CN116739182A - Electricity selling information output method and device, electronic equipment and storage medium - Google Patents

Electricity selling information output method and device, electronic equipment and storage medium Download PDF

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CN116739182A
CN116739182A CN202310920363.6A CN202310920363A CN116739182A CN 116739182 A CN116739182 A CN 116739182A CN 202310920363 A CN202310920363 A CN 202310920363A CN 116739182 A CN116739182 A CN 116739182A
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electricity
load
type load
price
time
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CN116739182B (en
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徐慧明
陈昊
张长浩
耿若曦
陈思安
何胜
喻洁
王堃
陶思成
高剑
洪福斌
刘波
施红明
郭佳
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State Grid Dianyi Digital Technology Xiong'an Co ltd
State Grid Digital Technology Holdings Co ltd
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State Grid Digital Technology Holdings Co ltd
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    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation

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Abstract

The disclosure relates to a method, a device, electronic equipment and a storage medium for outputting electricity selling information, and relates to the field of power dispatching. The method comprises the following steps: according to the historical load data, determining the electricity selling information of the base load and the electricity selling information of the elastic load of the target user in a preset time period in the future; optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period; and rolling and updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation output deviation, the received time-by-time wind power generation output deviation and the historical carbon reduction integral in the second time period. By the method and the device, the transaction cost of the electric carbon market of the polymer can be reduced, and the electricity consumption behavior of the user is optimized so as to further promote green electricity consumption.

Description

Electricity selling information output method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of power demand response technologies, and in particular, to a method and apparatus for outputting electricity selling information, an electronic device, and a storage medium
Background
The development of power marketing reforms, smart grid technology, advanced metering device technology and communication technology has driven the implementation and popularization of power demand responses. The implementation of the power demand response can improve the adjustment capability of the power grid peak period by adjusting the adjustable resources at the demand side, promote the consumption of renewable energy sources and realize the comprehensive optimal configuration of the resources, but the capability of the industrial and commercial users with the adjustable resources at the demand side for excavating the self demand response potential is limited, the response degree is not high enough, so that the adjustable resources of the industrial and commercial users cannot play an effective role in the supply and demand balance adjustment of the power grid, and the comprehensive optimal configuration of the power resources cannot be well realized. The industrial and commercial power load aggregator is a mechanism capable of integrating adjustable resources on the demand side of industrial and commercial users, realizing resource classification, aggregation, management and optimal configuration, and providing professional technical support and service.
Under the market background of carbon-to-carbon neutralization, how to consider carbon transactions to participate in the electric power market by an aggregator, and designing a multi-time-scale electricity selling strategy for the elastic load of a user so as to reduce the cost of the electric carbon market and improve the social competitiveness is a problem which needs to be considered by the aggregator.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, electronic equipment and a storage medium for outputting electricity selling information.
In a first aspect, an embodiment of the present disclosure provides an electricity vending information outputting method, including: acquiring historical load data of a target user and historical carbon reduction integration; according to the historical load data, determining electricity selling information of a base load and electricity selling information of an elastic load of a target user in a preset time period in the future, wherein the elastic load comprises a first excitation type load, a second excitation type load and a price type load, and the electricity selling information comprises electricity consumption and electricity selling price; optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period; rolling updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation power deviation, the received time-by-time wind power generation deviation and the historical carbon reduction integral in the second time period; and outputting the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load after rolling update and the electricity selling information of the price type load to a target user.
In a second aspect, embodiments of the present disclosure provide an electricity vending information output device, including: an acquisition unit configured to acquire historical load data of a target user and a historical carbon reduction integral; and a determining unit configured to determine, according to the historical load data, electricity selling information of the base load and electricity selling information of the elastic load of the target user in a preset time period in the future, wherein the elastic load comprises a first excitation type load, a second excitation type load and a price type load, and the electricity selling information comprises electricity consumption and electricity selling price. And the optimizing unit is configured to optimize the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period. The updating unit is configured to perform rolling updating on the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation output deviation, the received time-by-time wind power generation output deviation and the historical carbon reduction integral in the second time period; and an output unit configured to output the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the scrolling updated electricity selling information of the second excitation type load, and the electricity selling information of the price type load to the target user.
In a third aspect, embodiments of the present disclosure provide an electronic device comprising a memory, a processor, a bus, and a computer program stored on the memory and executable on the processor, the processor implementing the method of outputting electricity sales information as described in the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the electricity vending information outputting method as described in the first aspect.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is an exemplary system architecture diagram to which one embodiment of the electricity vending information output method of the present disclosure may be applied;
FIG. 2 is a flow chart of an embodiment of a method for outputting electricity vending information of the present disclosure;
FIG. 3 is a flowchart illustrating another embodiment of an electricity vending information outputting method according to the present disclosure;
FIG. 4 is a schematic diagram illustrating an embodiment of an electrical information output apparatus of the present disclosure;
fig. 5 is a schematic structural view of one embodiment of an electronic device of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
In order to make the technical scheme and advantages of the present disclosure more apparent, the present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the electricity vending information output method or apparatus of the present disclosure may be applied.
As shown in fig. 1, a system architecture 100 may include an aggregator 101 and service objects 102, 103, 104, 105, 106 of the aggregator 101. The aggregator 101 may obtain historical electricity data of the service objects 102, 103, 104, 105, 106, and may also obtain information of wind power generation and information of photovoltaic power generation. The aggregator 101 may integrate the power resources according to the requirements of the service objects 102, 103, 104, 105, 106 to increase the utilization of the power resources.
The service objects 102, 103, 104, 105, 106 may be public institutions, enterprise users, commercial buildings, charging posts, user-side energy storage, and the like.
It should be noted that, the method for outputting electricity selling information provided in the embodiment of the present disclosure is generally performed by the aggregator 101. Accordingly, the electricity vending information outputting device is generally provided in the aggregator 101.
Fig. 2 illustrates a flow 200 of one embodiment of a method of outputting electricity vending information of the present disclosure. The method in this embodiment may provide electricity vending information for individual users served by the method, where the electricity vending information may include the load amounts of different types of electricity and corresponding prices. And, the load amount of at least one type of electricity in the electricity selling information and the corresponding price may be changed according to the electricity using behavior of the target user. In particular, the price of the at least one type of electricity may be proportional to the amount of electricity used by the user over a historical period of time. That is, if the user uses less electricity for a historical period of time, the user is deemed to save electricity, and the price of at least one type of electricity may be lower than the normal price. In this way, the user can be guided to save electricity.
As shown in fig. 2, the electricity vending information output method of the present embodiment may include the following steps:
step 201, obtaining historical load data of a target user and historical carbon reduction integration.
In this embodiment, the execution subject of the electricity vending information output method (for example, the aggregator 101 shown in fig. 1) may first acquire the historical load data of the target user. Here, the target user may be a user who performs the subject service. The historical load data may be load data used by the target user over the past year, half year, or month.
The executive may also obtain historical carbon reduction credits for the target user. The value of the historical carbon reduction score may represent how much of the carbon reduction activity the target user is engaged in. The larger the number, the more carbon reduction behavior is represented for the target user. The smaller the value, the less the carbon reduction behavior is on behalf of the target user. The determination of the historical carbon reduction integral may be determined by the electricity usage behavior of the target user. For example, the historical carbon reduction integral may be a proportion of the electricity consumption of the green energy source by the target user to the total electricity consumption, a proportion of the electricity consumption of the target user to the total electricity consumption in the electricity consumption valley period, or the like.
The above historical carbon reduction integral can be calculated by a preset calculation formula, and it can be understood that the historical carbon reduction integral is changed in real time along with the electricity consumption behavior of the user.
Step 202, according to the historical load data, determining the electricity selling information of the base load and the electricity selling information of the elastic load of the target user in a preset time period in the future.
After the execution body acquires the historical load data of the target user, the execution body can split the historical load data according to the minimum time unit. The minimum time unit may be one day. That is, the execution subject can determine the electricity selling information of the base load and the electricity selling information of the elastic load of the target user in a preset time period in the future for the daily load in the history load data. Specifically, the execution body may use the minimum load electricity consumption in the historical load data as the electricity consumption of the base load, and use the price corresponding to the minimum load electricity consumption as the electricity selling price of the base load. And taking the difference between the maximum load electricity consumption and the minimum load electricity consumption in the historical load data as the electricity consumption of the elastic load. The electricity selling price of the elastic load can be determined according to the real-time electric energy price, etc.
In this embodiment, the electricity provided by the aggregator to the target user may include a base load and an elastic load. Wherein the elastic load may further include a first excitation type load, a second excitation type load, and a price type load. The basic load is a type supporting basic electricity consumption of a target user, the excitation type load is a type exciting according to electricity consumption behaviors of the target user, and the price type load is a type providing electricity according to real-time price. The sources of the different types of incentive type loads may be different, as may the corresponding prices. Alternatively, the generation times of the different types of excitation-type loads are different. The execution body may determine the amounts of the first excitation type load, the second excitation type load, and the price type load according to the preset ratio.
The future preset time period here may be a time period set according to an actual scene, for example, may be one day. I.e. the executing entity can determine the information of the selling electricity in the future day (the tomorrow at the present moment). The electricity selling information may include an amount of electricity consumption and an electricity selling price. Accordingly, the electricity selling information determined by the execution subject may include the electricity consumption amount and the electricity selling price of the base load, the electricity consumption amount and the electricity selling price of the first incentive type load, the electricity consumption amount and the electricity selling price of the second incentive type load, and the electricity consumption amount and the electricity selling price of the price type load.
Step 203, optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period.
In this embodiment, in order to optimize the electricity consumption experience of the target user, the electricity selling price of the first incentive type load generated in step 202 may be optimized. Specifically, the execution subject may first predict the load electricity consumption of the target user for the first period. Here, the first period may be one day or one week. The specific limitation is not particularly limited herein. The execution body may determine a predicted load electricity consumption of the target user in the first period of time based on the historical electricity consumption data of the target user. For example, the average value of the daily historical power consumption may be used as the predicted load power consumption.
The execution body may also obtain a predicted photovoltaic power generation amount in a first time period and a predicted wind power generation amount in the first time period. The predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period may be determined by the executing body, or may be obtained by the executing body from other electronic devices. Specifically, the executing body may obtain an output electric quantity of the photovoltaic power station every day, and take an average value of the output electric quantities as a predicted photovoltaic power generation amount in the first duration. Similarly, the execution water body can acquire daily output electric quantity of the wind power station, and the average value of the output electric quantity is used as the predicted wind power output force of the first duration.
The execution body may compare the predicted load electricity consumption of the target user in the first time period with a sum of the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period. If the predicted load electricity consumption of the target user in the first time period is smaller than or equal to the product of the sum value and the coefficient, which indicates that the electricity consumption of the target user is smaller, the electricity selling price of the first excitation load can be reduced. If the predicted load electricity consumption of the target user in the first time period is larger than the product of the sum and the coefficient, the electricity consumption of the target user is larger, and the electricity selling price of the first excitation type load can be increased.
And 204, rolling and updating the second excitation type load electricity consumption, the electricity selling price of the second excitation type load, the price type load electricity consumption and the electricity selling price of the price type load based on the predicted photovoltaic power generation output deviation, the received time-by-time wind power generation output deviation and the historical carbon reduction integral in the second time period.
In this embodiment, in order to encourage users to make green electricity, the historical carbon reduction integral of the users may be formed into benefits to participate in the electricity cost of the users. Specifically, the executive may determine the corresponding benefit based on the historical carbon reduction integral.
Then, the execution body can receive the wind power generation output deviation time by time, and can also predict the photovoltaic power generation output deviation in the second time period. Here, the second period of time may be one hour. The executive body can determine cost deviation based on the predicted photovoltaic power generation output deviation in the second time period and the received time-by-time wind power generation output deviation, and the cost deviation is compared with the benefit. Specifically, if the benefit is greater than the cost deviation, the electricity selling price of the second excitation type load is reduced, the electricity consumption of the second excitation type load is increased, meanwhile, the electricity consumption of the price type load is reduced, and the real-time electricity selling price of the market is taken as the electricity selling price of the price type load.
Meanwhile, in order to encourage the continuous carbon reduction behavior of the user, the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load can be updated in a rolling manner according to the real-time photovoltaic power generation output deviation and the real-time wind power generation output deviation.
And step 205, outputting the electricity selling information of the base load, the electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load and the electricity selling information of the price type load to the target user.
The execution body can output the determined electricity selling information of various types of loads to the target user, and the target user can adjust electricity consumption behavior after receiving the electricity selling information. In some specific practices, the target user can check the electricity selling information on the next day, and further can scientifically use electricity according to the electricity selling information, so that the full utilization of electric power resources is realized.
According to the electricity selling information output method provided by the embodiment of the disclosure, carbon transaction can be participated in an electric power market, an aggregator can extract the electricity consumption of bidding lattice type load and excitation type load by utilizing accumulated large-scale load historical data, so that a user can be guided to participate in electric power system dispatching in a targeted manner, and the transaction cost of the electricity carbon market of the aggregator can be reduced.
With continued reference to fig. 3, a flow 300 of another embodiment of an electricity vending information delivery method according to the present disclosure is shown. As shown in fig. 3, the method in this embodiment may include the steps of:
step 301, obtaining historical load data of a target user and historical carbon reduction integration.
Step 302, determining typical maximum load electricity consumption, minimum load electricity consumption, maximum load electricity consumption and average load electricity consumption according to historical load data; according to the minimum load electricity consumption, determining electricity selling information of the base load in a preset time period in the future; and determining the electricity selling information of the elastic load according to the typical maximum load electricity consumption, the average load electricity consumption and the electricity selling information of the base load.
After the execution body acquires the historical load data of the target user, the execution body can split the historical load data according to the minimum time unit. The minimum time unit may be one day. That is, the execution subject can determine a typical maximum load power consumption, a minimum load power consumption, a maximum load power consumption, and an average load power consumption for each day of load in the historical load data. Here, the execution subject may determine the minimum load electricity consumption amount, the maximum load electricity consumption amount, and the average load electricity consumption amount based on the daily load data of the target user. The target user may be referred to herein as a user i，Representing a useriAt the position oftTime minimum load power consumption, +.>Representing a useriAt the position oftMaximum load power consumption at time,/-)>Representing a useriAt the position oftThe average load electricity consumption at the moment. The execution body can order the historical load data of the target user from high to low, and average the load data of the first 10 percent to obtain the useriAt the position oftTypical maximum load power consumption at time>。
In this embodiment, the electricity selling information of the base load may include the electricity consumption of the base loadAnd a corresponding price. Here, the execution body may use the minimum load electricity amount +.>Directly as base load power consumption +.>I.e.The lowest price in the historical price data is taken as the price of the base load.
Can further use the electricity quantity according to the typical maximum loadAverage load power consumption->Base load->And (3) determining the electricity selling information of the elastic load. Specifically, the execution subject may use the typical maximum load electricity consumption +.>And the base load power consumption->The difference as the electricity consumption of the elastic load +.>I.e. +.>. Alternatively, a typical maximum load power consumption can be +.>And the base load power consumption->Product with coefficient and average load power consumption +.>The difference between the products of the coefficients is used as the power consumption of the elastic load.
In some alternative implementations of the present embodiment, the elastic load may include a first excitation-type load (also referred to as a class a excitation-type load), a second excitation-type load (also referred to as a class B excitation-type load), and a price-type load. The execution body may determine the electricity selling information of the elastic load through the following substeps 3021 to 3022:
sub-step 3021, determining electricity selling information of the first excitation type load and electricity selling information of the second excitation type load within a future preset time period according to the typical maximum load electricity consumption, the average load electricity consumption and the preset proportion.
Sub-step 3022, determining electricity sales information for the price type load within a future preset time period based on the typical maximum load electricity usage amount, the base load electricity sales information, and the incentive type load electricity sales information.
The execution subject can use the electric quantity according to the typical maximum loadAverage load power consumption +.>Determining the power consumption of the excitation load>. Specifically, the typical maximum load power consumption can be +.>And average load power consumption->The difference is used as the excitation load electricity consumption in the future preset time period +.>I.e.. Alternatively, the execution subject may use the amount of electricity for the average load +.>Setting a weight to make the typical maximum load power consumption +. >And average load power consumption->The difference between the product of the sum of the weights is used as the excitation load power consumption +.>. Here, the price of the incentive type load may float according to the electricity usage behavior of the user, i.e. the price of the incentive type load is a function related to the electricity usage behavior of the user.
The execution body may set in advance the duty ratio of the class a excitation type load and the class B excitation type load among the excitation type loads. Specifically, the class A excitation type response load ratio isClass B excitation type response load ratio is +.>. Then，/>。
After determining the electricity selling information of the excitation type load, the executing body can further combine the typical maximum load electricity consumption amount and the basic load electricity selling information to determine the future pre-predictionSetting the electricity selling information of the price type load in the time period. It is understood that the electricity selling information of the price type load in the present embodiment may include the electricity consumption amount of the price type loadAnd a corresponding electricity selling price. Wherein, price type load electricity consumption is +.>Can be typical maximum load +.>Subtracting the base load power consumptionSubtracting the excitation load power consumption +.>I.e. +.>. The electricity selling price of the price type load may be a real-time price of the electric power resource on the market.
In some specific applications, the predetermined future time period is a future day. The execution subject may determine the electricity selling information of the base load and the electricity selling information of the elastic load for the target user one day in advance.
Step 303, constructing a first objective function according to cost parameters of the aggregator; determining a first constraint condition according to the predicted photovoltaic power generation amount of the first duration, the predicted wind power generation amount of the first duration and the predicted load power consumption amount of the first preset duration; and optimizing the electricity selling price of the first excitation type load according to the first objective function and the first constraint condition.
The cost parameters of the polymerizer may include medium and long term thermal power market electricity purchasing costMarket electricity purchasing cost of middle-long term wind power->Spot market electricity purchase cost->And a first excitation load response cost +.>. The objective function is:
。
wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,for medium-long term wind power market electricity purchase price, +.>Decompose to the next day for the aggregatortWind power output at moment.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,for medium-long term wind power market electricity purchase price, +.>Decompose to the next day for the aggregatortWind power output at moment.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,wind power output for the next day is decomposed for the aggregator.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,is thattPrice of electricity purchased in spot market at moment +.>Is an aggregatortAnd the electric quantity is purchased in spot market at any moment.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,cost per unit for class a incentive-based load response for the aggregator,/->For user set- >Is thattTime of dayiUser class a incentive type load response. />，/>Is thatiThe duty cycle of the class a excitation type load at each moment of the user.
The execution subject can predict photovoltaic power generation amount according to the first durationPredicted wind power generation capacity for a first period of time>And a predicted load power consumption for a first preset time period +.>A first constraint is determined. Specifically, a first constraintThe conditions may include two of:
athe aggregator faces class a incentive type load response unit cost constraint:
。
in the method, in the process of the invention,、/>the upper limit and the lower limit of the unit cost of the class A excitation type load response are respectively, namely the unit cost of the class A excitation type load response needs to be kept between the upper limit and the lower limit of the cost.
bPower balance constraint:
。
wherein, the left side of the equation includes: the polymerizer breaks down to the next daytThe fire amount and the polymerization quotient at the moment are decomposed to the next daytMoment wind power output and aggregatortAnd (5) purchasing electricity quantity in spot market at moment and photovoltaic electricity generation quantity in a first preset time period. The sum of these four terms can be understood as the aggregate's time within the future preset time periodtThe time of day can provide the electric power. The right side of the equation includes: the response quantity of the user cluster to which the target user belongs to the A-class excitation type load and the predicted load electricity consumption of the first preset duration. I.e. the sum of these two terms is the electrical power consumed by the user cluster to which the target user belongs. The meaning of the above constraint is that the electric power provided by the aggregator reaches an equilibrium state with the electric power consumed by the user cluster.
And under the first constraint condition, continuously adjusting the parameters of the first objective function until the first objective function reaches the minimum value, and obtaining the electricity selling price of the class A excitation type load, namely the electricity selling price of the first excitation type load.
Step 304, acquiring real-time electricity utilization data of the target user during a preset time period in the future.
Considering the deviation of the external wind power output and the deviation of the internal photovoltaic power generation output, in the process of the future preset time period, the electricity selling strategy of the aggregator needs to be corrected time by time. In this embodiment, the executing body needs to acquire real-time electricity data of the target user to determine the response amount of the target user to the second excitation type load (B-type excitation type load) at each momentAnd the response of the target user to the price type load at each moment +.>。
And 305, determining a real-time carbon reduction integral according to the real-time electricity utilization data, the predicted photovoltaic power generation output deviation in the second time period, the time-by-time wind power generation output deviation and the historical carbon reduction integral.
The execution main body can subtract the real-time electricity consumption data from the predicted photovoltaic power generation deviation and the time-by-time wind power generation deviation in the second time period. Multiplying the obtained difference value by a preset coefficient, and adding the product to the historical carbon reduction integral to obtain the final real-time carbon reduction integral.
In some alternative implementations of the present embodiment, the executing entity may determine the real-time carbon reduction integral by: and determining the real-time carbon reduction integral according to the carbon reduction integral, the initial carbon reduction integral, the real-time photovoltaic power generation output deviation, the real-time wind power output deviation, the power consumption ratio of the target user, the response quantity of the second excitation type load and the response quantity of the price type load of the target user at the historical moment.
Specifically, the execution subject may calculate the real-time carbon reduction integral of the target user according to a predetermined rolling carbon reduction integral calculation formula:
。
wherein, the liquid crystal display device comprises a liquid crystal display device,kthe number of scrolling times.Is thatiUser' stTime of day carbon reduction integral->Is thatiUsers [ (user's name.)t-1) integration of carbon reduction at time point, +.>For the initial carbon reduction integration of the user, +.>Is the firstkAt the time of secondary scrollingtDeviation of photovoltaic power generation output at moment +.>Is the firstkAt the time of secondary scrollingtWind power output deviation at moment->Is thatiThe ratio of the total power consumption of the user power consumption occupation cluster is +.>Is thattTime of dayiResponse of user class B motivational load, +.>Is thattTime of dayiResponse amount of user price type load.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,is thattTime of dayiUser B-class excitation type load power consumption, +.>Is thattTime of dayiThe duty cycle of the user class B excitation type load.
Wherein, the liquid crystal display device comprises a liquid crystal display device,。
in the method, in the process of the invention,is thattTime of dayiUser price type load electricity consumption, < >>Is thattTime of dayiThe duty cycle of the user price type load.
The rollability of the calculation formula is reflected in correction of the photovoltaic power generation output deviation and the wind power generation output deviation at time by time:
when (when)In the time-course of which the first and second contact surfaces,
；
。
in the method, in the process of the invention,to achieve%k-2) temporal predictiontPhotovoltaic power generation at moment, < >>To achieve%k-1) temporal predictiontPhotovoltaic power generation at moment, < >>To achieve%k-2) time of receipttThe wind power generation amount information at the moment,to achieve%k-1) time of receipttAnd wind power generation amount information at the moment.
When (when)In the time-course of which the first and second contact surfaces,
；
。
and 306, based on the real-time carbon reduction integral, performing rolling update on the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load in the residual time in a preset time period in the future.
After determining the real-time carbon reduction integration, the executing body can update the electricity selling information of the B-class excitation type load and the electricity selling information of the price type load in a residual time within a preset time period in a rolling mode based on the real-time carbon reduction integration. Specifically, the executing body may preset a formula of electricity selling information of the B-type excitation type load and electricity selling information of the price type load, and the executing body may substitute the real-time carbon reduction integral into the formula to obtain the electricity selling information of the B-type excitation type load and the electricity selling information of the price type load.
In some alternative implementations of the present embodiment, a two-layer model may be built based on the electricity selling policies for base and elastic loads and rolling reduction carbon integral calculation methods, optimizing the aggregate electricity selling policies. The electricity selling information of the B-class excitation type load and the electricity selling information of the price type load are corrected time by time. The upper layer of the double-layer model aims at maximizing social benefit, the lower layer aims at minimizing user cost, and optimal electricity selling information of B-type excitation type loads and electricity selling information of price type loads are determined under corresponding constraint conditions.
Specifically, the rolling update may be implemented by the following sub-steps 3061-3065:
sub-step 3061, determining a second objective function according to the cost parameters of the aggregator and the electricity utility of the user cluster to which the target user belongs to the excitation type load and the electricity utility of the price type load.
Specifically, at the firstkAt the time of the secondary scrolling,t~24（t=k) The electricity selling strategy at the moment is as follows:
(1) The upper layer aims at maximizing social benefit, and a second objective function is constructed as follows:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the medium-to-long term electricity purchase cost within the day of the aggregator, which is determined by the following formula:
。
、/>、/>respectively isiThe user can determine the parameters of A-type excitation type load, B-type excitation type load and price type load according to self-consumption wish >Is a power consumption parameter. />Is thattTime of dayiResponse of user class a motivational load, +.>Is thattTime of dayiResponse of user class B motivational load, +.>Is thattTime of dayiResponse amount of user price type load. />
Sub-step 3062, determining a second constraint condition according to the purchase power of the aggregator, the power consumption of the user cluster to which the target user belongs, and the cost parameter of the aggregator.
The execution body may determine that the second constraint includes three of:
athe aggregator faces class B incentive type load unit cost constraint:
；
in the method, in the process of the invention,、/>the upper limit and the lower limit of the unit cost of the B-type excitation type load response are respectively defined.
bPower balance constraint:
；
the left side of the equation includes: the polymerizer breaks down to the next daytTime of day fire power, aggregatortThe method comprises the steps of taking the electricity quantity in the spot market at moment, decomposing the wind power output of a aggregator to the next time t moment, deviating the wind power output of each time, decomposing the photovoltaic power generation output of the aggregator to the next time t moment and summing the deviations of the photovoltaic power generation output of each time. I.e. the sum of the powers of the electrical energy sources that the polymerizer is able to provide. The right side of the equation includes: predicted user load electricity consumption in second time period of user cluster to which target user belongsAnd the sum of the response amounts of the various types of loads. I.e. the sum of the power of the electrical energy sources consumed by the user clusters.
cThe cost of the polymerizer guarantees:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the current market electricity purchase cost of the aggregator, < >>Representing the cost of the aggregator,/->Representing the maximum cost the aggregator incurs, +.>Representing the cost of carbon transactions by the aggregators participating in the carbon market,。
in the method, in the process of the invention,trade price per carbon->For total carbon emissions of the polymerizer +.>Is a carbon emission quota for the polymerizer. The aggregate can sell carbon emission rights in the carbon market by fully utilizing the internal photovoltaic power generation, and obtain a part of carbon market trading benefits, so that the trading cost of the aggregate electric carbon market is reduced.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
in the method, in the process of the invention,a、b、dand calculating a coefficient for the carbon emission of the thermal power generating unit corresponding to the purchased power.
In the method, in the process of the invention,wind power weight is absorbed for the aggregators corresponding to the annual power selling quantity.
Sub-step 3063, determining a third objective function based on the electricity cost of the target user and the real-time carbon reduction integral.
In this embodiment, the executing entity may add the real-time carbon reduction integral to the total power consumption of the user, i.e. convert the real-time carbon reduction integral into carbon reduction benefits. The difference between the electricity cost and the carbon reduction benefits then determines the electricity consumption, and the resulting electricity consumption may be used as a third objective function.
In some specific practices, the executing body may determine a cost of the second incentive type load according to the real-time electricity consumption of the second incentive type load and the initial electricity selling price and the electricity selling cost corresponding to the second incentive type load; determining the cost of the price type load according to the electricity consumption of the price type load and the corresponding real-time electricity selling price; determining the real-time carbon reduction benefits of the target user according to the real-time carbon reduction integral; and determining a third objective function according to the cost of the second excitation type load, the cost of the price type load and the real-time carbon reduction benefits.
(2) The lower layer of the double-layer model aims at minimizing the electricity cost of the user, namely a third objective function is constructed as follows:
；
in the method, in the process of the invention,initial electricity price for the aggregator towards the incentive type load, +.>Is oriented to the aggregation businessiUser class B incentive type load response unit cost, +.>Is oriented to the aggregation businessiUser price type load electricity selling price, < >>Is thattTime of dayiUser price type load response quantity. />Is a user unit benefit based on rolling carbon reduction points.
Sub-step 3064, determining a third constraint condition based on the response of the second excitation type load and the response of the price type load.
The third constraint may include three items:
aUpper and lower limit constraints of the response of class B excitation type load:
；
bupper and lower limit constraint of response quantity of price type load:
；
cprice-type load time period constraint:
。
and step 3065, rolling and updating the second excitation type load electricity consumption, the electricity selling price of the second excitation type load, the price type load electricity consumption and the electricity selling price of the price type load according to the second objective function, the second constraint condition, the third objective function and the third constraint condition.
Step 307, the electricity selling information of the base load, the electricity selling information of the optimized first excitation type load, the updated electricity selling information of the second excitation type load and the electricity selling information of the price type load are output to the target user.
According to the electricity selling information output method provided by the embodiment of the disclosure, the rolling carbon reduction integral calculation method is provided by considering the wind power output deviation and the photovoltaic power generation output deviation, so that the transaction cost of the electricity-carbon market of the polymer manufacturer is reduced, and the electricity consumption behavior of a user is optimized to further promote the green electricity consumption.
With further reference to fig. 4, as an implementation of the method shown in the foregoing figures, the present disclosure provides an embodiment of an electricity vending information outputting apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 4, the electricity vending information outputting device 500 of the present embodiment includes: an acquisition unit 401, a determination unit 402, an optimization unit 403, an update unit 404, and a model output unit 405.
An acquisition unit 401 configured to acquire historical load data of a target user and a historical carbon reduction integral;
and a determining unit 402 configured to determine, based on the historical load data, electricity selling information of the base load and electricity selling information of the elastic load of the target user in a preset time period in the future, wherein the elastic load includes a first excitation type load, a second excitation type load, and a price type load, and the electricity selling information includes a power consumption amount and an electricity selling price.
An optimizing unit 403 configured to optimize a selling price of electricity for the first excitation load based on the predicted load electricity consumption by the target user for the first period of time, the predicted photovoltaic power generation amount for the first period of time, and the predicted wind power generation amount for the first period of time.
An updating unit 404 configured to perform a rolling update on the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load, and the electricity selling price of the price type load based on the predicted photovoltaic power generation electricity deviation, the received time-by-time wind power generation electricity deviation, and the historical carbon reduction integral for the second period of time.
And an output unit 405 configured to output the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load after the scroll update, and the electricity selling information of the price type load to the target user.
In addition, in the technical scheme of the application, the application also provides electronic equipment.
Fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
As shown in fig. 5, the electronic device may include a processor 501, a memory 502, a bus 503, and a computer program stored on the memory 502 and executable on the processor 501, wherein the processor 501 and the memory 502 perform communication with each other via the bus 503. The steps of implementing the above method when the processor 501 executes the computer program include, for example: acquiring historical load data of a target user and historical carbon reduction integration; according to the historical load data, determining electricity selling information of a base load and electricity selling information of an elastic load of a target user in a preset time period in the future, wherein the elastic load comprises a first excitation type load, a second excitation type load and a price type load, and the electricity selling information comprises electricity consumption and electricity selling price; optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period; rolling updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation power deviation, the received time-by-time wind power generation deviation and the historical carbon reduction integral in the second time period; and outputting the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load after rolling update and the electricity selling information of the price type load to a target user.
In addition, in one embodiment of the present disclosure, there is also provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method, for example, including: acquiring historical load data of a target user and historical carbon reduction integration; according to the historical load data, determining electricity selling information of a base load and electricity selling information of an elastic load of a target user in a preset time period in the future, wherein the elastic load comprises a first excitation type load, a second excitation type load and a price type load, and the electricity selling information comprises electricity consumption and electricity selling price; optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in the first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period; rolling updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation power deviation, the received time-by-time wind power generation deviation and the historical carbon reduction integral in the second time period; and outputting the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load after rolling update and the electricity selling information of the price type load to a target user.
In summary, in the technical solution of the present disclosure, based on the accumulated historical load data, the user elastic load is classified into a class a incentive type response load (planning a day in advance), a class B incentive type response load (real-time response), and a price type response load; before the day, based on the short-term prediction of the user load electricity consumption, the short-term prediction of the photovoltaic power generation, and the reception of the next-day wind power output information, the aggregator aims at minimizing the cost and optimizes the class A excitation type response load electricity selling price; taking the external wind power output deviation and the internal photovoltaic power generation output deviation into consideration, and providing a rolling carbon reduction integral calculation method for the user power consumption behavior of an aggregator based on ultra-short-term prediction of the user load power consumption, ultra-short-term prediction of the photovoltaic power generation amount and reception of the time-by-time wind power output information; and in the day, based on a day-ahead electricity selling strategy and a rolling carbon reduction integral calculation method, a double-layer model with the maximum social benefit as a target and the minimum user electricity cost as a target is established at the upper layer, and the day-ahead electricity selling strategy of an aggregator is optimized. According to the rolling reduction carbon integral calculation method, the external wind power output deviation and the internal photovoltaic power generation output deviation are considered, so that the transaction cost of the polymer commercial electricity carbon market is reduced, the electricity consumption behavior of a user is optimized, and the green electricity consumption is further promoted.
The foregoing description of the preferred embodiments of the present disclosure is not intended to limit the disclosure, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present disclosure.

Claims (11)

1. An electricity selling information output method comprises the following steps:
acquiring historical load data of a target user and historical carbon reduction integration;
according to the historical load data, determining electricity selling information of a basic load and electricity selling information of an elastic load of the target user in a preset time period in the future, wherein the elastic load comprises a first excitation type load, a second excitation type load and a price type load, and the electricity selling information comprises electricity consumption and electricity selling price;
optimizing the electricity selling price of the first excitation type load based on the predicted load electricity consumption of the target user in a first time period, the predicted photovoltaic power generation amount in the first time period and the predicted wind power generation amount in the first time period;
rolling and updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load based on the predicted photovoltaic power generation output deviation, the received time-by-time wind power output deviation and the historical carbon reduction integral in a second time period;
And outputting the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the electricity selling information of the second excitation type load after rolling update and the electricity selling information of the price type load to the target user.
2. The method of claim 1, wherein the determining the electricity selling information of the base load and the electricity selling information of the elastic load of the target user according to the historical load data comprises:
determining typical maximum load electricity consumption, minimum load electricity consumption, maximum load electricity consumption and average load electricity consumption according to the historical load data;
according to the minimum load electricity consumption, determining electricity selling information of the base load in a preset time period in the future;
and determining the electricity selling information of the elastic load according to the typical maximum load electricity consumption, the average load electricity consumption and the electricity selling information of the base load.
3. The method of claim 2, wherein the determining the electricity vending information for the elastic load based on the typical maximum load electricity usage, the average load electricity usage, and the electricity vending information for the base load comprises:
determining electricity selling information of the first excitation type load and electricity selling information of the second excitation type load in the future preset time period according to the typical maximum load electricity consumption, the average load electricity consumption and the preset proportion;
And determining the electricity selling information of the price type load in the future preset time period according to the typical maximum load electricity consumption, the base load electricity selling information and the excitation type load electricity selling information.
4. The method of claim 1, wherein the optimizing the electricity selling price of the first incentive type load based on the predicted load electricity consumption of the target user for a first period of time, the predicted photovoltaic power generation amount for the first period of time, and the predicted wind power generation amount for the first period of time comprises:
constructing a first objective function according to cost parameters of the aggregators;
determining a first constraint condition according to the predicted load electricity consumption of the first duration, the predicted photovoltaic power generation of the first duration and the predicted wind power generation amount of the first duration;
and optimizing the electricity selling price of the first excitation load according to the first objective function and the first constraint condition.
5. The method of claim 1, wherein the rolling updating of the electricity usage of the second excitation type load, the electricity price of the second excitation type load, the electricity usage of the price type load, and the electricity price of the price type load based on the predicted photovoltaic power generation electricity deviation, the received time-by-time wind power generation electricity deviation, and the historical reduced carbon integral for the second period of time comprises:
Acquiring real-time electricity utilization data of the target user during the future preset time period;
determining a real-time carbon reduction integral according to the real-time electricity utilization data, the predicted photovoltaic power generation output deviation in the second time period, the time-by-time wind power output deviation and the historical carbon reduction integral;
and based on the real-time carbon reduction integral, rolling and updating the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load and the electricity selling price of the price type load in the residual time in the future preset time period.
6. The method of claim 5, wherein the determining a real-time carbon reduction integral from the real-time electricity usage data, the real-time photovoltaic power generation output bias, the real-time wind power generation output bias, and the historical carbon reduction integral comprises:
and determining the real-time carbon reduction integral according to the carbon reduction integral, the initial carbon reduction integral, the real-time photovoltaic power generation output deviation, the real-time wind power output deviation, the electricity consumption duty ratio of the target user, the response quantity of the second excitation type load and the response quantity of the price type load of the target user at the historical moment.
7. The method of claim 5, wherein the rolling updating of the second incentive type load electricity consumption, the electricity selling price of the second incentive type load, the price type load electricity consumption, and the electricity selling price of the price type load for a remaining time within the future preset time period based on the real-time carbon reduction integral comprises:
determining a second objective function according to cost parameters of an aggregator and the electricity utilization effect of the user cluster to which the target user belongs on the excitation type load and the electricity utilization effect of the price type load;
determining a third objective function according to the electricity cost of the target user and the real-time carbon reduction integral;
determining a second constraint condition according to the purchase power of the aggregation provider, the power of the user cluster to which the target user belongs and the cost parameter of the aggregation provider;
determining a third constraint condition according to the response quantity of the second excitation type load and the response quantity of the price type load;
and rolling and updating the second excitation type load electricity consumption, the second excitation type load electricity selling price, the price type load electricity consumption and the price type load electricity selling price of the residual time in the future preset time period according to the second objective function, the second constraint condition, the third objective function and the third constraint condition.
8. The method of claim 6, wherein the determining a third objective function based on the electricity cost of the target user and the real-time reduced carbon integral comprises:
determining the cost of the second excitation type load according to the real-time electricity consumption of the second excitation type load and the initial electricity selling price and the electricity selling cost corresponding to the second excitation type load;
determining the cost of the price type load according to the electricity consumption of the price type load and the corresponding real-time electricity selling price;
determining the real-time carbon reduction benefits of the target user according to the real-time carbon reduction integral;
and determining the third objective function according to the cost of the second excitation type load, the cost of the price type load and the real-time carbon reduction benefits.
9. An electricity vending information output device, comprising:
an acquisition unit configured to acquire historical load data of a target user and a historical carbon reduction integral;
a determining unit configured to determine, according to the historical load data, electricity selling information of a base load and electricity selling information of an elastic load of the target user in a preset time period in the future, wherein the elastic load includes a first excitation type load, a second excitation type load and a price type load, and the electricity selling information includes electricity consumption and electricity selling price;
An optimizing unit configured to optimize a selling price of the first excitation load based on a predicted load electricity consumption of the target user in a first period of time, a predicted photovoltaic power generation amount in the first period of time, and a predicted wind power generation amount in the first period of time;
an updating unit configured to perform rolling update on the electricity consumption of the second excitation type load, the electricity selling price of the second excitation type load, the electricity consumption of the price type load, and the electricity selling price of the price type load based on the predicted photovoltaic power generation output deviation, the received time-by-time wind power output deviation, and the historical carbon reduction integral within a second period of time;
and the output unit is configured to output the electricity selling information of the base load, the optimized electricity selling information of the first excitation type load, the scrolling updated electricity selling information of the second excitation type load and the electricity selling information of the price type load to the target user.
10. An electronic device comprising a memory, a processor, a bus and a computer program stored on the memory and executable on the processor, wherein the processor implements the electricity vending information outputting method as claimed in any one of claims 1 to 9 when the computer program is executed by the processor.
11. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the electricity vending information outputting method according to any one of claims 1 to 9.
CN202310920363.6A 2023-07-26 2023-07-26 Electricity selling information output method and device, electronic equipment and storage medium Active CN116739182B (en)

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