User electric energy sharing price calculation method and system based on flexible resource scheduling

Abstract

The invention discloses a user electric energy sharing price calculation method and system based on flexible resource scheduling, and the method comprises the steps: building a user aggregation coordinated operation model with the lowest power utilization cost during the cooperation of user groups as a target; defining constraint conditions during model operation; determining the overall power consumption cost of the user and resource scheduling results of different users; and performing electric energy sharing price optimization by using an improved particle swarm optimization algorithm in combination with the total power consumption cost and a resource scheduling result. According to the invention, flexible resources contained by different users can be fully utilized, the electricity consumption cost of each user is reduced while the daily electricity consumption demand of the user is met, and the user is promoted to participate in electric energy sharing.

Description

Technical Field

[0001] The present invention relates to the power system, and particularly to a method and system for calculating the user electric energy sharing price based on flexible resource scheduling. Background Art

[0002] At present, the electric energy sharing mode develops rapidly, and the flexible resources owned by users are increasing day by day, including distributed photovoltaic and distributed energy storage devices. Users can make full use of the "source storage" characteristics to meet their own electricity consumption needs, and can also share the surplus electric energy with other users to form a cooperation form of power mutual assistance. With the launch of the first distributed photovoltaic power generation market transaction pilot project traded to other third-party energy users by market means, direct settlement with users is carried out based on the bilateral transaction agreement. This marks the possibility of electric energy sharing between different users.

[0003] However, the pricing of the electric energy sharing price between users is not transparent, resulting in low sharing willingness, which may not effectively encourage users to use or share electric energy at the appropriate time, resulting in the failure to achieve the optimal resource allocation, causing some users to be unable to obtain electricity during high-demand periods, or resulting in energy waste. For users with energy storage devices, they may not be able to reasonably arrange the storage and release of electric energy, resulting in a reduction in the efficiency of the energy storage system and the failure to achieve the best timing of electric energy storage and release. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method and system for calculating the user electric energy sharing price based on flexible resource scheduling to promote users to participate in electric energy sharing, achieve reliable resource allocation, and avoid energy waste.

[0005] The technical solution of the present invention includes: a method for calculating the user electric energy sharing price based on flexible resource scheduling, including the following steps:

[0006] Obtain the daily electricity consumption information and the contained flexible resources of different users when participating in cooperative electric energy sharing; establish a user aggregation coordination operation model with the lowest electricity consumption cost of the user group as the goal;

[0007] Define the operation constraint conditions of the model;

[0008] Solve the model to obtain the total electricity consumption cost of the user group and the scheduling results of the flexible resources of different users;

[0009] Based on the scheduling results, obtain the proportion of flexible resources of different users at the same moment, establish an optimized model for the electricity consumption cost of user shared electric energy, determine the optimized goal of the sharing price, and optimize and obtain the electric energy sharing price by combining the improved particle swarm optimization algorithm.

[0010] The objective function of the user aggregation coordination operation model is:

[0011]

[0012] In the formula, f is the operating cost of the user group, and T is the sampling point within a day; are the electricity purchase and sale prices set by the power grid at time t; P t b 、P t s are the electricity purchase and sale energy of the user group at time t; γ is the annual loss rate of the user's energy storage construction; m is the operation life of the energy storage; r in is the energy storage construction price; S is the total energy storage capacity of the user group; p om is the loss price of the energy storage operation; P t c 、P t dis are the charge and discharge powers of the user group's energy storage at time t; Δt is the time interval.

[0013] Among them:

[0014]

[0015] In the formula, N is the total number of users; the energy storage capacity S is the sum of the energy storage users in the user group; are the electricity purchase and sale quantities of user n at time t, are the charge and discharge quantities when the energy storage device is called by user n at time t;

[0016] E n represents the energy storage capacity built by user n.

[0017] The constraint conditions include: load demand constraint, power balance constraint, energy storage operation constraint, electricity purchase and sale quantity constraint, and electricity sharing constraint among users.

[0018] The load demand constraint is:

[0019]

[0020] In the formula, L n,t is the actual load demand of user n in the t period; is the original load demand; is the transferable load quantity of user n; is the load curtailment quantity;

[0021] Among them,

[0022]

[0023] In the formula, is the load curtailment upper limit of user n at time t; The upper limit of load transfer for user n at time t;

[0024] The power balance constraint is:

[0025]

[0026] In the formula, represents the output of the photovoltaic panels of user n; are the electricity purchase and sale volumes of user n at time t, respectively; are the charging and discharging volumes of the energy storage device when it is called by user n at time t, respectively; represent the electricity shared out and received by user n at time t, respectively;

[0027] The energy storage operation constraint is:

[0028]

[0029] In the formula, E n,t is the energy storage capacity of the energy storage device of user n in the t time period; E n,t+1 represents the energy storage capacity of the energy storage device of user n at time t + 1; η is the energy storage charging and discharging efficiency;

[0030] Among them,

[0031]

[0032] In the formula, E max is the maximum capacity of the energy storage device; represent the maximum charging and discharging powers of the energy storage device, respectively; E 0 、E T are the capacities of the energy storage device at the beginning and end of a scheduling period, respectively; A and B are Boolean variables, representing the energy storage charging and discharging flags, respectively;

[0033] The electricity purchase and sale volume constraint is:

[0034]

[0035] In the formula, P n buy,max 、P n sell,max are the maximum values of the electricity purchase and sale volumes of the user, respectively; α and β are Boolean variables, representing the electricity purchase and sale states of the user, respectively; are the electricity purchase and sale volumes of user n at time t, respectively;

[0036] The electricity sharing constraint between users is:

[0037]

[0038] By using the CPLEX solver to solve the established model combined with the constraint conditions, the actual load demands of different users, the charge and discharge behaviors of energy storage, and the sharing of electric energy among users are obtained, that is, the scheduling results of the internal flexibility resources of different users.

[0039] By solving the scheduling results of the flexibility resources of users, the calculation formula for the sub-item electricity cost of each user is constructed:

[0040] The composition of the sub-item electricity cost of pure load users is:

[0041]

[0042] The composition of the sub-item electricity cost of prosumer users is:

[0043]

[0044] Among them,

[0045]

[0046] In the formula, represents the sub-item electricity cost of pure load users; represents the sub-item electricity cost of prosumer users; L n is the total electricity demand of user n; C pv is the cost of user using the PV of other users; C s is the cost of user using the energy storage of other users; m pv is the shared PV price; m s is the shared energy storage price; represents the total PV output of user n.

[0047] The objective function of the user shared electricity cost optimization model is:

[0048]

[0049] In the formula, F represents the price optimization objective function, represents the electricity cost when the user does not participate in electricity sharing.

[0050] The steps of the improved particle swarm algorithm are:

[0051] 1) Suppose there is a tribe composed of D shared price variables in a D-dimensional space:

[0052]

[0053] In the formula, represents D randomly generated shared PV prices; represents D randomly generated shared energy storage prices;

[0054] 2) The flight speed of the shared price variable is a D-dimensional vector, which is common to both types of variables:

[0055] V = (V 1 , V 2 , … V D )

[0056] where V represents the flight speed of the photovoltaic shared price and the energy storage shared price; V 1 ~V D represent the variable flight speeds common to the shared photovoltaic price and the shared energy storage price;

[0057] 3) The optimal positions searched so far for both types of variables are called individual values, denoted as:

[0058]

[0059] where respectively represent the individual optimal values of the photovoltaic shared price and the energy storage shared price,

[0060] represents the D individual optimal shared photovoltaic prices; represents the D individual optimal shared energy storage prices;

[0061] 4) The optimal position searched so far for the entire particle swarm is the global extreme value, denoted as:

[0062]

[0063] where J best represents the global optimal value of the photovoltaic and energy storage shared prices,

[0064] represents the D global optimal shared photovoltaic prices; represents the D global optimal shared energy storage prices;

[0065] 5) When the optimal value is found, the particles update their speeds and positions according to the following formula:

[0066]

[0067] where w is the inertia weight, w max 、w min respectively represent the maximum and minimum values of the inertia weight; i represents the current iteration number; I is the maximum iteration number; c 1 、c 2 respectively represent the acceleration constants; r 1 、r 2 are random numbers between 0 and 1; p i,d 、j i,drespectively represent the i-th generation of the photovoltaic or energy storage sharing price, the d-dimensional individual optimal value and the global optimal value; V(i) represents the speed correction value of the i-th generation of the photovoltaic or energy storage sharing price; V(i + 1) represents the speed correction value of the (i + 1)-th generation of the photovoltaic or energy storage sharing price; m i,d represents the specific value of the i-th generation of the photovoltaic or energy storage sharing price, d-dimensional, m i+1,d represents the specific value of the (i + 1)-th generation of the photovoltaic or energy storage sharing price, d-dimensional;

[0068] 6) The optimal value found by the entire particle swarm is:

[0069] globe = (F 1 , F 2 , …, F I )

[0070] In the formula, F 1 ~F I represent the optimal objective function values of the sharing price for each generation of optimization.

[0071] A user electricity sharing price calculation system based on flexible resource scheduling includes:

[0072] An aggregation module, which is used to obtain the daily electricity consumption information and the contained flexible resources when different users participate in cooperative electricity sharing; and establish a user aggregation coordination operation model with the goal of minimizing the electricity consumption cost of the user group;

[0073] A constraint module, which is used to clarify the operation constraint conditions of the model;

[0074] A solving module, which is used to solve the model to obtain the total electricity consumption cost of the user group and the scheduling results of the flexible resources of different users;

[0075] A sharing module, which is used to obtain the proportion of flexible resources of different users at the same moment based on the scheduling results, establish an optimization model for the electricity consumption cost of user shared electricity, determine the optimization goal of the sharing price, and optimize and obtain the electricity sharing price in combination with the improved particle swarm algorithm.

[0076] In the operation of the present invention, based on the lowest electricity consumption cost of users, the scheduling results of the flexible resources included in different users are obtained, and the composition of the electricity consumption cost of different types of users is designed according to the scheduling results and the resource proportions of different users. The sharing electricity price is optimized according to the bargaining power of users. Compared with the situation where users do not participate in electricity sharing, the electricity consumption cost of each user is significantly reduced, the willingness to share electricity is improved, and the dependence on the power grid is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is the flowchart of the present invention;

[0078] Figure 2It is the electricity demand diagram of 5 users collected by the present invention;

[0079] Figure 3 It is the photovoltaic output diagram of users with photovoltaics in the present invention;

[0080] Figure 4 It is the energy storage operation diagram of users with energy storage in the present invention;

[0081] Figure 5 It is the electricity balance diagram of the user group in the present invention;

[0082] Figure 6 It is the iteration diagram of the improved particle swarm optimization algorithm adopted by the present invention. Detailed implementation manners

[0083] The following describes the implementation plan and specific operation process of the present invention with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following specific description.

[0084] As shown in the present invention Figure 1 A method for calculating the user electricity sharing price based on the flexibility resource scheduling ratio is provided, including the following steps:

[0085] Step 1): Obtain the flexibility resource information of users participating in shared cooperation electricity sharing, such as rooftop photovoltaic equipment, energy storage equipment, loads that can perform demand response, etc.; establish a user aggregation coordinated operation model with the lowest electricity cost of the user group as the goal, and the specific objective function is:

[0086]

[0087] In the formula, T is the sampling point within a day, taking 24h; are the purchase and sale electricity prices formulated by the power grid at time t respectively; P t b and P t s are the purchased and sold electric energies of the user group at time t respectively; γ is the annual loss rate of the user's energy storage construction; m is the operation life of the energy storage; r in is the energy storage construction price; S is the total energy storage capacity of the user group; p om is the loss price of the energy storage operation; P t c and P t dis are the charge and discharge powers of the user group's energy storage at time t respectively; Δt is the time interval; taking 1h.

[0088] Among them:

[0089]

[0090] The above formula indicates that the electricity purchase and sale volume of the user group is the sum of the electricity of the included users, N is the total number of users; the energy storage capacity S is the sum of the users with energy storage in the user group; E n represents the energy storage capacity built by user n; if no energy storage is built, it is taken as 0, and correspondingly

[0091] Step 2): Determine the constraint conditions for the operation of the building system;

[0092] ① Load demand constraint

[0093]

[0094] Among them, L n,t is the actual load demand of user n at time t; is its original load demand; is the transferable load volume of user n; is the load curtailment volume;

[0095]

[0096] Among them, is the load curtailment upper limit of user n at time t; is the load transfer upper limit of user n at time t;

[0097] ② Power balance constraint

[0098]

[0099] Among them, represents the output of the photovoltaic panel of user n; are the electricity purchase and sale volumes of user n at time t respectively; L n,t is the actual load demand of user n at time t; are the charge and discharge volumes when the energy storage device is called by user n at time t respectively; represent the electricity shared out and received by user n at time t respectively.

[0100] ③ Energy storage operation constraint

[0101]

[0102] Among them, E n,t is the energy storage capacity of the energy storage device of user n at time t; E n,t+1 represents the energy storage capacity of the energy storage device of user n at time t + 1; η is the energy storage charge and discharge efficiency.

[0103]

[0104] It is the safety operation constraint of energy storage, including that the energy storage capacity at the beginning and end of each day of the energy storage is equal, the energy storage capacity boundary, the charge and discharge power boundary constraint, and the constraint that there can only be one charge and discharge state in the same time period. E max is the maximum capacity of the energy storage device; respectively represent the maximum charge and discharge powers of the energy storage device; are respectively the charge and discharge amounts when the energy storage device is called by user n at time t; E 0 、E T are respectively the capacities of the energy storage device at the beginning and end of a scheduling period; A and B are Boolean variables, representing the charge and discharge flags of the energy storage respectively;

[0105] ④ Purchase and sale electricity quantity constraint

[0106]

[0107] The above formula represents the purchase and sale electricity constraints and purchase and sale electricity state constraints of user n; P n buy,max 、P n sell,max are respectively the maximum values of the electricity purchase and sale quantities of the user; α and β are Boolean variables, representing the electricity purchase and sale states of the user respectively; are respectively the electricity purchase and sale quantities of user n at time t;

[0108] ⑤ Electric energy sharing constraint

[0109]

[0110] Step 3): Combining the constraint conditions, taking the charge and discharge behavior of the energy storage, the load demand, and the user's shared electric energy as optimization variables, borrowing matlab to call the commercial solver CPLEX to solve the user aggregation coordination operation model, and outputting the optimal solution.

[0111] By solving the user aggregation coordination operation model, the scheduling results of each device are obtained, and the proportion of different resources in scheduling is obtained.

[0112] Step 4): By solving the scheduling results of the user's flexible resources, the calculation formula for the sub-item electricity cost of each user is constructed:

[0113] The composition of the sub-item electricity cost of pure load users is:

[0114]

[0115] The composition of the sub-item electricity cost of prosumer users is:

[0116]

[0117] Among them, represents the sub-item electricity cost of pure load users; Represents the sub - item electricity consumption cost of prosumer users; L n Is the total electricity demand of user n; C pv Is the cost of user using other users' photovoltaic power, that is, the photovoltaic sharing income obtained by prosumer users according to the photovoltaic ratio; C s Is the cost of user using other users' energy storage, that is, the energy storage sharing income obtained by the energy storage - building users according to the energy storage capacity ratio; m pv Is the price of shared photovoltaic power; m s Is the price of shared energy storage; Represents the total photovoltaic output of user n.

[0118] Step 5): It can be seen from the user's electricity cost that it is related to the grid interaction cost, its own resources and the sharing price. Since the grid interaction cost and its own resources are determined and cannot be changed when scheduling users, in order to show the bargaining power of each subject, the objective function is:

[0119]

[0120] Represents the electricity cost when the user does not participate in electricity sharing, and F represents the price optimization objective function, that is, the cumulative product of the difference between the original cost and the optimized cost of each user is the largest.

[0121] The improved particle swarm algorithm is used to solve it so as to promote users to cooperate and share. Now the improved particle swarm algorithm is described in detail.

[0122] The improved particle swarm optimization algorithm is an evolutionary computing technology. The core idea is to use the sharing of information by individuals in the group to make the movement of the whole group evolve from disorder to order in the problem - solving space, so as to obtain a feasible solution to the problem.

[0123] The basic steps are as follows:

[0124] 1) Assume that there is a tribe composed of D sharing - price variables in a D - dimensional space:

[0125]

[0126] In the formula, Represents D randomly generated shared photovoltaic prices; Represents D randomly generated shared energy storage prices.

[0127] 2) The flying speed of the sharing - price variable is also a D - dimensional vector, which is common to the two types of variables:

[0128] V=(V 1 , V 2 ,…V D )

[0129] In the formula, V 1 ~V D represent the variable flight speeds common to the shared photovoltaic price and the shared energy storage price.

[0130] V represents the flight speed of the photovoltaic sharing price and the energy storage sharing price.

[0131] 3) The optimal positions searched so far for the two types of variables are called individual values, denoted as:

[0132]

[0133] In the formula, respectively represent the individual optimal values of the photovoltaic sharing price and the energy storage sharing price,

[0134] represents the D individual optimal shared photovoltaic prices; represents the D individual optimal shared energy storage prices.

[0135] 4) The optimal position searched so far for the entire particle swarm is the global extreme value, denoted as:

[0136]

[0137] In the formula, J best represents the global optimal value of the photovoltaic and energy storage sharing prices,

[0138] represents the D global optimal shared photovoltaic prices; represents the D global optimal shared energy storage prices.

[0139] 5) When the optimal value is found, the particles update their speeds and positions according to the following formula:

[0140]

[0141] In the formula, w is the inertia weight, w max , w min respectively represent the maximum and minimum values of the inertia weight; i represents the current iteration number; I is the maximum iteration number; c 1 , c 2 respectively represent the acceleration constants; r 1 , r 2 are random numbers between 0 and 1; p i,d , j i,d respectively represent the individual optimal value and the global optimal value of the i-th generation, d-dimensional photovoltaic (energy storage) sharing price; V(i) represents the speed correction value of the i-th generation photovoltaic (energy storage) sharing price; V(i + 1) represents the speed correction value of the (i + 1)-th generation photovoltaic (energy storage) sharing price; mi,d represents the specific value of the i-th generation of photovoltaic (energy storage) sharing price in d dimensions, m i+1,d represents the specific value of the (i + 1)-th generation of photovoltaic (energy storage) sharing price in d dimensions.

[0142] By improving w, the defect of falling into the local optimum of the conventional particle swarm can be avoided, thus realizing the optimization solution as a whole.

[0143] 6) The optimal value searched by the entire particle swarm is:

[0144] globe = (F 1 , F 2 , …, F I )

[0145] In the formula, F 1 ~F I represent the objective function values of the sharing price optimization for each generation of optimization, a total of I;

[0146] Select the m corresponding to the maximum value i,d which is the optimal photovoltaic and energy storage sharing price sought.

[0147] The present invention optimizes and calculates the sharing price for power sharing among users with multiple resources. First, the daily electricity consumption information and the contained flexibility resources of different users participating in cooperative power sharing are obtained. With the goal of minimizing the electricity consumption cost of the user group, a user cooperation operation objective function is established, the model operation constraint conditions are clarified, and the total electricity consumption cost of the user group and the scheduling results of the flexibility resources of different users are obtained by solving the model; based on the scheduling results, the proportion of flexibility resources of different users at the same moment is obtained, the sharing price optimization objective is determined, and the electricity sharing price is optimized by combining the improved particle swarm algorithm.

[0148] The present invention also provides a user electricity sharing price calculation system based on flexible resource scheduling, including:

[0149] An aggregation module, used to obtain the daily electricity consumption information and the contained flexibility resources of different users participating in cooperative power sharing; with the goal of minimizing the electricity consumption cost of the user group, establish a user aggregation coordination operation model;

[0150] A constraint module, used to clarify the model operation constraint conditions;

[0151] A solving module, used to solve the model to obtain the total electricity consumption cost of the user group and the scheduling results of the flexibility resources of different users;

[0152] A sharing module, used to obtain the proportion of flexibility resources of different users at the same moment based on the scheduling results, establish an optimization model for the electricity consumption cost of user shared electricity, determine the sharing price optimization objective, and optimize the electricity sharing price by combining the improved particle swarm algorithm.

[0153] In the operation of the present invention, the proportion of flexible resource scheduling highlights the contribution value of different users during sharing from the side, and based on this, a sharing price is formulated to encourage users to participate in electricity sharing.

[0154] The present invention selects 5 users, numbers them, collects their electricity consumption information and flexible resources, and optimizes their electricity consumption costs according to the established model. Table 1 shows the resource situation of the users. Figure 2 It is the actual electricity consumption situation of each user. Figure 3 It is the photovoltaic output diagram of the user with photovoltaic.

[0155] Table 1 Resource situation of each user

[0156]

[0157] Combined with Figures 2 to 3 the data, independent operation and electricity sharing for each user are respectively optimized and scheduled, and the operation results of different energy storages are obtained as Figure 4 shown. It can be seen that compared with independent operation, the energy storage can achieve "two charges and two discharges" during combined operation, and the electricity utilization efficiency is higher. The overall power balance diagram of the users is as Figure 5 shown. It can be seen that during combined operation, the energy storage charges at 1:00 - 7:00 and 23:00 - 24:00 when the electricity price is at the trough, and discharges at 8:00 - 12:00 and 15:00 - 22:00 when the electricity price is at the peak to meet the electricity consumption needs of each user, making good use of the "source-load" characteristics of the energy storage and effectively reducing the electricity consumption cost.

[0158] When optimizing the shared electricity price, the electricity consumption cost is calculated according to the shared electricity price and compared with the electricity consumption cost calculated during independent operation, and the electricity consumption cost comparison in Table 2 is obtained. From Figure 6 the improved particle swarm iteration algorithm, it can be seen that when iterating to the 16th generation, the objective function no longer changes. At this time, the corresponding photovoltaic sharing price and energy storage sharing price are 0.21 yuan / kWh and 0.18 yuan / kWh respectively.

[0159] Figure 6 In it, the ordinate represents the cumulative product of the difference between the original electricity consumption cost and the optimized electricity consumption cost of each user. The larger the value, the more the user cost is reduced. E is in scientific notation.

[0160] Table 2 Comparison of electricity consumption costs between cooperative operation and independent operation

[0161]

[0162] As can be seen from Table 2, when users conduct electricity sharing, the total electricity consumption costs of each user are all reduced. This is because the price of shared electricity formulated by the user group based on the proportion of flexible resource scheduling is lower than the maximum value of the electricity selling price of the power grid and higher than the minimum value of the electricity purchasing price of the power grid. This increases the electricity revenue of prosumer users and reduces the electricity purchasing cost of pure load users. Therefore, the overall electricity consumption cost of the user group is reduced by 32.4%, which will encourage users to actively participate in electricity sharing and ensure fairness in cost sharing.

[0163] The method steps and data described in the present invention are only specific embodiments of the present invention, which are general elaborations and examples of the spirit of the present invention. Those skilled in the art to which the present invention pertains can also realize various possibilities of variants or alternative embodiments. Inspired by the spirit and principle of the present invention, various modifications, supplements, improvements or substitutions can be made. It can be understood that these modifications, supplements, improvements or substitutions are considered to be included in the present invention without departing from the spirit of the present invention or exceeding the scope defined by the appended claims.

Claims

1. A method for calculating user power sharing prices based on flexible resource scheduling, characterized in that: The following steps are involved: Obtain daily electricity consumption information and flexibility resources of different users when they participate in cooperative power sharing; establish a user aggregation and coordination operation model with the goal of minimizing the electricity cost of the user group; Clarify the constraints of model operation; Solving the model obtains the total electricity cost of the user group and the scheduling results of different users' flexibility resources; Based on the scheduling results, the proportion of flexible resources of different users at the same time is obtained, and the user shared electricity cost optimization model is established. The shared price optimization target is determined, and the electricity sharing price is obtained by combining the improved particle swarm algorithm optimization.

2. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: The objective function of the user aggregation coordination operation model is: In the formula, f is the operating cost of the user group, and T is the sampling point in a day; are the purchase and sale prices of electricity set by the power grid at time t; are the purchase and sale of electricity by the user group at time t; γ is the annual loss rate of energy storage built by the user; m is the operating life of energy storage; r in is the energy storage construction price; S is the total energy storage capacity of the user group; p om The loss price for energy storage operation; are the charging and discharging power of the user group’s energy storage at time t; Δt is the time interval.

3. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: in: Where N is the total number of users; the energy storage capacity S is the sum of the users with energy storage in the user group; are the electricity purchase and sales of user n at time t, are the charge and discharge amounts of the energy storage device when it is called by user n at time t; E n Represents the energy storage capacity constructed by user n.

4. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: The constraints include: load demand constraints, power balance constraints, energy storage operation constraints, power purchase and sales constraints, and power sharing constraints among users.

5. The method for calculating user power sharing price based on flexible resource scheduling according to claim 4 is characterized in that: The load demand constraint is: Where, L n,t is the actual load demand of user n during period t; is the original load demand; is the transferable load of user n; To reduce the load; in, In the formula, is the load reduction upper limit of user n at time t; is the load transfer upper limit of user n at time t; The power balance constraint is: In the formula, represents the photovoltaic panel output of user n; are the electricity purchase and sales of user n at time t; are the charge and discharge amounts of the energy storage device when it is called by user n at time t; They represent the amount of electricity shared and received by user n at time t respectively; The energy storage operation constraints are: In the formula, E n,t E is the energy storage capacity of the energy storage device of user n in period t; n,t+1 represents the energy storage capacity of the energy storage device of user n at time t+1; η is the energy storage charging and discharging efficiency; in, In the formula, E max is the maximum capacity of the energy storage device; Respectively represent the maximum charging and discharging power of the energy storage device; E0, E T are the initial and final capacities of the energy storage device in a scheduling cycle; A and B are Boolean variables, representing the energy storage charge and discharge flags, respectively; The constraints on the amount of electricity purchased and sold are: In the formula, are the maximum values ​​of electricity purchased and sold by users, respectively; α and β are Boolean variables, representing the status of electricity purchased and sold by users, respectively; are the electricity purchase and sales of user n at time t; The constraints for power sharing between users are:

6. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: By using the CPLEX solver to solve the established model combined with the constraints, we can obtain the actual load demands of different users, the charging and discharging behaviors of energy storage, and the sharing of electric energy among users, that is, the scheduling results of the internal flexibility resources of different users.

7. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: By solving the user's flexibility resource scheduling results, the calculation formula for each user's sub-item electricity cost is constructed: The electricity cost of pure load users is composed of: The electricity cost of integrated production and consumption users is composed of: in, In the formula, Indicates the itemized electricity cost of pure load users; Indicates the electricity cost of each item for users of integrated production and consumption; L n is the total electricity demand of user n; C pv The cost of using other users’ photovoltaic power generation; C s Cost of using other users’ energy storage for users; m pv is the price of shared photovoltaic power generation; m s for the price of shared energy storage; Represents the total photovoltaic output of user n.

8. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1 is characterized in that: The objective function of the electricity cost optimization model for users sharing electricity is: In the formula, F represents the price optimization objective function, Indicates the electricity cost when the user does not participate in power sharing.

9. The method for calculating user power sharing price based on flexible resource scheduling according to claim 1, characterized in that: The steps of improving the particle swarm algorithm are: 1) Suppose there are D shared price variables in a D-dimensional space forming a tribe: In the formula, represents D randomly generated shared photovoltaic prices; represents D randomly generated shared energy storage prices; 2) The flight speed of the shared price variable is a D-dimensional vector, which is common to both types of variables: V=(V1,V2,…V D ) Where V represents the speed of the photovoltaic sharing price and the energy storage sharing price; V1~V D The variable flight speed represents the price common to shared PV and shared storage prices; 3) The optimal position of the two types of variables searched so far is called the individual value, which is recorded as: In the formula, They represent the individual optimal values ​​of photovoltaic sharing price and energy storage sharing price respectively, represents the optimal shared photovoltaic price of D individuals; represents the optimal shared energy storage price for D individuals; 4) The optimal position searched by the entire particle swarm so far is the global extreme value, which is recorded as: In the formula, J best represents the global optimal value of the photovoltaic and energy storage sharing price, represents D globally optimal shared PV prices; represents D globally optimal shared energy storage prices; 5) When the optimal value is found, the particle updates its speed and position according to the following formula: Where w is the inertia weight, w max 、w min Respectively represent the maximum and minimum values ​​of inertia weight; i represents the current iteration number; I is the maximum number of iterations; c1 and c2 are acceleration constants; r1 and r2 are random numbers between 0 and 1; p i,d 、j i,d They represent the individual optimal value and global optimal value of the ith and dth dimensions of the photovoltaic or energy storage sharing price respectively; V(i) represents the speed correction value of the ith generation photovoltaic or energy storage sharing price; V(i+1) represents the speed correction value of the i+1th generation photovoltaic or energy storage sharing price; m i,d represents the specific value of the d dimension of the ith generation of the photovoltaic or energy storage sharing price, m i+1,d represents the specific value of the d-dimensional PV or energy storage sharing price for the i+1th generation; 6) The optimal value searched by the entire particle swarm is: globe=(F1,F2,…,F I ) In the formula, F1~F I Represents the shared price optimization objective function value of each generation of optimization.

10. A user power sharing price calculation system based on flexible resource scheduling, characterized in that: include: Aggregation module, used to obtain daily electricity consumption information and flexibility resources contained in different users when participating in cooperative power sharing; With the goal of minimizing the electricity cost of the user group, a user aggregation and coordinated operation model is established; Constraint module, used to define model operation constraints; A solution module is used to solve the model to obtain the total electricity cost of the user group and the scheduling results of the flexibility resources of different users; The sharing module is used to obtain the proportion of flexible resources of different users at the same time based on the scheduling results, establish a user shared electricity cost optimization model, determine the shared price optimization target, and optimize the electricity sharing price by combining the improved particle swarm algorithm.

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