Power distribution network resource scheduling method and device and nonvolatile storage medium

Abstract

The invention discloses a power distribution network resource scheduling method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring equipment output data and load demand data of a county power distribution network; on the basis of a master-slave game theory, an optimization model is constructed, and objectives of the optimization model include county power distribution network income maximization, energy storage service provider income maximization and power consumption cost minimization; setting the electricity price of the county power distribution network according to the equipment output data and the load demand data; collecting user power consumption behavior feedback; based on user power consumption behavior feedback, solving the optimization model by adopting an iterative algorithm to obtain an optimization solving result; and scheduling resources in the county power distribution network according to an optimization solution result. According to the invention, the technical problem that the relation between cost and resource scheduling is difficult to balance due to the fact that resource shortage compensation is carried out on a power distribution network through generator sets capable of being rapidly started, but the generator sets are usually high in operation cost is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically, to a method, apparatus, and non-volatile storage medium for scheduling power distribution network resources. Background Technology

[0002] Currently, resource dispatching methods for county-level power distribution networks typically rely on traditional dispatching strategies, which often focus on optimizing a single objective, such as minimizing generation costs or maximizing system stability. However, with the widespread application of distributed energy resources (such as photovoltaic devices) and energy storage systems, the operating environment of power distribution networks has become more complex, and single-objective optimization methods are no longer sufficient to meet current needs. Currently, the dispatching of distributed energy resources such as photovoltaics and energy storage systems is often considered independently, lacking effective collaborative control mechanisms, resulting in low resource utilization and failure to maximize the economic benefits of the power distribution network.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and non-volatile storage medium for scheduling distribution network resources, at least to address the technical problem that current distribution networks compensate for resource shortages by using generator sets that can be started up quickly. However, these generator sets typically have high operating costs, making it difficult to balance the relationship between cost and resource scheduling.

[0005] According to one aspect of the present invention, a method for scheduling distribution network resources is provided, comprising: acquiring equipment output data and load demand data of a county-level distribution network, wherein the equipment output data includes output data of photovoltaic equipment and operating status of flexible generator sets; constructing an optimization model based on master-slave game theory, wherein the objectives of the optimization model include maximizing the revenue of the county-level distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs; setting the electricity price of the county-level distribution network according to the equipment output data and load demand data, wherein the electricity price includes real-time electricity price and peak electricity price; collecting user electricity consumption behavior feedback, wherein the user electricity consumption behavior feedback includes real-time electricity consumption and demand response capability of users; solving the optimization model using an iterative algorithm based on the user electricity consumption behavior feedback to obtain the optimization solution result; and scheduling resources in the county-level distribution network according to the optimization solution result.

[0006] Optionally, based on the master-slave game theory, an optimization model is constructed, including: determining the county-level distribution network as the leader and the energy storage service provider and users as followers based on the master-slave game theory, and constructing a master-slave game expression; setting objective functions and constraints for the county-level distribution network, the energy storage service provider, and the users respectively; and constructing an optimization model based on the master-slave game expression, the objective functions and constraints corresponding to the county-level distribution network, the energy storage service provider, and the users.

[0007] Optionally, the objective function for the county-level power distribution network is as follows:

[0008] ,

[0009] in, For the revenue of the county's power distribution network, and They are respectively t The electricity sales price and purchase price of the county-level power distribution network at any given time. and They are respectively t The power transmitted outward from the county-level power distribution network and the power input into the county-level power distribution network at any given time. T The number of hours in a day.

[0010] Optionally, the master-slave game expression is as follows:

[0011] ,

[0012] in, M As a leader, N For followers, and The first n Secondary leader and follower strategies Representing the leader in n In this game, strategy-based Strategies with Followers To maximize the corresponding objective function , Characterizing the strategies adopted by leaders Strategies with Followers It meets the respective constraints of the county-level power distribution network, energy storage service providers, and users. Characterizing the strategies adopted by leaders Strategies with Followers Satisfying the law of conservation of energy, For the leader based on the number of followers n-1 The result of the second game n Secondary strategy.

[0013] Optionally, electricity prices are set based on equipment output data and load demand data, including: inputting equipment output data and load demand data into a preset prediction model to obtain electricity supply and demand forecasts; setting real-time electricity prices based on electricity supply and demand forecasts; determining peak electricity consumption periods based on load demand data; determining peak electricity prices based on real-time electricity prices and peak electricity consumption periods; and setting electricity prices based on real-time electricity prices and peak electricity prices.

[0014] Optionally, the iterative algorithm includes gradient descent, Lagrange multiplier, and genetic algorithms.

[0015] Optionally, based on user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution result, including: determining the initial decision and initial Lagrange multiplier based on user electricity consumption behavior feedback, wherein the initial decision represents the setting of the electricity price; constructing the Lagrange function based on the optimization model and the initial Lagrange multiplier; solving for the extreme points of the Lagrange function using numerical methods to obtain the optimized decision; adjusting the electricity price based on the optimized decision and collecting user electricity consumption behavior feedback again; repeating the above steps until the Lagrange multiplier and the decision meet the preset conditions to obtain the optimization solution result.

[0016] According to another aspect of the present invention, a scheduling device for distribution network resources is also provided, comprising: an acquisition module for acquiring equipment output data and load demand data of a county-level distribution network, wherein the equipment output data includes output data of photovoltaic equipment and operating status of flexible generator sets; a construction module for constructing an optimization model based on master-slave game theory, wherein the objectives of the optimization model include maximizing the revenue of the county-level distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs; a setting module for setting the electricity price of the county-level distribution network according to the equipment output data and load demand data, wherein the electricity price includes real-time electricity price and peak electricity price; a collection module for collecting user electricity consumption behavior feedback, wherein the user electricity consumption behavior feedback includes real-time electricity consumption and demand response capability; a solution module for solving the optimization model using an iterative algorithm based on the user electricity consumption behavior feedback to obtain the optimization solution result; and a scheduling module for scheduling resources in the county-level distribution network according to the optimization solution result.

[0017] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located controls the execution of any of the above-described methods for scheduling power distribution network resources.

[0018] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program, when running, executes any of the above-described methods for scheduling power distribution network resources.

[0019] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for scheduling power distribution network resources.

[0020] In this embodiment of the invention, a method for scheduling distribution network resources is adopted. This involves acquiring equipment output data and load demand data of the county-level distribution network, where the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets. Based on master-slave game theory, an optimization model is constructed, with the objectives of maximizing the revenue of the county-level distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs. Electricity prices for the county-level distribution network are set based on the equipment output data and load demand data, including real-time electricity prices and peak electricity prices. User electricity consumption behavior feedback is collected, including real-time electricity consumption and demand response capabilities. Based on the user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model, obtaining the optimization solution. Based on the optimization solution, resources in the county-level distribution network are scheduled, achieving the goal of comprehensively considering revenue and cost in resource scheduling. This improves the economic efficiency of the electricity market and the allocation efficiency of electricity resources, thereby solving the technical problem that current distribution networks use rapidly starting generator sets to compensate for resource shortages, but these generator sets typically have high operating costs, making it difficult to balance the relationship between cost and resource scheduling. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for scheduling distribution network resources is shown.

[0023] Figure 2 This is a flowchart illustrating a method for scheduling power distribution network resources according to an embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a power distribution network resource scheduling device provided according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a 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.

[0027] According to an embodiment of the present invention, a method embodiment for scheduling distribution network resources is provided. 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. Furthermore, 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.

[0028] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method for scheduling distribution network resources is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0030] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power distribution network resource scheduling method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the power distribution network resource scheduling method of the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0032] Figure 2 This is a flowchart illustrating a method for scheduling distribution network resources according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0033] Step S202: Obtain the equipment output data and load demand data of the county power distribution network. The equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets.

[0034] In this step, when optimizing energy management in a county-level power distribution network, real-time output data and load demand data from various devices within the grid can be collected and integrated first. This data provides the necessary input for the optimization model, enabling the algorithm to formulate the most effective dispatch strategy based on current grid conditions and future forecasts. Photovoltaic (PV) output data typically comes from PV arrays installed at various locations. This data can be collected through PV inverters, monitoring devices, or integrated solar management systems. It is important to record the real-time power generation of each PV device, taking into account the effects of weather, sunlight intensity, and panel tilt angle. Flexible generator sets include combined heat and power (CHP) units, gas turbines, microturbines, and diesel generators. Operating status data typically includes power output, fuel consumption, equipment health status, available capacity, and response speed. This information is provided by the generator set monitoring system and may require real-time aggregation via remote data acquisition systems (such as SCADA). Load demand data can be obtained from the grid's load management center or smart meter network. Load data should include the demand of all users at a given point in time, distinguishing between residential, commercial, and industrial loads, and taking into account seasonality, diurnal variations, and other predictable load patterns.

[0035] Step S204: Based on the master-slave game theory, construct an optimization model. The objectives of the optimization model include maximizing the revenue of the county distribution network, maximizing the revenue of the energy storage service provider, and minimizing the electricity cost.

[0036] In this step, the optimization model based on master-follower game theory structures the problem as a game between a leader and one or more followers. The leader acts first, setting a strategy, and the followers then respond optimally based on the leader's strategy. In the context of a county-level distribution network, the leader might be a distribution network operator aiming to maximize revenue; the followers might be energy storage service providers and user groups, aiming to maximize revenue and minimize electricity costs, respectively. The county-level distribution network's objective is typically revenue maximization, achieved through effective management of power sources and loads, and participation in electricity market transactions, maximizing the economic benefits of grid operation. This may involve minimizing electricity purchase costs, maximizing electricity sales revenue, and reducing operational losses. The followers' objectives may include:

[0037] Maximizing revenue for energy storage service providers: Increase revenue by optimizing the charging and discharging strategies of energy storage devices, utilizing peak-valley electricity price differences, and providing value-added services such as frequency regulation and backup.

[0038] Minimize electricity costs for user groups: Reduce electricity expenses by adjusting load curves (peak shaving and valley filling), participating in demand response programs, or using their own distributed energy sources (such as rooftop solar power or small-scale wind power).

[0039] Through the steps and model structure described above, the optimization model based on master-slave game theory can comprehensively consider the interests of the county-level power distribution network, energy storage service providers, and user groups, seeking a fair and economical energy allocation and dispatch strategy. This achieves multi-objective optimization, including maximizing power distribution network revenue, maximizing energy storage service provider revenue, and minimizing user electricity costs. This approach not only improves the economic efficiency of the power system but also enhances system flexibility and user satisfaction.

[0040] Step S206: Based on the equipment output data and load demand data, set the electricity price for the county distribution network, whereby the electricity price includes real-time electricity price and peak electricity price.

[0041] In this step, electricity prices are dynamically adjusted within the county-level power distribution network based on equipment output data (especially production data from renewable energy sources such as photovoltaics) and load demand data. The setting of real-time and peak-hour prices requires comprehensive consideration of power supply and demand balance, energy storage status, user demand response, and electricity market rules. Historical data and current weather conditions can be used to predict the output of renewable energy sources such as photovoltaics over a future period. Future load demand is predicted based on historical load data, seasonal trends, weather forecasts, and user behavior models. The prediction results will guide the setting of peak-hour prices to encourage users to reduce electricity consumption during peak load periods.

[0042] Real-time electricity pricing strategies are typically adjusted based on real-time changes in electricity supply and demand. Their primary objective is to balance grid supply and demand and promote the integration of renewable energy. Real-time electricity pricing can be set based on the following principles:

[0043] Supply and demand matching: If the predicted load demand exceeds the output of power generation equipment, the real-time electricity price should increase to encourage users to reduce electricity consumption and stimulate more power generation resources to be connected to the grid.

[0044] Energy storage status: Considering the charging and discharging status of energy storage devices, when the energy storage is close to full charge or close to empty discharge, adjust the real-time electricity price to optimize the utilization of energy storage.

[0045] Renewable energy output: Adjust real-time electricity prices based on the projected output of renewable energy sources to encourage increased electricity consumption during peak renewable energy output periods, thereby maximizing the utilization efficiency of renewable energy.

[0046] Market rules: Adhere to the pricing mechanism of the electricity market, ensure that real-time electricity prices are within a reasonable range, and avoid negative impacts on market stability.

[0047] Peak electricity pricing is set during specific periods when electricity demand peaks. Its main objective is to reduce load demand and prevent grid overload through pricing mechanisms. Peak electricity pricing can be based on the following factors:

[0048] Predicted peak load: Predict the peak periods of load demand in advance. Electricity prices during these periods can be set as peak prices to encourage users to use electricity during off-peak hours through economic means.

[0049] Power system stability: Considering the carrying capacity and stability of the power grid, if it is predicted that the power system may be in a high-risk state at certain times, peak electricity prices can be announced in advance to alleviate system pressure.

[0050] Energy storage availability: When the available capacity of energy storage devices is low, higher peak electricity prices can be set to stimulate charging of energy storage devices, ensuring that they can discharge during peak demand periods and support grid stability.

[0051] Predictive analytics, machine learning, and optimization algorithms can be used to dynamically adjust electricity prices. For example, time series analysis can be used to predict future electricity supply and demand, and reinforcement learning algorithms can be used to adjust pricing strategies to maximize the economic benefits of the distribution network and user satisfaction, while ensuring the safe and stable operation of the grid.

[0052] Setting real-time and peak electricity prices for county-level power distribution networks is a dynamic optimization process that comprehensively considers electricity supply and demand, energy storage status, user demand response, and electricity market rules. By setting reasonable electricity prices, it is possible to promote the balance between supply and demand in the power grid, maximize the absorption of renewable energy, reduce users' electricity costs, and improve the economic efficiency and user satisfaction of the entire power system.

[0053] Step S208: Collect user electricity consumption behavior feedback, which includes real-time electricity consumption and demand response capability.

[0054] In this step, the feedback data on users' real-time electricity consumption and demand responsiveness not only helps grid operators predict load demand more accurately, but also optimizes electricity pricing strategies, increases user participation, and thus achieves more efficient energy allocation and management. Real-time electricity consumption data can be collected based on the smart meters connected to each user. Smart meters can measure and report users' instantaneous electricity consumption at a high frequency.

[0055] Demand responsiveness can be assessed by conducting short-term demand response tests, such as requiring users to reduce or shift their electricity consumption during specific periods. This involves observing whether users can adjust their electricity consumption as required, and to what extent. Based on the tests and user reports, predictive models of user demand response are developed. These models may include users' elastic price demand curves, response times, and statistical distributions of capacity.

[0056] The collected user feedback will be used to adjust electricity pricing strategies and demand response plans to better meet user needs, while maintaining grid stability and efficiency, achieving more refined demand-side management, promoting efficient grid operation, increasing user participation and satisfaction, and jointly promoting the development of sustainable energy.

[0057] Step S210: Based on user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution.

[0058] In this step, the optimization model is iteratively solved based on user electricity consumption behavior feedback. This process typically involves integrating user feedback into the model and then repeatedly optimizing it using an iterative algorithm until a solution that satisfies all objectives and constraints is found. First, initial parameters for the optimization model can be set, including equipment output data, load demand forecasts, energy storage status, and electricity pricing strategies. Simultaneously, the optimization objective function (e.g., maximizing distribution network revenue, maximizing energy storage service provider revenue, and minimizing user electricity costs) and constraints are defined. User electricity consumption behavior feedback (e.g., real-time electricity consumption, demand response capability) is integrated into the model to correct the forecasting model, more accurately reflecting users' actual responses and the real-time state of the power grid. This step includes updating the parameters of the demand forecasting model and adjusting the expected effects of the demand response plan.

[0059] Based on the complexity of the problem and the characteristics of the optimization objective, an appropriate iterative algorithm can be selected. Common algorithms include gradient descent, Newton's method, genetic algorithms, particle swarm optimization (PSO) and its improved versions. The upper limit of the number of iterations and convergence conditions of the algorithm are determined, such as the rate of change of the objective function being less than a preset threshold. In each iteration, the algorithm adjusts equipment scheduling, load management strategies, and electricity pricing strategies based on the current solution and user feedback to get closer to the optimization objective. If an improved PSO algorithm is used, this will include updating the position and velocity of particles and adaptively adjusting the inertia weights. After each iteration, the solution is evaluated to see if it satisfies all constraints, the value of the objective function is calculated, and it is checked whether the optimization objective has been reached or is close to being achieved. The optimization results are communicated to users, their feedback on the new electricity pricing strategy and power services is collected, and user satisfaction and participation are assessed. Based on user feedback and the verification results of the solution, model parameters are updated, such as the coefficients of the demand forecasting model and equipment output data. Using the updated model parameters, iterative optimization is performed again until the result meets the convergence condition or reaches the preset upper limit of the number of iterations.

[0060] Through this series of steps, the optimization model based on user electricity consumption behavior feedback can dynamically adjust strategies to adapt to changes in the electricity market and user demand, thereby achieving efficient grid operation and improved user satisfaction.

[0061] Step S212: Based on the optimization solution results, schedule the resources in the county power distribution network.

[0062] In this step, resources in the county's power distribution network are scheduled based on the optimization results. These results typically include detailed guidance on how to optimally allocate and utilize renewable energy, energy storage systems, and traditional energy resources, as well as how to set electricity prices to balance supply and demand and encourage user participation in demand response. Power generation plans for renewable energy sources such as distributed photovoltaic systems can be adjusted based on the optimization results. If favorable solar conditions are predicted and load demand is low, the charging of energy storage systems can be adjusted to discharge during peak load periods, balancing grid supply and demand. Energy storage system charging can be adjusted when electricity prices are low or renewable energy output is excessive; conversely, when electricity prices are high or renewable energy output is insufficient, the discharge of energy storage systems can be optimized to achieve spatiotemporal energy transfer and improve economic efficiency.

[0063] Based on the optimization results, real-time electricity pricing and peak-hour pricing strategies can be used to incentivize users to adjust their electricity consumption behavior during specific periods. For example, high-energy-consuming tasks can be scheduled during off-peak hours, or smart home systems can be used to automatically adjust the operating times of appliances such as air conditioners and water heaters. For loads that can be flexibly adjusted, such as electric vehicle charging stations, charging times can be intelligently scheduled based on the demand of the power distribution network and the energy storage status to avoid grid overload or reduce operating costs.

[0064] When renewable energy output is insufficient or load demand surges, the reserve capacity of traditional power generation resources such as combined heat and power units, gas turbines, and diesel generators is utilized based on optimization results to meet grid demand. The strategy for purchasing electricity from the upstream grid or external sources is adjusted according to optimization results, choosing to purchase electricity when costs are low or purchasing it in advance when load demand is predicted to exceed local capacity, thus avoiding the high costs of emergency power purchases.

[0065] Real-time electricity prices are dynamically adjusted based on optimization results to reflect the real-time status of electricity supply and demand, encouraging users to adjust their electricity consumption behavior according to price changes. Peak electricity prices are set in advance for periods when load demand is predicted to exceed the grid's stable operating range, using economic means to suppress load demand and prevent grid overload.

[0066] Through the above steps, the county-level power distribution network can intelligently schedule resources within the grid based on the optimization results, maximizing economic benefits while ensuring grid security and stability, improving user satisfaction, and promoting the efficient utilization of renewable energy. The implementation of this scheduling strategy requires not only advanced predictive analysis and optimization algorithms, but also a reliable data communication network and a flexible electricity pricing mechanism to ensure the real-time nature and effectiveness of grid scheduling decisions.

[0067] Through the above steps, the goal of resource scheduling can be achieved by comprehensively considering revenue and cost, thereby improving the economic efficiency of the power market and the efficiency of power resource allocation. This solves the technical problem that the current distribution network uses generators that can be started up quickly to make up for resource shortages, but these generators usually have high operating costs, making it difficult to balance the relationship between cost and resource scheduling.

[0068] As an optional implementation, an optimization model is constructed based on the master-slave game theory, including: determining the county-level distribution network as the leader and the energy storage service provider and users as followers based on the master-slave game theory, and constructing a master-slave game expression; setting objective functions and constraints for the county-level distribution network, the energy storage service provider, and the users respectively; and constructing an optimization model based on the master-slave game expression, the objective functions and constraints corresponding to the county-level distribution network, the energy storage service provider, and the users.

[0069] Optionally, in a master-slave game framework, the county-level distribution network is considered the leader because it has the dominant power to set electricity prices and dispatch strategies, while energy storage service providers and users act as followers, adjusting their behavior according to the distribution network's decisions to achieve an optimal state. The core of the master-slave game lies in the leader (distribution network) acting first, followed by the followers (energy storage service providers and users) making optimal responses. The game expression reflects this dynamic relationship:

[0070] Phase 1 (Leadership Decision-Making): The distribution network sets electricity prices and dispatch strategies in an attempt to maximize its own revenue or minimize its operating costs.

[0071] The second stage (follower response): Energy storage service providers and users adjust their charging and discharging behaviors and electricity consumption habits based on the decisions of the distribution network in order to maximize revenue or minimize costs.

[0072] Objective functions and constraints can be set for leaders and followers. For the county-level distribution network, the objective function can be maximizing total revenue or minimizing total operating costs. Constraints include power supply and demand balance, grid capacity limitations, technical limitations of generator sets and energy storage devices, and electricity market price restrictions. For energy storage service providers among the followers, the objective function can be maximizing their revenue, which can be achieved by charging when electricity prices are low and discharging when prices are high. Constraints include upper and lower limits on the charging and discharging power of energy storage devices, state of charge (SOC) limits, charging and discharging efficiency, and charge / discharge cycle limits. For users among the followers, who can be user aggregators, the objective function is to minimize user electricity costs while maximizing user comfort (e.g., maintaining indoor temperature within a comfortable range). Constraints include meeting basic electricity needs, flexibility limitations of adjustable loads, and user requirements for electricity service quality.

[0073] Based on the above settings, the optimization model can be formalized as a series of nested optimization problems, where the solution to the leader problem depends on the solution to the follower problem. Leader Problem (Outer Layer Optimization): Given technical, economic, and policy constraints, the distribution network determines the optimal electricity price and dispatch strategy to maximize its own revenue or minimize its costs. Follower Problem (Inner Layer Optimization): Energy storage service providers and users independently optimize their own behavior based on the distribution network's decisions.

[0074] Through the above steps, the optimization model based on master-slave game theory can effectively integrate the goals and constraints of the county-level power distribution network, energy storage service providers, and users, promoting the efficient allocation of power resources while taking into account the economic interests and electricity demand of all parties. The application of this model and solution strategy helps to build a more intelligent, flexible, and sustainable county-level power ecosystem.

[0075] This optional embodiment employs a leader-follower game model from game theory, capable of handling complex multi-party decision-making problems. By defining the county-level distribution network as the leader and energy storage service providers and users as followers, it simulates the behavioral patterns and strategy choices of different stakeholders in the electricity market, ensuring the model's rationality and effectiveness. Through model optimization, an equilibrium point acceptable to all parties is found, improving the overall efficiency of the system. In other optional embodiments, the model can be extended to consider more participants, such as electric vehicle charging stations, or more complex game strategies, such as hybrid strategies, can be introduced to cope with more complex and volatile market environments.

[0076] As an optional implementation, the objective function for a county-level power distribution network is as follows:

[0077] ,

[0078] in, For the revenue of the county's power distribution network, and They are respectively t The electricity sales price and purchase price of the county-level power distribution network at any given time. and They are respectively t The power transmitted outward from the county-level power distribution network and the power input into the county-level power distribution network at any given time. T The number of hours in a day.

[0079] As an optional implementation, the master-slave game expression is as follows:

[0080] ,

[0081] in, M As a leader, and The firstn Secondary leader and follower strategies Representing the leader in n In this game, strategy-based Strategies with Followers To maximize the corresponding objective function , Characterizing the strategies adopted by leaders Strategies with Followers It meets the respective constraints of the county-level power distribution network, energy storage service providers, and users. Characterizing the strategies adopted by leaders Strategies with Followers Satisfying the law of conservation of energy, For the leader based on the number of followers n-1 The result of the second game n Secondary strategy.

[0082] Optionally, the master-slave game expression is used to describe the decision-making process between leaders and followers in a dynamic environment. In a power system scenario where the county distribution network acts as the leader and energy storage service providers and users act as followers, the master-slave game expression can accurately capture how the leader sets strategies to maximize its own interests, while also considering the optimal responses of the followers. In the above expression... These represent the inequality constraints that leaders must satisfy to ensure that decisions are physically and technically feasible, such as grid capacity limitations and power supply and demand balance. This represents an equality constraint that ensures the system adheres to the principle of energy conservation, meaning that the total input and total output of the distribution network are equal.

[0083] Stackelberg equilibrium is a game in which the leader adopts an optimal strategy that also determines the optimal response of the followers. In other words, the leader's strategy maximizes its payoff given that the strategies of all other players (including other leaders and all followers) are fixed. The follower's strategy, given the leader's strategy, maximizes the follower's payoff or minimizes their costs. In the electricity market, reaching Stackelberg equilibrium means that a county-level distribution network, by setting optimal electricity prices and dispatch strategies, and with energy storage service providers and users adjusting their behavior accordingly (such as energy storage charging and discharging plans and user electricity consumption patterns), ultimately achieves a stable state that is both economically and technically feasible.

[0084] Solving master-follower games typically employs multi-level optimization methods. The leader first formulates an initial strategy, then iteratively adjusts it based on the followers' reactions until a Stackelberg equilibrium solution satisfying all constraints is found. In practical applications, this may involve complex mathematical optimization algorithms, such as dynamic programming, sequential quadratic programming, and the alternating direction multiplier method (ADMM), as well as real-time data analysis and prediction to ensure the accuracy and timeliness of decision-making.

[0085] By using master-slave game theory expressions, multi-objective optimization problems in power grid operation can be effectively addressed, promoting the rational allocation and utilization of power resources. At the same time, considering the needs and constraints of all participants, this approach can drive the efficient operation of the power market.

[0086] This embodiment employs a master-follower game theory expression, which can accurately describe the strategic interactions during the distribution network resource scheduling process. Through iterative optimization of the leader and follower strategies, a dynamic balance in resource scheduling is achieved, ensuring the stability and fairness of system operation. This effectively coordinates the interests of county-level distribution networks, energy storage service providers, and users, promoting the healthy development of the electricity market. In other optional embodiments, the concept of cooperative game theory can be introduced to explore a win-win strategy space for all parties, further enhancing the overall efficiency of the system.

[0087] As an optional embodiment, setting an electricity price based on equipment output data and load demand data includes: inputting equipment output data and load demand data into a preset prediction model to obtain the predicted power supply and demand situation; setting a real-time electricity price based on the predicted power supply and demand situation; determining peak electricity consumption periods based on load demand data; determining peak electricity prices based on real-time electricity prices and peak electricity consumption periods; and setting an electricity price based on real-time electricity prices and peak electricity prices.

[0088] Optionally, equipment output data (including the expected power contribution from distributed power sources, large generators, energy storage systems, etc.) and load demand data (users' expected electricity consumption at different time periods) are input into a pre-trained prediction model. These prediction models can be time-series prediction models based on historical data, such as ARIMA or Prophet, or more complex machine learning models, such as neural networks or support vector machines. The model's task is to predict future electricity supply and demand based on the input data, including electricity supply curves and electricity demand curves.

[0089] Once electricity supply and demand forecasts are available, real-time electricity prices can be set. Real-time prices are dynamically adjusted based on the relationship between electricity supply and demand to reflect the instantaneous supply and demand status of the electricity market. When supply exceeds demand, real-time prices may decrease to stimulate electricity consumption; conversely, if demand exceeds supply, real-time prices will increase to curb unnecessary electricity demand. The goal of setting real-time prices is to maintain a relatively balanced state between electricity supply and demand.

[0090] Based on load demand data, peak electricity demand periods are analyzed and predicted throughout the day. These periods often occur during the morning and evening rush hours on weekdays, or during periods of high electricity demand in specific seasons. Identifying peak electricity demand periods is crucial for designing effective peak pricing strategies, helping grid operators prepare in advance to ensure sufficient power supply during peak hours.

[0091] After determining the peak electricity consumption periods, a peak electricity price is set based on real-time electricity prices and electricity supply and demand forecasts. Peak electricity prices are typically much higher than real-time prices during normal periods. Their purpose is to quickly adjust the supply and demand balance and prevent grid overload in the event of a short-term surge in demand. Setting peak electricity prices can incentivize users to consume electricity during off-peak hours or participate in demand-side response programs, such as peak shaving and valley filling.

[0092] The final step is to integrate real-time pricing and peak pricing to form a complete pricing strategy. This includes establishing a standardized real-time pricing system and defining the trigger mechanism and price level for peak pricing. The pricing strategy should fully consider users' affordability, market rules, and the stable operation of the power grid. Furthermore, the effectiveness of the pricing strategy needs to be evaluated regularly, and adjustments made based on equipment output data, load demand data, and market feedback to ensure the strategy remains effective.

[0093] This optional embodiment employs a predictive model and dynamic pricing strategy, enabling flexible adjustment of electricity prices based on real-time changes in power supply and demand. By analyzing data on equipment output and load demand, future power supply and demand conditions are predicted, and electricity prices are set accordingly. This aligns with the economic principles of the electricity market, alleviates grid pressure, and improves the economic efficiency of power companies. In other optional embodiments, smart contracts can be introduced to automatically execute price adjustment rules, or big data analysis can be used to improve prediction accuracy and further optimize resource allocation.

[0094] As an alternative embodiment, iterative algorithms include gradient descent, Lagrange multiplier, and genetic algorithms.

[0095] These algorithms are all effective tools for solving optimization problems. By iteratively optimizing, they gradually approach the optimal solution, which aligns with the basic logic of optimization algorithms. The technique in this embodiment can efficiently solve complex optimization models and find the best strategy combination that satisfies the interests of all parties. In other optional embodiments, deep learning algorithms can be introduced to improve the intelligence level of optimization solving, or quantum computing technology can be used to accelerate the solution process and solve large-scale optimization problems.

[0096] As an optional embodiment, based on user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution result. This includes: determining an initial decision and an initial Lagrange multiplier based on user electricity consumption behavior feedback, wherein the initial decision represents the setting of the electricity price; constructing a Lagrange function based on the optimization model and the initial Lagrange multiplier; solving for the extreme points of the Lagrange function using numerical methods to obtain the optimized decision; adjusting the electricity price based on the optimized decision and collecting user electricity consumption behavior feedback again; repeating the above steps until the Lagrange multiplier and the decision meet preset conditions to obtain the optimization solution result.

[0097] Optionally, based on an understanding of the current electricity market conditions (such as equipment output and load forecasting), an initial electricity price is set, which forms the starting point of the optimization process. Lagrange multipliers are used to consider the constraints in the optimization problem; the initial values ​​can be chosen based on historical data or forecasts to reflect a preliminary assessment of the constraints. The Lagrange function is the combination of the objective function and all constraints, transforming the optimization problem into finding the extreme points of the Lagrange function. Numerical methods, such as gradient descent, Newton's method, or the conjugate gradient method, can be used to find the extreme points of the Lagrange function. This step aims to find the value of the decision variable (x) that minimizes (or maximizes, depending on the problem definition) the Lagrange function. During the solution process, the algorithm continuously adjusts the decision variables and Lagrange multipliers based on gradient information until the convergence condition is met. Based on the optimized decision variables, the electricity pricing strategy is adjusted, which may include adjustments to real-time and peak electricity prices. The new electricity pricing strategy is applied in practice, and user electricity consumption behavior data under the new pricing system is collected, which will serve as input for the next iteration.

[0098] Repeat the above steps, continuously adjusting the decision variables and Lagrange multipliers, until the preset convergence condition is met. The convergence condition may include the rate of change of the decision variables and Lagrange multipliers falling below a certain threshold, or the change in the objective function being sufficiently small. This process, through continuous learning and adjustment, gradually approaches the optimal solution.

[0099] Once the iterative algorithm converges, the resulting optimization solution will contain an optimal electricity pricing strategy. This strategy not only effectively reflects the supply and demand relationship in the electricity market but also promotes rational electricity consumption by users, reduces grid load fluctuations, and improves system stability. The optimization results should be analyzed to ensure their feasibility and economic viability in practical applications.

[0100] Through the aforementioned iterative optimization process based on user feedback, the county-level power distribution network can dynamically adjust electricity prices, achieving efficient allocation of power resources and meeting user needs while ensuring the stability and economy of power grid operation. The method employs an iterative optimization strategy based on user feedback, enabling dynamic price adjustments to adapt to changes in user electricity consumption behavior. By using the Lagrange multiplier algorithm to transform constraints into Lagrange functions and then solving for extreme points, it can respond promptly to market changes, optimize resource allocation, and improve system operating efficiency. In other optional embodiments, online learning mechanisms can be introduced to continuously optimize model parameters, or social network analysis can be used to understand user preferences and further improve service quality.

[0101] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that the power distribution network resource scheduling method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0103] According to embodiments of the present invention, a scheduling apparatus for distribution network resources for implementing the above-described scheduling method for distribution network resources is also provided. Figure 3 This is a structural block diagram of a power distribution network resource dispatching device provided according to an embodiment of the present invention, such as... Figure 3 As shown, the scheduling device for the distribution network resources includes: an acquisition module 302, a construction module 304, a setting module 306, a data acquisition module 308, a solution module 310, and a scheduling module 312. The scheduling device for the distribution network resources will be described below.

[0104] The acquisition module 302 is used to acquire equipment output data and load demand data of the county power distribution network. The equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets.

[0105] The construction module 304, connected to the acquisition module 302, is used to construct an optimization model based on the master-slave game theory. The objectives of the optimization model include maximizing the revenue of the county distribution network, maximizing the revenue of the energy storage service provider, and minimizing the electricity cost.

[0106] The setting module 306, connected to the construction module 304, is used to set the electricity price of the county distribution network based on the equipment output data and load demand data. The electricity price includes real-time electricity price and peak electricity price.

[0107] The data acquisition module 308, connected to the setting module 306, is used to collect user electricity consumption behavior feedback, which includes the user's real-time electricity consumption and demand response capability.

[0108] The solution module 310, connected to the acquisition module 308, is used to solve the optimization model based on user electricity consumption behavior feedback using an iterative algorithm to obtain the optimization solution result.

[0109] The scheduling module 312, connected to the solution module 310, is used to schedule resources in the county power distribution network based on the optimization solution results.

[0110] It should be noted that the acquisition module 302, construction module 304, setting module 306, acquisition module 308, solving module 310, and scheduling module 312 mentioned above correspond to steps S202 to S212 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0111] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0112] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power distribution network resource scheduling method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned power distribution network resource scheduling method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0113] The processor can access information and applications stored in memory via a transmission device to perform the following steps: acquiring equipment output data and load demand data for the county-level power distribution network, where the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets; constructing an optimization model based on master-slave game theory, where the objectives of the optimization model include maximizing the revenue of the county-level power distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs; setting the electricity price for the county-level power distribution network based on the equipment output data and load demand data, where the electricity price includes real-time electricity price and peak electricity price; collecting user electricity consumption behavior feedback, where the user electricity consumption behavior feedback includes real-time user electricity consumption and demand response capability; solving the optimization model using an iterative algorithm based on the user electricity consumption behavior feedback to obtain the optimization solution result; and scheduling resources in the county-level power distribution network based on the optimization solution result.

[0114] Optionally, the processor may also execute program code with the following steps: constructing an optimization model based on master-slave game theory, including: determining the county distribution network as the leader, energy storage service providers and users as followers based on master-slave game theory, and constructing a master-slave game expression; setting objective functions and constraints for the county distribution network, energy storage service providers and users respectively; and constructing an optimization model based on the master-slave game expression, the objective functions and constraints corresponding to the county distribution network, energy storage service providers and users respectively.

[0115] Optionally, the objective function for the county-level power distribution network in the above processor is as follows:

[0116] ,

[0117] in, For the revenue of the county's power distribution network, and They are respectively t The electricity sales price and purchase price of the county-level power distribution network at any given time. and They are respectively t The power transmitted outward from the county-level power distribution network and the power input into the county-level power distribution network at any given time. T The number of hours in a day.

[0118] Optionally, the master-slave game expression in the above processor is as follows:

[0119] ,

[0120] in, M As a leader, and The first n Secondary leader and follower strategies Representing the leader in n In this game, strategy-based Strategies with Followers To maximize the corresponding objective function , Characterizing the strategies adopted by leaders Strategies with Followers It meets the respective constraints of the county-level power distribution network, energy storage service providers, and users. Characterizing the strategies adopted by leaders Strategies with Followers Satisfying the law of conservation of energy, For the leader based on the number of followers n-1 The result of the second game n Secondary strategy.

[0121] Optionally, the processor may also execute program code for the following steps: setting electricity prices based on equipment output data and load demand data, including: inputting equipment output data and load demand data into a preset prediction model to obtain electricity supply and demand forecasts; setting real-time electricity prices based on electricity supply and demand forecasts; determining peak electricity consumption periods based on load demand data; determining peak electricity prices based on real-time electricity prices and peak electricity consumption periods; and setting electricity prices based on real-time electricity prices and peak electricity prices.

[0122] Optionally, the iterative algorithms in the processors described above include gradient descent, Lagrange multiplier, and genetic algorithms.

[0123] Optionally, the processor may also execute program code for the following steps: based on user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution result, including: based on user electricity consumption behavior feedback, determining the initial decision and the initial Lagrange multiplier, wherein the initial decision represents the setting of the electricity price; based on the optimization model and the initial Lagrange multiplier, constructing the Lagrange function; solving for the extreme points of the Lagrange function using numerical methods to obtain the optimized decision; based on the optimized decision, adjusting the electricity price and collecting user electricity consumption behavior feedback again; repeating the above steps until the Lagrange multiplier and the decision meet the preset conditions to obtain the optimization solution result.

[0124] This invention provides a method for scheduling distribution network resources. It involves acquiring equipment output data and load demand data for a county-level distribution network, including output data from photovoltaic equipment and the operating status of flexible generator sets. Based on master-slave game theory, an optimization model is constructed, with objectives including maximizing the revenue of the county-level distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs. Electricity prices for the county-level distribution network are set based on the equipment output data and load demand data, including real-time prices and peak prices. User electricity consumption behavior feedback is collected, including real-time electricity consumption and demand response capabilities. Based on this feedback, an iterative algorithm is used to solve the optimization model, yielding the optimization results. Resources in the county-level distribution network are then scheduled according to the optimization results. This method achieves the goal of comprehensively considering revenue and cost in resource scheduling, thereby improving the economic efficiency of the electricity market and the allocation efficiency of electricity resources. It also solves the technical problem that current distribution networks use rapidly starting generator sets to compensate for resource shortages, but these generator sets typically have high operating costs, making it difficult to balance the relationship between costs and resource scheduling.

[0125] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0126] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the power distribution network resource scheduling method provided in the above embodiments.

[0127] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring equipment output data and load demand data of the county-level power distribution network, wherein the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets; constructing an optimization model based on master-slave game theory, wherein the objectives of the optimization model include maximizing the revenue of the county-level power distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs; setting the electricity price of the county-level power distribution network according to the equipment output data and load demand data, wherein the electricity price includes real-time electricity price and peak electricity price; collecting user electricity consumption behavior feedback, wherein the user electricity consumption behavior feedback includes users' real-time electricity consumption and demand response capability; solving the optimization model using an iterative algorithm based on the user electricity consumption behavior feedback to obtain the optimization solution result; and scheduling resources in the county-level power distribution network according to the optimization solution result.

[0129] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing an optimization model based on master-slave game theory, including: determining the county distribution network as the leader, the energy storage service provider and the user as followers based on master-slave game theory, and constructing a master-slave game expression; setting objective functions and constraints for the county distribution network, the energy storage service provider and the user respectively; and constructing an optimization model based on the master-slave game expression, the objective functions and constraints corresponding to the county distribution network, the energy storage service provider and the user respectively.

[0130] Optionally, in this embodiment, the objective function corresponding to the county-level power distribution network in the non-volatile storage medium is as follows:

[0131] ,

[0132] in, For the revenue of the county's power distribution network, and They are respectively t The electricity sales price and purchase price of the county-level power distribution network at any given time. and They are respectively t The power transmitted outward from the county-level power distribution network and the power input into the county-level power distribution network at any given time. T The number of hours in a day.

[0133] Optionally, in this embodiment, the master-slave game expression in the non-volatile storage medium is as follows:

[0134] ,

[0135] in, M As a leader, and The first nSecondary leader and follower strategies Representing the leader in n In this game, strategy-based Strategies with Followers To maximize the corresponding objective function , Characterizing the strategies adopted by leaders Strategies with Followers It meets the respective constraints of the county-level power distribution network, energy storage service providers, and users. Characterizing the strategies adopted by leaders Strategies with Followers Satisfying the law of conservation of energy, For the leader based on the number of followers n-1 The result of the second game n Secondary strategy.

[0136] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: setting an electricity price based on equipment output data and load demand data, including: inputting equipment output data and load demand data into a preset prediction model to obtain a power supply and demand forecast; setting a real-time electricity price based on the power supply and demand forecast; determining peak electricity consumption periods based on load demand data; determining a peak electricity price based on the real-time electricity price and the peak electricity consumption periods; and setting the electricity price based on the real-time electricity price and the peak electricity price.

[0137] Optionally, in this embodiment, the iterative algorithm in the non-volatile storage medium includes gradient descent algorithm, Lagrange multiplier algorithm and genetic algorithm.

[0138] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: based on user electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution result, including: based on user electricity consumption behavior feedback, determining an initial decision and an initial Lagrange multiplier, wherein the initial decision represents the setting of the electricity price; constructing a Lagrange function based on the optimization model and the initial Lagrange multiplier; solving for the extreme points of the Lagrange function using numerical methods to obtain the optimized decision; adjusting the electricity price based on the optimized decision and collecting user electricity consumption behavior feedback again; repeating the above steps until the Lagrange multiplier and the decision meet preset conditions to obtain the optimization solution result.

[0139] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire equipment output data and load demand data of a county-level power distribution network, wherein the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets; construct an optimization model based on master-slave game theory, wherein the objectives of the optimization model include maximizing the revenue of the county-level power distribution network, maximizing the revenue of energy storage service providers, and minimizing electricity costs; set the electricity price of the county-level power distribution network according to the equipment output data and load demand data, wherein the electricity price includes real-time electricity price and peak electricity price; collect user electricity consumption behavior feedback, wherein the user electricity consumption behavior feedback includes users' real-time electricity consumption and demand response capability; solve the optimization model using an iterative algorithm based on the user electricity consumption behavior feedback to obtain the optimization solution result; and schedule resources in the county-level power distribution network according to the optimization solution result.

[0140] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0141] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0145] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0146] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for scheduling distribution network resources, characterized in that, include: Acquire equipment output data and load demand data of the county-level power distribution network, wherein the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets; Based on the master-slave game theory, an optimization model is constructed, wherein the objectives of the optimization model include maximizing the revenue of the county distribution network, maximizing the revenue of the energy storage service provider, and minimizing the electricity cost. Based on the equipment output data and load demand data, the electricity price of the county-level power distribution network is set, wherein the electricity price includes real-time electricity price and peak electricity price; Collect user electricity consumption behavior feedback, wherein the user electricity consumption behavior feedback includes real-time user electricity consumption and demand response capability; Based on the user's electricity consumption behavior feedback, an iterative algorithm is used to solve the optimization model to obtain the optimization solution result; Based on the optimization solution results, resources in the county-level power distribution network are scheduled.

2. The method according to claim 1, characterized in that, The optimization model constructed based on master-slave game theory includes: Based on the master-slave game theory, the county-level power distribution network is identified as the leader, the energy storage service provider and the user as the followers, and a master-slave game expression is constructed. Set objective functions and constraints for the county-level power distribution network, the energy storage service provider, and the user, respectively; The optimization model is constructed based on the master-slave game expression, the county power distribution network, the energy storage service provider, and the user's respective objective functions and constraints.

3. The method according to claim 2, characterized in that, The objective function corresponding to the county-level power distribution network is as follows: ， in, For the revenue of the aforementioned county-level power distribution network, and They are respectively t The electricity sales price and purchase price of the county-level power distribution network at the specified time. and They are respectively t The power transmitted outward from the county-level power distribution network and the power input into the county-level power distribution network at any given time. T The number of hours in a day.

4. The method according to claim 2, characterized in that, The master-slave game expression is as follows: ， in, M For the aforementioned leader, and The first n The strategies of the leaders and followers described below. The leader is represented by the strategy in the nth game. and the strategy of the followers To maximize the corresponding objective function , Characterizing the strategies adopted by the leader and the strategy of the followers The constraints corresponding to the county-level power distribution network, the energy storage service provider, and the user must be satisfied. Characterizing the strategies adopted by the leader and the strategy of the followers Satisfying the law of conservation of energy, For the leader according to the followers n-1 The result of the second game n Secondary strategy.

5. The method according to claim 1, characterized in that, The step of setting electricity prices based on the equipment output data and load demand data includes: The power output data of the equipment and the load demand data are input into a preset prediction model to obtain the power supply and demand forecast. Based on the predicted power supply and demand, the real-time electricity price is set. Based on the load demand data, determine the peak electricity consumption periods; The peak electricity price is determined based on the real-time electricity price and the peak electricity consumption period; The electricity price is set based on the real-time electricity price and the peak electricity price.

6. The method according to any one of claims 1 to 5, characterized in that, The iterative algorithms include gradient descent, Lagrange multiplier, and genetic algorithms.

7. The method according to claim 6, characterized in that, The optimization model is solved using an iterative algorithm based on the user's electricity consumption behavior feedback to obtain the optimization solution result, including: Based on the user's electricity consumption behavior feedback, an initial decision and an initial Lagrange multiplier are determined, wherein the initial decision represents the setting of the electricity price; Based on the optimization model and the initial Lagrange multipliers, construct the Lagrange function; The extreme points of the Lagrange function are solved by numerical methods to obtain the optimized decision. Based on the optimized decision, the electricity price is adjusted and the user's electricity consumption behavior feedback is collected again. Repeat the above steps until the Lagrange multipliers and decisions satisfy the preset conditions to obtain the optimized solution result.

8. A dispatching device for distribution network resources, characterized in that, include: The acquisition module is used to acquire equipment output data and load demand data of the county power distribution network, wherein the equipment output data includes the output data of photovoltaic equipment and the operating status of flexible generator sets; The module is used to build an optimization model based on the master-slave game theory. The objectives of the optimization model include maximizing the revenue of the county distribution network, maximizing the revenue of the energy storage service provider, and minimizing the electricity cost. The setting module is used to set the electricity price of the county distribution network based on the equipment output data and load demand data, wherein the electricity price includes real-time electricity price and peak electricity price; The data acquisition module is used to collect user electricity consumption behavior feedback, which includes real-time electricity consumption and demand response capability. The solution module is used to solve the optimization model using an iterative algorithm based on the user's electricity consumption behavior feedback, and obtain the optimization solution result; The scheduling module is used to schedule resources in the county-level power distribution network based on the optimization solution results.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the scheduling method for power distribution network resources according to any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the scheduling method for power distribution network resources according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the scheduling method for power distribution network resources according to any one of claims 1 to 7.

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