Dynamic time-of-use electricity price generation method, system, device and readable storage medium

Abstract

The application provides a dynamic time-of-use electricity price generation method, device and equipment and a readable storage medium. When it is necessary to fully tap the interests of each subject in the system under the background of coordinated operation of a smart power distribution network including multiple energy networks and multiple market subjects, the method provided in the embodiment of the application can independently optimize each microgrid, formulate a charging and discharging scheme and a power purchase and sale scheme; then, shared energy storage and each microgrid separately settle rental fees, and further realize low-storage high-generation arbitrage by using the sum of the rental capacity of each microgrid and the summed residual capacity; finally, the smart power distribution network aggregates the reported data on both sides, formulates a new time-of-use electricity price scheme with the maximum self-operation benefit as the target under the consideration of network constraints, and feeds back to the downstream, and iterates until the convergence criterion is met, so that the overall coordinated operation of interests can be realized through the adjustment of the time-of-use electricity price, and the maximization of the interests of each subject is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electricity pricing, in particular to a dynamic time-of-use electricity price generation method, system, device and readable storage medium. BACKGROUND

[0002] With the development of science and technology, in the environment of continuous development of intelligent power distribution system, emerging resources such as comprehensive energy microgrid, shared energy storage and demand response are connected to the intelligent power distribution network in large scale, and the coordination and interaction of multiple resources can improve the utilization efficiency of energy. However, various emerging resources are usually built and operated by third parties such as users and operators, and their operation is autonomous and has the demand of privacy protection. In order to mobilize the enthusiasm of flexible resources to participate in system regulation, price signal is needed as a guide, and the dynamic time-of-use electricity price of power distribution network is considered to be an effective means to mobilize distributed resources to participate in the optimal operation of the system.

[0003] For the comprehensive energy microgrid, by integrating renewable resources and loads within the microgrid, and by formulating power interaction schemes with other subjects, more refined local energy control is achieved. This efficient control allows the microgrid to flexibly adjust the production and consumption of internal energy, effectively solves the intermittency and volatility of new energy output, and significantly improves the operation efficiency of the microgrid. More and more shared energy storage is connected to the power grid, which can realize the space-time decoupling of electric energy and promote the coordinated use of energy among different subjects through electrical connection with different users and power systems; through sharing, the utilization rate of energy storage equipment is improved, and the payback period of equipment investment is reduced. Similarly, as an important flexible resource in the power system, demand response adjusts power usage behavior without affecting user power demand, and cooperates with the system to achieve various adjustments; in order to ensure the sustainability of the response mechanism and promote the development of the power system to be more efficient and reliable, economic and technical factors need to be considered, and demand response compensation needs to be given. The above flexible resources will have a significant impact on the operation of the power distribution network, but the power distribution market has just started, and how to formulate dynamic time-of-use electricity price and promote the coordinated operation of multiple flexible resources under the condition of reduced controllability and observability of the power distribution system is a difficult problem to be solved at present. SUMMARY

[0004] The present application aims to at least solve one of the above technical defects, and in view of this, the present application provides a dynamic time-of-use electricity price generation method, device, equipment and readable storage medium, which solves the technical defect that it is difficult to formulate dynamic time-of-use electricity price of power distribution system to promote the coordinated operation of multiple flexible resources in the prior art.

[0005] A dynamic time-of-use electricity price generation method comprises:

[0006] determine a first charging and discharging scheme between each of the integrated energy micro-grid users and the shared energy storage and a first power purchase and sale scheme between each of the integrated energy micro-grid users and the smart power grid according to the real-time time-of-use electricity price information provided by the smart power grid;

[0007] provide each of the first charging and discharging schemes to an operator of the shared energy storage and provide the first power purchase and sale scheme to an operator of the smart power grid;

[0008] settle a service lease fee between the shared energy storage and each of the integrated energy micro-grid users according to the first charging and discharging scheme between each of the integrated energy micro-grid users and the shared energy storage;

[0009] calculate a self remaining capacity of the shared energy storage according to an actual charging and discharging power of the shared energy storage, wherein the actual charging and discharging power of the shared energy storage is a sum of charging and discharging powers of each of the integrated energy micro-grid users;

[0010] optimize a second charging and discharging scheme between the shared energy storage and the smart power grid according to the self remaining capacity of the shared energy storage and the real-time time-of-use electricity price information provided by the smart power grid and provide the second charging and discharging scheme to the smart power grid;

[0011] determine second time-of-use electricity price information according to each of the first power purchase and sale scheme and the second charging and discharging scheme and provide the second time-of-use electricity price information to each of the integrated energy micro-grid users and the operator of the shared energy storage;

[0012] return to perform the operation of determining a first charging and discharging scheme between each of the integrated energy micro-grid users and the shared energy storage and a first power purchase and sale scheme between each of the integrated energy micro-grid users and the smart power grid according to real-time time-of-use electricity price information provided by the smart power grid until final time-of-use electricity price information meeting a preset benefit convergence condition is determined according to the second time-of-use electricity price information.

[0013] A dynamic time-of-use electricity price generation system, preferably applied to the method described in the foregoing introduction, the system comprising: a smart power grid, a shared energy storage, and at least one integrated energy micro-grid user;

[0014] wherein,

[0015] each of the integrated energy micro-grid users determines a first charging and discharging scheme between each of the integrated energy micro-grid users and the shared energy storage and a first power purchase and sale scheme between each of the integrated energy micro-grid users and the smart power grid according to real-time time-of-use electricity price information provided by the smart power grid, and provides each of the first charging and discharging schemes to an operator of the shared energy storage and provides the first power purchase and sale scheme to an operator of the smart power grid;

[0016] The shared energy storage aggregates first charge-discharge schemes of each of the integrated energy micro-grid users, and settles energy storage service leasing fees between each of the integrated energy micro-grid users and the shared energy storage according to the first charge-discharge schemes between each of the integrated energy micro-grid users and the shared energy storage.

[0017] The shared energy storage calculates its own residual capacity according to its actual charge-discharge power, wherein the actual charge-discharge power of the shared energy storage is the sum of the charge-discharge powers of each of the integrated energy micro-grid users.

[0018] The shared energy storage optimizes a second charge-discharge scheme between the shared energy storage and the smart power grid according to the own residual capacity and real-time time-of-use price information provided by the smart power grid, and provides the second charge-discharge scheme to the smart power grid.

[0019] The smart power grid optimizes real-time time-of-use price information according to each of the first power purchase and sale schemes and the second charge-discharge scheme, obtains second time-of-use price information, and provides the second time-of-use price information to each of the integrated energy micro-grid users and an operator of the shared energy storage.

[0020] Each of the integrated energy micro-grid users re-executes the operation of determining the first charge-discharge scheme between each integrated energy micro-grid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy micro-grid user and the smart power grid according to the real-time time-of-use price information provided by the smart power grid until the smart power grid determines final time-of-use price information that meets a preset benefit convergence condition.

[0021] Preferably, the operation of determining the first charge-discharge scheme between each integrated energy micro-grid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy micro-grid user and the smart power grid according to the real-time time-of-use price information provided by the smart power grid includes:

[0022] Each of the integrated energy micro-grid users optimizes the real-time time-of-use price information provided by the smart power grid to determine the first charge-discharge scheme between each integrated energy micro-grid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy micro-grid user and the smart power grid, with the goal of minimizing total operating costs.

[0023] wherein,

[0024] In the process of optimizing the real-time time-of-use price information provided by the smart power grid, the objective function of the optimization scheduling of each of the integrated energy micro-grid users is as shown in the following formula (1):

[0025]

[0026] wherein, in formula (1),

[0027] represents the objective function of the optimization scheduling of each of the comprehensive energy micro-grid users in the optimization process;

[0028] represents the electricity purchase unit price of each of the comprehensive energy micro-grid users at t moment in the Xth optimization process;

[0029] represents the electricity purchase unit price of each of the comprehensive energy micro-grid users at t moment in the Xth optimization process;

[0030] represents the electricity purchase power of the comprehensive energy micro-grid user i to the smart power grid at t moment in the Xth optimization process;

[0031] represents the electricity purchase power of the comprehensive energy micro-grid user i to the smart power grid at t moment in the Xth optimization process;

[0032] C ES represents the calling cost of the independent energy storage unit capacity in the comprehensive energy micro-grid;

[0033] represents the interaction power between the comprehensive energy micro-grid user i and the independent energy storage in the comprehensive energy micro-grid at t moment in the Xth optimization process, and the charging of the shared energy storage is positive, and the discharging of the shared energy storage is negative;

[0034] C SES represents the leasing cost of the energy storage unit capacity of the shared energy storage;

[0035] represents the interaction power between the comprehensive energy micro-grid user i and the shared energy storage at t moment in the Xth optimization process, and the charging of the shared energy storage is positive, and the discharging of the shared energy storage is negative;

[0036] Δt represents the time interval adopted when carrying out the time-of-use electricity price optimization.

[0037] Preferably, each of the comprehensive energy micro-grid users is connected to the smart power grid through a tie line;

[0038] wherein,

[0039] Each of the comprehensive energy micro-grid users needs to meet the tie line constraint condition with the smart power grid, wherein each of the comprehensive energy micro-grid users needs to meet the tie line constraint condition with the smart power grid as shown in the following formula (2):

[0040]

[0041] In equation (2),

[0042] and These represent the power purchased and the power sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization.

[0043] α1 and α2 represent the 0-1 variables of the electricity purchase and sale status of user i in the integrated energy microgrid;

[0044] This refers to the maximum capacity of the interconnection line between the integrated energy microgrid user i and the interconnection node of the smart distribution network.

[0045] Preferably, the independent energy storage within each integrated energy microgrid user needs to meet a preset first sequential constraint condition, wherein the preset first sequential constraint condition is shown in the following equation (3):

[0046]

[0047] In equation (3),

[0048] The remaining electricity of the independent energy storage within the integrated energy microgrid user i at time t during the Xth round of optimization;

[0049] η C With η D These represent the charging power and discharging efficiency of the independent energy storage within the user of the integrated energy microgrid, respectively.

[0050] E i,min With E i,max These represent the minimum and maximum states of charge (SOC) of independent energy storage within user i of the integrated energy microgrid, respectively.

[0051] Preferably, each of the integrated energy microgrid users is connected to the shared energy storage via a tie line;

[0052] in,

[0053] Each integrated energy microgrid user must satisfy the tie-line constraint condition with the shared energy storage, wherein the tie-line constraint condition that each integrated energy microgrid user must satisfy with the shared energy storage is as shown in the following equation (4):

[0054]

[0055] In equation (4),

[0056] represents the maximum capacity of the tie line between the integrated energy microgrid user i and the shared energy storage;

[0057] wherein,

[0058] The shared energy storage should satisfy a preset second sequential constraint condition, and the preset second sequential constraint condition is shown in the following formula (5):

[0059]

[0060] In formula (5),

[0061] E X E (t) represents the residual energy of the shared energy storage at time t in the Xth optimization process under the optimization scheduling of the plurality of integrated energy microgrid users;

[0062] E min E max respectively represent the minimum state of charge and the maximum state of charge of the shared energy storage.

[0063] Preferably, if the integrated energy microgrid user is configured with a thermal energy storage, the thermal energy storage operation constraint condition of the integrated energy microgrid user is shown in the following formula (6):

[0064]

[0065] In formula (6),

[0066] E (t) represents the residual energy of the thermal energy storage of the integrated energy microgrid user i at time t in the Xth optimization process;

[0067] η HSC and η HSD respectively represent the charging efficiency and discharging efficiency of the thermal energy storage of the integrated energy microgrid user;

[0068] and respectively represent the charging power and discharging power of the thermal energy storage of the plurality of integrated energy microgrid users at time t in the Xth optimization process;

[0069] k HS,min and k HS,max respectively represent the minimum thermal energy residual coefficient and the maximum thermal energy residual coefficient of the thermal energy storage of the integrated energy microgrid user;

[0070] and respectively represent the minimum charging power and the maximum charging power of the thermal energy storage of the integrated energy microgrid user i;

[0071] and respectively represent the minimum and maximum heat release power of the thermal storage of the comprehensive energy microgrid user i;

[0072] If the comprehensive energy microgrid user is configured with cold storage, the cold storage operation constraint condition of the comprehensive energy microgrid user is shown in the following formula (7):

[0073]

[0074] In formula (7), the minimum and maximum cold storage power of the comprehensive energy microgrid user i are represented by

[0075] Ei,t(X) represents the residual energy of the cold storage of the comprehensive energy microgrid user i at time t in the Xth optimization process;

[0076] η CSC and η CSD respectively represent the charging and refrigeration efficiency of the cold storage of the comprehensive energy microgrid user;

[0077] and respectively represent the charging and refrigeration power of the cold storage of the comprehensive energy microgrid user i at time t in the Xth optimization process;

[0078] k CS,min and k CS,max respectively represent the minimum and maximum cold energy residual coefficients of the cold storage of the comprehensive energy microgrid user;

[0079] and respectively represent the minimum and maximum charging power of the cold storage of the comprehensive energy microgrid user i;

[0080] and respectively represent the minimum and maximum refrigeration power of the cold storage of the comprehensive energy microgrid user i.

[0081] Preferably, if there is an energy coupling device such as an electric refrigerator and an electric boiler in the comprehensive energy microgrid user, the operation constraint of the energy coupling device such as the electric refrigerator and the electric boiler in the comprehensive energy microgrid user is shown in the following formula (8):

[0082]

[0083] In formula (8), the minimum and maximum charging power of the comprehensive energy microgrid user i are represented by

[0084] Ei,t(X) represents the residual energy of the cold storage of the comprehensive energy microgrid user i at time t in the Xth optimization process;

[0085] P i,eb,in,min and P i,eb,in,maxrespectively represent the minimum input power and the maximum input power of the electric boiler of the integrated energy microgrid user i;

[0086] respectively represent the minimum input power and the maximum input power of the electric boiler of the integrated energy microgrid user i; respectively represent the output power and the input power of the electric boiler of the integrated energy microgrid user i at time t in the Xth round of optimization process;

[0087] η eb represents the energy conversion efficiency of the electric boiler;

[0088] The operation constraint condition of the electric refrigerator in the integrated energy microgrid user is shown in the following formula (9):

[0089]

[0090] wherein,

[0091] respectively represent the output power and the input power of the electric refrigerator of the integrated energy microgrid user i at time t in the Xth round of optimization process;

[0092] P i,ec,in,min and P i,ec,in,max respectively represent the minimum input power and the maximum input power of the electric refrigerator of the integrated energy microgrid user i;

[0093] respectively represent the minimum input power and the maximum input power of the electric refrigerator of the integrated energy microgrid user i; respectively represent the output power and the input power of the electric refrigerator of the integrated energy microgrid user i at time t in the Xth round of optimization process;

[0094] η ec represents the energy conversion efficiency of the electric boiler;

[0095] In addition, the integrated energy microgrid user is also subjected to a multi-energy balance constraint, and the multi-energy balance constraint condition to which the integrated energy microgrid user is subjected is shown in the following formula (10):

[0096]

[0097] wherein,

[0098] and respectively represent the electric load prediction value, the heat load prediction value and the cold load prediction value of the integrated energy microgrid user i at time t.

[0099] A dynamic time-of-use electricity price generation device, comprising: one or more processors, and a memory;

[0100] The memory stores computer readable instructions which, when executed by the one or more processors, implement the steps of the dynamic time-of-use electricity price generation method of any of the preceding summary.

[0101] A readable storage medium stores computer readable instructions which, when executed by one or more processors, cause the one or more processors to implement the steps of the dynamic time-of-use electricity price generation method of any of the preceding summary.

[0102] From the above technical solutions, it can be seen that the method provided by the embodiments of the present application can determine the first charging and discharging scheme between each comprehensive energy microgrid user and the shared energy storage and the first electricity purchase and sale scheme between each comprehensive energy microgrid user and the smart power distribution network according to the real-time time-of-use electricity price information provided by the smart power distribution network; and can provide each first charging and discharging scheme to the operator of the shared energy storage and provide the first electricity purchase and sale scheme to the operator of the smart power distribution; so that each operator formulates an operation scheme. In actual application, the energy storage service of the shared energy storage needs operating costs, therefore, after the charging and discharging scheme is determined, the energy storage service leasing fee between the shared energy storage and each comprehensive energy microgrid user can be further settled according to the first charging and discharging scheme between each comprehensive energy microgrid user and the shared energy storage; the shared energy storage may have residual energy storage in addition to renting a part of the energy storage to each comprehensive energy microgrid user, therefore, the residual capacity of the shared energy storage can be further calculated according to the actual charging and discharging power of the shared energy storage so as to determine the operating cost of the shared energy storage, wherein the sum of the charging and discharging of each comprehensive energy microgrid user can be taken as the actual charging and discharging power of the shared energy storage; after the residual capacity of the shared energy storage is determined, the second charging and discharging scheme between the shared energy storage and the smart power distribution network can be further optimized according to the residual capacity of the shared energy storage and the real-time time-of-use electricity price information provided by the smart power distribution network and provided to the smart power distribution network; so that the second time-of-use electricity price information can be determined according to each first electricity purchase and sale scheme and the second charging and discharging scheme and provided to each comprehensive energy microgrid user and the operator of the shared energy storage; after the second time-of-use electricity price information is determined, in order to continuously optimize the time-of-use electricity price information so as to minimize the total operating cost, the operation of determining the first charging and discharging scheme between each comprehensive energy microgrid user and the shared energy storage and the first electricity purchase and sale scheme between each comprehensive energy microgrid user and the smart power distribution network according to the real-time time-of-use electricity price information provided by the smart power distribution network can be further performed according to the second time-of-use electricity price information until the final time-of-use electricity price information that meets the preset benefit convergence condition is determined, wherein the preset benefit convergence condition can be set as a price adjustment that meets the minimum total operating cost.

[0103] Therefore, when it is needed to fully tap the interests of each subject in the system in the background of the collaborative operation of the smart power distribution network containing multiple energy networks and multiple market subjects, the method provided by the embodiment of the application can independently optimize each microgrid, formulate the charging and discharging scheme and the power purchase and sale scheme, share the energy storage with each microgrid to separately settle the leasing cost, and further realize the low-storage high-discharge arbitrage by using the sum of the leasing capacity of each microgrid and the summed residual capacity. Finally, the smart power distribution network aggregates the reporting data on both sides, formulates a new time-of-use electricity price scheme with the maximum self-operation benefit as the target under the consideration of network constraints, and feeds back to the downstream, and iterates until the convergence criterion is met, so that the overall coordinated operation of the interests can be realized by adjusting the time-of-use electricity price, so as to maximize the interests of each subject. BRIEF DESCRIPTION OF DRAWINGS

[0104] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0105] Figure 1 A system framework structure diagram for implementing a dynamic time-of-use electricity price generation system is provided for the embodiment of the present application.

[0106] Figure 2 A schematic diagram of the use of residual capacity in the low-storage high-discharge case of shared energy storage is provided for the embodiment of the present application.

[0107] Figure 3 A flowchart of a dynamic time-of-use electricity price generation method is provided for the embodiment of the present application.

[0108] Figure 4 A hardware structure block diagram of a dynamic time-of-use electricity price generation device is disclosed for the embodiment of the present application. DETAILED DESCRIPTION

[0109] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0110] In view of the fact that most of the dynamic time-of-use electricity price generation schemes are difficult to adapt to complex and changeable business needs, for this reason, the applicant has researched a dynamic time-of-use electricity price generation scheme. When it is needed to fully tap the interests of each subject in the system in the context of the collaborative operation of the smart distribution network including multiple energy networks and multiple market subjects, the method provided by the embodiment of the application can independently optimize each microgrid, formulate charging and discharging schemes and power purchase and sale schemes; then, the shared energy storage and each microgrid individually settle the leasing cost, and further realize low-storage high-generation arbitrage by using the sum of the leasing capacity of each microgrid and the summed residual capacity; finally, the smart distribution network aggregates the reporting data on both sides, formulates a new time-of-use electricity price scheme with the maximum own operation benefit as the target under the consideration of network constraints, and feeds back to the downstream, and iterates until the convergence criterion is met, so that the overall coordinated operation of the interests can be realized through the adjustment of the time-of-use electricity price, so as to maximize the interests of each subject.

[0111] The method provided by the embodiment of the application can be used in a plurality of general-purpose or special-purpose computing device environments or configurations. For example, a personal computer, a server computer, a handheld device or a portable device, a tablet device, a multi-processor device, a distributed computing environment including any of the above devices or equipment, and the like.

[0112] The embodiment of the application provides a dynamic time-of-use electricity price generation method, which can be applied to various electricity price management systems and various computer terminals or smart terminals, and the execution subject can be a processor or a server of a computer terminal or a smart terminal.

[0113] The following will be described in combination with Figure 1 , an optional system architecture for formulating a dynamic time-of-use electricity price is introduced, as shown in Figure 1 , the system architecture can include a smart distribution network, shared energy storage, and at least one integrated energy microgrid user.

[0114] In actual application, in the context of large-scale access of emerging resources to traditional power distribution networks, each subject needs to flexibly combine dynamic time-of-use electricity price information and its own energy use to maximize its own benefit.

[0115] As shown in Figure 1 , a plurality of integrated energy microgrid users and shared energy storage need to respond to the dynamic time-of-use electricity price by optimizing their own power interaction schemes and feed back to the smart distribution network. Under the premise of accepting this feedback, the smart distribution network needs to comprehensively consider its own network loss, power purchase and sale income, demand response calling cost, and the like, to regenerate a time-of-use electricity price scheme for the downstream, so that each integrated energy microgrid user, shared energy storage, and smart distribution network iteratively optimize until the convergence criterion is met, so that the benefits of each subject are maximized.

[0116] Therefore, in the system provided by the embodiments of the present application, each integrated energy microgrid user can determine the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy microgrid user and the smart power distribution network according to the real-time time-of-use electricity price information provided by the smart power distribution network, and can provide each first charging and discharging scheme to the operator of the shared energy storage and the first power purchase and sale scheme to the operator of the smart power distribution network, so as to enable each operator to formulate an operation scheme.

[0117] For example,

[0118] It is assumed that in the actual application process, there can be N integrated energy microgrid users in the integrated energy microgrid user group, and there can be multiple energy loads, renewable energy outputs, and independent energy storages in each integrated energy microgrid user.

[0119] In the actual application process, each integrated energy microgrid user can be connected to the smart power distribution network and the shared energy storage through a tie line.

[0120] Therefore, each integrated energy microgrid user can determine the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy microgrid user and the smart power distribution network based on the Xth round of time-of-use electricity price information provided by the smart power distribution network and independently complete day-ahead optimization control with the minimum total operation cost as the target.

[0121] Among them,

[0122] which can represent the time-of-use electricity price of the smart power distribution network selling electricity to the microgrid and the shared energy storage;

[0123] which can represent the time-of-use electricity price of the smart power distribution network buying electricity from the microgrid and the shared energy storage.

[0124] As can be seen, each integrated energy microgrid user can optimize the real-time time-of-use electricity price information provided by the smart power distribution network with the minimum total operation cost as the target, so as to determine the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage;

[0125] Among them,

[0126] The objective function of the optimization dispatch corresponding to each integrated energy microgrid user in the process of optimizing the real-time time-of-use electricity price information provided by the smart power distribution network with the minimum total operation cost as the target can be as shown in the following formula (1):

[0127]

[0128] Among them, in formula (1),

[0129] The target function of the optimization scheduling corresponding to each integrated energy micro-grid user in the optimization process can be represented as:

[0130] The electricity purchase unit price of each integrated energy micro-grid user at t moment in the Xth round of optimization process can be represented as:

[0131] The electricity purchase unit price of each integrated energy micro-grid user at t moment in the Xth round of optimization process can be represented as:

[0132] The electricity purchase power of the integrated energy micro-grid user i to the smart power distribution network at t moment in the Xth round of optimization process can be represented as:

[0133] The electricity purchase power of the integrated energy micro-grid user i to the smart power distribution network at t moment in the Xth round of optimization process can be represented as:

[0134] C ES The calling cost of the independent energy storage unit capacity in the integrated energy micro-grid can be represented as:

[0135] The interaction power between the integrated energy micro-grid user i and the independent energy storage in the integrated energy micro-grid at t moment in the Xth round of optimization process can be represented as positive for charging the shared energy storage and negative for discharging the shared energy storage.

[0136] C SES The leasing cost of the energy storage unit capacity of the shared energy storage can be represented as:

[0137] The interaction power between the integrated energy micro-grid user i and the shared energy storage at t moment in the Xth round of optimization process can be represented as positive for charging the shared energy storage and negative for discharging the shared energy storage.

[0138] Δt represents the time interval adopted when carrying out the time-of-use price optimization.

[0139] As introduced above, in actual application process, each integrated energy micro-grid user is connected with the smart power distribution network through a tie line;

[0140] Wherein,

[0141] Each integrated energy micro-grid user needs to meet the tie line constraint condition with the smart power distribution network, and the tie line constraint condition between each integrated energy micro-grid user and the smart power distribution network can be shown as formula (2) as follows:

[0142]

[0143] In formula (2), wherein,

[0144] And These can be represented as the power purchased and sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization;

[0145] α1 and α2 can represent the 0-1 variables of the electricity purchase and sale status of user i in the integrated energy microgrid;

[0146] It can represent the maximum capacity of the tie line between integrated energy microgrid user i and the tie node of the smart distribution network.

[0147] As can be seen from the above introduction, each integrated energy microgrid user may have independent energy storage. Therefore, the independent energy storage within each integrated energy microgrid user also needs to meet the preset first sequential constraint condition.

[0148] The preset first sequential constraint condition can be represented by the following equation (3):

[0149]

[0150] In equation (3),

[0151] This can be represented as the remaining electricity of the independent energy storage within the integrated energy microgrid user i at time t during the Xth round of optimization;

[0152] η C With η D These can respectively represent the charging power and discharging efficiency of independent energy storage within a user of an integrated energy microgrid;

[0153] E i,min With E i,max These can represent the minimum and maximum state of charge of independent energy storage within user i of the integrated energy microgrid, respectively.

[0154] As can be seen from the above introduction, each integrated energy microgrid user is connected to the shared energy storage via a tie line;

[0155] in,

[0156] Each integrated energy microgrid user also needs to meet the tie-line constraint conditions with the shared energy storage. The tie-line constraint conditions that each integrated energy microgrid user needs to meet with the shared energy storage can be expressed as follows (4):

[0157]

[0158] In equation (4),

[0159] This can represent the maximum capacity of the interconnect between integrated energy microgrid user i and shared energy storage;

[0160] wherein,

[0161] The shared energy storage should meet a preset second sequential constraint condition.

[0162] The preset second sequential constraint condition can be shown in the following formula (5):

[0163]

[0164] wherein, in formula (5),

[0165] E X (t) can represent the residual energy of the shared energy storage at time t under the optimization scheduling of the plurality of integrated energy microgrid users in the Xth round of optimization process;

[0166] E min and E max may represent the minimum state of charge and the maximum state of charge of the shared energy storage, respectively.

[0167] In actual application, if the integrated energy microgrid user is configured with a thermal energy storage, the thermal energy storage operation constraint condition of the integrated energy microgrid user can be shown in the following formula (6):

[0168]

[0169] wherein, in formula (6)

[0170] may represent the residual energy of the thermal energy storage of the integrated energy microgrid user i at time t in the Xth round of optimization process;

[0171] η HSC and η HSD may represent the charging efficiency and discharging efficiency of the thermal energy storage of the integrated energy microgrid user, respectively;

[0172] and may represent the charging power and discharging power of the thermal energy storage of the plurality of integrated energy microgrid users at time t in the Xth round of optimization process, respectively;

[0173] k HS,min and k HS,max may represent the minimum thermal energy residual coefficient and the maximum thermal energy residual coefficient of the thermal energy storage of the integrated energy microgrid user, respectively;

[0174] and may represent the minimum charging power and the maximum charging power of the thermal energy storage of the integrated energy microgrid user i, respectively;

[0175] and Pmin,th,i(t) and Pmax,th,i(t) can represent the minimum heat release power and the maximum heat release power of the thermal storage energy of the comprehensive energy microgrid user i, respectively.

[0176] If the comprehensive energy microgrid user is configured with cold storage energy, the cold storage energy operation constraint condition of the comprehensive energy microgrid user can be shown as formula (7) as follows:

[0177]

[0178] In formula (7), Pmin,th,i(t) and Pmax,th,i(t) can represent the minimum heat release power and the maximum heat release power of the thermal storage energy of the comprehensive energy microgrid user i, respectively.

[0179] Pmin,th,i(t) and Pmax,th,i(t) can represent the minimum heat release power and the maximum heat release power of the thermal storage energy of the comprehensive energy microgrid user i, respectively.

[0180] η CSC and η CSD can represent the charging and refrigeration efficiency of the cold storage energy of the comprehensive energy microgrid user, respectively.

[0181] and can represent the charging and refrigeration power of the cold storage energy of the comprehensive energy microgrid user i at time t in the Xth optimization process, respectively.

[0182] k CS,min and k CS,max can represent the minimum and maximum cold energy remaining coefficients of the cold storage energy of the comprehensive energy microgrid user, respectively.

[0183] and can represent the minimum and maximum charging power of the cold storage energy of the comprehensive energy microgrid user i, respectively.

[0184] and can represent the minimum and maximum refrigeration power of the cold storage energy of the comprehensive energy microgrid user i, respectively.

[0185] If there are energy coupling devices such as electric refrigerators and electric boilers in the comprehensive energy microgrid user, the operation constraint condition of the energy coupling devices such as electric refrigerators and electric boilers in the comprehensive energy microgrid user can be shown as formula (8) as follows:

[0186]

[0187] In formula (8), Pmin,th,i(t) and Pmax,th,i(t) can represent the minimum heat release power and the maximum heat release power of the thermal storage energy of the comprehensive energy microgrid user i, respectively.

[0188] Pmin,th,i(t) and Pmax,th,i(t) can represent the minimum heat release power and the maximum heat release power of the thermal storage energy of the comprehensive energy microgrid user i, respectively.

[0189] P i,eb,in,min and P i,eb,in,maxThese can represent the minimum and maximum input power of the electric boiler of user i in the integrated energy microgrid, respectively;

[0190] and These can be represented as the output power and input power of the electric boiler of integrated energy microgrid user i at time t during the Xth round of optimization;

[0191] η eb This can represent the energy conversion efficiency of an electric boiler;

[0192] The operating constraints of the electric chillers in the integrated energy microgrid users are shown in equation (9) below:

[0193]

[0194] in,

[0195] This can represent the start / stop status of the electric chiller of integrated energy microgrid user i at time t during the Xth round of optimization;

[0196] P i,ec,in,min and P i,ec,in,max These can represent the minimum and maximum input power of the electric chiller for user i in the integrated energy microgrid, respectively;

[0197] and These can be represented as the output power and input power of the electric chiller of integrated energy microgrid user i at time t during the Xth round of optimization;

[0198] η ec This can represent the energy conversion efficiency of an electric boiler;

[0199] Furthermore, in practical applications, when the output of distributed generation is large, the integrated energy microgrid user i may consider appropriately discarding a portion of renewable energy to ensure the safe operation of the system. Therefore, there may be DG operation constraints, which can be expressed as follows (11):

[0200]

[0201] in,

[0202] This can represent the predicted output of distributed power sources in user i of the integrated energy microgrid at time t; It can represent the actual output of distributed power sources in user i of the integrated energy microgrid at time t.

[0203] In addition, the integrated energy micro-grid user can also be subject to multi-energy balance constraints, and the multi-energy balance constraint condition to which the integrated energy micro-grid user is subject can be shown in formula (10) as follows:

[0204]

[0205] wherein,

[0206] and respectively can represent the predicted value of the electrical load, the predicted value of the thermal load and the predicted value of the cold load of the integrated energy micro-grid user i at time t.

[0207] Based on the above constraint conditions, the N integrated energy micro-grid users can complete the day-ahead optimal dispatch, and provide the power interaction information and to the shared energy storage and smart power distribution network.

[0208] For example, the shared energy storage and smart power distribution network includes:

[0209]

[0210]

[0211] In actual application, the shared energy storage can rent part of the energy storage to each integrated energy micro-grid user for use, which can improve the energy utilization efficiency and also improve the operating income of the shared energy storage. Therefore, after receiving the charging and discharging scheme of each integrated energy micro-grid user, the shared energy storage can aggregate the first charging and discharging scheme of each integrated energy micro-grid user to determine the energy storage rental demand of each integrated energy micro-grid user, and according to the first charging and discharging scheme between each integrated energy micro-grid user and the shared energy storage, the energy storage service rental fee between each integrated energy micro-grid user and the shared energy storage can be settled respectively, so as to better operate the income of the shared energy storage.

[0212] In actual application, in addition to renting part of the energy storage, the shared energy storage can also have remaining energy storage. In order to improve the income of the shared energy storage, the shared energy storage can calculate its own remaining capacity according to its actual charging and discharging power, so as to continuously optimize the operation scheme according to its own remaining capacity.

[0213] For example, the shared energy storage can optimize the second charging and discharging scheme between itself and the smart power distribution network according to its own remaining capacity and the real-time time-of-use electricity price information provided by the smart power distribution network, and provide the second charging and discharging scheme to the smart power distribution network.

[0214] wherein,

[0215] ​​The actual charging and discharging power of the shared energy storage can be set as the sum of the charging and discharging power of each integrated energy microgrid user.

[0216] For example,

[0217] The total leasing benefit of the shared energy storage in the Xth round of optimization process can be shown in the following formula (14):

[0218]

[0219] The actual interaction power of the shared energy storage and the multiple integrated energy microgrid users at time t in the Xth round of optimization process can be:

[0220]

[0221] Taking charging the shared energy storage as positive and discharging as negative;

[0222] On the basis of the interaction power between the multiple integrated energy microgrid users and the shared energy storage, the residual power E X (t) of the shared energy storage at time t in the Xth round of optimization process under the optimized scheduling of the multiple integrated energy microgrid users can be formed, and the difference between the residual power E max and the maximum state of charge E of the shared energy storage is the residual capacity of the shared energy storage available for low-storage high-discharge arbitrage at time t.

[0223] If the shared energy storage has a residual amount, the residual capacity can be further utilized for low-storage high-discharge arbitrage.

[0224] For example, Figure 2 An example of a schematic diagram of the shared energy storage utilizing residual capacity for low-storage high-discharge is shown.

[0225] For example, the objective function of the optimized scheduling of the shared energy storage in the Xth round of optimization process is denoted as as shown in the following formula (16).

[0226]

[0227] The shared energy storage can take maximizing its own benefit as the optimization objective, and the objective function is divided into two parts;

[0228] is the energy storage leasing service fee paid by the multiple integrated energy microgrid users to the shared energy storage calculated in the foregoing description;

[0229] and respectively represent the power sold to the smart power distribution network and the power purchased from the smart power distribution network by the shared energy storage at time t in the Xth round of optimization process;

[0230] The shared energy storage represents the profit obtained by using the remaining capacity of the shared energy storage to store and release energy.

[0231] There is also a tie line constraint between the shared energy storage and the smart distribution network:

[0232]

[0233] wherein,

[0234] β1 and β2 can represent 0-1 variables of the shared energy storage buying and selling electricity state;

[0235] The maximum capacity of the tie line between the shared energy storage and the smart distribution network tie line node can be represented.

[0236] The shared energy storage can use the remaining capacity to complete the interaction with the distribution network, and there is a sequential constraint as shown in the following formula (18) and (19):

[0237]

[0238] E X '(0) = E X '(T) (19)

[0239] wherein,

[0240] E X '(t) is the actual remaining capacity of the shared energy storage after completing the interaction with the smart distribution network based on the charging and discharging of the multi-comprehensive energy micro-grid user to the shared energy storage at time t in the Xth round of optimization game process.

[0241] The shared energy storage provides the optimized power interaction information to the smart distribution network, wherein the optimized power interaction information of the shared energy storage can be as shown in the following formula (20):

[0242]

[0243] The smart distribution network can optimize the real-time electricity price information according to the first electricity buying and selling scheme and the second charging and discharging scheme of each comprehensive energy micro-grid user, so as to obtain the second time-of-use electricity price information and provide it to each comprehensive energy micro-grid user and the operator of the shared energy storage for adjusting their respective operation schemes.

[0244] For example,

[0245] In actual application process, the smart distribution network receives the power interaction scheme reported by the multi-comprehensive energy micro-grid user and the shared energy storage And After that, first, the interaction power of all comprehensive energy micro-grid users is summarized as shown in the following formula (21):

[0246]

[0247] At any time t within the optimization scheduling cycle, based on the power interaction scheme reported by the smart distribution network topology and multiple integrated energy microgrid users and shared energy storage, the power flow distribution T of the smart distribution network during the Xth round of optimization iterations can be obtained through power flow calculation methods. X .

[0248] Smart distribution networks optimize themselves to maximize their own efficiency and update current time-of-use electricity price information. Obtain the time-of-use electricity price information for the X+1th round. The technology was then distributed to downstream users to initiate the X+1th iteration.

[0249] The optimization objective of a smart distribution network can be denoted as: As shown in equation (22):

[0250]

[0251] in,

[0252] This can generate benefits for the interaction between the smart distribution network and the upper-level power grid;

[0253] Electricity pricing can be set for smart distribution networks. This enables the realization of benefits from multiple integrated energy microgrid users and shared energy storage interaction requests;

[0254] It can represent the network loss of a smart distribution network;

[0255] in,

[0256] E represents the set of all branches in the distribution network;

[0257] R ij This represents the branch resistance starting at node i and ending at node j.

[0258] This represents the square of the branch current at time t, with node i as the first segment and node j as the last segment, during the Xth round of optimization.

[0259] C t Costs related to network losses on behalf of the organization.

[0260] make

[0261] The steps for optimizing day-ahead time-of-use pricing in smart distribution networks using the particle swarm optimization algorithm are as follows:

[0262] Step S1: Create M particles, each representing a set of possible time-of-use electricity pricing vectors and initialize them.

[0263] Set the iteration number upper limit K, the current generation number d = 1 initializes the individual learning factor c1, the social learning factor c2 and the inertia weight w in the particle swarm algorithm, and initializes the position x m and the speed v m of each particle and

[0264] Step S2, based on The fitness function in the particle swarm algorithm can be shown as formula (23):

[0265]

[0266] Wherein,

[0267] The time-of-use electricity price information represented by the particle m in the Xth optimization process can be m∈[1, 2, …M]:

[0268]

[0269] Step S3: for each particle, according to the fitness function Calculate the fitness value, which is used to evaluate the quality of the electricity price setting. The higher the fitness value, the closer the electricity price setting is to the optimal.

[0270] Record and update the individual historical optimal position pbest m of each particle, that is, the individual historical electricity price setting that makes the particle m have the maximum fitness function;

[0271] Record and update the group historical optimal position pbest of the particle swarm, that is, find the historical electricity price setting with the maximum fitness function in all particles of the particle swarm.

[0272] Step S4: according to the speed update formula of the particle swarm algorithm:

[0273]

[0274] Wherein,

[0275] r1 and r2 are random numbers;

[0276] is the speed of the particle m moving to the next step in the dth generation, according to the current position of each particle in the dth generation and the moving speed update the particle position Guide the particle to move towards a more promising electricity price setting.

[0277] Step S5: Let d = d + 1, repeat the process in step S3, step S4.

[0278] When the maximum number of iterations is reached or the fitness function of all particles converges to a certain threshold, the algorithm ends, and the final electricity price setting will be the electricity price setting corresponding to the historical optimal position of the particle swarm, thereby obtaining the updated time-of-use electricity price information of the active power distribution network

[0279] In actual application, after receiving the new time-of-use electricity price information fed back by the intelligent power distribution network, each integrated energy microgrid user can perform the operation of determining the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy microgrid user and the intelligent power distribution network according to the second time-of-use electricity price information provided by the intelligent power distribution network based on the real-time time-of-use electricity price information provided by the intelligent power distribution network until the intelligent power distribution network can determine the final time-of-use electricity price information that meets the preset benefit convergence condition, wherein the preset benefit convergence condition can be that the benefits among the intelligent power distribution network, the shared energy storage, and each integrated energy microgrid user are maximized.

[0280] For example, in actual application, the multiple integrated energy microgrid users can perform the day-ahead optimal dispatch operation according to the updated time-of-use electricity price information of the intelligent power distribution network , thereby obtaining the updated power interaction scheme between the multiple integrated energy microgrid users and the shared energy storage , and the power interaction scheme with the intelligent power distribution network

[0281] The shared energy storage can perform the optimization process according to the interaction scheme and the current time-of-use electricity price information , thereby obtaining the updated power interaction scheme between the shared energy storage and the intelligent power distribution network

[0282] The intelligent power distribution network can perform the optimization process according to , thereby obtaining the time-of-use electricity price information and feeding back to the downstream again.

[0283] The process is repeated multiple times until the dynamic time-of-use electricity price information of the intelligent power distribution network and the benefits of each subject reach convergence, and the dynamic time-of-use electricity price can be determined.

[0284] The process is repeated multiple times until the dynamic time-of-use electricity price information of the intelligent power distribution network and the benefits of each subject reach convergence, and the dynamic time-of-use electricity price can be determined. Figure 3 , the flow of the dynamic time-of-use electricity price generation method given by the embodiments of the present application is introduced, as shown in Figure 1 , which can include the following steps:

[0285] Step S101, according to the real-time time-of-use electricity price information provided by the smart power grid, determining a first charging and discharging scheme between each integrated energy micro-grid user and the shared energy storage and a first electricity purchase and sale scheme between each integrated energy micro-grid user and the smart power grid.

[0286] Step S102, providing each first charging and discharging scheme to the operator of the shared energy storage and providing the first electricity purchase and sale scheme to the operator of the smart power grid.

[0287] Step S103, according to the first charging and discharging scheme between each integrated energy micro-grid user and the shared energy storage, respectively settling the energy storage service rental fee between the shared energy storage and each integrated energy micro-grid user.

[0288] Step S104, calculating the self remaining capacity of the shared energy storage according to the actual charging and discharging power of the shared energy storage, wherein the sum of the charging and discharging of each integrated energy micro-grid user is taken as the actual charging and discharging power of the shared energy storage.

[0289] Step S105, according to the self remaining capacity of the shared energy storage and the real-time time-of-use electricity price information provided by the smart power grid, optimizing a second charging and discharging scheme between the shared energy storage and the smart power grid and providing the second charging and discharging scheme to the smart power grid.

[0290] Step S106, according to each first electricity purchase and sale scheme and the second charging and discharging scheme, determining second time-of-use electricity price information and providing the second time-of-use electricity price information to each integrated energy micro-grid user and the operator of the shared energy storage.

[0291] Step S107, according to the second time-of-use electricity price information, returning to perform the operation of determining a first charging and discharging scheme between each integrated energy micro-grid user and the shared energy storage and a first electricity purchase and sale scheme between each integrated energy micro-grid user and the smart power grid according to the real-time time-of-use electricity price information provided by the smart power grid until a final time-of-use electricity price information meeting a preset benefit convergence condition is determined.

[0292] The specific processing procedure of the above dynamic time-of-use electricity price generation method can refer to the related description in the foregoing dynamic time-of-use electricity price generation system, which will not be described here again.

[0293] The dynamic time-of-use electricity price generation apparatus provided by the embodiments of the present application can be applied to a dynamic time-of-use electricity price generation device, such as a terminal: a mobile phone, a computer, etc. Optionally, Figure 4 The hardware structure block diagram of the dynamic time-of-use electricity price generation device is shown, which can refer to Figure 4The hardware structure of the dynamic time-of-use electricity price generation device can include at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4.

[0294] In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete communication with each other through the communication bus 4.

[0295] The processor 1 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application, etc.

[0296] The memory 3 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0297] The memory stores a program, and the processor can invoke the program stored in the memory, and the program is used to implement each processing flow in the terminal dynamic time-of-use electricity price generation scheme.

[0298] The embodiments of the present application also provide a readable storage medium, which can store a program suitable for processor execution, and the program is used to implement each processing flow in the terminal dynamic time-of-use electricity price generation scheme.

[0299] Finally, it should be noted that in this document, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0300] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between various embodiments can be referred to each other.

[0301] The above description of disclosed embodiments enables one of ordinary skill in the art to make and use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Various embodiments can be combined with each other. Thus, the present application is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating dynamic time-of-use electricity prices, characterized in that, include: Based on the real-time time-of-use electricity price information provided by the smart distribution network, a first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage, and a first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network are determined; Each of the first charging and discharging schemes is provided to the operator of the shared energy storage and the first power purchase and sale scheme is provided to the operator of the smart power distribution. Based on the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage, the energy storage service rental fee between the shared energy storage and each integrated energy microgrid user is settled respectively; The remaining capacity of the shared energy storage is calculated based on its actual charging and discharging power, wherein the actual charging and discharging power of the shared energy storage is the sum of the charging and discharging power of each of the integrated energy microgrid users; Based on the remaining capacity of the shared energy storage and the real-time time-of-use electricity price information provided by the smart distribution network, the second charging and discharging scheme between the shared energy storage and the smart distribution network is optimized and provided to the smart distribution network; Based on each of the first power purchase and sale schemes and the second charging and discharging schemes, the second time-of-use electricity price information is determined and provided to each of the integrated energy microgrid users and the shared energy storage operator; Based on the second time-of-use electricity price information, return to execute the operation of determining the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage and the first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network based on the real-time time-of-use electricity price information provided by the smart distribution network, until the final time-of-use electricity price information that meets the preset benefit convergence conditions is determined; The determination of a first charging and discharging scheme between each integrated energy microgrid user and shared energy storage, and a first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network, based on real-time time-of-use electricity price information provided by the smart distribution network, includes: Each integrated energy microgrid user aims to minimize total operating costs by optimizing the real-time time-of-use electricity price information provided by the smart distribution network, and determining the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage, as well as the first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network. in, The objective function for the optimized scheduling of each integrated energy microgrid user, with the goal of minimizing total operating cost, during the optimization process of the real-time time-of-use electricity price information provided by the smart distribution network, is shown in the following equation (1): （1） In equation (1), This represents the objective function for the optimal scheduling of each integrated energy microgrid user during the optimization process; This represents the electricity purchase price per unit for each integrated energy microgrid user at time t during the Xth round of optimization; This represents the electricity price per user of the integrated energy microgrid at time t during the Xth round of optimization; This represents the power purchased by user i of the integrated energy microgrid from the smart distribution network at time t during the Xth round of optimization. This represents the electricity sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization. This represents the cost of calling up the capacity of an independent energy storage unit within the integrated energy microgrid; denoted as the interaction power between user i of the integrated energy microgrid and the independent energy storage within the integrated energy microgrid at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative; This represents the rental cost per unit capacity of the shared energy storage; This represents the interaction power between the integrated energy microgrid user i and the shared energy storage at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative. This indicates the time interval used when implementing time-of-use pricing optimization.

2. A dynamic time-of-use electricity pricing system, characterized in that, The system applied to the method of claim 1 includes: a smart distribution network, shared energy storage, and at least one integrated energy microgrid user; in, Each integrated energy microgrid user determines a first charging and discharging scheme between itself and the shared energy storage and a first power purchase and sale scheme between itself and the smart distribution network based on the real-time time-of-use electricity price information provided by the smart distribution network, and provides each of the first charging and discharging schemes to the operator of the shared energy storage and the first power purchase and sale scheme to the operator of the smart distribution network. The shared energy storage aggregates the first charging and discharging schemes of each of the integrated energy microgrid users, and settles the energy storage service rental fees between each integrated energy microgrid user and the shared energy storage based on the first charging and discharging schemes between each integrated energy microgrid user and the shared energy storage. The shared energy storage calculates its remaining capacity based on its actual charging and discharging power, wherein the actual charging and discharging power of the shared energy storage is the sum of the charging and discharging power of each of the integrated energy microgrid users; The shared energy storage optimizes its second charging and discharging scheme with the smart distribution network based on its own remaining capacity and the real-time time-of-use electricity price information provided by the smart distribution network, and provides it to the smart distribution network. The smart distribution network optimizes the real-time electricity price information based on each of the first power purchase and sale schemes and the second charging and discharging schemes to obtain the second time-of-use electricity price information and provides it to each of the integrated energy microgrid users and the shared energy storage operators. Each integrated energy microgrid user, based on the second time-of-use electricity price information, re-executes the operation of determining the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage, and the first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network, based on the real-time time-of-use electricity price information provided by the smart distribution network, until the smart distribution network determines the final time-of-use electricity price information that meets the preset benefit convergence conditions; Based on the real-time time-of-use pricing information provided by the smart distribution network, each integrated energy microgrid user determines a first charging and discharging scheme between itself and the shared energy storage, and a first power purchase and sale scheme between itself and the smart distribution network, including: Each integrated energy microgrid user aims to minimize total operating costs by optimizing the real-time time-of-use electricity price information provided by the smart distribution network, and determining the first charging and discharging scheme between each integrated energy microgrid user and the shared energy storage, as well as the first power purchase and sale scheme between each integrated energy microgrid user and the smart distribution network. in, The objective function for the optimized scheduling of each integrated energy microgrid user, with the goal of minimizing total operating cost, during the optimization process of the real-time time-of-use electricity price information provided by the smart distribution network, is shown in the following equation (1): （1） In equation (1), This represents the objective function for the optimal scheduling of each integrated energy microgrid user during the optimization process; This represents the electricity purchase price per unit for each integrated energy microgrid user at time t during the Xth round of optimization; This represents the electricity price per user of the integrated energy microgrid at time t during the Xth round of optimization; This represents the power purchased by user i of the integrated energy microgrid from the smart distribution network at time t during the Xth round of optimization. This represents the electricity sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization. This represents the cost of calling up the capacity of an independent energy storage unit within the integrated energy microgrid; denoted as the interaction power between user i of the integrated energy microgrid and the independent energy storage within the integrated energy microgrid at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative; This represents the rental cost per unit capacity of the shared energy storage; This represents the interaction power between the integrated energy microgrid user i and the shared energy storage at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative. This indicates the time interval used when implementing time-of-use pricing optimization.

3. The system according to claim 2, characterized in that, Each of the integrated energy microgrid users is connected to the smart distribution network via a tie line; in, Each integrated energy microgrid user must meet the tie-line constraint condition with the smart distribution network, wherein the tie-line constraint condition that each integrated energy microgrid user must meet with the smart distribution network is as shown in the following equation (2): （2） In equation (2), and These represent the power purchased and the power sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization. and These are 0-1 variables representing the electricity purchase and sale status of user i in the integrated energy microgrid; This refers to the maximum capacity of the interconnection line between the integrated energy microgrid user i and the interconnection node of the smart distribution network.

4. The system according to claim 2, characterized in that, Each independent energy storage unit within the integrated energy microgrid user needs to meet a preset first sequential constraint condition, wherein the preset first sequential constraint condition is shown in the following equation (3): （3） In equation (3), The remaining electricity of the independent energy storage within the integrated energy microgrid user i at time t during the Xth round of optimization; and These represent the charging power and discharging efficiency of the independent energy storage within the user of the integrated energy microgrid, respectively. and These represent the minimum and maximum states of charge (SOC) of independent energy storage within user i of the integrated energy microgrid, respectively. denoted as the interaction power between user i of the integrated energy microgrid and the independent energy storage within the integrated energy microgrid at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative; This indicates the time interval used when implementing time-of-use pricing optimization.

5. The system according to claim 2, characterized in that, Each of the integrated energy microgrid users is connected to the shared energy storage via a tie line; in, Each integrated energy microgrid user must satisfy the tie-line constraint condition with the shared energy storage, wherein the tie-line constraint condition that each integrated energy microgrid user must satisfy with the shared energy storage is as shown in the following equation (4): （4） In equation (4), This indicates the maximum capacity of the interconnect line between the integrated energy microgrid user i and the shared energy storage; This represents the interaction power between the integrated energy microgrid user i and the shared energy storage at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative. in, The shared energy storage should satisfy a preset second sequential constraint condition, which is shown in equation (5) below: （5） In equation (5), The remaining power of the shared energy storage at time t under the optimized scheduling of multiple integrated energy microgrid users during the Xth round of optimization; and These represent the minimum state of charge and the maximum state of charge of the shared energy storage, respectively. This indicates the time interval used when implementing time-of-use pricing optimization; and These represent the charging power and discharging efficiency of the independent energy storage within the user of the integrated energy microgrid, respectively.

6. The system according to claim 2, characterized in that, If the integrated energy microgrid user is equipped with thermal energy storage, then the operating constraints of the thermal energy storage of the integrated energy microgrid user are as shown in equation (6): （6） Among them, equation (6) This represents the remaining energy of the thermal storage of integrated energy microgrid user i at time t during the Xth round of optimization; This indicates the maximum state of charge of the thermal energy storage of integrated energy microgrid user i; and These represent the heat storage charging efficiency and heat release efficiency of the integrated energy microgrid user, respectively. and Let represent the thermal energy storage charging power and thermal energy release power of multiple integrated energy microgrid users at time t during the Xth round of optimization; and These represent the minimum and maximum thermal energy surplus coefficients of the thermal energy storage of integrated energy microgrid users, respectively. and These represent the minimum and maximum thermal energy storage power of integrated energy microgrid user i, respectively. and These represent the minimum and maximum heat release power of the thermal energy storage of integrated energy microgrid user i, respectively. If the integrated energy microgrid user is equipped with cold storage, the operating constraints of the cold storage for the integrated energy microgrid user are as shown in equation (7): （7） In equation (7), This represents the remaining energy of the cold storage of integrated energy microgrid user i at time t during the Xth round of optimization; This indicates the maximum state of charge of the cold storage of integrated energy microgrid user i; and These represent the charging efficiency and cooling efficiency of the cold storage for integrated energy microgrid users, respectively. and Let represent the charging and cooling power of the cold storage of multiple integrated energy microgrid users i at time t during the Xth round of optimization; and These represent the minimum and maximum cold energy surplus coefficients of the cold storage for integrated energy microgrid users, respectively. and These represent the minimum and maximum charging power of the cold storage for integrated energy microgrid user i, respectively. and These represent the minimum and maximum cooling power of the cold storage for user i in the integrated energy microgrid, respectively.

7. The system according to claim 2, characterized in that, If the integrated energy microgrid user has energy coupling devices such as electric chillers and electric boilers, then the operating constraints of the energy coupling devices such as electric chillers and electric boilers in the integrated energy microgrid user are as shown in the following equation (8): （8） In equation (8), This indicates the start-up and shutdown status of the electric boiler of integrated energy microgrid user i at time t during the Xth round of optimization; and These represent the minimum and maximum input power of the electric boiler of user i in the integrated energy microgrid, respectively. and Let represent the output power and input power of the electric boiler of integrated energy microgrid user i at time t during the Xth round of optimization; This indicates the energy conversion efficiency of the electric boiler; The operating constraints of the electric chiller in the integrated energy microgrid user are shown in the following equation (9): （9） in, This indicates the start / stop status of the electric chiller of integrated energy microgrid user i at time t during the Xth round of optimization; and These represent the minimum and maximum input power of the electric chiller for user i in the integrated energy microgrid, respectively. and Let represent the output power and input power of the electric chiller of integrated energy microgrid user i at time t during the Xth round of optimization; This indicates the energy conversion efficiency of the electric boiler; In addition, the integrated energy microgrid users are also subject to multi-energy balance constraints, the multi-energy balance constraints of which are shown in the following equation (10): （10） in, , as well as These represent the predicted electrical load, predicted heat load, and predicted cooling load for integrated energy microgrid user i at time t, respectively. This represents the electricity sold by user i of the integrated energy microgrid to the smart distribution network at time t during the Xth round of optimization. denoted as the interaction power between user i of the integrated energy microgrid and independent energy storage within the integrated energy microgrid at time t during the Xth round of optimization, with shared energy storage charging as positive and shared energy storage discharging as negative; This represents the interaction power between the integrated energy microgrid user i and the shared energy storage at time t during the Xth round of optimization, with the shared energy storage charging being positive and the shared energy storage discharging being negative. This represents the power purchased by user i of the integrated energy microgrid from the smart distribution network at time t during the Xth round of optimization. This represents the actual output of distributed power sources in user i of the integrated energy microgrid at time t; and Let represent the thermal energy storage charging power and thermal energy release power of multiple integrated energy microgrid users at time t during the Xth round of optimization; and Let represent the charging and cooling power of the cold storage of multiple integrated energy microgrid users i at time t during the Xth round of optimization.

8. A dynamic time-of-use electricity price generation device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of the dynamic time-of-use pricing method as described in any one of claims 1.

9. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to implement the steps of the dynamic time-of-use pricing method as described in any one of claims 1.

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