An energy coordination scheduling method and system based on energy saving and carbon reduction

Abstract

The application discloses an energy coordination scheduling method and system based on energy saving and carbon reduction, and relates to the field of energy coordination scheduling.The method comprises the following steps: collecting community power grid data and meteorological prediction data of a target community; calculating carbon emission intensity and load margin data of the target community, and planning an initial amount of charging rights for the target community; screening out high-carbon charging communities and low-carbon charging communities, and completing low-carbon electric energy scheduling; correcting the initial amount of charging rights in combination with the low-carbon electric energy scheduling result, obtaining an amount of charging rights, and generating a charging right bidding price of the target community; and completing optimal charging time period distribution of all charging pile users in the target community, and obtaining a charging scheduling scheme of the target community. The application can effectively solve the problem of unstable residential power consumption caused by concentrated charging.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of energy coordinated scheduling, in particular to an energy coordinated scheduling method and system based on energy saving and carbon reduction. BACKGROUND

[0002] With the intensification of global climate change and the increasing problem of energy resource shortage, the transformation of energy systems towards clean, low-carbon and high-efficiency has become an inevitable trend. Under this background, electric vehicles gradually replace fuel vehicles and rapidly penetrate in high-density communities such as urban residential communities and office parks, thereby forming large-scale charging demand. Especially in some communities with high electric vehicle penetration rate, affected by the life habits of electric vehicle users, hundreds of electric vehicles access charging piles in the same period, forming charging tides, which may cause the overload of the distribution transformer in the community and accelerate its aging, while in some periods, the charging pile utilization rate is low, which may cause insufficient clean energy consumption in the community and energy waste.

[0003] The existing method for solving charging tides and realizing energy coordinated scheduling is to directly transfer the power resources of the electric vehicle low-penetration-rate community to the electric vehicle high-penetration-rate community through the energy scheduling system, which can alleviate the charging tide problem to some extent, but does not fundamentally solve the charging tide problem. Electric vehicle users will still use charging piles in a concentrated manner, forming charging tides and affecting the stability of residential electricity consumption. SUMMARY

[0004] Embodiments of the present application provide an energy coordinated scheduling method based on energy saving and carbon reduction, which is used to solve the problem of unstable residential electricity consumption caused by concentrated charging.

[0005] To achieve the above-mentioned purpose, embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, an energy coordinated scheduling method based on energy saving and carbon reduction is provided, which comprises:

[0007] For any target community in the target area, the community digital model constructed in advance is used to continuously collect community power grid data and meteorological prediction data of the target community;

[0008] Based on the community power grid data, the carbon emission intensity and the load margin data of the target community are calculated, and the initial amount of charging rights is planned for the target community in combination with the carbon emission intensity, the load margin data and the meteorological prediction data;

[0009] Based on the carbon emission intensity, the charging high-carbon community and the charging low-carbon community are selected from all target communities in the target area, and the low-carbon energy scheduling between all charging low-carbon communities and charging high-carbon communities is completed;

[0010] For any target community, the initial issuance amount of the charging right is corrected in combination with the low-carbon electric energy scheduling result to obtain a charging right issuance amount, and a charging right auction price of the target community is generated based on the charging right issuance amount and the low-carbon electric energy scheduling result;

[0011] The optimal charging time period distribution of all charging pile users in the target community is completed in combination with the charging right issuance amount and the charging right auction price to obtain a charging scheduling scheme of the target community.

[0012] Optionally, the community power grid data includes power grid monitoring data, power grid equipment data, power grid load data, historical power consumption data and historical power generation data of all target communities, and power grid topology data and meteorological prediction data of the target region, the meteorological prediction data including illumination intensity, community temperature and community wind speed.

[0013] Optionally, the carbon emission intensity and load margin data of the target community are calculated based on the community power grid data, and the initial issuance amount of the charging right for the target community is planned in combination with the carbon emission intensity, the load margin data and the meteorological prediction data, including the following steps:

[0014] The community power generation of the target community is predicted in combination with the historical power generation data and the meteorological prediction data, and the community power consumption and the community power consumption structure of the target community are predicted in combination with the historical power consumption data and the community power generation;

[0015] The corresponding carbon emission factor is obtained according to the community power consumption structure;

[0016] The carbon emission intensity of the target community is calculated in combination with the community power consumption and the carbon emission factor;

[0017] The community load data of the target community is calculated through the power grid monitoring data and the power grid load data, and the load margin data of the target community is predicted using the community load data;

[0018] The initial issuance amount of the charging right of the target community is planned in combination with the carbon emission intensity and the load margin data.

[0019] Optionally, the community load data of the target community is calculated through the power grid monitoring data and the power grid load data, and the load margin data of the target community is predicted using the community load data, including the following steps:

[0020] The load standard capacity of the target community is determined according to the transformer basic parameters in the power grid equipment data;

[0021] The temperature correction coefficient corresponding to the transformer monitoring temperature in the power grid monitoring data is determined, and the product of the temperature correction coefficient and the load standard capacity is calculated to obtain the load rated capacity;

[0022] community load data of the target community is calculated according to historical power consumption data, the community load data including community base load and community redundancy load;

[0023] The load margin data of the target community is predicted in combination with the load rated capacity, the community base load and the community redundancy load.

[0024] Optionally, the low-carbon power scheduling between the all-charging high-carbon community and the all-charging low-carbon community includes the following steps:

[0025] The power scheduling matching between the all-charging high-carbon community and the all-charging low-carbon community is completed based on community power grid data to obtain a plurality of community pairing combinations;

[0026] Based on the power grid topology data and the pre-acquired low-carbon abandoned power cost, a scheduling objective function and a scheduling constraint condition are constructed, and the iterative solution of the scheduling objective function is completed based on the scheduling constraint condition to obtain the globally optimal scheduling parameter of the community pairing combination;

[0027] The low-carbon power scheduling between the all-charging high-carbon community and the all-charging low-carbon community in the all community pairing combination is completed according to the globally optimal scheduling parameter.

[0028] Optionally, the power scheduling matching between the all-charging high-carbon community and the all-charging low-carbon community based on community power grid data to obtain a plurality of community pairing combinations includes the following steps:

[0029] The charging pile load characteristics of the all-charging high-carbon community are extracted based on historical power consumption data, wherein the charging pile load characteristics include a set of daily charging peak periods and a daily load fluctuation rate;

[0030] The power generation margin characteristics of the all-charging low-carbon community are extracted through community power generation, wherein the power generation margin characteristics include a set of daily power generation margin periods and a daily average power generation margin, and the community power generation is predicted based on historical power generation data and meteorological prediction data;

[0031] For any charging high-carbon community, the scheduling cost characteristics between the charging high-carbon community and all charging low-carbon communities are calculated according to the power grid topology data, wherein the scheduling cost characteristics include electrical scheduling distance and electrical scheduling loss;

[0032] The scheduling matching degree between the charging high-carbon community and all charging low-carbon communities is calculated according to the charging pile load characteristics, the load margin characteristics and the scheduling cost characteristics and by using an entropy weight method;

[0033] The power scheduling matching between the all-charging high-carbon community and the all-charging low-carbon community is completed according to the scheduling matching degree to obtain a plurality of community pairing combinations, and the community pairing combination at least includes one charging high-carbon community and one charging low-carbon community.

[0034] Optionally, based on the power grid topology data and the pre-acquired low-carbon abandoned power cost, a scheduling objective function and a scheduling constraint condition are constructed, and the iterative solution of the scheduling objective function is completed based on the scheduling constraint condition, to obtain the global optimal scheduling parameters of the community pairing combination, including the following steps:

[0035] The power grid topology data and the pre-acquired charging pile operation cost are used to construct a scheduling objective function with the minimum low-carbon power scheduling cost as the target;

[0036] The scheduling constraint condition corresponding to the scheduling objective function is constructed, and the scheduling constraint condition includes power balance constraint, charging pile operation constraint, tie line constraint and climbing constraint;

[0037] For any community pairing combination, virtual phase angles and virtual powers are introduced for all charging high-carbon communities and charging low-carbon communities in the community pairing combination;

[0038] Based on the scheduling objective function, a scheduling sub-objective function is constructed for all charging high-carbon communities and charging low-carbon communities introduced by the virtual phase angles and the virtual powers based on the augmented Lagrange function;

[0039] The local optimal scheduling parameters are obtained by solving all the scheduling sub-objective functions based on the scheduling constraint condition;

[0040] The deviation objective function is obtained by minimizing the scheduling deviation between all the local optimal scheduling parameters, and the global optimal scheduling parameters are obtained by iteratively correcting the local optimal scheduling parameters based on the deviation objective function.

[0041] Optionally, the optimal charging time period allocation of all charging pile users in the target community is completed by combining the charging right issuance amount and the charging right auction price, to obtain the charging scheduling scheme of the target community, including the following steps:

[0042] For any target community, the charging right auction parameters of all charging pile users in the target community are collected through the charging pile management platform of the target region, wherein the charging right auction parameters include a charging time period set, a highest auction unit price and a charging urgency;

[0043] The optimal charging time period allocation of all charging pile users is completed by combining the charging right issuance amount, the charging right auction price and the charging right auction parameters;

[0044] The optimal charging time period allocation result is integrated to obtain the charging scheduling scheme of the charging high-carbon community.

[0045] In a second aspect, the application provides a machine-readable storage medium, which stores instructions for causing a machine to execute the energy-saving and carbon-reducing based energy coordination scheduling method according to the first aspect.

[0046] In a third aspect, the application provides an energy coordination scheduling system based on energy saving and carbon reduction, comprising:

[0047] a memory configured to store instructions; and

[0048] a processor configured to call the instructions from the memory and enable the energy coordination scheduling method based on energy saving and carbon reduction according to the first aspect when the instructions are executed.

[0049] Through the above technical solution, by pre-constructing a community digital model, collecting community power grid data and meteorological prediction data, the load margin data of the target community is accurately calculated, and the initial issuance amount of charging rights is planned for the target community according to the load margin data, which can effectively avoid that the initial issuance amount of charging rights is too large, causing the power grid of the target community to be overloaded. In addition, based on the carbon emission intensity, the charging high-carbon community and the charging low-carbon community are selected from all target communities in the target area, and low-carbon electric energy scheduling is performed between all charging low-carbon communities and charging high-carbon communities, which avoids the waste of green clean energy in the charging low-carbon community and reduces the carbon emission of the charging high-carbon community, effectively reduces the idle of electric power resources, realizes energy saving and carbon reduction. After completing the low-carbon electric energy scheduling, the clean energy available to the charging high-carbon community increases, so the initial issuance amount of charging rights can be appropriately increased, the charging right issuance amount is obtained, and the charging right bidding price of the charging high-carbon community is generated according to the final charging right issuance amount. Then, the VCG mechanism is used to simulate the form of auction to allocate charging rights, and the optimal charging period is allocated to the charging pile users of the charging high-carbon community, so that the shunting charging is realized under the condition of maximizing the satisfaction of the charging pile users, avoiding the charging tide and affecting the stability of the power grid. In summary, the application not only realizes shunting charging and maintains the stable operation of the power grid, but also reduces the carbon emission intensity of the charging high-carbon community, and improves the utilization rate of clean energy in the target area.

[0050] Other features and advantages of the application embodiments will be described in detail in the subsequent specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A flowchart of a method for energy coordination scheduling based on energy saving and carbon reduction provided by the application embodiment;

[0052] Figure 2 A structural diagram of a community digital model provided by the application embodiment;

[0053] Figure 3 A flowchart of a method for planning the initial issuance amount of charging rights provided by the application embodiment. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation described herein is only used to explain and illustrate the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0055] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, motion condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications also change accordingly.

[0056] In addition, if the embodiments of the present application involve descriptions such as "first", "second", etc., the descriptions of "first", "second", etc. are only for description purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0057] Figure 1 A flowchart of a method for energy coordination and scheduling based on energy saving and carbon reduction according to an embodiment of the present application is schematically shown. As shown in Figure 1 The present application provides a method for energy coordination and scheduling based on energy saving and carbon reduction, which can include the following steps:

[0058] S101, for any target community in the target area, continuously collecting community power grid data and weather forecast data of the target community through a pre-constructed community digital model.

[0059] S102, calculating the carbon emission intensity and load margin data of the target community based on the community power grid data, and planning the initial amount of charging rights for the target community in combination with the carbon emission intensity, load margin data and weather forecast data.

[0060] S103, selecting the charging high-carbon community and the charging low-carbon community from all target communities in the target area based on the carbon emission intensity, and completing the low-carbon power scheduling between all charging low-carbon communities and charging high-carbon communities.

[0061] S104, for any target community, the initial issuance amount of charging right is corrected in combination with the low-carbon electric energy scheduling result to obtain the charging right issuance amount, and the charging right auction price of the target community is generated based on the charging right issuance amount and the low-carbon electric energy scheduling result.

[0062] S105, the optimal charging time period distribution of all charging pile users in the target community is completed in combination with the charging right issuance amount and the charging right auction price to obtain the charging scheduling scheme of the target community.

[0063] In the embodiment, there are multiple target communities in the target region, and the target communities include target communities with high electric vehicle penetration rate and target communities with low electric vehicle penetration rate. In addition, the target communities have self-built photovoltaic (roof / parking lot photovoltaic) and small wind turbines (such as distributed wind power in the target community), which can provide clean energy, i.e. low-carbon electric energy, for daily electricity consumption and electric vehicle charging of residents in the target community. When the clean energy is insufficient, the power grid of the target region will dispatch clean energy from other communities or purchase high-carbon energy, such as thermal power, to ensure stable electricity supply for residents in the target community. The community data model is a visual model constructed by digital twinning technology, which is used to monitor community power grid data and meteorological prediction data of all target communities in the target region. Referring to Figure 2 , the community data model includes multiple modules, such as a life electricity module, a charging pile module, a power grid monitoring module, and a meteorological prediction module. The life electricity module can visually display resident electricity distribution in each target community, such as elevator electricity consumption. Historical electricity consumption data is collected through the life electricity module and the charging pile module. The power grid monitoring module provides community power grid data, and the meteorological prediction module provides meteorological prediction data. The meteorological prediction module obtains meteorological station data of the target region through networking, and then displays the data visually through the community digital model.

[0064] First, the community power generation, community electricity consumption, and community electricity consumption structure of the target community are predicted in combination with historical power generation data and historical electricity consumption data of the target community. Then, the carbon emission factor of the corresponding type is obtained according to the type of electricity consumed by the target community. The carbon emission factor refers to the carbon dioxide emission coefficient generated by unit production, consumption activity or energy consumption. The electricity consumption of each type of electricity is calculated according to the community electricity consumption structure and the community electricity consumption, and the sum of the products between the electricity consumption of each type of electricity and the corresponding type of carbon emission factor is calculated, i.e. the carbon emission intensity of the target community in different time periods.

[0065] According to the transformer basic parameters in the power grid equipment data, the load standard capacity of the target community is determined, and the corresponding temperature correction coefficient is set according to the transformer monitoring temperature (winding temperature). The product of the temperature correction coefficient and the load standard capacity is calculated to obtain the load rated capacity. Then the community basic load and the community redundant load are calculated to obtain the community load data of the target community, which includes the community load of the target community in different time periods. The difference between the load rated capacity and the community load data is calculated to obtain the load margin data of the target community. Similarly, the load margin data includes the load margin of the target community in different time periods.

[0066] The load margin data is an absolute constraint for the issuance of charging rights, and it must be ensured that the transformer will not be overloaded. The carbon emission intensity is a dynamic adjustment factor for the allocation of charging rights in different time periods each day. For any time period, the lower the carbon emission intensity, the more charging rights will be issued under the constraint of the load margin data, thereby guiding the issuance of charging rights to tilt towards low-carbon periods, improving the consumption capacity of clean energy, and at the same time relieving the charging peak.

[0067] Then the target community with a carbon emission intensity less than or equal to a first preset intensity threshold (for example, 0) is regarded as a low-carbon charging community, and the target community with a carbon emission intensity greater than a second preset intensity threshold is regarded as a high-carbon charging community. The second preset intensity threshold is much greater than the first preset intensity threshold, and low-carbon electric energy scheduling is performed between the low-carbon charging community and the high-carbon charging community. Then, for the high-carbon charging community that completes the electric energy scheduling, the carbon emission intensity is recalculated according to the electric energy scheduling result, and the initial allocation of charging rights is adjusted according to the carbon emission result to obtain the issuance amount of charging rights. Based on the issuance amount of charging rights and the recalculated carbon emission intensity, the bidding price of the target community for charging rights is generated, that is, the bidding price of charging rights is dynamically regulated within the preset unit price limit, that is, the more the issuance amount of charging rights, the lower the carbon emission intensity, and the lower the corresponding bidding price of charging rights, and vice versa. Finally, the optimal charging time period distribution of all charging pile users in the target community is completed in combination with the issuance amount of charging rights and the bidding price of charging rights, and the charging scheduling scheme of the target community is obtained.

[0068] Through the above method, the problem of low-carbon electric energy consumption is solved, the utilization rate of low-carbon electric energy is improved, the load pressure of the charging pile is effectively relieved, and the occurrence of charging tides is avoided.

[0069] In one embodiment, the community power grid data includes power grid monitoring data, power grid equipment data, power grid load data, historical power consumption data and historical power generation data of all target communities, and power grid topology data and weather forecast data of the target area, including light intensity, community temperature and community wind speed.

[0070] In the embodiment, the grid monitoring data includes transformer monitoring temperature and other monitoring data such as voltage and current, the grid equipment data includes basic parameters of equipment in the target grid and equipment service life, including transformer basic parameters. The historical power generation data includes hourly photovoltaic power generation and wind power generation of the target community in the historical period and corresponding historical meteorological data (such as wind speed, light intensity and temperature). The historical power consumption data includes daily / weekly / monthly historical power consumption structure and historical power consumption of the target community in the historical period (such as the past few years), the historical power consumption structure refers to the consumption proportion of different types of electric energy of the target community, such as 20% photovoltaic, 30% wind power, 40% thermal power and 10% coal power, the historical power consumption includes distribution data of total power consumption of the target community in the historical period and distribution data of consumption amount of different types of power resources. The grid topology data includes grid line connection relationship in the target area and power supply range of the transformer station.

[0071] In one embodiment, the carbon emission intensity and load margin data of the target community are calculated based on the community grid data, and the initial issuance amount of the charging right of the target community is planned based on the carbon emission intensity, the load margin data and the meteorological prediction data, including the following steps: Figure 3

[0072] S201, the community power generation of the target community is predicted based on the historical power generation data and the meteorological prediction data, and the community power consumption and the community power consumption structure of the target community are predicted based on the historical power consumption data and the community power generation;

[0073] S202, the corresponding carbon emission factor is obtained according to the community power consumption structure;

[0074] S203, the carbon emission intensity of the target community is calculated based on the community power consumption and the carbon emission factor;

[0075] S204, the community load data of the target community is calculated based on the grid monitoring data and the grid load data, and the load margin data of the target community is predicted based on the community load data;

[0076] S205, the initial issuance amount of the charging right of the target community is planned based on the carbon emission intensity and the load margin data.

[0077] ​In the embodiment, the historical power generation data includes the hourly photovoltaic power generation and wind power generation of the target community in the historical time period, and the corresponding historical meteorological data (e.g., wind speed, light intensity and temperature). For example, when the data volume is sufficient, a power generation prediction model including a photovoltaic power generation prediction module and a wind power generation prediction module can be constructed and trained based on a long short-term memory network. Then, the meteorological prediction data is input into the power generation prediction model to obtain the hourly community power generation (e.g., the predicted community photovoltaic power generation of a certain day is 5 Wh, and the community wind power generation is 3 MWh). The community power generation includes the community photovoltaic power generation and the community wind power generation. Alternatively, when the data volume is insufficient, the historical power generation corresponding to the historical meteorological data closest to the meteorological prediction data is selected as the community power generation.

[0078] Next, analyze the historical electricity consumption data of the target community, including the historical electricity consumption structure and historical electricity consumption of the target community in the historical period (such as the past few years), the historical electricity consumption structure refers to the consumption proportion of different types of electricity in the target community, for example, 20% of photovoltaic power, 30% of wind power, 40% of thermal power and 10% of coal power, the historical electricity consumption includes the distribution data of the total electricity consumption of the target community in the historical period, and the distribution data of the consumption amount of different types of power resources, for example, the target community consumes 10000kwh of total electricity in a certain month, among which photovoltaic power accounts for 2000kwh, wind power accounts for 3000kwh, thermal power purchased through the power grid accounts for 4000kwh, and coal power accounts for 1000kwh. The XGBoost model can be used to input historical electricity consumption, historical meteorological data, community population change (such as new permanent residents), community electric car change (a resident newly purchases an electric car), thereby realizing day-level community electricity consumption prediction. In addition, if the target community's meteorological prediction information, community population, community electric car total number, community daily total electricity consumption, etc. do not change in the near future (such as within a week), or only the meteorological prediction information or the community daily total electricity consumption has a slight fluctuation, such as a temperature rise or fall of one or two degrees, etc., the average value of the target community's recent community daily electricity consumption distribution data (the average electricity consumption in the same time period of different days) can be used as the predicted community electricity consumption (hourly level), for example, the total electricity consumption from 7am to 10am. If the community electricity consumption in a certain time period (such as within three hours) is less than or equal to the community power generation capacity, the carbon emission intensity of the target community at this time is zero, and there is no need to continue to calculate the community electricity consumption structure, because the electricity consumption of the target area follows the principle of giving priority to using community power generation to reduce the rate of green power abandonment and carbon emissions in the target area. If the community electricity consumption per hour is greater than the community power generation capacity, the type and corresponding proportion of external purchased electricity in the historical electricity consumption structure are used, and if the change amplitude of the community electricity consumption and the corresponding community power generation capacity in the near future (such as within a week) of the target community is less than the preset threshold, the type and corresponding proportion of external purchased electricity in the corresponding hour before are directly used, for example, from 8am to 9am, the proportion of thermal power in external purchased electricity is 80%, and the proportion of coal power is 20%. Then, combined with the community electricity consumption, community power generation capacity and type and corresponding proportion of external purchased electricity, the community electricity consumption structure of the target community can be obtained, for example, the community electricity consumption structure from 7am to 10am is 20% photovoltaic power, 30% wind power, 40% thermal power and 10% coal power.

[0079] Then according to the type of electricity consumed by the target community, the corresponding type of carbon emission factor is obtained, which refers to the carbon dioxide emission coefficient generated by unit production, consumption activity or energy consumption, for example, the carbon emission of burning per ton of fuel or using per kilowatt hour of electricity. For example, the carbon emission factor of coal power is 750-900gCO2 / kWh, the carbon emission factor of hydropower is 10-50gCO2 / kWh, and the carbon emission factor of wind power and photovoltaic power is 0-10gCO2 / kWh. According to the community power consumption structure and community power consumption, the power consumption of each type of electricity is calculated, and the sum of the product of the power consumption of each type of electricity and the corresponding type of carbon emission factor is calculated, that is, the carbon emission intensity of the target community in different time periods, such as 7:00-10:00, 10:00-13:00, 13:00-16:00, etc.

[0080] The grid equipment data refers to the basic parameters of each device in the distribution network in the target area, as well as the service life of the device, which includes the basic parameters of the transformer, including the rated capacity and rated current of the transformer, insulation level, etc. The rated capacity refers to the long-term safe operation capacity of the transformer, which can be directly used as the load standard capacity of the target community. In addition, the actual carrying capacity of the transformer will change with the winding temperature (the higher the temperature, the weaker the insulation layer resistance), so it is necessary to set the corresponding temperature correction coefficient according to the transformer monitoring temperature (winding temperature) to calculate the product of the temperature correction coefficient and the load standard capacity to obtain the load rated capacity. The calculated load rated capacity is the upper limit of the safe operation capacity of the grid transformer in the target community on that day.

[0081] Community base load refers to other power consumption loads in the target community except for the electric vehicle power consumption, such as resident lighting, elevator operation, etc. The electric vehicle load can be removed from the historical power consumption data, and the average value of other power consumption loads in different time periods can be calculated as the community base load of the target community in different time periods. Then, the peak value of other power consumption loads in the recent period (such as one week) is analyzed, and the difference between the peak value of other power consumption loads and the average value of other power consumption loads in the same time period is calculated. The difference is multiplied by a fluctuation coefficient (for example, 0.9-1.1) as the community redundant load in the corresponding time period. The community base load and the community redundant load are integrated to obtain the community load data of the target community, which includes the community load of the target community in different time periods. In addition, all the time periods in the present application are the same time periods which are pre-divided. For example, if the next day planning charging scheduling scheme is planned for each target community, the next day is divided into multiple time periods, such as 7:00-10:00, 10:00-13:00, 13:00-16:00, etc. The charging scheduling scheme includes the scheduling scheme of each time period. The difference between the load rated capacity and the community load data is calculated to obtain the load margin data of the target community. Similarly, the load margin data includes the load margin of the target community in different time periods. The load margin data is the upper limit of the load that can be used by the charging pile of the target community. By accurately calculating the load margin data of the target community, a data basis is laid for the subsequent issuance amount of charging rights.

[0082] The load margin data is an absolute constraint for the issuance of charging rights, and it must be ensured that the transformer will not be overloaded. The carbon emission intensity is a dynamic adjustment factor for the allocation amount of charging rights in different time periods each day. For any time period, the lower the carbon emission intensity, the more charging rights are issued under the constraint of the load margin data, thereby guiding the issuance amount of charging rights to tilt towards low-carbon periods, improving the consumption capacity of clean energy, and at the same time, relieving the charging peak. For example, the maximum issuance amount for each time period can be set according to the load margin data, the maximum issuance amount = load margin / average charging power of single pile x T, T refers to the number of charging time slices in each time period (for example, the charging time slice is 15 minutes, and the total time of the time period is 3 hours, then the number of charging time slices is 12), and then the maximum issuance amount is dynamically adjusted by using the carbon emission intensity to obtain the initial issuance amount of charging rights of the target community in this time period.

[0083] In one embodiment, the community load data of the target community is calculated by the grid monitoring data and the grid load data, and the load margin data of the target community is predicted by using the community load data, including the following steps:

[0084] The load standard capacity of the target community is determined according to the transformer basic parameters in the grid equipment data;

[0085] According to the transformer monitoring temperature in the power grid monitoring data, the corresponding temperature correction coefficient is determined, the product between the temperature correction coefficient and the load standard capacity is calculated, and the load rated capacity is obtained;

[0086] According to the historical power consumption data, the community load data of the target community is calculated, and the community load data includes community basic load and community redundant load;

[0087] The load margin data of the target community is predicted in combination with the load rated capacity, the community basic load and the community redundant load.

[0088] In the embodiment, the power grid equipment data refers to the basic parameters of various equipment in the distribution network in the target area, and the service life of the equipment, which includes the transformer basic parameters, the transformer basic parameters including the rated capacity and rated current of the transformer, insulation level, etc. The rated capacity refers to the long-term safe operation capacity of the transformer, and the rated capacity can be directly used as the load standard capacity of the target community. If the rated capacity is not marked in the transformer basic parameters of some target communities, the rated capacity can be calculated according to the rated current and the rated voltage of the target community The calculation formula is In addition, the actual carrying capacity of the transformer will change with the winding temperature (the higher the temperature, the weaker the insulation layer resistance), so it is necessary to set the corresponding temperature correction coefficient according to the transformer monitoring temperature (winding temperature). For example, when the transformer monitoring temperature is less than or equal to 60 degrees Celsius, the temperature correction coefficient is 1.05, when the transformer monitoring temperature is greater than 60 degrees Celsius and less than or equal to 80 degrees Celsius, the temperature correction coefficient is 1.0, when the transformer monitoring temperature is greater than 80 degrees Celsius and less than or equal to 95 degrees Celsius, the temperature correction coefficient is 0.9, and when the transformer monitoring temperature is greater than 95 degrees Celsius, the temperature correction coefficient is 0.7. The product between the temperature correction coefficient and the load standard capacity is calculated, and the load rated capacity is obtained. The calculated load rated capacity is the upper limit of the safe operation capacity of the transformer in the target community on the day.

[0089] Community base load refers to other power loads in the target community except for the electric vehicle power load, such as resident lighting, elevator operation, etc. The electric vehicle power load can be removed from the historical power consumption data, and the average of other power loads is calculated as the community base load of the target community. Then, the peak of other power loads in the recent period (such as one week) is analyzed, and the difference between the peak of other power loads and the average of other power loads is calculated. The difference is multiplied by a fluctuation coefficient (such as 0.9-1.1) as the community redundant load. The community base load and the community redundant load are integrated to obtain the community load data of the target community. The difference between the rated capacity of the load and the community load data is calculated to obtain the load margin data of the target community. The load margin data is the upper limit of the load that can be used by the charging pile in the target community. Through accurate calculation of the load margin data of the target community, a data basis is laid for the subsequent issuance amount of charging rights.

[0090] In one embodiment, the low-carbon power scheduling between the all-charging low-carbon community and the all-charging high-carbon community includes the following steps:

[0091] Based on the community power grid data, the power scheduling matching between the all-charging high-carbon community and the all-charging low-carbon community is completed to obtain a plurality of community pairing combinations;

[0092] Based on the grid topology data and the pre-obtained low-carbon abandoned power cost, a scheduling objective function and a scheduling constraint condition are constructed, and the iterative solution of the scheduling objective function is completed based on the scheduling constraint condition to obtain the global optimal scheduling parameter of the community pairing combination;

[0093] According to the global optimal scheduling parameter, the low-carbon power scheduling between the all-charging high-carbon community and the all-charging low-carbon community in the all community pairing combination is completed.

[0094] In this embodiment, the charging pile charging data in the historical power consumption data is extracted, and the time period in which the charging load of the target community is greater than the preset load threshold is extracted according to the daily charging data, and is integrated into a daily charging peak period set, and the daily load fluctuation rate is calculated. Then, the power difference between the community power generation and the corresponding community power consumption in each time period of each day is calculated, and the power generation margin characteristics of all charging low-carbon communities are extracted according to the power difference, including a daily power generation margin period set and a daily average power generation margin. Then, the scheduling cost characteristics between the charging high-carbon community and all charging low-carbon communities are calculated according to the grid topology data, including the electrical scheduling distance and the electrical scheduling loss.

[0095] The time period overlap length between each daily charging peak period set and the whole daily power generation margin period set is calculated as a period complementarity. The dispatch priority is set according to the daily load fluctuation rate. The average charging load of the charging high-carbon community in the overlapping period is calculated, and the daily average power generation margin is divided by the average charging load to obtain the power matching degree. The closer the power matching degree is to 1, the higher the matching degree is. The electrical dispatch distance and the electrical dispatch loss are lower, and therefore the reciprocal of the electrical dispatch loss is taken as the dispatch matching degree.

[0096] For each charging high-carbon community, it is respectively paired with each charging low-carbon community to obtain multiple candidate pairings. Then, the pairing indicators such as the period complementarity, the daily load fluctuation rate, the power matching degree, and the dispatch matching degree are normalized. Then, for each normalized pairing indicator, its information entropy is calculated, and a weight is assigned to it according to the information entropy. The weighted sum is obtained. The comprehensive matching degree of each candidate pairing is obtained. For each charging high-carbon community, the charging low-carbon community with the highest comprehensive matching degree is selected as the optimal matching object. When multiple charging high-carbon communities are matched to the same low-carbon community, the charging high-carbon community with the highest daily load fluctuation rate is preferentially satisfied. Through the above steps, multiple community pairing combinations can be obtained, each of which contains at least one charging high-carbon community and one charging low-carbon community. Through the above steps, community matching combinations with high complementarity between supply and demand can be screened out, providing data support for subsequent electrical energy dispatching processes.

[0097] A dispatch target function is constructed to minimize the low-carbon electrical energy dispatching cost. The dispatch target function includes the renewable energy curtailment penalty of the charging low-carbon community L, the charging pile operation cost, the purchased high-carbon energy penalty of the charging high-carbon community H, the transmission loss, and the operation and maintenance cost. Dispatch constraint conditions corresponding to the dispatch target function are constructed. The dispatch constraint conditions include power balance constraints, charging pile operation constraints, tie line constraints, and climbing constraints. Through the above dispatch target function and dispatch constraint conditions, a community dispatch model of each community pairing combination is initially constructed. Then, the target cascade algorithm is used to complete the decomposition and solution of the community dispatch model, and the globally optimal dispatch parameters are obtained. The globally optimal dispatch parameters are used to guide the actual power dispatching, and the optimal operation of the entire power system is realized, such as determining the optimal transmission power of the tie line in different time periods, and completing the low-carbon electrical energy dispatching between all charging high-carbon communities and charging low-carbon communities in all community pairing combinations. The target cascade algorithm is a distributed optimization method specially used to solve multi-level, multi-subsystem coupled optimization problems. The core logic is to decompose the global target into local targets of each subsystem. Through target transmission and deviation coordination between levels, the global optimization is finally realized.

[0098] Through the above method, efficient energy dispatching between charging low-carbon communities and charging high-carbon communities can be realized, the use rate of green energy can be improved, and energy saving and carbon reduction can be achieved.

[0099] In one embodiment, the power scheduling matching between all charging high-carbon communities and charging low-carbon communities is completed based on community grid data, and a plurality of community pairing combinations are obtained, including the following steps:

[0100] The charging pile load characteristics of all charging high-carbon communities are extracted based on historical power consumption data, wherein the charging pile load characteristics include a daily charging peak period set and a daily load fluctuation rate.

[0101] The power generation margin characteristics of all charging low-carbon communities are extracted through community power generation, wherein the power generation margin characteristics include a daily power generation margin period set and a daily average power generation margin, and the community power generation is predicted based on historical power generation data and meteorological prediction data.

[0102] For any charging high-carbon community, the scheduling cost characteristics between the charging high-carbon community and all charging low-carbon communities are calculated according to grid topology data, wherein the scheduling cost characteristics include electrical scheduling distance and electrical scheduling loss.

[0103] The scheduling matching degree between the charging high-carbon community and all charging low-carbon communities is calculated according to the charging pile load characteristics, load margin characteristics, and scheduling cost characteristics and by using an entropy weight method.

[0104] The power scheduling matching between all charging high-carbon communities and charging low-carbon communities is completed according to the scheduling matching degree, and a plurality of community pairing combinations are obtained, wherein the community pairing combination at least includes one charging high-carbon community and one charging low-carbon community.

[0105] In this embodiment, the charging pile charging data in the historical power consumption data is extracted, the charging pile charging data contains the daily charging data of the charging high-carbon community in the historical time period, for example, 1000KW from 7:00 to 10:00 on April 9, 2023, and the time period in which the charging load of the target community is greater than the preset load threshold is extracted according to the daily charging data, and is integrated into the daily charging peak period set. The daily load fluctuation rate = (daily maximum load - daily minimum load) / daily average load.

[0106] Then, the power difference between the community power generation and the corresponding community power consumption in each time period is calculated, and the power generation margin characteristics of all charging low-carbon communities are extracted according to the power difference. The power generation margin characteristics refer to the surplus characteristics of renewable energy (such as photovoltaic and wind power). The daily power generation margin time period set refers to the time period set in which the power difference is greater than zero, i.e., the community power consumption is less than the community power generation. The daily average power generation margin refers to the average daily power generation margin. The electrical dispatching distance can be based on the distance between the boundary points of the charging high-carbon community and the charging low-carbon community in the power grid topology. The electrical dispatching loss refers to the electrical power loss of the power resource in the dispatching process, which can be calculated according to the electrical dispatching distance, for example, the empirical formula: electrical dispatching loss = 0.01 x electrical dispatching distance. The core constraint condition for completing the electrical energy dispatching matching between all charging high-carbon communities and charging low-carbon communities is to maximize the supply-demand complementarity between the charging high-carbon communities and the charging low-carbon communities, i.e., the renewable energy surplus time period of the charging low-carbon community highly coincides with the charging pile load peak time period of the charging high-carbon community, and the transmission cost of the tie line is the lowest.

[0107] The time period overlap length between each daily charging peak time period set and all daily power generation margin time period sets is calculated, which represents the time period complementarity between each charging high-carbon community and each charging low-carbon community. The daily load fluctuation rate represents the degree of change of the daily charging load of the high-carbon community. The higher the daily load fluctuation rate, the greater the risk of transformer overload, and therefore a higher dispatching priority needs to be given. The daily average power generation margin represents the energy dispatching capability of the charging low-carbon community. The average charging load of the charging high-carbon community in the overlapping time period is calculated, and the power matching degree is obtained by dividing the daily average power generation margin by the average charging load. The closer the power matching degree is to 1, the higher the matching degree. The electrical dispatching distance and the electrical dispatching loss are lower the better, and therefore the inverse of the electrical dispatching loss is taken as the dispatching matching degree.

[0108] For each charging high-carbon community, it is initially paired with each charging low-carbon community to obtain multiple candidate pairings. Then, the pairing indicators such as the time period complementarity, the daily load fluctuation rate, the power matching degree, and the dispatching matching degree are normalized. Then, for each normalized pairing indicator, its information entropy is calculated, and a weight is assigned to it according to the information entropy. The weighted sum is obtained, and the comprehensive matching degree of each candidate pairing is obtained. For each charging high-carbon community, the charging low-carbon community with the highest comprehensive matching degree is selected as the optimal matching object. When multiple charging high-carbon communities match to the same low-carbon community, the charging high-carbon community with the highest daily load fluctuation rate is prioritized. Through the above steps, multiple community pairing combinations can be obtained, each of which contains at least one charging high-carbon community and one charging low-carbon community. Through the above steps, the community matching combination with high supply-demand complementarity can be screened out, which provides data support for the subsequent electrical energy dispatching process.

[0109] In one embodiment, based on the grid topology data and the pre-obtained low-carbon curtailment cost, a scheduling objective function and scheduling constraint conditions are constructed, and the scheduling objective function is iteratively solved based on the scheduling constraint conditions to obtain the global optimal scheduling parameters of the community pairing combination, including the following steps:

[0110] Using the grid topology data and the pre-obtained charging pile operation cost, a scheduling objective function is constructed with the objective of minimizing the low-carbon power scheduling cost;

[0111] Scheduling constraint conditions corresponding to the scheduling objective function are constructed, including power balance constraints, charging pile operation constraints, tie line constraints, and ramping constraints;

[0112] For any community pairing combination, virtual phase angles and virtual powers are introduced for all charging high-carbon communities and charging low-carbon communities in the community pairing combination;

[0113] Based on the scheduling objective function, scheduling sub-objective functions are constructed for all charging high-carbon communities and charging low-carbon communities introduced by the virtual phase angles and virtual powers based on the augmented Lagrangian function;

[0114] Solving all scheduling sub-objective functions based on the scheduling constraint conditions obtains local optimal scheduling parameters;

[0115] Taking the scheduling deviation between all local optimal scheduling parameters as a deviation objective function, the local optimal scheduling parameters are iteratively corrected based on the deviation objective function to obtain global optimal scheduling parameters.

[0116] In this embodiment, the scheduling objective function is as follows:

[0117]

[0118] wherein, The renewable energy curtailment penalty and the charging pile operation cost of the charging low-carbon community L, the renewable energy curtailment penalty is the product of the curtailment penalty coefficient and the unabsorbed power generation, the curtailment penalty coefficient can be set according to the relevant regulations of the target area, such as the number of fines for unabsorbed green electricity, the charging pile operation cost includes the product of the charging pile unit power operation cost (such as energy consumption during operation, equipment wear and tear) and the charging power of the charging pile, and the fixed cost of starting and stopping the charging pile (such as the impact current during starting, the equipment starting loss).

[0119] The purchased high-carbon energy penalty of the charging high-carbon community H and the charging pile operation penalty, the purchased high-carbon energy penalty refers to the purchased high-carbon energy penalty coefficient and the purchased high-carbon energy power, and the purchased high-carbon energy penalty coefficient can be set according to the local carbon emission right price, the price difference between high-carbon energy and green electricity, and other factors.

[0120] The transmission loss is proportional to the square of the transmission power of the tie line between the charging high-carbon community and the charging low-carbon community in the same community pairing combination, and the operation and maintenance cost is proportional to the absolute value of the transmission power of the tie line. refers to a time period belongs to a set of dispatch time periods refers to all elements in the set of dispatch time periods.

[0121] The scheduling constraint conditions corresponding to the scheduling objective function are constructed, including power balance constraints, charging pile operation constraints, tie line constraints and climbing constraints. The power balance constraints include: the total power supply capacity of the charging low-carbon community is equal to its total consumption capacity, wherein the total power supply capacity includes community power generation and tie line input power, and the total consumption capacity includes the sum of charging power of the charging pile of the charging low-carbon community and unabsorbed abandoned power, i.e. the low-carbon energy of the charging low-carbon community is preferentially used for local charging, and the remaining part is dispatched to the charging high-carbon community in the same community pairing combination through the tie line (if there is still a surplus, it will be abandoned); the total power supply capacity of the charging high-carbon community is equal to its charging pile charging power, and the total power supply capacity of the charging high-carbon community includes community power generation, tie line input power and purchased high-carbon energy power, i.e. the charging high-carbon community preferentially uses low-carbon energy for charging, and the remaining part is dispatched from the charging high-carbon community in the same community pairing combination through the tie line, and if it is still insufficient, it is provided through the purchased energy. The charging pile operation constraint includes the power upper and lower limit constraint and the charging pile start-stop constraint, the output power of the charging pile must be within the preset power range, the lower limit of the preset power range prevents low load and low efficiency, and the upper limit prevents overload damage; the running time of the charging pile after single start is greater than or equal to the preset time threshold. The tie line constraint is that the transmission power of the tie line cannot exceed the rated capacity of the line to ensure the safety of the line and prevent overload trip. The climbing constraint is that the power change rate of the tie line must be within the preset range, and cannot be mutated to prevent excessive fluctuation of the power grid frequency / voltage. Through the above scheduling objective function and scheduling constraint conditions, the community scheduling model of each community pairing combination is preliminarily constructed. Then the decomposition and solution of the community scheduling model are completed through the target cascade algorithm.

[0122] ​In actual power systems, there is a complex power interaction relationship between high-carbon communities and low-carbon communities. If the entire system is directly modeled and solved, the model will become extremely complex, the calculation amount will be huge, and it may even be beyond the scope of current computing capacity due to the involvement of numerous devices, variables, and coupling constraints between them. For example, there are numerous charging piles in the community, the charging power, start-stop time, and the like of each charging pile need to be considered, and the power transmission of the tie line between the communities is also affected by various factors such as line capacity, resistance, and reactance. These factors are interwoven with each other, and for the above reasons, it is difficult to directly solve the global solution of the community scheduling model, so it is necessary to decouple the coupling relationship between communities by introducing virtual phase angles and virtual powers.

[0123] Specifically, for each charging high-carbon community, a virtual phase angle is defined, which can be understood as a virtual angle identifier for the charging high-carbon community to interact with the external power. It is not a real physical angle, but a kind of relative position or state of the community in power transmission. For example, a virtual phase angle can be set at the node connected to the tie line of the high-carbon community. For the charging low-carbon community, a virtual phase angle is also introduced at the corresponding node connected to the tie line. The virtual phase angle reflects a virtual state of the charging low-carbon community in the power transmission network, which is related to the community power output, local power load, and power transmission to the charging high-carbon community of the charging low-carbon community. Then, the virtual power is used to replace the actual tie line power between the charging low-carbon community and the charging high-carbon community. The virtual power can be set based on the virtual phase angle, for example, the virtual phase angle of the charging high-carbon community is θH, the virtual phase angle of the charging low-carbon community is θL, and the reactance of the tie line is x, then the virtual power Pv=(θH-θL) / x. The virtual power is an equivalent variable for decoupling the power interaction coupling relationship between the two communities, and is used as an equivalent replacement for the actual tie line power. Its value is equal to the actual tie line power between the two communities, and represents the scale and direction of power interaction between the two communities.

[0124] Through the above decoupling step, each community is regarded as an independent individual, and only the community power generation, community power consumption, and related calculations of the corresponding virtual variables need to be considered, without directly considering the complex physical connection and interaction between the communities. For example, when calculating the power demand satisfaction of the charging high-carbon community, the virtual phase angle and virtual power of the charging high-carbon community can be used in combination with the charging pile demand, power generation capacity, and the like of the charging high-carbon community, without directly considering the information such as the state of the power generation equipment of the low-carbon community in the same community group.

[0125] Then, by introducing Lagrange multipliers, the scheduling constraint conditions are converted into part of the scheduling objective function, so that the scheduling constraint conditions are guaranteed to be met while optimizing the scheduling objective function. For example, for the power balance constraint, it can be converted into an item in the scheduling objective function by augmenting the Lagrange function. During the optimization process, the algorithm will automatically adjust the variables so that the power balance constraint is satisfied. At the same time, the penalty term in the augmented Lagrange function can penalize the violation of the constraint condition. When the value of the variable causes the scheduling constraint condition to be not met, the value of the penalty term will increase, thereby making the value of the scheduling objective function larger. Therefore, during the optimization process, the algorithm will tend to find a solution that satisfies the scheduling constraint condition and minimizes the value of the scheduling objective function, thereby accelerating the convergence to the optimal solution. For example, if the operating power of a charging pile in a certain community exceeds its upper limit of rated power, the penalty term in the augmented Lagrange function will penalize this situation, prompting the algorithm to adjust the operating power of the charging pile so that it satisfies the scheduling constraint condition. Therefore, the scheduling sub-objective function is jointly constructed by the scheduling objective function and the scheduling constraint condition. Then, in the case of satisfying the scheduling constraint condition, all the scheduling sub-objective functions are solved in parallel using a pre-selected solver (such as Gurobi) to obtain local optimal scheduling parameters. Since the independently solved local optimal scheduling parameters have deviations in parameters such as virtual phase angle and virtual power, a deviation objective function is constructed to minimize the deviations between these local optimal scheduling parameters. The Lagrange multipliers are updated according to the phase angle deviation and other information obtained from the solution of each community sub-objective function. Then, a double-index convergence condition is set. One is the convergence of the phase angle deviation, which requires that the deviation between the virtual phase angle and the actual phase angle be less than a pre-set deviation threshold. The other is the convergence of the cost, which requires that the change amplitude of the total system cost in adjacent two iterations be less than a pre-set amplitude threshold. When both of the above two convergence conditions are met, the iteration is terminated, and the adjusted local optimal scheduling parameters obtained at this time become the global optimal scheduling parameters, which can be used to guide the actual power scheduling to achieve the optimal operation of the entire power system, for example, to determine the optimal transmission power of the tie line in different time periods.

[0126] By the above method, optimal scheduling in scheduling time and scheduling power can be ensured during the energy scheduling from the charging low-carbon community to the charging high-carbon community, which can effectively avoid the charging low-carbon community from scheduling electric energy when the low-carbon electric power resources of the charging high-carbon community are sufficient, and can also prevent the charging low-carbon community from blindly scheduling electric energy, resulting in insufficient low-carbon electric power resources. In summary, the above method can effectively improve the use rate of green energy and achieve energy saving and carbon reduction.

[0127] In one embodiment, the optimal charging time period allocation of all charging pile users in the target community is completed in combination with the charging right issuance amount and the charging right auction price, and the charging scheduling scheme of the target community includes the following steps:

[0128] For any target community, the charging right bidding parameters of all charging pile users in the target community are collected through the charging pile management platform of the target area, wherein the charging right bidding parameters include a charging time period set, a highest bidding unit price and a charging urgency;

[0129] The optimal charging time period distribution of all charging pile users is completed in combination with the charging right issuance amount, the charging right bidding price and the charging right bidding parameters;

[0130] The charging scheduling scheme of the high-carbon community is obtained by integrating the optimal charging time period distribution result.

[0131] In the embodiment, the charging pile user can make a charging reservation through the charging pile management platform. In the process of making the charging reservation, the charging pile user can select multiple desired charging time periods to form a charging time period set, and set the highest acceptable bidding unit price. The charging pile management platform pre-sets a reasonable unit price input limit to prevent malicious bidding caused by excessively low or high unit price. In addition, the target user can also input the charging urgency of the electric vehicle. The charging urgency includes five levels of emergency, relatively emergency, general, relatively relaxed and relaxed, and different levels correspond to different charging completion time requirements and priority weights. The charging urgency of the electric vehicle is set according to the current remaining power of the electric vehicle and whether there is a travel task in the future preset period (such as 24 hours), for example, when the current remaining power of the user's vehicle is less than 10% and regardless of whether there is a travel task, the charging urgency is emergency, when the current remaining power of the user's vehicle is 10%-30% of the total power and there is a travel task, the charging urgency is relatively emergency, when the current remaining power of the user's vehicle is 30%-50% of the total power and there is a travel task, the charging urgency is general, when the current remaining power of the user's vehicle is 50%-80% of the total power and there is a travel task, the charging urgency is relatively relaxed, when the current remaining power of the user's vehicle is more than 80% and there is a travel task, the charging urgency is relaxed, and when there is no travel task, the charging urgency is automatically reduced by one level, for example, when the current remaining power of the user's vehicle is 30%-50% of the total power but there is no travel task, the charging urgency is general. When the charging pile user charges the electric vehicle, the charging pile can obtain the electric vehicle battery power by detecting the jump of the charging current. If there is a large gap between the actual power of the electric vehicle and the power uploaded by the target user, for example, the target user uploads that the remaining power is less than 10%, but the actual remaining power is 50%-80% of the total power, the power abnormality upload is transmitted to the charging pile management platform, and the charging pile user is marked as abnormal. When multiple abnormal marks occur continuously, the charging urgency of the charging pile user is automatically reduced in subsequent optimal charging time period distribution, for example, from emergency to general.

[0132] Then, based on the charging right issuance amount, the charging right auction price and the charging right auction parameters, a three-dimensional data matrix is constructed, the dimensions including "user dimension" (user ID, charging urgency), "time period dimension" (each preset charging time period, corresponding issuance amount, upper limit of power grid load in the time period), "price dimension" (user's highest auction unit price, time period basic electricity price), to ensure that the data is directly associated with the charging right issuance amount and the auction price. Then, the allocation priority rules are set: in combination with the charging urgency, the auction price and the issuance amount, a multi-dimensional priority is set to avoid allocation imbalance caused by a single factor: the first priority is the charging urgency. The basic charging demand of "high urgency" users is preferentially guaranteed, and if the issuance amount is sufficient in a time period, the "high urgency" users are allocated first; if the issuance amount is insufficient, the price competition link is entered. The second priority is the highest auction unit price. In the same charging urgency level, the users are sorted from high to low according to the highest auction unit price, and the user with a high auction unit price is preferentially allocated with the charging right in the intended time period; if the unit prices are the same, the historical charging compliance rate (such as whether to charge on time, whether to occupy the charging pile for a long time) of the user is referred to, and the user with a high compliance rate is preferentially allocated. The third priority is the time period issuance amount matching. For each charging time period, the issuance amount is allocated from high priority users according to the issuance amount upper limit of the time period, and the allocation is continued until the issuance amount is used up; the users who do not obtain the intended time period are automatically recommended to a time period with sufficient remaining issuance amount, and are asked whether to accept, and if the answer is yes, the user is allocated to the recommended time period, and if the answer is no, the user is considered as not successfully allocated this time. The optimal charging time period allocation result is classified and arranged according to "time period-user", and the charging scheduling scheme of the high-carbon community is obtained.

[0133] The application also provides a machine readable storage medium, which stores instructions for causing a machine to execute the energy-saving and carbon-reducing based energy coordination scheduling method of any one of the above target areas.

[0134] The application also provides an energy-saving and carbon-reducing based energy coordination scheduling system, which comprises:

[0135] a memory configured to store instructions; and

[0136] a processor configured to call the target area instructions from the target area memory and capable of implementing the energy-saving and carbon-reducing based energy coordination scheduling method of any one of the above target areas when executing the target area instructions.

[0137] The processor can be a central processing unit (CPU), of course, according to the actual use, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), ready-to-program gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. can also be used, and the general-purpose processor can be a microprocessor or any conventional processor, etc. The application does not limit this.

[0138] The memory can be an internal storage unit of the computer device, for example, a hard disk or a memory of the computer device, or an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD), a flash card (FC) or the like, or a combination of the internal storage unit and the external storage device of the computer device. The memory is configured to store a computer program and other programs and data required by the computer device, and can be configured to temporarily store data that has been output or is to be output. The present application does not limit this.

[0139] The embodiment of the present application further provides a machine readable storage medium, which stores instructions for causing a machine to execute the energy-saving and carbon-reducing based energy coordination scheduling method.

[0140] Those skilled in the art should understand that embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage and the like) containing computer-usable program code.

[0141] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s).

[0142] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s).

[0143] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0144] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0145] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), or flash memory, for example. Memory is an example of computer readable media.

[0146] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0147] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0148] ​​The above merely provides an example of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. An energy-coordinated scheduling method based on energy saving and carbon reduction, characterized in that, The method comprises the following steps: For any target community in the target area, continuously collect community power grid data and meteorological prediction data of the target community through the pre-constructed community digital model; Calculate the carbon emission intensity and load margin data of the target community based on the community power grid data, and plan the initial issuance amount of charging rights for the target community in combination with the carbon emission intensity, load margin data and meteorological prediction data; Based on the carbon emission intensity, filter out the charging high-carbon community and the charging low-carbon community from all the target communities in the target area, and complete the electric energy dispatching matching between all the charging high-carbon communities and the charging low-carbon communities based on the community power grid data, to obtain a plurality of community pairing combinations; Use the power grid topology data and the pre-obtained charging pile operation cost to construct a dispatching target function with the minimum low-carbon electric energy dispatching cost as the target; Construct the dispatching constraint conditions corresponding to the dispatching target function, and the dispatching constraint conditions comprise power balance constraints, charging pile operation constraints, tie line constraints and climbing constraints; For any community pairing combination, introduce virtual phase angles and virtual powers for all the charging high-carbon communities and the charging low-carbon communities in the community pairing combination; Based on the dispatching target function, construct dispatching sub-target functions for all the charging high-carbon communities and the charging low-carbon communities introduced by the virtual phase angles and the virtual powers based on the augmented Lagrange function; Solve all the dispatching sub-target functions based on the dispatching constraint conditions to obtain local optimal dispatching parameters; Take the dispatching deviation between all the local optimal dispatching parameters as a deviation target function, and iteratively correct the local optimal dispatching parameters based on the deviation target function to obtain global optimal dispatching parameters; Complete the low-carbon electric energy dispatching between all the charging high-carbon communities and the charging low-carbon communities in all the community pairing combinations according to the global optimal dispatching parameters; For any target community, correct the initial issuance amount of charging rights in combination with the low-carbon electric energy dispatching result to obtain the issuance amount of charging rights, and generate the charging right auction price of the target community based on the issuance amount of charging rights and the low-carbon electric energy dispatching result; Complete the optimal charging time period distribution of all the charging pile users in the target community in combination with the issuance amount of charging rights and the charging right auction price to obtain the charging dispatching scheme of the target community.

2. The method of claim 1, wherein, The community power grid data comprises power grid monitoring data, power grid equipment data, power grid load data, historical power consumption data and historical power generation data of all the target communities, and power grid topology data of the target area, and the meteorological prediction data comprises illumination intensity, community temperature and community wind speed.

3. The method of claim 2, wherein, The calculation of the carbon emission intensity and the load margin data of the target community based on the community power grid data, and the planning of the initial issuance amount of charging rights for the target community in combination with the carbon emission intensity, the load margin data and the meteorological prediction data comprise the following steps: Predict the community power generation of the target community in combination with the historical power generation data and the meteorological prediction data, and predict the community power consumption and the community power consumption structure of the target community in combination with the historical power consumption data and the community power generation; Obtain the corresponding carbon emission factor according to the community power consumption structure; Calculate the carbon emission intensity of the target community in combination with the community power consumption and the carbon emission factor; Community load data of the target community is calculated based on grid monitoring data and grid load data, and load margin data of the target community is predicted based on the community load data; The initial issuance amount of the charging right of the target community is planned based on the carbon emission intensity and the load margin data.

4. The method of claim 3, wherein, The community load data of the target community is calculated based on grid monitoring data and grid load data, and the load margin data of the target community is predicted based on the community load data, including the following steps: The load standard capacity of the target community is determined according to the transformer basic parameters in the grid equipment data; The temperature correction coefficient corresponding to the transformer monitoring temperature in the grid monitoring data is determined, the product of the temperature correction coefficient and the load standard capacity is calculated, and the load rated capacity is obtained; The community load data of the target community is calculated based on historical electricity consumption data, and the community load data includes community basic load and community redundant load; The load margin data of the target community is predicted based on the load rated capacity, the community basic load and the community redundant load.

5. The method of claim 1, wherein, The energy scheduling matching between all charging high-carbon communities and charging low-carbon communities is completed based on community grid data, and a plurality of community pairing combinations are obtained, including the following steps: The charging pile load characteristics of all charging high-carbon communities are extracted based on historical electricity consumption data, wherein the charging pile load characteristics include a daily charging peak period set and a daily load fluctuation rate; The power generation margin characteristics of all charging low-carbon communities are extracted based on community power generation, wherein the power generation margin characteristics include a daily power generation margin period set and a daily average power generation margin, and the community power generation is predicted based on historical power generation data and weather forecast data; For any charging high-carbon community, the scheduling cost characteristics between the charging high-carbon community and all charging low-carbon communities are calculated based on grid topology data, wherein the scheduling cost characteristics include electrical scheduling distance and electrical scheduling loss; The scheduling matching degree between the charging high-carbon community and all charging low-carbon communities is calculated based on the charging pile load characteristics, the power generation margin characteristics and the scheduling cost characteristics by using the entropy weight method; The energy scheduling matching between all charging high-carbon communities and charging low-carbon communities is completed based on the scheduling matching degree, and a plurality of community pairing combinations are obtained, wherein the community pairing combination at least includes one charging high-carbon community and one charging low-carbon community.

6. The method of claim 1, wherein, The optimal charging time period allocation of all charging pile users in the target community is completed based on the charging right issuance amount and the charging right auction price, and a charging scheduling scheme of the target community is obtained, including the following steps: For any target community, the charging right auction parameters of all charging pile users in the target community are collected through the charging pile management platform of the target area, wherein the charging right auction parameters include a charging time period set, a highest auction unit price and a charging urgency; The optimal charging time period allocation of all charging pile users is completed based on the charging right issuance amount, the charging right auction price and the charging right auction parameters; The optimal charging time period allocation result is integrated to obtain the charging scheduling scheme of the charging high-carbon community.

7. A machine-readable storage medium, characterized in that, The machine readable storage medium stores instructions for causing the machine to execute the energy saving and carbon reduction based energy coordination scheduling method according to any one of claims 1 to 6.

8. An energy-coordinated scheduling system based on energy saving and carbon reduction, characterized in that, It includes: a memory configured to store instructions; and A processor configured to call the instructions from the memory and enable the energy-saving carbon-reducing based energy-coordinated scheduling method according to any one of claims 1-6 when the instructions are executed.

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