Micro-grid low-carbon scheduling method and related device
By constructing a low-carbon scheduling model that combines power consumption side and network parameters and using an optimized particle swarm optimization algorithm, the problem of insufficient accuracy in low-carbon scheduling of microgrids was solved, and more precise low-carbon scheduling was achieved.
Patent Information
- Application Number
- CN202510650824.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-20
- Publication Date
- 2025-11-28
AI Technical Summary
Existing microgrid low-carbon dispatch schemes have low consideration of the perception of the electricity consumption side, resulting in low accuracy of low-carbon dispatch.
The objective function of the low-carbon scheduling model is constructed by combining the power demand information and network parameter information of the microgrid, and the optimized particle swarm algorithm is used to solve it to obtain the low-carbon scheduling parameters.
It improves the accuracy of microgrid dispatching and achieves more precise low-carbon dispatching by comprehensively considering carbon emissions on both the power supply and consumption sides.
Smart Images
Figure CN121032011A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of data processing and microgrid technology, specifically to a low-carbon dispatching method and related devices for microgrids. Background Technology
[0002] With the emergence of the low-carbon concept, clean power generation technologies, represented by new clean energy sources such as solar and wind power, have been gradually integrated into the power grid on a large scale in recent years. Microgrids, as an important component of the smart grid, are increasingly being built with carbon reduction as their goal. However, due to the unique characteristics of microgrids, existing solutions for carbon reduction typically combine energy consumption costs and carbon emissions from the generation side for optimized scheduling. This results in low awareness of the electricity consumption side during low-carbon scheduling, leading to lower accuracy in low-carbon scheduling. Summary of the Invention
[0003] This application provides a low-carbon scheduling method and related apparatus for microgrids. It can combine the power demand information of the microgrid's power consumption side and the network parameter information of the microgrid to construct the objective function of the low-carbon scheduling model and solve it using an optimized particle swarm optimization algorithm to obtain the low-carbon scheduling parameters and perform scheduling, thereby improving the accuracy of microgrid scheduling.
[0004] A first aspect of this application provides a low-carbon dispatching method for microgrids, the method comprising:
[0005] Obtain the electricity demand information of the target microgrid on the consumer side, and obtain the network parameter information of the target microgrid;
[0006] The carbon emission cost information and energy consumption cost information of the target microgrid are determined based on the network parameter information.
[0007] The target cost function of the target microgrid is determined based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information.
[0008] The optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the objective cost function to obtain the low-carbon scheduling parameters.
[0009] The target microgrid is scheduled using the aforementioned low-carbon scheduling parameters.
[0010] In one possible implementation, determining the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information includes:
[0011] A first sub-cost function is constructed based on the electricity demand information on the electricity consumption side;
[0012] A second sub-cost function is constructed based on the carbon emission cost information;
[0013] Construct a third sub-cost function based on the energy consumption cost information;
[0014] The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
[0015] In one possible implementation, constructing the first sub-cost function based on the electricity demand information on the electricity consumption side includes:
[0016] Energy consumption cost information is determined based on the electricity demand information on the electricity consumption side.
[0017] The electricity demand information on the electricity consumption side is classified and processed to obtain k types of electricity demand.
[0018] Determine the extended carbon emission cost information corresponding to each of the k electricity demand types to obtain the extended carbon emission cost information for the k targets.
[0019] Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand;
[0020] Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information;
[0021] The first sub-cost function is constructed based on the energy consumption cost information and the final extended carbon emission cost information.
[0022] In one possible implementation, determining the final extended carbon emission cost information based on k target electricity demand and k target extended carbon emission cost information includes:
[0023] Extract the first extended carbon emission cost information corresponding to the first electricity demand from the extended carbon emission cost information of k targets, where the first electricity demand is any one of the k target electricity demands;
[0024] An energy demand curve is constructed based on the first energy demand during the current energy demand period.
[0025] Determine the electricity demand fluctuation information based on the electricity demand curve;
[0026] The factors influencing electricity demand fluctuations are determined based on the aforementioned demand fluctuation information;
[0027] Based on the aforementioned demand fluctuation factors, the optimized information of the first extended carbon emission cost information is determined, and the first optimized information is obtained;
[0028] The first extended carbon emission cost information is optimized using the first optimization information to obtain the second extended carbon emission cost information;
[0029] Repeat the above method of extracting the first extended carbon emission cost information corresponding to the first electricity demand, and then using the first optimization information to optimize the first extended carbon emission cost information to obtain the second extended carbon emission cost information, and determine the second extended carbon emission cost information corresponding to k target electricity demands respectively.
[0030] The second extended carbon emission cost information corresponding to each of the k target electricity demand is fused to obtain the final extended carbon emission cost information.
[0031] In one possible implementation, the step of using an optimized particle swarm optimization algorithm to solve the low-carbon scheduling model employing the objective cost function to obtain low-carbon scheduling parameters includes:
[0032] Determine the optimized particle velocity update function and optimized position update function corresponding to the optimized particle swarm algorithm;
[0033] Construct the optimized inertia weight factor and optimized learning factor for the optimized particle swarm optimization algorithm;
[0034] An optimized particle swarm optimization algorithm with optimized particle velocity update function, optimized position update function, optimized inertia weight factor, and optimized learning factor is used to iteratively optimize the low-carbon scheduling model using the objective cost function. After the preset number of iterations, the low-carbon scheduling parameters are obtained.
[0035] A second aspect of this application provides a microgrid low-carbon dispatching device, the device comprising:
[0036] The acquisition unit is used to acquire the power demand information of the target microgrid on the power consumption side, and to acquire the network parameter information of the target microgrid;
[0037] The first determining unit is used to determine the carbon emission cost information and energy consumption cost information of the target microgrid based on the network parameter information.
[0038] The second determining unit is used to determine the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information.
[0039] The solution unit is used to solve the low-carbon scheduling model using the target cost function using the optimized particle swarm algorithm to obtain the low-carbon scheduling parameters.
[0040] The scheduling unit is used to schedule the target microgrid using the low-carbon scheduling parameters.
[0041] In one possible implementation, the second determining unit is specifically used for:
[0042] A first sub-cost function is constructed based on the electricity demand information on the electricity consumption side;
[0043] A second sub-cost function is constructed based on the carbon emission cost information;
[0044] Construct a third sub-cost function based on the energy consumption cost information;
[0045] The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
[0046] In one possible implementation, in constructing the first sub-cost function based on the electricity demand information from the electricity consumption side, the second determining unit is specifically used for:
[0047] Energy consumption cost information is determined based on the electricity demand information on the electricity consumption side.
[0048] The electricity demand information on the electricity consumption side is classified and processed to obtain k types of electricity demand.
[0049] Determine the extended carbon emission cost information corresponding to each of the k electricity demand types to obtain the extended carbon emission cost information for the k targets.
[0050] Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand;
[0051] Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information;
[0052] The first sub-cost function is constructed based on the energy consumption cost information and the final extended carbon emission cost information.
[0053] In one possible implementation, regarding the determination of the final extended carbon emission cost information based on k target electricity demand and k target extended carbon emission cost information, the second determining unit is specifically used for:
[0054] Extract the first extended carbon emission cost information corresponding to the first electricity demand from the extended carbon emission cost information of k targets, where the first electricity demand is any one of the k target electricity demands;
[0055] An energy demand curve is constructed based on the first energy demand during the current energy demand period.
[0056] Determine the electricity demand fluctuation information based on the electricity demand curve;
[0057] The factors influencing electricity demand fluctuations are determined based on the aforementioned demand fluctuation information;
[0058] Based on the aforementioned demand fluctuation factors, the optimized information of the first extended carbon emission cost information is determined, and the first optimized information is obtained;
[0059] The first extended carbon emission cost information is optimized using the first optimization information to obtain the second extended carbon emission cost information;
[0060] Repeat the above method of extracting the first extended carbon emission cost information corresponding to the first electricity demand, and then using the first optimization information to optimize the first extended carbon emission cost information to obtain the second extended carbon emission cost information, and determine the second extended carbon emission cost information corresponding to k target electricity demands respectively.
[0061] The second extended carbon emission cost information corresponding to each of the k target electricity demand is fused to obtain the final extended carbon emission cost information.
[0062] In one possible implementation, the solving unit is specifically used for:
[0063] Determine the optimized particle velocity update function and optimized position update function corresponding to the optimized particle swarm algorithm;
[0064] Construct the optimized inertia weight factor and optimized learning factor for the optimized particle swarm optimization algorithm;
[0065] An optimized particle swarm optimization algorithm with optimized particle velocity update function, optimized position update function, optimized inertia weight factor, and optimized learning factor is used to iteratively optimize the low-carbon scheduling model using the objective cost function. After the preset number of iterations, the low-carbon scheduling parameters are obtained.
[0066] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.
[0067] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.
[0068] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.
[0069] Implementing the embodiments of this application has the following beneficial effects:
[0070] By acquiring the electricity demand information of the target microgrid on the consumer side and the network parameter information of the target microgrid, the carbon emission cost information and energy consumption cost information of the target microgrid are determined based on the network parameter information. The target cost function of the target microgrid is then determined based on the electricity demand information, the carbon emission cost information, and the energy consumption cost information. An optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the target cost function to obtain low-carbon scheduling parameters. These low-carbon scheduling parameters are then used to schedule the target microgrid. Therefore, by combining the electricity demand information of the microgrid on the consumer side and the network parameter information of the microgrid, the objective function of the low-carbon scheduling model can be constructed and solved using an optimized particle swarm optimization algorithm to obtain low-carbon scheduling parameters and perform scheduling, thus improving the accuracy of microgrid scheduling. Attached Figure Description
[0071] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0072] Figure 1 This application provides a schematic diagram of the structure of a target microgrid.
[0073] Figure 2 This application provides a flowchart illustrating a low-carbon dispatching method for microgrids.
[0074] Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;
[0075] Figure 4 This application provides a schematic diagram of the structure of a low-carbon dispatching device for a microgrid. Detailed Implementation
[0076] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0077] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0078] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.
[0079] To better understand the microgrid low-carbon dispatching method provided in this application, a brief introduction to existing microgrid low-carbon dispatching methods is given below. In existing methods, low-carbon dispatching schemes or parameters are typically generated using information from the power supply side. This information includes, for example, the characteristics of the power supply units, the purchase price and sales price of electricity in the microgrid, and carbon emission costs and energy consumption costs are constructed based on the unit power carbon emission factor and penalty coefficient of the power supply units. Furthermore, microgrid system constraints are introduced, and a particle swarm optimization algorithm using inertia weighting factors and learning factors is employed to solve the dispatching problem, resulting in a final dispatching scheme. However, this method has low sensitivity to electricity demand from the consumer side, while the consumer side has significant carbon reduction potential during grid dispatching, leading to low accuracy in the generated dispatching scheme.
[0080] To address the aforementioned issues, this application provides a low-carbon scheduling method for microgrids. This method combines the electricity demand information from the microgrid's consumer side with the network parameter information to construct the objective function of the low-carbon scheduling model. An optimized particle swarm optimization algorithm is then used to solve the objective function, obtaining the low-carbon scheduling parameters and performing the scheduling. This improves the accuracy of microgrid scheduling.
[0081] Please see Figure 1 , Figure 1 A schematic diagram of a target microgrid is shown. (For example...) Figure 1 As shown, the target microgrid package is divided into a power supply side and a power consumption side. The power supply side includes wind power generation systems, fossil fuel power generation systems, photovoltaic power generation systems, and energy storage systems, while the power consumption side includes electricity used by factories, residents, urban areas, and new energy vehicle charging stations. When performing low-carbon scheduling, the carbon emissions of the power supply side are fully considered, and the carbon emissions of the power consumption side are also taken into account for comprehensive evaluation. The optimal low-carbon scheduling parameters are obtained by combining the particle swarm optimization algorithm, which improves the accuracy of microgrid scheduling.
[0082] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a low-carbon dispatching method for microgrids. For example... Figure 2 As shown, a low-carbon dispatch method for microgrids is applied to the target microgrid. This method includes:
[0083] 201. Obtain the power demand information of the target microgrid on the power consumption side, and obtain the network parameter information of the target microgrid.
[0084] The electricity demand information on the consumer side can include electricity demand information for factories, residential areas, urban areas, and new energy vehicle charging stations. This electricity demand information can be assessed by time period. For example, a calendar day can be divided into 24 demand periods, with each hour representing one demand period. Alternatively, a calendar day can be divided into 12 demand periods, with each two-hour period representing one demand period. Specifically, the time periods could be divided as follows: 00:00 to 2:00 AM, 2:00 AM to 4:00 AM, and so on, resulting in 12 demand periods. Of course, other time period division methods can also be used; this is merely an example and not a specific limitation.
[0085] Electricity demand information on the consumer side can vary during different electricity demand periods. For example, during peak electricity consumption periods in the evening, the electricity demand of residents will be higher than that during normal electricity demand periods, so it can be a fluctuating value.
[0086] The network parameter information of the target microgrid can include parameters from wind power systems, fossil fuel power systems, photovoltaic power systems, and energy storage systems. For example, the parameter information for wind power systems includes the power generation capacity, carbon emissions, and operating costs. The parameter information for fossil fuel power systems includes the carbon emission factor, carbon emission power, and carbon emission cost coefficient for a given time period.
[0087] 202. Determine the carbon emission cost information and energy consumption cost information of the target microgrid based on the network parameter information.
[0088] Specifically, parameter information such as wind power generation systems, fossil fuel power generation systems, photovoltaic power generation systems, and energy storage systems can be extracted from network parameter information, and carbon emission cost information and energy consumption cost information can be determined based on the parameter information of wind power generation systems, fossil fuel power generation systems, photovoltaic power generation systems, and energy storage systems.
[0089] Carbon emission cost information and energy consumption cost information can be characterized by the following formula:
[0090] The system parameters of the fossil fuel power generation system in the network parameter information include the carbon emission coefficient per unit power of diesel generators, the carbon emission coefficient per unit power of gasoline generators, and the average carbon emission coefficient of thermal power plants. Of course, in other embodiments, carbon emissions from other fossil fuel power generation methods can also be included. Since the corresponding calculation methods after adding carbon emissions from other fossil fuel power generation methods can be considered the same as those using the three main fossil fuel power generation methods mentioned above, this application embodiment uses the carbon emissions of the three main fossil fuel power generation methods for explanation.
[0091] Carbon emission cost information can be determined by combining the carbon emission coefficient per unit power of diesel generators, the carbon emission coefficient per unit power of gasoline generators, the average carbon emission coefficient of thermal power plants, and the carbon emission power and quota power corresponding to the above three carbon emission coefficients.
[0092] Carbon emission cost information includes carbon emission cost information for fossil fuel power generation systems and carbon emission cost information for energy storage systems. The carbon emission cost of fossil fuel power generation systems includes carbon emission cost information for diesel generators, gasoline generators, and thermal power plants. Specifically, common cost information characterization methods can be used for cost characterization.
[0093] 203. Determine the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information.
[0094] Specifically, corresponding sub-cost functions can be determined based on electricity demand information, carbon emission cost information, and energy consumption cost information. These sub-cost functions are then merged to obtain the target cost function. During fusion, the sub-cost functions can be summed and their costs minimized to arrive at the target cost function.
[0095] Specifically, the objective cost function can be characterized by the following formula:
[0096] Fm = min(C1 + C2 + C3);
[0097] Where Fm is the target cost function value, min is the minimization process (which can be understood as performing maximum and minimum value processing to obtain the corresponding minimum value), C1 is the first sub-cost function corresponding to the electricity demand information on the electricity consumption side, C2 is the second sub-cost function corresponding to the carbon emission cost information, and C3 is the third sub-cost function corresponding to the energy consumption cost information.
[0098] 204. The optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the objective cost function to obtain the low-carbon scheduling parameters.
[0099] Before using the optimized particle swarm optimization algorithm to obtain the low-carbon scheduling parameters, it is necessary to set constraints for the low-carbon scheduling model. These constraints can be general, such as power balance constraints or operational constraints for the target microgrid. Operational constraints could include, for example, the power generation range of diesel generators, gasoline generators, or thermal power plants. Setting general constraints ensures that the target microgrid can operate normally under normal operating conditions.
[0100] The solution process involves: determining the optimized particle velocity update function and optimized position update function for the optimized particle swarm optimization algorithm, as well as constructing the optimized inertia weight factor and optimized learning factor. Then, the particle swarm optimization algorithm with the above four parameters is used to iteratively optimize the low-carbon scheduling model. After reaching a preset number of iterations, the low-carbon scheduling parameters are obtained. The preset number of iterations can be set using empirical values or historical data.
[0101] 205. The target microgrid is scheduled using the aforementioned low-carbon scheduling parameters.
[0102] By using low-carbon scheduling parameters to schedule the target microgrid, optimal scheduling can be achieved within a certain range, reducing the carbon emissions of the target microgrid during operation and improving the accuracy of microgrid scheduling.
[0103] In one possible implementation, when determining the target cost function, sub-cost functions corresponding to the electricity demand information on the power consumption side, the carbon emission cost information, and the energy consumption cost information can be constructed separately and then fused. During fusion, since the optimal system operating cost needs to be achieved when the function value of the target cost function is minimized, and this optimal system operating cost takes into account both the carbon emissions on the power supply side of the target microgrid and the electricity demand on the power consumption side, the constructed target cost function can be used to solve subsequent low-carbon scheduling models to obtain the optimal low-carbon scheduling parameters. Specifically, a method for determining the target cost function of the target microgrid based on the electricity demand information on the power consumption side, the carbon emission cost information, and the energy consumption cost information includes:
[0104] A1. Construct a first sub-cost function based on the electricity demand information on the electricity consumption side;
[0105] A2. Construct a second sub-cost function based on the carbon emission cost information;
[0106] A3. Construct a third sub-cost function based on the energy consumption cost information;
[0107] A4. The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
[0108] In constructing the first sub-cost function, energy consumption cost analysis can be performed on the electricity demand information on the electricity consumption side to obtain the corresponding energy consumption cost information. The electricity demand information on the electricity consumption side can be classified to obtain multiple electricity demand types. The electricity demand quantity corresponding to the electricity demand type can be extracted. Finally, the electricity demand type and energy consumption cost information are combined to construct the first sub-cost function.
[0109] When constructing the second and third sub-cost functions, a general cost function construction method can be used to obtain the second and third sub-cost functions.
[0110] Specifically, the objective cost function can be characterized by the following formula:
[0111] Fm = min(C1 + C2 + C3);
[0112] Where Fm is the target cost function value, min is the minimization process (which can be understood as performing maximum and minimum value processing to obtain the corresponding minimum value), C1 is the first sub-cost function corresponding to the electricity demand information on the electricity consumption side, C2 is the second sub-cost function corresponding to the carbon emission cost information, and C3 is the third sub-cost function corresponding to the energy consumption cost information.
[0113] In one possible implementation, when constructing the first sub-cost function, different types of electricity demand will generate corresponding carbon emissions during electricity use. For example, if the electricity demand type corresponds to factory electricity demand, the carbon emissions generated by factory electricity use need to be comprehensively considered; similarly, if the electricity demand type corresponds to residential electricity demand, the carbon emissions generated by residential electricity use need to be considered. Therefore, by comprehensively considering the extended carbon emission costs brought about by each type of electricity demand, and combining the direct and indirect carbon emissions (extended emission costs) in the target microgrid for subsequent scheduling parameter calculations, the low-carbon potential of the target microgrid can be further explored. Specifically, a method for constructing the first sub-cost function based on the electricity demand information on the electricity demand side includes:
[0114] B1. Determine energy consumption cost information based on the electricity demand information on the electricity consumption side;
[0115] B2. Classify the electricity demand information on the electricity consumption side to obtain k electricity demand types;
[0116] B3. Determine the extended carbon emission cost information corresponding to each of the k types of electricity demand to obtain the extended carbon emission cost information for the k targets.
[0117] B4. Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand.
[0118] B5. Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information;
[0119] B6. Construct the first sub-cost function based on the energy consumption cost information and the final extended carbon emission cost information.
[0120] Energy consumption cost information can be understood as the essential costs incurred during the transmission and interaction of electricity within the power grid when electricity is consumed. This cost fluctuates with some inherent characteristics of the power grid, and is specifically determined using common methods to capture energy consumption costs during the current electricity demand period.
[0121] During the current electricity demand period, there can be various types of electricity demand. These include electricity consumption by factories, residential electricity consumption, urban electricity consumption, and electricity consumption for new energy vehicle charging stations. Residential electricity consumption can also include low-power electricity consumption for lighting and high-power electricity consumption for lighting and high-power appliances such as air conditioners. Urban electricity consumption can also include electricity consumption for lighting and transportation facilities.
[0122] Therefore, we can classify the electricity demand based on the identification information corresponding to the demand source in the electricity demand information on the consumer side, resulting in k electricity demand types. Alternatively, we can use other common classification methods to obtain the same k electricity demand types.
[0123] The extended carbon emission costs vary depending on the type of electricity demand. For example, for residential electricity demand, the target extended carbon emission cost information can be generated by considering the electricity consumption scenarios. Specifically, residential electricity consumption scenarios can be divided into low-power and high-power scenarios. In low-power scenarios, the extended carbon emissions are lower than in high-power scenarios. High-power scenarios typically involve the use of high-power appliances. When these appliances are in use, they generate high temperatures. To ensure the equipment operates normally, heat dissipation is required, which introduces new energy consumption. This energy consumption can be considered an extended carbon emission cost. Meanwhile, when high-power electrical appliances are used, the heat they generate is still continuously released into the atmosphere, even though heat dissipation treatment is applied. Although the heat emitted by a single high-power device is not enough to cause an increase in ambient temperature, when a certain number of high-power devices are used simultaneously in a city, it can cause a certain increase in ambient temperature. After the ambient temperature increases, the respiration of plants and microorganisms (bacteria, etc.) in the environment will be enhanced, which in turn leads to an increase in carbon emissions.
[0124] Therefore, when determining extended carbon emission costs, for residential electricity consumption, a relatively fixed carbon emission cost can be set for low-power consumption scenarios, while for high-power consumption scenarios, extended carbon emission cost information can be dynamically generated based on power consumption. This can be represented by the following formula:
[0125] Cy = P1 * H1, high-power electricity consumption;
[0126] Cy = d1, low-power electricity consumption;
[0127] Where Cy represents the target extended carbon emission cost information for residential electricity consumption, P1 represents the power in high-power electricity consumption scenarios, H1 represents the carbon emission conversion factor (pre-set), and d1 represents the extended carbon emission cost information in low-power electricity consumption scenarios (usually a fixed value).
[0128] When the electricity demand type is factory electricity consumption, its extended carbon emission cost can be understood as the carbon emission cost caused by factory equipment during factory production and the carbon emission cost caused by the factory production process. The carbon emission cost caused by factory equipment is calculated using the same method as in the high-power electricity consumption scenario for residential use, with the specific difference being the conversion factor. The specific conversion factor is set according to the type of factory equipment. The carbon emission cost caused by the factory production process is determined based on the type of product produced by the factory; each product type has its corresponding carbon emission coefficient and carbon emission power. Specifically, the extended carbon emission cost of factory electricity consumption can be represented by the following formula:
[0129]
[0130] Among them, P q2 H represents the power of the factory equipment in the q-th factory. q2 Let P be the carbon emission conversion factor of the factory equipment in the q-th factory. q3 H represents the carbon emission power during the production process of the product in the q-th factory. q3 Let C be the carbon emission coefficient of the product produced in the q-th factory, and there are n factories. f Extend carbon emission cost information to the factory's electricity consumption goals.
[0131] For urban electricity consumption, due to its relatively small fluctuations, the target extended carbon emission information Cc cost can be set to a fixed value, which can be determined based on empirical values or historical data. The target extended carbon emission cost Cz for new energy vehicle charging stations can be calculated based on the number of charging piles used during the current period and the charging loss of each charging pile during operation. Specifically, it is represented by the following formula:
[0132]
[0133] H j M is the carbon emission conversion factor. j Let C be the charging loss of the i-th charging pile. z Extend carbon emission cost information to the goals of new energy vehicle charging stations.
[0134] After determining the carbon emission cost information for k targets, the electricity demand corresponding to each of the k electricity demand types can be extracted from the electricity demand information on the demand side. This electricity demand can be a predicted value, which can be obtained through historical data. Of course, this electricity demand can also be a requested value, which can be the demand value of quotas, etc.
[0135] The target extended carbon emission cost information can be optimized using the target electricity demand, and the resulting k extended carbon emission cost information points can be merged to obtain the final extended carbon emission cost information. Since the target electricity demand may fluctuate during quota allocation or forecasting, the extended carbon emission cost information needs to be corrected when energy demand fluctuates to obtain dynamic extended carbon emission cost information, thus improving the accuracy of the final extended carbon emission cost information determination.
[0136] Finally, the energy consumption cost information and the ultimate extended carbon emission cost information can be summed, and the final sum value can be determined as the first sub-cost function.
[0137] In this example, the extended carbon emission costs and total energy consumption costs faced by each type of electricity demand on the electricity demand side are analyzed in a targeted manner to jointly construct the first sub-cost function. In subsequent scheduling, factors such as extended carbon emission costs can be combined to carry out better low-carbon electricity scheduling, which is more accurate and more conducive to achieving the carbon reduction goal of the target microgrid. For example, when the extended carbon emission costs are high, it is possible to consider optimizing the equipment that causes extended carbon emissions for carbon reduction upgrades or low-carbon replacements, so as to improve the regional carbon reduction effect in the area covered by the target microgrid.
[0138] In one possible implementation, a method for determining final extended carbon emission cost information based on k target electricity demand and k target extended carbon emission cost information includes:
[0139] C1. Extract the first extended carbon emission cost information corresponding to the first electricity demand from the extended carbon emission cost information of the k targets, where the first electricity demand is any one of the k target electricity demands;
[0140] C2. Construct an energy demand curve for the current energy demand period based on the first energy demand;
[0141] C3. Determine the power demand fluctuation information based on the power demand curve;
[0142] C4. Determine the factors causing electricity demand fluctuations based on the aforementioned demand fluctuation information;
[0143] C5. Determine the optimized information of the first extended carbon emission cost information based on the aforementioned demand fluctuation factors to obtain the first optimized information;
[0144] C6. The first extended carbon emission cost information is optimized using the first optimization information to obtain the second extended carbon emission cost information.
[0145] C7. Repeat the above method of extracting the first extended carbon emission cost information corresponding to the first electricity demand, and then using the first optimization information to optimize the first extended carbon emission cost information to obtain the second extended carbon emission cost information, and determine the second extended carbon emission cost information corresponding to k target electricity demands respectively.
[0146] C8. The second extended carbon emission cost information corresponding to the k target electricity demand is fused to obtain the final extended carbon emission cost information.
[0147] The electricity demand includes residential electricity demand and factory electricity demand. When analyzing this demand, since the total residential electricity demand, urban electricity demand, and new energy vehicle charging station electricity demand do not fluctuate significantly within each time period, the analysis should focus primarily on factory electricity demand. The analysis can be performed on each type of electricity demand to further improve accuracy. The following example illustrates this with factory electricity demand.
[0148] The first electricity demand is the electricity demand corresponding to the factories' electricity consumption. This first electricity demand can include the electricity demand of each factory during the current electricity demand period. The electricity demand of each factory during the current electricity demand period can be represented by a segmented electricity demand curve, thus obtaining the electricity demand curve for each factory. The horizontal axis of this curve is the time axis, and the vertical axis is the electricity demand. In each segmented demand curve, the time length on the horizontal axis is a fixed value, meaning the current electricity demand period can be further divided equally, for example, into 12 sub-electricity demand periods. The electricity demand in different sub-electricity demand periods can be different.
[0149] Finally, the electricity demand of each factory in each sub-electricity demand period is superimposed to obtain the electricity demand curve for the current electricity demand period.
[0150] Fluctuation information can be extracted from the electricity demand range. In the absence of fluctuations, the electricity demand curve should be represented as a fixed straight line, meaning the value on the vertical axis of electricity demand should be the same, and the electricity demand in each sub-demand period within a given electricity demand period should also be the same. When fluctuations occur, the corresponding electricity demand curve within a sub-demand period will fluctuate. Therefore, the cause of this fluctuation might be a sudden increase or decrease in factory electricity consumption within a certain sub-demand period. A portion of the curve region can then be extracted from the electricity demand curve to obtain the electricity demand fluctuation curve. The electricity demand of multiple factories corresponding to this fluctuation curve can then be extracted, and from these multiple electricity demand values, the electricity demand information of the factories experiencing fluctuations (the first electricity demand fluctuation information) can be extracted, thus obtaining the electricity demand fluctuation information.
[0151] After extracting the initial power demand fluctuation information from the factory, the historical operating status information and the initial production demand information for the current power demand period are obtained. Based on these information, the factors causing the power demand fluctuation are determined. Since power demand information is obtained through prediction or setting, there may be prediction or setting errors, leading to abnormal power demand. Therefore, it is necessary to first identify anomalies in the initial power demand fluctuation information using the historical operating status information and the initial production demand information to determine whether the fluctuation is caused by abnormal or normal demand. If the fluctuation caused by the initial power demand fluctuation is caused by normal demand, the production demand information corresponding to the initial power demand fluctuation is identified as the power demand fluctuation factor. If the fluctuation caused by the initial power demand fluctuation is abnormal, the power demand fluctuation factor is considered abnormal, and the corresponding optimization information can be set to empty.
[0152] After obtaining the factors affecting electricity demand fluctuations, optimization information for the first extended carbon emission cost information can be generated using these factors. Specifically, the electricity demand fluctuation factor is the production demand information corresponding to the first electricity demand fluctuation information within the first production demand information. The optimization parameters in the first optimization information are then generated based on the electricity demand corresponding to this production demand information and the corresponding conversion coefficient. The conversion coefficient is set using empirical values or historical data. The optimization parameters can be obtained by multiplying the absolute value of the difference between the current electricity demand and the demand when there is no fluctuation by the conversion coefficient.
[0153] When performing optimization, the relevant parameters of the corresponding factory in the first extended carbon emission cost can be multiplied by the optimization parameter to obtain the second extended carbon emission cost information.
[0154] Therefore, based on the constructed electricity demand curve, it is possible to more intuitively analyze and determine whether electricity demand fluctuations occur. After electricity demand fluctuations occur, anomaly analysis is performed on the fluctuations. When the fluctuations are normal, the factors of electricity demand fluctuations are extracted. The optimization information generated by these factors is used to adjust the first extended carbon emission cost information, dynamically generating the second extended carbon emission cost information. Finally, the multiple second extended carbon emission cost information is merged to obtain the final extended carbon emission cost information, which can greatly improve the accuracy of determining the extended carbon emission cost information.
[0155] In one possible implementation, an optimized particle swarm optimization (PSO) algorithm can be used to determine the low-carbon scheduling parameters. This optimized PSO algorithm is adaptable to scenarios where the target microgrid considers electricity demand information from the consumer side during low-carbon scheduling, enabling more accurate determination of low-carbon scheduling parameters and enhancing the target microgrid's potential for carbon reduction. Specifically, a method for solving the low-carbon scheduling model using the target cost function with the optimized PSO algorithm to obtain the low-carbon scheduling parameters includes:
[0156] D1. Determine the optimized particle velocity update function and optimized position update function corresponding to the optimized particle swarm algorithm;
[0157] D2. Construct the optimized inertia weight factor and optimized learning factor for the optimized particle swarm algorithm;
[0158] D3. The low-carbon scheduling model using the objective cost function is iteratively optimized using an optimized particle swarm optimization algorithm with optimized particle velocity update function, optimized position update function, optimized inertia weight factor and optimized learning factor. After the preset number of iterations, the low-carbon scheduling parameters are obtained.
[0159] Specifically, the optimized particle velocity update function can be characterized by the following formula:
[0160] V ik+1 =w ik *V ik +c 1k *r1*(-s1*x ik +s2*A(x ik-d ,x ik ))+c 2k *r2(p g -x ik ));
[0161] Among them, V ik+1 Let w be the velocity of the i-th particle after k iterations. ik Let c be the optimal inertia weight factor for the i-th particle in the k-th iteration.1k Let c be the individual learning factor at the k-th iteration. 2k Let V be the social learning factor at the k-th iteration. ik Let r1 and r2 be random numbers between 0 and 1, and x be the particle velocity at the k-th iteration. ik Let be the position of the i-th particle in the k-th iteration, s1 be the particle optimization factor, s2 be the local feature optimization factor, and A(x) be the position of the i-th particle in the k-th iteration. i-dk ,x ik Let p be the local optimum of the i-th particle from its position in the kd-th iteration to its position in the k-th iteration. i p represents the optimal position that the i-th particle traverses. g This is the empirically optimal position for the particle swarm.
[0162] In the aforementioned particle velocities, adding local group characteristics to the individual consciousness of the particles can introduce local characteristics to influence the particle's behavior within a local area, which is more in line with the actual operating environment and improves the optimization effect.
[0163] The optimized position update function can be characterized by the following formula:
[0164] x ik+1 =x ik +V ik+1 ;
[0165] The optimization inertia weight factor can be characterized by the following formula:
[0166]
[0167] Among them, w ik w is the optimized inertia weight factor for the i-th particle in the k-th iteration. max w is the maximum inertia weighting factor. min b is the minimum inertia weighting factor, and b is the standardization factor. The numerical range is [0, π].
[0168] Therefore, using an initial smooth weight factor change in the inertial weight factor can achieve a strong global optimization process. Then, increasing the weight factor change in the optimization graph can accelerate the optimization process. Finally, using a smooth weight factor change again can maintain a relatively stable local optimization process, thereby improving the stability and accuracy of the final features.
[0169] The optimization learning factor can be characterized by the following formula:
[0170]
[0171] Among them, c 1kLet c be the individual learning factor at the k-th iteration. 2k Let c be the social learning factor at the k-th iteration. 1e c represents the final value of the individual learning factor. 1s c is the initial value of the individual learning factor. 2e c is the final value of the social learning factor. 2s Let θ1 be the initial value of the social learning factor, θ2 be the correction value of the individual learning factor, and K be the total number of iterations. Here, k is the current iteration number. 1s c is the initial value of the individual learning factor. 2e c is the final value of the social learning factor. 2s θ1 is the initial value of the social learning factor, θ2 is the correction value of the individual learning factor, and the corresponding values of the correction values of the social learning factors can be set through the attribute values of the target microgrid, specifically through empirical values or historical data.
[0172] In optimizing the learning factor, using an exponential function combined with a quadratic function for updating can reduce the amount of change in the learning factor, more stably combine self-features and global features, and improve the accuracy of optimization.
[0173] When performing the optimization process, other relevant parameters can be set according to existing common parameter setting methods. The low-carbon scheduling model using the objective cost function can be a low-carbon scheduling model that uses the objective function as the fitness function. When setting constraints, basic energy consumption and power setting logic must be met. The preset number of iterations can be 300, and the number of particles can be 120.
[0174] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 3 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.
[0175] Obtain the electricity demand information of the target microgrid on the consumer side, and obtain the network parameter information of the target microgrid;
[0176] The carbon emission cost information and energy consumption cost information of the target microgrid are determined based on the network parameter information.
[0177] The target cost function of the target microgrid is determined based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information.
[0178] The optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the objective cost function to obtain the low-carbon scheduling parameters.
[0179] The target microgrid is scheduled using the aforementioned low-carbon scheduling parameters.
[0180] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0181] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.
[0182] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the structure of a low-carbon dispatching device for a microgrid. For example... Figure 4 As shown, the device includes:
[0183] The acquisition unit 401 is used to acquire the power demand information of the target microgrid on the power consumption side, and to acquire the network parameter information of the target microgrid;
[0184] The first determining unit 402 is used to determine the carbon emission cost information and energy consumption cost information of the target microgrid based on the network parameter information.
[0185] The second determining unit 403 is used to determine the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information.
[0186] Solving unit 404 is used to solve the low-carbon scheduling model using the target cost function using the optimized particle swarm algorithm to obtain low-carbon scheduling parameters;
[0187] The scheduling unit 405 is used to schedule the target microgrid using the low-carbon scheduling parameters.
[0188] In one possible implementation, the second determining unit 403 is specifically used for:
[0189] A first sub-cost function is constructed based on the electricity demand information on the electricity consumption side;
[0190] A second sub-cost function is constructed based on the carbon emission cost information;
[0191] Construct a third sub-cost function based on the energy consumption cost information;
[0192] The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
[0193] In one possible implementation, in constructing the first sub-cost function based on the electricity demand information from the electricity consumption side, the second determining unit 403 is specifically used for:
[0194] Energy consumption cost information is determined based on the electricity demand information on the electricity consumption side.
[0195] The electricity demand information on the electricity consumption side is classified and processed to obtain k types of electricity demand.
[0196] Determine the extended carbon emission cost information corresponding to each of the k electricity demand types to obtain the extended carbon emission cost information for the k targets.
[0197] Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand;
[0198] Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information;
[0199] The first sub-cost function is constructed based on the energy consumption cost information and the final extended carbon emission cost information.
[0200] In one possible implementation, regarding the determination of the final extended carbon emission cost information based on k target electricity demand and k target extended carbon emission cost information, the second determining unit 403 is specifically used for:
[0201] Extract the first extended carbon emission cost information corresponding to the first electricity demand from the extended carbon emission cost information of k targets, where the first electricity demand is any one of the k target electricity demands;
[0202] An energy demand curve is constructed based on the first energy demand during the current energy demand period.
[0203] Determine the electricity demand fluctuation information based on the electricity demand curve;
[0204] The factors influencing electricity demand fluctuations are determined based on the aforementioned demand fluctuation information;
[0205] Based on the aforementioned demand fluctuation factors, the optimized information of the first extended carbon emission cost information is determined, and the first optimized information is obtained;
[0206] The first extended carbon emission cost information is optimized using the first optimization information to obtain the second extended carbon emission cost information;
[0207] Repeat the above method of extracting the first extended carbon emission cost information corresponding to the first electricity demand, and then using the first optimization information to optimize the first extended carbon emission cost information to obtain the second extended carbon emission cost information, and determine the second extended carbon emission cost information corresponding to k target electricity demands respectively.
[0208] The second extended carbon emission cost information corresponding to each of the k target electricity demand is fused to obtain the final extended carbon emission cost information.
[0209] In one possible implementation, the solving element 404 is specifically used for:
[0210] Determine the optimized particle velocity update function and optimized position update function corresponding to the optimized particle swarm algorithm;
[0211] Construct the optimized inertia weight factor and optimized learning factor for the optimized particle swarm optimization algorithm;
[0212] An optimized particle swarm optimization algorithm with optimized particle velocity update function, optimized position update function, optimized inertia weight factor, and optimized learning factor is used to iteratively optimize the low-carbon scheduling model using the objective cost function. After the preset number of iterations, the low-carbon scheduling parameters are obtained.
[0213] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the microgrid low-carbon dispatching methods described in the above method embodiments.
[0214] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the microgrid low-carbon dispatch methods described in the above method embodiments.
[0215] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0216] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0217] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.
[0218] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0219] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.
[0220] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0221] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.
[0222] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A low-carbon scheduling method for a microgrid, characterized in that, The method includes: Obtain the electricity demand information of the target microgrid on the consumer side, and obtain the network parameter information of the target microgrid; The carbon emission cost information and energy consumption cost information of the target microgrid are determined based on the network parameter information. The target cost function of the target microgrid is determined based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information. The optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the objective cost function to obtain the low-carbon scheduling parameters. The target microgrid is scheduled using the aforementioned low-carbon scheduling parameters.
2. The method of claim 1, wherein, The step of determining the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information includes: A first sub-cost function is constructed based on the electricity demand information on the electricity consumption side; A second sub-cost function is constructed based on the carbon emission cost information; Construct a third sub-cost function based on the energy consumption cost information; The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
3. The method of claim 2, wherein, The step of constructing the first sub-cost function based on the electricity demand information on the electricity consumption side includes: Energy consumption cost information is determined based on the electricity demand information on the electricity consumption side. The electricity demand information on the electricity consumption side is classified and processed to obtain k types of electricity demand. Determine the extended carbon emission cost information corresponding to each of the k electricity demand types to obtain the extended carbon emission cost information for the k targets. Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand; Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information; The first sub-cost function is constructed based on the energy consumption cost information and the final extended carbon emission cost information.
4. The method of claim 3, wherein, The process of determining the final extended carbon emission cost information based on k target electricity demand and k target extended carbon emission cost information includes: Extract the first extended carbon emission cost information corresponding to the first electricity demand from the extended carbon emission cost information of k targets, where the first electricity demand is any one of the k target electricity demands; An energy demand curve is constructed based on the first energy demand during the current energy demand period. Determine the electricity demand fluctuation information based on the electricity demand curve; The factors influencing electricity demand fluctuations are determined based on the aforementioned demand fluctuation information; Based on the aforementioned demand fluctuation factors, the optimized information of the first extended carbon emission cost information is determined, and the first optimized information is obtained; The first extended carbon emission cost information is optimized using the first optimization information to obtain the second extended carbon emission cost information; Repeat the above method of extracting the first extended carbon emission cost information corresponding to the first electricity demand, and then using the first optimization information to optimize the first extended carbon emission cost information to obtain the second extended carbon emission cost information, and determine the second extended carbon emission cost information corresponding to k target electricity demands respectively. The second extended carbon emission cost information corresponding to each of the k target electricity demand is fused to obtain the final extended carbon emission cost information. 5.The method of claim 1-4, wherein, The optimized particle swarm optimization algorithm is used to solve the low-carbon scheduling model using the objective cost function to obtain low-carbon scheduling parameters, including: Determine the optimized particle velocity update function and optimized position update function corresponding to the optimized particle swarm algorithm; Construct the optimized inertia weight factor and optimized learning factor for the optimized particle swarm optimization algorithm; An optimized particle swarm optimization algorithm with optimized particle velocity update function, optimized position update function, optimized inertia weight factor, and optimized learning factor is used to iteratively optimize the low-carbon scheduling model using the objective cost function. After the preset number of iterations, the low-carbon scheduling parameters are obtained.
6. A low-carbon dispatching device for microgrids, characterized in that, The device includes: The acquisition unit is used to acquire the power demand information of the target microgrid on the power consumption side, and to acquire the network parameter information of the target microgrid; The first determining unit is used to determine the carbon emission cost information and energy consumption cost information of the target microgrid based on the network parameter information. The second determining unit is used to determine the target cost function of the target microgrid based on the electricity demand information on the electricity consumption side, the carbon emission cost information, and the energy consumption cost information. The solution unit is used to solve the low-carbon scheduling model using the target cost function using the optimized particle swarm algorithm to obtain the low-carbon scheduling parameters. The scheduling unit is used to schedule the target microgrid using the low-carbon scheduling parameters.
7. The microgrid low-carbon dispatching device according to claim 6, characterized in that, The second determining unit is specifically used for: A first sub-cost function is constructed based on the electricity demand information on the electricity consumption side; A second sub-cost function is constructed based on the carbon emission cost information; Construct a third sub-cost function based on the energy consumption cost information; The first sub-cost function, the second sub-cost function, and the third sub-cost function are merged to obtain the target cost function.
8. The microgrid low-carbon dispatching device according to claim 7, characterized in that, In constructing the first sub-cost function based on the electricity demand information on the electricity consumption side, the second determining unit is specifically used for: Energy consumption cost information is determined based on the electricity demand information on the electricity consumption side. The electricity demand information on the electricity consumption side is classified and processed to obtain k types of electricity demand. Determine the extended carbon emission cost information corresponding to each of the k electricity demand types to obtain the extended carbon emission cost information for the k targets. Extract the electricity demand corresponding to each of the k electricity demand types to obtain the k target electricity demand; Based on the k target electricity demand and the k target extended carbon emission cost information, determine the final extended carbon emission cost information; The first sub-cost function is constructed based on the energy consumption cost information and the final extended carbon emission cost information.
9. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the microgrid low-carbon dispatching method as described in any one of claims 1-5.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the microgrid low-carbon dispatching method as described in any one of claims 1-5.