Multi-energy complementary power distribution control method and system

Abstract

The invention discloses a multi-energy complementary power distribution control method, belongs to the technical field of electric power system control, and aims to solve the problems that an existing multi-energy system control method based on periodic instruction issuing is heavy in communication burden, response lags when sudden disturbance is dealt with in two scheduling intervals, and the power distribution efficiency is poor. And the operation economy and the dynamic stability are difficult to consider at the same time. The method comprises the steps that an upper-layer optimization controller generates virtual impedance potential field function parameters and key topological characteristics and issues the virtual impedance potential field function parameters and the key topological characteristics to a local controller; the local controller executes power control based on the potential field gradient and monitors the topological position of the operation point; when a topology crossing event is detected, an asynchronous event triggering signal is sent upwards, and an upper-layer controller is triggered to carry out aperiodic re-planning. According to the method, decoupling of economic optimization and dynamic control is realized, the robustness and the adaptive capacity of the system under disturbance are improved, meanwhile, the communication burden is reduced, and the method is suitable for a system containing a large amount of distributed energy.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, specifically to a power distribution control method and system for multi-energy complementarity. Background Technology

[0002] As the penetration rate of renewable energy sources such as wind power and photovoltaics in power systems continues to increase, their inherent volatility and intermittency pose challenges to the safe and stable operation and economic dispatch of the system. To address this issue, existing technologies typically employ centralized or hierarchical control architectures. Centralized control methods collect global system information through a central controller, perform unified optimization calculations, and issue precise power commands to each distributed unit. Hierarchical control architectures decompose the control task into upper-level economic dispatch and lower-level real-time control. The upper-level controller periodically optimizes and sends power reference values ​​or adjustment ranges to the lower-level local controllers for execution.

[0003] However, the aforementioned existing technologies still have limitations in practical applications. On the one hand, the control method, which relies on periodically issuing precise power commands, places high demands on the bandwidth and real-time performance of the communication network. Especially when the number of distributed units is large, the communication burden becomes a bottleneck for system expansion. On the other hand, this time-driven control mode exhibits inherent lag when dealing with unforeseen system disturbances. When sudden events such as load changes or large fluctuations in renewable energy output occur between two optimized scheduling periods, the system can only rely on the local adjustment capabilities of the local controller to respond passively, and the overall control strategy cannot be updated in a timely manner. This may cause the system to deviate from its economic operating state, and in severe cases, even affect system stability. Summary of the Invention

[0004] To address the problems of existing multi-energy system control methods based on periodic command issuance, such as heavy communication burden and delayed response to sudden disturbances during scheduling intervals, making it difficult to balance operational economy and dynamic stability, this invention provides a multi-energy complementary power distribution control method and system.

[0005] In a first aspect, the multi-energy complementary power distribution control method of the present invention includes the following steps:

[0006] Upper-level optimization steps: The upper-level optimization controller performs optimization calculations based on the real-time operating data and prediction information of the multi-energy system, generates and issues control strategies. The control strategies include virtual impedance potential field function parameters used to define the dynamic response cost of each controllable unit, and key topological features used to define the shape of the potential field function.

[0007] Local control and monitoring steps: The local controller of each controllable unit performs real-time power control based on the received control strategy, and synchronously monitors the topological position of its own operating point in the virtual impedance potential field.

[0008] Asynchronous triggering step: When any of the local controllers detects a predefined topology traversal event at its operating point, it sends an asynchronous event trigger signal to the upper-layer optimization controller;

[0009] Forced replanning step: In response to the asynchronous event trigger signal, the upper-level optimization controller interrupts the current cycle and initiates an aperiodic replanning calculation to update the control strategy.

[0010] Preferably, the virtual impedance potential field function is a convex function with respect to the power regulation amount, and its parameters include at least a curvature coefficient characterizing the marginal cost of power regulation and a linear coefficient determining the economic optimum.

[0011] Preferably, the execution of real-time power control includes: generating a main control command; calculating the gradient of the current virtual impedance potential field function to generate a potential field gradient adjustment term; and superimposing the main control command and the potential field gradient adjustment term to form a final power command.

[0012] Preferably, the key topological feature includes at least one curvature threshold; the topology crossing event is defined as the real-time curvature crossing of the curvature threshold by the virtual impedance potential field function.

[0013] Preferably, the key topological features include a first curvature threshold and a second curvature threshold, wherein the second curvature threshold is smaller than the first curvature threshold, so as to divide the virtual impedance potential field into multiple operating regions including a flexible adjustment region, a normal region and a constraint boundary region.

[0014] Preferably, the forced replanning step includes: interrupting the current scheduling wait period and immediately performing the optimization calculation based on the latest system operating data when the trigger signal is received.

[0015] Preferably, the optimization calculation performed by the upper-level optimization controller is to solve a rolling optimization model that aims to minimize the overall operating cost of the system in future scheduling cycles and satisfies the system power balance constraints and the operating boundary constraints of each unit.

[0016] Preferably, the asynchronous event trigger signal includes at least the identifier of the controllable unit that issued the signal, the event type code, and the timestamp of the event occurrence.

[0017] Secondly, the multi-energy complementary power distribution control system of the present invention includes:

[0018] The information acquisition module is used to collect real-time operating data and forecast information of multi-energy systems;

[0019] The optimization and strategy generation module is used to perform optimization calculations based on the information to generate the virtual impedance potential field function parameters and key topological features.

[0020] The strategy execution module is configured in each local controller and is used to perform real-time power control based on the potential field function parameters.

[0021] A topology monitoring module, configured in each local controller, is used to monitor the topological location of the running point based on the key topological features.

[0022] The event triggering module, configured in each local controller, is used to generate asynchronous event triggering signals when a topology traversal event is detected.

[0023] An event response module, configured in the upper-level optimization controller, is used to receive the trigger signal and instruct the optimization and strategy generation module to start aperiodic replanning.

[0024] The beneficial effects of this invention are:

[0025] 1. This invention achieves decoupling between upper-level economic optimization and local dynamic control by generating a virtual impedance potential field function instead of directly issuing power setpoints. While the local controller performs its main transient control tasks, its output is continuously guided by the potential field gradient term, enabling its operating point to flexibly approach the global economic optimum. This approach balances the system's rapid dynamic response capability with the overall economic efficiency, improving the comprehensive performance of multi-energy systems under complex operating conditions.

[0026] 2. This invention establishes an asynchronous event triggering mechanism based on topology traversal judgment, and constructs a bottom-up fast feedback channel. When a controllable unit's operating state approaches the constraint boundary due to an unexpected disturbance, it can actively and in real time transmit a trigger signal upward to force the system to replan the control strategy, rather than passively waiting for the next fixed scheduling cycle. This enables the control system to quickly adapt to sudden events and significantly improves the system's robustness and adaptability in the face of large power fluctuations.

[0027] 3. The control method of the present invention optimizes the utilization of the system's communication resources. Under normal operating conditions, the upper-level controller only needs to periodically send potential field function parameters with a small amount of data. The local controller will only send asynchronous trigger signals when a critical topology crossing event occurs. Compared with control methods that require frequent issuance of dense power commands, the present invention significantly reduces the bandwidth requirements of the communication network, enhances the scalability of the system, and is more suitable for systems with a large number of distributed controllable units. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the control method of the present invention;

[0029] Figure 2 This is a schematic diagram of the overall system architecture and information flow of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some 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 are within the scope of protection of the present invention.

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0032] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0033] Specific Implementation Method 1: The following is combined with... Figures 1 to 2 This embodiment describes a multi-energy complementary power allocation control method, which includes an upper-level optimization step, a local control and monitoring step, an asynchronous triggering step, and a forced replanning step. Wherein:

[0034] Upper-level optimization steps: The upper-level optimization controller performs optimization calculations based on the real-time operating data and predictive information of the multi-energy system, generates and issues a control strategy. The control strategy includes virtual impedance potential field function parameters for defining the dynamic response cost of each controllable unit, and key topological features for defining the shape of this potential field function. The virtual impedance potential field function is a convex function with respect to power regulation, and its parameters include at least a curvature coefficient characterizing the marginal cost of power regulation and a linear coefficient determining the economic optimum. The key topological features include at least one curvature threshold; the topology crossing event is defined as the real-time curvature of the virtual impedance potential field function crossing the curvature threshold. The key topological features include a first curvature threshold and a second curvature threshold, where the second curvature threshold is smaller than the first curvature threshold, to divide the virtual impedance potential field into multiple operating regions, including a flexible adjustment region, a normal region, and a constraint boundary region. The optimization calculations performed by the upper-level optimization controller solve a rolling optimization model that aims to minimize the overall operating cost of the system within future scheduling cycles, while satisfying system power balance constraints and the operating boundary constraints of each unit.

[0035] Local control and monitoring steps: The local controller of each controllable unit performs real-time power control based on the received control strategy and synchronously monitors the topological position of its own operating point in the virtual impedance potential field; the execution of real-time power control includes: generating a main control command; calculating the gradient of the current virtual impedance potential field function to generate a potential field gradient adjustment term; and superimposing the main control command and the potential field gradient adjustment term to form a final power command.

[0036] Asynchronous triggering step: When any of the local controllers detects a predefined topology traversal event at its operating point, it sends an asynchronous event trigger signal to the upper-layer optimization controller;

[0037] Forced replanning step: In response to the asynchronous event trigger signal, the upper-level optimization controller interrupts the current cycle and initiates an aperiodic replanning calculation to update the control strategy. The forced replanning step includes: interrupting the current scheduling wait cycle and immediately executing the optimization calculation based on the latest system operating data at the time the trigger signal is received. The asynchronous event trigger signal includes at least the identifier of the controllable unit that issued the signal, the event type code, and the timestamp of the event occurrence.

[0038] See Figure 1 The above steps are divided into the following steps according to the actual sequence of events in the project:

[0039] S1. Information Collection and Forecasting: The upper-level optimization controller periodically collects real-time operating status data of each distributed power source and energy storage unit in the system, and integrates information such as load forecasting, renewable energy output forecasting and time-of-use pricing in the future scheduling cycle to form the data basis for optimization decision-making.

[0040] S2. Potential Field Strategy Generation and Distribution: Based on the information collected and predicted in step S1, the upper-level optimization controller constructs and solves a forward-looking rolling optimization model. The output of this model is not a traditional power setpoint, but rather a set of virtual impedance potential field function parameters defining the future dynamic response cost of each controllable unit, along with key topological characteristic thresholds used to define the potential field morphology. After calculation, this parameter set and characteristic thresholds are distributed to the corresponding local controller.

[0041] S3. Local Control and Status Monitoring: Each local controller receives and loads the potential field strategy sent down from the upper layer. On a millisecond timescale, the local controller executes a control law that incorporates potential field gradient information to respond to system disturbances in real time. On the other hand, it synchronously and continuously calculates the topological position of its current operating point in the potential field to determine its operating region.

[0042] S4. Topology Crossing Event Judgment: The local controller continuously determines whether a topology crossing has occurred from an unconstrained boundary region to a constrained boundary region at its operating point. If no crossing has occurred, it returns to step S3 and continues to execute the current control and monitoring tasks.

[0043] S5. Asynchronous event triggering: If the judgment result of step S4 is yes, that is, a topology traversal event is detected, the local controller immediately generates an asynchronous event triggering signal. This signal contains the cell identifier and event information, and sends it to the upper-level optimization controller.

[0044] S6. Forced Replanning Response: After receiving an asynchronous event trigger signal from any local controller, the upper-level optimization controller immediately interrupts the current scheduling waiting period and forces a new optimization calculation to start, that is, directly jumps back to step S2 to generate a new potential field strategy that is more adapted to the current system's actual operating conditions and issues it down.

[0045] The process of acquiring multidimensional system information and predicting state in step S1 is described in detail. This step provides the necessary and accurate data input for subsequent optimization and strategy generation, and its specific implementation may include the following sub-steps:

[0046] The system collects and processes real-time operating status data.

[0047] The upper-level optimization controller collects electrical quantities of key nodes and status quantities of each unit through the multi-energy system monitoring and data acquisition system, with a preset communication cycle.

[0048] The collected real-time data specifically includes: the grid exchange power, voltage amplitude and frequency at the common coupling point; the actual output active and reactive power of each renewable energy generation unit; the output active and reactive power of each controllable generator set; and the DC side voltage, current, state of charge and temperature reported by the battery management system of each energy storage unit.

[0049] Real-time data is transmitted to the data server of the upper-level optimization controller via standard industrial communication protocols. The information acquisition module processes the received raw data, including data cleaning to remove outliers, data synchronization to ensure time synchronization of each sampling point, and data filtering to smooth high-frequency noise.

[0050] Forward-looking prediction of source load power.

[0051] To support forward-looking optimization, the information acquisition module needs to call or integrate one or more prediction models to generate time-series prediction data of renewable energy output and load power for one or more future scheduling cycles.

[0052] Load power forecasting is a process that generates a sequence of predicted total system load demand over a future period, based on various input features such as historical load data, weather information, and date type. :

[0053]

[0054] in, Let N be the predicted load value for the k-th time segment in the future, and N be the total prediction step size.

[0055] Renewable energy output forecasting is a method for generating a predicted sequence of the maximum available output power for a future period of time, based on weather forecast data and the unit's own historical output data, targeting units such as photovoltaic and wind power. :

[0056]

[0057] in, This represents the predicted total renewable energy output at the k-th time segment in the future.

[0058] The specific model used to achieve the above-mentioned predictive function can be a statistical model or a machine learning model, such as an autoregressive integral moving average model, a support vector machine, or a long short-term memory network. The specific selection and implementation of the predictive model are well-known technologies in this field and will not be elaborated here.

[0059] Configuration and updating of economic and constraint information.

[0060] The information acquisition module is also responsible for acquiring and managing economic parameters and physical constraints for optimization from the electricity market system or local human-machine interface.

[0061] The economic information mainly includes the time-of-use electricity price schedule for the future dispatch cycle. This electricity price list defines the prices for purchasing or selling electricity to the main grid at different times. It also includes operating cost models for each controllable generating unit, which can be a function or a piecewise function that maps output power to operating cost per unit time.

[0062] The physical constraint information is a static or quasi-static dataset that defines the safe operating boundaries of each unit in the system. This information specifically includes: the upper and lower limits of the exchange power with the main power grid. and The upper and lower limits of active power output for each controllable generator set. and Its power ramp-up rate limitation; maximum charge / discharge power of the energy storage unit. and And the upper and lower limits of the state of charge set to ensure its safety and lifespan. and This information is stored in a structured manner for subsequent optimization models to access.

[0063] The optimization and generation of the virtual impedance potential field and its topological feature set in step S2 are described in detail. This step is the key difference between this invention and the prior art, as it does not directly calculate the power setpoint at future times, but instead generates a functional instruction that defines the system's dynamic response strategy.

[0064] This step is executed by the optimization and strategy generation module in the upper-level optimization controller, and may include the following sub-steps:

[0065] Construction and solution of a forward-looking rolling optimization model.

[0066] Based on the multi-dimensional information obtained in step S1, the optimization and strategy generation module constructs a rolling optimization model with the future scheduling cycle as the optimization time domain. The optimization objective of this model is to minimize a predefined comprehensive operating cost function. .

[0067] Comprehensive operating cost function It is a multivariate function of the power sequence of each controllable unit in the system. Its specific form can be defined according to the system configuration and operation objectives. This function includes, but is not limited to, the power interaction cost with the main grid, the fuel or operating cost of the controllable generator set, and the equivalent cost of life depreciation of the energy storage unit due to charging and discharging.

[0068] In solving this optimization model, a series of equality and inequality constraints must be satisfied. These constraints mathematically define the safe operating domain of the system. The main constraints include: system power balance constraints, i.e., the total output power of all power sources must be equal to the total load power at any given time; upper and lower limit constraints on the power output of each unit; and dynamic changes in the state of charge of the energy storage unit and its upper and lower limit safety constraints.

[0069] This optimization problem can be constructed as a standard mathematical programming problem, such as linear programming or quadratic programming. The optimization and strategy generation module calls the corresponding solver to solve the model. This is how to solve this type of optimization problem.

[0070] Parameterized characterization and generation of virtual impedance potential field function.

[0071] The optimization model does not yield a static sequence of power setpoints, but rather a set of time-varying parameters used to construct the virtual impedance potential function. The virtual impedance potential function maps the power regulation behavior of the controllable unit to its instantaneous overall cost.

[0072] In this embodiment, for each controllable unit i, in each discrete control interval k of a future scheduling period, its VIPF function is... Characterized as a measure of power regulation The parameterized convex function. A preferred, non-limiting implementation is to represent it using a quadratic function:

[0073] ;

[0074] in: This represents the adjustment of the real-time active power of controllable unit i relative to the reference point. and The time-varying parameters are determined by the optimization results of step S2.

[0075] parameter Defined as the curvature coefficient of the potential field. The magnitude of this parameter reflects the marginal cost of power regulation by unit i within the control interval k, or its impact on system stability. The optimization model comprehensively determines this parameter based on factors such as the system's reserve capacity, line margin, and the unit's own operating status. The value. Larger. The value indicates that the power regulation cost of unit i is high or it is crucial to the stability of the system, and its power output should not fluctuate significantly.

[0076] parameter It is defined as the linear coefficient of the potential field. This parameter is related to the curvature coefficient. Together they determine the economic optimum of the market. The location of this point, where the gradient of the potential field is zero, represents the most economical power output benchmark within the current control interval k. The relationship is as follows:

[0077] ;

[0078] The optimization and strategy generation module obtains the optimal parameter pair for each unit i in each control interval k by solving the optimization model in step S2. This constitutes a complete set of time-varying potential field strategies.

[0079] Definition and distribution of key topological feature sets of potential fields.

[0080] After generating the VIPF function parameter set, the optimization and policy generation module further defines key topological features for each potential field based on this parameter set. These topological features are a quantitative description of the potential field function's shape and are used for state awareness by the local controller.

[0081] Key topological features are primarily defined by the curvature of the potential field. The curvature of the potential field... In mathematics, it is its second derivative. For the quadratic function VIPF described above, its curvature is:

[0082] ;

[0083] The optimization and strategy generation module defines two curvature thresholds: a lower flexible adjustment zone threshold. and higher constraint boundary region threshold These two thresholds define three operating regions: when When, unit i is in the flexible adjustment zone; when At that time, it was in the ordinary area; when At that time, it is in the constrained boundary region.

[0084] Finally, the optimization and strategy generation module will include the VIPF parameter set. and key topological feature set The control strategy package is distributed to each corresponding local controller via the communication network.

[0085] The local control and topology traversal judgment based on the potential field gradient in steps S3 and S4 are explained in detail. This part is executed independently by the local controller of each controllable unit i on a millisecond time scale, and is a key link connecting the upper-level economic strategy and the lower-level physical execution.

[0086] This part is implemented collaboratively by the policy enforcement module and the topology monitoring module in the local controller, and may include the following sub-steps:

[0087] Loading and analysis of virtual impedance potential field strategy.

[0088] At the beginning of each scheduling cycle, the local controller receives and stores the latest control strategy package issued by the upper-level optimization controller. This strategy package contains the set of virtual impedance potential field function parameters for all discrete control intervals k within the current scheduling cycle. and key topological feature set The local controller loads the corresponding parameters in real time for each control interval k based on the internal clock, serving as the basis for the current control law and status monitoring.

[0089] Design and execution of local control laws that integrate potential field gradients.

[0090] The strategy execution module generates the power command that ultimately drives the power electronic actuator based on the loaded potential field strategy. This power command is issued by the main control command. Summation potential gradient adjustment term It is formed by stacking.

[0091] ;

[0092] in: This is a power reference generated by the local master controller. Its main goal is to quickly respond to changes in local electrical quantities to maintain system transient stability, as well as voltage and frequency stability. The specific implementation of this master controller can be droop control or a PI controller, etc. This is the core potential field gradient adjustment term of the invention, whose function is to dynamically and economically correct the main control command based on the upper-level economic strategy. This adjustment term is designed to be proportional to the current gradient of the VIPF:

[0093] ;

[0094] in: This is the gradient gain coefficient, a preset positive constant used to adjust the strength of the economy-oriented approach. It is the VIPF function for the current control interval k. It is the actual power output of unit i Relative to the economic optimal point deviation, that is .

[0095] Substituting the VIPF function, we obtain the specific form of the gradient adjustment term:

[0096] ;

[0097] By introducing this gradient adjustment term, when the actual power of unit i deviates from the economic optimum, a reverse adjustment force related to the degree of deviation and adjustment cost will be generated, thereby continuously and flexibly pulling the unit's operating point toward the economic optimum region without affecting the transient stable response.

[0098] Real-time calculation and determination of the topological location of the running point.

[0099] The topology monitoring module and the policy execution module work in parallel, and their task is to perceive the current operating status of the local controller in real time. This module is based on the loaded potential field parameters. Calculate the curvature of the current VIPF. :

[0100] ;

[0101] Subsequently, the topology monitoring module calculates the real-time curvature. Topological feature thresholds issued by the upper layer The comparison is performed to determine the topological region where the current running point is located.

[0102] Determining topology traversal events.

[0103] During the control cycle, the topology monitoring module continuously executes the calculation and comparison in step S3. Topology traversal events. It is defined as: the operating state of unit i undergoes a state transition from the unconstrained boundary region to the constrained boundary region between two adjacent monitoring times.

[0104] The logical condition for this judgment is: at the previous monitoring time. The potential curvature of the system satisfies At the current monitoring time k, the potential field curvature satisfies When this condition is met, the topology monitoring module determines that a topology traversal event has occurred and generates an internal event flag, driving subsequent event triggering modules. This judgment mechanism ensures that communication with the upper layer is only initiated when the system state deteriorates significantly and is pushed towards the preset operating boundary, avoiding frequent false triggers under normal fluctuations.

[0105] The asynchronous event triggering and forced replanning based on topology traversal in steps S5 and S6 are described in detail. This mechanism constructs a fast feedback closed loop from bottom to top, which is the core of ensuring that the method of this invention has high robustness and adaptability in the face of unexpected disturbances.

[0106] This part is implemented jointly by the event triggering module of the local controller and the event response module of the upper-layer optimization controller, and may include the following steps:

[0107] Generation and encapsulation of asynchronous event trigger signals.

[0108] When the topology monitoring module of the local controller determines that a topology traversal event has occurred in step S4, its internal event flag is set, and this action will drive the event triggering module.

[0109] Once the event triggering module is activated, it immediately generates a structured data packet, which serves as the asynchronous event trigger signal. To ensure that the upper-level controller can accurately parse the event and respond accordingly, the signal must contain at least the following information fields:

[0110] Unit identifier : A unique code used to indicate which controllable unit i issued the trigger signal.

[0111] Event type coding This field describes the type of event that occurred. In this embodiment, the encoding represents a constraint boundary region topology traversal event. Setting this field also provides an interface for future expansion to support other types of triggering events.

[0112] Event timestamp It records the precise moment when a topology traversal event is determined, so that the upper-level controller can perform logging, fault diagnosis, and decision synchronization.

[0113] Event context data This field is optional and is used to provide a system snapshot at the time the event occurs. It may further include the actual power value at the time of triggering, the local bus voltage value, or the specific value of the potential field curvature that caused the crossing.

[0114] The signal is encapsulated into a data frame suitable for the communication network protocol used, ready for transmission.

[0115] Asynchronous, high-priority transmission of trigger signals.

[0116] The event triggering module will encapsulate the asynchronous event triggering signal. The signal is pushed to the communication interface of the local controller. Its transmission is asynchronous; it is sent independently of the periodic data reporting sequence between the local controller and the upper-level controller, and is sent immediately after its generation.

[0117] To ensure low latency and high reliability in signal transmission, this signal is configured as a high-priority message in the communication network. Through the network's Quality of Service (QoS) mechanism, a priority transmission queue is allocated to this type of signal, ensuring that it is transmitted before regular telemetry data during network congestion.

[0118] Event response and forced replanning by the upper-level controller.

[0119] The event response module in the upper-layer optimization controller continuously listens for asynchronous event triggering channels from all local controllers. Upon receiving a valid asynchronous event triggering signal... Then, the module performs the following actions in sequence:

[0120] The signal is analyzed and verified to extract the unit identifier. Event type Information such as these confirms the legitimacy of the event.

[0121] Send an internal interrupt or force execution command to the optimization and policy generation module. This command will cause the optimization and policy generation module to interrupt its current scheduling wait state, even if the current scheduling cycle has not yet ended.

[0122] Upon receiving the forced execution command, the optimization and strategy generation module immediately uses the latest system state as input to initiate a completely new, non-periodic optimization calculation process, i.e., returning to execution step S2. This process generates a set of virtual impedance potential field parameters and topological characteristics that better reflect the current system's actual operating conditions, and broadcasts it to all local controllers to update the original control strategies of each unit. Through this mechanism, the control strategy of the entire system can achieve rapid and adaptive closed-loop updates after critical events occur.

[0123] Please see the appendix Figure 2A multi-energy complementary power distribution control system, comprising:

[0124] The information acquisition module, configured in the upper-level optimization controller, is used to periodically collect real-time operating data of each unit from the multi-energy system, and receive source-load power prediction data and economic information such as electricity prices from external systems.

[0125] The optimization and strategy generation module, configured within the upper-level optimization controller, is connected to the information acquisition module. This module constructs and solves a forward-looking rolling optimization problem based on received multi-dimensional information. Its calculation results in a set of parameters for one or more virtual impedance potential field functions for each controllable unit within a future scheduling cycle, used to define key topological feature thresholds for the potential field morphology.

[0126] The event response module, configured within the upper-level optimization controller, receives asynchronous event trigger signals from one or more local controllers. Upon receiving a signal, this module issues a command to the optimization and strategy generation module to initiate an aperiodic, immediate replanning calculation.

[0127] The strategy execution module, configured in each local controller, receives and parses the virtual impedance potential field function parameters issued by the upper-level optimization controller. Based on the parsed parameters, this module generates real-time power control commands to drive power electronic converters and other actuators.

[0128] The topology monitoring module, configured in each local controller, works in parallel with the policy execution module. This module utilizes the potential field parameters and topology characteristic thresholds sent down from the upper layer, combined with real-time operating data measured locally, to continuously calculate the topological position of the current operating point in the virtual impedance potential field.

[0129] The event triggering module is configured in each local controller and connected to the topology monitoring module. When the topology monitoring module determines that a predefined topology boundary has been crossed at the running point, the event triggering module is activated, generating and sending an asynchronous event trigger signal to the upper-level event response module.

[0130] In a complete workflow, the information acquisition module first collects and preprocesses global system information and transmits the data to the optimization and strategy generation module. The optimization and strategy generation module performs an optimization calculation over a longer time scale, generating a set of virtual impedance potential field parameters and topological characteristics that define the optimal dynamic response characteristics of the system over a future cycle, and then distributes them to each local controller.

[0131] In each local controller, the strategy execution module loads the potential field strategy and incorporates it as part of a millisecond-level real-time control law to address system disturbances such as load fluctuations or changes in renewable energy output. Simultaneously, the topology monitoring module continuously determines whether the current power output point is within a safe and economical topology region.

[0132] When the system encounters an unexpected and severe disturbance, causing the operating point of a local controller to be pushed towards its operating boundary, the topology monitoring module will detect this state change and determine it as a topology traversal event. This event will immediately activate the event triggering module, which generates a signal containing its own identity and event information and sends it directly to the event response module in the upper-level optimization controller.

[0133] Upon receiving the asynchronous signal, the event response module immediately initiates a forced update of the control strategy without waiting for the current scheduling cycle to end. The instruction optimization and strategy generation module, based on the latest system state, recalculates and issues a virtual impedance potential field strategy more suitable for the current operating conditions. Afterward, the system enters the next dynamic cycle of "optimization, execution, monitoring, and triggering."

[0134] The system in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so they will not be described again here.

[0135] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for power distribution control of multi-energy complementation, characterized in that, The method comprises the following steps: An upper layer optimization step: based on real-time operation data and prediction information of the multi-energy system, an upper layer optimization controller performs optimization calculation, generates and issues a control strategy, which comprises a virtual impedance potential field function parameter for defining a dynamic response cost of each controllable unit, and key topological features for defining a shape of the potential field function; A local control and monitoring step: based on the received control strategy, a local controller of each controllable unit performs real-time power control and synchronously monitors a topological position of an operating point in the virtual impedance potential field; An asynchronous triggering step: when any of the local controllers monitors a pre-defined topological crossing event of the operating point, an asynchronous event triggering signal is sent to the upper layer optimization controller; A forced re-planning step: the upper layer optimization controller responds to the asynchronous event triggering signal, interrupts a current period and initiates a non-periodic re-planning calculation to update the control strategy.

2. The method of claim 1, wherein, The virtual impedance potential field function is a convex function with respect to a power adjustment amount, and its parameters at least include a curvature coefficient representing a marginal cost of power adjustment and a linear coefficient determining an economic optimum.

3. The method according to claim 1 or 2, wherein, The real-time power control comprises: generating a main control instruction; calculating a gradient of the current virtual impedance potential field function to generate a potential field gradient adjustment term; and superimposing the main control instruction and the potential field gradient adjustment term to form a final power instruction.

4. The method of claim 1, wherein, The key topological features include at least one curvature threshold value; and the topological crossing event is defined as a real-time curvature of the virtual impedance potential field function crossing the curvature threshold value.

5. The method of claim 4, wherein, The key topological features include a first curvature threshold value and a second curvature threshold value, the second curvature threshold value being smaller than the first curvature threshold value, so as to divide the virtual impedance potential field into multiple operating regions including a flexible adjustment region, a normal region and a constraint boundary region.

6. The method of claim 1, wherein, The forced re-planning step comprises: interrupting a current scheduling waiting period, and immediately performing the optimization calculation based on the latest system operation data at the time of receiving the triggering signal.

7. The method of claim 1, wherein, The optimization calculation performed by the upper layer optimization controller is to solve a rolling optimization model with an objective of minimizing an integrated operation cost in a future scheduling period of the system, and satisfying a system power balance constraint and an operating boundary constraint of each unit.

8. The method of claim 1, wherein, The asynchronous event triggering signal at least includes an identifier of a controllable unit sending the signal, an event type code and a time stamp of the event occurrence.

9. A multi-energy complementary power distribution control system for implementing the method of any one of claims 1-8, characterized by, The method comprises: An information acquisition module for collecting real-time operation data and prediction information of a multi-energy system; An optimization and strategy generation module for performing optimization calculation based on the information to generate the virtual impedance potential field function parameter and the key topological features; A strategy execution module configured in each local controller for performing real-time power control based on the potential field function parameter; A topological monitoring module configured in each local controller for monitoring a topological position of an operating point according to the key topological features; An event triggering module configured in each local controller for generating an asynchronous event triggering signal when a topological crossing event is monitored. An event response module is configured in the upper layer optimization controller, and is used for receiving the trigger signal and instructing the optimization and strategy generation module to start non-periodic re-planning.

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