Comprehensive energy demand response scheduling method and platform for smart power grid

Abstract

The invention discloses a comprehensive energy demand response scheduling method and platform for a smart power grid, and relates to the technical field of energy management, and the method comprises the steps: predicting a user load demand through a future time zone window, and building a comprehensive energy demand scheduling space based on multi-energy cooperative scheduling; k energy supply units are modeled, and initial optimization and variation expansion optimization are performed in combination with a power grid energy scheduling evaluation channel, so that a better scheduling space is formed; and introducing an energy scheduling optimization operator, carrying out deep optimization on the scheduling space, obtaining a comprehensive energy demand scheduling strategy, and carrying out dynamic response scheduling. According to the invention, the technical problems of low energy utilization efficiency and insufficient scheduling flexibility caused by lack of efficient cooperative scheduling of multiple energy types and accurate adaptation capability of user load dynamic demands in the prior art are solved, and the purpose of improving the scheduling efficiency through multi-energy cooperative optimization and dynamic response scheduling is achieved. And the energy utilization efficiency and the scheduling flexibility are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a comprehensive energy demand response scheduling method and platform for smart grids. Background Art

[0002] With the rapid development of energy diversification and smart grid technology, the traditional single energy dispatching method has been unable to meet the increasingly complex user load demands and the requirements of multi-energy coordinated energy supply. In the existing technology, the dispatching of multi-energy supply units (such as thermal power, photovoltaics, wind power, energy storage, etc.) often lacks a unified optimization mechanism, making it difficult to take into account energy utilization efficiency, dispatching flexibility and carbon emission targets. In addition, the volatility of load demand and the instability of renewable energy further increase the complexity of dispatching, leading to problems such as unbalanced energy distribution and delayed dispatching response. Summary of the invention

[0003] This application provides a comprehensive energy demand response scheduling method and platform for smart grids, which is used to solve the technical problems in the existing technology that lack efficient coordinated scheduling of multiple energy types and accurate adaptation to user load dynamic demands, resulting in low energy utilization efficiency and insufficient scheduling flexibility.

[0004] The first aspect of the present application provides a comprehensive energy demand response scheduling method for smart grids, the method comprising: performing load forecasting on the user side of the power grid according to a future time zone window to determine the user load demand, wherein the power grid includes K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1; performing multi-energy coordinated scheduling of the K energy supply units according to the user load demand to establish a first space for comprehensive energy demand scheduling; modeling the K energy supply units to generate a multi-energy supply integrated model, and combining the power grid energy scheduling evaluation channel Perform initial optimization on the first space of comprehensive energy demand scheduling to obtain the second space of comprehensive energy demand scheduling; based on the power grid energy scheduling evaluation channel, perform variation expansion optimization on the second space of comprehensive energy demand scheduling according to the power grid energy scheduling variation function to generate the third space of comprehensive energy demand scheduling; introduce the first optimization operator of power grid energy scheduling and the second optimization operator of power grid energy scheduling to perform deep optimization on the third space of comprehensive energy demand scheduling to obtain the comprehensive energy demand scheduling strategy; based on the user load demand, perform response scheduling on the K energy supply units according to the comprehensive energy demand scheduling strategy.

[0005] The second aspect of the present application provides an integrated energy demand response scheduling platform for smart grids, the platform comprising: a user-side load prediction module, the user-side load prediction module is used to perform load prediction on the user side of the power grid according to a future time zone window to determine the user load demand, wherein the power grid comprises K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1; a scheduling first space generation module, the scheduling first space generation module is used to perform multi-energy coordinated scheduling of the K energy supply units according to the user load demand, and establish a first space for comprehensive energy demand scheduling; a scheduling second space generation module, the scheduling second space generation module is used to model according to the K energy supply units, generate a multi-energy supply integrated model, and combine the power grid energy scheduling The degree evaluation channel performs initial optimization on the first space of the comprehensive energy demand scheduling to obtain the second space of the comprehensive energy demand scheduling; a scheduling third space generation module, the scheduling third space generation module is used to perform mutation, expansion and optimization on the second space of the comprehensive energy demand scheduling based on the power grid energy scheduling evaluation channel according to the power grid energy scheduling variation function, to generate the third space of the comprehensive energy demand scheduling; a scheduling strategy optimization module, the scheduling strategy optimization module is used to introduce the first optimization operator of the power grid energy scheduling and the second optimization operator of the power grid energy scheduling to perform deep optimization on the third space of the comprehensive energy demand scheduling to obtain the comprehensive energy demand scheduling strategy; a response scheduling module, the response scheduling module is used to respond to the K energy supply units according to the comprehensive energy demand scheduling strategy based on the user load demand.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The integrated energy demand response scheduling method and platform for smart grids provided in the present application relate to the field of energy management technology. Through user load prediction and multi-energy coordinated scheduling, an integrated energy scheduling space is constructed, and initial optimization and variation expansion optimization are performed in combination with multi-energy supply modeling and power grid evaluation channels. By introducing deep optimization of the optimization operator, an integrated energy demand scheduling strategy is generated, and finally efficient response scheduling of K energy supply units is achieved. The technical problems of low energy utilization efficiency and insufficient scheduling flexibility caused by the lack of efficient coordinated scheduling of multiple energy types and accurate adaptation capabilities of user load dynamic demands in the prior art are solved, and the technical effect of improving energy utilization efficiency and scheduling flexibility is achieved through multi-energy coordinated optimization and dynamic response scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of a comprehensive energy demand response scheduling method for smart grids provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an integrated energy demand response scheduling platform for smart grids provided in an embodiment of the present application.

[0009] Explanation of the reference numerals: user-side load prediction module 11 , scheduling first space generation module 12 , scheduling second space generation module 13 , scheduling third space generation module 14 , scheduling strategy optimization module 15 , response scheduling module 16 . DETAILED DESCRIPTION

[0010] This application provides a comprehensive energy demand response scheduling method and platform for smart grids, which is used to solve the technical problems in the existing technology that lack efficient coordinated scheduling of multiple energy types and accurate adaptation to dynamic user load demands, resulting in low energy utilization efficiency and insufficient scheduling flexibility.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1As shown, the present application provides a comprehensive energy demand response scheduling method for smart grids, the method comprising: P10: Perform load forecasting on the user side of the power grid according to the future time zone window to determine the user load demand, wherein the power grid includes K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1.

[0014] Specifically, the load forecast of the power demand on the user side of the power grid is carried out according to the future time zone window, and the load demand of the user is finally determined. The future time zone window is a pre-set future time range (such as hours, days or weeks) used to predict the changing trend of the user's power demand during this period. Through the load forecast of the future time zone, the energy demand characteristics of the user can be understood in advance, providing data support and decision-making basis for the subsequent multi-energy coordinated scheduling.

[0015] The power grid contains K energy supply units, and the energy types of these units are different, for example, they may include traditional thermal power generation units, wind power generation units, photovoltaic power generation units, energy storage units, etc. Here K is a positive integer greater than 1, indicating that the types of energy and the number of energy supply units in the power grid are diversified. This diversified energy supply system enables the power grid to have the ability of multi-energy coordinated dispatch, and also lays the foundation for the subsequent optimization and allocation of energy resources.

[0016] For example, in order to accurately predict user load demand, first of all, it is necessary to analyze the user's historical electricity consumption data to dig out the regularity of user load demand, such as the electricity consumption characteristics during peak hours, valley hours, and special holidays. These regularities can be extracted using time series analysis models, such as ARIMA models, Prophet models, or deep learning models such as LSTM (long short-term memory network), which can capture complex load change trends and potential seasonal fluctuations. At the same time, in order to further improve the prediction accuracy, external influencing factors such as weather conditions (such as the impact of temperature on heating and cooling demand), policy adjustments (such as the impact of energy-saving and emission reduction policies on industrial electricity consumption) and social events (such as short-term electricity demand surges caused by large-scale activities) can be combined. In addition, through mathematical statistical analysis of user behavior characteristics, such as residential users' daily electricity habits or industrial users' production patterns, load forecasts can be further refined to meet the demand forecast requirements of different types of users.

[0017] The results of load forecasting are presented in numerical form, covering user load demands in various periods in future time zones, and may be further subdivided into the demands of different energy types, thereby providing accurate user demand information for the comprehensive energy dispatch of the power grid, supporting the coordinated optimization of multiple energy sources in subsequent steps, and improving the efficiency and reliability of energy distribution.

[0018] P20: Perform multi-energy coordinated scheduling of the K energy supply units according to the user load demand, and establish a first space for comprehensive energy demand scheduling.

[0019] Optionally, according to the user load demand, the K energy supply units in the power grid are dispatched in a multi-energy coordinated manner, thereby constructing the first space for comprehensive energy demand dispatch. The K energy supply units here refer to a variety of energy supply equipment or systems in the power grid, such as thermal power generation, photovoltaic power generation, wind power generation, energy storage units, etc. The energy types of these units are different, providing the power grid with diversified energy supply capabilities.

[0020] The core goal of multi-energy coordinated dispatch is to optimize the dispatch strategy of each energy unit based on the user's load demand, taking into account the power generation characteristics, energy supply efficiency, operating costs and environmental impact of different energy supply units. During the dispatch process, it is necessary to meet the needs of users to the greatest extent, reduce dispatch costs, and achieve efficient use of energy.

[0021] For example, first, the basis of scheduling is to establish an accurate user load demand forecasting model to provide specific energy demand distribution data. Next, K energy supply units are modeled to define the output capacity, response speed, operating restrictions (such as load upper and lower limits), and economic and environmental costs of each unit. Then, all units are jointly optimized through scheduling algorithms (such as linear programming, nonlinear programming, genetic algorithms, or reinforcement learning-based methods) to ensure that multi-energy systems work together to meet demand.

[0022] In this process, the first space of comprehensive energy demand scheduling constructed is a multi-dimensional solution space, which includes multiple parameters such as user load demand, energy supply unit status, power generation cost, energy type ratio, etc. This solution space can be defined by mathematical modeling (such as constrained optimization model) to find the best scheduling solution under the premise of meeting constraints (such as user demand, equipment capacity limitations, and environmental protection requirements). In addition, in order to improve the efficiency and accuracy of scheduling, it is necessary to combine real-time data feedback, such as the current operating status of each energy unit, weather information, load change trends, etc. By integrating this real-time information into the scheduling model, the energy allocation strategy can be dynamically adjusted to further optimize the operation effect.

[0023] Through the implementation of this step, the power grid can build a preliminary dispatch solution space (i.e. the first space of comprehensive energy demand dispatch), which provides basic data for optimization and expansion in subsequent steps. The establishment of this space marks the official launch of multi-energy coordinated dispatch, which is also an important part of comprehensive energy management.

[0024] P30: Modeling is performed based on the K energy supply units to generate a multi-energy supply integration model, and initial optimization is performed on the first space of comprehensive energy demand scheduling in combination with the power grid energy scheduling evaluation channel to obtain the second space of comprehensive energy demand scheduling.

[0025] Furthermore, step P30 of the embodiment of the present application further includes: P31: Extract the first decision of comprehensive energy demand scheduling according to the first space of comprehensive energy demand scheduling; P32: Evaluate the first decision of comprehensive energy demand scheduling based on the multi-energy energy supply integration model and the power grid energy dispatching evaluation channel to obtain the first energy dispatching evaluation result; P33: Determine whether the first energy dispatching evaluation result meets the energy dispatching evaluation constraint, wherein the energy dispatching evaluation constraint includes power supply quality evaluation constraint, power supply efficiency evaluation constraint, energy dispatching loss evaluation constraint and power grid dispatching safety evaluation constraint; P34: If the first energy dispatching evaluation result meets the energy dispatching evaluation constraint, add the first decision of comprehensive energy demand scheduling to the second space of comprehensive energy demand scheduling; P35: If the first energy dispatching evaluation result does not meet the second space of comprehensive energy demand scheduling, eliminate the first decision of comprehensive energy demand scheduling; P36: Based on the multi-energy energy supply integration model and the power grid energy dispatching evaluation channel, continue to optimize and select the first space of comprehensive energy demand scheduling according to the energy dispatching evaluation constraint to obtain the second space of comprehensive energy demand scheduling.

[0026] It should be understood that by modeling K energy supply units, a multi-energy supply integration model is generated. This model is a mathematical framework that uniformly expresses the characteristics of multiple energy types (such as thermal power, photovoltaics, wind power and energy storage), covering key parameters such as the output capacity, operating cost, response speed and environmental impact of energy units. At the same time, combined with the grid energy dispatch evaluation channel, the first phase of the comprehensive energy demand dispatch plan is analyzed to ensure that the dispatch plan can achieve a balance between performance and constraints.

[0027] Exemplarily, according to the first space of comprehensive energy demand scheduling, the initial scheduling scheme is extracted as the first batch of candidate decisions. These candidate schemes are generated based on user load demand and preliminary allocation rules of energy units, representing a direct application of the solution in the first space. The goal of this step is to narrow the scope of decision making and provide a relatively high-quality decision set for subsequent accurate evaluation.

[0028] Next, the multi-energy supply integration model and the grid energy dispatch evaluation channel are used to comprehensively evaluate each extracted first decision and generate the first energy dispatch evaluation results. The evaluation criteria include power supply quality (such as voltage and frequency stability), power supply efficiency (such as energy utilization), dispatch loss (such as energy loss in transmission), and grid dispatch safety (such as operation stability). This process ensures that the actual effect of the dispatch plan meets the comprehensive goals of the grid.

[0029] Next, based on the energy dispatch evaluation constraints, we determine whether each dispatch plan meets the requirements. These constraints include: power supply quality evaluation constraints, which can ensure the stability of user power demand in terms of voltage, frequency, etc.; power supply efficiency evaluation constraints, which can be used to optimize the efficiency of energy use; energy dispatch loss evaluation constraints, which can be used to reduce energy losses in transmission and dispatch; and power grid dispatch safety evaluation constraints, which can ensure that the dispatch plan does not affect the overall safety of the power grid.

[0030] Furthermore, for the first decision that meets all constraints, it is directly added to the second space of comprehensive energy demand scheduling as an optimized high-quality scheduling solution, ensuring that the solutions in the second space are screened and meet multi-dimensional performance requirements. If a scheduling solution fails to meet the evaluation constraints, it will be eliminated from the candidate set. This decision screening process eliminates solutions that may lead to reduced power supply quality or low scheduling efficiency, thereby further improving the overall scheduling quality.

[0031] Finally, based on the multi-energy supply integration model and dispatch evaluation channel, the remaining solutions in the first space are further optimized and screened according to the energy dispatch evaluation constraints to generate the second space for comprehensive energy demand dispatch. That is, through in-depth analysis and dynamic adjustment, it is ensured that the solutions in the second space can meet user load demand and grid performance goals in a better way.

[0032] Through the above steps, the second space of comprehensive energy demand scheduling is finally formed, marking the transition from initial scheduling to optimized scheduling, laying a solid foundation for subsequent in-depth optimization.

[0033] Furthermore, step P32 of the embodiment of the present application also includes: P32-1: According to the first decision of the comprehensive energy demand scheduling, the multi-energy supply integration model is simulated and scheduled to obtain the first simulated energy scheduling data set; P32-2: The power grid energy scheduling evaluation channel includes a power supply quality evaluation model, a power supply efficiency evaluation model, an energy scheduling loss evaluation model and a power grid scheduling safety evaluation model; P32-3: According to the first simulated energy scheduling data set, feature recognition is performed to obtain the first power supply quality related data, the first power supply efficiency related data, the first energy scheduling loss related data and the first power grid scheduling safety related data; P32-4: The first power supply quality related data is input into the power supply quality evaluation model to obtain the first power supply quality related data. Quality evaluation coefficient; P32-5: Input the first power supply efficiency related data into the power supply efficiency evaluation model to obtain the first power supply efficiency evaluation coefficient; P32-6: Input the first energy dispatching loss related data into the energy dispatching loss evaluation model to obtain the first energy dispatching loss evaluation coefficient; P32-7: Input the first power grid dispatching safety related data into the power grid dispatching safety evaluation model to obtain the first power grid dispatching safety evaluation coefficient; P32-8: Output the first power supply quality evaluation coefficient, the first power supply efficiency evaluation coefficient, the first energy dispatching loss evaluation coefficient and the first power grid dispatching safety evaluation coefficient as the first energy dispatching evaluation result.

[0034] Optionally, the process of generating the first energy dispatch evaluation result is further refined, and the multi-energy supply integration model is used to simulate the dispatching, and the dispatching data is feature identified and quantitatively evaluated through multiple evaluation models of the power grid energy dispatching evaluation channel, so as to generate a set of comprehensive evaluation coefficients to constitute a complete dispatching evaluation result.

[0035] First, the multi-energy supply integrated model is simulated and scheduled according to the first decision of comprehensive energy demand scheduling to obtain the first simulated energy scheduling data set. This step converts the allocation of different energy units in the first decision into specific energy supply status, scheduling path, operation characteristics and other data through simulation. These simulation results are the basis for the subsequent evaluation process.

[0036] Next, the composition of the power grid energy dispatch evaluation channel is clarified, including four core evaluation models: the power supply quality evaluation model, which is used to evaluate indicators such as voltage stability, frequency control and fluctuation range during the power supply process; the power supply efficiency evaluation model, which is used to measure energy utilization, power factor of the power grid system and the degree of optimization of resource allocation; the energy dispatch loss evaluation model, which is used to evaluate the transmission loss and system internal consumption generated by energy during the dispatch process; the power grid dispatch safety evaluation model, which is used to analyze the reliability of the dispatch process and its impact on the overall stability of the power grid.

[0037] Next, by performing feature recognition on the first simulated energy dispatch data set, feature data related to the four evaluation models are extracted, and first power supply quality-related data, first power supply efficiency-related data, first energy dispatch loss-related data, and first power grid dispatch safety-related data are generated respectively. The feature data recognition process may use a feature extraction algorithm, such as a dimensionality reduction method based on principal component analysis (PCA) or a deep learning feature extraction network, to ensure that the extracted data can accurately reflect the core characteristics of the simulated dispatch.

[0038] Subsequently, each type of associated data is input into the corresponding evaluation model, including inputting the first power supply quality associated data into the power supply quality evaluation model, inputting the first power supply efficiency associated data into the power supply efficiency evaluation model, inputting the first energy dispatch loss associated data into the energy dispatch loss evaluation model, and inputting the first power grid dispatch safety associated data into the power grid dispatch safety evaluation model, and respectively obtaining the first power supply quality evaluation coefficient, the first power supply efficiency evaluation coefficient, the first energy dispatch loss evaluation coefficient, and the first power grid dispatch safety evaluation coefficient. These evaluation coefficients quantify the performance of the dispatching scheme in terms of power supply quality, efficiency, loss, and safety through the calculation results of the model. For example, the power supply quality evaluation coefficient can calculate the voltage stability score through the fluctuation analysis function, while the power supply efficiency evaluation coefficient is obtained through a comprehensive analysis of energy conversion efficiency and load matching.

[0039] Finally, all evaluation coefficients are summarized and output as the first energy dispatch evaluation result. This result is a comprehensive quantitative evaluation of the dispatch plan, which can provide solid data support for selecting high-quality plans and optimizing dispatch strategies in subsequent steps.

[0040] P40: Based on the power grid energy dispatch evaluation channel, the second space of comprehensive energy demand dispatch is mutated, expanded and optimized according to the power grid energy dispatch variation function to generate the third space of comprehensive energy demand dispatch.

[0041] Furthermore, step P40 of the embodiment of the present application also includes: P41: According to the power grid energy dispatch variation function, the variation quantity characteristics of each comprehensive energy demand dispatching decision in the second space of the comprehensive energy demand dispatching are analyzed to obtain the variation quantity of each dispatching decision; P42: Based on the user load demand, the second space of the comprehensive energy demand dispatching is mutated according to the variation quantity of each dispatching decision to obtain the first space of energy dispatching decision variation; P43: According to the multi-energy supply integration model and the power grid energy dispatch evaluation channel, the first space of energy dispatching decision variation is optimized and selected to establish the second space of energy dispatching decision variation; P44: According to the second space of energy dispatching decision variation, the second space of comprehensive energy demand dispatching is expanded to obtain the third space of comprehensive energy demand dispatching.

[0042] It should be understood that based on the grid energy dispatch evaluation channel, the grid energy dispatch variation function is used to mutate, expand and optimize the dispatch decision in the second space of comprehensive energy demand dispatch, thereby generating the third space of comprehensive energy demand dispatch. This process introduces appropriate mutation operations, while retaining the high-quality solutions in the second space, explores more possible dispatch solutions, and improves the overall optimization capability.

[0043] First, according to the grid energy dispatch variation function, the variation quantity characteristics of each dispatch decision in the second space of comprehensive energy demand dispatch are analyzed, and the variation quantity of each dispatch decision is calculated. The grid energy dispatch variation function is a mathematical function used to quantify the range and frequency of changes in the energy supply characteristics of the dispatch decision. This process analyzes the fitness and sensitivity of each dispatch scheme when it varies by dynamically responding to user load demand, providing a basis for subsequent variation operations.

[0044] Then, according to the user load demand and the number of variations of each scheduling decision, the second space of comprehensive energy demand scheduling is mutated to generate the first space of energy scheduling decision variation. The specific operations of variation include adjusting the allocation ratio of energy units, changing the scheduling path, modifying the switching parameters between energy types, etc. These operations can be implemented through heuristic algorithms (such as mutation operations in genetic algorithms) or rule-based dynamic adjustment strategies. By introducing variation, potential high-quality scheduling solutions can be explored while enhancing the diversity of scheduling models.

[0045] Furthermore, after the mutation operation is completed, the multi-energy supply integration model and the power grid energy dispatch evaluation channel are used to comprehensively evaluate and optimize the first space of energy dispatch decision mutation, screen out high-quality solutions that meet the energy dispatch goals, and establish the second space of energy dispatch decision mutation. This process is similar to the preliminary screening of the first space, but because the mutation introduces new dispatch characteristics, the evaluation results may show higher optimization potential. Feature identification and evaluation rely on the efficient computing power of the model to ensure the scientificity and practicality of the mutation scheme.

[0046] Finally, the high-quality solutions of the second space of energy scheduling decision mutation are integrated into the second space of comprehensive energy demand scheduling to complete the expansion of the space and form a new third space of comprehensive energy demand scheduling. The expanded third space includes the excellent solutions of the original second space and the high-potential solutions obtained through mutation, laying a broader exploration foundation for subsequent deep optimization.

[0047] Through this step, the grid dispatching scheme is further improved in terms of stability, efficiency and flexibility, while providing rich decision-making support for the generation of the final comprehensive energy dispatching strategy.

[0048] Furthermore, step P41 of the embodiment of the present application further includes: P41-1: According to the power grid energy dispatch evaluation channel, multiple energy dispatch evaluation results corresponding to each comprehensive energy demand dispatch decision are retrieved; P41-2: According to the multiple energy dispatch evaluation results, the variation value of each comprehensive energy demand dispatch decision is evaluated to obtain the variation value coefficient of each dispatch decision; P41-3: The variation value coefficient of each dispatch decision is input into the power grid energy dispatch variation function to obtain the variation quantity of each dispatch decision, wherein the power grid energy dispatch variation function is: ; Among them, SDN represents the number of scheduling decision variations, FLOOR means rounding down, SDK represents the number of scheduled variations, SXC represents the scheduling decision variation value coefficient, and SXO represents the scheduled scheduling decision variation value coefficient.

[0049] In a possible embodiment of the present application, the specific process of obtaining the number of variations of the dispatch decision through the power grid energy dispatch evaluation channel can be further clarified. By combining the variation value evaluation and the power grid energy dispatch variation function, the number of variations is determined for each dispatch decision to support subsequent variation expansion optimization.

[0050] First, according to the power grid energy dispatching evaluation channel, retrieve multiple energy dispatching evaluation results corresponding to each integrated energy demand dispatching decision. The energy dispatching evaluation results include multiple dimensions such as power supply quality, power supply efficiency, energy dispatching loss, and power grid dispatching safety, comprehensively reflecting the comprehensive performance of the current dispatching decision in energy allocation and operation efficiency. These evaluation results provide the necessary input data for the variant value evaluation.

[0051] Next, through the analysis of multiple energy dispatching evaluation results, conduct a variant value evaluation for each dispatching decision to generate the corresponding variant value coefficient. The variant value coefficient is an important indicator for quantifying the potential benefits and risks of each decision's variation. Specifically, if a certain dispatching decision performs mediocrely in the current evaluation, its optimization potential may be improved through variation, and the variant value coefficient may be relatively high; conversely, for a decision with excellent performance, its variant value coefficient may be relatively low. This evaluation process relies on weighted analysis or multi-objective optimization algorithms to convert the evaluation results of multiple dimensions into a single variant value quantification index.

[0052] Furthermore, input the variant value coefficient of each dispatching decision into the power grid energy dispatching variation function to calculate its specific variation quantity. The definition of the variation function is as follows: ; where SDN represents the dispatching decision variation quantity, which is the output result of the function, FLOOR means rounding down, SDK represents the predetermined variation quantity, which is the set benchmark value, SXC represents the dispatching decision variant value coefficient, which is the result of the variant value evaluation, and SXO represents the predetermined dispatching decision variant value coefficient, which is the dispatching value standard as the benchmark.

[0053] This function dynamically adjusts the variation quantity according to the relationship between SXC and SXO (i.e., the relative size of the current variant value and the predetermined benchmark). When SXC < SXO, the variation quantity decreases; when SXC = SXO, the variation quantity remains unchanged; when SXC > SXO, the variation quantity increases. This dynamic adjustment mechanism ensures the accuracy of the variation operation and preferentially varies the decisions with higher optimization potential.

[0054] Through the above steps, based on the evaluation results and variant values of the dispatching decisions, it is possible to reasonably allocate variant resources, avoid unnecessary computational overhead, and at the same time explore more potential high-quality dispatching schemes. This process utilizes the comprehensiveness of the evaluation channel and the flexibility of the variation function, providing sufficient expansion space for subsequent dispatching optimization.

[0055] P50: Introduce the first optimization operator for power grid energy dispatching and the second optimization operator for power grid energy dispatching to deeply optimize the third space of the integrated energy demand dispatching, and obtain the integrated energy demand dispatching strategy.

[0056] Furthermore, step P50 of the embodiment of the present application also includes: P51: The first optimization operator of the power grid energy dispatching includes an energy dispatching optimality threshold, and the second optimization operator of the power grid energy dispatching is to minimize carbon emissions; P52: According to the first optimization operator of the power grid energy dispatching, the third space of the comprehensive energy demand dispatching is optimized and selected to obtain the fourth space of the comprehensive energy demand dispatching; P53: According to the second optimization operator of the power grid energy dispatching, the fourth space of the comprehensive energy demand dispatching is optimized and analyzed to generate the comprehensive energy demand dispatching strategy.

[0057] Optionally, by introducing the first optimization operator for power grid energy dispatch and the second optimization operator for power grid energy dispatch, the third space of comprehensive energy demand dispatch is deeply optimized, and finally the comprehensive energy demand dispatch strategy is obtained. This process ensures that the final dispatch strategy can achieve the best balance between energy utilization efficiency and environmental protection goals through the comprehensive effect of multi-dimensional optimization operators.

[0058] The first optimization operator of power grid energy dispatch takes the energy dispatch optimality threshold as the core and sets the basic adaptability standard that each dispatch scheme must meet. This operator screens out schemes with comprehensive adaptability better than the set threshold by evaluating the performance of each scheme in terms of power supply quality, power supply efficiency, energy loss and power grid security, ensuring that the optimized scheme has high feasibility and practical value.

[0059] The second optimization operator of the power grid energy dispatch focuses on minimizing carbon emissions and optimizing the environmental performance of energy dispatch schemes. The operator calculates the carbon emissions of each scheme and uses this as the main objective function for optimization, ensuring that the final dispatch strategy can meet load demand while minimizing carbon emissions and providing support for sustainable development.

[0060] First, on the basis of the third space of comprehensive energy demand scheduling, preliminary optimization is performed through the first optimization operator of power grid energy scheduling, which can screen out solutions that meet the optimal threshold of energy scheduling and generate the fourth space of comprehensive energy demand scheduling. The core of this step is to ensure that the optimized solution meets the basic requirements in terms of performance, efficiency and safety, thereby laying a solid foundation for further optimization. The specific optimization process can be achieved through multi-objective linear programming or heuristic algorithms, and solutions that do not meet the requirements are screened and eliminated one by one according to the set threshold.

[0061] Then, based on the fourth space, the second optimization operator of power grid energy dispatch is further introduced to conduct in-depth analysis and optimization of the carbon emissions of the scheme, and finally generate a comprehensive energy demand dispatch strategy. By quantifying the carbon emission characteristics of each scheme and using the objective function minimization method (such as genetic algorithm or particle swarm optimization algorithm), the scheme with the lowest carbon emissions is identified and prioritized to ensure that the dispatch strategy has high environmental performance while achieving efficient energy allocation.

[0062] Through the above steps, the optimized comprehensive energy demand scheduling strategy can effectively cope with complex and changeable load demands, while minimizing the impact of energy scheduling on the environment, and achieving the dual goals of smart grid in terms of economy and sustainability.

[0063] Furthermore, step P52 of the embodiment of the present application also includes: P52-1: Construct an energy scheduling optimal analytical function, wherein the energy scheduling optimal analytical function is: ; Among them, ECF represents the energy dispatching optimality, exp represents the exponential function with the natural constant e as the base, WC represents the power supply quality weight, K (PQC) represents the normalized power supply quality evaluation coefficient, WE represents the power supply efficiency weight, K (PQE) represents the normalized power supply efficiency evaluation coefficient, WS represents the power grid dispatching safety weight, K (PQS) represents the normalized power grid dispatching safety evaluation coefficient, WL represents the energy dispatching loss weight, and K (PQL) represents the normalized energy dispatching loss evaluation coefficient; P52-2: Based on the energy dispatching optimality analytical function, the energy dispatching optimality of the third space of the comprehensive energy demand dispatching is calculated to obtain multiple energy dispatching optimalities; P52-3: Determine whether the multiple energy dispatching optimalities meet the first optimization operator of the power grid energy dispatching, and obtain multiple decision optimality judgment results; P52-4: Based on the multiple decision optimality judgment results, the third space of the comprehensive energy demand dispatching is screened to obtain the fourth space of the comprehensive energy demand dispatching.

[0064] Specifically, by constructing and utilizing the energy dispatch optimal analytical function, the optimality of the third space of comprehensive energy demand dispatch can be calculated and screened to generate the fourth space of comprehensive energy demand dispatch. This process quantitatively evaluates the comprehensive performance of the dispatch scheme in multiple evaluation dimensions to ensure that the selected scheme meets the requirements of the first optimization operator of power grid energy dispatch.

[0065] First, an analytical function for energy scheduling optimization is constructed, and its mathematical expression is: ; Among them, ECF represents the optimality of energy scheduling and is a quantitative indicator for evaluating the overall quality of each scheduling scheme. exp represents an exponential function with the natural constant e as the base, which is used to enhance the nonlinear distribution characteristics of the score. WC represents the power supply quality weight, K(PQC) represents the normalized power supply quality evaluation coefficient, WE represents the power supply efficiency weight, K(PQE) represents the normalized power supply efficiency evaluation coefficient, WS represents the power grid scheduling safety weight, K(PQS) represents the normalized power grid scheduling safety evaluation coefficient, WL represents the energy scheduling loss weight, and K(PQL) represents the normalized energy scheduling loss evaluation coefficient, which is used to standardize indicators of different dimensions to ensure comparability.

[0066] The construction of this function aims to balance multiple key factors and comprehensively evaluate the adaptability and optimization potential of each scheduling scheme.

[0067] Next, based on the above-mentioned optimal analytical function, the optimality of each scheduling scheme in the third space of comprehensive energy demand scheduling is calculated to obtain multiple energy scheduling optimalities. This calculation process generates the corresponding optimality value by substituting the evaluation index of each scheme into the optimality function. The calculation results can directly reflect the comprehensive performance of the scheduling scheme in terms of performance and efficiency. The higher the optimality value, the better the overall performance of the scheme.

[0068] Furthermore, the optimality threshold set in the first optimization operator of the power grid energy dispatch is used to judge the multiple optimality values ​​calculated, and the dispatching schemes that meet the threshold conditions are screened out. The optimality judgment results divide the schemes into two categories: high-quality schemes that meet the conditions and eliminated schemes that do not meet the conditions. The core goal of this step is to exclude schemes that do not meet the basic adaptability requirements and provide a high-quality decision-making basis for subsequent optimization.

[0069] Finally, based on the optimality judgment results, the third space of comprehensive energy demand scheduling is screened, and the low optimality solutions are eliminated, and finally the fourth space of comprehensive energy demand scheduling is formed. The solution set of the fourth space is composed of solutions that meet the optimality threshold, which represents a set of scheduling strategies with outstanding performance in the current optimization stage, providing a solid foundation for deep optimization.

[0070] P60: Based on the user load demand, the K energy supply units are responsively scheduled according to the comprehensive energy demand scheduling strategy.

[0071] Optionally, based on user load demand and combined with the optimized comprehensive energy demand dispatching strategy, K energy supply units are dispatched in response to ensure efficient, safe and economical energy distribution and use while meeting user energy demand. This step is the specific implementation process of the dispatching strategy, which converts the optimization model into actual operation execution.

[0072] First of all, user load demand is the core basis for response scheduling. User load demand reflects the energy requirements in a specific period of time in the future, including load curves, peak loads, and special electricity demand. In response to these demands, energy resources are accurately allocated according to the demand intensity and distribution characteristics of different time periods. During the scheduling process, it is necessary not only to meet the energy demand on the user side, but also to find a balance between energy utilization efficiency and the stability of the power grid system.

[0073] Secondly, combined with the comprehensive energy demand scheduling strategy, K energy supply units are allocated and scheduled. The K energy supply units here refer to various energy types in the power grid, including but not limited to thermal power, wind power, photovoltaic power generation and energy storage units. These energy supply units have their own characteristics, such as output power, response speed, operating cost and environmental impact. The scheduling strategy sets specific operation plans and output targets for each energy unit by comprehensively optimizing these characteristics. For example, when electricity consumption is low, low-cost and high-efficiency energy units are given priority for output; when electricity consumption is peak, energy storage units are mobilized to participate in energy supply to alleviate load pressure.

[0074] To achieve efficient dispatch, it is also necessary to monitor the operating status of user loads and energy units in real time and dynamically adjust the dispatch plan. Through the real-time feedback mechanism, the output of the energy supply unit can be quickly adjusted according to sudden changes in load demand (such as unexpected load growth or renewable energy fluctuations caused by weather changes). For example, when the output of photovoltaic power generation decreases due to weather changes, the energy storage unit can be immediately called to supplement it to ensure the balance between supply and demand.

[0075] Multi-energy synergy is the key technical support for response scheduling. Through coordinated scheduling, different types of energy units can be organically combined to give full play to their respective advantages. For example, on sunny days, photovoltaic power generation is output first, and excess electricity is stored in the energy storage unit; at night or during peak hours, the energy storage unit releases electricity to supplement power supply demand. In addition, through the synergy of thermal power and renewable energy, it can not only meet long-term stable energy supply needs, but also reduce carbon emissions and improve the environmental friendliness of the power grid system.

[0076] Through the implementation of the comprehensive energy demand scheduling strategy, K energy supply units are responded to and scheduled, which can optimize energy utilization efficiency while meeting user load needs, ensuring the economy, safety and sustainability of power grid operation.

[0077] In summary, the embodiments of the present application have at least the following technical effects: This application predicts user load demand through future time zone windows, and establishes a comprehensive energy demand scheduling space based on multi-energy coordinated scheduling. By modeling K energy supply units, a multi-energy supply integration model is generated, and the initial optimization and variation expansion optimization are performed in combination with the power grid energy scheduling evaluation channel to form a better scheduling space. Further introduce the energy scheduling optimization operator, deeply optimize the scheduling space, obtain the comprehensive energy demand scheduling strategy, and finally dynamically respond to the K energy supply units according to the user load demand, so as to improve energy utilization efficiency and enhance scheduling flexibility.

[0078] The technical effect of improving energy utilization efficiency and scheduling flexibility has been achieved through multi-energy coordinated optimization and dynamic response scheduling.

[0079] Embodiment 2 is based on the same inventive concept as the integrated energy demand response scheduling method for smart grid in the above embodiment. Figure 2 As shown, the present application provides a comprehensive energy demand response scheduling platform for smart grids, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes: The user-side load prediction module 11 is used to perform load prediction on the user side of the power grid according to a future time zone window to determine the user load demand, wherein the power grid includes K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1.

[0080] The scheduling first space generation module 12 is used to perform multi-energy coordinated scheduling of the K energy supply units according to the user load demand and establish a first space for comprehensive energy demand scheduling.

[0081] The scheduling second space generation module 13 is used to model the K energy supply units, generate a multi-energy supply integration model, and perform initial optimization on the comprehensive energy demand scheduling first space in combination with the power grid energy scheduling evaluation channel to obtain the comprehensive energy demand scheduling second space.

[0082] The scheduling third space generation module 14 is used to mutate, expand and optimize the second space of comprehensive energy demand scheduling based on the power grid energy scheduling evaluation channel and the power grid energy scheduling variation function to generate a third space for comprehensive energy demand scheduling.

[0083] The scheduling strategy optimization module 15 is used to introduce the first optimization operator of power grid energy scheduling and the second optimization operator of power grid energy scheduling to perform deep optimization on the third space of comprehensive energy demand scheduling to obtain a comprehensive energy demand scheduling strategy.

[0084] The response scheduling module 16 is used to respond to and schedule the K energy supply units based on the user load demand and the comprehensive energy demand scheduling strategy.

[0085] Furthermore, the scheduling second space generation module 13 is further used to perform the following steps: According to the first space of comprehensive energy demand scheduling, extract the first decision of comprehensive energy demand scheduling; based on the multi-energy energy supply integration model and the power grid energy dispatching evaluation channel, evaluate the first decision of comprehensive energy demand scheduling to obtain the first energy dispatching evaluation result; judge whether the first energy dispatching evaluation result meets the energy dispatching evaluation constraint, wherein the energy dispatching evaluation constraint includes power supply quality evaluation constraint, power supply efficiency evaluation constraint, energy dispatching loss evaluation constraint and power grid dispatching safety evaluation constraint; if the first energy dispatching evaluation result meets the energy dispatching evaluation constraint, add the first decision of comprehensive energy demand scheduling to the second space of comprehensive energy demand scheduling; if the first energy dispatching evaluation result does not meet the second space of comprehensive energy demand scheduling, eliminate the first decision of comprehensive energy demand scheduling; based on the multi-energy energy supply integration model and the power grid energy dispatching evaluation channel, continue to optimize and select the first space of comprehensive energy demand scheduling according to the energy dispatching evaluation constraint to obtain the second space of comprehensive energy demand scheduling.

[0086] Furthermore, the scheduling second space generation module 13 is further used to perform the following steps: According to the first decision of the comprehensive energy demand scheduling, the multi-energy supply integrated model is simulated and scheduled to obtain a first simulated energy scheduling data set; the power grid energy scheduling evaluation channel includes a power supply quality evaluation model, a power supply efficiency evaluation model, an energy scheduling loss evaluation model and a power grid scheduling safety evaluation model; feature recognition is performed according to the first simulated energy scheduling data set to obtain first power supply quality related data, first power supply efficiency related data, first energy scheduling loss related data and first power grid scheduling safety related data; the first power supply quality related data is input into the power supply quality evaluation model to obtain a first power supply quality evaluation coefficient; the first power supply efficiency related data is input into the power supply efficiency evaluation model to obtain a first power supply efficiency evaluation coefficient; the first energy scheduling loss related data is input into the energy scheduling loss evaluation model to obtain a first energy scheduling loss evaluation coefficient; the first power grid scheduling safety related data is input into the power grid scheduling safety evaluation model to obtain a first power grid scheduling safety evaluation coefficient; the first power supply quality evaluation coefficient, the first power supply efficiency evaluation coefficient, the first energy scheduling loss evaluation coefficient and the first power grid scheduling safety evaluation coefficient are output as the first energy scheduling evaluation result.

[0087] Furthermore, the scheduling third space generation module 14 is further configured to perform the following steps: According to the power grid energy dispatch variation function, the variation quantity characteristics of each comprehensive energy demand dispatching decision in the second space of the comprehensive energy demand dispatching are analyzed to obtain the variation quantity of each dispatching decision; based on the user load demand, the second space of the comprehensive energy demand dispatching is mutated according to the variation quantity of each dispatching decision to obtain the first space of energy dispatching decision variation; according to the multi-energy supply integration model and the power grid energy dispatch evaluation channel, the first space of energy dispatching decision variation is optimized and selected to establish the second space of energy dispatching decision variation; according to the second space of energy dispatching decision variation, the second space of comprehensive energy demand dispatching is expanded to obtain the third space of comprehensive energy demand dispatching.

[0088] Furthermore, the scheduling third space generation module 14 is further configured to perform the following steps: According to the power grid energy dispatch evaluation channel, multiple energy dispatch evaluation results corresponding to each comprehensive energy demand dispatch decision are retrieved; according to the multiple energy dispatch evaluation results, the variation value of each comprehensive energy demand dispatch decision is evaluated to obtain the variation value coefficient of each dispatch decision; the variation value coefficient of each dispatch decision is input into the power grid energy dispatch variation function to obtain the variation quantity of each dispatch decision, wherein the power grid energy dispatch variation function is: ; Among them, SDN represents the number of scheduling decision variations, FLOOR means rounding down, SDK represents the number of scheduled variations, SXC represents the scheduling decision variation value coefficient, and SXO represents the scheduled scheduling decision variation value coefficient.

[0089] Furthermore, the scheduling strategy optimization module 15 is also used to perform the following steps: The first optimization operator for power grid energy dispatching includes an energy dispatching optimality threshold, and the second optimization operator for power grid energy dispatching is to minimize carbon emissions; according to the first optimization operator for power grid energy dispatching, the third space for comprehensive energy demand dispatching is optimized and selected to obtain the fourth space for comprehensive energy demand dispatching; according to the second optimization operator for power grid energy dispatching, the fourth space for comprehensive energy demand dispatching is optimized and analyzed to generate the comprehensive energy demand dispatching strategy.

[0090] Furthermore, the scheduling strategy optimization module 15 is also used to perform the following steps: Construct an energy scheduling optimal analytical function, wherein the energy scheduling optimal analytical function is: ; Wherein, ECF represents the energy dispatching optimality, exp represents the exponential function with the natural constant e as the base, WC represents the power supply quality weight, K (PQC) represents the normalized power supply quality evaluation coefficient, WE represents the power supply efficiency weight, K (PQE) represents the normalized power supply efficiency evaluation coefficient, WS represents the power grid dispatching safety weight, K (PQS) represents the normalized power grid dispatching safety evaluation coefficient, WL represents the energy dispatching loss weight, and K (PQL) represents the normalized energy dispatching loss evaluation coefficient; based on the energy dispatching optimality analytical function, the energy dispatching optimality of the third space of the comprehensive energy demand dispatching is calculated to obtain multiple energy dispatching optimalities; it is determined whether the multiple energy dispatching optimalities meet the first optimization operator of the power grid energy dispatching to obtain multiple decision optimality judgment results; based on the multiple decision optimality judgment results, the third space of the comprehensive energy demand dispatching is screened to obtain the fourth space of the comprehensive energy demand dispatching.

[0091] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0093] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A comprehensive energy demand response scheduling method for smart grid, characterized in that: The method comprises: Perform load forecasting on the user side of the power grid according to the future time zone window to determine the user load demand, wherein the power grid includes K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1; Perform multi-energy coordinated scheduling of the K energy supply units according to the user load demand, and establish a first space for comprehensive energy demand scheduling; Modeling is performed according to the K energy supply units to generate a multi-energy supply integration model, and initial optimization is performed on the first space of comprehensive energy demand scheduling in combination with the power grid energy scheduling evaluation channel to obtain the second space of comprehensive energy demand scheduling; Based on the power grid energy dispatch evaluation channel, the second space of comprehensive energy demand dispatch is mutated, expanded and optimized according to the power grid energy dispatch variation function to generate a third space of comprehensive energy demand dispatch; Introducing a first optimization operator for power grid energy dispatch and a second optimization operator for power grid energy dispatch to perform deep optimization on the third space of comprehensive energy demand dispatch to obtain a comprehensive energy demand dispatch strategy; Based on the user load demand, the K energy supply units are responsively scheduled according to the comprehensive energy demand scheduling strategy.

2. The method according to claim 1, characterized in that Modeling is performed based on the K energy supply units to generate a multi-energy supply integration model, and initial optimization is performed on the first space of comprehensive energy demand scheduling in combination with the power grid energy scheduling evaluation channel to obtain the second space of comprehensive energy demand scheduling, including: Extracting a first decision for comprehensive energy demand scheduling according to the first space for comprehensive energy demand scheduling; Based on the multi-energy supply integration model and the power grid energy dispatch evaluation channel, the first decision of the comprehensive energy demand dispatch is evaluated to obtain a first energy dispatch evaluation result; Determining whether the first energy dispatch evaluation result satisfies energy dispatch evaluation constraints, wherein the energy dispatch evaluation constraints include power supply quality evaluation constraints, power supply efficiency evaluation constraints, energy dispatch loss evaluation constraints, and power grid dispatch safety evaluation constraints; If the first energy scheduling evaluation result satisfies the energy scheduling evaluation constraint, adding the first comprehensive energy demand scheduling decision to the second comprehensive energy demand scheduling space; If the first energy scheduling evaluation result does not meet the second space of the comprehensive energy demand scheduling, the first decision of the comprehensive energy demand scheduling is eliminated; Based on the multi-energy supply integration model and the power grid energy dispatch evaluation channel, the first space of comprehensive energy demand dispatch is continuously optimized and selected according to the energy dispatch evaluation constraints to obtain the second space of comprehensive energy demand dispatch.

3. The method according to claim 2, characterized in that Based on the multi-energy supply integration model and the power grid energy dispatch evaluation channel, the first decision of the comprehensive energy demand dispatch is evaluated to obtain a first energy dispatch evaluation result, including: According to the first comprehensive energy demand scheduling decision, the multi-energy supply integration model is simulated and scheduled to obtain a first simulated energy scheduling data set; The power grid energy dispatch evaluation channel includes a power supply quality evaluation model, a power supply efficiency evaluation model, an energy dispatch loss evaluation model and a power grid dispatch safety evaluation model; Perform feature recognition according to the first simulated energy dispatching data set to obtain first power supply quality-related data, first power supply efficiency-related data, first energy dispatching loss-related data, and first power grid dispatching safety-related data; Inputting the first power supply quality associated data into the power supply quality evaluation model to obtain a first power supply quality evaluation coefficient; Inputting the first power supply efficiency associated data into the power supply efficiency evaluation model to obtain a first power supply efficiency evaluation coefficient; Inputting the first energy scheduling loss associated data into the energy scheduling loss evaluation model to obtain a first energy scheduling loss evaluation coefficient; Inputting the first power grid dispatching safety-related data into the power grid dispatching safety evaluation model to obtain a first power grid dispatching safety evaluation coefficient; The first power supply quality evaluation coefficient, the first power supply efficiency evaluation coefficient, the first energy dispatching loss evaluation coefficient and the first power grid dispatching safety evaluation coefficient are output as the first energy dispatching evaluation result.

4. The method according to claim 1, characterized in that Based on the power grid energy dispatch evaluation channel, the second space of comprehensive energy demand dispatch is mutated, expanded and optimized according to the power grid energy dispatch variation function to generate a third space of comprehensive energy demand dispatch, including: According to the power grid energy dispatch variation function, performing a variation quantity characteristic analysis on each comprehensive energy demand dispatch decision in the second space of the comprehensive energy demand dispatch, and obtaining the variation quantity of each dispatch decision; Based on the user load demand, the second space of comprehensive energy demand scheduling is mutated according to the number of each scheduling decision variation to obtain a first space of energy scheduling decision variation; According to the multi-energy energy supply integration model and the power grid energy dispatch evaluation channel, the first energy dispatch decision variation space is optimized and selected to establish a second energy dispatch decision variation space; According to the second space of energy scheduling decision variation, the second space of comprehensive energy demand scheduling is expanded to obtain the third space of comprehensive energy demand scheduling.

5. The method according to claim 4, characterized in that According to the power grid energy dispatch variation function, the variation quantity characteristics of each comprehensive energy demand dispatch decision in the second space of the comprehensive energy demand dispatch are analyzed to obtain the variation quantity of each dispatch decision, including: According to the power grid energy dispatch evaluation channel, multiple energy dispatch evaluation results corresponding to each comprehensive energy demand dispatch decision are retrieved; Performing variation value evaluation on each comprehensive energy demand scheduling decision according to the multiple energy scheduling evaluation results to obtain a variation value coefficient of each scheduling decision; Input the variation value coefficients of each dispatch decision into the power grid energy dispatch variation function to obtain the variation quantity of each dispatch decision, wherein the power grid energy dispatch variation function is: ； Among them, SDN represents the number of scheduling decision variations, FLOOR means rounding down, SDK represents the number of scheduled variations, SXC represents the scheduling decision variation value coefficient, and SXO represents the scheduled scheduling decision variation value coefficient.

6. The method according to claim 1, characterized in that The first optimization operator for power grid energy dispatch and the second optimization operator for power grid energy dispatch are introduced to perform deep optimization on the third space of comprehensive energy demand dispatch to obtain a comprehensive energy demand dispatch strategy, including: The first optimization operator for grid energy dispatching includes an energy dispatching optimality threshold, and the second optimization operator for grid energy dispatching is carbon emission minimization; According to the first optimization operator of the power grid energy dispatch, the third space of the comprehensive energy demand dispatch is optimized and selected to obtain the fourth space of the comprehensive energy demand dispatch; The fourth space of comprehensive energy demand scheduling is optimized and analyzed according to the second optimization operator of the power grid energy scheduling to generate the comprehensive energy demand scheduling strategy.

7. The method according to claim 6, characterized in that The third space of comprehensive energy demand scheduling is optimized and selected according to the first optimization operator of the power grid energy scheduling to obtain the fourth space of comprehensive energy demand scheduling, including: Construct an energy scheduling optimal analytical function, wherein the energy scheduling optimal analytical function is: ； Among them, ECF represents the optimality of energy dispatch, exp represents the exponential function with the natural constant e as the base, WC represents the power supply quality weight, K(PQC) represents the normalized power supply quality evaluation coefficient, WE represents the power supply efficiency weight, K(PQE) represents the normalized power supply efficiency evaluation coefficient, WS represents the power grid dispatch safety weight, K(PQS) represents the normalized power grid dispatch safety evaluation coefficient, WL represents the energy dispatch loss weight, and K(PQL) represents the normalized energy dispatch loss evaluation coefficient; Based on the energy scheduling optimality analytical function, the energy scheduling optimality of the third space of the comprehensive energy demand scheduling is calculated to obtain multiple energy scheduling optimalities; Determine whether the multiple energy dispatching optima satisfy the first optimization operator of the power grid energy dispatching, and obtain multiple decision optima determination results; Based on the multiple decision-making priority judgment results, the third space of comprehensive energy demand scheduling is screened to obtain the fourth space of comprehensive energy demand scheduling.

8. Comprehensive energy demand response dispatching platform for smart grid, characterized by: The platform includes: A user-side load prediction module, which is used to perform load prediction on the user side of the power grid according to a future time zone window to determine the user load demand, wherein the power grid includes K energy supply units, the energy types of the K energy supply units are different, and K is a positive integer greater than 1; A scheduling first space generation module, wherein the scheduling first space generation module is used to perform multi-energy coordinated scheduling of the K energy supply units according to the user load demand, and establish a comprehensive energy demand scheduling first space; A scheduling second space generation module, wherein the scheduling second space generation module is used to model the K energy supply units, generate a multi-energy supply integration model, and perform initial optimization on the first space of comprehensive energy demand scheduling in combination with the power grid energy scheduling evaluation channel to obtain the second space of comprehensive energy demand scheduling; A scheduling third space generation module, wherein the scheduling third space generation module is used to perform mutation, expansion and optimization on the second space of comprehensive energy demand scheduling based on the power grid energy scheduling evaluation channel and according to the power grid energy scheduling variation function to generate a third space of comprehensive energy demand scheduling; A scheduling strategy optimization module, wherein the scheduling strategy optimization module is used to introduce a first optimization operator for power grid energy scheduling and a second optimization operator for power grid energy scheduling to perform deep optimization on the third space of comprehensive energy demand scheduling to obtain a comprehensive energy demand scheduling strategy; A response scheduling module is used to respond to and schedule the K energy supply units based on the user load demand and the comprehensive energy demand scheduling strategy.

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