A multi-constraint based demand side response transaction mechanism design method and system

Abstract

The application provides a multi-constraint-based demand side response transaction mechanism design method and system, relates to the technical field of transaction mechanism design, and obtains meteorological data, energy characteristic parameters and power load data of an urban-rural joint area; analyzes the meteorological data and the power load data to obtain power load prediction results under different meteorological conditions considering multi-constraints; based on the results, the energy characteristic parameters are combined to obtain charging parameters of a power load low valley period and discharging parameters of a power load peak period; based on the first parameters, the distributed electric energy accessed by the urban-rural joint area is converted into integrated electric energy meeting the access standard of an adaptive power grid; an electric energy distribution scheme is generated according to the charging parameters, the discharging parameters and the power load prediction results; and based on the scheme, a demand side response transaction mechanism is constructed, so that intelligent electric energy distribution of the urban-rural joint area under complex conditions is realized.

Description

Technical Field

[0001] This application relates to the field of transaction mechanism design technology, and in particular to a method and system for designing a demand-side response transaction mechanism based on multiple constraints. Background Technology

[0002] In urban-rural fringe areas with dense distributed renewable energy connections, power system operation faces multiple complex constraints. Changes in meteorological conditions affect regional power load fluctuations and renewable energy generation output, increasing the difficulty of load forecasting. Simultaneously, limited by grid transmission capacity and the intermittent output characteristics of distributed power sources, the system is prone to power supply pressure during peak hours and faces difficulties in renewable energy absorption during off-peak hours.

[0003] Existing mainstream solutions have been studied in the past to establish predictive models by integrating meteorological information and historical load data, and to carry out regional power balance regulation by combining the charging and discharging scheduling strategies of energy storage systems.

[0004] However, this scheme has certain drawbacks. For example, the energy storage regulation strategy does not fully consider the efficiency fluctuations caused by changes in battery physical characteristics with operating conditions, resulting in a deviation between the actual charging and discharging capacity and the scheduling plan. The energy integration process of distributed energy lacks the ability to respond to variable weather and real-time load evolution paths. The energy allocation ratio and transmission path are based on experience and cannot be dynamically optimized according to system constraints, which affects the overall energy utilization efficiency and the flexibility of the trading mechanism. Summary of the Invention

[0005] The purpose of this application is to provide a design method and system for a demand-side response trading mechanism based on multiple constraints, so as to solve the problems of low overall energy utilization efficiency and insufficient flexibility of trading mechanisms in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a design method for a demand-side response transaction mechanism based on multiple constraints, comprising: Acquire meteorological data, energy characteristic parameters, and power load data for urban-rural fringe areas. The power load data includes the first parameter, the second parameter, and the third parameter. Analyzing meteorological and power load data, we obtain power load forecasting results considering multiple constraints under different meteorological conditions. These multiple constraints include the first constraint, the second constraint, and the third constraint. Based on the power load forecast results and combined with energy characteristic parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period. Based on the first parameter, the distributed power accessed in the urban-rural fringe is converted into integrated power that is compatible with the grid access standard. Based on charging parameters, discharging parameters, and power load forecast results, the allocation ratio and transmission path of integrated power are determined to generate a power allocation scheme. Based on the power allocation scheme, a demand-side response trading mechanism is constructed, which includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

[0007] Optionally, meteorological data and power load data are analyzed to obtain power load forecasting results considering multiple constraints under different meteorological conditions. These multiple constraints include a first constraint, a second constraint, and a third constraint, including: Based on the preset weather type, the weather data is classified to obtain the target weather data, which includes light intensity data, wind speed data, temperature data and precipitation data. Historical records are extracted from power load data, and these historical records are matched with target meteorological data to obtain a correlated dataset. Based on the upper limit threshold of multiple constraints, determine the constraint boundary values ​​of the associated dataset. The constraint boundary values ​​include the first boundary value, the second boundary value, and the third boundary value. Based on the associated dataset, the influence weights corresponding to the target meteorological data are calculated. The influence weights include the first weight, the second weight, the third weight, and the fourth weight. Based on the constraint boundary values, influence weights, and historical records, the target predicted value corresponding to the target meteorological data is calculated. The target predicted value includes the first predicted value, the second predicted value, the third predicted value, and the fourth predicted value. Based on the preset meteorological conditions, the target predicted values ​​are integrated to obtain power load prediction results considering multiple constraints under different meteorological conditions.

[0008] Optionally, based on the power load forecast results and combined with energy characteristic parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain charging parameters during off-peak hours and discharging parameters during peak hours, including: The power load values ​​for each time period are extracted from the power load forecast results. Based on the magnitude of the power load values, the periods of low power load and peak power load are determined. The maximum charging efficiency, maximum discharging efficiency, and capacity limitation parameters are extracted from the energy characteristic parameters. Based on the maximum charging efficiency, the charging reference value is calculated, and simultaneously, based on the maximum discharging efficiency, the discharging reference value is calculated. Based on the off-peak hours of power load and the corresponding power load values, combined with capacity limitation parameters, the charging adjustment coefficient of the energy storage unit during the off-peak hours of power load is calculated. Based on the peak hours of power load and the corresponding power load values, combined with capacity limitation parameters, the discharging adjustment coefficient of the energy storage unit during the peak hours of power load is calculated. Multiplying the charging adjustment coefficient by the charging reference value yields the charging parameters during off-peak hours, and multiplying the discharging adjustment coefficient by the discharging reference value yields the discharging parameters during peak hours. Based on the charging and discharging parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain the charging parameters during off-peak hours and the discharging parameters during peak hours.

[0009] Optionally, based on the first parameter, the distributed power accessed in the urban-rural fringe is converted into integrated power that conforms to the grid access standard, including: Based on the preset energy types, the distributed electricity connected to the urban-rural fringe areas is classified to obtain the classified distributed electricity, which includes photovoltaic power, wind power and biomass power. The maximum output power limits for photovoltaic power, wind power, and biomass power are extracted from the first parameter, and all the maximum output power limits are combined to obtain the capacity limit value. Obtain the access parameters of the power grid access standard, including voltage level parameters, frequency range parameters, and harmonic content parameters; Based on the maximum output power limit and access parameters, the classified distributed power energy is subjected to power electronic transformation processing to obtain photovoltaic power conversion, wind power conversion, and biomass power conversion. Based on the photovoltaic power conversion, wind power conversion, and biomass power conversion, the total power is calculated. Based on the total power, the photovoltaic power conversion, wind power conversion, and biomass power conversion are processed to obtain integrated power that is compatible with the grid access standard.

[0010] Optionally, based on the total power, the photovoltaic-converted electrical energy, wind-converted electrical energy, and biomass-converted electrical energy are processed to obtain integrated electrical energy adapted to grid access standards, including: If the total power is less than the total capacity limit of the first parameter, then photovoltaic power conversion, wind power conversion, and biomass power conversion will be used as the target photovoltaic power, target wind power, and target biomass power, respectively. If the total power is greater than or equal to the total capacity limit of the first parameter, then the photovoltaic power conversion, wind power conversion, and biomass power conversion are adjusted based on the capacity limit of each type of distributed power to obtain the target photovoltaic power, target wind power, and target biomass power. By merging the target photovoltaic power, target wind power, and target biomass power, an integrated power source adapted to grid access standards is obtained.

[0011] Optionally, based on charging parameters, discharging parameters, and power load forecast results, the allocation ratio and transmission path of integrated power are determined to generate a power allocation scheme, including: The first predicted load during the off-peak period and the second predicted load during the peak period are extracted from the power load forecast results. The first predicted load and the second predicted load are combined to obtain the predicted load dataset. Based on charging parameters, the planned charging amount during off-peak hours is calculated, and based on discharging parameters, the planned discharging amount during peak hours is calculated. The planned charging amount and planned discharging amount are then integrated to obtain the energy dispatch set. Based on the third parameter, the electricity priority of different users is determined. Based on the electricity priority, combined with the predicted load dataset, energy dispatch set and the second parameter, the allocation ratio of integrated power to each user is calculated. The allocation ratio of all users is combined to obtain the allocation ratio table. Based on the allocation ratio table and power load data, the transmission path for integrated power transmission to the corresponding users is determined. The allocation ratio table and transmission path are then integrated to obtain the power allocation scheme.

[0012] Optionally, based on the electricity distribution scheme, a demand-side response trading mechanism is constructed. This mechanism includes a target price mechanism, supply-demand matching rules, and a multi-constraint trading execution process, including: Based on the costs of new energy generation, grid transmission, and user electricity consumption in the power load data, and combined with the allocation ratio in the power allocation scheme, the basic transaction price for different time periods is calculated. Based on the supply and demand gap determined by the basic transaction price and the electricity load forecast, a price fluctuation coefficient is determined. The basic transaction price and the price fluctuation coefficient are then integrated to obtain the target price mechanism. Based on the electricity consumption priorities of different users, combined with the power allocation scheme and power load forecast results, the supply and demand matching rules are determined. According to multiple constraints and the power allocation scheme, the transaction execution process is determined. The transaction execution process includes constraints before transaction execution, real-time adjustment thresholds during transaction execution, and recording requirements after transaction execution. By integrating the target price mechanism, supply and demand matching rules, and transaction execution process, a demand-side response transaction mechanism is obtained.

[0013] Secondly, this application provides a design system for a demand-side response transaction mechanism based on multiple constraints, including: The acquisition module is used to acquire meteorological data, energy characteristic parameters, and power load data of urban-rural fringe areas. The power load data includes a first parameter, a second parameter, and a third parameter. The analysis module is used to analyze meteorological data and power load data to obtain power load forecast results considering multiple constraints under different meteorological conditions. The multiple constraints include the first constraint, the second constraint, and the third constraint. The adjustment module is used to adjust the charging and discharging efficiency of the energy storage unit based on the power load forecast results and energy characteristic parameters, so as to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period. The conversion module is used to convert distributed power accessed in urban-rural fringe areas into integrated power access that is compatible with the grid access standard, based on the first parameter. The module determines the allocation ratio and transmission path of integrated power based on charging parameters, discharging parameters, and power load forecast results, in order to generate a power allocation scheme. The module is used to build a demand-side response trading mechanism based on the power allocation scheme. The demand-side response trading mechanism includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute computer programs to implement the steps of a multi-constraint demand-side response transaction mechanism design method as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the design method for a demand-side response transaction mechanism based on multiple constraints as described in the first aspect above.

[0016] This application provides a design method for a demand-side response trading mechanism based on multiple constraints. The method involves acquiring meteorological data, energy characteristic parameters, and power load data for urban-rural fringe areas. The power load data includes a first parameter, a second parameter, and a third parameter. The meteorological and power load data are analyzed to obtain power load forecasting results considering multiple constraints under different meteorological conditions. These constraints include the first, second, and third constraints. Based on the power load forecasting results and combined with the energy characteristic parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain charging parameters during off-peak hours and discharging parameters during peak hours. Based on the first parameter, the distributed power accessed in the urban-rural fringe areas is converted into integrated power adapted to the grid access standard. According to the charging and discharging parameters and the power load forecasting results, the allocation ratio and transmission path of the integrated power are determined to generate a power allocation scheme. Based on the power allocation scheme, a demand-side response trading mechanism is constructed, which includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

[0017] By acquiring meteorological data, energy characteristic parameters, and power load data including renewable energy generation capacity, grid transmission capacity, and user electricity consumption behavior in urban-rural fringe areas, a comprehensive understanding of multi-dimensional factors affecting regional power operation has been achieved. This has improved the adaptability and accuracy of prediction models under multiple constraints, providing a scientific basis for formulating forward-looking dispatch strategies. It has also improved the response accuracy and energy utilization efficiency of energy storage systems; achieved standardized integration of heterogeneous energy sources, improving the quality and controllability of distributed power grid-connected power; enhanced the flexibility and resilience of power resource allocation; and increased the capacity for renewable energy absorption and the enthusiasm of demand-side resources to participate in regulation.

[0018] Furthermore, by classifying distributed power sources such as photovoltaic, wind power, and biomass energy according to their energy type, the maximum output power limits of each type of power source are extracted and combined to form an overall capacity constraint. Combined with standard parameters such as voltage, frequency, and harmonics required for grid connection, power electronic conversion is implemented for different types of power. Finally, based on the total power of the converted power, unified coordination processing is performed to generate integrated power that meets grid connection requirements. This solves the shortcomings of traditional static integration strategies in terms of dynamic environmental response. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of 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.

[0020] Figure 1 A flowchart illustrating a design method for a demand-side response transaction mechanism based on multiple constraints, provided for an embodiment of this application; Figure 2 A schematic diagram of a process for obtaining integrated electrical energy is provided for an embodiment of this application; Figure 3 A schematic diagram of the structure of a demand-side response transaction mechanism design system based on multiple constraints provided in this application embodiment; Detailed Implementation This addresses the multiple constraints faced by densely distributed renewable energy access areas in urban-rural fringe regions under complex weather conditions, including inaccurate load forecasting, limited grid transmission, energy storage response deviations, and rigid power distribution.

[0021] This application proposes a design method and system for a demand-side response trading mechanism based on multiple constraints. The aim is to overcome the technical bottlenecks in existing technologies, such as the neglect of the time-varying physical characteristics of energy storage batteries leading to charging and discharging efficiency mismatch, and the lack of dynamic response capabilities to real-time operational constraints in power integration and distribution strategies. By comprehensively collecting meteorological data, energy storage characteristic parameters, and key power load information including renewable energy output and grid capacity, and combining this with the actual operating status of the energy storage unit, the method dynamically optimizes its charging and discharging performance parameters during off-peak and peak periods, improving the accuracy of energy storage response and energy utilization. Based on renewable energy generation capabilities, the method classifies various types of distributed power sources and implements power electronic conversion adapted to grid access standards, achieving high-quality integration of heterogeneous energy sources. Ultimately, it constructs a demand-side response trading mechanism deeply coupled with the actual operating status of the system, enabling the power trading process to respond to renewable energy fluctuations and changes in grid carrying capacity, thereby enhancing the regional power grid's supply and demand balance capability and market regulation flexibility under volatile environments.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The core of this application is to provide a design method for a demand-side response transaction mechanism based on multiple constraints. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: Step 101: Obtain meteorological data, energy characteristic parameters, and power load data for the urban-rural fringe area. The power load data includes the first parameter, the second parameter, and the third parameter.

[0024] In this step, the urban-rural fringe refers to the transitional area between urban and rural areas. Meteorological data refers to data reflecting the meteorological conditions of this area. Energy characteristic parameters refer to parameters describing the performance of energy storage equipment. Electricity load data refers to core data related to electricity load. The first parameter refers to the maximum power generation capacity parameter of new energy power generation equipment. The second parameter refers to the power grid's ability to transmit electricity. The third parameter refers to parameters related to user electricity consumption behavior, specifically including historical benchmark load and load fluctuation coefficient. The historical benchmark load refers to the average power consumption value of users within a preset historical period; the load fluctuation coefficient is the ratio of the standard deviation of user power consumption to the average power consumption within a historical period, used to reflect the stability characteristics of user electricity demand.

[0025] In this embodiment of the application, meteorological data, energy characteristic parameters and power load data of urban-rural fringe areas are acquired by a data acquisition device, wherein the power load data includes a first parameter, a second parameter and a third parameter.

[0026] Step 102: Analyze meteorological data and power load data to obtain power load forecasting results considering multiple constraints under different meteorological conditions. The multiple constraints include the first constraint, the second constraint, and the third constraint.

[0027] In this step, the power load forecast result refers to the predicted value of the power load. The first constraint is the limitation on the maximum power generation capacity of new energy sources. The second constraint is the limitation on the maximum transmission capacity of the power grid. The third constraint is the limitation on the stability of user power consumption, specifically using load volatility as a metric. Load volatility is the ratio of the standard deviation of user power load to the average power load within a preset time window, used to indicate the degree to which the load deviates from the average level.

[0028] Step 103: Based on the power load forecast results and combined with energy characteristic parameters, adjust the charging and discharging efficiency of the energy storage unit to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period.

[0029] In this step, charge / discharge efficiency refers to the efficiency of the energy storage unit in charging and discharging. Charging parameters refer to the optimized charging parameters during off-peak hours. Discharging parameters refer to the optimized discharging parameters during peak hours.

[0030] Step 104: Based on the first parameter, convert the distributed power access in the urban-rural fringe into integrated power that is compatible with the grid access standard.

[0031] In this step, grid access standards refer to the technical specifications of the power grid for connected electrical energy. Integrated electrical energy refers to centralized electrical energy that conforms to grid access standards.

[0032] Step 105: Based on the charging parameters, discharging parameters, and power load forecast results, determine the allocation ratio and transmission path of integrated power to generate a power allocation scheme.

[0033] In this step, the allocation ratio refers to the proportion of consolidated electrical energy distributed to different users. The transmission path refers to the route along which consolidated electrical energy is transmitted from the source to the user. The energy allocation scheme refers to the plan regarding the allocation ratio and transmission path of consolidated electrical energy.

[0034] Step 106: Based on the power allocation scheme, construct a demand-side response trading mechanism, which includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

[0035] In this process, the demand-side response trading mechanism refers to the rule system for demand-side participation in electricity trading. The target price mechanism refers to the rules for determining electricity trading prices. The supply-demand matching rules refer to the rules for coordinating electricity supply and demand. The trading execution process refers to the operational steps of electricity trading.

[0036] The embodiments of this application realize the efficient utilization and standardized trading of electricity resources in urban-rural fringe areas, and improve the accuracy and feasibility of demand-side response.

[0037] This application provides a specific embodiment. Step 102 involves analyzing meteorological data and power load data to obtain power load forecasting results considering multiple constraints under different meteorological conditions. The multiple constraints include a first constraint, a second constraint, and a third constraint. Specifically, the steps are as follows: Step 201: Based on the preset meteorological type, classify the meteorological data to obtain the target meteorological data, which includes light intensity data, wind speed data, temperature data, and precipitation data.

[0038] In this step, the preset weather type refers to the pre-defined basic category used to classify meteorological data. The target meteorological data refers to the data set obtained after classifying meteorological data according to the preset weather type. Light intensity data refers to quantitative data reflecting the intensity of sunlight. Wind speed data refers to quantitative data reflecting the speed of air movement. Temperature data refers to quantitative data reflecting the degree of environmental temperature. Precipitation data refers to quantitative data reflecting the amount of rainfall or snowfall.

[0039] In this embodiment of the application, the meteorological data of the urban-rural fringe area is classified based on the preset meteorological type. The meteorological data is divided into several categories such as light intensity, wind speed, temperature and precipitation, thereby obtaining the target meteorological data. The data of each category corresponds to light intensity data, wind speed data, temperature data and precipitation data, respectively.

[0040] Step 202: Extract historical records from the power load data, match the historical records with the target meteorological data, and obtain the associated dataset.

[0041] In this step, historical records refer to electricity load data records of the urban-rural fringe area over a past period. The associated dataset refers to the dataset formed by linking historical records and target meteorological data over time.

[0042] In this embodiment of the application, historical records are extracted from the power load data, and then these historical records are matched with the target meteorological data according to the time dimension, so that the historical records at the same time point are associated with the corresponding light intensity data, wind speed data, temperature data and precipitation data, forming a related dataset containing time correlation.

[0043] Step 203: Determine the constraint boundary values ​​of the associated dataset based on the upper limit threshold of the multiple constraints. The constraint boundary values ​​include the first boundary value, the second boundary value, and the third boundary value.

[0044] In this step, the upper limit threshold refers to the maximum allowable value specified by each constraint in the multi-constraint set. The constraint boundary value refers to the numerical value used to limit the data range in the associated dataset, determined based on the upper limit threshold. The first boundary value is the boundary value determined based on the upper limit threshold of the first constraint. The second boundary value is the boundary value determined based on the upper limit threshold of the second constraint. The third boundary value is the boundary value determined based on the upper limit threshold of the third constraint. Specifically, the process for determining the third boundary value is as follows: First, obtain the upper limit threshold of the third constraint, which is the maximum allowable percentage of load fluctuation in the system; then, calculate the average electricity load value within a specific time period in the associated dataset; finally, multiply the average electricity load value by the maximum load fluctuation percentage, and the resulting product is the third boundary value. This third boundary value represents the maximum allowable absolute deviation of the load from the baseline value while meeting the requirements for stable power consumption.

[0045] In this embodiment of the application, the limitation range of each data item in the associated dataset is determined according to the upper limit threshold of multiple constraints, and a first boundary value, a second boundary value and a third boundary value are obtained. The first boundary value, the second boundary value and the third boundary value are integrated to obtain the constraint boundary value.

[0046] Step 204: Based on the associated dataset, calculate the influence weights corresponding to the target meteorological data. The influence weights include the first weight, the second weight, the third weight, and the fourth weight.

[0047] In this step, the influence weight refers to the quantitative index of the degree of influence of different meteorological elements on the power load. The first weight is the quantitative index of the influence of sunlight intensity on the power load. The second weight is the quantitative index of the influence of wind speed on the power load. The third weight is the quantitative index of the influence of temperature on the power load. The fourth weight is the quantitative index of the influence of precipitation on the power load.

[0048] In this embodiment of the application, based on the associated dataset, the degree of correlation between each meteorological element in the target meteorological data and historical records is analyzed, and the influence weight of each meteorological element on the power load is calculated. The specific calculation steps are as follows: First, extract the light intensity sequence, wind speed sequence, temperature sequence, precipitation sequence and corresponding power load sequence within a preset historical period from the associated dataset; then, use statistical methods to calculate the covariance between the light intensity sequence, wind speed sequence, temperature sequence and precipitation sequence and the power load sequence respectively, and take their absolute values ​​to obtain the light correlation value, wind speed correlation value, temperature correlation value and precipitation correlation value respectively; then, sum the light correlation value, wind speed correlation value, temperature correlation value and precipitation correlation value to obtain the total correlation feature value; finally, divide the correlation value corresponding to each meteorological element by the total correlation feature value, and the quotient is the influence weight of the corresponding meteorological element. The first weight is calculated by dividing the illumination correlation value by the total correlation characteristic value; the second weight is calculated by dividing the wind speed correlation value by the total correlation characteristic value; the third weight is calculated by dividing the temperature correlation value by the total correlation characteristic value; and the fourth weight is calculated by dividing the precipitation correlation value by the total correlation characteristic value.

[0049] Step 205: Based on the constraint boundary values, influence weights, and historical records, calculate the target predicted value corresponding to the target meteorological data. The target predicted value includes the first predicted value, the second predicted value, the third predicted value, and the fourth predicted value.

[0050] In this step, the target forecast value refers to the preliminary electricity load forecast data obtained after considering constraints and the influence of meteorological factors. The first forecast value refers to the electricity load forecast data mainly affected by sunlight intensity. The second forecast value refers to the electricity load forecast data mainly affected by wind speed. The third forecast value refers to the electricity load forecast data mainly affected by temperature. The fourth forecast value refers to the electricity load forecast data mainly affected by precipitation.

[0051] In this embodiment, adjustment coefficients are determined based on constraint boundary values. Historical records, corresponding influence weights, and adjustment coefficients are multiplied to obtain the target predicted value. Taking the calculation of the first weight as an example, if analysis of the associated dataset yields a covariance absolute value of 40 between the light intensity sequence and the power load sequence, 10 between the wind speed sequence and the load, 30 between the temperature sequence and the load, and 20 between the precipitation sequence and the load, then the total associated feature value is calculated to be 100. Subsequently, the first weight is calculated to be 0.4, the second weight to be 0.1, the third weight to be 0.3, and the fourth weight to be 0.2. After obtaining the above weights, a weighted calculation is performed using the adjustment coefficients determined by the constraint boundary values ​​and historical records to obtain the target predicted value. The first predicted value corresponds to the light intensity data, the second predicted value corresponds to the wind speed data, the third predicted value corresponds to the temperature data, and the fourth predicted value corresponds to the precipitation data.

[0052] Step 206: Based on the preset meteorological condition types, integrate the target predicted values ​​to obtain the power load prediction results considering multiple constraints under different meteorological conditions.

[0053] In this step, the preset meteorological condition type refers to the pre-defined classification type of meteorological conditions.

[0054] In this embodiment of the application, the target predicted values ​​under the corresponding meteorological conditions are integrated according to the preset meteorological condition types, such as sunny, cloudy, windy, and rainy days. The first, second, third, and fourth predicted values ​​belonging to the same meteorological condition type are integrated together to obtain the power load prediction results considering multiple constraints under different meteorological conditions.

[0055] The embodiments of this application enable accurate forecasting of power load under different meteorological conditions, thereby improving the scientific nature and applicability of the demand-side response trading mechanism design.

[0056] This application provides a specific embodiment. Step 103 involves adjusting the charging and discharging efficiency of the energy storage unit based on power load forecast results and energy characteristic parameters to obtain charging parameters during off-peak hours and discharging parameters during peak hours. This specifically includes the following steps: Step 301: Extract the power load values ​​for each time period from the power load forecast results, and determine the low-load and high-load periods based on the magnitude of the power load values.

[0057] In this step, the power load values ​​for each time period refer to the quantitative data of the power load size corresponding to different time periods in the power load forecast results.

[0058] In this embodiment of the application, the specific power load value corresponding to each hour of the day is extracted from the power load forecast results. A statistical threshold is calculated based on the historical power load data of the urban-rural fringe area for the same period, or the load threshold preset by the power grid operation is referenced as the standard for determining high and low load. Then, the power load value of each hour is compared with the above threshold. The time period when the load value of several consecutive hours is lower than the low threshold is determined as the power load low period, and the time period when the load value of several consecutive hours is higher than the high threshold is determined as the power load high period.

[0059] Step 302: Extract the maximum charging efficiency, maximum discharging efficiency, and capacity limitation parameters from the energy characteristic parameters. Calculate the charging reference value based on the maximum charging efficiency, and simultaneously calculate the discharging reference value based on the maximum discharging efficiency.

[0060] In this step, maximum charging efficiency refers to the highest charging efficiency that the energy storage unit can achieve under ideal conditions. Maximum discharging efficiency refers to the highest discharging efficiency that the energy storage unit can achieve under ideal conditions. Capacity limitation parameters are quantified indicators of the maximum allowable charge and discharge capacity of the energy storage unit. The charging reference value refers to the reference efficiency value determined based on the maximum charging efficiency for subsequent calculations. The discharging reference value refers to the reference efficiency value determined based on the maximum discharging efficiency for subsequent calculations.

[0061] In this embodiment, the maximum charging efficiency, maximum discharging efficiency, and capacity limitation parameters of the energy storage unit are extracted from the energy characteristic parameters. The maximum charging efficiency is multiplied by a preset reference ratio to obtain a charging reference value, and the maximum discharging efficiency is multiplied by a preset reference ratio to obtain a discharging reference value. The preset reference ratio refers to a fixed ratio that is pre-set to convert the maximum charging efficiency into a reference value.

[0062] Step 303: Based on the off-peak electricity load period and the corresponding electricity load value, and in combination with the capacity limitation parameters, calculate the charging adjustment coefficient of the energy storage unit during the off-peak electricity load period, and based on the peak electricity load period and the corresponding electricity load value, and in combination with the capacity limitation parameters, calculate the discharging adjustment coefficient of the energy storage unit during the peak electricity load period.

[0063] In this step, the charging adjustment factor refers to the coefficient used to adjust the charging efficiency based on the load conditions and capacity limitation parameters during periods of low electricity load. The discharging adjustment factor refers to the coefficient used to adjust the discharging efficiency based on the load conditions and capacity limitation parameters during periods of high electricity load.

[0064] In this embodiment, for periods of low power load, a charging adjustment coefficient is obtained by combining the corresponding power load value and capacity limit parameter. The ratio of the power load value to the capacity limit parameter during the low power load period is multiplied by the adjustment ratio to obtain the charging adjustment coefficient. Meanwhile, for periods of high power load, a discharging adjustment coefficient is obtained by combining the corresponding power load value and capacity limit parameter. The ratio of the power load value to the capacity limit parameter during the high power load period is multiplied by the adjustment ratio to obtain the discharging adjustment coefficient. The adjustment ratio refers to a proportional coefficient set according to the actual scenario to balance the relationship between load and capacity.

[0065] Step 304: Multiply the charging adjustment coefficient and the charging reference value to obtain the charging parameters during the off-peak period of power load, and multiply the discharging adjustment coefficient and the discharging reference value to obtain the discharging parameters during the peak period of power load.

[0066] In this embodiment, the charging adjustment coefficient is multiplied by the charging reference value to obtain the charging parameters during the off-peak period of power load, and the discharging adjustment coefficient is multiplied by the discharging reference value to obtain the discharging parameters during the peak period of power load.

[0067] Step 305: Based on the charging and discharging parameters, adjust the charging and discharging efficiency of the energy storage unit to obtain the charging parameters during off-peak hours and the discharging parameters during peak hours.

[0068] In this embodiment, based on the charging parameters, the charging and discharging efficiency of the energy storage unit during periods of low power load is adjusted so that the charging and discharging efficiency during these periods matches the charging parameters; simultaneously, based on the discharging parameters, the charging and discharging efficiency of the energy storage unit during periods of high power load is adjusted so that the charging and discharging efficiency during these periods matches the discharging parameters, thus obtaining the charging parameters during periods of low power load and the discharging parameters during periods of high power load.

[0069] The embodiments of this application achieve precise control of charging and discharging efficiency at different times, enabling the energy storage unit to better adapt to the peak and off-peak changes in power load.

[0070] For example, in a scenario of a suburban area (A), the power load values ​​for each time period of the 24-hour day are extracted from the power load forecast results. The values ​​from 0:00 to 6:00 are between 200 and 300 kW, identified as low-load periods; the values ​​from 18:00 to 22:00 are between 800 and 1000 kW, identified as high-load periods. From the energy characteristic parameters, the maximum charging efficiency is extracted to be 90%, the maximum discharging efficiency to be 85%, and the capacity limit parameter to be 500 kW. With a preset baseline ratio of 0.9, the calculated charging baseline value is 81%, and the discharging baseline value is 76.5%. Simultaneously, for multiple constraints, an upper limit threshold for the third constraint is set. The maximum load fluctuation rate is 15%. If the historical average load of a certain period in the associated dataset is 600kW, then the calculated third boundary value is 90kW. The power load value corresponding to the power load off-peak period is 250kW, the adjustment ratio is 1.2, and the charging adjustment coefficient is 0.6. The power load value corresponding to the power load peak period is 900kW, and the discharging adjustment coefficient is 2.16. The charging parameter is 48.6%, and the discharging parameter is 165.24%. The charging and discharging efficiency of the energy storage unit is adjusted according to the charging and discharging parameters, and finally the charging parameter during the off-peak period is determined to be 48.6%, and the discharging parameter during the peak period is corrected to 85%.

[0071] This application provides a specific embodiment, such as Figure 2 As shown, step 104, based on the first parameter, converts the distributed power access in the urban-rural fringe into integrated power that is compatible with the grid access standard, specifically including the following steps: Step 401: Based on the preset energy type, classify the distributed power connected to the urban-rural fringe area to obtain the classified distributed power, which includes photovoltaic power, wind power and biomass power.

[0072] In this step, the preset energy type refers to the pre-defined category of energy sources used to classify distributed electricity. Classified distributed electricity refers to the collection of electricity obtained after classifying distributed electricity accessed in urban-rural fringe areas according to the preset energy type. Photovoltaic electricity refers to electricity generated through solar photovoltaic devices. Wind power refers to electricity generated through wind power generation devices. Biomass electricity refers to electricity generated through biomass power generation devices.

[0073] In this embodiment of the application, the distributed power accessed in the urban-rural fringe is classified based on the preset energy type, and divided into photovoltaic power, wind power and biomass power, thereby obtaining the classified distributed power.

[0074] Step 402: Extract the maximum output power limits of photovoltaic power, wind power and biomass power from the first parameter respectively, and combine all the maximum output power limits to obtain the capacity limit value.

[0075] In this step, the maximum output power limit refers to the maximum permissible output power specified in the first parameter for each type of distributed power. The capacity limit is the sum of the maximum output power limits for all types of distributed power.

[0076] In this embodiment of the application, the maximum output power limit corresponding to photovoltaic power, wind power, and bioelectric power are extracted from the first parameter respectively. The maximum output power limit corresponding to photovoltaic power, wind power, and bioelectric power is added together to obtain the capacity limit value.

[0077] Step 403: Obtain the access parameters of the power grid access standard. The access parameters include voltage level parameters, frequency range parameters, and harmonic content parameters.

[0078] In this step, the grid access standard access parameters refer to the technical requirements parameters of the grid for the accessed electrical energy; the voltage level parameters refer to the voltage standards that the grid requires the accessed electrical energy to meet; the frequency range parameters refer to the frequency range that the grid requires the accessed electrical energy to meet; and the harmonic content parameters refer to the allowable content of harmonic components in the accessed electrical energy.

[0079] In this application embodiment, the various technical requirements parameters specified in the power grid access standard are obtained, namely the access parameters of the power grid access standard. These parameters specifically include the voltage level parameters that the power must meet, the allowable frequency range parameters, and the harmonic content parameters of the harmonic components.

[0080] Step 404: Based on the maximum output power limit and access parameters, perform power electronic transformation processing on the classified distributed power to obtain photovoltaic power conversion, wind power conversion, and biomass power conversion.

[0081] In this step, photovoltaic power conversion refers to the electrical energy that meets the grid connection parameter requirements after photovoltaic power has undergone power electronic transformation. Wind power conversion refers to the electrical energy that meets the grid connection parameter requirements after wind power has undergone power electronic transformation. Biomass power conversion refers to the electrical energy that meets the grid connection parameter requirements after biomass power has undergone power electronic transformation.

[0082] Photovoltaic energy conversion is electrical energy formed after power electronic transformation. Its voltage and frequency are key parameters constituting this electrical energy, and must meet grid connection standards to achieve grid compatibility. For photovoltaic energy in the classified distributed power generation, power electronic transformation is performed in combination with its corresponding maximum output power limit and grid connection standard parameters. The voltage of the photovoltaic energy conversion is equal to the voltage level parameter in the connection parameters, so that the voltage of the photovoltaic energy conversion is consistent with the voltage level required by the grid. The frequency of the photovoltaic energy conversion is equal to the target value within the frequency range parameter in the connection parameters. At the same time, the harmonic content of the photovoltaic energy conversion is controlled to be less than or equal to the harmonic content parameter in the connection parameters. Through processing, the harmonic components in the electrical energy are made to not exceed the allowable content specified by the grid, and finally, photovoltaic energy conversion is obtained.

[0083] For wind power in the classified distributed energy sector, power electronic transformation processing is performed based on its corresponding maximum output power limit and grid access standard parameters. The voltage of the converted wind power is equal to the voltage level parameter in the access parameters; the frequency of the converted wind power is equal to the target value within the frequency range parameter in the access parameters, ensuring that the frequency is within the allowable range; simultaneously, the harmonic content of the converted wind power is controlled to be less than or equal to the harmonic content parameter in the access parameters, thus obtaining the converted wind power energy. For biomass power in the classified distributed energy sector, power electronic transformation processing is performed based on its corresponding maximum output power limit and grid access standard parameters. The voltage of the converted biomass power is equal to the voltage level parameter in the access parameters; the frequency of the converted biomass power is equal to the target value within the frequency range parameter in the access parameters; simultaneously, the harmonic content of the converted biomass power is controlled to be less than or equal to the harmonic content parameter in the access parameters, thus obtaining the converted biomass energy.

[0084] Step 405: Calculate the total power based on photovoltaic power conversion, wind power conversion, and biomass power conversion. Based on the total power, process the photovoltaic power conversion, wind power conversion, and biomass power conversion to obtain integrated power that is compatible with the grid access standard.

[0085] In this step, total power refers to the sum of the power converted from photovoltaic power, wind power, and biomass power.

[0086] In this embodiment of the application, the power of photovoltaic power conversion, wind power conversion, and biomass power conversion are added together to obtain the total power. Then, based on the total power, the photovoltaic power conversion, wind power conversion, and biomass power conversion are processed. If the total power exceeds the capacity limit, the power of each power conversion is reduced proportionally. If it does not exceed the limit, it is directly integrated to finally obtain integrated power that is compatible with the grid access standard.

[0087] The embodiments of this application realize the conversion of distributed power from multiple sources into integrated power that conforms to grid access standards, thereby improving the compatibility and stability of power access in urban-rural fringe areas.

[0088] For example, in a scenario of a suburban area (A), the preset energy types are photovoltaic (PV), wind power, and biomass energy. After classifying the distributed power access, we obtain PV power, wind power, and biomass power. From the first parameter, the maximum output power limit is extracted as 500kW for PV power, 300kW for wind power, and 200kW for biomass power, with a calculated capacity limit of 1000kW. The grid access standard access parameters are obtained: voltage level parameter is 380V, frequency range parameter is 50±0.5Hz, and harmonic content parameter is below 5%. Based on the maximum output power limit and access parameters, the PV power is processed to achieve a voltage of 380V, a frequency of 50Hz, and a harmonic content of 3%, resulting in PV-converted power of 450kW. Similarly, wind-converted power of 280kW and biomass-converted power of 180kW are obtained. The total calculated power is 910kW. Since it does not exceed the capacity limit of 1000kW, the three are directly combined to obtain integrated power that conforms to the grid access standard.

[0089] This application provides a specific embodiment. Step 405 involves processing the photovoltaic-converted electrical energy, wind-converted electrical energy, and biomass-converted electrical energy based on the total power to obtain integrated electrical energy adapted to the grid access standard. This specifically includes the following steps: Step 411: If the total power is less than the total capacity limit of the first parameter, then photovoltaic power conversion, wind power conversion, and biomass power conversion are taken as target photovoltaic power, target wind power, and target biomass power.

[0090] In this step, the total capacity limit refers to the upper limit of the total power of all types of distributed electrical energy specified in the first parameter.

[0091] In this embodiment of the application, the relationship between the calculated total power and the total capacity limit of the first parameter is determined. If the total power is less than the total capacity limit, it means that the total power of each converted power has not exceeded the specified upper limit and no power adjustment is required. Therefore, photovoltaic converted power is directly used as the target photovoltaic power, wind power converted power is used as the target wind power, and biomass converted power is used as the target biomass power.

[0092] Step 412: If the total power is greater than or equal to the total capacity limit of the first parameter, then based on the capacity limit of each type of distributed power, the photovoltaic power conversion, wind power conversion, and biomass power conversion are adjusted to obtain the target photovoltaic power, target wind power, and target biomass power.

[0093] In this step, the capacity limit values ​​for each type of distributed power refer to the maximum output power limit for photovoltaic power, wind power, and biomass power, respectively.

[0094] In this embodiment, if the total power is greater than or equal to the total capacity limit of the first parameter, it indicates that the current total power exceeds the prescribed upper limit and needs to be adjusted. The adjustment ratio is determined based on the capacity limit values ​​of each type of distributed power, and the ratio of the total capacity limit to the total power is used as the adjustment ratio. The photovoltaic power conversion power, wind power conversion power, and biomass power conversion power are multiplied by the adjustment ratio respectively to obtain the target photovoltaic power, target wind power, and target biomass power. The target photovoltaic power, target wind power, and target biomass power are obtained through the above calculations to ensure that the total power does not exceed the total capacity limit.

[0095] Step 413: Combine the target photovoltaic power, target wind power, and target biomass power to obtain integrated power that is compatible with the grid access standard.

[0096] In this step, the integrated power energy that adapts to the grid access standard refers to the power energy that conforms to the grid access standard after the various types of converted power energy have been combined and adjusted.

[0097] In this embodiment of the application, the target photovoltaic power, target wind power, and target bio-power are merged. During the merging process, the voltage, frequency, and harmonic content of each power are kept in accordance with the grid access standard access parameters, and finally integrated power that is adapted to the grid access standard is obtained.

[0098] The embodiments of this application ensure that the integrated power meets both capacity limitation requirements and grid access standards, thereby improving the compliance and reliability of power integration.

[0099] This application provides a specific embodiment. Step 105 involves determining the allocation ratio and transmission path of integrated electrical energy based on charging parameters, discharging parameters, and power load prediction results to generate an electrical energy allocation scheme. This specifically includes the following steps: Step 501: Extract the first predicted load during the low-load period and the second predicted load during the peak-load period from the power load forecast results, and combine the first predicted load and the second predicted load to obtain the predicted load dataset.

[0100] In this step, the first predicted load refers to the quantified data of the predicted load size during off-peak hours. The second predicted load refers to the quantified data of the predicted load size during peak hours. The predicted load dataset is the dataset formed by combining the first and second predicted loads.

[0101] In this embodiment of the application, the predicted load values ​​corresponding to the low-load period and the predicted load values ​​corresponding to the high-load period are selected from the power load forecast results. These two types of values ​​are then arranged and combined in chronological order to form a predicted load dataset containing predicted load information for different time periods.

[0102] Step 502: Based on the charging parameters, calculate the planned charging amount during the off-peak period of power load, and based on the discharging parameters, calculate the planned discharging amount during the peak period of power load. Integrate the planned charging amount and the planned discharging amount to obtain the energy dispatch set.

[0103] In this step, the planned charging amount during off-peak hours refers to the total amount of electrical energy planned to be charged into the energy storage unit based on charging parameters during off-peak hours. The planned discharging amount during peak hours refers to the total amount of electrical energy planned to be discharged from the energy storage unit based on discharging parameters during peak hours. The energy dispatch set refers to the set of parameters used to dispatch energy storage units, formed by integrating the planned charging amount and planned discharging amount.

[0104] In this embodiment, the charging parameters are multiplied by the off-peak period duration to obtain the planned charging amount during the off-peak period, where the off-peak period duration refers to the duration of the off-peak period. The planned discharging amount during the peak period is equal to the discharging parameters multiplied by the peak period duration, where the peak period duration refers to the duration of the peak period. Subsequently, the calculated planned charging amount and planned discharging amount are integrated according to the time period correspondence to obtain an energy scheduling set, which is used to guide the charging and discharging scheduling of energy storage units.

[0105] Step 503: Based on the third parameter, determine the electricity priority of different users. Based on the electricity priority, combined with the predicted load dataset, energy dispatch set and the second parameter, calculate the allocation ratio of integrated power to each user. Combine the allocation ratios of all users to obtain the allocation ratio table.

[0106] In this step, the electricity priority of different users refers to the order in which users are allocated electricity, determined according to the third parameter. The allocation ratio for each user refers to the proportion of total integrated electricity allocated to each user. The allocation ratio table is a table formed by combining the allocation ratios of all users at different time periods.

[0107] In this embodiment, determining the electricity priority of different users specifically involves: obtaining the historical baseline load and load fluctuation coefficient of each user; multiplying the historical baseline load of each user by the load fluctuation coefficient to obtain the comprehensive weight value of each user's electricity consumption; and arranging the comprehensive weight values ​​of each user from largest to smallest to determine the electricity priority of different users. In this process, the electricity priority serves as a proportional weight for electricity allocation, ensuring that when the total amount is limited, each user can obtain a proportionally equitable electricity allocation based on their own basic electricity consumption scale and fluctuation characteristics. Based on the electricity priority, combined with the predicted load dataset, energy storage scheduling parameters, and a second parameter, the integrated electricity proportion to be allocated to each user in different time periods is calculated, i.e., the allocation proportion of each user. The allocation proportion of each user is obtained by dividing the product of the user's electricity priority coefficient and the user's predicted load by the sum of the products of all users' electricity priority coefficients and their respective predicted loads. The electricity priority coefficient is a quantified coefficient assigned based on the electricity priority; the higher the priority, the larger the coefficient. The allocation proportions of all users during off-peak and peak periods are combined to obtain an allocation proportion table.

[0108] Step 504: Based on the allocation ratio table and power load data, determine the transmission path for integrated power transmission to the corresponding users, integrate the allocation ratio table and transmission path to obtain the power allocation scheme.

[0109] In this step, the transmission path refers to the power transmission lines through which the integrated electrical energy is transmitted from the power supply end to each user end.

[0110] In this embodiment of the application, based on the power allocation of each user in the allocation ratio table at different time periods, and combined with the second parameter in the power load data, a transmission line that can meet the power allocation transmission needs of each user at different time periods is selected, and the transmission path for integrating power transmission to the corresponding user is determined; then the allocation ratio table and the determined transmission path are integrated according to the time and user correspondence to form a power allocation scheme containing allocation ratio and transmission path information.

[0111] This application embodiment processes load forecasting, energy storage scheduling, and user allocation information in different time periods, and determines the allocation ratio and transmission path in combination with the grid transmission capacity to generate a comprehensive power allocation scheme, thereby improving the rationality and feasibility of power allocation.

[0112] For example, in the scenario of a suburban area A, the first predicted load during off-peak hours is 300kW, and the second predicted load during peak hours is 900kW, extracted from the power load forecast results. These are combined to obtain the predicted load dataset. The charging parameters are 50kW / h, the off-peak period lasts for 6 hours, and the planned charging amount is 300kW. h; discharge parameters are 80kW / h, peak period duration is 4 hours, planned discharge capacity is 320kW. h, integrate to obtain the energy dispatch set; based on the historical benchmark load and load fluctuation coefficient of industrial user A in the third parameter, its comprehensive power consumption weight value is calculated to be 150, which is higher than the comprehensive power consumption weight value of residential user B calculated based on the historical benchmark load and load fluctuation coefficient, which is 80. Thus, industrial user A is determined to have a higher priority than residential user B, with power consumption priority coefficients of 1.5 and 1.0 respectively. The allocation ratio of A during off-peak hours is calculated to be 0.75, and that of B is 0.25. Combine to obtain the allocation ratio table; according to the allocation ratio and the second parameter, it is determined that user A is transmitted by line 1, and user B is transmitted by line 2. After integration, the power allocation scheme is obtained.

[0113] This application provides a specific embodiment. Step 106 involves constructing a demand-side response trading mechanism based on an electricity allocation scheme. The demand-side response trading mechanism includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints. Specifically, it includes the following steps: Step 601: Based on the renewable energy generation cost, grid transmission cost, and user electricity cost in the power load data, and combined with the allocation ratio in the power allocation scheme, calculate the basic transaction price for different time periods.

[0114] In this step, the cost of renewable energy generation refers to a quantitative indicator used to measure the costs incurred during the renewable energy generation process. The grid transmission cost refers to a quantitative indicator used to measure the costs incurred during the transmission of electricity through the power grid. The user electricity cost refers to a quantitative indicator used to measure the costs incurred by users during the use of electricity. The base transaction price for different time periods refers to the benchmark price for electricity trading calculated based on cost parameters and allocation ratios for different time periods.

[0115] In this embodiment of the application, the product of the new energy power generation cost and cost weight one, the product of the grid transmission cost and cost weight two, and the product of the user electricity cost and cost weight three are added together and then multiplied by the allocation ratio coefficient to obtain the basic transaction price. Among them, cost weight one, cost weight two, and cost weight three are weight coefficients that are preset according to the importance of each type of cost in the total transaction price, and the allocation ratio coefficient is a coefficient that adjusts the total cost according to the allocation ratio.

[0116] Step 602: Based on the supply and demand gap determined by the basic transaction price and the electricity load forecast, determine the price fluctuation coefficient, integrate the basic transaction price and the price fluctuation coefficient to obtain the target price mechanism.

[0117] In this step, the supply-demand gap in the electricity load forecast refers to the difference between the predicted electricity demand and the predicted electricity supply. The price fluctuation factor is a coefficient used to adjust the base transaction price based on the supply-demand gap.

[0118] In this embodiment, based on the power load forecast results, the difference between the power demand and supply for each time period is calculated, i.e., the supply-demand gap of the power load forecast results. If the demand is greater than the supply, the gap is positive; if the demand is less than the supply, the gap is negative. A price fluctuation coefficient is determined based on this supply-demand gap. When the gap is positive, the price fluctuation coefficient is greater than 1, indicating an upward price movement; when the gap is negative, the price fluctuation coefficient is less than 1, indicating a downward price movement. The price fluctuation coefficient is determined by multiplying the quotient of the supply-demand gap and the total supply by a fluctuation ratio and then adding 1. The fluctuation ratio is a pre-set proportional coefficient used to control the price fluctuation range. Subsequently, the base transaction price is multiplied by the corresponding price fluctuation coefficient to obtain the actual transaction price calculation method for each time period, and these are integrated to form a target price mechanism. This mechanism clarifies the rules for determining transaction prices under different supply and demand conditions.

[0119] Step 603: Based on the electricity priority of different users, combined with the power allocation scheme and power load forecast results, determine the supply and demand matching rules, and determine the transaction execution process according to multiple constraints and the power allocation scheme. The transaction execution process includes constraints before transaction execution, real-time adjustment thresholds during transaction execution, and recording requirements after transaction execution.

[0120] In this step, the supply-demand matching rule refers to the rules used to coordinate the matching relationship between electricity supply and user demand. The transaction execution process refers to the operational steps and requirements of the entire electricity transaction process, from preparation to completion. Pre-transaction constraints refer to items that need to be verified to meet multiple constraint requirements before transaction execution. Real-time adjustment thresholds during transaction execution refer to the boundary values ​​that require real-time adjustments when certain parameters reach a certain value during the transaction execution process. Post-transaction recording requirements refer to the specific specifications for recording the transaction results after the transaction is completed.

[0121] In this embodiment, based on the electricity consumption priorities of different users, users with higher priorities are given priority in supply matching. Combining the power allocation scheme and power load forecast results, a supply and demand matching rule is determined. The rule stipulates that power should be allocated to high-priority users first, and the remaining power should be allocated to low-priority users proportionally. At the same time, based on multiple constraints and the power allocation scheme, a transaction execution process is determined: before the transaction is executed, it is necessary to check whether each constraint is met, i.e., the constraints before the transaction is executed, such as whether the generation capacity meets the standard and whether the transmission capacity is sufficient; during the transaction execution, a real-time adjustment threshold is set, and if the load fluctuation exceeds the threshold, the allocation amount needs to be adjusted; after the transaction is executed, the recording requirements are clearly defined, such as recording the actual allocated power and the transaction price, thus forming a complete transaction execution process.

[0122] Step 604: Integrate the target price mechanism, supply and demand matching rules, and transaction execution process to obtain the demand-side response transaction mechanism.

[0123] In this embodiment, the target price mechanism, supply and demand matching rules, and transaction execution process are organically integrated to ensure that the three are interconnected in terms of time and logic, forming a complete and executable system, namely the demand-side response transaction mechanism, which can guide the electricity trading activities in urban-rural fringe areas.

[0124] The embodiments of this application improve the standardization and flexibility of electricity trading and promote supply and demand balance.

[0125] Figure 3 This application provides a schematic diagram of a specific implementation of a demand-side response transaction mechanism design system based on multiple constraints, as illustrated in the embodiments of this application. Figure 3 The system may include: The acquisition module 21 is used to acquire meteorological data, energy characteristic parameters and power load data of urban-rural fringe areas. The power load data includes a first parameter, a second parameter and a third parameter.

[0126] Analysis module 22 is used to analyze meteorological data and power load data to obtain power load forecast results considering multiple constraints under different meteorological conditions. The multiple constraints include the first constraint, the second constraint, and the third constraint.

[0127] The adjustment module 23 is used to adjust the charging and discharging efficiency of the energy storage unit based on the power load forecast results and in combination with the energy characteristic parameters, so as to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period.

[0128] The conversion module 24 is used to convert the distributed power accessed in the urban-rural fringe into integrated power that is compatible with the grid access standard, based on the first parameter.

[0129] The determination module 25 determines the allocation ratio and transmission path of integrated power based on charging parameters, discharging parameters, and power load prediction results, in order to generate a power allocation scheme.

[0130] Module 26 is used to build a demand-side response trading mechanism based on the power allocation scheme. The demand-side response trading mechanism includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

[0131] This application provides a multi-constraint-based demand-side response transaction mechanism design system to implement the aforementioned multi-constraint-based demand-side response transaction mechanism design method. Therefore, the specific implementation of the multi-constraint-based demand-side response transaction mechanism design system can be found in the embodiment section of the multi-constraint-based demand-side response transaction mechanism design method above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0132] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a multi-constraint demand-side response transaction mechanism design method.

[0133] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described design methods for a demand-side response transaction mechanism based on multiple constraints.

[0134] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0135] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the demand-side response transaction mechanism design method based on multiple constraints.

[0136] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software 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 implementations should not be considered beyond the scope of this invention.

[0137] The foregoing has provided a detailed description of the design method and system for a demand-side response transaction mechanism based on multiple constraints provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A design method for a demand-side response transaction mechanism based on multiple constraints, characterized in that, include: Meteorological data, energy characteristic parameters, and power load data of urban-rural fringe areas are acquired, wherein the power load data includes a first parameter, a second parameter, and a third parameter; The meteorological data and the power load data are analyzed to obtain power load forecasting results considering multiple constraints under different meteorological conditions. The multiple constraints include a first constraint, a second constraint, and a third constraint. Based on the power load forecast results and combined with the energy characteristic parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period. Based on the first parameter, the distributed power accessed in the urban-rural fringe is converted into integrated power that is compatible with the grid access standard. Based on the charging parameters, the discharging parameters, and the power load prediction results, the allocation ratio and transmission path of the integrated power are determined to generate a power allocation scheme. Based on the aforementioned power allocation scheme, a demand-side response trading mechanism is constructed, which includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

2. The method according to claim 1, characterized in that, The meteorological data and the power load data are analyzed to obtain power load forecasting results considering multiple constraints under different meteorological conditions. These multiple constraints include a first constraint, a second constraint, and a third constraint, including: Based on a preset weather type, the weather data is classified to obtain target weather data, which includes light intensity data, wind speed data, temperature data, and precipitation data. Historical records are extracted from the power load data, and the historical records are matched with the target meteorological data to obtain a related dataset; Based on the upper limit threshold of the multiple constraints, the constraint boundary values ​​of the associated dataset are determined, and the constraint boundary values ​​include a first boundary value, a second boundary value, and a third boundary value. Based on the associated dataset, the influence weights corresponding to the target meteorological data are calculated, and the influence weights include a first weight, a second weight, a third weight, and a fourth weight. Based on the constraint boundary value, the influence weight, and the historical record, a target predicted value corresponding to the target meteorological data is calculated. The target predicted value includes a first predicted value, a second predicted value, a third predicted value, and a fourth predicted value. Based on preset meteorological conditions, the target predicted values ​​are integrated to obtain power load prediction results considering multiple constraints under different meteorological conditions.

3. The method according to claim 1, characterized in that, Based on the power load forecast results and combined with the energy characteristic parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain the charging parameters during off-peak hours and the discharging parameters during peak hours, including: The power load values ​​for each time period are extracted from the power load forecast results, and the low-load and high-load periods are determined based on the magnitude of the power load values. The maximum charging efficiency, maximum discharging efficiency, and capacity limitation parameters are extracted from the energy characteristic parameters. Based on the maximum charging efficiency, a charging reference value is calculated. At the same time, based on the maximum discharging efficiency, a discharging reference value is calculated. Based on the off-peak electricity load period and the corresponding electricity load value, and in conjunction with the capacity limitation parameter, the charging adjustment coefficient of the energy storage unit during the off-peak electricity load period is calculated, and based on the peak electricity load period and the corresponding electricity load value, and in conjunction with the capacity limitation parameter, the discharging adjustment coefficient of the energy storage unit during the peak electricity load period is calculated. Multiplying the charging adjustment coefficient and the charging reference value yields the charging parameters during off-peak hours, and multiplying the discharging adjustment coefficient and the discharging reference value yields the discharging parameters during peak hours. Based on the charging parameters and the discharging parameters, the charging and discharging efficiency of the energy storage unit is adjusted to obtain the charging parameters during off-peak hours and the discharging parameters during peak hours.

4. The method according to claim 1, characterized in that, Based on the first parameter, the distributed power accessed in the urban-rural fringe is converted into integrated power that conforms to the grid access standard, including: Based on the preset energy types, the distributed power accessed in the urban-rural fringe areas is classified to obtain the classified distributed power, which includes photovoltaic power, wind power and biomass power. The maximum output power limits of the photovoltaic power, the wind power, and the bioelectric power are extracted from the first parameter respectively, and all the maximum output power limits are combined to obtain the capacity limit value. Obtain the access parameters of the power grid access standard, including voltage level parameters, frequency range parameters, and harmonic content parameters; Based on the maximum output power limit and the access parameters, the classified distributed power energy is subjected to power electronic transformation processing to obtain photovoltaic power conversion, wind power conversion, and biomass power conversion. Based on the photovoltaic-converted electrical energy, the wind-converted electrical energy, and the biomass-converted electrical energy, the total power is calculated. Based on the total power, the photovoltaic-converted electrical energy, the wind-converted electrical energy, and the biomass-converted electrical energy are processed to obtain integrated electrical energy that is adapted to the grid access standard.

5. The method according to claim 4, characterized in that, Based on the total power, the photovoltaic-converted electrical energy, the wind-converted electrical energy, and the biomass-converted electrical energy are processed to obtain integrated electrical energy adapted to grid access standards, including: If the total power is less than the total capacity limit of the first parameter, then the photovoltaic power conversion, the wind power conversion, and the biomass power conversion will be taken as the target photovoltaic power, the target wind power, and the target biomass power. If the total power is greater than or equal to the total capacity limit of the first parameter, then the photovoltaic power conversion, the wind power conversion, and the biomass power conversion are adjusted based on the capacity limit of each type of distributed power to obtain the target photovoltaic power, the target wind power, and the target biomass power. The target photovoltaic power, the target wind power, and the target bio-power are combined to obtain integrated power that is compatible with grid access standards.

6. The method according to claim 1, characterized in that, Based on the charging parameters, the discharging parameters, and the power load forecast results, the allocation ratio and transmission path of the integrated power are determined to generate a power allocation scheme, including: The first predicted load during the off-peak period and the second predicted load during the peak period are extracted from the power load forecast results. The first predicted load and the second predicted load are combined to obtain the predicted load dataset. Based on the charging parameters, the planned charging amount during the off-peak period of power load is calculated, and based on the discharging parameters, the planned discharging amount during the peak period of power load is calculated. The planned charging amount and the planned discharging amount are integrated to obtain the energy dispatch set. Based on the third parameter, the electricity priority of different users is determined. Based on the electricity priority, combined with the predicted load dataset, the energy dispatch set and the second parameter, the allocation ratio of the integrated power to each user is calculated. The allocation ratios of all users are combined to obtain an allocation ratio table. Based on the allocation ratio table and the power load data, the transmission path for the integrated power transmission to the corresponding user is determined, and the allocation ratio table and the transmission path are integrated to obtain the power allocation scheme.

7. The method according to claim 1, characterized in that, Based on the aforementioned power allocation scheme, a demand-side response trading mechanism is constructed. This mechanism includes a target price mechanism, supply-demand matching rules, and a multi-constraint trading execution process, comprising: Based on the new energy generation cost, grid transmission cost, and user electricity cost in the power load data, and combined with the allocation ratio in the power allocation scheme, the basic transaction price for different time periods is calculated. Based on the supply and demand gap determined by the basic transaction price and the power load forecast, a price fluctuation coefficient is determined, and the basic transaction price and the price fluctuation coefficient are integrated to obtain a target price mechanism. Based on the electricity consumption priorities of different users, combined with the power allocation scheme and the power load forecast results, supply and demand matching rules are determined. According to the multiple constraints and the power allocation scheme, the transaction execution process is determined. The transaction execution process includes constraints before transaction execution, real-time adjustment thresholds during transaction execution, and recording requirements after transaction execution. By integrating the target price mechanism, the supply and demand matching rules, and the transaction execution process, a demand-side response transaction mechanism is obtained.

8. A design system for a demand-side response transaction mechanism based on multiple constraints, characterized in that, include: The acquisition module is used to acquire meteorological data, energy characteristic parameters, and power load data of urban-rural fringe areas. The power load data includes a first parameter, a second parameter, and a third parameter. The analysis module is used to analyze the meteorological data and the power load data to obtain power load forecast results considering multiple constraints under different meteorological conditions. The multiple constraints include a first constraint, a second constraint, and a third constraint. The adjustment module is used to adjust the charging and discharging efficiency of the energy storage unit based on the power load forecast results and the energy characteristic parameters, so as to obtain the charging parameters during the power load off-peak period and the discharging parameters during the power load peak period. The conversion module is used to convert the distributed power accessed in the urban-rural fringe into integrated power that is compatible with the grid access standard, based on the first parameter. The determination module determines the allocation ratio and transmission path of the integrated power based on the charging parameters, the discharging parameters, and the power load prediction results, in order to generate a power allocation scheme. The module is used to construct a demand-side response trading mechanism based on the power allocation scheme. The demand-side response trading mechanism includes a target price mechanism, supply and demand matching rules, and a transaction execution process under multiple constraints.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of a multi-constraint demand-side response transaction mechanism design method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the design method of a demand-side response transaction mechanism based on multiple constraints as described in any one of claims 1 to 7.

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