A source-load adjustable resource regulation method, device, equipment and medium

Abstract

This application belongs to the field of data processing technology and discloses a method, device, equipment, and medium for regulating adjustable resources on the source-load side. The method includes: obtaining the particle scale based on the quantity of adjustable resources on the source-load side; obtaining the particle region position and particle velocity based on the particle scale; calculating the comprehensive performance index value of each particle region position based on a renewable energy absorption capacity index and a modularity index based on electrical distance; calculating the comprehensive performance index value based on a penalty function to obtain a fitness evaluation result; updating the particle region position and particle velocity based on the fitness evaluation result to obtain the globally optimal partition position; obtaining the partitioning result based on the globally optimal partition position; calculating the scheduling instruction based on the partitioning result; and calculating the adjustable resources on the source-load side based on the scheduling instruction to obtain the resource regulation result. This application can reasonably and accurately regulate adjustable resources on the source-load side.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment and medium for source-load adjustable resource regulation. Background Technology

[0002] With the continuous increase in the proportion of renewable energy integration and the increasing complexity of user load patterns, the traditional power system operation mode centered on centralized dispatch is facing unprecedented challenges. On the one hand, the output of distributed renewable energy is highly volatile and uncertain, making it more difficult to achieve real-time grid balance. On the other hand, the introduction of various types of adjustable resources, such as sources, loads, and storage, makes the system operation exhibit complex characteristics of multiple scales, multiple objectives, and multiple couplings.

[0003] Existing control strategies mainly fall into two categories: centralized optimization control and distributed collaborative control. Centralized optimization control systems rely on a master control center to obtain information from all nodes and uniformly calculate control strategies. Representative methods include AGC (Automatic Generation Control) based on a master-slave structure and SCADA / EMS systems. However, this type of method suffers from severe communication bottlenecks, insufficient real-time performance, and poor system robustness. In particular, it is difficult to meet the development needs of future smart grids when dealing with large-scale renewable energy access and load-side adjustable resource coordination.

[0004] It is evident that existing methods still suffer from shortcomings such as lagging cross-regional collaboration and weak regional autonomy. How to achieve regional autonomous optimization, cross-regional power mutual assistance, and heterogeneous resource collaboration on a time scale of seconds has become an unsolved problem. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for regulating adjustable resources on the source and load sides, which can effectively improve the absorption level of renewable energy, enhance the rationality of the grid zoning structure and the tightness of electrical coupling, and ensure that the zoning scheme simultaneously meets various preset constraints. Furthermore, by optimizing scheduling, it significantly improves the coordination and control capability of adjustable resources on the source and load sides and the overall energy utilization efficiency, and constructs a progressive regulation framework of dynamic generation of zoning, hierarchical coordination and optimization, and distributed autonomous execution, which enables reasonable and precise regulation of adjustable resources on the source and load sides.

[0006] In a first aspect, embodiments of this application provide a method for adjusting source and load resources, the method comprising: The particle size is obtained based on the number of adjustable resources on the source and load side; the particle size is then used for initialization to obtain the particle region position and particle velocity. The comprehensive performance index value of each particle region is obtained by calculating based on the renewable energy absorption capacity index and the modularity index based on electrical distance. Determine whether there are isolated nodes in the current partition based on the adjacency matrix; If so, the comprehensive performance index value is calculated based on the penalty term function to obtain the fitness evaluation result; The particle region position and particle velocity are updated based on the fitness evaluation results to obtain the globally optimal partition position; Determine whether all parameters corresponding to the current partitioning scheme meet the preset constraints; if so, obtain the partitioning result based on the globally optimal partitioning position. The scheduling instructions are obtained by calculating based on the partitioning results. The adjustable resources on the source and load sides are calculated according to the scheduling instructions to obtain the resource regulation results.

[0007] Furthermore, the method also includes: The renewable energy absorption capacity index is calculated based on the actual power value of renewable energy sources, the maximum power that renewable energy sources can generate, the original load power value, and the adjustment power value of all controllable equipment in the region. According to the node and nodes The weights of the edges and their connections to the nodes The modularity index based on electrical distance is obtained by calculating the sum of the weights of connected edges and the sum of the weights of each node in the network.

[0008] Furthermore, the method also includes: The single-node impact factor is calculated based on multiple weighting coefficients, load weight, voltage level, and reliability level. The penalty term function is obtained by multiplying the adjustment coefficient, the single-node influence factor, and the partition penalty factor.

[0009] Furthermore, the preset constraints include: Power system power flow constraints, node voltage constraints, branch power flow constraints, and distributed generation power injection constraints.

[0010] Furthermore, the method also includes: According to the injection node Active and reactive power, nodes The voltage magnitude, the real part of the nodal admittance matrix, the imaginary part of the nodal admittance matrix, and the node and nodes The phase angle difference between the phase angles is calculated to obtain the power flow constraints of the power system. According to the node Upper limit of voltage amplitude and node The lower limit of the voltage amplitude is calculated to obtain the node voltage constraint; According to the route Active power and lines The maximum allowed active power is calculated to obtain the branch power flow constraints; The distributed generation injection power constraint is obtained by comparing the active power of the distributed generation injected into the distribution network with the maximum active power of the distributed generation injected into the distribution network.

[0011] Furthermore, based on the partitioning results, scheduling instructions are calculated, including: Based on the zoning results, obtain the upper limit and lower limit of the regulation capacity for each region; The total power deviation of the system is calculated based on the upper limit and lower limit of the regulation capacity of each region. Based on the total power deviation of the system, power adjustment commands for each region are obtained; Based on the zoning results, multiple parameters corresponding to the adjustable resources on the source-load side are obtained and calculated to obtain the injected power, voltage amplitude, upper limit of the regulation capacity of the region, and lower limit of the regulation capacity of the region at the grid connection point. The control commands for adjustable resources on the source-load side are obtained by processing the injected power, voltage amplitude, upper limit of the regulation capacity of the region and lower limit of the regulation capacity of the region at the grid connection point. Scheduling instructions are obtained based on the power adjustment instructions for each region and the control instructions for adjustable resources on the source and load sides.

[0012] Furthermore, based on the scheduling instructions, the adjustable resources on the source and load sides are calculated to obtain the resource regulation results, including: Constructing an adjacency matrix based on a partitioned communication network; The control output is calculated based on the adjacency matrix and scheduling instructions. The update and adjustment instructions are obtained by processing the local control update formula and control output. Adjust the adjustable resources on the source and load side according to the update adjustment instructions to obtain the resource regulation results.

[0013] Secondly, embodiments of this application provide a source-load adjustable resource regulation device, the device comprising: The acquisition module is used to obtain the particle size based on the number of adjustable resources on the source load side; and to initialize based on the particle size to obtain the particle region position and particle velocity. The indicator module is used to calculate the comprehensive performance index value of each particle region position based on the renewable energy absorption capacity index and the modularity index based on the electrical distance. The judgment module is used to determine whether there are isolated nodes in the current partition based on the adjacency matrix; The evaluation module is used to calculate the comprehensive performance index value based on the penalty term function if the condition is met, and obtain the fitness evaluation result. The update module is used to update the particle region position and particle velocity based on the fitness evaluation results to obtain the globally optimal partition position. The constraint module is used to determine whether all parameters corresponding to the current partitioning scheme meet the preset constraint conditions; if so, the partitioning result is obtained based on the globally optimal partitioning position. The scheduling module is used to calculate and obtain scheduling instructions based on the partitioning results; The results module is used to calculate the adjustable resources on the source and load side according to the scheduling instructions and obtain the resource regulation results.

[0014] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps of a source-load adjustable resource control method as described in any of the above embodiments.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a source-load adjustable resource control method as described in any of the above embodiments.

[0016] In summary, compared with the prior art, the beneficial effects of the technical solution provided in this application include at least the following: This application provides a source-load adjustable resource regulation method. The method can effectively improve the absorption level of renewable energy, enhance the rationality of the power grid zoning structure and the tightness of electrical coupling, and ensure that the zoning scheme meets all preset constraints at the same time. Furthermore, by optimizing the scheduling, it significantly improves the coordination and control capability of source-load adjustable resources and the overall energy utilization efficiency, and constructs a progressive regulation framework of dynamic generation of zoning, hierarchical coordination optimization and distributed autonomous execution, so as to carry out reasonable and precise regulation of source-load adjustable resources. Attached Figure Description

[0017] Figure 1 A flowchart of a source-load adjustable resource regulation method provided as an exemplary embodiment of this application.

[0018] Figure 2 This is a structural diagram of a source-load adjustable resource regulation device provided as an exemplary embodiment of this application. Detailed Implementation

[0019] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Please see Figure 1 This application provides a method for adjusting source load resources, which specifically includes the following steps: Step S1: Obtain the particle size based on the number of adjustable resources on the source load side; initialize based on the particle size to obtain the particle region position and particle velocity.

[0022] By dynamically determining the particle size based on the number of adjustable resources on the source and load sides, the computational complexity of the optimization algorithm is matched with the actual system scale, reducing the consumption of computational resources while ensuring optimization accuracy. Furthermore, the particle size is used to initialize the particle region position and particle velocity, providing reasonable and diverse initial search points for subsequent iterative optimization processes. This helps improve the algorithm's convergence speed and global optimization capability, avoids premature convergence, and thus lays a solid foundation for the overall optimization of power grid zoning and resource regulation.

[0023] In one feasible implementation, the quantity of adjustable resources on the source side, such as distributed photovoltaics and wind turbines, and on the load side, such as adjustable loads and energy storage devices, can be counted first. Based on this quantity, the particle size can be calculated using a linear mapping method. Subsequently, the region position vector and velocity vector corresponding to each particle are randomly generated in a predefined solution space to complete the initialization of the particle swarm, providing an initial particle swarm state that can be directly iterated for subsequent comprehensive performance index calculation and partition optimization.

[0024] Step S2: Calculate the comprehensive performance index value of each particle region position based on the renewable energy absorption capacity index and the modularity index based on electrical distance.

[0025] In some embodiments, the method further includes: The renewable energy absorption capacity index is calculated based on the actual power value of renewable energy sources, the maximum power that renewable energy sources can generate, the original load power value, and the adjustment power value of all controllable equipment in the region. According to the node and nodes The weights of the edges and their connections to the nodes The modularity index based on electrical distance is obtained by calculating the sum of the weights of connected edges and the sum of the weights of each node in the network.

[0026] In one feasible implementation, the comprehensive performance index takes into account the structural and functional aspects of the zoning. It uses the renewable energy absorption capacity index to coordinate the node combination within the zoning, promoting a balance between source, load, and storage within the region; and uses a modularity index based on electrical distance to measure the electrical coupling strength within the region, ensuring the structural integrity of the zoning. The formula for calculating the comprehensive performance index can be expressed as follows: ; In the formula, Indicators representing the capacity to absorb renewable energy. This represents a modularity index based on electrical distance. , These are the weight values ​​for the renewable energy absorption capacity index and the modularity index based on electrical distance, respectively.

[0027] Among them, renewable energy absorption capacity indicators Defined as: ; In the formula, express Time of the first The renewable energy absorption capacity indicators for each region for Nodes within the time region Actual value of renewable energy power supply. for Nodes within the time region The maximum power output of renewable energy sources. for Nodes within the time region The original load power value, for The adjustable power value of all controllable devices within the time zone, including responsive loads, energy storage devices, etc.

[0028] To facilitate understanding, a set of typical examples are given. Suppose a certain partition... If a time period includes 3 nodes with maximum renewable energy outputs of 0.6 MW, 0.9 MW, and 0.5 MW respectively, then: ; The corresponding original load powers are 0.7 MW, 0.8 MW, and 0.5 MW, respectively, that is: ; Assuming that this zone can generate an additional 0.1 MW of electricity demand through adjustable load and energy storage, that is: ; because According to the above definition, we have: ; Modularity index based on electrical distance The definition is as follows: .

[0029] In the formula, Represents a node and nodes The weight of the edge; For all nodes The sum of the weights of connected edges. This is the sum of the weights of all nodes in the network. If a node... and nodes If they are assigned to the same area, then ,otherwise, . The value is generally between 0 and 1. A larger value indicates a tighter electrical connection between nodes within the same area and a clearer partitioning structure. For example, when the calculation yields... At that time, the current partition can be considered to have good electrical coupling and modular structure; if only This indicates that the internal connections of the partition are relatively loose, and the partition division needs to be adjusted.

[0030] edge weight It can be defined according to different physical quantities, such as impedance, voltage sensitivity and short-circuit capacity. Different weight definitions will lead to differences in the modularity results.

[0031] When using line impedance as the weight, we can set: ; And on Normalization is performed. If the per-unit impedances of branches ij and ik are 0.05 pu and 0.20 pu respectively, the normalized result is... , .

[0032] When using voltage sensitivity as the weight, we can set: ; Represents a node Active injection changes to nodes The degree of influence of voltage. If at a certain operating point there is... ,but .

[0033] When using short-circuit capacity as the weight, it can be set as follows: With connection to node short-circuit capacity Proportional. Assume it is related to the node. Two connected nodes , The short-circuit capacities are 25 MVA and 10 MVA, respectively, and after normalization, we get... , .

[0034] This method combines renewable energy absorption capacity indicators with modularity indicators based on electrical distance to calculate comprehensive performance index values ​​for each particle region. This achieves structural and functional coordination during grid partitioning, thereby improving renewable energy absorption levels, promoting a balance between energy sources, loads, and storage within the partition, and reducing energy waste. Simultaneously, the modularity indicator based on electrical distance ensures tight electrical connections between nodes within the partition, enhancing the structural clarity and coupling strength of the region. The introduction of comprehensive performance index values ​​means that partitioning optimization not only focuses on energy utilization efficiency but also considers the topological rationality of the grid, thus improving the overall stability, reliability, and management efficiency of grid operation. This provides strong support for the efficient integration of renewable energy and the safe and economical dispatch of the grid.

[0035] Step S3: Determine whether there are isolated nodes in the current partition based on the adjacency matrix.

[0036] In one feasible implementation, an isolated node is a node that has no direct connections to other nodes in a certain area; that is, the connectivity of this node with other nodes is 0. The specific determination method is as follows: First, construct the adjacency matrix: This requires constructing the adjacency matrix for the entire network. ,in, Represents a node and nodes Is there a connection between them? If there is a connection, then... ,otherwise .

[0037] Secondly, traverse all nodes: traverse every node in the network. Calculate the relationship between this node and all other nodes in the partition. Connectivity. If for all All This indicates that the node It is an isolated node.

[0038] By constructing and analyzing the adjacency matrix to determine whether isolated nodes exist within the current partition, isolated nodes without direct electrical connections to other nodes in the region can be accurately identified. This avoids electrical islanding problems caused by improper partitioning and ensures the connectivity of the network within each partition. Early detection of isolated nodes provides crucial information for subsequent adjustments and optimizations to the partitioning scheme, significantly improving the practicality and security of the power grid partitioning strategy and enhancing the reliability of the entire partitioning optimization process.

[0039] Step S4: If yes, calculate the comprehensive performance index value according to the penalty term function to obtain the fitness evaluation result.

[0040] In some embodiments, the method further includes: The single-node impact factor is calculated based on multiple weighting coefficients, load weight, voltage level, and reliability level. The penalty term function is obtained by multiplying the adjustment coefficient, the single-node influence factor, and the partition penalty factor.

[0041] In one feasible implementation, if there are isolated nodes within the partition under the current conditions, a penalty term is added to the overall performance index value before proceeding to the next step. Penalty term function. It can be represented as: ; In the formula, It is an adjustment factor used to control the intensity of the penalty term; isolated node The region to which it belongs; For partitioning The number of isolated nodes; It is a single-node influence factor; This is the partitioning penalty factor.

[0042] in, ; In the formula, , , These are the weighting coefficients; For load weighting, nodes can be used. The ratio of active / reactive load to total system load reflects the importance of nodes to system power contribution; The voltage level represents the impact of the node voltage level on the isolation risk. The reliability level indicates the node's power supply reliability requirements. For ease of engineering application, this application specifies the voltage level. and reliability level The judgment rules are as follows: voltage level Judgment rules: The rated voltage level of each node is determined according to the power grid design or operation procedures. Select the highest voltage level in the system. As a reference voltage, its per-unit value is 1.0, then the node Calculate using the following formula: ; For example, taking 110 kV as the highest voltage level, that is... There are 110 kV nodes. 35kV node 10 kV node 0.4 kV node For nodes at other voltage levels, the calculation can be performed according to the ratio of rated voltage to maximum voltage, following the rules described above. When the actual maximum voltage level of the system differs, only corresponding adjustments are needed. The calculation method remains unchanged. High-voltage level nodes... A larger value means that its isolation will lead to greater regulatory complexity.

[0043] Reliability level Judgment rules: Reliability levels are classified based on the power supply reliability requirements of the loads connected to the nodes, and can be referenced to the importance of the loads or the general classification of load levels by official / industry authorities: Level 1 (High Reliability) Nodes: Connecting users such as hospitals, major transportation hubs, key substations, and important information centers where prolonged power outages are unacceptable. .

[0044] Level 2 (Medium Reliability) Nodes: Connect general industrial and commercial users, important residential communities, important public buildings, and other users who can tolerate short-term power outages but require high power supply reliability. .

[0045] Level 3 (Ordinary Reliability) Node: Connects general residential users or loads with low power supply reliability requirements, and takes... For nodes that only serve as intermediate network connections and do not directly carry loads, their impact on system power supply reliability can be considered minimal, and therefore... .

[0046] When the same node is simultaneously connected to loads of different levels, the above-mentioned level values ​​can be weighted and averaged according to the capacity ratio of each type of load to obtain the overall reliability level of the node. : ; In the formula, The partition size, i.e., the partition The number of nodes included, and isolated nodes in small partitions (e.g.) This will significantly reduce the efficiency of regional regulation; Partition connectivity, which is the ratio of the number of edges within a partition to the number of fully connected edges, reflects the compactness of the partition structure.

[0047] In this method, comprehensive performance indicators are used. The fitness function formed by the penalty term P: ; The fitness function described above is used to evaluate the merits of each particle's current partitioning scheme, yielding the fitness evaluation result F. For each particle... The partition position corresponding to the maximum fitness value obtained in each iteration is denoted as the individual optimal position of the particle. Among all the historical best positions of individual particles, the partition with the highest fitness value is recorded as the population's optimal position. .

[0048] As the BPSO algorithm iterates, the fitness of each particle's current partitioning scheme is recalculated in each round and compared with its own historical best and the group's historical best. If the current fitness is greater, the corresponding individual's optimal position is updated with the current partitioning position. Or the group's optimal position (i.e., the global optimal position). .

[0049] This study improves the rationality and practicality of power grid zoning optimization by introducing a penalty function to evaluate the fitness of zoning schemes with isolated nodes. The penalty function comprehensively considers the single-node impact factor of isolated nodes and the connectivity of the zoning. The single-node impact factor accurately quantifies the impact of isolation on system operation by integrating the node's load weight, voltage level, and reliability level, ensuring priority is given to the connectivity of highly important nodes. The zoning penalty factor further evaluates the structural characteristics of the zoning itself, avoiding the negative impact of small-scale or low-connectivity zoning on overall control efficiency. Combining this penalty term with comprehensive performance indicators to form a fitness function enables the optimization algorithm to automatically identify and avoid zoning schemes that generate isolated nodes, guiding the search process towards zoning schemes with complete structure, tight electrical coupling, and favorable renewable energy consumption. This enhances the engineering feasibility of the zoning results, improves the overall quality and operational reliability of the power grid zoning strategy, and provides a better decision-making basis for subsequent power grid planning and dispatching.

[0050] Step S5: Update the particle region position and particle velocity based on the fitness evaluation results to obtain the globally optimal partition position.

[0051] In one feasible implementation, the specific formulas for updating particle velocity and particle region position are as follows: ; ; In the formula, The velocity of the particle; The position of the particle, i.e., the particle of The partition number where the node is located; For particles of The partition number where the node is located, the node For any one of the nodes Connected nodes; This represents the current iteration number; This is the inertia weight, typically set to 1; , The learning factor is generally taken as... ; , These are random numbers uniformly distributed within the interval [0,1]. For the first The individual in the first The optimal position of an individual particle is the partition position corresponding to the particle when it obtains the maximum fitness value in each iteration. For the population in the first The optimal position of the population in dimension 1 is the partition position corresponding to the group with the highest fitness value among all the optimal positions of individual particles in the history of the particles. Dimensional partition number; This is a bitwise OR operation; it returns 1 if the two numbers are the same, and 0 if they are different.

[0052] Specifically, by updating the particle region positions and particle velocities based on fitness evaluation results, the particle swarm is effectively guided to evolve towards a better partitioning scheme, thereby obtaining the globally optimal partitioning position. The particle velocity update formula integrates individual historical optimal experience with swarm historical optimal information, and by introducing XOR operations to process discrete partition number data, the update process is more adapted to the discrete characteristics of power grid partitioning. The particle region position update is dynamically adjusted according to the updated velocity, ensuring effective development during the search process. The above update mechanism not only balances the ability of global search and local fine-tuning, but also helps the algorithm avoid getting trapped in local optima, improving the convergence speed and solution quality of the partitioning optimization process. The final globally optimal partitioning position provides a structurally reasonable and functionally coordinated optimal partitioning scheme for power grid planning and operation, enhancing the practicality of the partitioning strategy.

[0053] Step S6: Determine whether all parameters corresponding to the current partitioning scheme meet the preset constraints; if so, obtain the partitioning result based on the globally optimal partitioning position.

[0054] In some embodiments, the preset constraints include: Power system power flow constraints, node voltage constraints, branch power flow constraints, and distributed generation power injection constraints.

[0055] In some embodiments, the method further includes: According to the injection node Active and reactive power, nodes The voltage magnitude, the real part of the nodal admittance matrix, the imaginary part of the nodal admittance matrix, and the node and nodes The phase angle difference between the phase angles is calculated to obtain the power flow constraints of the power system. According to the node Upper limit of voltage amplitude and node The lower limit of the voltage amplitude is calculated to obtain the node voltage constraint; According to the route Active power and lines The maximum allowed active power is calculated to obtain the branch power flow constraints; The distributed generation injection power constraint is obtained by comparing the active power of the distributed generation injected into the distribution network with the maximum active power of the distributed generation injected into the distribution network.

[0056] In one feasible implementation, the preset constraints specifically include power system flow constraints, node voltage constraints, branch flow constraints, and distributed generation injection power constraints; the specific calculation methods for each preset constraint can be as follows: ① Power flow constraints in power systems: ; In the formula, , For injection nodes Active power and reactive power, For nodes voltage amplitude, Includes all nodes in the system Connected nodes, Let be the real part of the nodal admittance matrix. Let be the imaginary part of the nodal admittance matrix. For nodes and nodes The phase angle difference between them.

[0057] ② Node voltage constraints: ; In the formula, For nodes The upper limit of voltage amplitude, For nodes The lower limit of voltage amplitude.

[0058] ③ Branch flow constraints: ; In the formula, For the line active power, For the line The maximum active power allowed to pass.

[0059] ④ Distributed power injection power constraints: ; In the formula, Active power of distributed generation injected into the distribution network This represents the maximum active power of distributed generation that can be injected into the distribution network.

[0060] This application incorporates comprehensive pre-defined constraints to perform a final verification of the partitioning scheme, ensuring that the obtained globally optimal partitioning location not only meets the optimization objective but also aligns with the safety and stability requirements of actual power system operation. Power flow constraints guarantee the balance and rational distribution of power within and outside the partition, node voltage constraints effectively prevent voltage exceedance risks, branch flow constraints avoid line overloads, and distributed generation power injection constraints promote the safe absorption of renewable energy. This application combines theoretically optimized partitioning schemes with physical laws, giving the final partitioning results practical value for directly guiding power grid planning and operation. It fundamentally avoids the infeasibility of schemes due to violations of system operation constraints, thus improving the reliability and practicality of the partitioning method.

[0061] Step S7: Calculate the scheduling instructions based on the partitioning results.

[0062] In some embodiments, scheduling instructions are calculated based on the partitioning results, including: Based on the zoning results, obtain the upper limit and lower limit of the regulation capacity for each region; The total power deviation of the system is calculated based on the upper limit and lower limit of the regulation capacity of each region. Based on the total power deviation of the system, power adjustment commands for each region are obtained; Based on the zoning results, multiple parameters corresponding to the adjustable resources on the source-load side are obtained and calculated to obtain the injected power, voltage amplitude, upper limit of the regulation capacity of the region, and lower limit of the regulation capacity of the region at the grid connection point. The control commands for adjustable resources on the source-load side are obtained by processing the injected power, voltage amplitude, upper limit of the regulation capacity of the region and lower limit of the regulation capacity of the region at the grid connection point. Scheduling instructions are obtained based on the power adjustment instructions for each region and the control instructions for adjustable resources on the source and load sides.

[0063] In one feasible implementation, the above embodiments yielded a set of optimal partitioning results, i.e., a region set, that satisfies constraints such as voltage and power flow. Furthermore, it is possible to construct a hierarchical coordination and optimization module, which includes a regional coordination layer and an intra-regional autonomy layer.

[0064] Specifically, the regional coordination layer, as the upper-level decision-making unit, is responsible for global power balance across regions.

[0065] Its state space (the set of input data received and utilized by the algorithm at each level) can include the total power deviation of the system. Upper limit of adjustment capacity in each region and the lower limit of regulation capacity in each region .

[0066] ; in, This represents the total active power load of the system at the current moment. It is the sum of the active power output of all controllable power sources in the system (conventional units, renewable energy, grid power purchase, etc.). A positive sign indicates that there is a power deficit in the system and it needs to be adjusted upwards, while a negative sign indicates that there is a power surplus and it needs to be adjusted downwards.

[0067] No. The upper and lower limits of the adjustment capacity for each region at the current moment are calculated as follows: ; ; In the formula, , , They are respectively regions Internal collection of conventional / renewable power sources, energy storage devices, and adjustable loads; For equipment Currently, I have made contributions and exerted my efforts; , To its upper and lower force limits; , These refer to the remaining dischargeable and rechargeable power capacity of the energy storage device under energy constraints. , These represent the maximum allowable reduction and maximum allowable increase of the adjustable load, respectively. Indicates the area The maximum power can still be increased; This indicates the maximum power that can still be reduced (a negative value).

[0068] Its action space (the set of control decision outputs given by the algorithm at each level) can include the inter-region power exchange matrix. ,in Indicates partition To partition The power transfer amount. For any time, we have: , ; In the formula, Assigning regions to the coordination layer Adjusting the workload. The plus sign indicates a region. Net output (power supplied to other areas), the negative sign indicates net input. The diagonal elements in the matrix are always 0, i.e. The coordination layer optimizes... Cause total power deviation Eliminated by all regions, while simultaneously satisfying the capacity constraints of each region: Specifically, the autonomous level within the region, as the lower-level execution unit, can act according to regional coordination instructions. Complete the power distribution within the region.

[0069] Its state space can include the injected power of the regional grid connection point. and Voltage amplitude Regional adjustment capacity limit and lower limit .area The active and reactive power injection at the grid connection point are calculated as follows: ; ; In the formula, A collection of connecting lines to areas outside the region; , For connecting lines Inflow area The power flow power (obtained from power flow calculation results); , These are the power sources / loads within the area. Active and reactive power. Voltage amplitude at the regional grid connection point. Obtained from the power flow equations: ; in, The system node voltage vector; the autonomous layer uses the grid connection point voltage magnitude from the power flow calculation results. As a state quantity And it is required to meet voltage constraints. . and It is shared by the autonomous layer and the coordination layer, and can be updated in real time according to the formula above.

[0070] Its action space is based on the consensus algorithm described below, which integrates upper-level instructions. This is broken down into specific power regulation values ​​for each flexible resource within the region (such as the charging and discharging power of a single energy storage device, and the start-stop status of controllable loads). Let the region... shared within Let there be an adjustable device, and its active power regulation vector be denoted as: ; Then the autonomous level must satisfy: ; in, and The upper and lower limits are adjusted for individual device k. A typical consistency allocation can be written as: ; in, Let k be the adjacency set of device k. For adjacency weight, , This is the step size coefficient; through iteration, the power adjustment of each device converges to a point that can achieve the regional regulation task while satisfying the constraints. A set of solutions yields specific adjustment quantities such as the charging / discharging power of a single energy storage device and the start / stop status of controllable loads.

[0071] Furthermore, a reward function can be set, which includes system tuning costs and penalties for exceeding constraint limits. System tuning costs Specifically as follows: ; In the formula, For the number of regions, For the region The power regulation amount satisfies the following formula: ; Regional adjustment cost functions are generally in quadratic or linear piecewise form, for example: ; In the formula, , , All coefficients are obtained by fitting the marginal cost of regional adjustment resources. The penalty for exceeding the constraint limit includes a penalty for exceeding the adjustment power limit. Penalties for exceeding the limits of branch road trends The details are as follows: ; ; In the formula, and These are the penalty factors for scenarios involving excessive power and excessive branch power flow, respectively. and These represent the upper and lower limits of the regional adjustment capacity. and Branch roads Trends and their limits For the set of branches.

[0072] At each time step Each regional agent, based on the observed state (Composed of the above state variables) Execute actions (correspond and ), and act upon the environment to obtain rewards from the environment: ; and cause the environmental state to change Continue until one training round is completed. The first... The cumulative discount reward for a given training round is obtained by adding up the discount rewards at each time step within that round: ; in, As a discount factor, Let be the end time of the k-th round. Let the average cumulative discounted reward for all agents in the k-th training round be: ; In the formula, For the number of agents in the region, For the kth round, the first... The cumulative discount reward for each agent. Through interaction with the environment, each agent in each region optimizes its own strategy during training, making the sequence... The convergence rate gradually increases with the number of training rounds until it stabilizes. Specifically, the convergence criterion is set as follows: ; And the above conditions are continuous If the condition is met in all training rounds, the cumulative reward function is considered to have stabilized, and training ends. The preset convergence threshold is typically set to 1. or On the order of 1% of the absolute value; This is the minimum number of training rounds required to continuously satisfy the threshold condition, for example, 50 to 100 rounds. When the number of training rounds reaches the maximum value... If the stability criterion is still not met, training is stopped and the policy obtained at this time is taken as the approximate optimal policy.

[0073] After training stabilizes, a hierarchical coordination optimization model is obtained, consisting of upper-level regional coordination strategies and lower-level intra-regional autonomy strategies. In actual operation, using the current system state as input, this model can output an inter-regional power exchange matrix. Or power adjustment commands for each region and specific control instructions for each flexible resource. The above results are used to drive energy storage devices, controllable loads and distributed power sources to perform power adjustment, so as to realize dynamic power exchange between regions and economic and safe power distribution within the region.

[0074] This application constructs a hierarchical coordination and optimization module based on the partitioning results and calculates scheduling instructions, realizing a complete closed loop from theoretical partitioning schemes to actual control instructions, ensuring that the partitioning optimization results can safely guide the real-time operation of the power grid. Global power balance decisions are made through the regional coordination layer, accurately calculating the regulation capacity of each region and allocating regulation tasks, utilizing the adjustable resources of the entire network to eliminate the total system power deviation. Simultaneously, the autonomous layer within each region, based on the upper-level instructions and combined with the electrical status of the regional grid connection point and the internal resource regulation capabilities, decomposes the regional-level instructions into precise control quantities for flexible resources such as energy storage devices, controllable loads, and distributed power sources within the region through a consensus algorithm, achieving autonomous optimization and rapid response within the region. The collaborative architecture between upper and lower layers balances global optimization and local execution, ensuring that the scheduling instructions meet both system-level safety requirements and the physical characteristics of the equipment layer. The final generated regional power regulation instructions and source-load-side adjustable resource control instructions together constitute a scheduling instruction set that can directly drive equipment actions, significantly improving the grid's coordinated control capabilities in the face of renewable energy fluctuations and load changes.

[0075] Step S8: Calculate the adjustable resources on the source-load side according to the scheduling instructions to obtain the resource regulation results.

[0076] In some embodiments, the adjustable resources on the source-load side are calculated according to the scheduling instructions to obtain the resource regulation results, including: Constructing an adjacency matrix based on a partitioned communication network; The control output is calculated based on the adjacency matrix and scheduling instructions. The update and adjustment instructions are obtained by processing the local control update formula and control output. Adjust the adjustable resources on the source and load side according to the update adjustment instructions to obtain the resource regulation results.

[0077] In one feasible implementation, a leader node election is first performed: the leader node is elected based on voltage sensitivity ranking, and the adjustable resource with the highest sensitivity automatically becomes the leader node, responsible for receiving the various instructions obtained in the above embodiments and broadcasting them to the partitions. The voltage sensitivity formula is expressed as follows: ; In the formula, The reactive power sensitivity is the voltage change when a unit reactive power is injected into a node. The larger the value, the stronger the voltage regulation capability of the node. Active power sensitivity is the voltage fluctuation when a node injects a unit of active power; the larger the value, the more sensitive the node is to power fluctuations.

[0078] Furthermore, information exchange topology calculations are performed: an adjacency matrix is ​​constructed based on the partitioned communication network. If node With nodes If a communication connection exists, then ,otherwise In consensus algorithms, the adjacency matrix and node state variables are... Together, they form the weighted state difference term for each node relative to its neighbors: ; in, This is the "output" of the adjacency matrix in the information exchange topology, used to characterize the nodes. Its set of neighboring nodes The weighted difference term is the cumulative state deviation between nodes. This weighted difference term serves as the input to the subsequent consistent collaborative control law, determining the direction and magnitude of each node's local control input based on the state differences with its neighbors. This enables effective propagation and coupling of information within the partition, playing a crucial role in the final collaborative control convergence process. For example, when node i's voltage is 0.97 pu, and the voltages of its three neighboring nodes are 1.00 pu, 0.99 pu, and 1.01 pu respectively, the weighted difference term is positive overall, and the direction of the local control input is calculated as "increasing voltage." The corresponding specific action is to increase the reactive power output of the inverter at this node or appropriately reduce the active power load at this node. Conversely, when the average voltage of neighboring nodes is approximately 0.96 pu while node i's voltage is 1.00 pu, the weighted difference term is negative overall, and the direction of the local control input is "decreasing voltage," such as reducing the reactive power output of the inverter or appropriately increasing the active power load at this node. For example, under the same voltage difference, if a neighboring node has a larger communication weight with node i, its state difference will account for a higher proportion in the weighted difference term, which will increase the adjustment range calculated by the local control quantity accordingly. This will allow the adjustment to be prioritized along the direction indicated by the neighboring node, thus accelerating the convergence speed of the overall coordinated control.

[0079] Furthermore, consensus coordination control calculations are performed: an improved finite-time consensus protocol is adopted to achieve second-level response, as detailed below: ; In the formula, To control the output, the node is represented. Control commands (such as inverter voltage regulation); To control the gain, Determines the convergence speed; It is a sign function, ensuring finite-time convergence; The elements are adjacent matrix elements (1 if they are in the same partition, 0 otherwise); and These are the state variables of the neighbor and the local state variables (such as voltage amplitude), respectively.

[0080] When the control quantity is obtained Then, it is substituted into the local control update formula to complete the coordinated control of each flexible resource (i.e., adjustable resources on the source-load side). The specific operation of the local control update formula is as follows: 1) Mapped to power / voltage regulation commands: If node If reactive power regulation is performed using an inverter, then: ; in, For nodes reactive power reference value, This is the updated reactive power output; If node For active, adjustable resources (such as adjustable loads and some distributed power sources), then: ; in, The original active power reference value, The active power output / load level after implementing coordinated control.

[0081] 2) Discrete-time unified update form: In discrete control implementation, the above process can be uniformly represented as: ; Soon As a node The state (or control quantity) increment within the current sampling period is used to update the state / control setpoint for the next time step. .

[0082] 3) Collaborative control process: After all nodes perform the above update operations in parallel, the new system operating state is obtained through power flow calculation or state estimation. Subsequently, each node, based on the updated state variables (i.e., update adjustment instructions), uses the adjacency matrix to exchange information again and recalculate. The process iterates continuously. As the iterations proceed, the state differences between nodes gradually decrease, converging to the expected consistent or quasi-consistent state within a finite time, thereby achieving distributed collaborative control of adjustable resources on the source and load sides within the partition.

[0083] For example, a quasi-consistency criterion can be preset as "the maximum absolute value of the voltage difference between adjacent nodes is less than 0.01 per unit and remains unchanged for 5 consecutive control cycles": when the voltage difference between all adjacent nodes in the system converges to within 0.01 per unit and no longer changes significantly within a time interval T=2, it indicates that the voltage distribution has reached the desired quasi-consistency state, and the voltage level within the partition is basically balanced; if it is further required that "the maximum voltage difference is less than 0.005 per unit and remains unchanged for 10 consecutive control cycles," then the system operation state can be considered to have reached a more stringent consistency state. Similarly, for active power regulation, it can be set that "the power sharing deviation of each parallel adjustable resource does not exceed 2% of its rated capacity and continues for several sampling cycles." When the actual iteration results meet the above conditions, it can be determined that the power sharing of each node is basically consistent, the system enters a stable and coordinated operation state, and the resource regulation results are obtained.

[0084] This application efficiently transforms upper-level dispatch instructions into actual control actions for adjustable resources on the source and load sides, achieving precise execution of power grid control. Based on voltage sensitivity, a leader node is dynamically elected, ensuring that the node with the strongest voltage regulation capability receives and broadcasts instructions first, thereby optimizing the instruction transmission path and response efficiency. By constructing an adjacency matrix to characterize the information interaction topology within a partition and utilizing an improved finite-time consistency protocol for calculation, each node can quickly form a collaborative control output based on its local and neighbor states, achieving rapid response within seconds and finite-time convergence. The local control update formula transforms the consistent control quantity into specific power or voltage regulation instructions, driving flexible resources such as inverters and adjustable loads to adaptively adjust, and through continuous iterative calculation, the entire partition's operating state converges collaboratively to the expected target. This not only significantly improves the real-time performance and accuracy of dispatch instruction execution but also enhances the system's self-organization and collaborative recovery capabilities in the face of fluctuations, effectively ensuring stability and balance within the partition and significantly improving the intelligence level of power grid operation.

[0085] The source-load adjustable resource regulation method provided in the above embodiments can effectively improve the absorption level of renewable energy, enhance the rationality of the power grid zoning structure and the tightness of electrical coupling, and ensure that the zoning scheme meets all preset constraints at the same time. Furthermore, by optimizing scheduling, it significantly improves the coordination and control capability of source-load adjustable resources and the overall energy utilization efficiency, and constructs a progressive regulation framework of dynamic generation of zoning, hierarchical coordination optimization and distributed autonomous execution, so as to carry out reasonable and precise regulation of source-load adjustable resources.

[0086] Please see Figure 2 Another embodiment of this application provides a source-load adjustable resource regulation device, the device comprising: The acquisition module 101 is used to obtain the particle size based on the number of adjustable resources on the source load side; and to initialize based on the particle size to obtain the particle region position and particle velocity.

[0087] The indicator module 102 is used to calculate the comprehensive performance index value of each particle region position based on the renewable energy absorption capacity index and the modularity index based on the electrical distance.

[0088] The judgment module 103 is used to determine whether there are isolated nodes in the current partition based on the adjacency matrix.

[0089] The evaluation module 104 is used to calculate the comprehensive performance index value according to the penalty term function if the condition is met, and obtain the fitness evaluation result.

[0090] The update module 105 is used to update the particle region position and particle velocity based on the fitness evaluation results to obtain the globally optimal partition position.

[0091] The constraint module 106 is used to determine whether each parameter corresponding to the current partitioning scheme meets the preset constraint conditions; if so, the partitioning result is obtained based on the globally optimal partitioning position.

[0092] The scheduling module 107 is used to calculate and obtain scheduling instructions based on the partitioning results.

[0093] Result module 108 is used to calculate the adjustable resources on the source-load side according to the scheduling instructions and obtain the resource regulation results.

[0094] The specific limitations of the source-load adjustable resource control device provided in this embodiment can be found in the embodiment of the source-load adjustable resource control method described above, and will not be repeated here. Each module in the above-described source-load adjustable resource control device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0095] This application provides a computer device that may include a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it causes the processor to perform the steps of a load-adjustable resource control method as described in any of the above embodiments.

[0096] The working process, working details and technical effects of the computer device provided in this embodiment can be found in the embodiment of a source-load adjustable resource regulation method described above, and will not be repeated here.

[0097] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of a source-load adjustable resource control method as described in any of the above embodiments. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0098] The working process, working details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the embodiment of a source-load adjustable resource regulation method described above, and will not be repeated here.

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0100] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for adjusting source load resources, characterized in that, The method includes: The particle size is obtained based on the number of adjustable resources on the source load side; the particle size is then used for initialization to obtain the particle region position and particle velocity. The comprehensive performance index value of each particle region is obtained by calculating based on the renewable energy absorption capacity index and the modularity index based on electrical distance. Determine whether there are isolated nodes in the current partition based on the adjacency matrix; If so, the comprehensive performance index value is calculated based on the penalty term function to obtain the fitness evaluation result; The particle region position and particle velocity are updated based on the fitness evaluation results to obtain the globally optimal partition position; Determine whether all parameters corresponding to the current partitioning scheme meet the preset constraints; if so, obtain the partitioning result based on the globally optimal partitioning position. The scheduling instructions are calculated based on the partitioning results. The adjustable resources on the source-load side are calculated according to the scheduling instructions to obtain the resource regulation results.

2. The source-load adjustable resource regulation method according to claim 1, characterized in that, The method further includes: The renewable energy absorption capacity index is calculated based on the actual power value of renewable energy sources, the maximum power that renewable energy sources can generate, the original load power value, and the adjustment power value of all controllable equipment in the region. According to the node and nodes The weights of the edges and their connections to the nodes The modularity index based on electrical distance is obtained by calculating the sum of the weights of connected edges and the sum of the weights of each node in the network.

3. The source-load adjustable resource regulation method according to claim 1, characterized in that, The method further includes: The single-node impact factor is calculated based on multiple weighting coefficients, load weight, voltage level, and reliability level. The penalty term function is obtained by multiplying the adjustment coefficient, the single-node influence factor, and the partition penalty factor.

4. The source-load adjustable resource regulation method according to claim 1, characterized in that, The preset constraints include: Power system power flow constraints, node voltage constraints, branch power flow constraints, and distributed generation power injection constraints.

5. The source-load adjustable resource regulation method according to claim 4, characterized in that, The method further includes: According to the injection node Active and reactive power, nodes The voltage magnitude, the real part of the nodal admittance matrix, the imaginary part of the nodal admittance matrix, and the node and nodes The phase angle difference between the phase angles is calculated to obtain the power flow constraints of the power system. According to the node Upper limit of voltage amplitude and node The lower limit of the voltage amplitude is calculated to obtain the node voltage constraint; According to the route Active power and lines The maximum allowed active power is calculated to obtain the power flow constraints of the branch; The distributed power injection constraint is obtained by comparing the active power of the distributed source injected into the distribution network with the maximum active power of the distributed source injected into the distribution network.

6. The source-load adjustable resource regulation method according to claim 1, characterized in that, The step of calculating and obtaining scheduling instructions based on the partitioning results includes: Based on the partitioning results, obtain the upper limit and lower limit of the regulation capacity for each region; The total power deviation of the system is calculated based on the upper limit and lower limit of the regulation capacity of each region. Based on the total power deviation of the system, power adjustment commands for each region are obtained. Based on the partitioning results, multiple parameters corresponding to the adjustable resources on the source-load side are obtained and calculated to obtain the injected power, voltage amplitude, upper limit of the regulation capacity of the region, and lower limit of the regulation capacity of the region at the grid connection point. The control commands for adjustable resources on the source-load side are obtained by processing the injected power, voltage amplitude, upper limit of the regulation capacity of the region and lower limit of the regulation capacity of the region at the grid connection point of the region. Scheduling instructions are obtained based on the power adjustment instructions for each region and the control instructions for adjustable resources on the source and load sides.

7. The source-load adjustable resource regulation method according to claim 1, characterized in that, The step of calculating the adjustable resources on the source-load side according to the scheduling instruction to obtain the resource regulation result includes: Constructing an adjacency matrix based on a partitioned communication network; The control output quantity is calculated based on the adjacency matrix and the scheduling instruction. The update adjustment command is obtained by processing the local control update formula and the control output quantity. Adjust the adjustable resources on the source load side according to the update adjustment command to obtain the resource regulation result.

8. A source-load adjustable resource regulation device, characterized in that, The device includes: The acquisition module is used to acquire the particle size based on the number of adjustable resources on the source load side; and to initialize the particle region position and particle velocity based on the particle size. The indicator module is used to calculate the comprehensive performance index value of each particle region position based on the renewable energy absorption capacity index and the modularity index based on the electrical distance. The judgment module is used to determine whether there are isolated nodes in the current partition based on the adjacency matrix; The evaluation module is used to calculate the comprehensive performance index value according to the penalty term function if the condition is met, and obtain the fitness evaluation result. The update module is used to update the particle region position and the particle velocity based on the fitness evaluation result to obtain the globally optimal partition position; The constraint module is used to determine whether all parameters corresponding to the current partitioning scheme meet the preset constraint conditions; if so, the partitioning result is obtained based on the globally optimal partitioning position. The scheduling module is used to calculate and obtain scheduling instructions based on the partitioning results; The results module is used to calculate the adjustable resources on the source-load side according to the scheduling instructions and obtain the resource regulation results.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the source load adjustable resource control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the source load adjustable resource control method as described in any one of claims 1 to 7.

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