Retail electricity price optimization method, system and equipment based on electricity utilization characteristics and medium

Abstract

The invention discloses a retail electricity price optimization method, system, equipment and medium based on electricity utilization characteristics, and relates to the field of retail market electricity price optimization, and the main points of the technical scheme are that a first objective function for maximizing the revenue of an electricity selling company and a second objective function for minimizing the electricity utilization cost of a user are established; based on the weighted sum of the first objective function and the second objective function, determining an objective function of retail electricity price optimization; establishing a retail electricity price optimization model in combination with the objective function under a pre-configured constraint condition; wherein the constraint conditions comprise a retail electricity price constraint, an electricity demand constraint, an energy storage equipment constraint and an electricity price strategy constraint; and solving the retail electricity price optimization model to obtain an income optimization result of the electricity selling company and an electricity utilization cost optimization result of the user. According to the invention, the problem that the user satisfaction is reduced due to the instability of the current pricing mode is solved.

Description

Technical Field

[0001] The present invention relates to the field of retail market electricity price optimization, and more specifically, to a retail electricity price optimization method, system, device and medium based on electricity consumption characteristics. Background Art

[0002] At present, the electricity price in the electricity retail market is diversified, gradually improved and constantly adapted to the current situation of market changes. Specifically, there are several aspects. On the one hand, there are relatively rich pricing models, including tiered electricity price model, linkage model, fixed agency fee model, proportional sharing model, etc. Among them, the tiered electricity price model belongs to a static pricing model. It can provide relatively stable electricity cost expectations for users with low electricity elasticity, but it is difficult for new users to accurately judge the electricity discounts and benefits after entering the market through this model, and the power sales company needs to bear the risk of wholesale market price fluctuations. The linkage model, such as linkage with the power grid agency price or linkage with the monthly wholesale market average price, has the risk of price difference. The fixed agency fee and proportional sharing models are prone to the user's electricity cost being affected by the wholesale price of the power sales company. Therefore, the current pricing model has the problem of instability, which leads to a decline in user satisfaction. Summary of the invention

[0003] The purpose of the present invention is to provide a method, system, device and medium for optimizing retail electricity prices based on electricity consumption characteristics, so as to solve the problem that the current pricing model is unstable and leads to a decrease in user satisfaction.

[0004] In a first aspect of the present application, a method for optimizing retail electricity prices based on electricity consumption characteristics is provided, the method comprising:

[0005] Establishing a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determining an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function;

[0006] Under the pre-configured constraint conditions, combined with the objective function, a retail electricity price optimization model is established; wherein the constraint conditions include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints and electricity price strategy constraints;

[0007] Solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0008] In one implementation, the expression of the first objective function is: Among them, 1+ρ is market growth, θ is market share, and λ j and γ j Represent different user types, β krepresents the electricity sales revenue after the influence of competitors’ pricing strategies, p i is the retail electricity price of user i, q i is the electricity sales of user i, I j is the set of users of the jth user type, p k-competitor is the retail electricity price of the kth competitor, p is the average retail electricity price of the power sales company, S is the policy subsidy, T is the tax, V is the value-added service income, C V is the cost of value-added services, C P is the electricity purchase cost, C O is the operating cost, C R For risk cost.

[0009] In one implementation, the risk cost includes market risk cost, credit risk cost, strategy adjustment risk cost and operational risk cost;

[0010] Among them, the market risk cost includes the risk cost of electricity price fluctuations and the risk cost of market demand changes, the credit risk cost includes credit assessment costs and bad debt loss costs, and the operating risk cost includes system construction and maintenance costs and failure loss costs.

[0011] In one implementation, the expression of the second objective function is: in, Represents user u i The electricity cost of n is the total number of users.

[0012] In one implementation, the user u i The electricity cost includes one or more of electricity cost, basic electricity cost, power factor adjustment cost, power equipment cost, load fluctuation adjustment cost, renewable energy adjustment cost, and energy cost.

[0013] In one implementation, the energy storage device constraints include energy storage device efficiency constraints and energy storage device power constraints.

[0014] In one implementation, a genetic algorithm and a particle swarm algorithm are combined to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0015] In a second aspect of the present application, a retail electricity price optimization system based on electricity consumption characteristics is provided, the system comprising:

[0016] An objective function establishment module is used to establish a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determine an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function;

[0017] An optimization model building module is used to build a retail electricity price optimization model under pre-configured constraints and in combination with the objective function; wherein the constraints include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints, and electricity price strategy constraints;

[0018] The optimization solution module is used to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0019] The third aspect of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for optimizing retail electricity prices based on electricity usage characteristics as provided in the first aspect of the present application are implemented.

[0020] The fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a retail electricity price optimization method based on electricity consumption characteristics provided in the first aspect of the present application are implemented.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] In a retail electricity price optimization method based on electricity consumption characteristics provided in an embodiment of the present invention, the present invention optimizes the user's electricity price cost calculation according to the user's electricity consumption characteristics, such as the proportion and duration of electricity consumption during peak and valley periods; accurately calculates the costs caused by different load sizes, properties, equipment efficiency, and usage frequencies based on the load characteristics and equipment usage in the electricity consumption type characteristics; reasonably adjusts the fluctuation costs and high-reliability power supply costs through the load fluctuation conditions and power supply reliability requirements in the electricity consumption stability characteristics; scientifically calculates the self-generation costs and the costs of using multiple energy sources in combination with the renewable energy utilization and energy combination diversity in the energy consumption structure characteristics, promotes users to utilize renewable energy and optimize energy combinations, and ultimately achieves effective reduction of user electricity costs and rational use of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0024] Figure 1 A schematic diagram of a flow chart of a method for optimizing retail electricity prices based on electricity consumption characteristics provided by an embodiment of the present invention;

[0025] Figure 2 A functional block diagram of a retail electricity price optimization system based on electricity usage characteristics provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0027] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0028] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any combination or all combinations of the words listed at the same time. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0029] It should be understood that terms such as "first" and "second" are only used for descriptive purposes and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0030] Please refer to Figure 1 , Figure 1 A schematic diagram of a process flow of a retail electricity price optimization method based on electricity consumption characteristics provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0031] S101, establishing a first objective function for maximizing the profit of the power sales company and a second objective function for minimizing the user's electricity cost, and determining an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function.

[0032] In this embodiment, the expression of the objective function is: Z=αZ1+βZ2; where: Z is the comprehensive objective function, Z1 is the maximum profit of the power sales company, Z2 is the minimum electricity cost of the user, α and β are weights assigned to the objectives, where α+β=1.

[0033] The expression of the first objective function is: Among them, 1+ρ is the market growth, θ is the market share, λj and γj represent different user types, β k represents the electricity sales revenue after the influence of competitors’ pricing strategies, p i is the retail electricity price of user i, q i is the electricity sales of user i, I j is the set of users of the jth user type, p k-competitor is the retail electricity price of the kth competitor, p is the average retail electricity price of the power sales company, S is the policy subsidy, T is the tax, V is the value-added service income, C V is the cost of value-added services, C P is the electricity purchase cost, C O is the operating cost, C R For risk cost. Where: j For different types of users, the sum of the proportions of different user types is 1.

[0034] Specifically, risk costs include market risk costs, credit risk costs, strategy adjustment risk costs and operational risk costs; among them, the market risk costs include electricity price fluctuation risk costs and market demand change risk costs, the credit risk costs include credit assessment costs and bad debt loss costs, and the operational risk costs include system construction and maintenance costs and failure loss costs.

[0035] Market risk costs include the risk of electricity price fluctuations and the risk of changes in market demand. As for the risk of electricity price fluctuations, power sales companies can hedge the risk of electricity price fluctuations by participating in the electricity futures and options markets. Suppose the number of contracts for purchasing electricity futures is n f , the price of each futures contract is p f , the transaction cost is C f At the same time, the number of contracts for purchasing electricity options is n0, the price of each option is p0, and the transaction cost is C0. The risk cost of electricity price fluctuations can be expressed as: C1 = n f p f +C f +n0p0+C0.

[0036] As for the risk of market demand changes, changes in market demand may lead to user churn, especially when competitors offer more attractive prices or services. Assume that the user churn rate is μ and the average lifetime value of each user is V u , then the risk cost of market demand change caused by user loss is C c The expression is: Where n is the total number of users, V u-iIt is the average lifetime value of the ith user, including the expected future electricity sales revenue, value-added service revenue, etc.

[0037] Credit assessment cost C ce The calculation formula is: C ce =C cef +nc cev , where: C cef is the fixed cost of credit assessment, c cev is the variable cost of each evaluation, and n is the number of users.

[0038] Bad debt loss cost C bd The expression is Among them: β is the bad debt rate, A bd,i is the amount owed by user i.

[0039] Strategy adjustment risk cost C pc The expression is: C pc =C pf +fc pv , where: C pf is the fixed cost of policy compliance, f is the frequency of policy adjustment, c pv is the variable cost after each policy adjustment.

[0040] System construction and maintenance cost C sb The expression is: Where: T is the system service life, C sm The annual maintenance cost.

[0041] Failure loss cost C sf The expression is: C sf =πL sf , where: π is the system failure probability, L sf is the average loss caused by each failure.

[0042] Assume that the user set is U = {u1,u2,…,u n}, user u i The electricity cost is Then the expression of the second objective function Z2 for minimizing the user's electricity cost is: in, Represents user u i The electricity cost of n is the total number of users.

[0043] Considering the characteristics of various users, the electricity cost of the above users can be composed of the following aspects: iThe electricity cost includes one or more of the electricity cost, basic electricity cost, power factor adjustment cost, power equipment cost, load fluctuation adjustment cost, renewable energy adjustment cost and energy cost. The calculation method is as follows:

[0044] 1. Electricity cost C ui-elec :C ui-elec =p ui-peak q ui-peak +p ui-valley q ui-valley +p us-flat q ui-flat +p us-spike q ui-spike , where: p ui-peak 、p ui-valley 、p us-flat 、p us-spike For user u i The electricity price at peak, valley, flat and peak times, q ui-peak ,q ui-valley ,q ui-flat ,q ui-spike are the power consumption at the corresponding moments respectively.

[0045] 2. Basic electricity cost C ui-basic (For heavy load users): C ui-basic =p basic S ui , where: p basic is the basic electricity price, S ui For the power capacity.

[0046] 3. Power factor adjustment cost C ui-compensation (For users with inductive load): C ui-compensation =C ui-compensation-investment +C ui-compensation-operation , where: C ui-compensation-investment is the investment cost of reactive power compensation equipment, C ui-compensation-operation Its annual operating and maintenance costs.

[0047] 4. Cost of electrical equipment C ui-equipment :C ui-equipment =∑ j (p ui f ui-equipment-j e ui-equipment-j ), where: j is different electrical equipment, p ui f ui-equipment-j is the usage frequency of device j, e ui-equipment-j It is the power consumption per use.

[0048] 5. Load fluctuation adjustment cost C ui-fluctuation :C ui-fluctuation =Cui-elec α ui-fluctuation , where: α ui-fluctuation is the fluctuation cost adjustment factor.

[0049] 6. Renewable energy adjustment costs C ui-renewable :

[0050] C ui-renewable =-p ui-sell q ui-renewable +C ui-renewable-generation ;, where: p ui-sell is the on-grid electricity price, q ui-renewable is the self-generated electricity, C ui-renewable-generation The cost of renewable energy generation equipment.

[0051] 7. Multiple Energy Costs C ui-multiple-energy (For users who use multiple energy sources): C ui-multiple-energy =p ui-gas q ui-gas +C ui-conversion (1-η ui-conversion ), where: p ui-gas is the natural gas price, q ui-gas is the natural gas usage, C ui-conversion is the energy conversion cost, η ui-conversion For energy conversion efficiency.

[0052] S102, under pre-configured constraints, in combination with the objective function, establishing a retail electricity price optimization model; wherein the constraints include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints and electricity price strategy constraints.

[0053] In this embodiment, the expression of the retail electricity price constraint is: min <p i <p max , where: p i Retail electricity prices need to meet the upper and lower limits set by government regulation. min is the price lower limit, p max The price ceiling.

[0054] The expression of power demand constraint is: in,

[0055] The energy storage device constraints include energy storage device efficiency constraints and energy storage device power constraints. The expression of energy storage device efficiency constraints is: Where: η ui-equipment-j is the efficiency of device j, which must meet its technical parameter range. The expression of the energy storage device power constraint is: Among them, P ui-storage-kis the charging and discharging power of the energy storage device k, which must be within the specified range.

[0056] The expression of electricity price strategy constraint is: Ensure that all electricity prices comply with the price policy of the power sales company and regulations. At the same time, other parameters (such as power consumption capacity, self-generated electricity, natural gas usage, etc.) must also comply with the constraints of actual physical and market conditions. By solving the minimum value of the objective function under the above constraints, a solution can be obtained to minimize the electricity cost of users under different electricity consumption characteristics.

[0057] S103, solving the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0058] In this embodiment, a genetic algorithm and a particle swarm algorithm are combined to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0059] The combination process of genetic algorithm and particle swarm algorithm is as follows:

[0060] 1. Initialize the population:

[0061] Genetic algorithm and particle swarm algorithm are used to initialize two populations respectively. The individuals in the population represent a set of possible decision variable values.

[0062] For genetic algorithms, a set of initial chromosomes can be randomly generated, each chromosome corresponding to a set of values ​​of decision variables;

[0063] For the particle swarm algorithm, the positions and velocities of a group of particles are randomly initialized, and the position vector represents the value of the decision variable.

[0064] 2. Fitness evaluation: For the genetic algorithm, the fitness of each chromosome is calculated. The fitness function is a combination of the objective function of maximizing the revenue of the power sales company and the objective function of minimizing the user's electricity cost. For example, it can be the revenue of the power sales company minus the user's electricity cost.

[0065] For the particle swarm algorithm, the above fitness function is also used to calculate the fitness of each particle.

[0066] 3. Genetic algorithm operation:

[0067] Selection: According to the fitness value, select a part of excellent chromosomes to enter the next generation population. Roulette selection, tournament selection and other methods can be used.

[0068] Crossover: Perform a crossover operation on the selected chromosome to generate a new chromosome. For example, single-point crossover, two-point crossover, etc. can be used.

[0069] Mutation: Mutate some chromosomes to increase the diversity of the population. Mutation can be a random change to a gene value on a chromosome.

[0070] 4. Particle swarm algorithm operation: Update the speed and position of each particle based on the particle’s own historical optimal position and the global optimal position of the entire population.

[0071] Speed ​​update formula: in: is the velocity of particle i in the dth dimension at the tth iteration, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the range [0,1], p id is the historical optimal position of the particle, p gd is the global optimal position of the entire population, is the position of the particle in the dimension at the iteration.

[0072] Position update formula:

[0073] 5. Information exchange and fusion: Regularly pass the excellent individual information in the genetic algorithm population to the particle swarm algorithm population as a new particle of the particle swarm algorithm population to introduce new search directions and excellent genes;

[0074] Similarly, the excellent particle information in the particle swarm algorithm population is fed back to the genetic algorithm population as the new chromosome of the genetic algorithm population to enhance the search ability of the genetic algorithm.

[0075] 6. Termination condition judgment: When the predetermined termination condition is met, the algorithm stops. The termination condition can be reaching the maximum number of iterations, the objective function value converges to a certain degree, etc.

[0076] Combining genetic algorithm and particle swarm algorithm can give full play to the advantages of both, efficiently handle the complex nonlinear relationship, multiple decision variables and multiple constraints in the objective function of maximizing the profit of power sales companies and minimizing the electricity costs of users, quickly and accurately find better parameter solutions, and improve optimization effects and efficiency.

[0077] According to the above optimized decision variables, the revenue of the power sales company and the electricity cost of the user can be calculated. The revenue changes of the power sales company under different market conditions and the degree of reduction of the user's electricity cost can be analyzed.

[0078] In summary of the embodiments described above, in a retail electricity price optimization method based on electricity consumption characteristics provided in an embodiment of the present invention, the present invention optimizes the user's electricity price cost calculation according to the user's electricity consumption characteristics, such as the proportion and duration of electricity consumption during peak and valley periods and peak periods; accurately calculates the costs caused by different load sizes, properties, equipment efficiency, and usage frequencies based on the load characteristics and equipment usage in the electricity consumption type characteristics; reasonably adjusts the fluctuation costs and high-reliability power supply costs through the load fluctuation conditions and power supply reliability requirements in the electricity consumption stability characteristics; scientifically calculates the self-generation costs and the costs of using multiple energy sources in combination with the renewable energy utilization and energy combination diversity in the energy consumption structure characteristics, promotes users to utilize renewable energy and optimize energy combinations, and ultimately achieves effective reduction of user electricity costs and rational use of energy.

[0079] Please refer to Figure 2 , Figure 2 A principle block diagram of a retail electricity price optimization system based on electricity consumption characteristics provided by an embodiment of the present invention, such as Figure 2 As shown, the system includes:

[0080] An objective function establishment module 210 is used to establish a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determine an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function;

[0081] The optimization model building module 220 is used to build a retail electricity price optimization model under pre-configured constraints and in combination with the objective function; wherein the constraints include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints, and electricity price strategy constraints;

[0082] The optimization solution module 230 is used to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

[0083] It can be seen that in a retail electricity price optimization system based on electricity consumption characteristics provided by an embodiment of the present invention, the present invention optimizes the user's electricity price cost calculation according to the user's electricity consumption characteristics, such as the proportion and duration of electricity consumption during peak and valley periods and peak periods; accurately calculates the costs caused by different load sizes, properties, equipment efficiency, and usage frequencies based on the load characteristics and equipment usage in the electricity consumption type characteristics; reasonably adjusts the fluctuation costs and high-reliability power supply costs through the load fluctuation conditions and power supply reliability requirements in the electricity consumption stability characteristics; scientifically calculates the self-generation costs and the costs of using multiple energy sources in combination with the renewable energy utilization and energy combination diversity in the energy consumption structure characteristics, promotes users to utilize renewable energy and optimize energy combinations, and ultimately achieves effective reduction of user electricity costs and rational use of energy.

[0084] The embodiment of the present application also provides an electronic device, which includes a processor, a memory, a communication interface and at least one communication bus for connecting the processor, the memory and the communication interface. The memory includes but is not limited to a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (PROM) or a portable CD-ROM, and the memory is used for related instructions and data.

[0085] The communication interface is used to receive and send data. The processor may be one or more CPUs. When the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processor in the electronic device is used to read one or more programs stored in the memory and perform the following operations: establish a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determine the objective function for optimizing the retail electricity price based on the weighted sum of the first objective function and the second objective function; establish a retail electricity price optimization model in combination with the objective function under pre-configured constraints; wherein the constraints include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints, and electricity price strategy constraints; solve the retail electricity price optimization model to obtain the revenue optimization results of the power sales company and the electricity cost optimization results of the user.

[0086] It should be noted that the specific implementation of each operation can be Figure 1 The corresponding description of the method embodiment shown, the electronic device can be used to execute a retail electricity price optimization method based on electricity usage characteristics of the above method embodiment, which will not be described in detail here.

[0087] In the embodiment of the present application, a computer-readable storage medium is also provided, and the computer-readable storage medium is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a method for optimizing a retail electricity price based on electricity consumption characteristics in the above embodiment. It should be understood by those skilled in the art that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0088] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing retail electricity prices based on electricity consumption characteristics, characterized in that: include: Establishing a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determining an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function; Under the pre-configured constraint conditions, combined with the objective function, a retail electricity price optimization model is established; wherein the constraint conditions include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints and electricity price strategy constraints; Solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

2. The method according to claim 1, characterized in that: The expression of the first objective function is: Among them, (1+ρ) is market growth, θ is market share, λ is j and γ j Represent different user types, β k represents the electricity sales revenue after the influence of competitors’ pricing strategies, p i is the retail electricity price of user i, q i is the electricity sales of user i, I j is the user set of the jth user type, p k-competitor is the retail electricity price of the kth competitor, p is the average retail electricity price of the power sales company, S is the policy subsidy, T is the tax, V is the value-added service income, C V is the cost of value-added services, C P is the electricity purchase cost, C O is the operating cost, C R For risk cost.

3. The method according to claim 2, characterized in that The risk costs include market risk costs, credit risk costs, strategy adjustment risk costs and operational risk costs; Among them, the market risk cost includes the risk cost of electricity price fluctuations and the risk cost of market demand changes, the credit risk cost includes credit assessment costs and bad debt loss costs, and the operating risk cost includes system construction and maintenance costs and failure loss costs.

4. The method according to claim 1, characterized in that: The expression of the second objective function is: in, Represents user u i The electricity cost of n is the total number of users.

5. The method according to claim 4, characterized in that The user u i The electricity cost includes one or more of electricity cost, basic electricity cost, power factor adjustment cost, power equipment cost, load fluctuation adjustment cost, renewable energy adjustment cost, and energy cost.

6. The method according to claim 5, characterized in that The energy storage device constraints include energy storage device efficiency constraints and energy storage device power constraints.

7. The method according to claim 1, characterized in that The genetic algorithm and the particle swarm algorithm are combined to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

8. A retail electricity price optimization system based on electricity consumption characteristics, characterized in that: The system includes: An objective function establishment module is used to establish a first objective function for maximizing the revenue of the power sales company and a second objective function for minimizing the user's electricity cost, and determine an objective function for optimizing the retail electricity price based on a weighted sum of the first objective function and the second objective function; An optimization model building module is used to build a retail electricity price optimization model under pre-configured constraints and in combination with the objective function; wherein the constraints include retail electricity price constraints, electricity demand constraints, energy storage equipment constraints, and electricity price strategy constraints; The optimization solution module is used to solve the retail electricity price optimization model to obtain the revenue optimization result of the power sales company and the electricity cost optimization result of the user.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a retail electricity price optimization method based on electricity consumption characteristics as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of a method for optimizing retail electricity prices based on electricity usage characteristics as described in any one of claims 1 to 7 are implemented.

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