Optimization Scheduling Method, System, Device and Medium Based on Active Support of Microgrid

Abstract

The present invention discloses an optimization scheduling method, system, device and medium based on the active support of a microgrid. By sampling, a number of target source-load prediction error scenarios, scenario probabilities and net load scenarios are obtained; based on the frequency change amount on the grid side, the active support power demand on the grid side is calculated; based on the scenario probabilities, the active support power demand on the grid side and the inertia support form of the microgrid, a first evaluation equation for the active support power demand of the microgrid is constructed; with a number of net load scenarios as constraint conditions, a microgrid optimization scheduling model is constructed based on a scheduling cost equation and a risk cost equation constructed according to the loss cost, cyclic energy cost and the first evaluation equation in the microgrid scheduling process; and an active support scheduling scheme for the microgrid is obtained based on the microgrid optimization scheduling model. The method provided by this application fully considers and utilizes the active support frequency of the microgrid, significantly enhances the frequency response ability of the distribution network, and improves the operation efficiency and stability of the distribution network.

Description

Technical Field

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

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

[0003] Traditional power dispatching models are mainly based on large-scale centralized power generation and rely on generator sets on the transmission side to provide necessary inertia and frequency regulation response. This model performs well in dealing with large-scale and stable power supply, but it seems a bit inadequate when facing the complex and changeable situations brought by the access of new energy sources such as distributed photovoltaics.

[0004] Therefore, 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 needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides an optimal dispatching method and system based on the active support of a microgrid to solve the technical 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, and to achieve the effects of enhancing the frequency response ability of the distribution network, effectively suppressing frequency fluctuations, reducing the frequency regulation pressure on the transmission side, and improving the operation efficiency and stability of the entire power system.

[0006] In a first aspect, the present invention provides an optimal dispatching method based on the active support of a microgrid, where the microgrid includes at least a distributed photovoltaic energy type, and the method includes:

[0007] Sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain a number of target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario. Based on the number of source-load prediction error scenarios, obtain a number of net load scenarios;

[0008] Calculate the grid-side active support power demand based on the grid-side frequency change in the target area;

[0009] Based on the scenario probability, the grid-side active support power demand, and the inertia support form of the microgrid, construct a first evaluation equation for the microgrid active support power demand, and the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities;

[0010] Based on the loss cost, the renewable energy cost, and the first evaluation equation in the microgrid dispatching process, construct a dispatching cost equation and a risk cost equation corresponding to the grid-side active support power demand;

[0011] Using the net load fluctuation intervals corresponding to the number of net load scenarios as constraints, construct a microgrid optimal dispatching model based on the dispatching cost equation and the risk cost equation;

[0012] During the grid dispatching process, input the real-time grid-side frequency change in the target area, the power prediction value of the microgrid, and the power prediction value of the load obtained in real time into the microgrid optimal dispatching model, and obtain the microgrid active support dispatching plan according to the output result of the microgrid optimal dispatching model.

[0013] Preferably, the sampling of the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area and the sampling of the cumulative distribution function of the day-ahead power prediction error of the load in the target area to obtain a number of target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario includes:

[0014] Use the Latin hypercube sampling method to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid to obtain a number of day-ahead microgrid prediction error scenarios;

[0015] Use the Latin hypercube sampling method to sample the cumulative distribution function of the day-ahead power prediction error of the load to obtain a number of day-ahead load prediction error scenarios;

[0016] Combine the number of day-ahead microgrid prediction error scenarios and the day-ahead load prediction error scenarios to obtain a number of day-ahead source-load prediction error scenarios;

[0017] The sampling backward reduction method optimizes the day-ahead source-load prediction error scenarios to obtain a number of the target source-load prediction error scenarios and the scenario probability corresponding to each target source-load prediction error scenario.

[0018] Preferably, constructing a first evaluation equation for 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 includes:

[0019] Sampling the cumulative distribution function of the historical prediction error of the active support demand power on the grid side by the sampling Latin hypercube sampling method, and obtaining the demand probability of the power support required on the grid side according to the analysis result;

[0020] Constructing the first evaluation equation for the active support power demand of the microgrid based on the scenario probability, the active support power demand on the grid side, the power of the inertia support form of the microgrid, and the demand probability;

[0021] The first evaluation equation is:

[0022]

[0023] Wherein, represents the active support power demand of the microgrid, is the target source-load prediction error scenario is the scenario probability, is the number of target source-load prediction error scenarios, is the demand probability of the power support required on the grid side at time is the active support power demand on the grid side, is the power released by the energy storage, is the power absorbed by the energy storage, is the power released by the wind turbine, is the power absorbed by the wind turbine, is the energy storage coefficient, is the wind turbine coefficient, , .

[0024] Preferably, constructing a scheduling cost equation and a risk cost equation corresponding to the active support power demand on the grid side based on the loss cost, the cyclic energy cost, and the first evaluation equation in the microgrid scheduling process includes:

[0025] Characterizing the life loss in the microgrid scheduling process by the degradation costs of the electrolyzer, fuel cell, hydrogen storage tank, and electrical energy storage to obtain a second evaluation equation for the loss cost;

[0026] Characterize the circulating energy cost by the purchase and sale electricity cost and the energy loss penalty cost, and obtain the third evaluation equation of the circulating energy cost;

[0027] Construct a scheduling cost equation according to the first evaluation equation, the second evaluation equation and the third evaluation equation;

[0028] The scheduling cost equation is:

[0029]

[0030] Wherein, is the electrolyzer degradation coefficient, is the electrolyzer power at time is the scheduling time interval, , is the fuel cell degradation coefficient, is the fuel cell power at time is the hydrogen storage tank degradation coefficient, and are respectively the amount of hydrogen generated by the electrolyzer at time and the amount of hydrogen consumed by the fuel cell at time , is the energy storage system degradation coefficient, and are respectively the charging power and the discharging power of the energy storage system at time , is the electricity price at time and are respectively the micro-grid purchase electricity power and the selling electricity power at time is the scenario probability of the target source-load prediction error scenario , is the unit penalty cost for abandoned light, is the target source-load prediction error scenario under the abandoned light power at time is the unit penalty cost for abandoned wind, is the target source-load prediction error scenario under the abandoned wind power at time is the unit penalty cost for lost power load, is the target source-load prediction error scenario under the lost power load power at time is the unit penalty cost for lost heat load, is the lost heat load power at time is the penalty cost for unit cooling loss load, is the cooling loss load power at time is the penalty cost for unit hydrogen loss load, is the amount of substance of hydrogen loss load at time

[0031] Evaluate the risk cost of the dispatch according to the dispatch cost equation to obtain the risk cost equation.

[0032] Preferably, the evaluating the risk cost of the dispatch according to the dispatch cost equation to obtain the risk cost equation includes:

[0033] Calculate the dispatch cost data set at each time under all the target source-load prediction error scenarios according to the dispatch cost equation;

[0034] Determine the confidence level of the risk cost, and perform an optimization solution on the dispatch cost data set based on the confidence level to obtain auxiliary variables;

[0035] Construct 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;

[0036] The risk cost equation is expressed as:

[0037]

[0038] where is the risk cost, is the auxiliary variable, is the confidence level of the risk cost, represents the target source-load prediction error scenario the dispatch cost under.

[0039] Preferably, using the net load fluctuation intervals corresponding to several of the net load scenarios as constraint conditions, constructing a microgrid optimal dispatch model based on the dispatch cost equation and the risk cost equation includes:

[0040] Determine the net load fluctuation intervals corresponding to several of the net load scenarios based on the confidence level;

[0041] Using the net load fluctuation intervals as constraint conditions, construct a comprehensive dispatch cost equation based on the dispatch cost equation and the risk cost equation;

[0042] Construct a microgrid optimal dispatch model according to the comprehensive dispatch cost equation;

[0043] The objective function of the microgrid optimal dispatch model is:

[0044]

[0045] Among them, is the risk coefficient.

[0046] Preferably, the constraint conditions at least further include: electric power balance constraint, thermal power balance constraint, cooling power balance constraint, equipment constraint, battery energy storage system operation constraint, hydrogen energy subsystem constraint, heat storage system operation constraint in the heat storage type electric boiler, power purchase and sale constraint, and constraint on the active support power provided by the fan.

[0047] In a second aspect, the present invention further provides an optimal scheduling system based on the active support of the microgrid to implement the above-mentioned optimal scheduling method based on the active support of the microgrid. The microgrid at least includes a distributed photovoltaic energy type. 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;

[0048] 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 sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area, to obtain a number of target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario, and based on the number of source-load prediction error scenarios, obtain a number of net load scenarios;

[0049] 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 amount in the target area;

[0050] 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. The first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities;

[0051] The cost equation construction unit: is used to construct a scheduling cost equation and a risk cost equation corresponding to the grid-side active support power demand based on the loss cost, the cyclic energy cost, and the first evaluation equation in the microgrid scheduling process;

[0052] The scheduling model construction unit: is used to construct a microgrid optimal scheduling model based on the scheduling cost equation and the risk cost equation with the net load fluctuation intervals corresponding to a number of the net load scenarios as constraint conditions;

[0053] The scheduling optimization unit: in the process of power grid scheduling, it inputs the real-time frequency change amount on the power 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 into the microgrid optimal scheduling model, and obtains the microgrid active support scheduling plan according to the output result of the microgrid optimal scheduling model.

[0054] In a third aspect, the present invention further provides a computer device, which includes a memory, a processor, and a transceiver, and they are connected through 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 optimal scheduling method based on microgrid active support.

[0055] In a fourth aspect, the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is run, the above-mentioned optimal scheduling method based on microgrid active support is realized.

[0056] The present application provides a method, system, device, and medium for evaluating the failure probability of sensitive devices. Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows:

[0057] The optimal scheduling method based on microgrid active support disclosed in the present application fully considers and utilizes the active support of the microgrid for frequency, significantly enhances the frequency response ability of the distribution network, effectively suppresses frequency fluctuations, can also greatly reduce the frequency regulation pressure on the transmission side, and improves the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the bearing capacity of the distribution network for distributed power sources, and creates more favorable conditions for the large-scale access and efficient utilization of renewable energy in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the steps of an optimal scheduling method based on microgrid active support provided by a preferred embodiment of the present invention;

[0059] Figure 2 It is a schematic diagram of the microgrid structure provided by a preferred embodiment of the present invention;

[0060] Figure 3 It is a schematic diagram of the 24-hour frequency change amount on the power grid side of the target area obtained according to specific parameter settings provided by a preferred embodiment of the present invention;

[0061] Figure 4 It is a schematic diagram of the active support power demand on the power grid side calculated based on the frequency change amount on the power grid side provided by a preferred embodiment of the present invention;

[0062] Figure 5It is a schematic diagram of the operation result of a distribution network provided by a preferred embodiment of the present invention without considering the active support of the microgrid;

[0063] Figure 6 It is a schematic diagram of the operation result of a distribution network provided by a preferred embodiment of the present invention considering the active support of the microgrid;

[0064] Figure 7 It is a schematic diagram of the power change of an energy storage system provided by a preferred embodiment of the present invention;

[0065] Figure 8 It is a schematic diagram of the SOC change curve of the energy storage system before and after considering the active support of the microgrid provided by a preferred embodiment of the present invention;

[0066] Figure 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;

[0067] Figure 10 It is a schematic diagram of the structure of an optimization scheduling system based on the active support of the microgrid provided by a preferred embodiment of the present invention;

[0068] Figure 11 It is the internal structure diagram of a computer device in an embodiment of the present invention. Detailed implementation manners

[0069] The following specifically clarifies the implementation manners of the present invention in conjunction with the accompanying drawings. The given embodiments are only for illustrative purposes and should not be construed as limitations on the present invention. The accompanying drawings are only for reference and illustration 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 those of ordinary skill in the art without creative efforts fall 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 construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality" is two or more.

[0070] In the description of the present invention, it should be noted that, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation to the present invention. The term "and / or" used herein includes any and all combinations of one or more of the 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.

[0071] 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 meanings as those commonly understood by those skilled in the technical field to which the present invention belongs. 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 according to specific circumstances.

[0072] As a new form of power organization, microgrids have shown unique advantages in the integration and optimal utilization of distributed energy due to their flexible and autonomous characteristics. With the rapid development of microgrid technology, they can quickly respond when the 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 and other resources. This fast and accurate adjustment ability is of great significance for improving the stability of the entire distribution network system. However, traditional power dispatch models often ignore the great potential of microgrids in frequency stability and inertia support.

[0073] In view of this, in the embodiments of the present invention, an optimized dispatch method based on the active support of a microgrid is provided. The microgrid includes at least a distributed photovoltaic energy type. Please refer to Figure 1 and Figure 2 , and the method includes:

[0074] S1. Sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and sample the cumulative distribution function of the day-ahead power prediction error of the load in the target area to obtain a number of target source-load prediction error scenarios in the target area and the scenario probability corresponding to each target source-load prediction error scenario. Based on the number of source-load prediction error scenarios, a number of net load scenarios are obtained. The microgrid in this application includes, but is not limited to, new energy power generation power sources such as distributed photovoltaic energy types and wind power energy types. The day is divided into 24 time periods. Based on the power prediction models of the microgrid and the load respectively, the cumulative distribution function of the day-ahead power prediction error of each time period of the microgrid and the load is obtained through histogram analysis or kernel density estimation methods. In an embodiment of this application, the Latin hypercube sampling method is used to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area to generate a number of day-ahead microgrid prediction error scenarios, and the Latin hypercube sampling method is used to sample the cumulative distribution function of the day-ahead power prediction error of the load to generate a number of day-ahead load prediction error scenarios. Combine the number of day-ahead microgrid prediction error scenarios and the day-ahead load prediction error scenarios to obtain a number of day-ahead source-load prediction error scenarios.

[0075] The Latin hypercube sampling method is a stratified sampling technique that can divide the variable into M intervals with the same probability. At this time, select 1 sample point that satisfies the Latin hypercube condition from each of the M intervals to form a sample set containing M sample points, which can obtain a higher sampling accuracy under a smaller sampling scale, reduce the calculation cost and time.

[0076] In a preferred embodiment of this application, the cumulative distribution function of the power prediction error of each time period of the microgrid and the cumulative distribution function of the power prediction error of each time period of the load are respectively evenly divided into intervals, and a value is randomly taken in each interval , then the sampling cumulative probability of the th interval is:

[0077]

[0078] Among them, is a random number with a uniform distribution, and , the cumulative distribution function of the uniform distribution is:

[0079]

[0080] Among them, .

[0081] Furthermore, use the inverse function of the cumulative distribution function to convert the sampling cumulative probability into the actual sampling value , then:

[0082]

[0083] Continue sampling from the remaining interval using the above sampling method. After one round of sampling, a day-ahead source-load prediction error scenario is generated. Repeat the above Latin hypercube sampling process until the sampling ends ( ), and multiple day-ahead source-load prediction error scenarios are generated.

[0084] Due to the large number of day-ahead source-load prediction error scenarios generated by the Latin hypercube sampling method, there are a large number of similar scenarios. 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, obtaining several target source-load prediction error scenarios and the scenario probability of each target source-load prediction error scenario, and maximizing the fitting accuracy of the remaining scenarios to the original samples. Specifically, assume that the number of day-ahead source-load prediction error scenarios generated by Latin hypercube sampling is , and the number of reduced target day-ahead source-load prediction error scenarios is . The detailed steps of the backward reduction method are as follows:

[0085] Step 1: Initialize the scenario probability and the initial number of scenarios for each scenario. Then, the initial scenario probability and the initial number of scenarios are expressed as:

[0086] ,

[0087] where represents the initial scenario probability of the day-ahead source-load prediction error scenario , and represents the initial number of scenarios.

[0088] Step 2: Calculate the distance between each pair of day-ahead source-load prediction error scenarios. The calculation formula is:

[0089]

[0090] where is the th element in the day-ahead source-load prediction error scenario , is the th element in the day-ahead source-load prediction error scenario , is the number of elements in each day-ahead source-load prediction error scenario. In the present application, , representing 24 hours, three uncertainty factors of photovoltaic power, wind power, and load demand.

[0091] Step 3: Select the scenario that is closest to the specified scenario The scenario with the minimum distance , that is:

[0092]

[0093] Calculate the probability of the scenario and multiply it by the distance . .

[0094] Step 4: For each day-ahead source-load prediction error scenario, repeat Step 3, select the day-ahead source-load prediction error scenario that makes the smallest, eliminate this day-ahead source-load prediction error scenario, and at the same time let , update the scenario probability of the day-ahead source-load prediction error scenario . of the scenario .

[0095] Step 5: Repeat Step 2 - Step 4 until is reached, obtaining several target source-load prediction error scenarios and the scenario probability of each target source-load prediction error scenario.

[0096] After obtaining the samples of the target source-load prediction error scenarios, calculate the difference between the load power and the microgrid power corresponding to each target source-load prediction error scenario, obtaining several net load scenarios. Based on the net load scenarios, the net load fluctuation interval under a given confidence level can be determined. This net load fluctuation interval serves as a constraint for the optimal scheduling based on the active support of the microgrid in this application, which can reasonably balance the revenue risk brought by the prediction error and improve the accuracy of power grid scheduling.

[0097] S2. Based on the frequency change amount on the power grid side of the target area, calculate the active support power demand on the power grid side; consider the frequency change demand and inertia support demand of the distribution network in the target area, and evaluate the active support capacity that the distribution network generally requires the microgrid to provide in each time period, so as to represent the active support power demand on the power grid side at time period , is the frequency coefficient, is the inertia coefficient, is the frequency change amount on the power grid side, is the time step, then the calculation formula for the active support power demand on the power grid side is:

[0098]

[0099] When is positive, it means that the distribution network requires the microgrid to provide power support to it. When is negative, it means that the distribution network requires the microgrid to absorb the excess power.

[0100] S3. Based on the scenario probability, the active support power demand on the grid side, and the inertia support form of the microgrid, construct a first evaluation equation for the active support power demand of the microgrid. 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: the energy storage release power , the energy storage absorption power , the wind turbine release power , and the wind turbine absorption power . Based on the scenario probability, the active support power demand on the grid side, and the inertia support form of the microgrid, construct a first evaluation equation for the active support power demand of the microgrid. The relational expression of the first evaluation equation is:

[0101]

[0102] wherein, is the scenario probability of the target source-load prediction error scenario , is the number of target source-load prediction error scenarios, is the demand probability that the grid side needs power support at time is the energy storage coefficient, is the wind turbine coefficient. The different magnitudes of the two represent the priority degrees of different support methods. The larger the coefficient, the more it needs to be considered preferentially, , .

[0103] 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. For the parameter , the present application also adopts 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 that the grid side needs power support according to the analysis result. This process is the same as the method of S1 for obtaining the target source-load prediction error scenario and the corresponding scenario probability for each target source-load prediction error scenario, and will not be elaborated here.

[0104] S4. Based on the loss cost, cyclic 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; in this application, the comprehensive scheduling cost is used as the objective function for the optimal 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, which includes the loss cost, cyclic 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 electrical energy storage is used to characterize the life loss in the microgrid scheduling process, and the second evaluation equation for the loss cost is obtained. The cyclic energy cost is characterized by the power purchase and sale cost and the energy loss penalty cost, and the third evaluation equation for the cyclic energy cost is obtained. The scheduling cost is calculated according to the first evaluation equation, the second evaluation equation, and the third evaluation equation. The scheduling cost at least includes: the electrolyzer degradation cost , the fuel cell degradation cost , the hydrogen storage tank degradation cost , the energy storage degradation cost , the power purchase and sale cost , the curtailment penalty cost for light , the curtailment penalty cost for wind , the penalty cost for lost power load , the penalty cost for lost heat load , the penalty cost for lost cooling load , the penalty cost for lost hydrogen load and the active support power demand of the microgrid . The scheduling cost calculation equation is:

[0105]

[0106] Wherein,

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] Among them, is the degradation coefficient of the electrolyzer, is the electrolyzer power at time is the scheduling time interval, , is the degradation coefficient of the fuel cell, is the fuel cell power at time is the degradation coefficient of the hydrogen storage tank, and are respectively the amount of hydrogen generated by the electrolyzer at time and the amount of hydrogen consumed by the fuel cell at time , is the degradation coefficient of the energy storage system, and are respectively the charging power and discharging power of the energy storage system at time , is the electricity price at time and are respectively the power of microgrid power purchase and power sale at time is the scenario probability of the target source-load prediction error scenario , is the unit penalty cost for curtailed light, is the target source-load prediction error scenario under the curtailed light power at time is the unit penalty cost for curtailed wind, is the target source-load prediction error scenario under the curtailed wind power at time is the unit penalty cost for lost load, is the target source-load prediction error scenario under the lost load power at time is the unit penalty cost for lost heat load, is the lost heat load power at time is the unit penalty cost for lost cooling load, is the lost cooling load power at time is the unit penalty cost for lost hydrogen load, is the amount of hydrogen for lost hydrogen load at time

[0118] For Target source-load prediction error scenario at the moment, the curtailment power of photovoltaic power, Target source-load prediction error scenario at the moment, the curtailment power of wind power and Target source-load prediction error scenario at the moment, the power of the lost load are not only closely related to the power balance constraint of the microgrid and the operating states of various devices, but also restricted by both the net load scenario and the moment. Under different target source-load prediction error scenarios, the predicted values of photovoltaic and wind power as well as the load demand are different. Therefore, the power balance situation under each scenario is different, which in turn leads to different curtailment power of photovoltaic power, curtailment power of wind power and power of the lost load. The occurrence probabilities of different target source-load prediction error scenarios affect , and the values at each moment. In actual calculations, it is necessary to calculate the , and at each moment for each target source-load prediction error scenario respectively to accurately reflect the operating state of the microgrid under different conditions.

[0119] For the risk cost, due to the uncertainties of photovoltaic power, wind turbines and loads, the risk cost of the dispatch is evaluated according to the dispatch cost equation, and the risk cost equation is obtained as follows:

[0120]

[0121]

[0122] where is the risk cost, is the auxiliary variable, is the confidence level of the risk cost, represents the dispatch cost under the target source-load prediction error scenario .

[0123] For the determination of the auxiliary variable, it is necessary to comprehensively consider the gap of the dispatch costs corresponding to each target source-load prediction error scenario and the scenario probability for optimization and solution. The specific steps are as follows:

[0124] 1) According to the dispatch cost equation, calculate the dispatch costs at each moment under all target source-load prediction error scenarios, and summarize the dispatch costs at each moment under all target source-load prediction error scenarios to form a dispatch cost data set .

[0125] 2) Arrange the elements in the dispatch cost data set in ascending order.

[0126] 3) Confidence level of the given risk cost , and the auxiliary variable is the value that the scheduling cost does not exceed with a probability under the confidence level of the given risk cost.

[0127] 4) Optimally solve the scheduling cost data set based on the confidence level to obtain the auxiliary variable. The auxiliary variable is related to the confidence levels of the scheduling cost and the risk cost, and is used to balance the scheduling cost and the risk cost. By adjusting the auxiliary variable, an optimal microgrid active support optimization scheduling scheme 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 that the scheduling cost exceeds a certain threshold; when the risk preference is high, the auxiliary variable can be appropriately increased to accept a higher scheduling cost fluctuation to a certain extent in pursuit of better economic benefits.

[0128] S5. Use the net load fluctuation intervals corresponding to several of the above-mentioned net load scenarios as constraint conditions, and construct a microgrid optimization scheduling model based on the scheduling cost equation and the risk cost equation; in the actual scheduling process, based on the confidence level, determine the net load fluctuation intervals corresponding to several net load scenarios, use the net load fluctuation intervals corresponding to several net load scenarios as the interval constraint conditions for scheduling, construct a comprehensive scheduling cost equation based on the scheduling cost equation and the risk cost equation, and construct a microgrid optimization scheduling model according to the comprehensive scheduling cost equation. The objective function of the microgrid optimization scheduling model is:

[0129]

[0130] where is the risk coefficient.

[0131] For the constraint conditions of the microgrid optimization scheduling model, it also at least includes: electric power balance constraint, thermal power balance constraint, cooling power balance constraint, equipment constraint, battery energy storage system operation constraint, hydrogen energy subsystem constraint, operation constraint of the heat storage system in the heat storage electric boiler, power purchase and sale constraint, and constraint on the active support power provided by the wind turbine.

[0132] Among them, the electric power balance constraint is expressed as:

[0133]

[0134] where , and are respectively the predicted values of the photovoltaic, wind power, and electric load powers at the moment; , and are respectively the target source-load prediction error scenarios Lower Power prediction errors of photovoltaic, wind power and load at the moment; Is the target source-load prediction error scenario Lower The maximum power that the photovoltaic system may supply after considering support at the moment, Is The electric power of the thermal energy storage electric boiler at the moment.

[0135] The thermal power balance constraint is expressed as:

[0136]

[0137] Among them, Is the predicted value of the thermal load power, Is the heat loss load power, Is the input power of the absorption chiller, Represents the electrical efficiency of the fuel cell, Represents the electrical efficiency of the electrolyzer, Represents the thermal utilization efficiency of the heat exchanger, Is the heat storage and heat release efficiency, And Are the heat storage and heat release powers of the thermal energy storage electric boiler respectively.

[0138] The cooling power balance constraint is expressed as:

[0139]

[0140] Among them, Is the predicted value of the cooling load power, Is the cooling loss load power, Is the operating efficiency of the absorption chiller.

[0141] The equipment constraint is expressed as:

[0142] The following formula is the upper and lower limit constraints of the electric power of the absorption chiller and the thermal energy storage electric boiler:

[0143] ,

[0144] Among them, Is The input power of the absorption chiller at the moment, Is the upper limit of the input power of the absorption chiller, Is The electric power of the thermal energy storage electric boiler at the moment, Is the upper limit of the electric power of the thermal energy storage electric boiler.

[0145] The operation constraint of the battery energy storage system is expressed as:

[0146] The SOC (State of Charge of the electrical energy storage system) in adjacent periods shall satisfy the following relationship:

[0147]

[0148] where, is the State of Charge of the battery energy storage system in the period; is the charge-discharge efficiency, is the support power time, which is set to 1 minute in the application, , are the power absorbed and supplied by the energy storage system support respectively, represents the capacity of the battery energy storage system.

[0149] During the operation of the battery energy storage system, the remaining capacity at each moment shall satisfy the upper and lower limit constraints as shown below:

[0150]

[0151] where, is the minimum value of the State of Charge of the battery energy storage system, is the maximum value of the State of Charge of the battery energy storage system.

[0152] In addition, large current charge and discharge will shorten the life of the battery energy storage system. Therefore, the charge and discharge power of the battery energy storage system during operation also needs to be limited within the following range:

[0153]

[0154] where, is a binary variable representing the charge and discharge state of the battery energy storage system, represents that the battery energy storage system is in the charging state, represents the discharging state, is the maximum charge and discharge rate of the battery energy storage system.

[0155] The hydrogen energy subsystem constraint is expressed as:

[0156] The fuel cell power and electrolyzer power are related to the amount of hydrogen substance as follows:

[0157] ,

[0158] where, and are respectively the amount of hydrogen substance consumed by the fuel cell and the amount of hydrogen substance produced by the electrolyzer at time It represents the lower heating value of hydrogen, which refers to the heat released when a unit amount of substance of hydrogen is completely burned.

[0159] Since the hydrogen storage tank cannot store and release hydrogen simultaneously, the power of the fuel cell and the electrolyzer need to satisfy the following constraints:

[0160]

[0161]

[0162] Among them, is a binary variable representing the state of the hydrogen storage tank. represents that the fuel cell consumes hydrogen and the hydrogen storage tank releases hydrogen. represents that the electrolyzer produces hydrogen and the hydrogen storage tank stores hydrogen. and are the rated powers of the fuel cell and the electrolyzer respectively.

[0163] The hydrogen storage volume of the hydrogen storage tank in adjacent time periods needs to satisfy the following relationship:

[0164]

[0165]

[0166]

[0167] Among them, represents the hydrogen storage volume at time represents the hydrogen storage volume at time represents the hydrogen load demand. represents the capacity of the hydrogen storage tank. represents the unmet hydrogen load demand.

[0168] The upper and lower limit constraints of the hydrogen storage volume at each moment during the operation of the hydrogen storage tank need to satisfy the following formula:

[0169]

[0170] Among them, represents the minimum value of the hydrogen storage volume. represents the maximum value of the hydrogen storage volume.

[0171] The operation constraints of the heat storage system in the heat storage type electric boiler are expressed as:

[0172] The HOC in adjacent time periods needs to satisfy the following relationship:

[0173]

[0174] Among them, For the heat storage system The heat storage state during a time period For the heat storage system The heat storage state during a time period Indicates the capacity of the heat storage system

[0175]

[0176] Among them, Is the minimum value of the heat storage state of heat storage Is the minimum value of the heat storage state of heat storage

[0177] During the operation of the heat storage system, the remaining capacity at each moment needs to satisfy the upper and lower limit constraints shown in the above formula

[0178]

[0179]

[0180] Among them, Is a binary variable representing the heat storage and heat release states of the heat storage system Indicates that the heat storage system is in the heat storage state Indicates the heat release state

[0181] The power purchase and sale constraint is

[0182] The power purchase and sale constraint during grid-connected operation is

[0183]

[0184]

[0185] Among them, Is a binary variable representing the power purchase and sale state Indicates that the microgrid purchases power from the distribution grid Indicates that the microgrid sells power to the distribution grid And Are the maximum power purchase and sale powers respectively, and the power purchase and sale powers are 0 during off-grid operation

[0186] The constraint for the fan to provide active support power is

[0187] The energy provided for active support is provided by the energy storage and the fan, and its calculation formula is as follows

[0188]

[0189] Among them, Indicates The predicted value of the fan output at time Indicates the target source-load prediction error scenario Prediction error of the lower fan power Indicates the capacity of the fan Is the power that the fan actually participates in the power balance of the power grid after considering the support Indicates that after the fan considers the support Actual value absorbed by the fan at time Indicates that after the fan considers the support Actual value of the fan output at time. For the 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.

[0190] S6. During the power grid dispatching process, input the real-time frequency change amount on the power 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 into the microgrid optimal dispatching model, and obtain the microgrid active support dispatching plan according to the output result of the microgrid optimal dispatching model; in the actual power grid dispatching process, input the real-time frequency change amount on the power 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 into the microgrid optimal dispatching model, and through the operation of the microgrid optimal dispatching model, obtain the corresponding microgrid active support dispatching plan. The microgrid active support dispatching plan includes the output data of each microgrid, shows the actual output of photovoltaic and wind power at each time period, as well as the power of fuel cells and electrolyzers, and is one of the core results of the dispatching plan. The microgrid active support dispatching plan also includes energy storage charge and discharge data, power purchase and sale data, abandoned energy level and load deficit data, and cost data to evaluate the economy of the dispatching plan, optimize the microgrid optimal dispatching model, and improve the fit between the microgrid active support dispatching plan output by the microgrid optimal dispatching model and the actual situation.

[0191] In an embodiment of the present application, the optimal dispatching method based on microgrid active support disclosed in the present application is verified according to the parameter settings in Table 1, Table 2, and Table 3.

[0192] Table 1

[0193]

[0194] Table 2

[0195]

[0196] Table 3

[0197]

[0198] Figure 3Schematic diagram of the 24-hour grid-side frequency change of the target area obtained according to the above parameter settings, with the reference frequency being 50 Hz. Figure 4 Schematic diagram of the grid-side active support power demand calculated based on the grid-side frequency change, with the unit being kW. Figure 5 Schematic diagram of the operation result of the distribution network without considering the active support of the microgrid. Figure 6 Schematic diagram of the operation result of the distribution network after considering the active support of the microgrid. From Figure 5 and Figure 6 It can be seen that there are certain changes in the overall operation of the distribution network before and after considering the active support of the microgrid. For example, the charge and discharge time of the energy storage changes. For example, the amplitudes of the photovoltaic power in each hour before 13 hours are not the same, and the charge and discharge time of the energy storage also changes. However, it is difficult to accurately judge the gap between the two before and after considering the active support of the microgrid. Since the set energy storage support coefficient is greater than the fan support coefficient, the system makes more use of the energy storage to provide support for the power grid. Next, the power release situation and SOC change of the energy storage system are used to further analyze the gap in the optimal dispatching of the distribution network before and after considering the support. Figure 7 Schematic diagram of the power change of the energy storage system. Among them, the bar chart is the power change of the energy storage system before considering the support, and the line chart is the power change after considering the support. Figure 8 Schematic diagram of the SOC change curve of the energy storage system before and after considering the active support of the microgrid. Through Figure 7 It can be seen that the distribution network has a positive power support demand in the 1st and 2nd hours and needs the microgrid to supply energy. After considering the support, the electric energy storage system provides power output in the 1st and 2nd hours to meet the support demand on the grid side. The power output in the 1st and 2nd hours also corresponds to the Figure 7 relative decrease in the electric energy storage SOC in the 1st and 2nd hours after considering the support. Observing Figure 8 It can be seen that the distribution network needs the microgrid to supply power to it in the 11th hour. Before considering the support, the SOC curve of the energy storage system was 0.1 at the end of the 10th hour, reaching the lowest point of the electricity volume. At this time, no power can be supplied outward 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 outward in the 11th hour to meet the support needs of the power grid.

[0199] Figure 9 Schematic diagram of the satisfaction of the support power by the distribution network after considering the active support of the microgrid. Through Figure 9 It can be found that the power support demands represented by the green dotted line are all well met. After considering the active support of the microgrid, it can better meet the frequency and inertia support of the distribution network, and can respond to the disturbance of the distribution network frequency within the corresponding time, avoiding excessive oscillation of the distribution network frequency.

[0200] In a preferred embodiment of the present invention, the optimized scheduling method based on the active support of the microgrid 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 a number of target source-load prediction error scenarios in the target area and the scenario probabilities corresponding to each target source-load prediction error scenario. Based on the number of source-load prediction error scenarios, a number of net load scenarios are obtained; based on the grid-side frequency change amount in the target area, the grid-side active support power demand is calculated; based on the scenario probabilities, the grid-side active support power demand, and the inertia support form of the microgrid, a first evaluation equation for the microgrid active support power demand is constructed, and 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 cyclic energy cost, and the first evaluation equation during the microgrid scheduling process, a scheduling cost equation and a risk cost equation corresponding to the grid-side active support power demand are constructed; with the net load fluctuation intervals corresponding to the number of net load scenarios as the constraint conditions, a microgrid optimized scheduling model is constructed based on the scheduling cost equation and the risk cost equation; during the grid scheduling process, the grid-side real-time frequency change amount, the power prediction value of the microgrid, and the power prediction value of the load in the target area obtained in real time are input into the microgrid optimized scheduling model, and the microgrid active support scheduling scheme is obtained according to the output result of the microgrid optimized scheduling model. The optimized scheduling method based on the active support of the microgrid provided by this application fully considers and utilizes the active support frequency of the microgrid, significantly enhances the frequency response ability of the distribution network, effectively suppresses frequency fluctuations, can also greatly reduce the frequency regulation pressure on the transmission side, and improves the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the bearing capacity of the distribution network for distributed power sources, and creates more favorable conditions for the large-scale access and efficient utilization of renewable energy in the distribution network.

[0201] Correspondingly, as Figure 10 shown, based on an optimized scheduling method based on the active support of the microgrid, an embodiment of the present invention further provides an optimized scheduling system based on the active support of the microgrid to implement the optimized scheduling method based on the active support of the microgrid disclosed in the embodiment of the present invention. The microgrid includes at least a distributed photovoltaic energy type. 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;

[0202] The scenario construction unit 1: is configured to sample the cumulative distribution function of the day-ahead power prediction error of the microgrid in the target area, and 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 the scenario probabilities corresponding to each of the target source-load prediction error scenarios, and based on the plurality of source-load prediction error scenarios, obtain a plurality of net load scenarios;

[0203] The grid-side active support power demand calculation unit 2: is configured to calculate the grid-side active support power demand based on the grid-side frequency change amount in the target area;

[0204] The microgrid active support power demand evaluation unit 3: is configured to construct a first evaluation equation for the microgrid active support power demand based on the scenario probabilities, the grid-side active support power demand, and the inertia support form of the microgrid, and the first evaluation equation is set to reflect the inertia support power of the microgrid under all the scenario probabilities;

[0205] The cost equation construction unit 4: is configured to construct a scheduling cost equation and a risk cost equation corresponding to the grid-side active support power demand based on the loss cost, the cyclic energy cost, and the first evaluation equation in the microgrid scheduling process;

[0206] The scheduling model construction unit 5: is configured to construct a microgrid optimal scheduling model based on the scheduling cost equation and the risk cost equation with the net load fluctuation intervals corresponding to the plurality of net load scenarios as constraint conditions;

[0207] The scheduling optimization unit 6: is configured to, during the grid scheduling process, input the real-time grid-side frequency change amount, the power prediction value of the microgrid, and the power prediction value of the load in the target area obtained in real time into the microgrid optimal scheduling model, and obtain a microgrid active support scheduling plan according to the output result of the microgrid optimal scheduling model.

[0208] For the specific limitations of an optimization scheduling system based on microgrid active support, reference may be made to the above limitations on an optimization scheduling method based on microgrid active support, which will not be elaborated here. Those of ordinary skill in the art can realize that, in combination with the various modules and steps described in the embodiments disclosed in the present invention, they can be implemented in hardware, software, or a combination of both. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians 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.

[0209] Such as Figure 11As shown, a computer device provided by an embodiment of the present invention includes 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, it implements the steps in the above-mentioned optimization scheduling embodiment based on active support of the microgrid, such as Figure 1 the steps S1 to S6 described in

[0210] Those skilled in the art can understand that the schematic Figure 11 is only an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may further include input / output devices, network access devices, buses, etc.

[0211] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the computer device and connects various parts of the entire computer device through various interfaces and lines.

[0212] The memory can be used to store the computer program and / or modules. By running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory, the processor realizes various functions of the computer device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0213] Among them, if the modules integrated in the computer device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0214] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-described embodiment methods, it can be completed by a computer program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0215] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the above-described optimal scheduling based on active support of the microgrid, such as Figure 1 the steps S1 to S6 described therein.

[0216] In summary, an optimized scheduling method, system, device, and medium based on the active support of a microgrid provided by the embodiments of the present application are used 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. 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 to obtain a number of target source-load prediction error scenarios in the target area and the scenario probabilities corresponding to each target source-load prediction error scenario, and obtaining a number of net load scenarios based on the number of source-load prediction error scenarios; calculating the power demand for active support on the grid side based on the frequency change amount on the grid side of the target area; constructing a first evaluation equation for the power demand for active support of the microgrid based on the scenario probabilities, the power demand for active support on the grid side, and the inertia support form of the microgrid, and the first evaluation equation is set to reflect the inertia support power of the microgrid under all scenario probabilities; constructing a scheduling cost equation and a risk cost equation corresponding to the power demand for active support on the grid side based on the loss cost, the renewable energy cost, and the first evaluation equation during the microgrid scheduling process; constructing a microgrid optimized scheduling model based on the scheduling cost equation and the risk cost equation with the net load fluctuation intervals corresponding to the number of net load scenarios as the constraint conditions; during the grid scheduling process, inputting the real-time frequency change amount on 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 into the microgrid optimized scheduling model, and obtaining the active support scheduling scheme of the microgrid according to the output result of the microgrid optimized scheduling model. The optimized scheduling method based on the active support of the microgrid provided by the present application fully considers and utilizes the active support of the microgrid for frequency, significantly enhances the frequency response ability of the distribution network, effectively suppresses frequency fluctuations, and can also greatly reduce the frequency regulation pressure on the transmission side, improving the operation efficiency and stability of the entire power system. At the same time, it also helps to improve the bearing capacity of the distribution network for distributed power sources, creating more favorable conditions for the large-scale access and efficient utilization of renewable energy in the distribution network.

[0217] Each embodiment in this specification is described in a progressive manner. For parts that are the same or similar in each embodiment, they can be referred to each other. 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 embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0218] The above-described embodiments merely represent several preferred embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, 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 subject to 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; 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, , .

2. The optimization scheduling method based on active support of microgrid 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 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, represents the scheduling cost, 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.

4. The optimization scheduling method based on active support of microgrid according to claim 3, 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 .

5. The optimization scheduling method based on active support of microgrid according to claim 4, 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, constructing a comprehensive dispatching cost equation 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.

6. 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.

7. 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 6, 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.

8. 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 6.

9. 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 active support of a microgrid as described in any one of claims 1 to 6 is implemented.

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