Power distribution system control strategy determination method and device, and electronic device

Abstract

The application discloses a kind of control strategy determination method, device and electronic equipment of power distribution system.Therein, the method comprises: receiving and responding to the splitting event signal of power distribution system, obtaining the full amount operating state information corresponding to power distribution system;According to full amount operating state information, the on-off state of controllable line is combined, and power distribution system is divided into multiple initial island sets;According to local operating state information, a multi-agent dynamic frequency response model is constructed;Solving multi-agent dynamic frequency response model, according to the initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively, the updated island set is obtained by updating, and the loss value and control strategy operation are determined iteratively until the target condition, to obtain the target control strategy corresponding to the target island set.The application solves the technical problem that the strategy loss is high when determining the control strategy corresponding to multiple island systems in the related art under the splitting condition.

Description

Technical Field

[0001] This invention relates to the field of power distribution systems, and more specifically, to a method, apparatus, and electronic device for determining control strategies for power distribution systems. Background Technology

[0002] In related technologies, with the large-scale integration of new resources such as distributed power sources, energy storage devices, and electric vehicles into power distribution systems, the operating mode of power distribution systems is gradually evolving from the traditional single-power supply mode to a multi-entity collaborative power supply mode. In scenarios such as extreme natural disasters, major equipment failures, or external attacks, the power distribution system may disconnect from the upstream power grid and enter an islanded operation state. At this time, how to rationally divide the islanded areas and coordinate the participation of multiple resources in frequency regulation and load management is a key technical issue in ensuring continuous power supply to critical loads and preventing system collapse.

[0003] Existing islanded operation and emergency control methods mostly employ centralized optimization models, making unified scheduling decisions for the system and generally assuming that all types of power sources and loads within the system completely obey unified control commands. However, in new power distribution systems, resources such as synchronous generators, virtual synchronous machines, energy storage power sources, photovoltaic power sources, and interruptible loads exhibit significant differences in dynamic response capabilities, control costs, and operational constraints. Each resource actually possesses independent decision-making preferences and behavioral characteristics during emergency operation, making it difficult to characterize the dynamic interactions between resources using purely static or centralized optimization methods. Furthermore, in the case of de-isolating systems, determining control strategies for multiple islanded systems presents the technical challenge of high strategy losses.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for determining control strategies of a power distribution system, in order to at least solve the technical problem in the related art where the determined strategies have high losses when determining control strategies for multiple islanded systems in the case of disconnection.

[0006] According to one aspect of the present invention, a method for determining a control strategy for a power distribution system is provided, comprising: receiving and responding to a disconnection event signal of the power distribution system, and acquiring full-scale operating state information corresponding to the power distribution system; based on the full-scale operating state information, and under the premise of satisfying network connectivity and radial operating constraints, combining the on / off states of controllable lines to divide the power distribution system into multiple initial island sets; and constructing a multi-agent dynamic frequency response model based on the local operating state information corresponding to each island system in the multiple initial island sets, wherein the multi-agent dynamic frequency response model includes a power loss... The objective function is to minimize the loss; the multi-agent dynamic frequency response model is solved to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets; based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, the updated island set is obtained, and the iterative operation to determine the loss value and control strategy is performed until the target condition is met, and the target control strategy corresponding to the target island set is obtained. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

[0007] Optionally, based on the full operational status information, and under the premise of satisfying network connectivity and radial operational constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets, including: invoking target constraints, wherein the target constraints include node affiliation uniqueness constraints, power node island binding constraints, node affiliation topology connectivity constraints, and line affiliation node consistency constraints; using a discrete particle swarm optimization algorithm, the on / off states of the controllable lines are combined to obtain multiple candidate island sets; from the multiple candidate island sets, the multiple initial island sets that meet the target constraints are selected.

[0008] Optionally, based on the local operating state information corresponding to each island system in the multiple initial island sets, a multi-agent dynamic frequency response model is constructed, including: retrieving an objective function, wherein the objective function includes a first function term representing the power of the energy storage battery, a second function term representing the load shedding power, a third function term representing the power of the synchronous generator, and a fourth function term representing the virtual synchronous machine; and constructing the multi-agent dynamic frequency response model based on the objective function and the local operating state information corresponding to each island system in the multiple initial island sets.

[0009] Optionally, solving the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets includes: discretizing the off-grid transient process into multiple time steps; controlling multiple game agents to execute update strategy operations according to the objective based on the game state parameters of the current time step until a predetermined convergence condition is reached, thereby obtaining the initial total loss value and preliminary control strategy corresponding to the current time step. The multiple game agents include synchronous generators, virtual synchronous machines, energy storage power supplies, and load entities; the game state parameters include the system inertial center frequency and the power corresponding to each of the multiple game agents; and controlling the execution of the strategy determination operation corresponding to the next time step until all strategy determination operations corresponding to the multiple time steps have been executed, thereby obtaining the initial total loss value and initial control strategy corresponding to the multiple initial island sets.

[0010] Optionally, from the multiple sets of candidate islands, the multiple sets of initial islands that meet the target constraints are selected, including: invoking steady-state constraints, wherein the steady-state constraints include power flow balance constraints within the island, power output constraints, load shedding constraints, node voltage constraints, and line capacity constraints; and selecting the multiple sets of initial islands that meet both the target constraints and the steady-state constraints from the multiple sets of candidate islands.

[0011] Optionally, solving the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets includes: invoking steady-state constraints and transient constraints, wherein the transient constraints include frequency minimum point constraints, frequency change rate constraints, power output transient constraints, maximum power angle constraints, and limit cut-off time constraints; and solving the multi-agent dynamic frequency response model under the constraints of the steady-state constraints and the transient constraints to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets.

[0012] Optionally, based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, an updated island set is obtained, and iterative operations to determine the loss value and control strategy are performed until the target condition is met. After obtaining the target control strategy corresponding to the target island set, the method further includes: controlling the corresponding island system to execute the corresponding initial control strategy.

[0013] According to one aspect of the present invention, a control strategy determination device for a power distribution system is provided, comprising: an acquisition module, configured to receive and respond to a disconnection event signal of the power distribution system, and acquire full-scale operating state information corresponding to the power distribution system; a division module, configured to, based on the full-scale operating state information, combine the on / off states of controllable lines under the premise of satisfying network connectivity and radial operating constraints, and divide the power distribution system into multiple initial island sets; and a construction module, configured to, based on the local operating state information corresponding to each island system in the multiple initial island sets, construct a multi-agent dynamic frequency response model, wherein the multi-agent dynamic frequency response model includes power... The objective function is to minimize the power loss. A first determining module is used to solve the multi-agent dynamic frequency response model and determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets. A second determining module is used to update the updated island set based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, and perform iterative operations to determine the loss value and control strategy until the target condition is met, thus obtaining the target control strategy corresponding to the target island set. The target condition includes at least one of the following: total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the control strategy determination method for a power distribution system as described in any of the preceding claims.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the control strategy determination method for a power distribution system as described above.

[0016] In this embodiment of the invention, a disconnection event signal of the power distribution system is received and responded to to obtain the full operating status information of the power distribution system. Based on the full operating status information, under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets. Based on the local operating status information corresponding to each island system in the multiple initial island sets, a multi-subject dynamic frequency response model is constructed, wherein the multi-subject dynamic frequency response model includes an objective function with the goal of minimizing power loss. The multi-subject dynamic frequency response model is solved to determine the initial total loss value and initial control strategy corresponding to each of the multiple initial island sets. Based on the initial total loss value and initial control strategy corresponding to each of the multiple initial island sets, the updated island set is obtained. Iterative operations to determine the loss value and control strategy are performed until the target condition is met, and the target control strategy corresponding to the target island set is obtained. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems. This paper adopts a combination of multi-agent dynamic game theory and iterative optimization. By dividing the initial island set under the constraints of network connectivity and radial operation, a multi-agent dynamic frequency response model with the objective function of minimizing power loss is constructed. The initial total loss value and control strategy are obtained by solving the model. The island set is then iteratively updated and the loss value and control strategy are repeatedly optimized until the preset objective conditions are met. This achieves the goals of minimizing the total power loss of the distribution system under off-grid operation, optimizing the island topology and multi-resource collaborative emergency control strategy, thereby significantly reducing the power loss of the island system and improving the economy of the distribution system's off-grid operation. This also solves the technical problem in related technologies where the determined strategy has high loss when determining the control strategies for multiple island systems under the case of disconnection. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a method for determining the control strategy of a power distribution system according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the frequency response model structure of a multi-resource system provided by an optional embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the frequency response reduction model structure of a multi-resource system provided by an optional embodiment of the present invention;

[0021] Figure 4This is a schematic diagram of the complete optimization decision model structure provided by an optional embodiment of the present invention;

[0022] Figure 5 This is a structural block diagram of a control strategy determination device for a power distribution system according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Example 1

[0026] According to an embodiment of the present invention, an embodiment of a control strategy determination method for a power distribution system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a control strategy determination method for a power distribution system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Receive and respond to the disconnection event signal of the power distribution system, and obtain the full operating status information of the power distribution system.

[0029] Among them, the power distribution system refers to the power distribution system composed of power distribution networks, various distributed power sources, energy storage devices, load nodes and control equipment, which can realize the local distribution, consumption and supply-demand balance of power, such as urban power distribution networks containing synchronous generators, virtual synchronous machines and photovoltaic power sources.

[0030] Among them, the disconnection event signal refers to the trigger signal that the power distribution system is electrically disconnected from the upper-level power grid due to extreme disasters, equipment failures, external attacks, etc. It can trigger the system's islanded operation detection and emergency control process in real time, such as the electrical signal generated by the tripping of the tie line between the system and the main grid.

[0031] Among them, the full operational status information refers to the collection of various electrical parameters and equipment information that can reflect the overall operational status of the power distribution system. It can provide comprehensive and accurate basic data support for subsequent islanding and model construction, such as the power distribution network topology, the load and importance level of each node, the location / capacity / control parameters of distributed power sources, and system frequency / voltage / power flow information.

[0032] In this step, the system monitors the operating status of the power distribution system in real time. Upon detecting a disconnection event signal, it immediately initiates a data acquisition process to comprehensively acquire all current operating status information of the power distribution system. This step provides complete and accurate foundational data for all subsequent islanding, model building, and control strategy formulation, avoiding subsequent decision-making errors due to missing or biased information. It also enables rapid response to disconnection events, buying time for islanding emergency control and ensuring timely emergency response after power distribution system disconnection.

[0033] Step S104: Based on the full operational status information, and under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets.

[0034] Network connectivity refers to the requirement that nodes and lines within an island form a continuous electrical connection, with no isolated nodes or disconnected branches, ensuring the normal transmission and distribution of electrical energy within the island. For example, a power node within an island can supply power to all load nodes through lines.

[0035] Among them, radial operation constraints refer to the requirement that the topology of the island must follow the radial operation requirements of the power distribution network, without the ring network operation situation, which can adapt to the traditional operation characteristics of the power distribution system, reduce the complexity of network control and the risk of failure. For example, starting from the power supply node, the line extends to each load node in a tree-like manner.

[0036] Among them, controllable lines refer to power distribution lines in the power distribution system that can be controlled by switching equipment to achieve on / off control. They can adjust the line connection status according to operational needs and provide a carrier for topology adjustment for islanding, such as 10kV power distribution lines with intelligent circuit breakers.

[0037] The on / off state refers to the operating state of a controllable line, which can be either on or off. It can be used to reconfigure the topology of the power distribution system by changing the on / off state of the line, such as disconnecting some tie lines to achieve partition isolation of the power distribution system.

[0038] The initial island set refers to a set of multiple island topology schemes obtained by combining controllable line on / off states based on full operational status information and under the basic topology constraints. It can provide multiple candidate schemes for subsequent model solving and strategy optimization, such as a set of schemes containing 3 or 5 different island partitioning methods.

[0039] In this step, based on the full operational status information obtained in S102 and following the basic topology operation rules of the power distribution system, the original overall power distribution system is divided into multiple sets of initially isolated islands that meet the requirements of network connectivity and radial operation by combining different combinations of the on / off states of all controllable lines. This step achieves preliminary topology reconstruction after the power distribution system is decoupled, breaking down the complex overall system into multiple independently operable candidate island units. Furthermore, by adhering to basic topology constraints, the feasibility of all initial islanding schemes in static operation is ensured, preventing invalid topology schemes from entering subsequent processes and improving the efficiency of subsequent emergency control decisions.

[0040] Step S106: Based on the local operating state information corresponding to each island system in the multiple initial island sets, construct a multi-agent dynamic frequency response model, wherein the multi-agent dynamic frequency response model includes an objective function with the goal of minimizing power loss.

[0041] An islanded system refers to a single island in an initial set of islands that has independent power supply and operation control capabilities, and can operate independently off-grid from the upper-level power grid. For example, an independent distribution island containing one synchronous generator and several load nodes.

[0042] Among them, local operating status information refers to the operating parameters and equipment characteristics of each islanded system. It can accurately reflect the operating status of a single island and provide exclusive data for the model construction of a single island, such as the power output, load power, and internal circuit topology of a certain islanded system.

[0043] Among them, the multi-agent dynamic frequency response model refers to the frequency dynamic response model that considers the interaction of multiple game agents such as synchronous generators, virtual synchronous machines, energy storage power sources, and load subjects for the off-grid transient operation characteristics of islanded systems. It can characterize the frequency regulation behavior and frequency evolution law of multiple resources within the island, and provide theoretical model support for the formulation of control strategies. For example, it includes frequency response game models such as rapid regulation of energy storage, primary frequency regulation of generators, and load reduction.

[0044] Among them, power loss refers to the sum of various power losses, such as active / reactive power loss and load shedding loss, generated by the islanded system during off-grid operation and emergency control. It can quantify the degree of energy loss during islanded operation, such as active power loss caused by load shedding and active power loss of line transmission.

[0045] The objective function refers to a mathematical function that quantifies the cost and power loss of multi-resource control with the goal of minimizing power loss. It can provide optimization guidance for solving multi-agent dynamic frequency response models, such as a multivariate power loss minimization function that includes energy storage power regulation, load shedding power, and generator output.

[0046] In this step, for the multiple initial island sets obtained in S104, local operating state information of each island system is extracted. With minimizing power loss as the core optimization objective, a multi-agent dynamic frequency response model adapted to the transient operation of a single island in the off-grid environment is constructed. This step collaboratively models island partitioning and frequency emergency control, overcoming the limitation of traditional centralized models in depicting the interaction of multi-agent strategies. Simultaneously, by focusing on minimizing power loss, the model solution always revolves around reducing island operating losses, ensuring the economic efficiency of subsequent control strategies. Furthermore, the model's construction based on local information of a single island improves its accuracy and adaptability.

[0047] Step S108: Solve the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively;

[0048] The initial total loss value refers to the sum of the overall power loss of each initial island set under the corresponding control strategy after solving the multi-agent dynamic frequency response model. It can quantify the degree of operational loss of each initial island set and provide a reference benchmark for loss values ​​for subsequent iterative optimization. For example, the initial total loss value of a certain initial island set is 500kW. h.

[0049] The initial control strategy refers to the off-grid emergency control scheme adapted to each initial island set after solving the multi-subject dynamic frequency response model. It can clarify the specific action mode and parameters of multiple resources within the island and provide specific control guidance for island operation, such as the energy storage power adjustment amount, load shedding list, and synchronous generator output adjustment value of a certain island.

[0050] In this step, a game theory equilibrium solution method is used to solve the multi-agent dynamic frequency response model constructed in S106. For each initial island set, the corresponding initial total loss value is calculated, and an initial control strategy adapted to that island set is formulated. Through this step, the theoretical model is transformed into specific quantified loss values ​​and executable control strategies, providing a clear benchmark for subsequent iterative optimization. Simultaneously, because the model considers the policy interactions of multiple agents, the solved initial control strategy can adapt to the dynamic frequency regulation characteristics of multiple resources within the islands, ensuring the feasibility and effectiveness of the strategy. Furthermore, the quantification of the initial total loss value provides a clear numerical evaluation standard for subsequent optimization of the island sets, avoiding biases from subjective decisions.

[0051] Step S110: Based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, update the island set to obtain the updated island set, perform iterative operation to determine the loss value and control strategy until the target condition is met, and obtain the target control strategy corresponding to the target island set. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

[0052] Among them, updating the island set refers to the new island set obtained by adjusting and optimizing the topology of the initial island set based on the initial total loss value and the initial control strategy. It can iteratively improve the operational economy and stability of the island set, such as the new island scheme obtained by fine-tuning the line on / off status of the initial island.

[0053] In this context, iteration refers to the repeated execution of the process of updating the island set, calculating the loss value, and formulating the control strategy. This process can gradually optimize the island set and the control strategy, allowing the result to continuously approach the optimal solution. For example, through 5 iterations, the total loss value of the island set can be reduced from 500kW. h drops to 300kW h.

[0054] Among them, the target condition refers to the termination condition of the iterative process, which can define the boundary for iterative optimization, avoid meaningless iterative calculations, and ensure decision-making efficiency.

[0055] The predetermined loss threshold refers to a pre-set upper limit for the total power loss, which can quantify the economic objective of iterative optimization. For example, the predetermined loss threshold can be set to 300kW. h.

[0056] The target island set refers to the set of optimal island topology schemes obtained after iterative optimization to meet the target conditions. It can take into account the economy, stability and supply guarantee of the off-grid operation of the power distribution system. For example, it is a set of islands that includes multiple independent islands and whose total loss value is lower than a predetermined threshold.

[0057] Among them, the target control strategy refers to the optimal off-grid emergency control strategy adapted to the target island set, which can minimize the power loss and frequency stability control of the island system and ensure continuous power supply to important loads.

[0058] In this step, based on the initial total loss value and initial control strategy obtained in S108, the initial island set is topologically adjusted to obtain an updated island set. The model building and solution process of S106-S108 is repeated to determine the loss value and control strategy of the updated island set until the predetermined loss threshold, predetermined number of iterations, and other target conditions are met, ultimately obtaining the target island set and its corresponding target control strategy. Through this step, iterative optimization of the island set and control strategy is achieved, gradually reducing the total power loss of the distribution system and allowing the final target island set and target control strategy to achieve the optimal balance between economy and stability. At the same time, by setting multi-dimensional target conditions, the economy of the control strategy (total power loss below the threshold) is ensured, while decision-making efficiency (reaching the predetermined number of iterations) is also taken into account, as well as the power supply guarantee capability of the distribution system (including multiple target island systems). In addition, the iterative optimization process allows the control strategy to adapt to the adjustment of the island topology, improving the adaptability and optimality of the strategy and effectively solving the technical problem of high loss in the control strategy of traditional methods.

[0059] Through the above steps S102-S110, the system receives and responds to the disconnection event signal of the power distribution system to obtain the full operational status information of the power distribution system. Based on the full operational status information, and under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets. Based on the local operational status information corresponding to each island system in the multiple initial island sets, a multi-subject dynamic frequency response model is constructed, wherein the multi-subject dynamic frequency response model includes an objective function with the goal of minimizing power loss. The multi-subject dynamic frequency response model is solved to determine the initial total loss value and initial control strategy corresponding to each of the multiple initial island sets. Based on the initial total loss value and initial control strategy corresponding to each of the multiple initial island sets, the updated island set is obtained. Iterative operations to determine the loss value and control strategy are performed until the target conditions are met, and the target control strategy corresponding to the target island set is obtained. The target conditions include at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems. This paper adopts a combination of multi-agent dynamic game theory and iterative optimization. By dividing the initial island set under the constraints of network connectivity and radial operation, a multi-agent dynamic frequency response model with the objective function of minimizing power loss is constructed. The initial total loss value and control strategy are obtained by solving the model. The island set is then iteratively updated and the loss value and control strategy are repeatedly optimized until the preset objective conditions are met. This achieves the goals of minimizing the total power loss of the distribution system under off-grid operation, optimizing the island topology and multi-resource collaborative emergency control strategy, thereby significantly reducing the power loss of the island system and improving the economy of the distribution system's off-grid operation. This also solves the technical problem in related technologies where the determined strategy has high loss when determining the control strategies for multiple island systems under the case of disconnection.

[0060] As an optional implementation, based on the full operational status information, and under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets. This includes: invoking target constraints, where the target constraints include node affiliation uniqueness constraints, power node island binding constraints, node affiliation topology connectivity constraints, and line affiliation node consistency constraints; using a discrete particle swarm optimization algorithm, the on / off states of controllable lines are combined to obtain multiple candidate island sets; and from the multiple candidate island sets, multiple initial island sets that meet the target constraints are selected.

[0061] Among them, the target constraint refers to the core set of topological constraints used to screen legitimate island topologies. It can ensure the logical rationality and physical feasibility of island division and prevent invalid topologies from entering the subsequent process. It includes node ownership uniqueness constraint, power node island binding constraint, node ownership topology connectivity constraint, and line ownership node consistency constraint.

[0062] Among them, the node affiliation uniqueness constraint refers to the constraint condition that restricts a single node to a maximum of one island, which can avoid topological logic conflicts caused by duplicate node allocation. For example, node A cannot belong to both island 1 and island 2 at the same time.

[0063] Among them, the power node islanding constraint refers to the constraint condition that a power node must belong to the island it is on. This can ensure that the power supply resources are forcibly bound to the island and prevent the power supply from leaving the power supply unit. For example, the node where a synchronous generator is located must be fixed to the island it is connected to.

[0064] Among them, the node affiliation topology connectivity constraint refers to the constraint condition that a node belongs to an island only if its parent node also belongs to the island and the connection is made. This constraint can ensure the connectivity of the island topology and avoid the existence of isolated nodes. For example, if node B belongs to island 3, its directly connected parent node C must also belong to island 3 and the connection line must be made.

[0065] Among them, the line affiliation node consistency constraint refers to the constraint condition that the premise of a line affiliation to an island is that both ends of the node belong to the island. This can avoid cross-island lines and ensure the rationality of the physical isolation of islands. For example, the premise of line MN affiliation to island 4 is that both node M and node N belong to island 4.

[0066] Among them, the Discrete Particle Swarm Optimization (DPSO) algorithm refers to an optimization algorithm based on swarm intelligence. It can efficiently achieve the combined optimization of controllable line on / off states by searching for the optimal solution through particle iteration, and quickly generate a large number of potential island topology combinations.

[0067] Among them, the candidate island set refers to the set of multiple potential island topology schemes obtained by unconstrained combination of controllable line on / off states through discrete particle swarm optimization algorithm. It can provide sufficient candidate samples for subsequent constraint screening, such as multiple island partitioning schemes containing effective and invalid topologies.

[0068] In this embodiment, based on the full operational status information, the basic constraints of network connectivity and radial operation are first clarified, and then the target constraints such as node uniqueness and power node islanding are invoked. The on / off states of controllable lines are batch-combined using the discrete particle swarm optimization algorithm to generate multiple sets of candidate islands covering various potential topologies. Finally, from the candidate island sets, schemes that simultaneously satisfy the basic constraints and target constraints are selected to obtain multiple sets of initial islands.

[0069] This approach leverages the efficient search capabilities of the Discrete Particle Swarm Optimization (DPSO) algorithm to rapidly generate a large number of candidate topologies, avoiding the omission of optimal island structures. Furthermore, rigorous screening based on objective constraints eliminates invalid schemes such as duplicate node affiliations and disconnected topologies, ensuring the initial island set possesses logical rationality and physical feasibility. This lays a reliable foundation for subsequent model construction and strategy solving, while simultaneously improving the efficiency and accuracy of island partitioning.

[0070] As an optional embodiment, a multi-agent dynamic frequency response model is constructed based on the local operating state information corresponding to each island system in multiple initial island sets. This includes: retrieving an objective function, wherein the objective function includes a first function term representing the power of the energy storage battery, a second function term representing the load shedding power, a third function term representing the power of the synchronous generator, and a fourth function term representing the virtual synchronous machine; and constructing a multi-agent dynamic frequency response model based on the objective function and the local operating state information corresponding to each island system in multiple initial island sets.

[0071] Among them, the first function term of the energy storage battery power refers to the function term in the objective function that quantifies the cost of energy storage battery power regulation. It can reflect the economic cost of energy storage power participating in frequency regulation and provide a cost reference for energy storage side for strategy optimization.

[0072] The second function term of the load shedding power refers to the function term in the objective function that quantifies the cost of load shedding power loss. It can reflect the economic loss caused by load shedding and guide the strategy to prioritize avoiding excessive load shedding.

[0073] Among them, the third function term of synchronous generator power refers to the function term in the objective function that quantifies the cost of synchronous generator power regulation. It can reflect the economic cost of synchronous generators participating in frequency regulation and provide a basis for power supply side strategy optimization.

[0074] Among them, the fourth function term of the virtual synchronous machine refers to the function term in the objective function that quantifies the power regulation cost of the virtual synchronous machine. It can reflect the economic cost of the virtual synchronous machine participating in frequency regulation and improve the cost accounting of multiple power sources.

[0075] In this embodiment, firstly, an objective function containing four function terms is retrieved. This objective function quantifies the control costs and losses of the energy storage battery, load shedding, synchronous generator, and virtual synchronous machine through different function terms. Then, using the local operating state information of each island system in multiple initial island sets as data support, the objective function is integrated into the construction process of the multi-subject dynamic frequency response model to form a dedicated model adapted to the off-grid transient operation of a single island.

[0076] In this way, the four function terms of the objective function fully cover the control costs and losses of core resources, making the model optimization direction more in line with the economic demands of engineering practice. At the same time, by combining the local operating state information of a single island to build the model, the problem of insufficient adaptability of general models is avoided, and the model’s pertinence and accuracy for different island systems are improved, providing reliable theoretical support for subsequent solutions to economical and efficient control strategies.

[0077] As an optional embodiment, solving the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to multiple initial island sets includes: discretizing the off-grid operation transient process into multiple time steps; controlling multiple game agents to execute update strategy operations according to the objective based on the game state parameters of the current time step until a predetermined convergence condition is reached, thereby obtaining the initial total loss value and preliminary control strategy corresponding to the current time step. The multiple game agents include synchronous generators, virtual synchronous machines, energy storage power supplies, and load entities; the game state parameters include the system inertial center frequency and the power corresponding to each of the multiple game agents; and controlling the execution of the strategy determination operation corresponding to the next time step until all strategy determination operations corresponding to multiple time steps have been executed, thereby obtaining the initial total loss value and initial control strategy corresponding to the multiple initial island sets.

[0078] Among them, the off-grid operation transient process refers to the dynamic process of the island transitioning from a disturbed state to a stable operating state after it is disconnected from the upper-level power grid. It can reflect the instantaneous change characteristics of system parameters such as frequency and power, and is the core scenario of frequency response control.

[0079] Among them, the time step refers to the smallest time unit after discretizing the transient process of off-grid operation. It can transform a continuous dynamic process into a step-by-step computable stage, which is suitable for the step-by-step solution logic of dynamic games. For example, a 10-second transient process can be discretized into 10 1-second time steps.

[0080] Among them, the predetermined convergence condition refers to the condition for determining the termination of the game process. It can ensure that the strategies of each player reach a stable equilibrium state and avoid meaningless strategy iteration. For example, the change in each player's strategy within two consecutive time steps is ≤ ±0.5% of the rated capacity.

[0081] The initial control strategy refers to the control scheme adapted to the system state at each time step after the game converges. It can provide phased control guidance for the transient operation of the island, such as the energy storage power adjustment amount and load shedding ratio at a certain time step.

[0082] In this embodiment, the off-grid operation transient process of the isolated island is first discretized into multiple time steps. Within each time step, the game entities such as the synchronous generator, virtual synchronous machine, energy storage power supply, and load entity are controlled to update their own strategies according to preset objectives based on the game state parameters such as the system inertial center frequency and the power of various entities at the current time step, until the predetermined convergence condition is reached, thereby obtaining the local initial total loss value and preliminary control strategy corresponding to that time step. Subsequently, the strategy determination operation of the subsequent time steps is executed in sequence. After all time steps have been executed, the results of each stage are integrated to form the complete initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively.

[0083] This approach discretizes the continuous transient process into time steps, adapting to the step-by-step solution logic of dynamic games, making the complex frequency response process easier to compute. The setting of predetermined convergence conditions ensures that the strategy at each time step is a stable equilibrium solution under the current state, avoiding distortion of loss values ​​caused by strategy fluctuations. The integration of strategies at each time step forms a complete control scheme, providing a comprehensive and accurate benchmark for subsequent iterative optimization, and improving the reliability and effectiveness of the control strategy.

[0084] As an optional embodiment, multiple initial island sets that meet the target constraints are selected from multiple candidate island sets, including: invoking steady-state constraints, wherein the steady-state constraints include power flow balance constraints within the island, power output constraints, load shedding constraints, node voltage constraints, and line capacity constraints; and selecting multiple initial island sets that meet both the target constraints and steady-state constraints from multiple candidate island sets.

[0085] Among them, steady-state constraints refer to the set of constraints that ensure the feasibility of islanded static operation. They can limit the boundaries of the system's static operation and prevent static parameters from exceeding the limits. These constraints include power flow balance constraints, power output constraints, load shedding constraints, node voltage constraints, and line capacity constraints within the island.

[0086] Among them, the power flow balance constraint within the island refers to the constraint conditions that ensure the balance between active and reactive power supply and demand within the island. It is the foundation for ensuring the stable operation of the island and avoids system instability caused by excessive power deficit or surplus.

[0087] Among them, power output constraints refer to the constraints that limit the upper and lower limits of the active and reactive power output of the power source and the reserve capacity. These constraints can ensure the safe operation of power equipment and avoid overload damage. For example, the active power output of a synchronous generator shall not exceed 1.1 times its rated capacity.

[0088] Among them, load shedding constraints refer to the constraints that limit the range and proportion of load shedding in emergency situations. This can prevent a decline in power supply levels caused by excessive load shedding, while ensuring the rationality of load shedding. For example, the load shedding ratio must be consistent with the active and reactive power ratio of the original load of the node.

[0089] Among them, node voltage constraint refers to the constraint condition that limits the voltage deviation of each node in the island to within the allowable range. It can ensure the normal operation of electrical equipment and avoid damage to the equipment due to excessively high or low voltage. For example, the node voltage amplitude deviation is ≤ ±5% of the rated voltage.

[0090] Among them, line capacity constraints refer to the constraints that limit the maximum power carried by a line, which can avoid thermal stability problems caused by line overload and ensure the safe operation of the line. For example, the maximum active power carried by a certain 10kV line shall not exceed 20MW.

[0091] In this embodiment, in addition to invoking the target constraint, a steady-state constraint including sub-constraints such as power flow balance, power output, and load shedding is also invoked. From multiple sets of candidate islands generated by the discrete particle swarm optimization algorithm, schemes that meet both the target constraint (topological rationality) and the steady-state constraint (static operational feasibility) are simultaneously selected, and finally multiple sets of initial islands are obtained.

[0092] This approach superimposes steady-state constraints on topological constraints, ensuring both the legitimacy of the island topology and the safety boundaries of its static operation. This prevents invalid schemes with legal topologies but exceeding static parameter limits from entering subsequent processes. For example, power flow balance constraints eliminate islands with unbalanced power, and line capacity constraints prevent line overload risks. This further improves the reliability and practicality of the initial island set, reduces invalid calculations in subsequent model solving, and enhances overall decision-making efficiency.

[0093] As an optional implementation, the multi-agent dynamic frequency response model is solved to determine the initial total loss value and initial control strategy corresponding to multiple initial island sets, including: invoking steady-state constraints and transient constraints, wherein the transient constraints include frequency minimum point constraints, frequency change rate constraints, power output transient constraints, maximum power angle constraints, and limit cut-off time constraints; under the constraints of steady-state constraints and transient constraints, the multi-agent dynamic frequency response model is solved to determine the initial total loss value and initial control strategy corresponding to multiple initial island sets.

[0094] Transient constraints refer to the set of constraints that ensure the safety of islanded transient operation. They can limit the boundaries of dynamic changes in the system and prevent transient parameters from exceeding limits, which could lead to system collapse. These constraints include minimum frequency point constraints, frequency change rate constraints, power output transient constraints, maximum power angle constraints, and limit cut-off time constraints.

[0095] Among them, the minimum frequency constraint refers to the constraint condition that limits the system frequency from falling below the minimum safe value. This can prevent the low frequency load protection from being triggered when the frequency is too low, and ensure the stability of the system frequency. For example, the minimum safe value of the system frequency is set to 49.5Hz.

[0096] Among them, the frequency change rate constraint refers to the constraint condition that limits the rate of change of the system frequency. It can suppress drastic frequency fluctuations and avoid generator loss of synchronization. For example, the frequency change rate range is set to -2Hz / s to +2Hz / s.

[0097] Among them, power output transient constraints refer to the constraints that limit the power output ramp rate and instantaneous overshoot, which can protect power electronic equipment from overcurrent damage. For example, the power output ramp rate must not exceed 5% of rated capacity / second.

[0098] Among them, the maximum power angle constraint refers to the constraint condition that limits the maximum power angle under system disturbance, which can ensure the transient synchronous stability of the generator and avoid loss of synchronization and collapse. For example, the maximum power angle is ≤120°.

[0099] Among them, the limit clearing time constraint refers to the constraint condition that the system limit clearing time is not less than the protection device action time, which can ensure that the protection device has enough time to clear the fault and improve the islanding transient resilience. For example, the limit clearing time is ≥100ms.

[0100] In this embodiment, before solving the multi-agent dynamic frequency response model, steady-state constraints and transient constraints are invoked. During the solution process, these two types of constraints are used as boundary conditions to ensure that the model solution meets both static operation requirements (such as power output not exceeding the upper limit and node voltage within the allowable range) and dynamic safety requirements (such as frequency not lower than the safety threshold and power angle not exceeding the stability limit). Finally, by solving the model, the initial total loss value and initial control strategy corresponding to the multiple initial island sets are determined.

[0101] This approach provides dual protection through steady-state and transient constraints, preventing equipment damage caused by exceeding static parameter limits and mitigating system collapse risks such as frequency instability and power angle loss during transient processes. The inclusion of constraints ensures that the initial control strategy obtained is not only economical (optimal total loss value) but also comprehensively safe and feasible, effectively reducing the safety risks of islanded off-grid operation and addressing the shortcomings of traditional methods that focus only on losses while neglecting transient safety.

[0102] As an optional embodiment, based on the initial total loss value and initial control policy corresponding to multiple initial island sets, an updated island set is obtained, and iterative operations to determine the loss value and control policy are performed until the target condition is met. After obtaining the target control policy corresponding to the target island set, the method further includes: controlling the corresponding island system to execute the corresponding initial control policy.

[0103] In this embodiment, after obtaining the target control strategy corresponding to the target island set through iterative optimization, an additional control execution link is added. The system sends target control strategy instructions to the field execution devices corresponding to each island system, including line on / off control signals, power output adjustment parameters, load shedding execution instructions, etc., to control each island system to perform specific operations according to the target control strategy, so as to realize the orderly operation of the islands and the power supply guarantee.

[0104] This approach perfects the technical closed loop of partitioning, modeling, solving, optimizing, and executing, transforming the optimized target control strategy from a theoretical solution into a practically implementable one, thus enhancing the engineering applicability of the technical solution. The precise execution of the field execution device ensures the effective implementation of the target control strategy, guarantees that the islanded system can operate strictly according to the optimal solution, further consolidates the technical effects of minimizing power loss, stabilizing frequency, and ensuring the supply of important loads, and makes the entire technical solution fully feasible.

[0105] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0106] In related technologies, with the large-scale integration of new resources such as distributed power sources, energy storage devices, and electric vehicles into power distribution systems, the operating mode of power distribution systems is gradually evolving from the traditional single-power supply mode to a multi-entity collaborative power supply mode. In scenarios such as extreme natural disasters, major equipment failures, or external attacks, the power distribution system may disconnect from the upstream power grid and enter an islanded operation state. At this time, how to rationally divide the islanded areas and coordinate the participation of multiple resources in frequency regulation and load management is a key technical issue in ensuring continuous power supply to critical loads and preventing system collapse.

[0107] Existing islanded operation and emergency control methods mostly employ centralized optimization models, making unified scheduling decisions for the system and generally assuming that all types of power sources and loads within the system completely obey unified control commands. However, in new power distribution systems, resources such as synchronous generators, virtual synchronous machines, energy storage power sources, photovoltaic power sources, and interruptible loads exhibit significant differences in dynamic response capabilities, control costs, and operational constraints. In fact, each resource has independent decision-making preferences and behavioral characteristics during emergency operation, making it difficult to characterize the dynamic interaction relationships between resources using only static or centralized optimization methods.

[0108] Furthermore, the formation and transient operation of islands exhibit significant time evolution characteristics. The response behaviors of various resources during frequency emergency regulation, power support, and load reduction processes show phased and strategic features. Traditional methods struggle to theoretically characterize the competition, cooperation, and strategic evolution relationships among multiple resources, which can easily lead to problems such as frequency overruns, transient desynchronization, or excessive load shedding, resulting in substantial losses in the end.

[0109] Therefore, there is an urgent need for a method that can introduce the concept of multi-agent dynamic game in the process of island operation, systematically characterize the strategic interaction behavior of various resources in the off-grid operation, and realize the collaborative decision-making of island division and emergency control.

[0110] In view of this, an optional embodiment of the present invention provides a method for determining the control strategy of a power distribution system.

[0111] Step S1: Island operation triggering and state awareness;

[0112] When a disconnection event between the distribution system and the upstream power grid is detected, the current operating status information of the system is obtained, including: the distribution network topology; the load of each node and its importance level; the location, capacity and control parameters of distributed resources such as synchronous generators, virtual synchronous machines, energy storage power sources, and photovoltaic power sources within the system; and the system frequency, voltage and power flow information.

[0113] Step S2: Construct an island self-configuration partitioning model based on dynamic game theory;

[0114] The islanding process in a power distribution system is modeled as a multi-agent spatial game problem. The game participants include grid-connected power source entities, grid-linked power source entities, load entities, and a system coordination entity. The islanding strategy is determined by line on / off states and node affiliation. The payoff function for each island comprehensively considers load loss, resource control costs, and transient stability risks. Through game theory solutions, an islanding equilibrium strategy that satisfies power balance, priority for critical loads, and transient safety constraints is obtained.

[0115] Step S3: Construct a frequency response game model that considers the dynamic evolution of multiple resources;

[0116] After the island is formed, the off-grid transient process within the island is discretized into multiple time steps, and a multi-agent dynamic game model is constructed. The game state variables include the system's inertial center frequency, the output of various power sources, energy storage power, and load shedding. Synchronous generators and virtual synchronous machines participate in the game through inertial response and primary frequency regulation strategies. Energy storage power sources participate through rapid power adjustment strategies. Load subjects participate in load reduction strategy selection based on incentive or penalty mechanisms. The system frequency dynamic response is described using a multi-resource aggregation reduced-order model, which serves as the state evolution constraint for the dynamic game.

[0117] Step S4: Emergency frequency control decision based on dynamic game equilibrium;

[0118] Within each discrete time step, each player updates its strategy based on the current system state and the principle of minimizing control costs. The system coordinating entity guides the game process to converge to an equilibrium solution that satisfies the following constraints by adjusting incentive factors and penalty weights: the system frequency is not lower than the safety threshold; the rate of frequency change meets the restriction requirements; the power output change meets the ramp rate and overload capacity constraints; and the system's maximum power angle and limit cut-off time meet the transient stability requirements.

[0119] Step S5: Game Theory - Optimization Integration Solution and Strategy Execution.

[0120] A discrete particle swarm optimization algorithm is used to generate candidate topologies for islanding, and these topologies are then input into a dynamic game model as a set of game strategies. The outer optimization layer searches for islanding structures; the inner game layer evaluates the dynamic payoffs of the system under each structure; and the particle fitness is adjusted based on the game results. The optimal islanding partitioning and emergency control strategy are obtained through iterative solving. The line on / off status, power output adjustment, and load shedding scheme under the final game equilibrium strategy are sent to the field execution device to achieve power supply control under islanding operation.

[0121] For example, Figure 2 This is a schematic diagram of the frequency response model structure of a multi-resource system provided by an optional embodiment of the present invention. Figure 3 This is a schematic diagram of the frequency response reduction model structure of a multi-resource system provided by an optional embodiment of the present invention. Figure 4 This is a schematic diagram of the complete optimization decision model structure provided by an optional embodiment of the present invention, such as... Figure 2-4 As shown, it will be introduced below.

[0122] In a typical power distribution system with distributed power sources, the system includes grid-connected power sources, grid-linked power sources, and interruptible loads. Each load node is classified according to its power supply importance.

[0123] When an external fault or extreme event causes the distribution system to disconnect from the upstream power grid, the system enters an islanded operation monitoring state. The control device collects real-time data on the distribution system's network topology, node load power and its importance level, distributed generation output status, system frequency, and power flow information.

[0124] The system frequency response characteristics are represented by the widely used center of inertia (COI), as shown in the following equation:

[0125] (1)

[0126] In the formula, The system's inertial center frequency, Let be the overall inertial time constant of the system. is the inertial time constant of generator i (the unit type includes synchronous generators and virtual synchronous machines, i.e., units that can provide inertial support). Let be the frequency of generator node i.

[0127] instantaneous disturbance The system rate of change of frequency (RoCoF) is:

[0128] (2)

[0129] In the formula, This represents the change in system power.

[0130] Table 1 shows the control methods and characteristics used for various resources in the system.

[0131] Table 1

[0132]

[0133] The structure of the established multi-resource system frequency response model is as follows: Figure 2 As shown, the frequency response model of a multi-resource system obtained by aggregating similar resources into equivalent and simplified versions is as follows: Figure 3 As shown, the parameters for aggregation are given by formula (3):

[0134] (3)

[0135] In the formula, and The power equivalent aggregation of energy storage batteries and load shedding are respectively. Equivalent aggregation of rated power variation for photovoltaic power sources after considering uncertainties. , and These refer to the quantity of energy storage batteries, photovoltaic power sources, and load shedding, respectively. This indicates the battery output power at the current moment. This indicates the current output power of the photovoltaic power source. This indicates the number of loads being shed at the current moment.

[0136] The equivalent aggregation of control parameters for a power supply with inertia support capability is as follows:

[0137] (4)

[0138] In the formula, , , and This represents the overall system's inertial time constant, damping coefficient, capacity, and control time constant. , and This represents the inertial time constant, damping coefficient, and capacity of the i-th synchronous generator. , and Let represent the inertial time constant, damping coefficient, and capacity of the j-th virtual synchronizer.

[0139] (5)

[0140] In the formula, and This is a proportionality coefficient, representing the proportion of the rated capacity of the i-th synchronous generator and the j-th virtual synchronous machine to the total system capacity. This represents the droop coefficient of the i-th synchronous generator after calculation using the proportional coefficient. Let represent the droop coefficient of the j-th virtual synchronizer after calculation using the proportional coefficient. and Let represent the droop coefficient of the i-th synchronous generator and the j-th virtual synchronous machine. This represents the overall droop coefficient of the system. is the weighting coefficient of the k-th feedback control branch. Let represent the control time constant of the k-th generator. and These represent the number of synchronous generators and virtual synchronous machines, respectively.

[0141] Finally, the established frequency response model for the multi-resource system is as follows:

[0142] (6)

[0143] The discrete difference is used to obtain the frequency response difference model of the multi-resource system:

[0144] (7)

[0145] In the formula, This represents the differential time step. Under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to generate multiple candidate islanded operation structures. For each candidate islanded operation structure, the active power deficit and the guarantee level of important loads are calculated.

[0146] (a) Steady-state constraints

[0147] After a fault occurs, the power distribution system will be divided into multiple islanded systems, requiring the introduction of node 01 state variables. and Line 01 state variables This is used to describe the internal topology of an islanded system after a failure.

[0148] variable and Must meet:

[0149] (8)

[0150] (9)

[0151] (10)

[0152] (11)

[0153] In the formula, A collection of multiple isolated islands. Let x be the set of nodes within the island. Let x be the set of nodes containing the power source. The set of routes within island x. This represents the parent node of node i within the isolated island x. Indicates the line state, Indicates the line It is in the disconnected state. This represents the state variable of the parent node h.

[0154] Formula (8) indicates that a node belongs to at most one island; Formula (9) indicates that a power node must belong to the island where the power source is located; Formula (10) indicates that the prerequisite for node i to belong to island x is that its parent node h also belongs to island x, and the line Not disconnected; Formula (11) indicates the line The premise for belonging to island x is that both nodes h and i at the two ends of the line belong to that island.

[0155] (1) Linear power flow constraints

[0156] The linear DistFlow model simplifies the solution by ignoring the nonlinear terms in the traditional DistFlow model, making it a simplified model suitable for power flow calculations in radial network structures of distribution systems. The power flow constraints of island x in a radial structure using the linear DistFlow model are shown below:

[0157] (12)

[0158] (13)

[0159] (14)

[0160] (15)

[0161] (16)

[0162] In the formula, and It represents the active and reactive power flow along the path from parent node h to child node i. and It represents the active and reactive power flow along the path from parent node i to child node j. and This represents the active and reactive power output by the power source g at node i. and This represents the active and reactive power of the load at node i. and This represents the active and reactive power of load shearing at node i. M represents a very large constant in the Big M method. This represents the square of the voltage amplitude at the grid-connected node before disconnection. This represents the square of the voltage amplitude at the power supply node. It is the square of the rated voltage amplitude. and It is the square of the voltage magnitude at nodes i and j. and These are the resistance and reactance of the lines at nodes i and j. and It refers to the maximum active and reactive power that can flow through the line during operation.

[0163] Formula (12) represents the active and reactive power flow balance at each node; Formula (13) defines the voltage at the grid connection point and the power supply node as the reference value; Formula (14) describes the voltage drop between adjacent nodes on the line. Formula (15) constrains the node voltage deviation; Formula (16) limits the amount of power flowing on the line.

[0164] (2) Power output constraint

[0165] Power output constraints include upper and lower limits for active and reactive power, as well as reserve capacity limits, as follows:

[0166] (17)

[0167] In the formula, and These are the lower limits of the active and reactive power output of the power source g. and It is the upper limit of the active and reactive power output of the power supply g. This represents the reserve capacity factor, ranging from (0,1]. This represents the state variable of the power source g.

[0168] (3) Load shedding constraint

[0169] Load shedding constraints are used to limit the range and proportion of load shedding in emergency situations. The constraints are as follows:

[0170] (18)

[0171] (19)

[0172] In the formula, and These are the lower limits of active and reactive load shedding at node i. and It is the upper limit of active and reactive load shedding for node i.

[0173] Formula (19) ensures that the load is cut off according to the ratio of active and reactive power of the original load of node i.

[0174] (ii) Transient constraints

[0175] After islanding, due to the radial network structure, the power deficit of each island can be represented by the power flowing along the lines of the island's boundary nodes, that is, the difference between the power originally flowing into the island's boundary nodes and the power flowing out of the island's boundary nodes:

[0176] (20)

[0177] In the formula, This represents the power flowing into the boundary node line of island x. This represents the power flowing out of the boundary node line of island x.

[0178] If the system frequency drops below a safe threshold or the rate of frequency change is too large after being disturbed, the low-frequency load shedding protection will be triggered, causing system instability. Therefore, it is necessary to constrain the frequency transient process.

[0179] (twenty one)

[0180] In the formula, This represents the minimum safe value for the system frequency. and These represent the minimum and maximum values ​​of the rate of change of frequency. The system frequency of island x. During the transient process, each step k must satisfy the frequency transient constraint.

[0181] During frequency regulation, the power supply's power regulation must meet the output ramp-up speed constraint, limiting the maximum rate of power change between adjacent moments. Furthermore, considering the limited overcurrent withstand capability of power electronic interface power supplies, excessive power overshoot can lead to equipment burnout; therefore, it is also necessary to limit the instantaneous maximum power. The constraints are as follows:

[0182] (twenty two)

[0183] In the formula, and It is the maximum rate at which the power supply output climbs upwards and downwards. It is the multiple by which the power supply can withstand the maximum instantaneous power exceeding its maximum power limit. (Power output within the island x environment) During the transient process, the power transient constraint must be satisfied at each step k. This represents the active power output of power source g at time k.

[0184] Transient synchronization stability constraints ensure that generators in an islanded system do not lose synchronization during transient processes following disturbances, thereby maintaining the overall stable operation of the system.

[0185] During the islanding process, the disturbance caused by the power deficit is a small disturbance, corresponding to a Type II fault. Considering calculation errors and the conservative requirements in practical engineering applications, the maximum power angle of the system under the disturbance can be constrained to not exceed [a certain value]. The constraints are as follows:

[0186] (twenty three)

[0187] In the formula, It is the maximum power angle of the system during the transient process of island x under disturbance.

[0188] Considering the 100ms activation time of the protection equipment, if the islanded system experiences a secondary disaster and its critical clearing time under a large Type I fault disturbance is less than the activation time of the protection equipment, the protection system may fail to clear the fault in time, leading to power supply desynchronization within the island and ultimately causing the islanded system to collapse. Therefore, the formed island must possess a certain degree of resilience, meaning the system's critical clearing time must be greater than 100ms, as constrained by the following:

[0189] (twenty four)

[0190] In the formula, It is the limit clearing time of the system during the transient process of island x under disturbance.

[0191] Based on the above calculation results, and combining the inertia characteristics of grid-connected power sources, the fast response capability of grid-connected power sources, and the flexibility of interruptible load shedding, power regulation constraint ranges and regulation priorities for different resources within each candidate island are dynamically generated. Grid-connected power sources are primarily used to provide frequency references and inertia support, grid-connected power sources are used to perform fast power compensation, and interruptible loads are used as a last resort for power balancing.

[0192] Candidate islanded operation structures that meet power regulation constraints and operational safety requirements are screened to determine the final islanded self-configuration operation structure, and emergency control is implemented under this structure according to the generated power regulation constraint range and regulation priority.

[0193] Overall modeling of the optimized decision-making model:

[0194] The objective function of the off-grid emergency control optimization decision model for a distribution system considering islanded flexible self-configuration partitioning is to minimize load loss and resource control quantity, as shown in the following formula:

[0195] (25)

[0196] In the formula, It is the objective function. For the control cost weighting coefficients of different resources, , , , It is the cost of controlling each resource.

[0197] The weighting coefficients reflect the priority of different resources in emergency control. The smaller the weighting factor, the smaller the increment of the objective function, and the higher the control priority. Generally, the control cost of power-side resources such as energy storage, synchronous power sources (SG), and distributed power sources (VSG) is much lower than the control cost of load shedding. Different control costs are set for primary, secondary, and tertiary loads during emergency load shedding to ensure that non-critical loads are prioritized for shedding. In practical applications, the weighting coefficients and control costs of different control resources can be set according to safety requirements.

[0198] After comprehensively considering both steady-state and transient constraints, the complete optimization decision model structure is as follows: Figure 4 As shown.

[0199] In summary, the optimized decision-making model for off-grid emergency control of the power distribution system, considering the flexible self-configuration partitioning of islanded areas, is as follows:

[0200]

[0201]

[0202]

[0203]

[0204]

[0205] In the formula, Indicates synchronous power output. This indicates the output of distributed power sources. In the initial stage of islanded operation, the system prioritizes suppressing frequency drops by adjusting the output of distributed power sources. When the frequency drops further and still cannot meet safety requirements, non-critical loads are systematically disconnected according to their importance level. After the system's operating status returns to the safe threshold range, power supply to the loads is gradually restored according to a preset sequence.

[0206] Through the above implementation scheme, the power distribution system can maintain frequency stability under off-grid operation conditions, achieve continuous power supply to important loads, and avoid the overall collapse of the island due to frequency instability.

[0207] The above optional implementation methods can achieve at least the following beneficial effects:

[0208] 1. Strong dynamic adaptability. This invention dynamically generates power adjustment constraint ranges and adjustment priorities for multiple types of distributed resources during islanded operation, which can adapt to changes in the operating characteristics of different resources and system operating conditions, avoiding the operational risks caused by fixed control strategies.

[0209] 2. High frequency safety. During emergency control, the system comprehensively considers frequency deviation, frequency change rate, and equipment physical constraints, effectively suppressing drastic frequency fluctuations in the early stages of off-grid operation and reducing the probability of low-frequency load shedding and generator loss of synchronization.

[0210] 3. Strong capacity to guarantee critical loads. By introducing load importance level and guarantee level assessment during the islanding self-configuration stage, priority power supply to critical loads under limited resource conditions is achieved, improving the targeting and reliability of power supply.

[0211] 4. Avoid excessive load shedding. Before implementing load shedding, this invention prioritizes coordinating multiple types of distributed power sources to participate in power regulation, effectively reducing unnecessary load shedding and improving the overall power supply level of the system.

[0212] 5. High feasibility of engineering implementation. The method makes decisions based on real-time acquireable electrical quantities and equipment operating status, with clear control logic. It can be directly integrated into existing power distribution automation systems and emergency control platforms, making it suitable for practical engineering applications.

[0213] 6. Wide range of applications. This invention is applicable to new power distribution systems with a high proportion of new energy sources, power electronic interface resources, and flexible loads, and can significantly improve the operational resilience of the power distribution system under extreme disasters and complex disturbances.

[0214] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0215] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0216] Example 2

[0217] According to embodiments of the present invention, an apparatus for implementing the above-described method for determining control strategies for a power distribution system is also provided. Figure 5 This is a structural block diagram of a control strategy determination device for a power distribution system according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 502, a division module 504, a construction module 506, a first determination module 508, and a second determination module 510. The device will be described in detail below.

[0218] Acquisition module 502 is used to receive and respond to the disconnection event signal of the power distribution system and acquire the full operating status information of the power distribution system; partitioning module 504, connected to the acquisition module 502, is used to combine the on / off states of controllable lines based on the full operating status information, under the premise of satisfying network connectivity and radial operation constraints, and divide the power distribution system into multiple initial island sets; construction module 506, connected to the partitioning module 504, is used to construct a multi-agent dynamic frequency response model based on the local operating status information corresponding to each island system in the multiple initial island sets, wherein the multi-agent dynamic frequency response model includes an objective function with the goal of minimizing power loss; the first determination The first determination module 508, connected to the first determination module 506, is used to solve the multi-agent dynamic frequency response model and determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively. The second determination module 510, connected to the first determination module 508, is used to update the updated island set based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively, and perform iterative operations to determine the loss value and control strategy until the target condition is met, thereby obtaining the target control strategy corresponding to the target island set. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

[0219] It should be noted that the above-mentioned acquisition module 502, division module 504, construction module 506, first determination module 508 and second determination module 510 correspond to steps S102 to S110 in the method for determining the control strategy of the power distribution system. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0220] Example 3

[0221] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the control strategy determination method for a power distribution system as described above.

[0222] Example 4

[0223] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the control strategy determination method for a power distribution system described above.

[0224] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0225] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0226] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0227] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0228] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0229] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0230] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the control strategy of a power distribution system, characterized in that, include: Receive and respond to the disconnection event signal of the power distribution system, and obtain the full operating status information of the power distribution system. Based on the full operational status information, and under the premise of satisfying network connectivity and radial operation constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets. Based on the local operating state information corresponding to each island system in the multiple initial island sets, a multi-agent dynamic frequency response model is constructed, wherein the multi-agent dynamic frequency response model includes an objective function with the goal of minimizing power loss; Solve the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets respectively; Based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, the updated island set is obtained. Iterative operations are performed to determine the loss value and control strategy until the target condition is met, and the target control strategy corresponding to the target island set is obtained. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

2. The method according to claim 1, characterized in that, Based on the full operational status information, and under the premise of satisfying network connectivity and radial operational constraints, the on / off states of controllable lines are combined to divide the power distribution system into multiple initial island sets, including: Invoke target constraints, wherein the target constraints include node affiliation uniqueness constraints, power node islanding constraints, node affiliation topology connectivity constraints, and line affiliation node consistency constraints; By using the discrete particle swarm optimization algorithm, the on / off states of the controllable circuit are combined to obtain multiple sets of candidate islands. From the multiple sets of candidate islands, select the multiple sets of initial islands that meet the target constraints.

3. The method according to claim 1, characterized in that, Based on the local operating state information corresponding to each island system in the multiple initial island sets, a multi-agent dynamic frequency response model is constructed, including: The objective function is retrieved, wherein the objective function includes a first function term representing the power of the energy storage battery, a second function term representing the load shedding power, a third function term representing the power of the synchronous generator, and a fourth function term representing the virtual synchronous machine; Based on the objective function and the local operating state information corresponding to each island system in the multiple initial island sets, the multi-agent dynamic frequency response model is constructed.

4. The method according to claim 1, characterized in that, Solving the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets includes: The off-grid operation transient process is discretized into multiple time steps. Multiple game entities are controlled to perform update strategy operations according to the target based on the game state parameters of the current time step until the predetermined convergence condition is reached, thereby obtaining the initial total loss value and preliminary control strategy corresponding to the current time step. The multiple game entities include synchronous generators, virtual synchronous machines, energy storage power supplies, and load entities. The game state parameters include the system inertial center frequency and the power corresponding to each of the multiple game entities. The control executes the policy determination operation corresponding to the next time step until the policy determination operations corresponding to the multiple time steps are completed, thereby obtaining the initial total loss value and the initial control policy corresponding to the multiple initial island sets.

5. The method according to claim 2, characterized in that, From the multiple sets of candidate islands, the multiple sets of initial islands that meet the target constraints are selected, including: Call steady-state constraints, wherein the steady-state constraints include islanded power flow balance constraints, power output constraints, load shedding constraints, node voltage constraints, and line capacity constraints; From the multiple sets of candidate islands, select the multiple sets of initial islands that meet the target constraints and the steady-state constraints.

6. The method according to claim 1, characterized in that, Solving the multi-agent dynamic frequency response model to determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets includes: Call steady-state constraints and transient constraints, wherein the transient constraints include frequency minimum point constraints, frequency change rate constraints, power output transient constraints, maximum power angle constraints, and limit cut-off time constraints; Under the constraints of the steady-state constraints and the transient constraints, the multi-agent dynamic frequency response model is solved to determine the initial total loss value and the initial control strategy corresponding to the multiple initial island sets.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the initial total loss value and initial control policy corresponding to the multiple initial island sets, the updated island set is obtained. Iterative operations are performed to determine the loss value and control policy until the target condition is met. After obtaining the target control policy corresponding to the target island set, the process further includes: Control the corresponding isolated system to execute the corresponding initial control strategy.

8. A control strategy determination device for a power distribution system, characterized in that, include: The acquisition module is used to receive and respond to the disconnection event signal of the power distribution system, and acquire the full operating status information corresponding to the power distribution system; The partitioning module is used to combine the on / off states of controllable lines based on the full operational status information, under the premise of satisfying network connectivity and radial operation constraints, and divide the power distribution system into multiple initial island sets. The construction module is used to construct a multi-agent dynamic frequency response model based on the local operating state information corresponding to each island system in the multiple initial island sets. The multi-agent dynamic frequency response model includes an objective function with the goal of minimizing power loss. The first determining module is used to solve the multi-agent dynamic frequency response model and determine the initial total loss value and initial control strategy corresponding to the multiple initial island sets, respectively. The second determining module is used to update the updated island set based on the initial total loss value and initial control strategy corresponding to the multiple initial island sets, and perform iterative operations to determine the loss value and control strategy until the target condition is met, thereby obtaining the target control strategy corresponding to the target island set. The target condition includes at least one of the following: the total power loss is less than a predetermined loss threshold, the number of iterations reaches a predetermined number, and the target island set includes multiple target island systems.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the control strategy determination method for the power distribution system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the control strategy determination method for the power distribution system as described in any one of claims 1 to 7.

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