Power distribution system regional energy autonomous optimization method under new energy bearing capacity constraint

Abstract

The invention provides a power distribution system area energy autonomous optimization method under new energy bearing capacity constraint. The method comprises the following steps: acquiring basic data of a target power distribution system area; a Voronoi-energy flow entropy dynamic space partitioning method is adopted, autonomous units are divided, and energy flow entropy features are calculated; generating initial new energy installed capacity, energy storage capacity and bearing capacity threshold values according to the space boundary and energy flow entropy characteristics of each autonomous unit; a Stackelberg game model is adopted, a dynamic electricity price, a carbon price and a deviation reward and punishment mechanism are introduced, a distributed iteration method is combined, and an operation scheduling strategy of each autonomous unit is determined; and performing rolling optimization and scheduling adjustment according to the feedback data, and dynamically updating the space boundary, the bearing capacity threshold and the scheduling strategy of the autonomous unit. According to the method, the energy management capability of the power distribution system is effectively improved, the new energy resource configuration is optimized, the scheduling cost is reduced, and the flexibility and stability of the system are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of regional energy autonomy technology in power systems, specifically relating to an optimization method for regional energy autonomy in distribution systems under the constraint of new energy carrying capacity. Background Technology

[0002] With the increasing penetration of renewable energy sources, traditional power distribution systems face a series of challenges, particularly regarding the growing regional capacity to support renewable energy. The contradiction between the volatility and unpredictability of renewable energy and the existing capacity of the power distribution network exposes the limitations of traditional power grid architecture in adapting to high-penetration renewable energy, necessitating innovative solutions to break this vicious cycle of high-penetration access and low-capacity operation.

[0003] In traditional power distribution systems, power dispatch primarily relies on a central control center, using a single dispatch strategy to balance power supply and demand. While this centralized dispatch approach is feasible in certain scenarios, it often exhibits significant limitations when dealing with renewable energy integration, load fluctuations, and electricity market changes. In particular, the volatility and uncertainty of renewable energy sources pose a significant challenge to the stable operation of the power system. Furthermore, with the rapid development of renewable energy, especially the large-scale integration of solar and wind power, the installed capacity of renewable energy in distribution systems is increasing daily, leading to greater complexity and uncertainty in power dispatch. Traditional dispatch methods often neglect the coordination and optimization between various distribution units, resulting in inefficient use of energy resources and unstable energy supply.

[0004] In the prior art, Chinese patent CN118709885A discloses a method and system for optimizing the carrying capacity of a regional power grid under the access of new energy sources. The method includes clustering historical data of wind and solar power output and load to obtain a typical scenario set of load and wind and solar power; constructing a regional power grid carrying capacity model with the objectives of maximizing the installed capacity of new energy sources in the regional power grid and maximizing the effective reactive power reserve of the transmission network; decomposing the model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model; using a step-by-step iterative algorithm for transmission and distribution to collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model to obtain a solution set of new energy installed capacity; constructing an evaluation system of operation indicators, economic indicators, and environmental indicators; and calculating the time-sharing score based on the evaluation system and the solution set of new energy installed capacity to obtain the optimal carrying capacity. However, this method has the following limitations: 1. Traditional methods rely on static regional division or fixed capacity configuration, which cannot be flexibly adjusted according to the fluctuations in new energy output and changes in load demand in different regions, resulting in unreasonable division of autonomous units and reducing the system's adaptive capability and overall energy utilization efficiency. 2. Existing methods in regional energy autonomous dispatch often employ static rules or fixed algorithms, making it difficult to cope with changes in the electricity market and fluctuations in renewable energy output. The dispatch strategies lack real-time response capabilities, resulting in insufficient economic efficiency and dispatch effectiveness. 3. Traditional methods rely heavily on fixed load and renewable energy output data, lacking dynamic adjustment mechanisms. This easily leads to irrational resource allocation, low system dispatch efficiency, and uneven energy utilization. 4. When dealing with optimization involving multiple autonomous units or multiple stakeholders, existing methods often fail to achieve coordinated optimization of global and local dispatch, neglecting the interactive effects between autonomous units, resulting in insufficient overall system dispatch accuracy and stability.

[0005] In summary, existing technologies have significant shortcomings in autonomous unit division, scheduling strategy design, multi-entity coordination, and spatiotemporal dynamic modeling, making it difficult to meet the operational needs of power distribution systems in the context of high-penetration renewable energy access. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a regional energy autonomous optimization method for power distribution systems under the constraint of new energy carrying capacity.

[0007] The objective of this invention can be achieved through the following technical solutions: This invention provides a method for optimizing regional energy autonomy in a power distribution system under the constraint of renewable energy carrying capacity, comprising the following steps: Collect basic data on the target power distribution system area; Based on the basic data, the Voronoi-energy flow entropy dynamic spatial partitioning method is used to divide autonomous units, determine the spatial boundaries of each autonomous unit, and calculate the energy flow entropy characteristics. Based on the spatial boundaries, energy flow entropy characteristics, and basic data of each autonomous unit, the initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold of each autonomous unit are generated. Under the constraints of the initial new energy installation and energy storage capacity scheme and carrying capacity threshold, the Stackelberg game model is adopted, combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, and the operation scheduling strategy of each autonomous unit is determined by distributed iterative solution; Based on the feedback data after the execution of the operation scheduling strategy, rolling optimization and scheduling adjustments are performed to dynamically update the spatial boundaries, carrying capacity thresholds, and operation scheduling strategies of autonomous units.

[0008] Furthermore, the basic data for the target power distribution system area includes the distribution of new energy resources, load data, geographic information, energy storage and adjustable load capacity, as well as electricity prices and carbon trading prices.

[0009] Furthermore, based on the fundamental data, the Voronoi-energy flow entropy dynamic spatial partitioning method is used to divide autonomous units and determine the spatial boundaries of each autonomous unit, specifically including: Based on the distribution of new energy resources in the basic data, the geographical coordinates of all new energy power stations within the target power distribution system area are extracted to form a set of power station coordinates. : in, Indicates the first i Geographic coordinates of a new energy power station on a two-dimensional plane; n Indicates the total number of new energy power stations; Based on the load data in the basic data, obtain the power data of each load center. And calculate the relative weights of each load center: in, For the first j The relative weight of each load center reflects its proportion in the overall load; For the first j The load power of each load center; This represents the total number of load centers. With each new energy power station For the generation point, construct autonomous units according to the load-weighted Voronoi partitioning rule: in, Indicates the first i The spatial boundaries of each autonomous unit, namely, the new energy power station The power supply responsibility area is formed around the core; Represents any point on a two-dimensional plane; The Euclidean distance formula is used. This is the load weighting adjustment coefficient, used to control the degree of influence of the load center on spatial division; For points The relative weight of the nearest load center; According to the load-weighted Voronoi partitioning rule, the following is obtained: Each autonomous unit.

[0010] Furthermore, the calculation of energy flow entropy characteristics specifically includes: According to each autonomous unit Data on renewable energy output and load demand within the autonomous unit are used to calculate the regional energy flow entropy of the autonomous unit. The formula is: in, For the first i The regional energy flow entropy characteristics of each autonomous unit reflect the balance between the output of new energy sources and the demand for load. For the first i Each autonomous unit in time period t Total output of new energy sources; For the first i Each autonomous unit in time period t The total load demand, according to the first i The loads of all load centers in each autonomous unit are determined; This represents the total number of time periods included in the statistics.

[0011] Furthermore, the process of generating the initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold for each autonomous unit based on its spatial boundaries, energy flow entropy characteristics, and basic data specifically includes: Based on the distribution and load data of new energy resources in the basic data, and the first i Spatial boundaries of autonomous units Entropy characteristics of energy flow Calculate the first i Initial installed capacity of new energy in each autonomous unit : in, Indicates the first i The available capacity of new energy resources within each autonomous unit; Based on the energy flow entropy characteristics of autonomous units Adjusted capacity coefficient; Indicates the first i The available new energy resource capacity of each autonomous unit; Based on thei Load center power data within each autonomous unit Data on power output from new energy sources and energy flow entropy characteristics Calculate the first i Initial energy storage capacity of each autonomous unit : in, This is the energy storage capacity adjustment coefficient; To count the total number of time periods; For the first i All load centers within an autonomous unit during the time period t Total load; For the first i Each autonomous unit in time period t Total output of new energy sources; Based on the i Initial installed capacity of new energy in each autonomous unit and initial energy storage capacity Calculate the first i Initial bearing capacity threshold of each autonomous unit : in, This is the bearing capacity ratio coefficient, representing the contribution of energy storage capacity to the bearing capacity of the autonomous unit; Calculated , and These parameters serve as initial planning parameters for subsequent multi-entity operation scheduling optimization.

[0012] Furthermore, under the constraints of the initial new energy installed capacity and energy storage capacity scheme and carrying capacity threshold, the Stackelberg game model is adopted, combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, to determine the operation scheduling strategy of each autonomous unit through distributed iterative solution, specifically including: Based on the i Initial installed capacity of new energy in each autonomous unit Energy storage capacity and bearing capacity threshold Combined with the first i Load demand of each autonomous unit And new energy power output data , and formulate scheduling objectives for each autonomous unit; Dynamic electricity pricing and carbon price Under the influence of market and policy constraints, a deviation reward and punishment mechanism is introduced, a deviation function is set, and the cost function of each autonomous unit is defined. ; The leader-follower strategy in the Stackelberg game model is adopted. The leader is the decision-making body for optimizing the energy dispatch of the entire region and is responsible for setting the global optimization goal. Each follower is an autonomous unit and determines the dispatch strategy of each autonomous unit according to its own cost function and deviation function, under the condition of satisfying the constraints. The leader obtains the global optimal solution by coordinating the dispatch strategies of all autonomous units.

[0013] Furthermore, the scheduling target for each autonomous unit is given by the following formula: in, , The first i The scheduling target weight coefficient of each autonomous unit; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For the first i Each autonomous unit in time period t The output of new energy sources; For the first i Each autonomous unit in time period t Total load demand; This represents the total number of data points over a given time period.

[0014] Furthermore, the cost function The formula is: in, For time period t Dynamic electricity pricing; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For time period t Carbon price; For the first i Each autonomous unit in time period t Carbon emissions; This is the deviation reward / penalty coefficient, used to convert scheduling deviations into costs; For the first i Each autonomous unit in time period t The deviation function is defined as follows: , For the first i Each autonomous unit in time period t The target power for scheduling; To count the total number of time periods; For the first i Each autonomous unit in time period t The cost.

[0015] Furthermore, the leader-follower strategy in the Stackelberg game model is adopted. The leader is the decision-making entity for optimizing energy dispatch across the entire region and is responsible for setting the global optimization objective. Each follower is an autonomous unit that, based on its own cost function and deviation function, determines its dispatch strategy according to the dispatch objectives of each autonomous unit while satisfying constraints. The leader coordinates the dispatch strategies of all autonomous units to ultimately obtain the global optimal solution, specifically including: Step A1: The leader, based on global constraints, including the initial installed capacity of new energy sources in each autonomous unit within the region,... Energy storage capacity and bearing capacity threshold and the scheduling objectives of each autonomous unit. With cost function To formulate overall optimization goals for regional energy dispatch; Step A2: Each follower autonomous unit, under the premise of satisfying its own constraints, uses its cost function Sum and deviation functions To optimize the scheduling strategy, a distributed iterative solution is used. ; Step A3: The leader collects the scheduling strategies of all autonomous units. Calculate the coordination adjustment amount based on the global optimization objective. The coordinated adjustment amount will be fed back to each follower autonomous unit, using the following formula: in, For the first i Each autonomous unit in time period t Coordination and adjustment amount; The scheduling power is calculated by the leader based on the global optimization objective; The scheduling power is initially calculated by the followers based on their own cost function and constraints. Step A4: Each follower autonomous unit adjusts the amount according to the leader's coordination. Re-optimize its scheduling strategy Update its cost function Sum and deviation functions ; Step A5: Repeat steps A2-A4 until the scheduling strategies of all autonomous units converge under the given constraints, forming a leader-follower game equilibrium solution, thereby obtaining the globally optimal regional energy scheduling scheme.

[0016] Furthermore, the global optimization objective is expressed as: in, n The total number of autonomous units; T To count the total number of time periods; For the first i Each autonomous unit in time period t The scheduling power; For the first i Each autonomous unit in time period t Carbon emissions; For the first i Deviation function of each autonomous unit; This is the deviation reward / penalty coefficient. For time period t Dynamic electricity pricing; For time period t The price of carbon.

[0017] Compared with the prior art, the present invention has the following advantages: (1) In the optimization of regional energy autonomy in distribution systems under the constraint of new energy carrying capacity, existing technologies generally face the problem of how to effectively divide autonomous units and rationally allocate energy resources. Traditional methods rely on static regional division or fixed energy capacity allocation schemes, lacking the ability to dynamically adjust and flexibly respond to regional energy fluctuations. To solve this problem, this invention adopts a dynamic spatial partitioning method based on Voronoi-energy flow entropy to divide the target distribution system region into autonomous units. By calculating the energy flow entropy characteristics, the matching degree between new energy output and load demand in each autonomous unit can be accurately reflected. This method effectively improves the rationality of autonomous unit division, enabling the system to better adapt to load fluctuations and new energy output changes in different times and spaces, thereby optimizing the overall regional energy autonomy capability and avoiding the energy waste and load imbalance problems in traditional methods.

[0018] (2) In existing technologies, the design of regional autonomous systems often overlooks the impact of complex market and policy factors on new energy dispatch, especially under the electricity market and carbon trading mechanism, where the design of reasonable dispatch strategies remains a significant challenge. Traditional optimization methods are mostly based on static rules, lacking flexibility and responsiveness. To address this issue, this invention introduces the Stackelberg game model, combined with dynamic electricity prices, carbon prices, and deviation reward and punishment mechanisms, to achieve intelligent and economic optimization of regional energy dispatch. Through the leader-follower strategy, the leader makes decisions based on the global objective, while the followers optimize dispatch based on their own constraints. This game mechanism can effectively coordinate the dispatch behavior of each autonomous unit, ensuring the global optimal solution while incentivizing each unit to flexibly adjust its dispatch strategy according to market changes, thereby improving the system's economic efficiency and environmental protection.

[0019] (3) Currently, traditional scheduling strategies rely heavily on fixed load and renewable energy output data, neglecting the dynamic changes in scheduling objectives and costs, which can easily lead to unreasonable resource allocation and low system scheduling efficiency. To address this issue, this invention, based on the Stackelberg game model, introduces dynamic electricity prices, carbon emissions, and a deviation reward / penalty mechanism to establish dynamic scheduling objectives and cost functions for each autonomous unit. Specifically, the scheduling objectives of each autonomous unit are dynamically adjusted according to the differences in renewable energy output and load demand, while the cost function comprehensively considers multiple factors such as electricity prices, carbon prices, and carbon emissions, enabling flexible responses to market price fluctuations and policy changes, thereby achieving the lowest-cost energy scheduling while ensuring stable system operation. This innovative feature effectively improves the responsiveness of the power distribution system, reduces system operating costs, and optimizes the achievement of carbon emission control and environmental protection goals.

[0020] (4) Existing technologies often suffer from fragmentation when dealing with multi-agent optimization scheduling problems, especially with the continuous increase in the amount of renewable energy access. Traditional optimization methods cannot effectively handle the coupling between deep learning and physical laws such as power flow equations. To address this issue, this invention proposes a multi-agent collaborative scheduling optimization strategy and achieves coordination between global and local scheduling through the leader-follower mechanism in the Stackelberg game model. This method not only considers the physical constraints of power flow equations but also integrates the predictive capabilities of deep learning. Through distributed iterative solutions, it ensures the physical rationality of the system scheduling strategy and the efficiency of data-driven approaches, thereby improving the accuracy and stability of the optimization solution and ensuring the efficient operation of the system under complex conditions. Attached Figure Description

[0021] Figure 1 This is a flowchart of the regional energy autonomy optimization method for power distribution systems according to an embodiment of the present invention; Figure 2 This is a model diagram of a regional energy autonomous optimization system for a power distribution system according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0023] Example 1: This embodiment provides a method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity, such as... Figure 1 As shown, it includes the following steps: Step S1: Collect basic data of the target power distribution system area; the basic data of the target power distribution system area includes the distribution of new energy resources, load data, geographic information, energy storage and adjustable load capacity, as well as electricity price and carbon trading price.

[0024] Step S2: Based on the basic data, the Voronoi-energy flow entropy dynamic spatial partitioning method is used to divide autonomous units, determine the spatial boundaries of each autonomous unit, and calculate the energy flow entropy characteristics, specifically including: Based on the distribution of new energy resources in the basic data, the geographical coordinates of all new energy power stations within the target power distribution system area are extracted to form a set of power station coordinates. : in, Indicates the first i Geographic coordinates of a new energy power station on a two-dimensional plane; n Indicates the total number of new energy power stations; Based on the load data in the basic data, obtain the power data of each load center. And calculate the relative weights of each load center: in, For the first j The relative weight of each load center reflects its proportion in the overall load; For the first j The load power of each load center; This represents the total number of load centers. With each new energy power station For the generation point, construct autonomous units according to the load-weighted Voronoi partitioning rule: in, Indicates the first i The spatial boundaries of each autonomous unit, namely, the new energy power station The power supply responsibility area is formed around the core; Represents any point on a two-dimensional plane; The Euclidean distance formula is used. This is the load weighting adjustment coefficient, used to control the degree of influence of the load center on spatial division; For points The relative weight of the nearest load center; According to the load-weighted Voronoi partitioning rule, the following is obtained: Each autonomous unit.

[0025] According to each autonomous unit Data on renewable energy output and load demand within the autonomous unit are used to calculate the regional energy flow entropy of the autonomous unit. The formula is: in, For the first i The regional energy flow entropy characteristics of each autonomous unit reflect the balance between the output of new energy sources and the demand for load. For the first i Each autonomous unit in time period t Total output of new energy sources; For the first i Each autonomous unit in time period t The total load demand, according to the first i The loads of all load centers in each autonomous unit are determined; This represents the total number of time periods included in the statistics.

[0026] In step S2, the power distribution system area is dynamically spatially partitioned based on Voronoi-energy flow entropy. This aims to address the problem that traditional static partitioning methods cannot reflect the spatiotemporal differences between renewable energy output and load demand. By using renewable energy power plants as generation points and combining load-weighted Voronoi partitioning rules, dynamic adaptive adjustment of autonomous unit boundaries is achieved, enabling high-load areas to obtain a more reasonable energy supply range, thereby avoiding energy supply-demand imbalances and local overload phenomena. Simultaneously, energy flow entropy characteristics are introduced to quantitatively evaluate the matching balance between renewable energy output and load demand within each autonomous unit. This ensures that the partitioning results not only consider geographical and electrical topology relationships but also reflect energy flow characteristics and output fluctuation patterns. This method achieves spatiotemporal coupling mapping between renewable energy output and load demand, improves the energy self-balancing capability of autonomous units and the scientific nature of system partitioning, and provides a more accurate and dynamic foundation for subsequent energy storage configuration, carrying capacity assessment, and operational optimization.

[0027] Step S3: Based on the spatial boundaries, energy flow entropy characteristics, and basic data of each autonomous unit, generate the initial installed capacity of new energy sources, energy storage configuration capacity, and carrying capacity threshold for each autonomous unit, specifically including: Based on the distribution and load data of new energy resources in the basic data, and the first i Spatial boundaries of autonomous units Entropy characteristics of energy flow Calculate the first i Initial installed capacity of new energy in each autonomous unit : in, Indicates the first i The available capacity of new energy resources within each autonomous unit; Based on the energy flow entropy characteristics of autonomous units Adjusted capacity coefficient; Indicates the first i The available new energy resource capacity of each autonomous unit; Based on the i Load center power data within each autonomous unit Data on power output from new energy sources and energy flow entropy characteristics Calculate the first i Initial energy storage capacity of each autonomous unit : in, This is the energy storage capacity adjustment coefficient; To count the total number of time periods; For the first i All load centers within an autonomous unit during the time period t Total load; For the first i Each autonomous unit in time period t Total output of new energy sources; Based on the i Initial installed capacity of new energy in each autonomous unit and initial energy storage capacity Calculate the first i Initial bearing capacity threshold of each autonomous unit : in, This is the bearing capacity ratio coefficient, representing the contribution of energy storage capacity to the bearing capacity of the autonomous unit; Calculated , and These parameters serve as initial planning parameters for subsequent multi-entity operation scheduling optimization.

[0028] In step S3, initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold are generated based on the spatial boundaries and energy flow entropy characteristics of each autonomous unit, aiming to achieve scientific allocation and dynamic balance of regional energy resources. By utilizing energy flow entropy to reflect the matching degree between new energy output and load demand within each autonomous unit, an adjustment coefficient reflecting spatiotemporal matching characteristics can be introduced into the installed capacity calculation, thereby making resource allocation more consistent with the actual supply and demand balance of the autonomous unit and avoiding over-installation or resource waste. Simultaneously, load-output difference and energy flow entropy adjustment parameters are introduced into the energy storage configuration. Energy storage resources are adaptively allocated through a capacity adjustment coefficient, making the energy storage system more resilient and stable in the face of new energy fluctuations and load uncertainties. Furthermore, the carrying capacity threshold is calculated based on the new energy installed capacity and energy storage capacity, and the contribution of energy storage to improving the system's carrying capacity is quantitatively reflected through a carrying capacity ratio coefficient. This process achieves a quantitative connection from spatial division to capacity allocation, making the initial parameters both physically reasonable and dynamically adaptable, providing accurate basic conditions for the subsequent game-theoretic optimization stage, and effectively improving the system's energy allocation efficiency and operational robustness.

[0029] Step S4: Under the constraints of the initial renewable energy installation and energy storage capacity plan and carrying capacity threshold, the Stackelberg game model is adopted, combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, to determine the operation scheduling strategy of each autonomous unit through distributed iterative solution, specifically including: Based on the i Initial installed capacity of new energy in each autonomous unit Energy storage capacity and bearing capacity threshold Combined with the first i Load demand of each autonomous unit And new energy power output data , and formulate scheduling objectives for each autonomous unit; Dynamic electricity pricing and carbon price Under the influence of market and policy constraints, a deviation reward and punishment mechanism is introduced, a deviation function is set, and the cost function of each autonomous unit is defined. ; The leader-follower strategy in the Stackelberg game model is adopted. The leader is the decision-making body for optimizing the energy dispatch of the entire region and is responsible for setting the global optimization goal. Each follower is an autonomous unit and determines the dispatch strategy of each autonomous unit according to its own cost function and deviation function, under the condition of satisfying the constraints. The leader obtains the global optimal solution by coordinating the dispatch strategies of all autonomous units.

[0030] The scheduling objectives for each autonomous unit are given by the following formula: in, , The first i The scheduling target weight coefficient of each autonomous unit; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For the first i Each autonomous unit in time period t The output of new energy sources; For the first i Each autonomous unit in time period t Total load demand; This represents the total number of data points over a given time period.

[0031] Cost function The formula is: in, For time period t Dynamic electricity pricing; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For time period t Carbon price; For the first i Each autonomous unit in time period t Carbon emissions; This is the deviation reward / penalty coefficient, used to convert scheduling deviations into costs; For the first i Each autonomous unit in time period t The deviation function is defined as follows: , For the first i Each autonomous unit in time period t The target power for scheduling; To count the total number of time periods; For the first i Each autonomous unit in time period t The cost.

[0032] The Stackelberg game model employs a leader-follower strategy. The leader, acting as the decision-maker for optimizing energy dispatch across the entire region, is responsible for setting the global optimization objective. Each follower, representing an autonomous unit, determines its own dispatch strategy based on its cost and deviation functions, while adhering to constraints and the dispatch objectives of each autonomous unit. The leader coordinates the dispatch strategies of all autonomous units to ultimately obtain the globally optimal solution, specifically including: Step A1: The leader, based on global constraints, including the initial installed capacity of new energy sources in each autonomous unit within the region,... Energy storage capacity and bearing capacity threshold and the scheduling objectives of each autonomous unit. With cost function To formulate overall optimization goals for regional energy dispatch; Step A2: Each follower autonomous unit, under the premise of satisfying its own constraints, uses its cost function Sum and deviation functions To optimize the scheduling strategy, a distributed iterative solution is used. ; Step A3: The leader collects the scheduling strategies of all autonomous units. Calculate the coordination adjustment amount based on the global optimization objective. The coordinated adjustment amount will be fed back to each follower autonomous unit, using the following formula: in, For the first i Each autonomous unit in time period t Coordination and adjustment amount; The scheduling power is calculated by the leader based on the global optimization objective; The scheduling power is initially calculated by the followers based on their own cost function and constraints. Step A4: Each follower autonomous unit adjusts the amount according to the leader's coordination. Re-optimize its scheduling strategy Update its cost function Sum and deviation functions ; Step A5: Repeat steps A2-A4 until the scheduling strategies of all autonomous units converge under the given constraints, forming a leader-follower game equilibrium solution, thereby obtaining the globally optimal regional energy scheduling scheme.

[0033] The global optimization objective is expressed as: in, nThe total number of autonomous units; T To count the total number of time periods; For the first i Each autonomous unit in time period t The scheduling power; For the first i Each autonomous unit in time period t Carbon emissions; For the first i Deviation function of each autonomous unit; This is the deviation reward / penalty coefficient. For time period t Dynamic electricity pricing; For time period t The price of carbon.

[0034] Step S4 introduces a distributed iterative solution based on Stackelberg game theory under the constraints of initial installed capacity, energy storage, and carrying capacity. This aims to organically connect the static capacity determined by the plan with the dynamic scheduling during operation, achieving coordinated optimization of both economic and physical objectives. A leader-follower (Stackelberg) structure is adopted because the regional coordinator can act as the leader, proposing constrained objectives and signals from a global perspective (e.g., dynamic electricity price, carbon price, and deviation penalty coefficient in the global optimization objective). Each autonomous unit, as a follower, minimizes its local objective based on its own initial capacity, energy storage, and carrying capacity thresholds, as well as local data, thus achieving guided convergence of the global objective while ensuring physical constraints and autonomy. The deviation term is included in the cost function to endogenize scheduling accuracy and prediction reliability into economic signals, reducing over-limit or power curtailment caused by output deviations. The reasons for using distributed iterative solvers such as ADMM are: firstly, it can decompose large-scale problems into local sub-problems of each autonomous unit, reducing computational complexity and preserving the privacy and local decision-making power of each unit; secondly, it achieves global consistency constraints (e.g., overall consistency) through coordination variables and multiplier updates. The gradual satisfaction of energy balance and carrying capacity limits facilitates scalable real-time or near-real-time optimization in communication-constrained and multi-entity scenarios. The technical effects and advantages include: First, coupling economic incentives (electricity price, carbon price, penalties) with physical constraints (installed capacity, energy storage, carrying capacity) prompts autonomous units to spontaneously make scheduling decisions that balance cost and reliability under market signals, thereby improving economic efficiency and promoting low-carbon operation; second, reducing the mismatch between prediction and execution through deviation penalties, reducing power curtailment and exceeding limits, and improving scheduling reliability; third, distributed solution enhances the scalability and privacy protection of the algorithm, facilitating deployment in distribution networks with a large number of autonomous units; fourth, the leader-follower iterative coordination mechanism can gradually approach the global optimum while ensuring local autonomy, balancing autonomy and system reliability, thereby improving system self-balancing rate, reducing purchased electricity, and enhancing robustness to fluctuating output in the context of high-penetration renewable energy.

[0035] Step S5: Based on the feedback data after the execution of the operation scheduling strategy, perform rolling optimization and scheduling adjustments, dynamically updating the spatial boundary, carrying capacity threshold, and operation scheduling strategy of the autonomous unit, specifically including: Based on the feedback data collected during the operating cycle, including the renewable energy output, load demand, energy storage status of charge, carbon emissions, and scheduling deviations of each autonomous unit, the operating status of each autonomous unit is analyzed, the actual energy flow entropy characteristics are calculated, and compared with the energy flow entropy of the previous cycle.

[0036] when (in When the threshold value is reached, it is determined that the energy supply and demand matching relationship of the autonomous unit has changed, and the spatial division needs to be readjusted. At this time, based on the latest load data and the distribution of new energy output, the spatial boundaries of each autonomous unit are redefined using the load-weighted Voronoi partitioning rule, and the updated energy flow entropy characteristics are calculated.

[0037] Based on the updated energy flow entropy characteristics, the initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold of each autonomous unit are recalculated; the updated parameters are used as input conditions for a new round of game optimization.

[0038] Based on the updated parameters, a new Stackelberg game model is established. The leader formulates a global optimization objective according to the latest system state and constraints, while the followers update their scheduling strategies according to the new cost and deviation functions. The leader-follower game equilibrium is solved through a distributed iterative approach to obtain the optimal scheduling strategy for each autonomous unit in the current cycle. Repeat the above process until the system operation is stable, the spatial division of autonomous units, the carrying capacity threshold and the operation scheduling strategy converge within the set error range, forming a rolling optimization closed loop, ensuring that the regional energy autonomous system can achieve energy supply and demand balance and optimal economic operation in multiple time periods and multiple scenarios.

[0039] Example 2: This embodiment provides a regional energy self-governance optimization system for a power distribution system under the constraint of new energy carrying capacity, such as... Figure 2 As shown, it includes: The data acquisition module is used to acquire basic data within the target power distribution system area, including new energy resource distribution data, load center power data, energy storage operation status data, electricity price and carbon price time series data, meteorological forecast data, and power grid topology information, providing input support for subsequent autonomous optimization. The autonomous unit partitioning module is used to partition the target power distribution system area into autonomous units based on the aforementioned basic data using the Voronoi-energy flow entropy dynamic spatial partitioning method, determine the spatial boundaries of each autonomous unit, and calculate the energy flow entropy characteristics of each autonomous unit to characterize the degree of matching between new energy output and load demand. The initial planning parameter generation module is used to generate the initial new energy installed capacity, energy storage configuration capacity and carrying capacity threshold of each autonomous unit based on the spatial boundary, energy flow entropy characteristics and basic data of each autonomous unit. The capacity coefficient is corrected by energy flow entropy to achieve capacity allocation optimization among autonomous units. The distributed scheduling optimization module is used to construct and solve a multi-agent operation optimization problem based on the Stackelberg game model under the constraints of the initial new energy installed capacity and energy storage capacity scheme and carrying capacity threshold. Combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, the operation scheduling strategy of each autonomous unit is determined. Among them, the leader is responsible for setting the global optimization goal, and the followers perform local scheduling optimization according to their own cost function and constraints. The global optimal scheduling scheme is achieved through distributed iterative solution. The rolling optimization and feedback module is used to dynamically update the energy flow entropy characteristics, spatial boundaries, initial planning parameters and operation scheduling strategies of each autonomous unit based on the feedback data after the execution of the operation scheduling strategy, forming a multi-cycle rolling optimization mechanism to realize the adaptive adjustment of autonomous unit division and scheduling strategy.

[0040] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity, characterized in that, Includes the following steps: Collect basic data on the target power distribution system area; Based on the basic data, the Voronoi-energy flow entropy dynamic spatial partitioning method is used to divide autonomous units, determine the spatial boundaries of each autonomous unit, and calculate the energy flow entropy characteristics. Based on the spatial boundaries, energy flow entropy characteristics, and basic data of each autonomous unit, the initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold of each autonomous unit are generated. Under the constraints of the initial new energy installation and energy storage capacity scheme and carrying capacity threshold, the Stackelberg game model is adopted, combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, and the operation scheduling strategy of each autonomous unit is determined by distributed iterative solution; Based on the feedback data after the execution of the operation scheduling strategy, rolling optimization and scheduling adjustments are performed to dynamically update the spatial boundaries, carrying capacity thresholds, and operation scheduling strategies of autonomous units.

2. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity as described in claim 1, characterized in that, The basic data for the target power distribution system area includes the distribution of new energy resources, load data, geographic information, energy storage and adjustable load capacity, and electricity and carbon trading prices.

3. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity as described in claim 1, characterized in that, The method of dividing autonomous units based on fundamental data and using the Voronoi-energy flow entropy dynamic spatial partitioning method to determine the spatial boundaries of each autonomous unit specifically includes: Based on the distribution of new energy resources in the basic data, the geographical coordinates of all new energy power stations within the target power distribution system area are extracted to form a set of power station coordinates. : in, Indicates the first i Geographic coordinates of a new energy power station on a two-dimensional plane; n Indicates the total number of new energy power stations; Based on the load data in the basic data, obtain the power data of each load center. And calculate the relative weights of each load center: in, For the first j The relative weight of each load center reflects its proportion in the overall load; For the first j The load power of each load center; This represents the total number of load centers. With each new energy power station For the generation point, construct autonomous units according to the load-weighted Voronoi partitioning rule: in, Indicates the first i The spatial boundaries of each autonomous unit, namely, the new energy power station The power supply responsibility area is formed around the core; Represents any point on a two-dimensional plane; The Euclidean distance formula is used. This is the load weighting adjustment coefficient, used to control the degree of influence of the load center on spatial division; For points The relative weight of the nearest load center; According to the load-weighted Voronoi partitioning rule, the following is obtained: Each autonomous unit.

4. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity according to claim 1, characterized in that, The calculation of energy flow entropy characteristics specifically includes: According to each autonomous unit Data on renewable energy output and load demand within the autonomous unit are used to calculate the regional energy flow entropy of the autonomous unit. The formula is: in, For the first i The regional energy flow entropy characteristics of each autonomous unit reflect the balance between the output of new energy sources and the demand for load. For the first i Each autonomous unit in time period t Total output of new energy sources; For the first i Each autonomous unit in time period t The total load demand, according to the first i The loads of all load centers in each autonomous unit are determined; This represents the total number of time periods covered in the statistics.

5. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity according to claim 1, characterized in that, The process of generating the initial new energy installed capacity, energy storage configuration capacity, and carrying capacity threshold for each autonomous unit based on its spatial boundaries, energy flow entropy characteristics, and basic data specifically includes: Based on the distribution and load data of new energy resources in the basic data, and the first i Spatial boundaries of autonomous units Entropy characteristics of energy flow Calculate the first i Initial installed capacity of new energy in each autonomous unit : in, Indicates the first i The available capacity of new energy resources within each autonomous unit; Based on the energy flow entropy characteristics of autonomous units Adjusted capacity coefficient; Indicates the first i The available new energy resource capacity of each autonomous unit; Based on the i Load center power data within each autonomous unit Data on power output from new energy sources and energy flow entropy characteristics Calculate the first i Initial energy storage capacity of each autonomous unit : in, This is the energy storage capacity adjustment coefficient; To count the total number of time periods; For the first i All load centers within an autonomous unit during the time period t Total load; For the first i Each autonomous unit in time period t Total output of new energy sources; Based on the i Initial installed capacity of new energy in each autonomous unit and initial energy storage capacity Calculate the first i Initial bearing capacity threshold of each autonomous unit : in, This is the bearing capacity ratio coefficient, representing the contribution of energy storage capacity to the bearing capacity of the autonomous unit; Calculated , and These parameters serve as initial planning parameters for subsequent multi-entity operation scheduling optimization.

6. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity according to claim 1, characterized in that, Under the constraints of the initial new energy installed capacity and energy storage capacity scheme and carrying capacity threshold, the Stackelberg game model is adopted, combined with dynamic electricity price, carbon price and deviation reward and punishment mechanism, to determine the operation scheduling strategy of each autonomous unit through distributed iterative solution, specifically including: Based on the i Initial installed capacity of new energy in each autonomous unit Energy storage capacity and bearing capacity threshold Combined with the first i Load demand of each autonomous unit And new energy power output data , and formulate scheduling objectives for each autonomous unit; Dynamic electricity pricing and carbon price Under the influence of market and policy constraints, a deviation reward and punishment mechanism is introduced, a deviation function is set, and the cost function of each autonomous unit is defined. ; The leader-follower strategy in the Stackelberg game model is adopted. The leader is the decision-making body for optimizing the energy dispatch of the entire region and is responsible for setting the global optimization goal. Each follower is an autonomous unit and determines the dispatch strategy of each autonomous unit according to its own cost function and deviation function, under the condition of satisfying the constraints. The leader obtains the global optimal solution by coordinating the dispatch strategies of all autonomous units.

7. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity as described in claim 6, characterized in that, The scheduling target for each autonomous unit is given by the following formula: in, , The first i The scheduling target weight coefficient of each autonomous unit; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For the first i Each autonomous unit in time period t The output of new energy sources; For the first i Each autonomous unit in time period t Total load demand; This represents the total number of data points over a given time period.

8. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity as described in claim 6, characterized in that, The cost function The formula is: in, For time period t Dynamic electricity pricing; For the first i Each autonomous unit in time period t The scheduling power, i.e., the scheduling strategy; For time period t Carbon price; For the first i Each autonomous unit in time period t Carbon emissions; This is the deviation reward / penalty coefficient, used to convert scheduling deviations into costs; For the first i Each autonomous unit in time period t The deviation function is defined as follows: , For the first i Each autonomous unit in time period t The target power for scheduling; To count the total number of time periods; For the first i Each autonomous unit in time period t The cost.

9. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity according to claim 6, characterized in that, The strategy adopted is the leader-follower strategy in the Stackelberg game model. The leader is the decision-making body for optimizing the energy dispatch of the entire region and is responsible for setting the global optimization goal. The followers are each autonomous unit, and based on their own cost function and deviation function, they make decisions according to the dispatch goals of each autonomous unit under the condition of satisfying the constraints. The scheduling strategy for each autonomous unit is determined, and the leader coordinates the scheduling strategies of all autonomous units to ultimately obtain the globally optimal solution, which specifically includes: Step A1: The leader, based on global constraints, including the initial installed capacity of new energy sources in each autonomous unit within the region,... Energy storage capacity and bearing capacity threshold and the scheduling objectives of each autonomous unit. With cost function To formulate overall optimization goals for regional energy dispatch; Step A2: Each follower autonomous unit, under the premise of satisfying its own constraints, uses its cost function Sum and deviation functions To optimize the scheduling strategy, a distributed iterative solution is used. ; Step A3: The leader collects the scheduling strategies of all autonomous units. Calculate the coordination adjustment amount based on the global optimization objective. The coordinated adjustment amount will be fed back to each follower autonomous unit, using the following formula: in, For the first i Each autonomous unit in time period t Coordination and adjustment amount; The scheduling power is calculated by the leader based on the global optimization objective; The scheduling power is initially calculated by the followers based on their own cost function and constraints. Step A4: Each follower autonomous unit adjusts the amount according to the leader's coordination. Re-optimize its scheduling strategy Update its cost function Sum and deviation functions ; Step A5: Repeat steps A2-A4 until the scheduling strategies of all autonomous units converge under the given constraints, forming a leader-follower game equilibrium solution, thereby obtaining the globally optimal regional energy scheduling scheme.

10. The method for regional energy autonomy optimization of a power distribution system under the constraint of new energy carrying capacity according to claim 9, characterized in that, The global optimization objective is expressed as: in, n The total number of autonomous units; T To count the total number of time periods; For the first i Each autonomous unit in time period t The scheduling power; For the first i Each autonomous unit in time period t Carbon emissions; For the first i Deviation function of each autonomous unit; This is the deviation reward / penalty coefficient. For time period t Dynamic electricity pricing; For time period t The price of carbon.

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