Network cost recovery method and system

Abstract

The invention discloses a network cost recovery method and system. The method comprises the following steps: obtaining terminal user related parameters in a target power grid, wherein the terminal user related parameters at least comprise an electrical load parameter; establishing a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model comprises a first objective function and a first constraint condition; and solving the first cost calculation model according to the real-time related parameters of the terminal user, and carrying out network cost recovery. By optimizing a power consumption bill structure, a terminal user is stimulated to increase the power consumption when the electricity price is low and reduce the power consumption when the electricity price is high, so that dynamic balance of a power grid load is realized. The electricity price strategy can be dynamically adjusted according to the real-time operation state of the power grid and the electricity consumption behavior of the user so as to guide the user to reasonably use electricity. By introducing the flexibility of the terminal user, the operation efficiency of the power grid can be improved, and the extra cost caused by capacity expansion of the power grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of network cost recovery, and in particular to a network cost recovery method and system. Background Art

[0002] As the global energy transition deepens, the power system is undergoing unprecedented changes. Especially in the context of the increasing penetration of renewable energy, how to ensure the stability and reliability of the power grid has become an urgent problem to be solved. Renewable energy sources such as wind power and photovoltaics are volatile and intermittent, which brings new challenges to the traditional power system. The traditional power system relies on conventional power sources such as thermal power and hydropower to respond to changes in power demand by dispatching these power generation facilities. However, with the increase in the installed capacity of new energy, the regulatory role of these conventional power sources has gradually weakened, and the demand for power system flexibility has gradually increased.

[0003] Driven by the acceleration of electrification and the rapid development of renewable energy, the structure of electricity demand is also undergoing profound changes worldwide. The gradual popularization of new power loads such as electric vehicles and heat pumps has further exacerbated the complexity and uncertainty of grid loads. This trend forces grid operators to make large-scale investments in transmission networks to cope with the ever-changing demand for electricity. At the same time, with the increasing openness of the power market and the frequent fluctuations in electricity prices, the role of end users has gradually changed from traditional electricity consumers to "active participants" with load regulation capabilities.

[0004] In this context, demand-side response and end-user flexibility have become important tools to ensure grid stability. By rationally designing the electricity price structure and encouraging end-users to adjust their electricity consumption behavior according to electricity price fluctuations, the peak load pressure on the power grid can be effectively alleviated and the need for power system expansion can be reduced. In fact, the flexibility of end-users can be achieved not only by directly participating in demand response programs, but also by optimizing their electricity bill structure and indirectly encouraging them to adjust their loads, thus playing a positive role. Therefore, exploring how to optimize grid operation through the flexibility of end-users has become one of the important research directions in current power system planning.

[0005] The network cost recovery method that takes into account the flexibility of end users can not only optimize the load distribution of the power grid, but also ensure that the grid operator can continue to recover its operating costs when the power demand changes and the user load flexibility increases. This model is proposed to provide higher operating efficiency and flexibility for the power system through reasonable electricity price design and load management tools, and provide an important basis for future transmission network planning. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides a network cost recovery method and system, which can solve the problems mentioned in the background technology.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a network cost recovery method, comprising:

[0011] Acquire terminal user related parameters in the target power grid, wherein the terminal user related parameters at least include power load parameters;

[0012] Establishing a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model includes a first objective function and a first constraint condition;

[0013] The first cost calculation model is solved according to the real-time relevant parameters of the terminal user to recover the network cost.

[0014] As a preferred solution of the network cost recovery method of the present invention, the first cost calculation model includes:

[0015] The first cost calculation model is an arbitrary model that minimizes the user's bill;

[0016] The first cost calculation model includes a first objective function and a first constraint condition;

[0017] The goal of the first objective function is to minimize the user bill.

[0018] As a preferred solution of the network cost recovery method of the present invention, the network cost recovery by solving the first cost calculation model according to the real-time relevant parameters of the terminal user includes:

[0019] Obtain electricity price impact rules based on real-time relevant parameters of end users;

[0020] updating the first cost calculation model according to the electricity price impact rule;

[0021] The updated first cost calculation model is solved.

[0022] As a preferred solution of the network cost recovery method described in the present invention, the first constraint condition at least includes an electricity price elasticity constraint condition.

[0023] As a preferred solution of the network cost recovery method of the present invention, the first objective function includes:

[0024] Design the objective function to minimize the user's bill by minimizing the total amount the user pays each year;

[0025] The first objective function is designed by retail electricity price, net interactive power of the network, network tariff paid to the network operator and calculating total network cost parameters.

[0026] As a preferred solution of the network cost recovery method of the present invention, the updating of the first cost calculation model according to the electricity price impact rule comprises:

[0027] Reconstruct the power demand for transmission and distribution handover, and update the retail electricity price part of the first cost calculation model;

[0028] The retail electricity price includes a fixed electricity price and a time-varying electricity price;

[0029] The updating of the retail electricity price portion in the first cost calculation model is updating the time-varying electricity price portion in the first cost calculation model.

[0030] As a preferred solution of the network cost recovery method of the present invention, the reconstruction of the power demand for power transmission and distribution handover and updating the retail electricity price part of the first cost calculation model include:

[0031] Establishing a second objective function and a second constraint condition;

[0032] The portion containing the retail electricity price in the first cost calculation model is updated according to the second objective function and the second constraint condition.

[0033] In a second aspect, the present invention provides a network cost recovery system, comprising:

[0034] A data acquisition module, used to acquire terminal user related parameters in the target power grid, wherein the terminal user related parameters at least include power load parameters;

[0035] A modeling module, used to establish a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model includes a first objective function and a first constraint condition;

[0036] A solution module is used to solve the first cost calculation model according to the real-time relevant parameters of the terminal user to recover the network cost.

[0037] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0039] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a network cost recovery method and system, obtains terminal user related parameters in the target power grid, the terminal user related parameters at least include power load parameters; establishes a first cost calculation model based on the terminal user related parameters, the first cost calculation model includes a first objective function and a first constraint condition; solves the first cost calculation model according to the real-time related parameters of the terminal user to recover the network cost. By optimizing the electricity bill structure, the terminal user is encouraged to increase the electricity consumption when the electricity price is low and reduce the electricity consumption when the electricity price is high, so as to achieve the dynamic balance of the power grid load. The power price strategy can be dynamically adjusted according to the real-time operation status of the power grid and the user's electricity consumption behavior to guide the user to use electricity reasonably. By introducing the flexibility of the terminal user, the system can improve the operation efficiency of the power grid and reduce the additional cost caused by the expansion of the power grid. The network cost recovery system of the present application can also provide decision support for the power grid operator to help it manage costs and risks more effectively in the power market. The design of the method of the present application takes into account a variety of factors, including but not limited to the user's electricity consumption habits, the characteristics of the power grid load and the fluctuation of electricity prices, so as to ensure the fairness and efficiency of cost recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 A method flow chart of a network cost recovery method and system provided by an embodiment of the present invention;

[0042] Figure 2 An internal structural diagram of a computer device of a network cost recovery method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a network cost recovery method and system, including:

[0046] There are some problems in the existing related technologies, such as low cost recovery efficiency and difficulty in guiding users' electricity usage behavior.

[0047] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the network cost recovery method;

[0048] Figure 1 A method flow chart of a network cost recovery method and system is shown, including:

[0049] S101, obtaining terminal user related parameters in a target power grid, where the terminal user related parameters at least include power load parameters;

[0050] In an optional embodiment, the terminal user-related parameters in the target power grid may include power load parameters and historical power consumption data. For example, the user's past power consumption records can help predict the user's future power consumption behavior, including daily, weekly, and monthly power consumption.

[0051] In an optional embodiment, the end-user related parameters in the target power grid may also include electricity payment records and user types. For example, different user types (such as residential users, commercial users, industrial users, etc.) have different electricity usage characteristics and needs. This information is very important for formulating personalized cost recovery strategies.

[0052] In an optional embodiment, the terminal user-related parameters in the target power grid may also include geographic location information and power equipment information, such as the type, quantity and frequency of use of power equipment owned by the user. Such data can help more accurately assess the user's power demand and potential energy saving potential.

[0053] In an optional embodiment, the terminal user-related parameters in the target power grid may also include response behavior data and other data, such as the behavior pattern of reducing electricity consumption during peak electricity price periods and increasing electricity consumption during low electricity price periods, which helps to optimize electricity price strategies and improve power grid operation efficiency.

[0054] In the embodiment of the present application, the terminal user-related parameters include at least power load parameters, which can reflect the user's power demand in different time periods, including average power load, peak load, etc.

[0055] It should be noted that the terminal user-related parameters at least include power load parameters because power load parameters are key factors in the operation of power grids and the formulation of cost recovery strategies. First, power load parameters can reflect the power demand patterns of terminal users, which is crucial for power grid operators because they need to predict the load of the power grid based on the user's power consumption habits, so as to reasonably arrange the allocation and scheduling of power resources. Secondly, by analyzing the power load parameters, the peak and valley periods in the power grid can be identified, which is crucial for implementing peak and valley electricity price strategies, encouraging users to increase power consumption during low power price periods, and reducing power consumption during high power price periods, thereby achieving dynamic balance of power grid load. In addition, the analysis of power load parameters can also help power grid operators optimize the operation efficiency of the power grid and reduce the additional costs caused by unbalanced power grid load. Therefore, the terminal user-related parameters at least include power load parameters to ensure that the network cost recovery method and system can effectively dynamically adjust the power price strategy according to the real-time operation status of the power grid and the user's power consumption behavior, guide users to use electricity reasonably, improve the operation efficiency of the power grid, and reduce the additional costs caused by the expansion of the power grid.

[0056] In an optional embodiment, the terminal user's related parameters can be obtained through smart meter data, which can collect users' electricity consumption data in real time, including power consumption, power consumption time, instantaneous power, etc. These data can be transmitted to the data center through wireless communication technology (such as GPRS, LoRa, NB-IoT, etc.) for further analysis and processing.

[0057] In an optional embodiment, the terminal user-related parameters can also be obtained through the user information system. The user information system of the power company stores a large amount of basic user information and historical electricity consumption records. This information can be obtained through database query, including the user's electricity consumption, electricity bill payment record, user type, etc.

[0058] In an optional embodiment, the terminal user-related parameters can also be obtained through user surveys and feedback, and the user's electricity usage habits, equipment information, reactions to electricity price policies, etc. can be collected through questionnaires, telephone interviews, etc. User feedback information can provide more detailed user behavior data to help optimize electricity price strategies.

[0059] In an embodiment of the present application, terminal user-related parameters are obtained through a user information system, specifically weekly or daily electricity consumption, retail electricity prices, network charges, net interactive power of the network, etc.

[0060] It should be noted that obtaining relevant parameters of end users in the target power grid, which at least include power load parameters, can more accurately evaluate the real-time load situation of the power grid and provide reliable data support for subsequent cost calculation and electricity price strategy formulation.

[0061] S102, establishing a first cost calculation model based on terminal user related parameters, the first cost calculation model including a first objective function and a first constraint condition;

[0062] In the embodiment of the present application, the first cost calculation model includes:

[0063] The first cost calculation model is an arbitrary model that minimizes the user's bill;

[0064] The first cost calculation model includes a first objective function and a first constraint condition;

[0065] The goal of the first objective function is to minimize the user's bill.

[0066] In an optional embodiment, the first cost calculation model can be designed in an equivalent manner, and the objective of the objective function can be equivalently processed, for example, minimizing the user's bill is equivalent to minimizing the total amount paid by the user each year or minimizing the average amount paid by the user each month. Equivalence processing helps to simplify the complexity of the model while maintaining the accuracy of cost calculation.

[0067] In an optional embodiment, the first cost calculation model can also be designed in other ways, for example, using a neural network model to predict the user's electricity consumption behavior, thereby optimizing cost calculation. The neural network model can process a large amount of nonlinear data, and by learning historical electricity consumption data, it can predict the user's future electricity consumption pattern, thereby providing a more accurate basis for cost calculation.

[0068] In an optional embodiment, the first cost calculation model can also be implemented by a machine learning algorithm, which can dynamically adjust the parameters of the cost calculation model according to the user's historical electricity consumption data and real-time electricity consumption to adapt to changes in the grid load and fluctuations in the user's electricity consumption behavior. In this way, the first cost calculation model can respond more flexibly to changes in market and user demand, thereby improving the efficiency and accuracy of cost recovery.

[0069] In the embodiment of the present application, the first cost calculation model is designed in an equivalent manner, changing the goal from minimizing the user's bill to minimizing the total amount paid by the user each year.

[0070] In the embodiment of the present application, the first objective function includes:

[0071] Design the objective function to minimize the user's bill by minimizing the total amount the user pays each year;

[0072] The first objective function is designed by the retail electricity price, the net interactive power of the network, the network tariff paid to the network operator and the calculation of the total network cost parameters.

[0073] In the embodiment of the present application, the first objective function can be as follows:

[0074]

[0075] Among them, the collection is the set of all time periods; the set and represents the corresponding set of days and weeks; the set k∈{d,w} distinguishes daily or weekly electricity consumption. The objective function represents the total amount paid by the user each year, where is the retail electricity price; is the net interaction power with the network; f NT To pay network charges of network operators; It is a parameter used to calculate the total network cost.

[0076] In the embodiment of the present application, the first constraint condition includes at least an electricity price elasticity constraint condition.

[0077] In an optional embodiment, the first constraint may also include power balance constraints, photovoltaic power generation constraints, daily and weekly balance constraints, battery capacity and charge and discharge rate limit constraints, electricity fee constraints, electricity price elasticity constraints, etc., to ensure that the power supply and demand of the power grid are balanced in different time periods. The electricity price elasticity constraint can reflect the user's sensitivity to changes in electricity prices, thereby considering the user's behavior in response to changes in electricity prices in the cost calculation model. For example, when electricity prices rise, users may reduce their electricity consumption, and vice versa. By setting reasonable electricity price elasticity constraints, users' electricity consumption behavior can be predicted more accurately, thereby optimizing the operation and cost recovery strategy of the power grid.

[0078] It should be noted that in this application, the electricity price is divided into fixed electricity price and time-varying electricity price. The fixed electricity price fixes the electricity price per hour as a constant. The time-varying electricity price applies different electricity prices to each hour of the day by proposing a time-varying contract to the user. Usually, the electricity price per hour of the day will be notified in advance so that the user can adjust his electricity consumption habits.

[0079] In the embodiment of the present application, the time-varying electricity price is explained by the elastic model of the bill minimization problem constructed above. Unlike using a fixed electricity price vector, this method allows the electricity price to be updated according to the consumption behavior of the end user. The basic assumption means that all end users associated with the load curve follow the same behavior, that is, the overall behavior of all end users may therefore have an impact on the electricity price. Therefore, the first constraint condition includes at least the electricity price elasticity constraint condition.

[0080] In an optional embodiment, power balance constraints, photovoltaic power generation constraints, daily and weekly balance constraints, battery capacity and charge and discharge rate limit constraints, electricity fee constraints, and electricity price elasticity constraints are established as the first constraint conditions, specifically:

[0081] Constraint 1 is the power balance constraint. The interaction with the net power of the grid needs to meet the sum of the basic load demand, daily / weekly charging, daily / weekly discharge and photovoltaic power generation. is the base load demand; and For daily and weekly charging; and For daily and weekly discharges; is the photovoltaic power generation, expressed as:

[0082]

[0083] The second constraint is the photovoltaic power generation constraint. The photovoltaic power generation output needs to meet the installed capacity constraint, where is the photovoltaic power generation load factor, which represents the photovoltaic power generation capacity in time period t; CAP pv is the installed capacity of photovoltaic power generation, expressed as:

[0084]

[0085] Constraint three is the battery status constraint, which represents the daily and weekly battery status updates, where is the battery charging state during time period t; η ch and η dc are the charging and discharging efficiencies, respectively; and The charge and discharge capacity is expressed as:

[0086]

[0087] Constraint four is the daily and weekly balance constraint, expressed as:

[0088]

[0089] Constraint five is the battery capacity and charge and discharge rate limit constraints, where CAP k is the maximum capacity of the battery; CR k The maximum rate of charge and discharge is expressed as:

[0090]

[0091] Constraint six is ​​the electricity cost constraint, where w bill Indicates electricity cost, dependent on grid interaction And the network fee structure is expressed as:

[0092]

[0093] Constraint seven is the electricity price elasticity constraint, λ t is the electricity price related to electricity demand, expressed as:

[0094]

[0095] It should be noted that the establishment of a first cost calculation model based on terminal user-related parameters, including a first objective function and a first constraint condition, can more accurately reflect the user's electricity consumption behavior in response to changes in electricity prices, thereby providing a more flexible and dynamic pricing strategy for power grid operators. In this way, operators can adjust electricity prices according to real-time grid conditions and user needs to achieve the dual goals of cost recovery and stable operation of the grid. In addition, the model can also help users better understand their electricity costs, thereby encouraging users to increase electricity consumption when electricity prices are low and reduce electricity consumption when electricity prices are high, achieving demand-side management and optimizing grid load. Ultimately, through this network cost recovery system, efficient utilization and sustainable development of grid resources can be achieved.

[0096] S103, solving a first cost calculation model according to real-time relevant parameters of the terminal user to recover the network cost.

[0097] In an optional embodiment, when the electricity price only considers the fixed electricity price, the first cost calculation model is directly solved according to the real-time relevant parameters of the terminal user to recover the network cost. When the electricity price only considers the fixed electricity price, it is impossible to update the electricity price according to the user's consumption behavior;

[0098] In an embodiment of the present application, the electricity price is divided into a fixed electricity price and a time-varying electricity price. The fixed electricity price fixes the hourly electricity price as a constant. The time-varying electricity price applies different electricity prices to each hour of the day by proposing a time-varying contract to the user. Usually, the hourly electricity price of the day will be notified in advance so that the user can adjust his electricity consumption habits. In the application, the time-varying electricity price is explained by the elastic model of the bill minimization problem constructed above. Unlike using a fixed electricity price vector, this method allows the electricity price to be updated according to the consumption behavior of the end user. The basic assumption means that all end users associated with the load curve follow the same behavior, that is, the overall behavior of all end users may therefore have an impact on the electricity price.

[0099] In the embodiment of the present application, solving the first cost calculation model according to the real-time relevant parameters of the terminal user to recover the network cost includes:

[0100] Obtain electricity price impact rules based on real-time relevant parameters of end users;

[0101] updating the first cost calculation model according to the electricity price impact rule;

[0102] The updated first cost calculation model is solved.

[0103] In an optional embodiment, the electricity price influencing rules can be obtained based on the real-time relevant parameters of the terminal user by analyzing the terminal user's electricity consumption history data and the current power grid conditions to determine the strategy for adjusting the electricity price. The specific steps include collecting information such as the user's electricity consumption, electricity consumption time, and type of power equipment, as well as the real-time load of the power grid and the supply of renewable energy. Then, using data mining and machine learning techniques, analyze these data to identify the patterns of user electricity consumption behavior and the fluctuation laws of the power grid load. Based on these analysis results, electricity price influencing rules are constructed, which can reflect the relationship between user electricity consumption behavior and electricity prices, as well as the impact of changes in power grid load on electricity prices.

[0104] In an optional embodiment, the first cost calculation model is updated according to these rules. This involves adjusting the parameters in the model, such as the electricity price elasticity coefficient, electricity demand forecast, etc., to reflect the current market conditions and user behavior. After the model is updated, the model is solved to calculate the user's optimal electricity consumption strategy under the current electricity price structure. The solution process may involve linear programming, nonlinear programming or other optimization algorithms to ensure that the goal of minimizing costs is achieved while meeting the user's electricity demand. Ultimately, in this way, grid operators can dynamically adjust electricity prices based on real-time data and user behavior to achieve cost recovery, while users can also reduce electricity bills by optimizing electricity consumption behavior, achieving a win-win effect.

[0105] In an embodiment of the present application, updating the first cost calculation model according to the electricity price impact rule includes:

[0106] Reconstruct the power demand for transmission and distribution handover, and update the retail electricity price part of the first cost calculation model;

[0107] Retail electricity prices include fixed electricity prices and time-varying electricity prices;

[0108] Updating the portion of the first cost calculation model that includes the retail electricity price is updating the portion of the first cost calculation model that includes the time-varying electricity price.

[0109] In the embodiment of the present application, the relationship between the retail electricity price and the total transmission and distribution load is as follows:

[0110] λ t =a·W t T / D +b

[0111] Where W t T / D is the power interaction through transmission and distribution in time period t, which represents the power demand for transmission and distribution handover; a is the sensitivity constant of power load to electricity price, which measures the influence of power load on electricity price. The larger the value, the more significant the influence of load change on electricity price; b is the fixed part of electricity price, which represents the benchmark electricity price under zero load condition.

[0112] In an alternative embodiment, the optimal flexible behavior of the user can be used to reconstruct W t T / D By reaggregating the behavior of all users, the formula is as follows:

[0113]

[0114] Where J is the set of user types; P t Ind. is the industrial load in time period t; is the renewable energy power generation at time t.

[0115] In the embodiment of the present application, reconstructing the power demand for power transmission and distribution handover and updating the retail electricity price portion of the first cost calculation model includes:

[0116] Establishing a second objective function and a second constraint condition;

[0117] The portion of the first cost calculation model containing the retail electricity price is updated according to the second objective function and the second constraint condition.

[0118] In an optional embodiment, the second objective function and the second constraint condition may be based on optimizing the grid operation efficiency and the user's electricity cost.

[0119] In an optional embodiment, the second objective function aims to minimize the operating costs of the grid operator while meeting the electricity demand of users, while the second constraint ensures the safety and stability of the grid operation.

[0120] In an optional embodiment, the second objective function may include the operation cost and maintenance cost of the power grid, as well as the loss of user satisfaction caused by the adjustment of electricity prices. The second constraint condition may include the transmission capacity limit, voltage stability requirements, frequency stability requirements, etc. of the power grid. By solving the second objective function and satisfying the second constraint condition, an optimized electricity price structure can be obtained, which can not only promote the cost recovery of the power grid operator, but also encourage users to reasonably arrange electricity consumption time and reduce electricity consumption during peak hours, so as to achieve a balanced distribution of power grid load. Ultimately, this network cost recovery system can achieve efficient utilization of power grid resources and promote the sustainable development of the power grid.

[0121] In the embodiment of the present application, the electricity price λ t is part of the decision variable, thus generating a bilinear term. The bilinear term is relaxed by McCormick. The specific formula is as follows, including the second objective function and the second constraint:

[0122]

[0123] The variables and are the lower and upper bounds of the electricity price and the net interaction between user j and the grid.

[0124] In an optional embodiment, the above modeling simulates the consumption behavior of each user separately. At the same time, the transmission system operator needs to ensure that the annual network cost is recovered regardless of the user behavior, which is achieved by iteratively adjusting the network tariff. The network tariff includes three forms: fixed network tariff, capacity-based network tariff and volume network tariff. In order to ensure that the flexible behavior of the end user does not cause losses to the grid operator during the network cost recovery process, an initial value is set for the network cost and a system operator agent is defined to ensure continuous income from the user. The total economic transaction is converted into the following format:

[0125]

[0126] Where EC is the total energy cost of all users associated with the load profile and NC is the total network cost of all users.

[0127] In summary, the present invention proposes a network cost recovery method, which obtains terminal user related parameters in the target power grid, wherein the terminal user related parameters at least include power load parameters; establishes a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model includes a first objective function and a first constraint condition; and solves the first cost calculation model according to the real-time related parameters of the terminal user to recover the network cost. By optimizing the electricity bill structure, the terminal user is encouraged to increase the power consumption when the electricity price is low and reduce the power consumption when the electricity price is high, thereby achieving a dynamic balance of the power grid load. The power price strategy can be dynamically adjusted according to the real-time operation status of the power grid and the user's power consumption behavior to guide the user to use electricity reasonably. By introducing the flexibility of the terminal user, the system can improve the operation efficiency of the power grid and reduce the additional costs caused by the expansion of the power grid. The network cost recovery system of the present application can also provide decision support for power grid operators to help them manage costs and risks more effectively in the power market. The design of the method of the present application takes into account a variety of factors, including but not limited to user power consumption habits, power grid load characteristics, and power price fluctuations, to ensure the fairness and efficiency of cost recovery.

[0128] This embodiment also provides a network cost recovery system, including:

[0129] A data acquisition module is used to acquire terminal user related parameters in the target power grid, and the terminal user related parameters at least include power load parameters;

[0130] A modeling module, used to establish a first cost calculation model based on terminal user related parameters, the first cost calculation model including a first objective function and a first constraint condition;

[0131] The solution module is used to solve the first cost calculation model according to the real-time relevant parameters of the terminal user to recover the network cost.

[0132] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0133] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a network cost recovery method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0134] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0135] Acquire relevant parameters of end users in the target power grid, where the relevant parameters of end users at least include power load parameters;

[0136] Establishing a first cost calculation model based on terminal user related parameters, the first cost calculation model including a first objective function and a first constraint condition;

[0137] The first cost calculation model is solved according to the real-time relevant parameters of the terminal users to recover the network cost.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0139] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A network cost recovery method, characterized in that: include: Acquire terminal user related parameters in the target power grid, wherein the terminal user related parameters at least include power load parameters; Establishing a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model includes a first objective function and a first constraint condition; The first cost calculation model is solved according to the real-time relevant parameters of the terminal user to recover the network cost.

2. The network cost recovery method according to claim 1, characterized in that: The first cost calculation model includes: The first cost calculation model is an arbitrary model that minimizes the user's bill; The first cost calculation model includes a first objective function and a first constraint condition; The goal of the first objective function is to minimize the user bill.

3. The network cost recovery method according to claim 2, characterized in that: The solving the first cost calculation model according to the real-time relevant parameters of the terminal user to recover the network cost includes: Obtain electricity price impact rules based on real-time relevant parameters of end users; updating the first cost calculation model according to the electricity price impact rule; The updated first cost calculation model is solved.

4. The network cost recovery method according to claim 3, characterized in that: The first constraint condition includes at least an electricity price elasticity constraint condition.

5. The network cost recovery method according to claim 4, characterized in that: The first objective function includes: Design the objective function to minimize the user's bill by minimizing the total amount the user pays each year; The first objective function is designed by retail electricity price, net interactive power of the network, network tariff paid to the network operator and calculating total network cost parameters.

6. The network cost recovery method according to claim 5, characterized in that: The updating of the first cost calculation model according to the electricity price impact rule comprises: Reconstruct the power demand for transmission and distribution handover, and update the retail electricity price part of the first cost calculation model; The retail electricity price includes a fixed electricity price and a time-varying electricity price; The updating of the retail electricity price portion in the first cost calculation model is updating the time-varying electricity price portion in the first cost calculation model.

7. The network cost recovery method according to claim 6, characterized in that: The reconstructing the power demand for power transmission and distribution handover and updating the retail electricity price portion in the first cost calculation model includes: Establishing a second objective function and a second constraint condition; The portion containing the retail electricity price in the first cost calculation model is updated according to the second objective function and the second constraint condition.

8. A network cost recovery system, characterized in that: include: A data acquisition module, used to acquire terminal user related parameters in the target power grid, wherein the terminal user related parameters at least include power load parameters; A modeling module, used to establish a first cost calculation model based on the terminal user related parameters, wherein the first cost calculation model includes a first objective function and a first constraint condition; A solution module is used to solve the first cost calculation model according to the real-time relevant parameters of the terminal user to recover the network cost.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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