Method, device, computer program product and electronic equipment for determining power utilization strategy

Abstract

This application discloses a method, apparatus, computer program product, and electronic device for determining electricity consumption strategies. Relating to the field of smart grids, the method includes: acquiring all users managed by a power grid company and identifying a first type of equipment for each user; determining the user's objective function based on the operating time of the first type of equipment; acquiring the first objective function of the target user and the second objective functions of other users, and determining the target correlation through the first and second objective functions; determining the total power consumption of all users within a preset period, and determining the target user's payoff function through the total power consumption and the target correlation; using each user as a game participant and electricity consumption strategies as a strategy selection space, inputting each user's payoff function into a game model to obtain a Nash equilibrium solution, and determining the Nash equilibrium solution as the target electricity consumption strategy for all users. This application solves the problem in related technologies where users' electricity consumption strategies lead to significant peak-shaving pressure on the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart grid, in particular, to a method and apparatus for determining power consumption strategy, a computer program product and an electronic device. BACKGROUND

[0002] DSM (Demand side management) refers to that, in order to achieve the purposes of power grid safety, energy saving and emission reduction, etc., a power grid enterprise or the like controls the size and time period of power consumption on the demand side through price guidance, centralized scheduling and the like. The DSM can realize safe and efficient operation of the power grid without increasing investment in power grid infrastructure. The volatility of distributed photovoltaic and other power sources significantly widens the load peak-valley difference, resulting in huge pressure on the power grid for peak shaving. The demand side response can shift the load use time period, and is an important means to reduce the PAR (Peak to average ratio).

[0003] The control object of the DSM is residential buildings and industrial and commercial users, and the control method includes direct control and intelligent pricing control. In the direct control scheme, a utility company can monitor adjustable electrical equipment such as heating, ventilation and air conditioning in a user building in real time according to the agreement between the utility company and the user, but this scheme has the possibility of infringing on the privacy of the user and is difficult to effectively carry out. In the intelligent pricing scheme, the user changes the power consumption time period by himself / herself through means such as peak electricity price, time-of-use pricing or real-time electricity price.

[0004] However, the DSM scheme in the related art mainly focuses on the interaction between the power grid enterprise and the user. For example, in the intelligent pricing control strategy, the power grid enterprise expects the user to respond to the price difference of each time period and shift his / her own load from the high-price time period to the low-price time period. In this mode, each user needs to directly communicate with the power grid enterprise. However, this control method cannot be effectively applied to the new power system with extensive access of distributed power sources, because the existence of distributed power sources leads to correlation between the load use conditions of the users, and the adjustment of the power consumption strategy of a single user cannot effectively reduce the pressure on the power grid for peak shaving.

[0005] At present, there is no effective solution to the problem that the power consumption strategy of the user in the related art leads to large pressure on the power grid for peak shaving. SUMMARY

[0006] The main purpose of the present application is to provide a method and apparatus for determining power consumption strategy, a computer program product and an electronic device, to solve the problem that the power consumption strategy of the user in the related art leads to large pressure on the power grid for peak shaving.

[0007] To achieve the above object, according to one aspect of the present application, a method for determining a power consumption strategy is provided. The method comprises: obtaining all users managed by a power grid enterprise, for each user, determining a first type of equipment of the user, wherein the first type of equipment is power consumption equipment whose running period is allowed to be adjusted; determining a target function of the user based on the running period of the first type of equipment of the user, wherein the target function is a relationship between power consumption cost of the user and a power consumption strategy of the user, and the power consumption strategy comprises the running period of the first type of equipment of the user; obtaining a target function of a target user to obtain a first target function, and obtaining target functions of other users to obtain second target functions, wherein the other users are users other than the target user among all the users, and the target correlation is a correlation between power consumption cost of the target user and power consumption cost of the other users; determining total power consumption power of all the users in a preset period, and determining a benefit function of the target user based on the total power consumption power and the target correlation, wherein the benefit function is a relationship between saved cost of the target user and the power consumption strategy under influence of the power consumption strategy of the other users; taking each user as a game participant, taking the power consumption strategy of each user as a strategy selection space of the user, inputting the benefit function of each user into a game model to obtain a Nash equilibrium solution, and determining the Nash equilibrium solution as a target power consumption strategy of all the users, wherein the Nash equilibrium solution is used to represent the power consumption strategy of each user under maximization of saved cost.

[0008] Optionally, determining the target function of the user based on the running period of the first type of equipment of each user comprises: determining a plurality of periods contained in a preset period, for each user, determining a second type of equipment of the user, wherein the second type of equipment is power consumption equipment with a fixed running period; determining a first running period and a first running power of the first type of equipment of the user, and determining a second running period and a second running power of the second type of equipment; inputting the first running period, the first running power, the second running period and the second running power into a power generation cost function to obtain a power generation cost of total power consumption power of the user, wherein the power generation cost function is a relationship between power consumption period, power consumption power and power generation cost; determining a proportional coefficient of total power consumption cost and total power generation cost of all the users, calculating a product of the power generation cost and the proportional coefficient to obtain the target function.

[0009] Optionally, determining the target correlation based on the first target function and the second target function comprises: determining a first power consumption power of the target user in the preset period, and determining a second power consumption power of the other users in the preset period; calculating a ratio of the first target function to the second target function to obtain a first ratio, and calculating a ratio of the first power consumption power to the second power consumption power to obtain a second ratio; inputting the first ratio and the second ratio into a preset linear relationship expression to obtain the target correlation.

[0010] Optionally, the determining the benefit function of the target user through the total power consumption and the target correlation comprises: determining a proportional coefficient of total power consumption cost and total power generation cost of all users, calculating a ratio of the first target power consumption of the target user in a preset period to the total power consumption, and calculating a product of the ratio and the proportional coefficient to obtain a benefit coefficient; determining the second target power consumption of all other users except the target user among all users, wherein the second target power consumption is an accumulated value of power consumption of all other users in the preset period; determining the target power consumption cost of all other users based on the second target power consumption, and calculating a product of the target power consumption cost and the benefit coefficient to obtain the benefit function of the target user.

[0011] Optionally, the constraint condition of the target function is determined in the following manner: determining a power constraint of the target function, wherein the power constraint is used to represent that the power consumption of the first type of equipment under different power consumption strategies is the same; determining a time period constraint of the target function, wherein the time period constraint is used to represent that the running time of the first type of equipment under different power consumption strategies is the same; and determining a device running power constraint of the target function, wherein the device running power constraint is used to represent that the running power of the first type of equipment belongs to a target power range.

[0012] Optionally, the Nash equilibrium solution is determined as the target power consumption strategy of all users, comprising: in the case that there is only one Nash equilibrium solution, determining the Nash equilibrium solution as the target power consumption strategy; in the case that there are multiple Nash equilibrium solutions, determining a to-be-determined power consumption strategy corresponding to each Nash equilibrium solution; calculating a load peak-to-average ratio of each to-be-determined power consumption strategy, and determining the to-be-determined power consumption strategy corresponding to the minimum load peak-to-average ratio as the target power consumption strategy, wherein the load peak-to-average ratio is used to represent the peak regulation pressure of the power grid enterprise.

[0013] Optionally, the calculation of the load peak-to-average ratio of each to-be-determined power consumption strategy comprises: determining a target running time period of the first type of equipment of each user in the to-be-determined power consumption strategy, determining power consumption of all users in each time period in a preset period to obtain a time period power consumption of each time period; determining a maximum time period power consumption from the time period power consumptions of all time periods, and calculating a ratio of a sum of the time period power consumptions of all time periods to a number of time periods in the preset period to obtain an average time period power consumption; and calculating a ratio of the maximum time period power consumption to the average time period power consumption to obtain the load peak-to-average ratio of the to-be-determined power consumption strategy.

[0014] In order to achieve the above object, according to another aspect of the present application, a device for determining power consumption strategy is provided. The device comprises: an acquisition unit configured to acquire all users managed by a power grid enterprise, and determine, for each user, a first type of equipment of the user, wherein the first type of equipment is power consumption equipment whose running time period is allowed to be adjusted; a first determination unit configured to determine, based on the running time period of the first type of equipment of each user, a target function of the user, wherein the target function is a relationship between power consumption cost of the user and power consumption strategy of the user, and the power consumption strategy comprises the running time period of the first type of equipment of the user; a second determination unit configured to acquire the target function of a target user to obtain a first target function, acquire the target function of other users to obtain a second target function, and determine a target correlation by using the first target function and the second target function, wherein the other users are users other than the target user among all the users, and the target correlation is a correlation between power consumption cost of the target user and power consumption cost of the other users; a third determination unit configured to determine total power consumption of all the users in a preset period, and determine a benefit function of the target user by using the total power consumption and the target correlation, wherein the benefit function is a relationship between saved cost of the target user and power consumption strategy under influence of power consumption strategy of the other users; and a fourth determination unit configured to take each user as a game participant, take power consumption strategy of each user as strategy selection space of the user, input the benefit function of each user into a game model, obtain a Nash equilibrium solution, and determine the Nash equilibrium solution as target power consumption strategy of all the users, wherein the Nash equilibrium solution is used to represent power consumption strategy of each user under condition of maximizing saved cost.

[0015] In order to achieve the above object, according to another aspect of the present application, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements steps of the method for determining power consumption strategy in various embodiments of the present application.

[0016] This application employs the following steps: First, obtain all users managed by the power grid company. For each user, determine the user's first-class equipment, where the first-class equipment is the electrical equipment whose operating time is adjustable. Second, determine the user's objective function based on the operating time of each user's first-class equipment, where the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the operating time of the user's first-class equipment. Third, obtain the objective function of the target user to obtain the first objective function. Fourth, obtain the objective functions of other users to obtain the second objective function. Fifth, determine the target correlation relationship through the first and second objective functions, where other users are users other than the target user, and the target correlation relationship is the target user's electricity consumption. This study investigates the correlation between electricity costs and those of other users. It determines the total electricity consumption of all users within a preset period and uses this correlation to determine the target user's payoff function. The payoff function represents the relationship between the target user's cost savings and their electricity consumption strategies under the influence of other users' strategies. Each user is treated as a game player, and their electricity consumption strategy is used as their strategy choice space. The payoff function of each user is input into a game model to obtain a Nash equilibrium solution. This Nash equilibrium solution is then used as the target electricity consumption strategy for all users, representing each user's strategy to maximize cost savings. This addresses the problem of high grid peak-shaving pressure caused by user electricity consumption strategies in related technologies. By determining the target correlation between electricity costs of various users managed by the power grid company, and using this correlation to determine the user's payoff function, a game model is introduced. The user's electricity consumption strategy is used as the decision variable, and the user's payoff function is input into the game model for game solving. The resulting Nash equilibrium solution serves as the target electricity consumption strategy for all users, thereby reducing the grid peak-shaving pressure. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart of a method for determining an electricity consumption strategy according to an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of a smart grid structure for power grid enterprise management of users provided according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram illustrating the total electricity consumption and electricity cost without adopting the electricity consumption strategy of this embodiment, according to an embodiment of this application.

[0021] Figure 4This is a schematic diagram showing the total electricity consumption and electricity cost after adopting the electricity consumption strategy of this embodiment according to the embodiments of this application;

[0022] Figure 5 This is a schematic diagram showing the comparison results of electricity costs based on whether or not the electricity consumption strategy of this embodiment is used, according to the embodiments of this application;

[0023] Figure 6 This is a schematic diagram illustrating the impact of different proportions of the first type of equipment on the peak-to-average load ratio according to the embodiments of this application;

[0024] Figure 7 This is a schematic diagram of a device for determining power consumption strategies according to an embodiment of this application;

[0025] Figure 8 This is a schematic diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0030] It should be noted that the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0031] The present invention will now be described in conjunction with preferred implementation steps. Figure 1 This is a flowchart of a method for determining electricity consumption strategies according to embodiments of this application, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S101: Obtain all users managed by the power grid company. For each user, determine the user's first type of equipment, where the first type of equipment is the electrical equipment that can be adjusted during the operating period.

[0033] In step S101, the first category of equipment can be devices that can participate in demand-side response, such as washing machines, dryers, refrigerators, dishwashers, air conditioners, and PHEVs (Plug-in Hybrid Electric Vehicles), which are devices capable of adjusting load periods. Refrigerators, lights, and similar devices are rigid load devices that need to be used during fixed operating periods and belong to the second category. For demand-side management, the user's electricity consumption strategy can only adjust the first category of equipment. Therefore, to determine the user's electricity consumption strategy, it is necessary to first determine the first and second categories of equipment used by the user.

[0034] Step S102: Determine the user's objective function based on the runtime of each user's first type of equipment. The objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy. The electricity consumption strategy includes the runtime of the user's first type of equipment.

[0035] In step S102, the objective function can be the following formula:

[0036]

[0037] Among them, X n Let D be the set of first-class devices for user n. n / X n It is the set of second-class devices for user n, T is the preset period, such as a day, and t is the time period within the preset period, such as dividing a day into 24 time periods by hour. Let be the load power of the xth type I device of user n during time period t. Let be the load power of the y-th type II device of user n during time period t. f() is the power generation cost function of the power grid company, and ρ is the ratio coefficient of the power grid company's electricity consumption cost to its power generation cost.

[0038] Step S103: Obtain the objective function of the target user to obtain the first objective function; obtain the objective functions of other users to obtain the second objective function; determine the target correlation relationship through the first objective function and the second objective function, where other users are users other than the target user among all users, and the target correlation relationship is the correlation between the electricity cost of the target user and the electricity cost of other users.

[0039] In step S103, to assess the inter-user impact of electricity costs among the power grid company's users, target correlations are determined based on the first and second objective functions of different users. The target correlation characterizes how the electricity costs of the target user and other users influence each other. The target user can be any user managed by the power grid company, and the revenue function for each target user can be obtained based on the target correlations between each user and other users.

[0040] Step S104: Determine the total power consumption of all users within a preset period, and determine the revenue function of the target user through the correlation between the total power consumption and the target. The revenue function is the relationship between the cost savings of the target user and the power consumption strategy under the influence of the power consumption strategies of other users.

[0041] In step S104, for adjusting the electricity consumption strategy of users managed by the power grid company, it is necessary to first ensure that the electricity consumption of each user remains unchanged, and only adjust the user's load power application period. Therefore, it is necessary to determine the total electricity consumption of all users managed by the power grid company within a preset period, and then determine the revenue function of the target user based on the total electricity consumption and the target correlation.

[0042] Step S105: Taking each user as a game participant and each user's electricity consumption strategy as the user's strategy choice space, input each user's payoff function into the game model to obtain the Nash equilibrium solution. The Nash equilibrium solution is determined as the target electricity consumption strategy for all users. The Nash equilibrium solution is used to characterize each user's electricity consumption strategy to maximize cost savings.

[0043] In step S105, the game theory model is a model that uses game theory to analyze and make decisions about a user's electricity consumption strategy. Game theory is a mathematical theory that studies decision-making problems with competitive and cooperative characteristics. In a multi-participant environment (multiple users in this scenario), it examines how each participant chooses the most advantageous strategy while considering the actions of other participants. In determining the electricity consumption strategy, the game theory model is mainly used to solve for Nash equilibrium. Nash equilibrium is a state in which, given a combination of strategies (i.e., the electricity consumption strategies of all users), no participant can increase its own revenue by unilaterally changing its own strategy without other participants changing their strategies. The payoff function describes the relationship between the electricity cost savings of a user under the influence of other users' electricity consumption strategies and the electricity consumption strategy itself. The payoff function considers the mutual influence of the user's own electricity costs and the electricity costs of other users in the power grid.

[0044] In some examples, each user is treated as a player in a game, with the goal of maximizing electricity cost savings. The users' strategy choice space represents their electricity consumption strategies, i.e., the operating time of their first-class devices. By inputting each user's payoff function into the game model, a Nash equilibrium is found. This equilibrium represents the solution where each user, given the strategies of other users, is unwilling to change their own strategy, as any unilateral change would not result in further cost savings. The Nash equilibrium, serving as the target electricity consumption strategy for all users, ensures the economic efficiency and effectiveness of the power grid while reducing peak-shaving pressure.

[0045] Game theory models can handle the interdependence and conflict among users in electricity consumption strategy optimization. By solving for Nash equilibrium, a globally optimal combination of electricity consumption strategies can be found, which is optimal for all users and does not increase the peak-shaving pressure on the power grid. Game theory models emphasize the optimal choices of each individual user managed by the power grid company and their mutual influence, thereby achieving optimization at the global level of the power grid company.

[0046] In some examples, Figure 2 This is a schematic diagram of a smart grid structure for power grid enterprise management users provided according to an embodiment of this application, such as... Figure 2As shown, in this power grid structure, the power grid company manages N users. Each user consists of a random combination of Category II and Category I devices. Each user has 10 to 20 Category II devices, including refrigerators / freezers (daily power consumption: 1.32 kWh), electric stoves (daily power consumption: 1.89 kWh for self-cleaning, 2.01 kWh for regular use), lighting (1.00 kWh daily for 10 standard bulbs), and heating equipment (daily power consumption: 7.1 kWh). In addition, each user also has 10 to 20 Category I devices, including dishwashers (daily power consumption: 1.44 kWh), washing machines (daily power consumption: 1.94 kWh for regular use), dryers (daily power consumption: 2.50 kWh), and PHEVs (daily power consumption: 9.9 kWh).

[0047] Figure 3 This is a schematic diagram illustrating the total electricity consumption and electricity cost without adopting the electricity consumption strategy of this embodiment, according to an embodiment of this application. Figure 4 This is a schematic diagram illustrating the total electricity consumption and electricity cost after adopting the electricity consumption strategy of this embodiment, as provided in the embodiments of this application. Figure 3 and Figure 4 As shown, when the power consumption strategy provided in this embodiment is not adopted, the PAR is calculated to be 2.1, and the total power cost is 44.77 yuan; while when the power consumption strategy provided in this embodiment is adopted, the PAR is reduced to 1.8 (i.e., a reduction of 17%), and the total power cost is reduced to 37.90 yuan (i.e., a reduction of 18%). Figure 4 As can be seen, after adopting the power consumption strategy provided in this embodiment, the load distribution is more even at different times of the day. However, in both cases, the total energy consumed by each user is the same. It is just that the power consumption strategy provided in this embodiment makes the user's power consumption more reasonable.

[0048] The above examples demonstrate that the electricity consumption strategy of this embodiment can minimize the total electricity cost for the power grid company. However, only when the electricity cost for each user is reduced can the application of demand-side response in actual production be effectively promoted. Figure 5 This is a schematic diagram showing the comparison of electricity costs based on whether or not the electricity consumption strategy provided in this embodiment is used, according to an embodiment of this application. Figure 5 As shown, the curve with no demand-side response represents the electricity cost without using the electricity consumption strategy of this embodiment, while the curve with demand-side response represents the electricity cost using the electricity consumption strategy of this embodiment. After using the electricity consumption strategy of this embodiment, the electricity cost of almost all users has decreased.

[0049] In the example above, the proportion of users' first-category devices is 50%. To fully understand the impact of the proportion of first-category devices on demand-side response... Figure 6 This is a schematic diagram illustrating the impact of different proportions of the first type of equipment on the peak-to-average load ratio, according to embodiments of this application.Figure 6 As shown, Figure 6 The diagram illustrates the peak-to-average load ratio (PAR) as the proportion of Category I equipment varies from 10% to 90%. As the proportion of Category I equipment increases, the PAR gradually decreases. When the proportion of Category I equipment reaches 90%, the PAR can be reduced to 1.34, almost equal to the balanced load, which is extremely beneficial for grid operation.

[0050] The method for determining electricity consumption strategies provided in this application involves acquiring all users managed by the power grid company. For each user, a first category of equipment is identified, where the first category of equipment refers to electrical equipment whose operating time is adjustable. Based on the operating time of each user's first category of equipment, a user's objective function is determined. This objective function is the relationship between the user's electricity cost and their electricity consumption strategy, where the electricity consumption strategy includes the operating time of the user's first category of equipment. The objective function of the target user is obtained, resulting in a first objective function. The objective functions of other users are also obtained, resulting in a second objective function. A target correlation is determined using the first and second objective functions. Here, other users are those other than the target user, and the target correlation is the target user's... This study investigates the correlation between a user's electricity costs and those of other users. It determines the total electricity consumption of all users within a preset period and uses this correlation to determine the target user's payoff function. This payoff function represents the relationship between the target user's cost savings and their electricity consumption strategies under the influence of other users' strategies. Each user is treated as a game player, and their electricity consumption strategy is used as their strategy choice space. The payoff function of each user is input into a game model to obtain a Nash equilibrium solution. This Nash equilibrium solution is then used as the target electricity consumption strategy for all users, representing each user's strategy to maximize cost savings. This addresses the problem of high grid peak-shaving pressure caused by user electricity consumption strategies in related technologies. By determining the target correlation between electricity costs of various users managed by the power grid company, and using this correlation to determine the user's payoff function, a game model is introduced. The user's electricity consumption strategy is used as the decision variable, and the user's payoff function is input into the game model for game solving. The resulting Nash equilibrium solution serves as the target electricity consumption strategy for all users, thereby reducing the grid peak-shaving pressure.

[0051] To determine the relationship between a user's electricity cost and their electricity consumption strategy, an objective function is determined based on the operating segments of the user's first and second types of devices. Optionally, in the method for determining the electricity consumption strategy provided in this application embodiment, determining the user's objective function based on the operating segments of each user's first type of devices includes: determining multiple time periods included within a preset period; for each user, determining the user's second type of devices, wherein the second type of devices are electrical devices with fixed operating segments; determining the first operating segment and first operating power of the user's first type of devices, and determining the second operating segment and second operating power of the second type of devices; inputting the first operating segment, first operating power, second operating segment, and second operating power into a power generation cost function to obtain the power generation cost of the user's total electricity consumption, wherein the power generation cost function is a relationship between electricity consumption time periods, electricity consumption, and power generation cost; determining the ratio coefficient between the total electricity cost and the total power generation cost for all users, calculating the product of the power generation cost and the ratio coefficient to obtain the objective function.

[0052] In some examples, the preset period can be set to T, the time period included within the preset period is t, the number of users is n, the first type of device is x, and the second type of device is y. The total load of user n in time period t can be calculated using the following formula:

[0053]

[0054] in, Let be the total load of user n during time period t. The formula for calculating the power generation cost is as follows:

[0055] C t =f(E t );

[0056] Among them, C t Let f() be the power generation cost, and f be a function of the power generation cost. The proportionality coefficient can be calculated using the following formula:

[0057]

[0058] Among them, b n This represents the electricity cost for user n, which can be calculated based on the electricity price at different times and the power consumption of user n during those times. Since the fees charged by the power grid company to users are greater than the generation cost (i.e., the electricity cost is greater than the generation cost), the proportionality coefficient is greater than or equal to 1. Based on the above analysis, the objective function can be expressed as follows:

[0059]

[0060] This embodiment calculates the ratio of total electricity cost to total generation cost for all users, ensuring that the economic interests of the power grid company are considered while optimizing user electricity consumption strategies. This balanced approach helps find a equilibrium between cost savings for users and increased economic efficiency for the power grid company, promoting the sustainable development of the smart grid. The objective function combines user electricity cost, generation cost, and the ratio, reflecting not only the direct economic benefits of users but also indirectly considering the peak-shaving pressure on the power grid. By optimizing the objective function, the smart grid can achieve the goals of reducing the peak-to-average load ratio and total electricity cost, improving the efficiency and economy of power grid operation.

[0061] After determining the objective function, the target correlation is determined based on the objective functions of different users. Optionally, in the method for determining the electricity consumption strategy provided in this application embodiment, determining the target correlation through the first objective function and the second objective function includes: determining the first power consumption of the target user within a preset period, and determining the second power consumption of other users within the preset period; calculating the ratio of the first objective function to the second objective function to obtain a first ratio, and calculating the ratio of the first power consumption to the second power consumption to obtain a second ratio; inputting the first ratio and the second ratio into a preset linear relationship expression to obtain the target correlation.

[0062] In some examples, the constructed objective function and proportionality coefficient minimize the user's electricity cost while also minimizing the grid company's PAR (Parity Rate). This means increasing electricity prices during peak load periods and decreasing them during off-peak periods. With total load demand remaining constant, each user can reduce their own electricity cost by changing their electricity consumption strategy. Considering the game-like dynamics among users' strategies, it's necessary to determine the target user's relationship with other users. Since a user's electricity cost is directly proportional to their power consumption (i.e., load), the pre-defined linear relationship expression can be expressed as follows:

[0063]

[0064] Among them, b n Let b be the electricity cost for user n (i.e., the target user). m The electricity cost for user m (i.e., other users) That is, the first ratio. Let n be the load of user n during time period t. For user m, the load during time period t. That is, the second ratio. Further derivation of the above formula yields the following formula:

[0065]

[0066] Combining the formula for calculating the proportional coefficient with the formula above, the target correlation between the electricity cost of a single user n and the electricity consumption strategies of other users can be obtained as follows:

[0067]

[0068]

[0069] Among them, E n E is the load power of user n during all time periods within a preset period. total It is the total load power (i.e., total power consumption) of all users in all time periods within the preset cycle.

[0070] This embodiment quantifies the degree of mutual influence of electricity costs among users by determining target correlations. This helps smart grid systems understand the impact of user electricity consumption strategy adjustments on the overall grid and the costs between users, providing a data foundation for optimizing electricity consumption strategies. By encouraging users to consider the overall grid benefits when adjusting their electricity consumption strategies, it promotes collaborative optimization among users and jointly reduces the peak-shaving pressure on the grid.

[0071] After determining the target correlation and total power consumption, the revenue function of the target user is determined through the total power consumption and the target correlation. Optionally, in the method for determining the power consumption strategy provided in this application embodiment, determining the revenue function of the target user through the total power consumption and the target correlation includes: determining the ratio coefficient of the total power consumption cost to the total power generation cost of all users; calculating the ratio of the first target power consumption of the target user to the total power consumption within a preset period; and calculating the product of the ratio and the ratio coefficient to obtain the revenue coefficient; determining the second target power consumption of all users other than the target user, wherein the second target power consumption is the cumulative value of the power consumption of all other users within the preset period; determining the target power consumption cost of all other users based on the second target power consumption; and calculating the product of the target power consumption cost and the revenue coefficient to obtain the revenue function of the target user.

[0072] In some examples, the payoff function can be expressed as follows:

[0073]

[0074] Among them, Ω n (p n ;p -n Let ) represent the revenue function for target user n. p represents the electricity consumption strategy of target user n. -n =[p1,...,p n-1 ,p n+1 ,...,p N] represents the set of electricity consumption strategies of users other than the target user n. ω n This represents the profit coefficient. Indicates the second target power consumption. This represents the target electricity cost.

[0075] This embodiment determines the revenue function of the target user, realizes personalized evaluation of user electricity consumption behavior, fair cost sharing, and correlation analysis of electricity consumption strategies among users, thereby promoting collaborative optimization, reducing the peak-shaving pressure of the power grid, and improving the power grid operation efficiency and economic benefits.

[0076] The user's power consumption strategy needs to meet the constraints. Optionally, in the method for determining the power consumption strategy provided in this application embodiment, the constraints of the objective function are determined in the following ways: determining the power constraint of the objective function, wherein the power constraint is used to characterize that the power consumption of the first type of equipment is the same under different power consumption strategies; determining the time constraint of the objective function, wherein the time constraint is used to characterize that the operating time of the first type of equipment is the same under different power consumption strategies; determining the equipment operating power constraint of the objective function, wherein the equipment operating power constraint is used to characterize that the operating power of the first type of equipment belongs to the target power range.

[0077] In some examples, since the goal of the electricity consumption strategy optimized in this embodiment is to reduce the peak-valley difference without changing the total electricity consumption, it should be ensured that the daily load electricity consumption after adopting the electricity consumption strategy of this embodiment is the same as before. For example, the start time and end time of the adjusted operating period of user n's first type of equipment x are respectively The start and end times of the electricity load before the electricity consumption strategy is used are respectively: The total daily load power is The power constraint of the objective function can be expressed by the following formula:

[0078]

[0079] in, This indicates the runtime segment that is not allowed to be adjusted for Class I devices.

[0080] For user n's first-class device x, the time interval for the user to adjust the device's operating time must be greater than or equal to the time interval required to complete the device's function. For example, for plug-in hybrid electric vehicles, their normal charging time is generally not less than 3 hours, therefore, it is necessary to meet the following requirements. For example, appliances such as refrigerators need to operate continuously to meet [the requirements of]... The time constraint of the objective function can be expressed by the following formula:

[0081]

[0082] Where, Δtthre That is, the runtime, t1, and t2 values ​​of the first type of equipment are determined according to the specific type of the first type of equipment.

[0083] For user n's first type of device x, its standby power is: Maximum operating power is The device operating power constraint of the objective function can be expressed by the following formula:

[0084]

[0085] This embodiment ensures that the power consumption of the first type of equipment remains consistent under different power consumption strategies by determining the power constraint of the objective function. This avoids instantaneous power fluctuations in the power grid caused by strategy adjustments, ensuring stable grid operation and preventing overload or underload situations. By determining the time constraint of the objective function, the operating time of the first type of equipment is guaranteed to be the same under different power consumption strategies. This not only helps maintain the normal operating cycle of the equipment and prevents the equipment's lifespan from being affected by excessive delays or early operation, but also ensures that users' basic power needs are not affected, enhancing user experience and acceptance. By determining the equipment operating power constraint of the objective function, the operating power of the first type of equipment is ensured to be within the target power range, avoiding the equipment from operating at inefficient or unsafe power levels. This helps optimize the allocation of power resources, improve energy utilization efficiency, and reduce the risk of energy waste and equipment damage.

[0086] If there are multiple Nash equilibrium solutions, the target electricity consumption strategy is determined by the load peak-to-average power ratio. Optionally, in the method for determining the electricity consumption strategy provided in this application embodiment, determining the Nash equilibrium solution as the target electricity consumption strategy for all users includes: if there is only one Nash equilibrium solution, determining the Nash equilibrium solution as the target electricity consumption strategy; if there are multiple Nash equilibrium solutions, determining the undetermined electricity consumption strategy corresponding to each Nash equilibrium solution; calculating the load peak-to-average power ratio for each undetermined electricity consumption strategy, and determining the undetermined electricity consumption strategy corresponding to the smallest load peak-to-average power ratio as the target electricity consumption strategy, wherein the load peak-to-average power ratio is used to characterize the peak-shaving pressure of the power grid enterprise.

[0087] In some examples, multiple Nash equilibrium solutions can be obtained through game theory models. When there is only one Nash equilibrium solution, it is directly used as the target electricity consumption strategy. If multiple Nash equilibrium solutions exist, the peak-to-average load ratio of the power grid enterprise under different undetermined electricity consumption strategies is calculated, and the target electricity consumption strategy is selected based on the minimum peak-to-average load ratio. This embodiment can effectively reduce the peak-shaving pressure on the power grid during high-load periods by selecting the target electricity consumption strategy with the minimum peak-to-average load ratio.

[0088] Optionally, in the method for determining the power consumption strategy provided in the embodiments of this application, calculating the peak-to-average load ratio of each pending power consumption strategy includes: determining the target operating period of the first type of equipment for each user in the pending power consumption strategy; determining the power consumption of all users in each time period within a preset period to obtain the time period power consumption; determining the maximum time period power consumption from the time period power consumption of all time periods; and calculating the ratio of the sum of the time period power consumption of all time periods to the number of time periods within the preset period to obtain the average time period power consumption; and calculating the ratio of the maximum time period power consumption to the average time period power consumption to obtain the peak-to-average load ratio of the pending power consumption strategy.

[0089] In some examples, since the primary objective of demand-side management is to reduce the load peak-to-valley difference, the load peak-to-average power ratio is used to characterize the load peak-shaving pressure on the power grid company. The power system has N users, and the set of users is... The load of user n during time period t is The peak-to-average load ratio can be calculated using the following formula:

[0090]

[0091] Where T represents the number of electricity consumption periods within the preset cycle. This represents the power consumption of the power grid company during time period t. This represents the total power consumption over all time periods. This represents the average power consumption over a given period. This represents the maximum power consumption during any given time period.

[0092] A higher peak-to-average load ratio indicates a higher peak load and greater pressure on the power grid for peak regulation. This embodiment quantifies the peak regulation pressure of different electricity consumption strategies by calculating the peak-to-average load ratio, enabling refined management and optimization of user electricity consumption strategies. By selecting target electricity consumption strategies through the peak-to-average load ratio, the peak regulation pressure on the power grid is effectively reduced.

[0093] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0094] This application also provides an apparatus for determining electricity consumption strategies. It should be noted that this apparatus can be used to execute the method for determining electricity consumption strategies provided in this application. The apparatus for determining electricity consumption strategies provided in this application will be described below.

[0095] Figure 7This is a schematic diagram of a device for determining power consumption strategies according to embodiments of this application. Figure 7 As shown, the device includes:

[0096] The acquisition unit 701 is used to acquire all users managed by the power grid enterprise. For each user, it determines the user's first type of equipment, wherein the first type of equipment is the electrical equipment that can be adjusted during the operating period.

[0097] The first determining unit 702 is used to determine the user's objective function based on the runtime of the first type of equipment for each user, wherein the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the runtime of the user's first type of equipment.

[0098] The second determining unit 703 is used to obtain the objective function of the target user, obtain the first objective function, obtain the objective functions of other users, obtain the second objective function, and determine the target correlation relationship through the first objective function and the second objective function. Here, other users are users other than the target user among all users, and the target correlation relationship is the correlation between the electricity cost of the target user and the electricity cost of other users.

[0099] The third determining unit 704 is used to determine the total power consumption of all users within a preset period, and to determine the benefit function of the target user through the total power consumption and the target correlation. The benefit function is the relationship between the cost saving of the target user and the power consumption strategy under the influence of the power consumption strategies of other users.

[0100] The fourth determining unit 705 is used to take each user as a game participant, take each user's electricity consumption strategy as the user's strategy selection space, input each user's payoff function into the game model, obtain the Nash equilibrium solution, and determine the Nash equilibrium solution as the target electricity consumption strategy of all users. The Nash equilibrium solution is used to characterize each user's electricity consumption strategy to maximize cost savings.

[0101] The power consumption strategy determination device provided in this application embodiment includes an acquisition unit 701 for acquiring all users managed by the power grid company, and for each user, determining the user's first type of equipment, wherein the first type of equipment is power consumption equipment whose operation period allows adjustment; a first determination unit 702 for determining the user's objective function based on the operation period of each user's first type of equipment, wherein the objective function is the relationship between the user's electricity cost and the user's power consumption strategy, and the power consumption strategy includes the operation period of the user's first type of equipment; a second determination unit 703 for acquiring the objective function of the target user to obtain the first objective function, acquiring the objective functions of other users to obtain the second objective function, and determining the target correlation relationship through the first objective function and the second objective function, wherein other users are users other than the target user among all users, and the target correlation relationship is the correlation between the target user's electricity cost and the electricity costs of other users; and a third determination unit 704 for determining the total power consumption of all users within a preset period, and determining the total power consumption of all users within a preset period. The relationship between power consumption and target cost determines the payoff function of the target user. This payoff function represents the relationship between the target user's cost savings and their electricity consumption strategy under the influence of other users' strategies. The fourth determining unit 705 uses each user as a game participant and their electricity consumption strategy as their strategy choice space. It inputs each user's payoff function into a game model to obtain a Nash equilibrium solution. This Nash equilibrium solution is then used as the target electricity consumption strategy for all users. The Nash equilibrium solution characterizes each user's strategy for maximizing cost savings, addressing the problem of high grid peak-shaving pressure caused by user electricity consumption strategies in related technologies. By determining the target correlation of electricity costs among users managed by the power grid company, the payoff function of each user is determined from this correlation. By introducing a game model and using user electricity consumption strategies as decision variables, the payoff function of each user is input into the game model for game solving, yielding a Nash equilibrium solution as the target electricity consumption strategy for all users, thereby reducing the grid peak-shaving pressure.

[0102] Optionally, in the power consumption strategy determination device provided in the embodiments of this application, the first determination unit 702 includes: a first determination module, used to determine multiple time periods included in a preset period, and for each user, determine the user's second type of equipment, wherein the second type of equipment is a power consumption device with a fixed operating period; a second determination module, used to determine the first operating period and first operating power of the user's first type of equipment, and determine the second operating period and second operating power of the second type of equipment; a first input module, used to input the first operating period, first operating power, second operating period and second operating power into a power generation cost function to obtain the power generation cost of the user's total power consumption, wherein the power generation cost function is a relationship between power consumption period, power consumption and power generation cost; and a third determination module, used to determine the ratio coefficient of the total power consumption cost to the total power generation cost of all users, calculate the product of the power generation cost and the ratio coefficient, and obtain the objective function.

[0103] Optionally, in the power consumption strategy determination device provided in the embodiments of this application, the second determination unit 703 includes: a fourth determination module, used to determine the first power consumption of a target user within a preset period, and to determine the second power consumption of other users within a preset period; a first calculation module, used to calculate the ratio of a first objective function to a second objective function to obtain a first ratio, and to calculate the ratio of the first power consumption to the second power consumption to obtain a second ratio; and a second input module, used to input the first ratio and the second ratio into a preset linear relationship expression to obtain a target correlation relationship.

[0104] Optionally, in the power consumption strategy determination device provided in this application embodiment, the third determination unit 704 includes: a fifth determination module, used to determine the ratio coefficient of the total power consumption cost to the total power generation cost of all users, calculate the ratio of the first target power consumption of the target user to the total power consumption within a preset period, and calculate the product of the ratio and the ratio coefficient to obtain the revenue coefficient; a sixth determination module, used to determine the second target power consumption of all users except the target user, wherein the second target power consumption is the cumulative value of the power consumption of all other users within the preset period; and a seventh determination module, used to determine the target power consumption cost of all other users based on the second target power consumption, calculate the product of the target power consumption cost and the revenue coefficient to obtain the revenue function of the target user.

[0105] Optionally, in the power consumption strategy determination device provided in the embodiments of this application, the device further includes: a fifth determining unit, used to determine the power constraint of the objective function, wherein the power constraint is used to characterize that the power consumption of the first type of equipment is the same under different power consumption strategies; a sixth determining unit, used to determine the time period constraint of the objective function, wherein the time period constraint is used to characterize that the operating time of the first type of equipment is the same under different power consumption strategies; and a seventh determining unit, used to determine the equipment operating power constraint of the objective function, wherein the equipment operating power constraint is used to characterize that the operating power of the first type of equipment belongs to the target power range.

[0106] Optionally, in the power consumption strategy determination device provided in the embodiments of this application, the fourth determination unit 705 includes: an eighth determination module, used to determine the Nash equilibrium solution as the target power consumption strategy when there is only one Nash equilibrium solution; a ninth determination module, used to determine the undetermined power consumption strategy corresponding to each Nash equilibrium solution when there are multiple Nash equilibrium solutions; and a second calculation module, used to calculate the peak-to-average load ratio of each undetermined power consumption strategy and determine the undetermined power consumption strategy corresponding to the minimum peak-to-average load ratio as the target power consumption strategy, wherein the peak-to-average load ratio is used to characterize the peak-shaving pressure of the power grid enterprise.

[0107] Optionally, in the power consumption strategy determination device provided in this application embodiment, the second calculation module includes: a determination submodule, used to determine the target operating period of the first type of equipment of each user in the power consumption strategy to be determined, determine the power consumption of all users in each time period within a preset period, and obtain the time period power consumption of each time period; a first calculation submodule, used to determine the maximum time period power consumption from the time period power consumption of all time periods, and calculate the ratio of the sum of the time period power consumption of all time periods to the number of time periods within the preset period, and obtain the average time period power consumption; and a second calculation submodule, used to calculate the ratio of the maximum time period power consumption to the average time period power consumption, and obtain the load peak-to-average ratio of the power consumption strategy to be determined.

[0108] The device for determining the power consumption strategy includes a processor and a memory. The aforementioned acquisition unit 701, first determination unit 702, second determination unit 703, third determination unit 704, and fourth determination unit 705 are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0109] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can reduce the pressure on the power grid for peak shaving.

[0110] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0111] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements a method for determining an electricity consumption strategy.

[0112] This invention provides a processor for running a program, wherein the program executes a method for determining power consumption strategies during runtime.

[0113] Figure 8 This is a schematic diagram of an electronic device provided according to an embodiment of this application. For example... Figure 8 As shown, the electronic device 801 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring all users managed by the power grid company; for each user, determining the user's first type of equipment, where the first type of equipment is the electrical equipment whose operating time is adjustable; determining the user's objective function based on the operating time of each user's first type of equipment, where the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the operating time of the user's first type of equipment; acquiring the objective function of the target user to obtain the first objective function; acquiring the objective functions of other users to obtain the second objective function; and determining the target correlation relationship through the first and second objective functions. Other users refer to all users excluding the target user. The target correlation is the relationship between the target user's electricity cost and the electricity costs of other users. The total electricity consumption of all users within a preset period is determined. The target user's payoff function is determined based on the total electricity consumption and the target correlation. This payoff function represents the relationship between the target user's cost savings and their electricity consumption strategies under the influence of other users' strategies. Each user is treated as a game participant, and their electricity consumption strategy is used as their strategy choice space. Each user's payoff function is input into the game model to obtain a Nash equilibrium solution. This Nash equilibrium solution is determined as the target electricity consumption strategy for all users, representing each user's strategy to maximize cost savings. The devices mentioned in this paper can be servers, PCs, tablets, mobile phones, etc.

[0114] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: obtaining all users managed by the power grid enterprise; for each user, determining the user's first type of equipment, wherein the first type of equipment is electrical equipment that can be adjusted during the operating period; determining the user's objective function based on the operating period of each user's first type of equipment, wherein the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the user's first type of equipment's operating period; obtaining the objective function of the target user to obtain a first objective function; obtaining the objective functions of other users to obtain a second objective function; determining the target correlation relationship through the first objective function and the second objective function, wherein the other users... The user is any user other than the target user. The target correlation is the relationship between the target user's electricity cost and the electricity costs of other users. The total electricity consumption of all users within a preset period is determined. The target user's payoff function is determined by the total electricity consumption and the target correlation. The payoff function is the relationship between the target user's cost savings and their electricity consumption strategies under the influence of other users' electricity consumption strategies. Each user is treated as a game participant, and each user's electricity consumption strategy is used as their strategy choice space. The payoff function of each user is input into the game model to obtain the Nash equilibrium solution. The Nash equilibrium solution is determined as the target electricity consumption strategy for all users. The Nash equilibrium solution is used to characterize each user's electricity consumption strategy that maximizes cost savings.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0120] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0121] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0122] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining an electricity consumption strategy, characterized in that, include: Obtain all users managed by the power grid company, and for each user, determine the first type of equipment of the user, wherein the first type of equipment is electrical equipment that can be adjusted during the operating period; The objective function of a user is determined based on the runtime of the first type of device for each user, wherein the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the runtime of the user's first type of device; Obtain the objective function of the target user to obtain the first objective function, obtain the objective functions of other users to obtain the second objective function, and determine the target correlation relationship through the first objective function and the second objective function. The other users are users other than the target user among all users, and the target correlation relationship is the correlation between the electricity cost of the target user and the electricity cost of the other users. Determine the total power consumption of all users within a preset period, and determine the benefit function of the target user through the total power consumption and the target correlation, wherein the benefit function is the relationship between the cost savings of the target user and the power consumption strategy under the influence of the power consumption strategies of other users; Each user is taken as a game participant, and each user's electricity consumption strategy is taken as the strategy selection space of the user. The payoff function of each user is input into the game model to obtain the Nash equilibrium solution. The Nash equilibrium solution is determined as the target electricity consumption strategy of all users. The Nash equilibrium solution is used to characterize the electricity consumption strategy of each user to maximize cost savings. The determination of the target correlation through the first objective function and the second objective function includes: determining the first power consumption of the target user within the preset period, and determining the second power consumption of the other users within the preset period; calculating the ratio of the first objective function to the second objective function to obtain a first ratio, and calculating the ratio of the first power consumption to the second power consumption to obtain a second ratio; inputting the first ratio and the second ratio into a preset linear relationship expression to obtain the target correlation.

2. The method according to claim 1, characterized in that, Determining the user's objective function based on the runtime segment of each user's first type of device includes: The preset period includes multiple time periods. For each user, the user's second type of device is determined, wherein the second type of device is an electrical device with a fixed operating period. Determine the first operating segment and first operating power of the user's first type of device, and determine the second operating segment and second operating power of the second type of device; The first operating period, the first operating power, the second operating period, and the second operating power are input into the power generation cost function to obtain the power generation cost of the user's total power consumption. The power generation cost function is a relationship between the power consumption period, the power consumption, and the power generation cost. Determine the ratio of the total electricity cost to the total power generation cost for all users, calculate the product of the power generation cost and the ratio, and obtain the objective function.

3. The method according to claim 1, characterized in that, Determining the revenue function for the target user based on the total power consumption and the target correlation includes: Determine the ratio of the total electricity cost to the total power generation cost for all users, calculate the ratio of the first target power consumption of the target user to the total power consumption within the preset period, and calculate the product of the ratio and the ratio to obtain the revenue coefficient; Determine the second target power consumption of all users other than the target user, wherein the second target power consumption is the cumulative value of the power consumption of all other users within the preset period; Based on the second target power consumption, the target power consumption cost for all other users is determined, and the product of the target power consumption cost and the revenue coefficient is calculated to obtain the revenue function for the target user.

4. The method according to claim 1, characterized in that, The constraints of the objective function are determined in the following manner: Determine the power constraint of the objective function, wherein the power constraint is used to characterize that the power consumption of the first type of equipment is the same under different power consumption strategies; Determine the time period constraint of the objective function, wherein the time period constraint is used to characterize that the first type of equipment has the same operating time under different power consumption strategies; Determine the device operating power constraint of the objective function, wherein the device operating power constraint is used to characterize that the operating power of the first type of device belongs to the target power range.

5. The method according to claim 1, characterized in that, Determining the Nash equilibrium solution as the target electricity consumption strategy for all users includes: If there is only one Nash equilibrium solution, the Nash equilibrium solution is determined as the target power consumption strategy. When there are multiple Nash equilibrium solutions, determine the undetermined power consumption strategy corresponding to each Nash equilibrium solution; Calculate the peak-to-average load ratio for each pending power consumption strategy, and determine the pending power consumption strategy corresponding to the minimum peak-to-average load ratio as the target power consumption strategy, wherein the peak-to-average load ratio is used to characterize the peak-shaving pressure of the power grid enterprise.

6. The method according to claim 5, characterized in that, The calculation of the peak-to-average load ratio for each pending electricity consumption strategy includes: Determine the target operating period of the first type of equipment for each user in the pending power consumption strategy, determine the power consumption of all users in each time period within the preset period, and obtain the power consumption of each time period; The maximum power consumption during all time periods is determined from the power consumption during all time periods, and the average power consumption during all time periods is obtained by calculating the ratio of the sum of the power consumption during all time periods to the number of time periods within the preset period. Calculate the ratio of the maximum power consumption during the period to the average power consumption during the period to obtain the peak-to-average load ratio of the undetermined power consumption strategy.

7. A device for determining an electricity consumption strategy, characterized in that, include: The acquisition unit is used to acquire all users managed by the power grid enterprise, and for each user, to determine the first type of equipment of the user, wherein the first type of equipment is electrical equipment that can be adjusted during the operating period; The first determining unit is configured to determine the user's objective function based on the runtime of each user's first type of device, wherein the objective function is the relationship between the user's electricity cost and the user's electricity consumption strategy, and the electricity consumption strategy includes the runtime of the user's first type of device; The second determining unit is used to obtain the objective function of the target user to obtain a first objective function, obtain the objective functions of other users to obtain a second objective function, and determine the target correlation relationship through the first objective function and the second objective function, wherein the other users are users other than the target user among all users, and the target correlation relationship is the correlation relationship between the electricity cost of the target user and the electricity cost of the other users; The third determining unit is used to determine the total power consumption of all users within a preset period, and to determine the benefit function of the target user through the total power consumption and the target correlation, wherein the benefit function is the relationship between the cost saving of the target user and the power consumption strategy under the influence of the power consumption strategies of other users; The fourth determining unit is used to take each user as a game participant, take each user's electricity consumption strategy as the user's strategy selection space, input each user's payoff function into the game model to obtain the Nash equilibrium solution, and determine the Nash equilibrium solution as the target electricity consumption strategy of all users, wherein the Nash equilibrium solution is used to characterize each user's electricity consumption strategy to maximize cost savings. The second determining unit includes: a fourth determining module, used to determine the first power consumption of the target user within the preset period, and to determine the second power consumption of the other users within the preset period; a first calculating module, used to calculate the ratio of the first objective function to the second objective function to obtain a first ratio, and to calculate the ratio of the first power consumption to the second power consumption to obtain a second ratio; and a second input module, used to input the first ratio and the second ratio into a preset linear relationship expression to obtain the target correlation relationship.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the power consumption strategy as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the electricity consumption strategy according to any one of claims 1 to 6.

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