Demand response pricing method and system, electronic equipment and storage medium

Abstract

The invention discloses a demand response pricing method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a demand response load capacity, carrying out the calculation according to the demand response load capacity and operator parameters, and determining a target model; wherein the operator parameters comprise an expenditure parameter, a cost parameter and user information, and the target model comprises an operator cost function and a user revenue function; performing verification calculation on the target model, comparing a calculation result with a preset value, if the calculation result is smaller than or equal to the preset value, returning to perform calculation according to the demand response load capacity and the operator parameters, and determining the target model; and if the calculation result is greater than a preset value, performing game calculation on the target model to obtain a target price. A cost function of an operator and a revenue function of a user are established according to the demand response load capacity, game calculation is carried out, and the user at a demand end participates in a pricing game process, so that the pricing reasonability is improved; the embodiment of the invention can be widely applied to the field of electricity markets.

Description

Technical Field

[0001] The present invention relates to the field of power market, and in particular to a demand response pricing method, system, electronic equipment and storage medium. Background Art

[0002] The existing electricity service market uses a demand response mechanism to allocate resources in the electricity market, enabling the grid dispatcher to smooth out power flow fluctuations and fill peaks and troughs. Through the pricing of demand response, it guides users and power generation ends to participate in demand response and adjust their respective electricity consumption behaviors or power generation scales to gain profits. The existing demand response pricing mainly adopts the game theory method, in which the dispatching department and power generators conduct multiple rounds of games to adjust demand response plans and pricing. Electricity users do not participate in the game process, and the demand response pricing is not reasonable enough. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a demand response pricing method, system, electronic device and storage medium, which can improve the rationality of demand response pricing.

[0004] To achieve the above objective, an embodiment of the present invention provides a demand response pricing method, the method comprising:

[0005] Obtaining a demand response load, performing calculations based on the demand response load and operator parameters, and determining a target model; wherein the operator parameters include expenditure parameters, cost parameters, and user information, and the target model includes a cost function of the operator and a revenue function of the user;

[0006] Perform verification calculation according to the target model to obtain a calculation result; compare the calculation result with a preset value;

[0007] If the calculation result is less than or equal to the preset value, return to perform the calculation according to the demand response load and the operator parameters to determine the target model until the calculation result is greater than the preset value;

[0008] If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

[0009] In some embodiments, the calculating according to the demand response load and the operator parameters to determine the target model specifically includes:

[0010] Calculate according to the demand response load and the expenditure parameter in the operator parameter to obtain expenditure data; calculate according to the demand response load and the cost parameter in the operator parameter to obtain power supply cost data;

[0011] Calculate according to the demand response load and the user information in the operator parameters to obtain cost sharing data;

[0012] A cost function is determined according to the expenditure data and the power supply cost data, and a benefit function is determined according to the expenditure data and the cost allocation data; and the target model is determined according to the cost function and the benefit function.

[0013] In some embodiments, performing verification calculation according to the target model to obtain a calculation result specifically includes:

[0014] Performing partial derivative calculation on the benefit function of the target model to obtain a first function; performing analysis based on the first function to obtain a demand response load parameter;

[0015] The demand response load parameter is associated with the cost function of the target model to obtain a second function; and a partial derivative of the second function is calculated to obtain the calculation result.

[0016] In some embodiments, performing game calculation on the target model to obtain the target price specifically includes:

[0017] Calculating according to a first preset formula and a cost function in the target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user;

[0018] Calculate the error value based on the first extreme value and the preset threshold value; compare the error value with the preset range;

[0019] If the error value satisfies the preset range, the target pricing is determined according to the first data set; otherwise, the calculation is returned to be performed according to the first preset formula and the cost function in the target model to obtain the first data set.

[0020] In some embodiments, performing game calculation on the target model to obtain the target price further includes:

[0021] Calculating according to a first preset formula and a cost function in the target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user;

[0022] Recording the current number of iterations, and comparing the current number of iterations with a preset value;

[0023] If the current number of iterations is equal to the preset value, the target pricing is determined according to the first data set; if the current number of iterations is less than the preset value, the calculation is returned to be performed according to the first preset formula and the cost function in the target model to obtain the first data set.

[0024] In some embodiments, the first preset formula is:

[0025]

[0026] in, is the fitness value of the best strategy for the i-th user, is the optimal strategy for the i-th user, is the best strategy for users other than the i-th user, is the profit function of the ith user in his own strategy and the strategies of other users except the ith user, and S is the set of all feasible strategies.

[0027] In some embodiments, the second preset formula is:

[0028]

[0029] in, is the extreme value of the i-th user at the t-th iteration, is the fitness value of the best strategy for the i-th user, is the extreme value of the i-th user at the t-1th iteration.

[0030] To achieve the above object, another aspect of an embodiment of the present invention provides a demand response pricing system, including:

[0031] The first module is used to obtain the demand response load, calculate according to the demand response load and operator parameters, and determine the target model; wherein the operator parameters include expenditure parameters, cost parameters and user information, and the target model includes the operator's cost function and the user's benefit function;

[0032] The second module is used to perform verification calculation according to the target model to obtain a calculation result; and compare the calculation result with a preset value;

[0033] A third module is used for returning to execute the calculation according to the demand response load and the operator parameters to determine the target model if the calculation result is less than or equal to the preset value, until the calculation result is greater than the preset value;

[0034] If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

[0035] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0036] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0037] Implementation of the embodiments of the present invention includes the following beneficial effects: The embodiments provide a demand response pricing method, system, electronic device and storage medium, which obtains the demand response load, performs calculations based on the demand response load and operator parameters, and determines a target model; wherein the operator parameters include expenditure parameters, cost parameters and user information, and the target model includes the operator's cost function and the user's benefit function; the target model is verified and calculated to obtain a calculation result and compared with a preset value, and if the calculation result is less than or equal to the preset value, the target model is returned to be calculated based on the demand response load and the operator parameters to determine the target model; if the calculation result is greater than the preset value, the target model is gamed and calculated to obtain a target price; a model including operator costs and user benefits is constructed based on the demand response load, and a game is performed based on the verified model, and an equilibrium solution is obtained through the game as the target pricing result, and the user is taken into account in the game process of demand response pricing to improve the rationality of demand response pricing. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the steps of a demand response pricing method provided by an embodiment of the present invention;

[0039] Figure 2 It is a schematic flow chart of the steps of determining a target model in a demand response pricing method provided by an embodiment of the present invention;

[0040] Figure 3 It is a flowchart of the steps of performing verification calculation in a demand response pricing method provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic flow chart of the steps of determining a first data set in a demand response pricing method provided by an embodiment of the present invention;

[0042] Figure 5 This is another flowchart of steps for determining a first data set in a demand response pricing method provided by an embodiment of the present invention;

[0043] Figure 6It is a schematic diagram of a step flow of a specific embodiment provided by an embodiment of the present invention;

[0044] Figure 7 It is a structural block diagram of a demand response pricing system provided by an embodiment of the present invention;

[0045] Figure 8 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0047] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0048] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0049] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meanings as those commonly understood by those skilled in the art of the present invention. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0050] Figure 1 is an optional flow chart of a demand response pricing method provided by an embodiment of the present invention. Figure 1 The method may include but is not limited to steps S101 to S104.

[0051] Step S101, obtaining the demand response load, performing calculations based on the demand response load and operator parameters, and determining a target model; wherein the operator parameters include expenditure parameters, cost parameters, and user information, and the target model includes the operator's cost function and the user's revenue function;

[0052] Step S102, performing verification calculation on the target model to obtain a calculation result; comparing the calculation result with a preset value;

[0053] Step S103, if the calculation result is less than or equal to the preset value, return to perform calculation according to the demand response load and operator parameters to determine the target model until the calculation result is greater than the preset value;

[0054] Step S104: If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

[0055] In steps S101 to S104 shown in the embodiment of the present application, by obtaining the demand response load that the operator is responsible for, the operator allocates the demand response load that it is responsible for to the users who have signed a service agreement with it; at the same time, the operator also needs to pay compensation to the users who participate in the demand response, and the users who participate in the demand response thereby obtain benefits; the operator's goal of participating in demand response is to minimize costs, while the goal of users participating in demand response is to maximize benefits; therefore, the operator's cost function and the user's benefit function are constructed based on the acquired demand response load, and a model of the operator and the user is established; in this embodiment, the master-slave Stackelberg game method is adopted to determine the pricing of demand response based on the model of the operator and the user, The operator makes decisions first as a leader, and then the users make decisions as followers. The specific pricing of demand response is determined through multiple rounds of games. Users on the demand side participate in the pricing process of demand response through games to improve the rationality of pricing; the benefits of users participating in demand response are maximized, while operators minimize costs, that is, the pricing of demand response is the equilibrium solution of the master-slave game; in order to make the results of the game meaningful, or to determine that there is an equilibrium solution to the master-slave game, the constructed model needs to be verified to determine whether the constructed model is a Nash equilibrium; if the model passes the verification, the master-slave game is carried out according to the constructed model to obtain the equilibrium solution as the pricing of demand response; if the model does not pass the verification, the operator and user models are rebuilt according to the demand response load.

[0056] In step S101 of some embodiments, the demand response load amount may be determined based on a load forecast curve published by a power grid; it may also be determined by bidding in a demand response market, but is not limited thereto.

[0057] See also Figure 2 In some embodiments, step S101 may include but is not limited to steps S201 to S203:

[0058] Step S201, calculating according to the demand response load and the expenditure parameter in the operator parameters to obtain expenditure data; calculating according to the demand response load and the cost parameter in the operator parameters to obtain power supply cost data;

[0059] Step S202, calculating according to the demand response load and the user information in the operator parameters to obtain cost sharing data;

[0060] Step S203, determining a cost function according to the expenditure data and the power supply cost data, determining a benefit function according to the expenditure data and the cost allocation data; and determining a target model according to the cost function and the benefit function.

[0061] In step S201 of some embodiments, the cost of the operator's participation in demand response includes compensation paid by the operator to the corresponding users participating in the demand response. The users participate in demand response to reduce the corresponding loads, and the operator reduces the amount of power supplied to the users, thereby reducing the power supply income; but since the amount of power supplied is reduced, the operator can reduce the power generation of the units, thereby reducing the power generation cost; therefore, the cost function of the operator is:

[0062] C idr =E p -ΔU

[0063] Among them, C idr is the net cost of the operator participating in demand response, E p is the compensation paid by the operator to the users participating in the demand response, ΔU is the power generation cost reduced by the operator participating in the demand response; the compensation paid by the operator to the user is related to the demand response load that the user is responsible for, and the demand response price of different users is different; the power generation cost of the operator is related to the number of units and the unit power generation cost, so the cost function of the operator can be further expressed as:

[0064]

[0065] Among them, C idr is the net cost of the operator participating in demand response, η i is the demand response load of the i-th user, λ is the power supply price given by the operator to the user, and ε i is the compensation price paid by the operator to the i-th user, Q is the power generation reduced by the operator due to demand response, m is the number of units whose power generation is reduced by the operator due to demand response, and ρ is the unit power generation cost of the unit.

[0066] In step S202 of some embodiments, the user performs load reduction according to the demand response load amount allocated by the operator and receives compensation paid by the operator. Since different users can be responsible for different scales of demand response and have different bargaining power, the operator provides different demand response pricing to different users; at the same time, the user's load reduction is equivalent to the user reducing the amount of electricity purchased from the operator, reducing the expenditure on purchasing electricity, and also incurring losses due to participation in demand response, such as reduced production due to load reduction. This part of the loss is numerically the same as the operator's reduced power generation cost due to participation in demand response, and each user is apportioned according to the proportion of demand response executed; therefore, the user's profit function can be expressed as:

[0067]

[0068] Among them, R i is the profit function of the ith user participating in demand response, η i is the demand response load of the i-th user, λ is the power supply price given by the operator to the user, and ε i is the compensation price paid by the operator to the i-th user, Q is the power generation reduced by the operator due to the demand response, m is the number of units whose power generation is reduced by the operator due to the demand response, and ρ is the unit power generation cost of the unit.

[0069] In step S203 of some embodiments, the goals of the operator and the user participating in the demand response are to minimize costs and maximize benefits, respectively. According to the operator cost function and the user benefit function obtained above, a target model of the operator and the user participating in the demand response is established, which is specifically as follows:

[0070]

[0071] In order to ensure that the target model is meaningful and to facilitate subsequent game calculations, constraints are imposed on the constructed target model. The specific constraints are as follows:

[0072]

[0073] in, is the initial load demand of the ith user in a day.

[0074] See also Figure 3 In some embodiments, step S102 may include but is not limited to steps S301 to S302:

[0075] Step S301, performing partial derivative calculation on the benefit function of the target model to obtain a first function; performing analysis based on the first function to obtain a demand response load parameter;

[0076] Step S302, associating the demand response load parameter with the cost function of the target model to obtain a second function; performing partial derivative calculation on the second function to obtain a calculation result.

[0077] In step S301 of some embodiments, after establishing a target model for operators and users to participate in demand response, it is necessary to verify the target model to determine whether the target model is a Nash equilibrium, and perform subsequent game calculations based on the model verified to be a Nash equilibrium to obtain the pricing of demand response; in this embodiment, the users who sign the agreement with the operator make independent decisions, and the decision of a single user will not affect the decisions of other users; first, the user's benefit function is sequentially subjected to first-order partial derivatives and second-order partial derivatives, and the following partial derivative functions are obtained respectively:

[0078]

[0079] According to the constraints of the target model, it can be seen that the first-order partial derivative is greater than zero, while the second-order partial derivative is less than zero; therefore, it can be determined that the user's benefit function has a maximum value; the maximum value of the user's benefit function is calculated based on the first-order partial derivative, and the first-order partial derivative is set equal to zero. After calculation, the following parameters are obtained:

[0080]

[0081] This parameter indicates the amount of demand response load that the i-th user is responsible for when achieving maximum revenue.

[0082] In step S302 of some embodiments, the converted value is substituted into the user's cost function for calculation to obtain the following formula:

[0083]

[0084] For ε i Taking the second-order derivative, we get the following parameters:

[0085]

[0086] According to the constraints in the target model, the parameters is greater than zero, so the operator's cost function has a minimum value. Therefore, the target model has an equilibrium solution that can simultaneously maximize user benefits and minimize operator costs; the equilibrium solution is determined through subsequent game calculations.

[0087] See also Figure 4 In some embodiments, step S104 may include but is not limited to steps S401 to S403:

[0088] Step S401, calculating according to a first preset formula and a cost function in a target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user;

[0089] Step S402, calculating according to the first extreme value and the preset threshold value to obtain an error value; comparing the error value with a preset range;

[0090] Step S403, if the error value meets the preset range, determine the target pricing according to the first data set; otherwise, return to execute the calculation according to the first preset formula and the cost function in the target model to obtain the first data set.

[0091] In step S401 of some embodiments, a particle swarm algorithm is used to perform a master-slave game on a model verified as a Nash equilibrium. First, a fitness function of the particle swarm algorithm is constructed to calculate the fitness value of each user under different pricing and different demand response allocation strategies during the game, and whether it is an equilibrium solution is determined according to the fitness value; before the particle swarm algorithm is used for the game, the parameters of the algorithm are initialized, including particle position and speed parameters, as well as the power supply price and demand response pricing of the operator; the demand response load of each user and the demand response pricing of the operator are allocated through the algorithm, and the fitness value set of each user and operator is calculated according to the constructed fitness function, and the cost function of the operator is used as the constraint condition of the algorithm; the extreme value in the current fitness value set of each user and operator is calculated according to the calculated fitness value set and the extreme value update formula; the specific formula is as follows:

[0092]

[0093] in, is the fitness value of the best strategy for the i-th user, is the optimal strategy for the i-th user, is the best strategy for users other than the i-th user, is the profit function of the ith user in his own strategy and the strategies of other users except the ith user, and S is the set of all feasible strategies;

[0094]

[0095] in, is the extreme value of the i-th user at the t-th iteration, is the fitness value of the best strategy for the i-th user, is the extreme value of the i-th user at the t-1th iteration.

[0096] In step S402 of some embodiments, an error calculation is performed based on the calculated extreme value and a preset threshold, and the obtained error is compared with a preset error range to determine whether the currently allocated user demand response load and the operator's demand response pricing are a balanced solution.

[0097] In step S403 of some embodiments, if the calculated error satisfies the preset error range, it is determined that the currently allocated user demand response load and the operator's demand response pricing are equilibrium solutions, the algorithm stops running, outputs the current equilibrium solution, and determines the currently allocated user demand response load and the operator's demand response pricing based on the obtained equilibrium solution; if the calculated error does not satisfy the preset error range, it is determined that the currently allocated user demand response load and the operator's demand response pricing are not equilibrium solutions, returns to reallocate the user demand response load and the operator's demand response pricing, and recalculates the fitness value and extreme value according to the preset formula.

[0098] See also Figure 5 In some embodiments, step S104 may include but is not limited to steps S501 to S503:

[0099] Step S501, calculating according to a first preset formula and a cost function in a target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user;

[0100] Step S502, record the current iteration number, and compare the current iteration number with a preset value;

[0101] Step S503, if the current number of iterations is equal to the preset value, determine the target pricing according to the first data set; if the current number of iterations is less than the preset value, return to execute the calculation according to the first preset formula and the cost function in the target model to obtain the first data set.

[0102] In step S501 of some embodiments, a particle swarm algorithm is used to perform a master-slave game on a model verified as a Nash equilibrium. First, a fitness function of the particle swarm algorithm is constructed to calculate the fitness value of each user under different pricing and different demand response allocation strategies during the game, and whether it is an equilibrium solution is determined according to the fitness value; before the particle swarm algorithm is used for the game, the parameters of the algorithm are initialized, including particle position and speed parameters, as well as the power supply price and demand response pricing of the operator; the demand response load of each user and the demand response pricing of the operator are allocated through the algorithm, and the fitness value set of each user and the operator is calculated according to the constructed fitness function, and the cost function of the operator is used as the constraint condition of the algorithm; the extreme value in the current fitness value set of each user and the operator is calculated according to the calculated fitness value set and the extreme value update formula;

[0103] In step S502 of some embodiments, after allocating the user's demand response load and the operator's demand response pricing through the algorithm and calculating the current allocated fitness value according to a preset formula, the particle swarm algorithm completes an iteration, and a game is completed between the user and the operator; the current number of iterations is recorded to determine whether to stop the algorithm.

[0104] In step S503 of some embodiments, the recorded number of iterations is compared with a preset number. If the recorded number of iterations is less than the preset number, the algorithm repeats the iteration to assign different demand response loads to users and different demand response pricing to operators. If the recorded number of iterations is equal to the preset number, the algorithm stops iterating to avoid falling into a local optimum. The algorithm uses the result of the last iteration as the target strategy and extracts the operator's demand response pricing from the target strategy as the target pricing.

[0105] The following is a detailed description and explanation of the solution of the embodiment of the present invention in conjunction with a specific application example:

[0106] See also Figure 6, obtain the demand response load obtained by the operator's participation in demand response, and determine the user information and operator information of the service agreement determined by the operator; determine the price information of the operator's power supply to the user, the unit cost of power generation by the generator set and other information based on the operator information; construct the cost function of the operator's participation in demand response based on the obtained demand response load, the operator's user information and the operator's information, including the compensation paid by the operator to the users participating in the demand response, the power supply income from load reduction by participating in the demand response, and the power supply cost reduced by load reduction; at the same time, construct the user's benefit function for participating in demand response based on the obtained demand response load and user information, including the user receiving the compensation paid by the operator and the reduced electricity purchase cost by participating in the demand response for load reduction; at the same time, the user also needs to share the cost saved by the operator due to load reduction, and the specific sharing amount is determined according to the proportion of the demand response responsible by the user to the total demand response load. A game model between users and operators is established based on the established cost function and benefit function, and the particle swarm algorithm is used to iteratively calculate the game model to simulate the demand response pricing game and demand response allocation game between users and operators; before the game is carried out, the established game model is verified for Nash equilibrium, the verified game model is iteratively calculated, and the game model that fails the verification is reconstructed; for the verified game model, the parameters of the particle swarm algorithm, the demand response pricing in the game model and the demand response load allocated to each user are initialized, and the particle swarm algorithm randomly allocates the demand response pricing and the allocated demand response load after the initialization is completed; then, the fitness value of each user and operator in the current iteration and the extreme value in the fitness value are calculated according to the preset fitness function and extreme value update function, the error value is determined according to the calculated extreme value and the preset threshold, and the error value is compared with the preset error range; if the error value meets the preset error range, or the number of iterations of the algorithm reaches the set maximum number, the algorithm stops iterating, outputs the demand response pricing and the allocated demand response load of the current iteration as the equilibrium solution, and uses the demand response pricing in the equilibrium solution as the target pricing.

[0107] Implementation of the embodiments of the present invention includes the following beneficial effects: The embodiments provide a demand response pricing method, system, electronic device and storage medium, which obtains the demand response load, performs calculations based on the demand response load and operator parameters, and determines a target model; wherein the operator parameters include expenditure parameters, cost parameters and user information, and the target model includes the operator's cost function and the user's benefit function; the target model is verified and calculated to obtain a calculation result and compared with a preset value, and if the calculation result is less than or equal to the preset value, the target model is returned to be calculated based on the demand response load and the operator parameters to determine the target model; if the calculation result is greater than the preset value, the target model is gamed and calculated to obtain target pricing; a model including operator costs and user benefits is constructed based on the demand response load, and a game is performed based on the verified model, and an equilibrium solution is obtained through the game as the target pricing result, and the user is taken into consideration in the game process of demand response pricing to improve the rationality of demand response pricing.

[0108] See also Figure 7 The embodiment of the present invention further provides a demand response pricing system, including:

[0109] The first module is used to obtain the demand response load, calculate according to the demand response load and operator parameters, and determine the target model; wherein the operator parameters include expenditure parameters, cost parameters and user information, and the target model includes the operator's cost function and the user's benefit function;

[0110] The second module is used to perform verification calculation according to the target model to obtain a calculation result; and compare the calculation result with a preset value;

[0111] A third module is used for returning to perform the calculation according to the demand response load and the operator parameters to determine the target model if the calculation result is less than or equal to the preset value, until the calculation result is greater than the preset value;

[0112] If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

[0113] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned demand response pricing method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0115] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0116] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0117] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0118] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes a demand response pricing method of the embodiment of this application;

[0119] Input / output interface 803, used to implement information input and output;

[0120] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0121] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0122] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0123] Among them, the memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. The memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a remote memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] In addition, the embodiment of the present application also discloses a computer program product or a computer program, and the computer program product or the computer program is stored in a computer-readable storage medium. The processor of the computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the above method. Similarly, the contents in the above method embodiment are all applicable to the storage medium embodiment, and the functions specifically implemented by the storage medium embodiment are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those achieved by the above method embodiment.

[0125] An embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor. The program executable by the processor is used to implement the above method when executed by the processor.

[0126] It is understood that all or some steps and systems in the disclosed method above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0127] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A pricing method for demand response, characterized in that: The method comprises: Obtaining a demand response load, performing calculations based on the demand response load and operator parameters, and determining a target model; wherein the operator parameters include expenditure parameters, cost parameters, and user information, and the target model includes a cost function of the operator and a revenue function of the user; Performing verification calculation on the target model to obtain a calculation result; comparing the calculation result with a preset value; If the calculation result is less than or equal to the preset value, return to perform the calculation according to the demand response load and the operator parameters to determine the target model until the calculation result is greater than the preset value; If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

2. The method according to claim 1, characterized in that The calculating according to the demand response load and the operator parameters to determine the target model specifically includes: Calculate according to the demand response load and the expenditure parameter in the operator parameter to obtain expenditure data; calculate according to the demand response load and the cost parameter in the operator parameter to obtain power supply cost data; Calculate according to the demand response load and the user information in the operator parameters to obtain cost sharing data; A cost function is determined according to the expenditure data and the power supply cost data, and a benefit function is determined according to the expenditure data and the cost sharing data; and the target model is determined according to the cost function and the benefit function.

3. The method according to claim 1, characterized in that The verification calculation of the target model to obtain the calculation result specifically includes: Performing partial derivative calculation on the benefit function of the target model to obtain a first function; performing analysis based on the first function to obtain a demand response load parameter; The demand response load parameter is associated with the cost function of the target model to obtain a second function; and a partial derivative of the second function is calculated to obtain the calculation result.

4. The method according to claim 1, characterized in that: The performing game calculation on the target model to obtain the target pricing specifically includes: Calculating according to a first preset formula and a cost function in the target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user; Calculate the error value based on the first extreme value and the preset threshold value; compare the error value with the preset range; If the error value satisfies the preset range, the target pricing is determined according to the first data set; otherwise, the calculation is returned to be performed according to the first preset formula and the cost function in the target model to obtain the first data set.

5. The method according to claim 1, characterized in that The performing game calculation on the target model to obtain the target pricing specifically includes: Calculating according to a first preset formula and a cost function in the target model to obtain a first data set, and calculating according to the first data set and a second preset formula to obtain a first extreme value; wherein the first data set includes a user's fitness value, and the first extreme value represents a maximum fitness value of the user; Recording the current number of iterations, and comparing the current number of iterations with a preset value; If the current number of iterations is equal to the preset value, the target pricing is determined according to the first data set; if the current number of iterations is less than the preset value, the calculation is returned to be performed according to the first preset formula and the cost function in the target model to obtain the first data set.

6. The method according to any one of claims 4-5, characterized in that: The first preset formula is: in, is the fitness value of the best strategy for the i-th user, is the optimal strategy for the i-th user, is the best strategy for users other than the i-th user, is the profit function of the ith user in his own strategy and the strategies of other users except the ith user, and S is the set of all feasible strategies.

7. The method according to any one of claims 4 to 5, characterized in that: The second preset formula is: in, is the extreme value of the i-th user at the t-th iteration, is the fitness value of the best strategy for the i-th user, is the extreme value of the i-th user at the t-1th iteration.

8. A demand response pricing system, characterized in that: include: The first module is used to obtain the demand response load, calculate according to the demand response load and operator parameters, and determine the target model; wherein the operator parameters include expenditure parameters, cost parameters and user information, and the target model includes the operator's cost function and the user's benefit function; The second module is used to perform verification calculation according to the target model to obtain a calculation result; and compare the calculation result with a preset value; A third module is used for returning to perform the calculation according to the demand response load and the operator parameters to determine the target model if the calculation result is less than or equal to the preset value, until the calculation result is greater than the preset value; If the calculation result is greater than the preset value, a game calculation is performed on the target model to obtain a target price.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 7 when executed by the processor.

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