its output is intermittent and uncertain, which directly aggravates the fluctuation of active power in local distribution networks. This frequent change in power not only increases the risk of frequency fluctuations, but also brings unprecedented pressure to the frequency regulation of the power system.

[0003] The traditional power dispatch model is mainly based on large-scale centralized power generation, relying on the generator sets on the transmission side to provide the necessary inertia and frequency response. This model performs well in dealing with large-scale, stable power supply, but it is somewhat powerless when faced with the complex and changing situations brought about by the access of new energy sources such as distributed photovoltaics.

[0004] It can be seen that how to solve the problem that the traditional power dispatching model cannot cope with the power fluctuations caused by the access of a large number of distributed photovoltaic power generation systems, resulting in poor stability of the distribution network, has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides an optimization scheduling method and system based on active support of microgrids to solve the technical problem that the traditional power scheduling model cannot cope with the power fluctuations caused by the access of a large number of distributed photovoltaic power generation systems, resulting in poor stability of the distribution network, thereby enhancing the frequency response capability of the distribution network, effectively suppressing frequency fluctuations, reducing the frequency regulation pressure on the transmission side, and improving the operating efficiency and stability of the entire power system.

[0006] In a first aspect, the present invention provides an optimization scheduling method based on active support of a microgrid, wherein the microgrid includes at least a distributed photovoltaic energy type, and the method includes: Sampling the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sampling the cumulative distribution function of the day-ahead power prediction error of the load in the target area, obtaining a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, and obtaining a plurality of net load scenarios based on the plurality of source-load prediction error scenarios; Calculating the active support power demand on the grid side based on the grid side frequency change in the target area; Based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, a first evaluation equation for the active support power demand of the microgrid is constructed, wherein the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities; Based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process, constructing a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side; Taking the net load fluctuation ranges corresponding to the plurality of net load scenarios as constraints, a microgrid optimization scheduling model is constructed based on the scheduling cost equation and the risk cost equation; During the grid dispatching process, the real-time frequency change of the grid side of the target area, the power forecast value of the microgrid, and the power forecast value of the load acquired in real time are input into the microgrid optimization dispatching model, and the microgrid active support dispatching plan is obtained according to the output result of the microgrid optimization dispatching model.

[0007] Preferably, the cumulative distribution function of the day-ahead power forecast error of the microgrid in the target area is sampled, and the cumulative distribution function of the day-ahead power forecast error of the load in the target area is sampled to obtain a plurality of target source-load forecast error scenarios in the target area and a scenario probability corresponding to each of the target source-load forecast error scenarios, including: The cumulative distribution function of the day-ahead power prediction error of the microgrid is sampled by a Latin hypercube sampling method to obtain several day-ahead microgrid prediction error scenarios; The cumulative distribution function of the day-ahead power forecast error of the load is sampled by a Latin hypercube sampling method to obtain several day-ahead load forecast error scenarios; Combining a plurality of the day-ahead microgrid prediction error scenarios with the day-ahead load prediction error scenarios to obtain a plurality of day-ahead source-load prediction error scenarios; The sampling backward reduction method optimizes the day-ahead source load prediction error scenario to obtain a plurality of target source load prediction error scenarios and a scenario probability corresponding to each target source load prediction error scenario.

[0008] Preferably, the first evaluation equation for the active support power demand of the microgrid is constructed based on the scenario probability, the active support power demand of the grid side and the inertia support form of the microgrid, including: The sampling Latin hypercube sampling method performs sampling analysis on the cumulative distribution function of the historical prediction error of the active support demand power on the grid side, and obtains the demand probability of power support on the grid side according to the analysis result; Based on the scenario probability, the grid-side active support power demand, the power of the inertial support form of the microgrid and the demand probability, construct a first evaluation equation for the microgrid active support power demand; The first evaluation equation is: in, Indicates that the microgrid actively supports the power demand, Prediction error scenario for target source load The probability of the scenario, is the number of target source load prediction error scenarios, for The probability of power demand support required by the grid side at the moment, Actively support power demand on the grid side, Release power for energy storage, Absorbing power for energy storage, Release power for the fan, The fan absorbs power. is the energy storage coefficient, is the fan coefficient, , .

[0009] Preferably, the scheduling cost equation and risk cost equation corresponding to the grid-side active support power demand are constructed based on the loss cost, circulating energy cost and the first evaluation equation in the microgrid scheduling process, including: The degradation costs of the electrolyzer, the fuel cell, the hydrogen storage tank, and the electric energy storage are used to characterize the life loss during the microgrid dispatching process, and a second evaluation equation for the loss cost is obtained; Characterizing the circulating energy cost by the electricity purchase and sale cost and the energy loss penalty cost, and obtaining a third evaluation equation of the circulating energy cost; constructing a scheduling cost equation according to the first evaluation equation, the second evaluation equation and the third evaluation equation; The scheduling cost equation is: in, is the electrolytic cell degradation coefficient, for Electrolyzer power at the moment, is the scheduling time interval, , is the fuel cell degradation coefficient, for Fuel cell power at all times, is the degradation coefficient of the hydrogen storage tank, and Battery slot The amount of hydrogen produced at any one time and the fuel cell The amount of hydrogen consumed at any given moment, is the degradation coefficient of the energy storage system, and Energy storage system Charging power and discharging power at all times, for Electricity price at any time, and They are The power purchased and sold by the micro-grid at all times. Prediction error scenario for target source load The probability of the scenario, is the unit abandonment penalty cost, Prediction error scenario for target source load Down Always discard optical power. is the penalty cost for unit wind curtailment, Prediction error scenario for target source load Down Wind power is abandoned at all times. is the penalty cost per unit power failure load, Prediction error scenario for target source load Down The load power at the moment of power failure, is the penalty cost per unit heat loss load, for Heat loss load power at any time, is the penalty cost per unit cooling load, for Cooling load power at any moment, is the penalty cost per unit hydrogen loss load, for The amount of material losing hydrogen load at any moment; The risk cost of scheduling is evaluated according to the scheduling cost equation to obtain the risk cost equation.

[0010] Preferably, the risk cost of scheduling is evaluated according to the scheduling cost equation to obtain the risk cost equation, including: According to the scheduling cost equation, calculating the scheduling cost data set at each moment under all the target source load prediction error scenarios; Determining the confidence level of the risk cost, and optimizing and solving the scheduling cost data set based on the confidence level to obtain auxiliary variables; Constructing the risk cost equation based on the auxiliary variables, the dispatch cost under the target source load prediction error scenario, the scenario probability and the confidence level; The risk cost equation is expressed as: in, is the risk cost, is an auxiliary variable, is the confidence level of risk cost, Represents the target source load prediction error scenario The scheduling cost under .

[0011] Preferably, the net load fluctuation intervals corresponding to the plurality of net load scenarios are used as constraints, and a microgrid optimization scheduling model is constructed based on the scheduling cost equation and the risk cost equation, including: Based on the confidence level, determining a net load fluctuation range corresponding to a plurality of the net load scenarios; Taking the net load fluctuation range as a constraint condition, a comprehensive dispatching cost equation is constructed based on the dispatching cost equation and the risk cost equation; Constructing a microgrid optimization dispatch model according to the comprehensive dispatch cost equation; The objective function of the microgrid optimization scheduling model is: in, is the risk factor.

[0012] Preferably, the constraints include at least: electric power balance constraints, thermal power balance constraints, cold power balance constraints, equipment constraints, battery energy storage system operation constraints, hydrogen energy subsystem constraints, heat storage system operation constraints in thermal storage electric boilers, electricity purchase and sales constraints, and constraints on the active support power provided by fans.

[0013] In a second aspect, the present invention further provides an optimization scheduling system based on active support of a microgrid, which implements the above-mentioned optimization scheduling method based on active support of a microgrid, wherein the microgrid includes at least a distributed photovoltaic energy type, and the system includes: a scenario construction unit, a grid-side active support power demand calculation unit, a microgrid active support power demand evaluation unit, a cost equation construction unit, a scheduling model construction unit, and a scheduling optimization unit; The scenario construction unit is used to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and to sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, and to obtain a plurality of net load scenarios based on the plurality of source-load prediction error scenarios; The grid-side active support power demand calculation unit is used to calculate the grid-side active support power demand based on the grid-side frequency change of the target area; The microgrid active support power demand evaluation unit is used to construct a first evaluation equation for the microgrid active support power demand based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, wherein the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities; The cost equation construction unit is used to construct a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process; The dispatch model construction unit is used to construct a microgrid optimization dispatch model based on the dispatch cost equation and the risk cost equation, taking the net load fluctuation ranges corresponding to the plurality of net load scenarios as constraints; The dispatching optimization unit is used to input the real-time frequency change of the grid side of the target area, the power prediction value of the microgrid, and the power prediction value of the load into the microgrid optimization dispatching model during the grid dispatching process, and obtain the microgrid active support dispatching plan according to the output result of the microgrid optimization dispatching model.

[0014] In a third aspect, the present invention also provides a computer device, comprising a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to execute the above-mentioned optimization scheduling method based on active support of microgrids.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. When the computer program is executed, the above-mentioned optimization scheduling method based on active support of microgrid is implemented.

[0016] The present application provides a method, system, device and medium for evaluating the probability of failure of a sensitive device. Compared with the prior art, the embodiments of the present application have the following beneficial effects: The optimization scheduling method based on active support of microgrid disclosed in this application fully considers and utilizes the active support frequency of microgrid, significantly enhances the frequency response capability of distribution network, effectively suppresses frequency fluctuation, and can also greatly reduce the frequency regulation pressure on the transmission side, and improve the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the carrying capacity of distribution network for distributed power sources, and create more favorable conditions for large-scale access of renewable energy to distribution network and efficient utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of an optimization scheduling method based on active support of a microgrid provided by a preferred embodiment of the present invention; Figure 2 It is a schematic diagram of a microgrid structure provided by a preferred embodiment of the present invention; Figure 3 It is a schematic diagram of a 24-hour grid-side frequency variation of a target area obtained according to specific parameter settings provided by a preferred embodiment of the present invention; Figure 4 It is a schematic diagram of the active support power demand of the grid side calculated according to the frequency change of the grid side provided by a preferred embodiment of the present invention; Figure 5 It is a schematic diagram of the operation results of the distribution network without considering the active support of the microgrid provided by a preferred embodiment of the present invention; Figure 6 It is a schematic diagram of the operation results of a distribution network considering active support of a microgrid provided by a preferred embodiment of the present invention; Figure 7 It is a schematic diagram of power variation of an energy storage system provided by a preferred embodiment of the present invention; Figure 8 It is a schematic diagram of the SOC variation curve of the energy storage system before and after the active support of the microgrid provided by a preferred embodiment of the present invention; Fig. 9 It is a schematic diagram of the satisfaction of the distribution network for the support power after considering the active support of the microgrid provided by a preferred embodiment of the present invention; Fig.10 It is a structural schematic diagram of an optimization dispatching system based on active support of a microgrid provided by a preferred embodiment of the present invention; Fig.11 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following is a detailed explanation of the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided only for illustrative purposes and cannot be understood as limiting the present invention. The accompanying drawings are only for reference and illustration purposes and do not constitute a limitation on the scope of patent protection of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0019] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0020] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0021] As a new form of power organization, microgrids, with their flexible and autonomous characteristics, have shown unique advantages in the integration and optimal utilization of distributed energy. With the rapid development of microgrid technology, they can respond quickly when frequency fluctuates, and provide effective frequency support and inertia response for the distribution network through the coordinated scheduling of internal energy storage devices, controllable loads, and distributed power sources. This fast and accurate regulation capability is of great significance to improving the stability of the entire distribution network system. However, traditional power dispatching models often ignore the huge potential of microgrids in frequency stability and inertia support.

[0022] In view of this, in an embodiment of the present invention, an optimization scheduling method based on active support of a microgrid is provided, wherein the microgrid at least includes a distributed photovoltaic energy type, see Figure 1 and Figure 2 , the method comprising: S1. Sampling the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sampling the cumulative distribution function of the day-ahead power prediction error of the load in the target area, obtaining several target source-load prediction error scenarios in the target area and the scenario probability corresponding to each of the target source-load prediction error scenarios, and obtaining several net load scenarios based on the several source-load prediction error scenarios; the microgrid of the present application includes but is not limited to new energy power generation sources such as distributed photovoltaic energy types and wind power energy types, and divides a day into 24 time periods. Based on the power prediction models of the microgrid and the load, the cumulative distribution function of the day-ahead power prediction error of the microgrid and the load in each time period is obtained by histogram analysis or kernel density estimation method. In one embodiment of the present application, a Latin hypercube sampling method is used to sample the cumulative distribution function of the day-ahead power forecast error of the microgrid in the target area to generate a number of day-ahead microgrid forecast error scenarios. A Latin hypercube sampling method is used to sample the cumulative distribution function of the day-ahead power forecast error of the load to generate a number of day-ahead load forecast error scenarios. The several day-ahead microgrid forecast error scenarios and the day-ahead load forecast error scenarios are combined to obtain a number of day-ahead source-load forecast error scenarios.

[0023] The Latin hypercube sampling method is a stratified sampling technique that can divide a variable into M intervals with equal probability. At this time, one sample point that meets the Latin hypercube condition is selected from each of the M intervals to form a sample set containing M sample points. This can achieve higher sampling accuracy at a smaller sampling scale and reduce computational cost and time.

[0024] In a preferred embodiment of the present application, the cumulative distribution function of the power prediction error of the microgrid in each period and the cumulative distribution function of the power prediction error of the load in each period are evenly divided into intervals, and randomly select a value in each interval , then The cumulative probability of sampling an interval is: in, is a uniformly distributed random number, and , the cumulative distribution function of the uniform distribution is: in, .

[0025] Further, using the inverse function of the cumulative distribution function The cumulative probability of sampling Convert to actual sample value ,but: Sampling The above sampling method continues sampling from the remaining interval. After one round of sampling is completed, a day-ahead source-load forecast error scenario is generated. The above Latin hypercube sampling process is repeated until the sampling is completed ( ), generate multiple day-ahead source-load forecast error scenarios.

[0026] The day-ahead source load prediction error scenarios generated by the Latin hypercube sampling method are huge in number and have a large number of similar scenarios. In order to ensure the calculation accuracy and solution speed, in a preferred embodiment of the present application, the sampling backward reduction method is used to optimize the day-ahead source load prediction error scenarios, obtain several target source load prediction error scenarios and the scenario probability of each target source load prediction error scenario, and maximize the fitting accuracy of the remaining scenarios to the original samples. Specifically, assuming that the number of day-ahead source load prediction error scenarios generated by Latin hypercube sampling is , the number of target day-ahead source load forecast error scenarios after reduction is , the detailed steps of the backward reduction method are as follows: Step 1: Initialize the scene probability and the number of initial scenes for each scene. The initial scene probability and the initial scene number are expressed as: , in, Represents the day-ahead source-load forecast error scenario The initial scene probability, Indicates the number of initial scenes.

[0027] Step 2: Calculate the distance between each pair of day-ahead source-load forecast error scenarios using the following formula: in, This is the day-ahead source load prediction error scenario Middle elements, This is the day-ahead source load prediction error scenario Middle elements, is the number of elements in each day-ahead source load forecast error scenario. In this application, , representing the three uncertain factors of 24 hours, photovoltaic power, wind power and load demand.

[0028] Step 3: Select and specify the scene The scene with the smallest distance ,Right now: Calculation scenario Probability and distance The product of .

[0029] Step 4: For each day-ahead source-load forecast error scenario, repeat step 3 and select Minimum day-ahead source-load forecast error scenario And eliminate the scenario of source load forecast error on the day before, and let , update the day-ahead source load forecast error scenario The probability of the scene .

[0030] Step 5: Repeat steps 2 to 4 until So far, several target source load prediction error scenarios and the scenario probability of each target source load prediction error scenario are obtained.

[0031] After obtaining samples of target source-load prediction error scenarios, the difference between the load power and the microgrid power corresponding to each target source-load prediction error scenario is calculated to obtain several net load scenarios, based on which the net load fluctuation range under a given confidence level can be determined. This net load fluctuation range is used as a constraint for the optimized scheduling based on the active support of the microgrid in this application, which can reasonably balance the benefit risk brought by the prediction error and improve the accuracy of the grid scheduling.

[0032] S2. Calculate the active support power demand of the grid side based on the grid side frequency change of the target area; Consider the frequency change demand and inertia support demand of the distribution network in the target area, and evaluate the overall distribution network's need for the microgrid to provide active support capacity in each time period. Indicates time period The grid side actively supports power demand, is the frequency coefficient, is the inertia coefficient, is the frequency change on the grid side, is the time step, the calculation formula for the active support power demand on the grid side is: when When it is positive, it means that the distribution network needs the microgrid to provide power support. When it is negative, it means that the distribution network requires the microgrid to absorb excess power.

[0033] S3. Based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, a first evaluation equation for the active support power demand of the microgrid is constructed. The first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities. The inertia support form of the microgrid includes: energy storage release power , Energy storage absorption power , fan release power And the fan absorbed power Based on the scenario probability, the active support power demand of the grid side and the inertial support form of the microgrid, the first evaluation equation for the active support power demand of the microgrid is constructed. The relationship of the first evaluation equation is: in, Prediction error scenario for target source load The probability of the scenario, is the number of target source load prediction error scenarios, yes The probability of power demand support required by the grid side at the moment, is the energy storage coefficient, is the fan coefficient. The difference between the two indicates the priority of different support methods. The larger the coefficient, the more priority it needs to be given. , .

[0034] The first evaluation equation constructed in the embodiment of the present application fully reflects the inertia support power of the microgrid under all scenario probabilities. This application also uses the Latin hypercube sampling method to sample and analyze the cumulative distribution function of the historical prediction error of the active support demand power on the grid side, and obtains the demand probability of power support on the grid side according to the analysis results. This process is the same as the method of S1 to obtain the target source load prediction error scenario and the scenario probability corresponding to each target source load prediction error scenario, and will not be repeated here.

[0035] S4. Based on the loss cost, circulating energy cost and the first evaluation equation in the microgrid scheduling process, construct the scheduling cost equation and risk cost equation corresponding to the active support power demand on the grid side; the present application uses the comprehensive scheduling cost as the objective function of the optimized scheduling based on the active support of the microgrid, and the comprehensive scheduling cost includes the scheduling cost and the risk cost. For the scheduling cost including the loss cost, circulating energy cost and the cost of meeting the active support demand of the microgrid, the degradation cost of the electrolyzer, fuel cell, hydrogen storage tank and electric energy storage is used to characterize the life loss in the microgrid scheduling process to obtain the second evaluation equation of the loss cost, and the circulating energy cost is characterized by the purchase and sale cost of electricity and the energy loss penalty cost to obtain the third evaluation equation of the circulating energy cost, and the scheduling cost is calculated according to the first evaluation equation, the second evaluation equation and the third evaluation equation. The scheduling cost includes at least: the degradation cost of the electrolyzer , Fuel cell degradation costs , Hydrogen storage tank degradation cost , Energy storage degradation costs , Power purchase and sales costs , Abandonment penalty cost , Wind power curtailment penalty costs , Power failure load penalty cost , Heat loss load penalty cost , Cooling loss load penalty cost , Hydrogen loss load penalty cost and microgrids actively support power demand , the scheduling cost calculation equation is: in, in, is the electrolytic cell degradation coefficient, for Electrolyzer power at the moment, is the scheduling time interval, , is the fuel cell degradation coefficient, for Fuel cell power at all times, is the degradation coefficient of the hydrogen storage tank, and Battery slot The amount of hydrogen produced at any one time and the fuel cell The amount of hydrogen consumed at any given moment, is the degradation coefficient of the energy storage system, and Energy storage system Charging power and discharging power at all times, for Electricity price at any time, and They are The power purchased and sold by the micro-grid at all times. Prediction error scenario for target source load The probability of the scenario, is the unit abandonment penalty cost, Prediction error scenario for target source load Down Always discard optical power. is the penalty cost for unit wind curtailment, Prediction error scenario for target source load Down Wind power is abandoned at all times. is the penalty cost per unit power failure load, Prediction error scenario for target source load Down The load power at the moment of power failure, is the penalty cost per unit heat loss load, for Heat loss load power at any time, is the penalty cost per unit cooling load, for Cooling load power at any moment, is the penalty cost per unit hydrogen loss load, for The amount of substance losing hydrogen load at any moment.

[0036] for Target source load prediction error scenario Down Always discard optical power. Target source load prediction error scenario Down Wind power curtailment at all times and Target source load prediction error scenario Down The power of the load at the moment of power failure is not only closely related to the power balance constraint of the microgrid and the operating status of each device, but also subject to the dual restrictions of the net load scenario and time. Under different target source and load prediction error scenarios, the photovoltaic and wind power prediction values ​​and load demand are different, so the power balance situation in each scenario is different, which leads to different abandoned photovoltaic power, abandoned wind power and power failure load power. The impact of the probability of occurrence of different target source and load prediction error scenarios , and In the actual calculation, the value of each moment should be calculated for each target source load prediction error scenario. , and , in order to accurately reflect the operating status of the microgrid under different conditions.

[0037] As for the risk cost, due to the uncertainty of photovoltaic, wind turbine and load, the risk cost of scheduling is evaluated according to the scheduling cost equation, and the risk cost equation is obtained as follows: in, is the risk cost, is an auxiliary variable, is the confidence level of risk cost, Represents the target source load prediction error scenario The scheduling cost under .

[0038] The determination of auxiliary variables requires comprehensive consideration of the scheduling cost gaps corresponding to each target source load forecast error scenario and the scenario probability for optimization and solution. The specific steps are as follows: 1) According to the dispatch cost equation, calculate the dispatch cost at each time under all target source load forecast error scenarios, summarize the dispatch cost at each time under all target source load forecast error scenarios, and form a dispatch cost data set .

[0039] 2) Arrange the elements in the scheduling cost data set in ascending order.

[0040] 3) Confidence level for a given risk cost , the auxiliary variable is that under the confidence level of a given risk cost, there is The probability that the scheduling cost does not exceed the value of .

[0041] 4) Based on the confidence level, the dispatch cost data set is optimized and solved to obtain auxiliary variables. Auxiliary variables are related to the confidence level of dispatch cost and risk cost, and are used to balance dispatch cost and risk cost. By adjusting the auxiliary variables, the optimal dispatch scheme for active support of microgrids can be found under different risk preferences. For example, when the risk preference is low, a smaller auxiliary variable can be selected to reduce the risk of dispatch cost exceeding a certain threshold; when the risk preference is high, the auxiliary variable can be appropriately increased, and higher dispatch cost fluctuations can be accepted to a certain extent to pursue better economic benefits.

[0042] S5. Taking the net load fluctuation intervals corresponding to the several net load scenarios as constraints, a microgrid optimization scheduling model is constructed based on the scheduling cost equation and the risk cost equation; in the actual scheduling process, based on the confidence level, the net load fluctuation intervals corresponding to the several net load scenarios are determined, and the net load fluctuation intervals corresponding to the several net load scenarios are taken as the interval constraints for scheduling, and a comprehensive scheduling cost equation is constructed based on the scheduling cost equation and the risk cost equation. According to the comprehensive scheduling cost equation, a microgrid optimization scheduling model is constructed, and the objective function of the microgrid optimization scheduling model is: in, is the risk factor.

[0043] The constraints of the microgrid optimization scheduling model also include at least: electric power balance constraints, thermal power balance constraints, cold power balance constraints, equipment constraints, battery energy storage system operation constraints, hydrogen energy subsystem constraints, thermal storage system operation constraints in thermal storage electric boilers, power purchase and sales constraints, and constraints on active support power provided by wind turbines.

[0044] The electric power balance constraint is expressed as: in, , and They are The predicted power values ​​of photovoltaic, wind power and electric load at each moment; , and They are target source load prediction error scenarios Down Power prediction errors of photovoltaic, wind power and load at each moment; is the target source load prediction error scenario Down The maximum power that the photovoltaic system can supply after considering the support at any time. for The electrical power of the thermal storage electric boiler at all times.

[0045] The thermal power balance constraint is expressed as: in, is the predicted value of heat load power, is the heat loss load power, is the input power of the absorption chiller, represents the fuel cell electrical efficiency, represents the electrolytic cell electrical efficiency, It represents the heat utilization efficiency of the heat exchanger. is the heat storage and release efficiency, and They are the heat storage and heat release power of the thermal storage electric boiler respectively.

[0046] The cold power balance constraint is expressed as: in, is the cooling load power prediction value, is the cooling load power, It is the working efficiency of absorption chiller.

[0047] The device constraints are expressed as: The following formula is the upper and lower limits of the electric power of the absorption chiller and the thermal storage electric boiler: , in, for The input power of the absorption chiller at this moment, is the upper limit of the input power of the absorption chiller, for The electrical power of the thermal storage electric boiler at any given moment, It is the upper limit of the electric power of the thermal storage electric boiler.

[0048] The operating constraints of the battery energy storage system are expressed as: The SOC (state of charge of the energy storage system) in adjacent time periods must satisfy the following relationship: in, For battery energy storage systems State of charge during the time period; is the charge and discharge efficiency, To support power time, the application is set to 1 minute. , are the power absorbed and supplied by the energy storage system, Indicates the capacity of the battery energy storage system.

[0049] The remaining capacity at each moment during the operation of the battery energy storage system must meet the upper and lower limit constraints shown below: in, is the minimum state of charge of the battery energy storage system, It is the maximum value of the state of charge of the battery energy storage system.

[0050] In addition, high current charging and discharging will shorten the life of the battery energy storage system. Therefore, during operation, the charging and discharging power of the battery energy storage system must be limited to the following range: in, A binary variable representing the charging and discharging status of the battery energy storage system, Indicates that the battery energy storage system is in charging state. Indicates that it is in the discharge state. It is the maximum charge and discharge rate of the battery energy storage system.

[0051] The hydrogen energy subsystem constraints are expressed as: The fuel cell power and electrolyzer power satisfy the following relationship with the amount of hydrogen: , in, and They are The amount of hydrogen consumed by the fuel cell and the amount of hydrogen produced by the electrolyzer at any given moment, It indicates the lower calorific value of hydrogen, which refers to the heat released when a unit amount of hydrogen is completely burned.

[0052] Since the hydrogen storage tank cannot store and release hydrogen at the same time, the fuel cell power and electrolyzer power must meet the following constraints: in, is a binary variable representing the status of the hydrogen storage tank, Indicates that the fuel cell consumes hydrogen and the hydrogen storage tank releases hydrogen. It means that the electrolyzer produces hydrogen and the hydrogen storage tank stores hydrogen. and are the rated powers of the fuel cell and electrolyzer, respectively.

[0053] The hydrogen storage capacity of the hydrogen storage tank in adjacent time periods must satisfy the following relationship: in, express Hydrogen storage capacity at the moment, express Hydrogen storage capacity at the moment, represents the hydrogen load demand, Indicates the capacity of the hydrogen storage tank, Indicates unmet hydrogen load demand.

[0054] The hydrogen storage capacity at each moment during the operation of the hydrogen storage tank must meet the upper and lower limit constraints shown in the following formula: in, Indicates the minimum value of hydrogen storage capacity, Indicates the maximum amount of hydrogen storage.

[0055] The operating constraints of the heat storage system in the thermal storage electric boiler are expressed as: The HOCs of adjacent time periods must satisfy the following relationship: in, For thermal storage system The heat storage state of the time period, For thermal storage system The heat storage state of the time period, Indicates the capacity of the thermal storage system.

[0056] in, is the minimum value of the heat storage state of heat storage, It is the minimum value of the heat storage state.

[0057] The remaining capacity at each moment during the operation of the thermal storage system must satisfy the upper and lower limit constraints shown in the above formula: in, is a binary variable representing the heat storage and heat release status of the thermal storage system, Indicates that the thermal storage system is in the thermal storage state. Indicates an exothermic state.

[0058] The restrictions on electricity purchase and sales are as follows: The power purchase and sales constraints during grid-connected operation are as follows: in, A binary variable representing the state of electricity purchase and sale, Indicates that the microgrid purchases electricity from the distribution network. Indicates that the microgrid sells electricity to the distribution network. and They are the maximum values ​​of power purchase and power sale respectively. The power purchase and power sale during off-grid operation is 0.

[0059] The constraint on the active support power provided by the wind turbine is: The energy for active support is provided by energy storage and wind turbines, and its calculation formula is as follows: in, express The predicted value of wind turbine output at the moment, Represents the target source load prediction error scenario The prediction error of the downstream wind turbine power is Indicates the capacity of the fan. It is the power of the wind turbine that actually participates in the grid power balance after considering the support. Indicates that the fan takes into account the support The actual value absorbed by the fan at the moment, Indicates that the fan takes into account the support The actual value of the fan output at a given moment. For a fan, the capacity that can be absorbed is the maximum power of the fan minus the current fan output, and the output that can be provided is the current fan output.

[0060] S6. In the process of grid dispatching, the real-time frequency change amount of the grid side of the target area, the power forecast value of the microgrid, and the power forecast value of the load obtained in real time are input into the microgrid optimization dispatching model, and the microgrid active support dispatching scheme is obtained according to the output result of the microgrid optimization dispatching model; in the actual grid dispatching process, the real-time frequency change amount of the grid side of the target area, the power forecast value of the microgrid, and the power forecast value of the load obtained in real time are input into the microgrid optimization dispatching model, and the microgrid active support dispatching scheme corresponding thereto is obtained through the operation of the microgrid optimization dispatching model. The microgrid active support dispatching scheme includes the output data of each microgrid, and displays the actual output of photovoltaic and wind power in each period, as well as the power of fuel cells and electrolyzers, which is one of the core results of the dispatching scheme. The microgrid active support dispatching scheme also includes energy storage charging and discharging data, power purchase and sales data, abandoned energy level load shortage data and cost data, so as to evaluate the economic feasibility of the dispatching scheme, optimize the microgrid optimization dispatching model, and improve the fit between the microgrid active support dispatching scheme output by the microgrid optimization dispatching model and the actual situation.

[0061] In one embodiment of the present application, the optimization scheduling method based on active support of microgrid disclosed in the present application is verified according to the parameter settings of Table 1, Table 2 and Table 3.

[0062] Table 1 Table 2 Table 3 Figure 3 This is a schematic diagram of the 24-hour grid-side frequency variation in the target area obtained based on the above parameter settings. The reference frequency is 50Hz. Figure 4 This is a schematic diagram of the active support power demand on the grid side calculated based on the frequency change on the grid side, in kW. Figure 5 This is a schematic diagram of the distribution network operation results without considering the active support of the microgrid. Figure 6 This is a schematic diagram of the distribution network operation results after considering the active support of the microgrid. Figure 5 and Figure 6 It can be seen that the overall operation of the distribution network has changed before and after considering the active support of the microgrid. For example, the charging and discharging time of the energy storage has changed. For example, the amplitude of the photovoltaic power in each hour before 13 hours is not the same, and the charging and discharging time of the energy storage has also changed. However, it is difficult to accurately judge the difference between the two before and after considering the active support of the microgrid. Since the set energy storage support coefficient is greater than the wind turbine support coefficient, the system mostly uses energy storage to support the power grid. Next, the power release and SOC changes of the energy storage system are used to further analyze the difference in the optimized scheduling of the distribution network before and after considering the support. Figure 7 It is a schematic diagram of the power change of the energy storage system, where the bar graph shows the power change of the energy storage system before considering the support, and the line graph shows the power change after considering the support. Figure 8 This is a schematic diagram of the SOC change curve of the energy storage system before and after considering the active support of the microgrid. Figure 7 It can be seen that the distribution network has a positive power support demand in the first and second hours, and the microgrid needs to supply energy. After considering the support, the electric energy storage system provides power output in the first and second hours to meet the support needs of the grid side. The power output in the first and second hours is also Figure 7 Considering the support, the SOC of the electric energy storage decreases in the first and second hours. Figure 8It can be seen that the distribution network needs the microgrid to provide power to it in the 11th hour. Before considering the support, the SOC curve of the energy storage system is 0.1 at the end of the 10th hour, reaching the lowest point of electricity. At this time, it will not be able to supply power to the outside in the 11th hour. After considering the support demand, the electric energy storage system is charged in the 10th hour, so that the energy storage can supply power to the outside in the 11th hour to meet the support needs of the power grid.

[0063] Fig. 9 This is a schematic diagram of the distribution network's satisfaction with the support power after considering the active support of the microgrid. Fig. 9 It can be found that the power support requirements represented by the green dotted line are well met. After considering the active support of the microgrid, the frequency and inertia support of the distribution network can be better met, and the disturbance of the distribution network frequency can be responded to within the corresponding time to avoid excessive oscillation of the grid frequency.

[0064] In a preferred embodiment of the present invention, the optimization scheduling method based on active support of the microgrid disclosed in the present application samples the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and samples the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain several target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario, and obtains several net load scenarios based on the several source-load prediction error scenarios; calculates the active support power demand on the grid side based on the grid-side frequency change in the target area; constructs a first evaluation method of the active support power demand of the microgrid based on the scenario probability, the active support power demand on the grid side and the inertia support form of the microgrid. The first evaluation equation is set to reflect the inertia support power of the microgrid under all scenario probabilities; based on the loss cost, circulating energy cost and the first evaluation equation in the microgrid dispatching process, the dispatching cost equation and risk cost equation corresponding to the active support power demand on the grid side are constructed; the net load fluctuation range corresponding to several net load scenarios is used as a constraint condition, and a microgrid optimization dispatching model is constructed based on the dispatching cost equation and the risk cost equation; in the grid dispatching process, the real-time frequency change of the grid side of the target area, the power prediction value of the microgrid, and the power prediction value of the load are input into the microgrid optimization dispatching model, and the microgrid active support dispatching scheme is obtained according to the output result of the microgrid optimization dispatching model. The optimization dispatching method based on the active support of the microgrid provided in this application fully considers and utilizes the active support frequency of the microgrid, significantly enhances the frequency response capability of the distribution network, effectively suppresses frequency fluctuations, and can also greatly reduce the frequency regulation pressure on the transmission side, and improve the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the carrying capacity of the distribution network for distributed power sources, and create more favorable conditions for the large-scale access and efficient utilization of renewable energy to the distribution network.

[0065] Accordingly, if Fig.10 As shown, based on an optimization scheduling method based on active support of a microgrid, an embodiment of the present invention further provides an optimization scheduling system based on active support of a microgrid, to implement the optimization scheduling method based on active support of a microgrid disclosed in an embodiment of the present invention, wherein the microgrid at least includes a distributed photovoltaic energy type, and the system includes: a scenario construction unit 1, a grid-side active support power demand calculation unit 2, a microgrid active support power demand evaluation unit 3, a cost equation construction unit 4, a scheduling model construction unit 5 and a scheduling optimization unit 6; The scenario construction unit 1 is used to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and to sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, and to obtain a plurality of net load scenarios based on the plurality of source-load prediction error scenarios; The grid side active support power demand calculation unit 2 is used to calculate the grid side active support power demand based on the grid side frequency change amount of the target area; The microgrid active support power demand evaluation unit 3 is used to construct a first evaluation equation for the microgrid active support power demand based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, wherein the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities; The cost equation construction unit 4 is used to construct a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process; The dispatch model construction unit 5 is used to construct a microgrid optimization dispatch model based on the dispatch cost equation and the risk cost equation, taking the net load fluctuation ranges corresponding to the plurality of net load scenarios as constraints; The dispatching optimization unit 6 is used to input the real-time frequency change of the grid side of the target area, the power prediction value of the microgrid, and the power prediction value of the load into the microgrid optimization dispatching model during the grid dispatching process, and obtain the microgrid active support dispatching plan according to the output result of the microgrid optimization dispatching model.

[0066] For the specific definition of an optimization scheduling system based on active support of a microgrid, please refer to the above-mentioned definition of an optimization scheduling method based on active support of a microgrid, which will not be repeated here. A person of ordinary skill in the art can appreciate that the various modules and steps described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0067] like Fig.11 As shown, an embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps in the above-mentioned optimization scheduling embodiment based on active support of microgrids are implemented, for example Figure 1 Steps S1 to S6 described in .

[0068] Those skilled in the art will understand that the Fig.11 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0069] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0070] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the computer device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0071] Wherein, if the module integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0072] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0073] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to perform the steps in the optimized scheduling based on active support of microgrids in the above embodiment, for example Figure 1 Steps S1 to S6 described in .

[0074] In summary, the embodiments of the present application provide an optimization scheduling method, system, device and medium based on active support of microgrids to solve the technical problem that traditional power scheduling models cannot cope with power fluctuations caused by the access of a large number of distributed photovoltaic power generation systems, resulting in poor stability of the distribution network. The method includes: sampling the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sampling the cumulative distribution function of the day-ahead power prediction error of the load in the target area, obtaining several target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario, and obtaining several net load scenarios based on the several source-load prediction error scenarios; calculating the active support power demand on the grid side based on the grid-side frequency change in the target area; and calculating the active support power demand on the grid side based on the scenario probability, the grid-side active support power demand, and the grid-side active support power demand. The first evaluation equation of the active support power demand of the microgrid is constructed based on the rate demand and the inertia support form of the microgrid. The first evaluation equation is set to reflect the inertia support power of the microgrid under all scenario probabilities; based on the loss cost, the cycle energy cost and the first evaluation equation in the microgrid dispatching process, the dispatching cost equation and the risk cost equation corresponding to the active support power demand on the grid side are constructed; the net load fluctuation range corresponding to several net load scenarios is used as a constraint condition, and a microgrid optimization dispatching model is constructed based on the dispatching cost equation and the risk cost equation; in the grid dispatching process, the real-time frequency change amount of the grid side of the target area, the power prediction value of the microgrid, and the power prediction value of the load obtained in real time are input into the microgrid optimization dispatching model, and the microgrid active support dispatching scheme is obtained according to the output result of the microgrid optimization dispatching model. The optimization dispatching method based on the active support of the microgrid provided in this application fully considers and utilizes the active support frequency of the microgrid, significantly enhances the frequency response capability of the distribution network, effectively suppresses frequency fluctuations, and can also greatly reduce the frequency regulation pressure on the transmission side, and improve the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the carrying capacity of the distribution network for distributed power sources, and create more favorable conditions for the large-scale access and efficient utilization of renewable energy to the distribution network.

[0075] 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.

[0076] The above-described embodiments only express several preferred implementations of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several improvements and substitutions can be made without departing from the technical principles of the present application, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent application shall be based on the protection scope of the claims.

Claims

1. An optimization scheduling method based on active support of microgrid, characterized in that: The microgrid includes at least a distributed photovoltaic energy type, and the method includes: Sampling the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sampling the cumulative distribution function of the day-ahead power prediction error of the load in the target area, obtaining a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, and obtaining a plurality of net load scenarios based on the plurality of source-load prediction error scenarios; Calculating the active support power demand on the grid side based on the grid side frequency change in the target area; Based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, a first evaluation equation for the active support power demand of the microgrid is constructed, wherein the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities; Based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process, constructing a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side; Taking the net load fluctuation ranges corresponding to the plurality of net load scenarios as constraints, a microgrid optimization scheduling model is constructed based on the scheduling cost equation and the risk cost equation; During the grid dispatching process, the real-time frequency change of the grid side of the target area, the power forecast value of the microgrid, and the power forecast value of the load acquired in real time are input into the microgrid optimization dispatching model, and the microgrid active support dispatching plan is obtained according to the output result of the microgrid optimization dispatching model.

2. The optimization scheduling method based on microgrid active support according to claim 1, characterized in that: The cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area is sampled, and the cumulative distribution function of the day-ahead power prediction error of the load in the target area is sampled to obtain a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, including: The cumulative distribution function of the day-ahead power prediction error of the microgrid is sampled by a Latin hypercube sampling method to obtain several day-ahead microgrid prediction error scenarios; The cumulative distribution function of the day-ahead power forecast error of the load is sampled by a Latin hypercube sampling method to obtain several day-ahead load forecast error scenarios; Combining a plurality of the day-ahead microgrid prediction error scenarios with the day-ahead load prediction error scenarios to obtain a plurality of day-ahead source-load prediction error scenarios; The sampling backward reduction method optimizes the day-ahead source load prediction error scenario to obtain a plurality of target source load prediction error scenarios and a scenario probability corresponding to each target source load prediction error scenario.

3. The optimization scheduling method based on microgrid active support according to claim 1, characterized in that: The first evaluation equation for the active support power demand of the microgrid is constructed based on the scenario probability, the active support power demand of the grid side and the inertia support form of the microgrid, including: The sampling Latin hypercube sampling method performs sampling analysis on the cumulative distribution function of the historical prediction error of the active support demand power on the grid side, and obtains the demand probability of power support on the grid side according to the analysis result; Based on the scenario probability, the grid-side active support power demand, the power of the inertial support form of the microgrid and the demand probability, construct a first evaluation equation for the microgrid active support power demand; The first evaluation equation is: in, Indicates that the microgrid actively supports the power demand, Prediction error scenario for target source load The probability of the scenario, is the number of target source load prediction error scenarios, for The probability of power demand support required by the grid side at the moment, Actively support power demand on the grid side, Release power for energy storage, Absorbing power for energy storage, Release power for the fan, The fan absorbs power. is the energy storage coefficient, is the fan coefficient, , .

4. The optimization scheduling method based on microgrid active support according to claim 1 is characterized in that: The method of constructing a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process includes: The degradation costs of the electrolyzer, the fuel cell, the hydrogen storage tank, and the electric energy storage are used to characterize the life loss during the microgrid dispatching process, and a second evaluation equation for the loss cost is obtained; Characterizing the circulating energy cost by the electricity purchase and sale cost and the energy loss penalty cost, and obtaining a third evaluation equation of the circulating energy cost; constructing a scheduling cost equation according to the first evaluation equation, the second evaluation equation and the third evaluation equation; The scheduling cost equation is: in, is the electrolytic cell degradation coefficient, for Electrolyzer power at the moment, is the scheduling time interval, , is the fuel cell degradation coefficient, for Fuel cell power at all times, is the degradation coefficient of the hydrogen storage tank, and Battery slot The amount of hydrogen produced at any one time and the fuel cell The amount of hydrogen consumed at any given moment, is the degradation coefficient of the energy storage system, and Energy storage system Charging power and discharging power at all times, for Electricity price at any time, and They are The power purchased and sold by the micro-grid at all times. Prediction error scenario for target source load The probability of the scenario, is the unit abandonment penalty cost, Prediction error scenario for target source load Down Always discard optical power. is the penalty cost for unit wind curtailment, Prediction error scenario for target source load Down Wind power is abandoned at all times. is the penalty cost per unit power failure load, Prediction error scenario for target source load Down The load power at the moment of power failure, is the penalty cost per unit heat loss load, for Heat loss load power at any time, is the penalty cost per unit cooling load, for Cooling load power at any moment, is the penalty cost per unit hydrogen loss load, for The amount of material losing hydrogen load at any moment; The risk cost of scheduling is evaluated according to the scheduling cost equation to obtain the risk cost equation.

5. The optimization scheduling method based on active support of microgrid according to claim 4, characterized in that: The step of evaluating the risk cost of scheduling according to the scheduling cost equation to obtain the risk cost equation includes: According to the scheduling cost equation, calculating the scheduling cost data set at each moment under all the target source load prediction error scenarios; Determining the confidence level of the risk cost, and optimizing and solving the scheduling cost data set based on the confidence level to obtain auxiliary variables; Constructing the risk cost equation based on the auxiliary variables, the dispatch cost under the target source load prediction error scenario, the scenario probability and the confidence level; The risk cost equation is expressed as: in, is the risk cost, is an auxiliary variable, is the confidence level of risk cost, Represents the target source load prediction error scenario The scheduling cost under .

6. The optimization scheduling method based on active support of microgrid according to claim 5, characterized in that: The method uses the net load fluctuation intervals corresponding to the plurality of net load scenarios as constraints and constructs a microgrid optimization scheduling model based on the scheduling cost equation and the risk cost equation, including: Based on the confidence level, determining a net load fluctuation range corresponding to a plurality of the net load scenarios; Taking the net load fluctuation range as a constraint condition, a comprehensive dispatching cost equation is constructed based on the dispatching cost equation and the risk cost equation; Constructing a microgrid optimization dispatch model according to the comprehensive dispatch cost equation; The objective function of the microgrid optimization scheduling model is: in, is the risk factor.

7. The optimization scheduling method based on active support of microgrid according to claim 1, characterized in that: The constraints include at least: electric power balance constraints, thermal power balance constraints, cold power balance constraints, equipment constraints, battery energy storage system operation constraints, hydrogen energy subsystem constraints, thermal storage system operation constraints in thermal storage electric boilers, electricity purchase and sales constraints, and constraints on active support power provided by fans.

8. An optimization dispatching system based on active support of a microgrid, realizing the optimization dispatching method based on active support of a microgrid as described in any one of claims 1 to 7, characterized in that: The microgrid includes at least a distributed photovoltaic energy type, and the system includes: a scenario construction unit, a grid-side active support power demand calculation unit, a microgrid active support power demand evaluation unit, a cost equation construction unit, a scheduling model construction unit, and a scheduling optimization unit; The scenario construction unit is used to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and to sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain a plurality of target source-load prediction error scenarios in the target area and a scenario probability corresponding to each of the target source-load prediction error scenarios, and to obtain a plurality of net load scenarios based on the plurality of source-load prediction error scenarios; The grid-side active support power demand calculation unit is used to calculate the grid-side active support power demand based on the grid-side frequency change of the target area; The microgrid active support power demand evaluation unit is used to construct a first evaluation equation for the microgrid active support power demand based on the scenario probability, the grid-side active support power demand and the inertia support form of the microgrid, wherein the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities; The cost equation construction unit is used to construct a dispatching cost equation and a risk cost equation corresponding to the active support power demand on the grid side based on the loss cost, the circulating energy cost and the first evaluation equation in the microgrid dispatching process; The dispatch model construction unit is used to construct a microgrid optimization dispatch model based on the dispatch cost equation and the risk cost equation, taking the net load fluctuation ranges corresponding to the plurality of net load scenarios as constraints; The dispatching optimization unit is used to input the real-time frequency change of the grid side of the target area, the power prediction value of the microgrid, and the power prediction value of the load into the microgrid optimization dispatching model during the grid dispatching process, and obtain the microgrid active support dispatching plan according to the output result of the microgrid optimization dispatching model.

9. A computer device, characterized in that: The computer device includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to execute the optimization scheduling method based on active support of microgrids as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the optimization scheduling method based on microgrid active support as described in any one of claims 1 to 7 is implemented.

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