CN116777497A - E-retailer retail scheme optimization method considering multi-time scale green electricity consumption points - Google Patents
E-retailer retail scheme optimization method considering multi-time scale green electricity consumption points Download PDFInfo
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Abstract
The application provides an optimization method for an e-commerce retail scheme considering multi-time scale green electricity consumption points, and relates to the field of electric power markets. The method for optimizing the retail scheme of the electric vendor taking the green electricity consumption points of multiple time scales into consideration comprises the steps of constructing an electric vendor monthly market transaction model based on acquired signing electric quantity and price of an electric vendor annual thermal power contract, signing electric quantity and price of the annual green power contract, monthly market electricity price probability distribution function and monthly rolling corrected user electricity consumption; distributing annual green electricity contract monthly decomposition amount, annual thermal power contract monthly decomposition amount and monthly market thermal power purchase electric quantity to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption sections of the users, the peak and valley period of the spot market electricity price and the electricity consumption amount of the users; and constructing an electric vendor-user double-layer model. The application optimizes the real-time electricity selling price to guide the electricity consumption behavior of the user, thereby improving the enthusiasm of green electricity consumption at the user side and reducing the electricity purchasing cost of the spot market of the electricity seller.
Description
Technical Field
The application relates to the technical field of electric power markets, in particular to an optimization method for an electric vendor retail scheme considering multi-time scale green electricity consumption points.
Background
Under the background of deep reform of the electric power market, the electric power vendor takes part in purchasing electric energy in the multi-time scale market by proxy of electric power users. With the large-scale development of renewable energy sources, the electricity selling side needs to bear the burden of being absorbed corresponding to the annual electricity selling quantity. For an electricity seller, how to design a medium-long time market electricity distribution strategy, a spot market electricity purchasing strategy and a retail package for users is a key problem to be considered for improving electricity purchasing and selling profits and promoting green electricity consumption of users.
At present, domestic related researches mainly consider the electricity purchasing and selling strategy of an electricity seller in the electricity market, but no deep research exists on how the electricity seller can consume green electricity based on multiple time scales and optimize retail packages for users. Therefore, the electronic seller issues annual contract green electricity consumption points and spot market green electricity consumption points for users, optimizes retail packages, and softens the electricity consumption requirements of users so as to adapt to the requirements of larger-scale green electricity consumption.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides an optimization method for the retail scheme of the electronic commerce by considering the green electricity consumption points of multiple time scales so as to optimize the retail scheme of the electronic commerce.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
in a first aspect, there is provided an e-commerce retail scheme optimization method that considers multi-time scale green electricity consumption credits, the method comprising the steps of:
a transaction model construction step, namely constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market price probability distribution function and the monthly rolling corrected user power consumption;
the distribution step is that the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are obtained based on a constructed monthly market transaction model of an electricity seller, and the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are distributed to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption sections of the users, the spot market electricity price peak flat valley period and the user electricity consumption amount;
an optimization step, namely constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and completing optimization of an electric vendor retail scheme through the electric vendor-user double-layer model;
the electric vendor-user double-layer model comprises an upper layer model and a lower layer model.
Preferably, the objective function of the monthly market transaction model of the electronic seller is as follows:
wherein F is med_long Represents the total electricity purchasing cost of the medium-and-long-term market of an electricity seller, wherein M is 12 months, p m For the month-to-month market price of electricity,CVaR is the amount of electricity purchased in the market at month m m For the monthly market electricity price fluctuation risk cost of the mth month, p cth Price for annual thermal power contract, < +_>For the decomposition amount of the thermal power of the mth month and the same month, p cgreen Price is entered for annual green electricity contracts,the decomposition amount of the green electricity contract month is the annual green electricity contract month of the mth month; the electricity seller has determined the year according to the monthly output level of the green electricity while signing the annual green electricity with the new energy generatorGreen electricity contract month decomposition amount.
Preferably, in the objective function of the monthly market transaction model of the electronic seller, the monthly market electricity price p m Obeys normal distributionThe probability density function is:
wherein mu is m For the expected value of the electricity price of the mth month,the electricity price variance of the mth month is the fluctuation value; the method comprises the following steps of approximately calculating historical electricity price data:
in the method, in the process of the application,approximating the expected value of electricity price for month m, < >>For the approximate electricity price variance of the mth month, K is the number of electricity price data of the mth month history, and +.>For the mth month, the kth historical month electricity price,/->For the probability of its occurrence.
Preferably, the monthly market of the electronic sellerIn the objective function of the transaction model, monthly electricity purchasing quantityThe optimization process is as follows:
wherein k represents the number of times of rolling optimization, and Δt is 1 month time scale;
monthly electricity purchasing quantityOptimization procedure +.>For controlling the variables, the deviation amount is predicted by the electricity consumption>Is a disturbance input;
in the objective function, the moon market electricity price fluctuation risk cost CVaR of the mth month m The method comprises the following steps:
where eta is a risk value variable, alpha is a confidence level,for the loss function in the mth and kth scenes, the auxiliary variables are constructed as follows:
in the formula, the loss functionFor selling and purchasing electricityCost and electricity sales profit:
in the formula e r Is the average benefit of the unit electric quantity of the medium-long-term market,and predicting the electricity consumption for the user in the month m.
Preferably, the constraint condition of the monthly market transaction model of the electronic seller comprises:
risk function constraints
Wherein S is k Is an auxiliary variable;
month electricity consumption constraint considering deviation assessment
Wherein, delta represents the deviation checking quantity;
annual market purchase electricity constraints
Wherein q is cth Contract electricity quantity for annual thermal power;
maximum decomposition power constraint
In the method, in the process of the application,representing annual thermal powerTogether with the maximum decomposable charge at month m.
Preferably, the annual green electricity contract monthly decomposition amount, the annual thermal electricity contract monthly decomposition amount and the monthly market thermal electricity purchase amount are distributed to each user every moment, and the method specifically comprises the following steps:
according to the actual green electricity output and the electricity consumption quantity of the users, the annual green electricity contract month decomposition quantity is distributed to each user at every moment:
green electricity is shown in the formula A m The power output does not differ every day in the mth month formed by the days, and the electric vendor distributes electric quantity in the annual green electricity contract month of the mth monthAverage to daily and from moment to moment according to green electricity output characteristics. the green power output level at time t is:
in the method, in the process of the application,for the green power output at the moment t, q green The total output of the green electricity is one day;
the annual green electricity contract resolution of the user at time i is:
wherein r is i The power consumption ratio of the user is i;
distributing annual thermal power contract month decomposition amount to each user every moment according to the power consumption amount of the user at the high, medium and low stages; and distributing thermal power purchase quantity of the monthly market to each user every moment according to the current market electricity peak and valley period and the electricity consumption quantity of the user.
Preferably, the annual contract green electricity consumption points are defined based on the long-term electricity consumption behavior of the user:
in the method, in the process of the application,represents the annual contract green electricity consumption integral, < ->I is the middle-long-term electric quantity purchased by the user at the time (t+1) and the time t respectively,/user>The annual green electricity contract quantity distributed by the user at the time (t+1) and the time t is respectively shown as i;
the spot market green electricity consumption integral is defined based on the user spot electricity consumption behavior:
in the method, in the process of the application,represents the spot market green electricity consumption integral, < ->For the thermal power selling price of the spot market at the moment t, < ->For the thermal power day maximum selling price of spot market, < ->And (5) absorbing the green electric quantity of the spot market for the i user t moment. Preferably, the upper model aims at maximizing profit of the electronic vendor and aims at the function B pr The method comprises the following steps:
B pr ＝max(R sell +R gss -C ml -C sg -C sth -C fit -C csg )
in the objective function, R sell Representing the sales revenue of the retail packages formulated by the electronic vendors to the users:
wherein I is the total number of users, P t ml 、P t spot The price of the medium-long-term electric quantity and the spot electric quantity which are sold by the electric seller at the moment t,the medium-and-long-term electricity purchasing quantity and the spot market electricity purchasing quantity of the user at the moment i are respectively;
in the objective function, R gss The green electricity allowance income of the market-oriented market of the electricity seller is shown:
wherein p is green For the rest green electricity online electricity price,green electricity purchasing quantity of the spot market of the electricity seller at the moment t;
in the objective function, C ml Representing the medium-and-long-term market electricity purchase cost of an electricity seller:
wherein p is cth 、p cgreen 、p mon The electricity prices of the annual thermal power contracts, the annual green electricity contracts and the monthly market respectively,the electric quantity is distributed to the moment by moment for the annual thermal power contract, the annual green power contract and the monthly market electric quantity respectively;
in the objective function, C sg The green electricity purchase cost of the spot market of the electricity seller is shown:
wherein p is sgreen Green electricity purchase price for the spot market of the electricity seller;
in the objective function, C sth The thermal power purchase cost of the spot market of the electric vendor is represented:
in the method, in the process of the application,the thermal power purchase quantity of the spot market at the moment t of the electric seller;
in the objective function, C fit The method comprises the steps of representing that an electronic vendor issues annual contract green electricity to absorb point rewards cost:
wherein p is aw,fit Issuing annual contract green electricity consumption point unit change rewards for the electric vendor;
in the objective function, C csg The method comprises the following steps of indicating that an electric vendor distributes spot market green electricity to absorb point rewards cost:
wherein p is aw,csg Issuing a spot market green electricity consumption point unit change reward for an electric vendor;
the corresponding constraint conditions are:
electricity selling price constraint for users by electricity sellers:
in the method, in the process of the application,the price upper limit and the price lower limit of the medium-term and long-term electric quantity sold by the electric seller facing the user are respectively +.> The upper and lower limits of the price of the spot electric quantity sold by the electric vendor facing the user are respectively set;
power balance constraint:
preferably, the lower layer of the lower layer model aims at maximizing the comprehensive benefit of the user and aims at a functionThe method comprises the following steps:
in the case of the objective function,representing i the electricity utility of the user:
wherein alpha is i Representing parameters respectively determined by users according to self consumption will, and beta represents an electric energy consumption parameter;
in the case of the objective function,representing i annual contract green electricity consumption point rewards benefit for the user:
in the case of the objective function,representing i the spot market green electricity consumption point rewards benefit of the user:
in the case of the objective function,representing i the purchase cost of the user:
the corresponding constraint conditions are:
user electricity consumption constraint:
in the method, in the process of the application,the upper limit and the lower limit of the electricity consumption of the user are respectively the ratio, < ->And (5) the historical electricity consumption of the user is t time i.
In a second aspect, there is provided an electronic retailer retail package computing system that considers multi-time scale green electricity consumption credits, the system comprising the following modules:
the transaction model construction module is used for constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market electricity price probability distribution function and the monthly rolling corrected user electricity consumption;
the distribution module is used for acquiring annual green electricity contract monthly decomposition amount, annual thermal power contract monthly decomposition amount and monthly market thermal power purchase electric quantity based on the constructed monthly market transaction model of the electric vendor, and distributing the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption stages of the users, the current market electricity price peak and valley period and the user electricity consumption volume;
the optimizing module is used for constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and optimizing an electric vendor retail scheme through the electric vendor-user double-layer model;
the electric vendor-user double-layer model comprises an upper layer model and a lower layer model.
(III) beneficial effects
According to the method for optimizing the retail scheme of the electric vendor, which considers the multi-time scale green electricity consumption points, is designed by considering the multi-time scale green electricity purchasing behavior of the electric vendor, so that the real-time electricity selling price is optimized to guide the electricity consumption behavior of a user, the enthusiasm of green electricity consumption of the user side is improved, and the electricity purchasing cost of the spot market of the electric vendor is reduced.
Drawings
FIG. 1 is a schematic flow chart of an optimization method of an e-retailer retail scheme of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Examples
As shown in fig. 1, one embodiment of the present application provides an e-commerce retail scheme optimization method that considers multi-time scale green electricity consumption credits, the method comprising the steps of:
a transaction model construction step, namely constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market price probability distribution function and the monthly rolling corrected user power consumption; the electric vendor makes annual thermal power contracts with thermal power generators and annual green power contracts with new energy generators respectively at the end of the last year based on the predicted electricity consumption of the user and the burden of green power dissipation responsibility, and the monthly market transaction model of the electric vendor aims at minimizing the medium-and-long-term market electricity purchasing cost.
The distribution step is that the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are obtained based on a constructed monthly market transaction model of an electricity seller, and the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are distributed to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption sections of the users, the spot market electricity price peak flat valley period and the user electricity consumption amount;
an optimization step, namely constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and completing optimization of an electric vendor retail scheme through the electric vendor-user double-layer model; annual contract green electricity consumption points are defined based on the medium-term and long-term electricity consumption behaviors of the user, and spot market green electricity consumption points are defined based on the spot electricity consumption behaviors of the user.
The electric vendor-user double-layer model comprises an upper layer model and a lower layer model. The upper model aims at maximizing profit of an electric vendor, and profit of the upper model is composed of electric selling income, multi-time scale market electricity purchasing cost, issuing annual contract green electricity consumption point rewarding cost and issuing spot market green electricity consumption point rewarding cost. The lower model aims at maximizing the comprehensive benefit of the user, wherein the comprehensive benefit consists of electricity utilization effect, green electricity consumption integral benefit in multiple time scales and electricity purchasing cost. The electricity purchasing quantity of the spot market thermal power of the electricity seller, the electricity selling price of the electricity seller facing to the user, namely the real-time electricity purchasing scheme of the electricity seller, are optimized, and the electricity utilization strategy of the user is optimized.
Further, in the monthly market transaction model of the electric vendor, the electric vendor aims at minimizing the medium-and-long-term market electricity purchasing cost, the cost is composed of contract cost of annual market, monthly market electricity purchasing cost and monthly electricity price fluctuation risk cost, and the objective function is as follows:
wherein F is med_long Represents the total electricity purchasing cost of the medium-and-long-term market of an electricity seller, wherein M is 12 months, p m For the month-to-month market price of electricity,CVaR is the amount of electricity purchased in the market at month m m For the monthly market electricity price fluctuation risk cost of the mth month, p cth Price for annual thermal power contract, < +_>For the decomposition amount of the thermal power of the mth month and the same month, p cgreen Price is entered for annual green electricity contracts,the decomposition amount of the green electricity contract month is the annual green electricity contract month of the mth month; the power seller and the new energy power producer sign an annual green power contract, and the annual green power contract month decomposition amount is determined according to the annual green power month output level.
Further, in the objective function of the monthly market transaction model of the electronic seller, the monthly market electricity price p m Obeys normal distributionThe probability density function is:
wherein mu is m For the expected value of the electricity price of the mth month,the electricity price variance of the mth month is the fluctuation value; the method comprises the following steps of approximately calculating historical electricity price data:
in the method, in the process of the application,approximating the expected value of electricity price for month m, < >>For the approximate electricity price variance of the mth month, K is the number of electricity price data of the mth month history, and +.>For the mth month, the kth historical month electricity price,/->For the probability of its occurrence.
Further, in the objective function of the monthly market transaction model of the electric vendor, monthly electricity purchasing is performedMeasuring amountThe optimization process is as follows:
wherein k represents the number of times of rolling optimization, and Δt is 1 month time scale;
monthly electricity purchasing quantityOptimization procedure +.>For controlling the variables, the deviation amount is predicted by the electricity consumption>Is a disturbance input;
in the objective function, the moon market electricity price fluctuation risk cost CVaR of the mth month m The method comprises the following steps:
where eta is a risk value variable, alpha is a confidence level,for the loss function in the mth and kth scenes, the auxiliary variables are constructed as follows:
in the formula, the loss functionThe difference between the electricity selling cost and the electricity selling profit is as follows:
in the formula e r Is the average benefit of the unit electric quantity of the medium-long-term market,and predicting the electricity consumption for the user in the month m.
Further, constraints of the e-commerce monthly market transaction model include:
risk function constraints
Wherein S is k Is an auxiliary variable;
month electricity consumption constraint considering deviation assessment
Wherein, delta represents the deviation checking quantity;
annual market purchase electricity constraints
Wherein q is cth Contract electricity quantity for annual thermal power;
maximum decomposition power constraint
In the method, in the process of the application,the maximum decomposable electric quantity of the annual thermal power generation at the mth month is shown.
Further, the annual green electricity contract monthly decomposition amount, the annual thermal electricity contract monthly decomposition amount and the monthly market thermal electricity purchase amount are distributed to each user every moment, and the method specifically comprises the following steps:
according to the actual green electricity output and the electricity consumption quantity of the users, the annual green electricity contract month decomposition quantity is distributed to each user at every moment:
green electricity is shown in the formula A m The power output does not differ every day in the mth month formed by the days, and the electric vendor distributes electric quantity in the annual green electricity contract month of the mth monthAverage to daily and from moment to moment according to green electricity output characteristics. the green power output level at time t is:
in the method, in the process of the application,for the green power output at the moment t, q green The total output of the green electricity is one day;
the annual green electricity contract resolution of the user at time i is:
wherein r is i The power consumption ratio of the user is i;
distributing annual thermal power contract month decomposition amount to each user every moment according to the power consumption amount of the user at the high, medium and low stages; distributing thermal power purchase quantity of the monthly market to each user every moment according to the current market electricity peak and valley period and the electricity consumption quantity of the user;
pair A is composed of m The historical total load data of the mth month consisting of the days is analyzed and can be divided into holidays and non-holidays, wherein the non-holidays are divided into working days and non-working days, and the daily decomposition medium-term and long-term are calculated as follows:
the basis for the annual thermal power co-month decomposition amount distribution is the power consumption amount of the power consumption high-middle-low section of the power selling agent user and the user, namely the power consumption high-middle-low section of the power selling agent user is divided into each time period according to the ratio of 3:2:1, and then the power consumption amount is evenly divided into each time, and finally the power consumption amount is distributed to each user according to the power consumption amount of the user; the thermal power purchase electric quantity distribution in the monthly market is based on the current market electricity price and the electricity consumption quantity of users, namely the electricity consumption quantity is distributed to each user according to the electricity price peak and valley of 3:2:1 to each period and then to each moment on average.
Further, annual contract green electricity consumption credits are defined based on the user's medium-to-long term electricity usage behavior:
in the method, in the process of the application,represents the annual contract green electricity consumption integral, < ->I is the middle-long-term electric quantity purchased by the user at the time (t+1) and the time t respectively,/user>The annual green electricity contract quantity distributed by the user at the time (t+1) and the time t is respectively shown as i;
the spot market green electricity consumption integral is defined based on the user spot electricity consumption behavior:
in the method, in the process of the application,represents the spot market green electricity consumption integral, < ->For the thermal power selling price of the spot market at the moment t, < ->For the thermal power day maximum selling price of spot market, < ->And (5) absorbing the green electric quantity of the spot market for the i user t moment.
Further, the upper model aims at maximizing profit of the electronic vendor and aims at function B pr The method comprises the following steps:
B pr ＝max(R sell +R gss -C ml -C sg -C sth -C fit -C csg )
in the objective function, R sell Representing the sales revenue of the retail packages formulated by the electronic vendors to the users:
wherein I is the total number of users, P t ml 、P t spot The price of the medium-long-term electric quantity and the spot electric quantity which are sold by the electric seller at the moment t,the medium-and-long-term electricity purchasing quantity and the spot market electricity purchasing quantity of the user at the moment i are respectively;
in the objective function, R gss The green electricity allowance income of the market-oriented market of the electricity seller is shown:
wherein p is green For the rest green electricity online electricity price,green electricity purchasing quantity of the spot market of the electricity seller at the moment t;
in the objective function, C ml Representing the medium-and-long-term market electricity purchase cost of an electricity seller:
wherein p is cth 、p cgreen 、p mon The electricity prices of the annual thermal power contracts, the annual green electricity contracts and the monthly market respectively,the electric quantity is distributed to the moment by moment for the annual thermal power contract, the annual green power contract and the monthly market electric quantity respectively;
in the objective function, C sg The green electricity purchase cost of the spot market of the electricity seller is shown:
wherein p is sgreen Green electricity purchase price for the spot market of the electricity seller;
in the objective function, C sth The thermal power purchase cost of the spot market of the electric vendor is represented:
in the method, in the process of the application,spot market fire for t moment of electric vendorElectricity purchasing quantity;
in the objective function, C fit The method comprises the steps of representing that an electronic vendor issues annual contract green electricity to absorb point rewards cost:
wherein p is aw,fit Issuing annual contract green electricity consumption point unit change rewards for the electric vendor;
in the objective function, C csg The method comprises the following steps of indicating that an electric vendor distributes spot market green electricity to absorb point rewards cost:
wherein p is aw,csg Issuing a spot market green electricity consumption point unit change reward for an electric vendor;
the corresponding constraint conditions are:
electricity selling price constraint for users by electricity sellers:
in the method, in the process of the application,the price upper limit and the price lower limit of the medium-term and long-term electric quantity sold by the electric seller facing the user are respectively +.> The upper and lower limits of the price of the spot electric quantity sold by the electric vendor facing the user are respectively set;
power balance constraint:
further, the lower model layer aims at maximizing the comprehensive benefit of the user and aims at functionThe method comprises the following steps:
in the case of the objective function,representing i the electricity utility of the user:
wherein alpha is i Representing parameters respectively determined by users according to self consumption will, and beta represents an electric energy consumption parameter;
in the case of the objective function,representing i annual contract green electricity consumption point rewards benefit for the user:
in the case of the objective function,representing i the spot market green electricity consumption point rewards benefit of the user:
/>
in the case of the objective function,representing i the purchase cost of the user:
the corresponding constraint conditions are:
user electricity consumption constraint:
in the method, in the process of the application,the upper limit and the lower limit of the electricity consumption of the user are respectively the ratio, < ->And (5) the historical electricity consumption of the user is t time i.
In a second aspect, there is provided an electronic retailer retail package computing system that considers multi-time scale green electricity consumption credits, the system comprising the following modules:
the transaction model construction module is used for constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market electricity price probability distribution function and the monthly rolling corrected user electricity consumption;
the distribution module is used for acquiring annual green electricity contract monthly decomposition amount, annual thermal power contract monthly decomposition amount and monthly market thermal power purchase electric quantity based on the constructed monthly market transaction model of the electric vendor, and distributing the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption stages of the users, the current market electricity price peak and valley period and the user electricity consumption volume;
the optimizing module is used for constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and optimizing an electric vendor retail scheme through the electric vendor-user double-layer model;
the electric vendor-user double-layer model comprises an upper layer model and a lower layer model.
Embodiments of the present application may be provided as a method or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto 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 flow or flows and/or block diagram block or blocks.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," 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 but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An optimization method for an e-commerce retail scheme considering multi-time scale green electricity consumption points, which is characterized by comprising the following steps:
a transaction model construction step, namely constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market price probability distribution function and the monthly rolling corrected user power consumption;
the distribution step is that the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are obtained based on a constructed monthly market transaction model of an electricity seller, and the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity are distributed to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption sections of the users, the spot market electricity price peak flat valley period and the user electricity consumption amount;
an optimization step, namely constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and completing optimization of an electric vendor retail scheme through the electric vendor-user double-layer model;
the electric vendor-user double-layer model comprises an upper layer model and a lower layer model.
2. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 1, wherein: the objective function of the monthly market transaction model of the electronic seller is as follows:
wherein F is med_long Represents the total electricity purchasing cost of the medium-and-long-term market of an electricity seller, wherein M is 12 months, p m For the month-to-month market price of electricity,CVaR is the amount of electricity purchased in the market at month m m For the monthly market electricity price fluctuation risk cost of the mth month, p cth Price for annual thermal power contract, < +_>For the decomposition amount of the thermal power of the mth month and the same month, p cgreen Price is entered for annual green electricity contracts,the decomposition amount of the green electricity contract month is the annual green electricity contract month of the mth month; the electricity seller has taken root when signing annual green electricity with the new energy power generatorAnd determining the annual green electricity contract month decomposition amount according to the green electricity month output level.
3. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 2, wherein: in the objective function of the monthly market transaction model of the electronic seller, the monthly market electricity price p m Obeys normal distributionThe probability density function is:
wherein mu is m For the expected value of the electricity price of the mth month,the electricity price variance of the mth month is the fluctuation value; the method comprises the following steps of approximately calculating historical electricity price data:
in the method, in the process of the application,approximating the expected value of electricity price for month m, < >>For the approximate electricity price variance of the mth month, K is the number of electricity price data of the mth month history, and +.>For the mth month, the kth historical month electricity price,/->For the probability of its occurrence.
4. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 3, wherein: in the objective function of the monthly market transaction model of the electronic seller, monthly electricity purchasing quantityThe optimization process is as follows:
wherein k represents the number of times of rolling optimization, and Δt is 1 month time scale;
monthly electricity purchasing quantityOptimization procedure +.>For controlling the variables, the deviation amount is predicted by the electricity consumption>Is a disturbance input;
in the objective function, the moon market electricity price fluctuation risk cost CVaR of the mth month m The method comprises the following steps:
where eta is a risk value variable, alpha is a confidence level,for the loss function in the mth and kth scenes, the auxiliary variables are constructed as follows:
in the formula, the loss functionThe difference between the electricity selling cost and the electricity selling profit is as follows:
in the formula e r Is the average benefit of the unit electric quantity of the medium-long-term market,and predicting the electricity consumption for the user in the month m.
5. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 4, wherein: the constraint conditions of the monthly market transaction model of the electronic seller comprise:
risk function constraints
S k ≥0,k＝1,2,…K
Wherein S is k Is an auxiliary variable;
month electricity consumption constraint considering deviation assessment
Wherein, delta represents the deviation checking quantity;
annual market purchase electricity constraints
Wherein q is cth Contract electricity quantity for annual thermal power;
maximum decomposition power constraint
In the method, in the process of the application,the maximum decomposable electric quantity of the annual thermal power generation at the mth month is shown.
6. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 5, wherein: the method for distributing the annual green electricity contract monthly decomposition amount, the annual thermal electricity contract monthly decomposition amount and the monthly market thermal electricity purchasing power to each user every moment specifically comprises the following steps:
according to the actual green electricity output and the electricity consumption quantity of the users, the annual green electricity contract month decomposition quantity is distributed to each user at every moment:
green electricity is shown in the formula A m The power output does not differ every day in the mth month formed by the days, and the electric vendor distributes electric quantity in the annual green electricity contract month of the mth monthAverage to daily and from moment to moment according to green electricity output characteristics. the green power output level at time t is:
in the method, in the process of the application,for the green power output at the moment t, q green The total output of the green electricity is one day;
the annual green electricity contract resolution of the user at time i is:
wherein r is i The power consumption ratio of the user is i;
distributing annual thermal power contract month decomposition amount to each user every moment according to the power consumption amount of the user at the high, medium and low stages; and distributing thermal power purchase quantity of the monthly market to each user every moment according to the current market electricity peak and valley period and the electricity consumption quantity of the user.
7. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 1, wherein: the annual contract green electricity consumption points are defined based on the medium-and-long-term electricity consumption behavior of the user:
in the method, in the process of the application,represents the annual contract green electricity consumption integral, < ->I is the middle-long-term electric quantity purchased by the user at the time (t+1) and the time t respectively,/user>I users are respectively at (t+1), tThe annual green electricity contract amount distributed at the moment;
the spot market green electricity consumption integral is defined based on the user spot electricity consumption behavior:
in the method, in the process of the application,represents the spot market green electricity consumption integral, < ->For the thermal power selling price of the spot market at the moment t, < ->For the thermal power day maximum selling price of spot market, < ->And (5) absorbing the green electric quantity of the spot market for the i user t moment.
8. The method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 7, wherein: the upper model aims at maximizing profit of the electronic vendor and aims at a function B pr The method comprises the following steps:
B pr ＝max(R sell +R gss -C ml -C sg -C sth -C fit -C csg )
in the objective function, R sell Representing the sales revenue of the retail packages formulated by the electronic vendors to the users:
wherein I is the total number of users, P t ml 、P t spot The price of the medium-long-term electric quantity and the spot electric quantity which are sold by the electric seller at the moment t,the medium-and-long-term electricity purchasing quantity and the spot market electricity purchasing quantity of the user at the moment i are respectively;
in the objective function, R gss The green electricity allowance income of the market-oriented market of the electricity seller is shown:
wherein p is green For the rest green electricity online electricity price,green electricity purchasing quantity of the spot market of the electricity seller at the moment t;
in the objective function, C ml Representing the medium-and-long-term market electricity purchase cost of an electricity seller:
wherein p is cth 、p cgreen 、p mon The electricity prices of the annual thermal power contracts, the annual green electricity contracts and the monthly market respectively,the electric quantity is distributed to the moment by moment for the annual thermal power contract, the annual green power contract and the monthly market electric quantity respectively;
in the objective function, C sg The green electricity purchase cost of the spot market of the electricity seller is shown:
in the method, in the process of the application,p sgreen green electricity purchase price for the spot market of the electricity seller;
in the objective function, C sth The thermal power purchase cost of the spot market of the electric vendor is represented:
in the method, in the process of the application,the thermal power purchase quantity of the spot market at the moment t of the electric seller;
in the objective function, C fit The method comprises the steps of representing that an electronic vendor issues annual contract green electricity to absorb point rewards cost:
wherein p is aw,fit Issuing annual contract green electricity consumption point unit change rewards for the electric vendor;
in the objective function, C csg The method comprises the following steps of indicating that an electric vendor distributes spot market green electricity to absorb point rewards cost:
wherein p is aw,csg Issuing a spot market green electricity consumption point unit change reward for an electric vendor;
the corresponding constraint conditions are:
electricity selling price constraint for users by electricity sellers:
in the method, in the process of the application,the price upper limit and the price lower limit of the medium-term and long-term electric quantity sold by the electric seller facing the user are respectively +.> The upper and lower limits of the price of the spot electric quantity sold by the electric vendor facing the user are respectively set;
power balance constraint:
9. the method for optimizing an e-retailer retail scheme taking into account multi-time scale green electricity consumption credits of claim 8, wherein: the lower layer of the lower layer model aims at maximizing the comprehensive benefit of the user and aims at a functionThe method comprises the following steps:
in the case of the objective function,representing i the electricity utility of the user:
wherein alpha is i Representing parameters respectively determined by users according to self consumption will, and beta represents an electric energy consumption parameter;
in the case of the objective function,representing i annual contract green electricity consumption point rewards benefit for the user:
in the case of the objective function,representing i the spot market green electricity consumption point rewards benefit of the user:
in the case of the objective function,representing i the purchase cost of the user:
the corresponding constraint conditions are:
user electricity consumption constraint:
in the method, in the process of the application,up and down of the power consumption of the user respectivelyLimit ratio (S/P)>And (5) the historical electricity consumption of the user is t time i.
10. An electronic retailer retail package computing system that considers multi-time scale green electricity consumption credits, the system comprising the following modules:
the transaction model construction module is used for constructing an electricity vendor monthly market transaction model based on the acquired signing electric quantity and price of the annual thermal power contract of the electricity vendor, the signing electric quantity and price of the annual green power contract, the monthly market electricity price probability distribution function and the monthly rolling corrected user electricity consumption;
the distribution module is used for acquiring annual green electricity contract monthly decomposition amount, annual thermal power contract monthly decomposition amount and monthly market thermal power purchase electric quantity based on the constructed monthly market transaction model of the electric vendor, and distributing the annual green electricity contract monthly decomposition amount, the annual thermal power contract monthly decomposition amount and the monthly market thermal power purchase electric quantity to each user from moment to moment according to the actual green electricity output, the high, medium and low electricity consumption stages of the users, the current market electricity price peak and valley period and the user electricity consumption volume;
the optimizing module is used for constructing an electric vendor-user double-layer model based on the defined annual contract green electricity consumption points and spot market green electricity consumption points, and optimizing an electric vendor retail scheme through the electric vendor-user double-layer model;
the electric vendor-user double-layer model comprises an upper layer model and a lower layer model.
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| CN117350821B (en) * | 2023-12-01 | 2024-02-23 | 国网安徽省电力有限公司经济技术研究院 | Main body volume reporting and quoting method of power utilization side considering green electricity and electric energy market joint operation |
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