Source-network-load interactive renewable energy supply flexibility control method and system

Abstract

The invention belongs to the technical field of power system control, and provides a source-grid-load interactive renewable energy supply flexibility control method and system, and the method comprises the steps: obtaining a power grid state deviation according to related physical information; generating and broadcasting synthetic pheromones according to the power grid state deviation to obtain a pheromone gradient field; secondly, extracting field features of a position pheromone gradient field where the distributed flexible resources are located; the field features comprise pheromone concentration and pheromone concentration gradient; according to the field feature and the state of the flexible resource, calculating a power regulation amount through a preset response vector graph; and finally, according to the power regulation quantity, controlling the flexible resource to carry out power output regulation. According to the method, the communication and calculation bottlenecks of centralized control can be avoided, and the limitation of global coordination deficiency of distributed control can be overcome, so that rapid, efficient and stable cooperative control of massive distributed flexible resources is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system control, and particularly relates to a source-grid-load interactive renewable energy supply flexibility control method and system. BACKGROUND

[0002] The penetration rate of renewable energy represented by wind energy and solar energy in the power system is growing at an unprecedented rate. The inherent intermittency, volatility and prediction uncertainty of such power sources pose a serious challenge to the real-time supply and demand balance and safe and stable operation of the power system. At the same time, with the development of power electronics technology, energy storage technology and information communication technology, the number of distributed flexible resources such as battery energy storage systems, electric vehicles and intelligent temperature control loads in the power grid is growing rapidly. These resources have fast and accurate power regulation capabilities and are ideal carriers for providing flexibility services for the power grid.

[0003] To effectively regulate and control the massive distributed flexible resources, one mainstream approach is to use a centralized control architecture, that is, a central control center or aggregator platform collects state information, performs optimization calculation and issues precise control instructions for all resources within its jurisdiction. In theory, this architecture can achieve globally optimal resource allocation. However, with the exponential growth of the number of connected resources, it puts extremely high requirements on the bandwidth, latency and reliability of the communication network. The massive data interaction and complex centralized optimization calculation also bring heavy communication bottlenecks and computing burdens, limiting the response speed and scalability of the control, and there is a single point of failure risk. To avoid the inherent defects of centralized control, another technical path is to use a decentralized control strategy so that each resource unit can respond autonomously based on local information without relying on high-speed wide-area communication, thus having the advantages of fast response and strong scalability. However, due to the lack of awareness of the global state of the power grid, this control method based solely on local information cannot effectively coordinate resources. In the face of complex power grid disturbances, it may lead to blind regulation actions, causing over-regulation in some areas and under-regulation in other areas, making it difficult to achieve optimal resource allocation at the system level, and even may cause local oscillation, affecting the overall stability of the system. SUMMARY

[0004] The present application is proposed to solve the above problems, and provides a source-grid-load interactive renewable energy supply flexibility control method and system. The present application solves the problem that the prior art cannot balance fast response, low communication dependence and global coordination when regulating and controlling massive distributed flexible resources.

[0005] To achieve the above purpose, the present application is implemented by the following technical solutions: In a first aspect, the present application provides a method for renewable energy supply flexibility control based on source-grid-load interaction. First, pheromone generators are deployed at multiple pre-defined nodes in the power grid. Each pheromone generator acquires the grid state deviation at its location, such as frequency deviation, voltage deviation, etc.

[0006] Subsequently, the multiple pheromone generators generate and broadcast synthetic pheromone signals according to the acquired grid state deviation through a pre-defined transfer function. Due to the broadcasting of multiple pheromone generators at different geographical locations and the natural attenuation of signals during propagation, these signals superimpose to form a dynamic, non-uniform pheromone gradient field in the power grid. Specifically, the k-th pheromone generator generates the concentration of the k-th type of pheromone , which is a function of the k-th type of grid state deviation , measured by the pheromone generator: where is a pre-defined non-linear transfer function, which includes a response dead zone and a response saturation zone to avoid responding to normal fluctuations in the power grid and limit the output under extreme disturbances.

[0007] Then, the pheromone sensing and actuator deployed on various types of distributed flexible resources senses the field characteristics of the pheromone gradient field at its location in real time. The field characteristics include at least two dimensions: one is the pheromone concentration, which is a scalar value representing the intensity of local grid demand; the other is the pheromone concentration gradient, which is a vector value pointing to the grid area with the most urgent demand and its magnitude representing the spatial variation rate of demand intensity.

[0008] Next, the pheromone sensing and actuator calculates the power adjustment amount according to the sensed field characteristics and the internal state of the flexible resource itself through a pre-defined response vector atlas. This calculation process takes into account both external grid demand and the adjustable capacity and constraints of the resource itself. Specifically, for the k-th type of grid flexibility demand, the calculation of the power adjustment amount satisfies the following relationship: ​​​​​​​​​It is a state-dependent response gain function, the value of which is determined by its own state. The decision is used to characterize the responsiveness of resources in the current state; This is a pheromone response function used to define the specific response pattern of a resource to external field characteristics.

[0009] Furthermore, when flexible resources are used as energy storage devices, their It is in the state of charge (SOC). The function is configured to have a large value when the SOC is in a preset middle working range, and its value tends to zero when the SOC is close to its upper or lower limit. As another specific implementation, the... The function can be configured as and The weighted sum.

[0010] Finally, the pheromone sensor and actuator generates specific power control commands based on the calculated total power adjustment amount, and controls the flexible resources bound to it to perform power output adjustment. This process allows the adjustment behaviors of massive flexible resources to converge and act on the power grid together until the power grid state deviation recovers to the preset dead zone range. At this point, the pheromone gradient field disappears, the resources automatically stop adjusting, and the system reaches a new stable equilibrium state.

[0011] Secondly, the present invention also provides a source-grid-load interactive renewable energy supply flexibility control system, comprising: The data acquisition module is configured to acquire relevant physical information of multiple nodes in the power grid. The pheromone gradient field determination module is configured to: obtain the power grid state deviation based on relevant physical information; generate and broadcast synthetic pheromones based on the power grid state deviation to obtain the pheromone gradient field; The field feature extraction module is configured to: extract field features of the pheromone gradient field at the location of the distributed flexible resource; the field features include pheromone concentration and pheromone concentration gradient; The power adjustment amount determination module is configured to: calculate the power adjustment amount based on the field characteristics and the state of the flexible resource through a preset response vector spectrum; The control module is configured to control the flexible resource to adjust its power output according to the power adjustment amount.

[0012] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the source-grid-load interaction renewable energy supply flexibility control method described in the first aspect.

[0013] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps of the source-grid-load interaction renewable energy supply flexibility control method described in the first aspect.

[0014] Fifthly, the present invention also provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the source-grid-load interaction renewable energy supply flexibility control method described in the first aspect.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention first obtains the power grid state deviation based on relevant physical information; then, it generates and broadcasts synthetic pheromones based on the power grid state deviation to obtain a pheromone gradient field; next, it extracts the field characteristics of the pheromone gradient field at the location of the distributed flexible resources; the field characteristics include pheromone concentration and pheromone concentration gradient; based on the field characteristics and the state of the flexible resources themselves, it calculates the power adjustment amount through a preset response vector spectrum; finally, based on the power adjustment amount, it controls the flexible resources to adjust their power output. This invention avoids the communication and computing bottlenecks of centralized control and overcomes the limitations of the lack of global coordination in decentralized control, thereby achieving rapid, efficient, and stable collaborative control of massive distributed flexible resources.

[0016] 2. This invention achieves deep decentralization of control logic by constructing a pheromone gradient field as a common medium for power grid state information and enabling autonomous decision-making by pheromone sensors and actuators deployed on distributed resources. This approach eliminates the need for a central control station for global information collection and centralized optimization calculations, and avoids reliance on large-scale, high-speed bidirectional communication networks, thereby reducing system complexity and communication overhead. It also possesses plug-and-play access capabilities to massive heterogeneous resources and exhibits excellent scalability.

[0017] 3. This invention constructs a physical closed-loop control system with an extremely short response link by generating real-time grid state deviations using a pheromone generator and rapidly executing power regulation using pheromone sensors and actuators. This system directly generates negative feedback to the pheromone gradient field by changing the physical state of the grid, enabling the overall response of massive resources to automatically converge to a stable state as the grid state recovers, without external intervention or command cancellation, thus achieving system-level self-stabilizing control.

[0018] 4. This invention introduces the spatial vector feature of pheromone concentration gradient into the response decision-making process, enabling spatially differentiated power regulation of distributed flexible resources. Resources closer to the grid demand source and with larger gradient values ​​will respond more strongly, while resources farther from the source will respond weakly or not at all. This mechanism guides massive resources to spontaneously form an implicit synergy, precisely applying flexibility support to the areas of the grid most in need, effectively addressing localized grid problems, and improving control accuracy and resource utilization efficiency. Attached Figure Description

[0019] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0020] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system architecture diagram of Embodiment 1 of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0023] Example 1: Against the backdrop of a global energy transition towards cleaner and lower-carbon energy, the penetration rate of renewable energy sources, such as wind and solar power, in the power system is growing at an unprecedented rate. However, the inherent intermittency, volatility, and predictability of these power sources pose serious challenges to the real-time supply-demand balance and safe and stable operation of the power system. To absorb a high proportion of renewable energy and maintain grid reliability, improving the flexibility of system operation has become a core technological requirement for the current development of the power system.

[0024] Meanwhile, with the development of power electronics, energy storage, and information and communication technologies, the number of distributed flexible resources in the power grid, such as battery energy storage systems, electric vehicles, and intelligent temperature-controlled loads, is growing rapidly. These resources possess rapid and precise power regulation capabilities, making them ideal carriers for providing the necessary flexibility services to the power grid. Therefore, how to effectively and economically coordinate and regulate massive, geographically dispersed distributed flexible resources, enabling them to aggregate into substantial virtual power plants or flexibility resource pools to respond to the dynamic needs of the power grid, has become a research hotspot and technical challenge in this field.

[0025] To effectively manage massive, distributed, and flexible resources, existing technologies have explored various control architectures. A mainstream approach is a centralized control architecture, where a central control center or aggregator platform collects and optimizes the status information of all resources within its jurisdiction and issues precise control commands. Theoretically, this architecture can achieve globally optimal resource allocation. However, with the exponential growth in the number of access resources, extremely high demands are placed on the bandwidth, latency, and reliability of the communication network. The massive data interaction and complex centralized optimization calculations also introduce heavy communication bottlenecks and computational burdens, limiting control response speed and scalability, and introducing the risk of single points of failure.

[0026] To circumvent the inherent drawbacks of centralized control, another technical approach is to employ distributed control strategies, such as droop control based on local grid frequency or voltage measurements. This method allows each resource unit to respond autonomously based on local information, without relying on high-speed wide-area communication, thus offering advantages such as fast response and strong scalability. However, due to the lack of awareness of the overall grid state, this type of control method, entirely based on local information, struggles to achieve effective coordination among resources. When facing complex grid disturbances, it may lead to blind adjustments, resulting in over-regulation in some areas and under-regulation in others, hindering system-level resource optimization and potentially even triggering local oscillations, affecting the overall stability of the system.

[0027] To address at least one of the aforementioned problems, this embodiment provides a source-grid-load interactive renewable energy supply flexibility control method. This method avoids the communication and computing bottlenecks of centralized control and overcomes the limitations of distributed control's lack of global coordination, thereby achieving rapid, efficient, and stable collaborative control of massive distributed flexible resources. Figure 1 As shown, the method includes: S1: Multiple pheromone generators deployed at different nodes of the power grid acquire the power grid state deviation of their respective nodes in real time; In this embodiment, multiple pheromone generators deployed at different nodes of the power grid and the methods by which they acquire the power grid state deviation of their respective nodes in real time are described in detail.

[0028] A pheromone generator is a physical device deployed within the physical network of a power system. To accurately reflect the operating status of the entire power grid or a specific area, the deployment location of the pheromone generator is pre-selected. Preferably, it is deployed at key nodes in the power grid, such as substation buses, the beginning of distribution network feeders, and large renewable energy grid connection points. These nodes are chosen because they are key convergence or divergence points for power flow and state changes, and the power grid state variables at these locations can more representatively reflect the overall or local supply-demand balance of the system.

[0029] Each pheromone generator is equipped with a high-precision power grid state variable measurement unit. To ensure the temporal consistency and comparability of the state information acquired by pheromone generators at different locations, the measurement unit preferably has a synchronous phasor measurement function, such as achieving nanosecond-level time synchronization through timing sources like the Global Positioning System or the BeiDou Navigation Satellite System.

[0030] The pheromone generator acquires the power grid state deviation of its node in real time. This process includes two steps: real-time monitoring of state variables and quantitative calculation of deviation values.

[0031] In the real-time monitoring step of the state variable, the pheromone generator continuously measures its location. One or more preset power grid physical quantities (physical information). These physical quantities are the types of power grid flexibility requirements that the present invention aims to address. And predefined. For example, when flexibility requirements are system-level frequency support. At that time, all pheromone generators monitor the same physical quantity, namely the real-time operating frequency of the entire synchronous power grid system. When flexibility requirements are localized voltage support .

[0032] At the same time, each pheromone generator monitors the real-time voltage amplitude at its own grid connection point. .

[0033] In the quantification calculation step of the deviation value, the pheromone generator compares the physical quantity monitored in real time with a preset reference value to obtain the quantified power grid state deviation. Specifically, regarding frequency support requirements, grid state deviation... The calculation method is as follows: ; in, The rated frequency of the power grid is a constant reference value at the system level.

[0034] Regarding voltage support requirements, located at... The pheromone generator calculates its power grid state deviation. The method is as follows: ; in, The rated voltage for this node is a localized reference value.

[0035] In this way, the pheromone generator transforms the continuously changing physical power grid state into a standardized digital quantity that can be directly used for subsequent pheromone concentration synthesis, namely, the power grid state deviation. The magnitude of this deviation value directly reflects the degree to which the power grid deviates from its normal operating state, while its positive or negative sign reflects the direction of the deviation, providing accurate and real-time input for the subsequent generation of synthetic pheromones that characterize the intensity and type of demand.

[0036] S2: Multiple pheromone generators generate and broadcast synthetic pheromones based on grid state deviations to form a dynamic, non-uniform pheromone gradient field in the grid. In this embodiment, the process of multiple pheromone generators generating and broadcasting synthetic pheromones based on power grid state deviations to form a dynamic, non-uniform pheromone gradient field in the power grid is described in detail.

[0037] After obtaining standardized power grid state deviations Subsequently, the core function of each pheromone generator is to convert it into a broadcastable physical signal, namely, synthetic pheromone. This process ensures that the abstract requirements of the power grid can be materialized into environmental information that can be perceived by distributed devices.

[0038] Specifically, the pheromone generator is internally configured with a preset transfer function. Used to input power grid state deviation Mapped to the output pheromone concentration Index here The type of pheromone represents a specific grid flexibility requirement, while the index... This identifies the specific pheromone generator. The mapping relationship is as follows: ; To ensure system stability and response effectiveness, the transfer function... Preferably, it is designed as a piecewise nonlinear function, whose structure includes a response dead zone and a response saturation zone. Its specific form can be: ; in, This is the absolute value of the input power grid state deviation; The preset response dead zone threshold is set so that the system remains unresponsive to normal and minor fluctuations in the power grid, thus avoiding unnecessary adjustments. In response to the saturation threshold, when the power grid state deviation exceeds this value, the output pheromone concentration will no longer increase, in order to prevent excessive response commands from being generated in extreme power grid events and to ensure the stability of the system. This is the gain coefficient in the linear response region, which determines the sensitivity of pheromone concentration to changes in state deviation. This represents the maximum permissible concentration of this type of pheromone.

[0039] Calculated pheromone concentration It is a numerical value that needs to be encoded into a physical signal before it can be broadcast. In this embodiment, the type of synthetic pheromone... The pheromone is distinguished by specific physical characteristics of the signal. Preferably, different types may correspond to different carrier frequencies, specific digital coding sequences, or unique modulation waveforms. The concentration of this type of pheromone... It is then modulated to the amplitude of the signal.

[0040] Subsequently, the pheromone generator broadcasts the encoded synthetic pheromone signal unidirectionally through its signal transmission unit. Preferably, the synthetic pheromone is broadcast via power line carrier communication or a dedicated low-power wide-area wireless channel. This unidirectional, broadcast communication method eliminates the need to establish a point-to-point communication link or receive any return information.

[0041] The key aspect of this embodiment is that multiple geographically dispersed pheromone generators exist simultaneously within the power grid. Each generator independently generates and broadcasts its synthetic pheromone. Therefore, at any location within the power grid... The total pheromone concentration it can perceive It is all The result of signals broadcast by each pheromone generator propagating and attenuating along their respective paths, superimposed at this point. This physical process can be described by the following equation: ; in, It is the first A pheromone generator in Concentration generated at any given time; It is a characterization signal from the source point propagate to target point The attenuation function is related to the propagation distance and the properties of the medium.

[0042] Due to two key factors, firstly, generators at different locations may produce pheromones of varying intensities due to the measurement of different local grid state deviations; secondly, signal propagation in physical space inevitably involves attenuation, resulting in a non-uniform distribution of the final superimposed field across the entire grid space. When a severe state deviation occurs in a local area of ​​the grid, pheromone generators near that area will release high concentrations of pheromones, thus creating a concentration peak in that region.

[0043] This concentration distribution, which varies with spatial location, constitutes a dynamic, non-uniform scalar field of pheromone concentration. This scalar field necessarily possesses a gradient in space, namely the pheromone gradient field. The vector direction of this gradient field points to the direction of the fastest concentration increase, thus implicitly pointing to the source region where the grid demand is most urgent; the magnitude of its vector represents the rate of change of demand intensity in space. Meanwhile, since the grid state changes in real time, the concentration generated by each pheromone generator is also updated in real time, so the entire pheromone gradient field is also dynamically changing.

[0044] Ultimately, the pheromone gradient field, as a public information medium, carries rich information about the type, global intensity, spatial location, and urgency of power grid demand, and transmits it to every distributed resource in the power grid that has deployed pheromone sensors and actuators, providing a decision-making basis for subsequent autonomous and coordinated responses.

[0045] S3: Multiple pheromone sensors and actuators deployed on distributed flexible resources sense the field characteristics of the pheromone gradient field at their location. The field characteristics include pheromone concentration and pheromone concentration gradient. In this embodiment, the process of how multiple pheromone sensors and actuators deployed on distributed flexible resources perceive the field characteristics of the pheromone gradient field at their location is described in detail.

[0046] Pheromones sensing and actuators are hardware and software modules embedded in or attached to the controllers of various distributed flexible resources as autonomous response units. Distributed flexible resources may include battery energy storage systems, photovoltaic inverters, electric vehicle charging stations, intelligent temperature-controlled loads, or building energy management systems. Their core function is to act as an interface between flexible resources and grid environmental information, decoding the physical information field constructed by the pheromone generator.

[0047] Specifically, the process of pheromone perception and actuator perception of field characteristics forms the data basis for subsequent response decisions. Field characteristics are not a single numerical value, but rather include two interrelated dimensions with different physical meanings: pheromone concentration and pheromone concentration gradient.

[0048] First, the purpose of sensing pheromone concentration is to quantify the grid demand intensity at the location of flexible resources. Each pheromone sensor and actuator is equipped with a signal receiving unit. This unit is configured to tune to a preset value corresponding to a specific pheromone type. The corresponding signal frequency band or decoding of a specific encoded sequence. By receiving and demodulating the broadcast synthetic pheromone signal, the signal receiving unit extracts the signal amplitude. This amplitude, after calibration, directly corresponds to its location. Real-time pheromone concentration It provides a preliminary basis for flexible resources to judge whether or not they respond and the magnitude of the response.

[0049] However, simply obtaining the scalar value of pheromone concentration is insufficient to achieve spatial differentiation and precise response. Therefore, this invention further proposes that the pheromone sensor and actuator also sense the pheromone concentration gradient. This gradient is a vector value that provides spatial information about the source of grid demand, enabling the response of flexible resources to have directionality. (Pheromone concentration gradient) Mathematically, this is represented as a vector consisting of the partial derivatives of the concentration scalar field in a spatial coordinate system: ; The size of the vector It represents the degree of drastic change in demand intensity with spatial variation, and its direction points to the source area where demand is most urgent.

[0050] To estimate the pheromone concentration gradient, the pheromone sensor and actuator can employ at least one of the following techniques: As a preferred embodiment, the signal receiving unit of the pheromone sensor and actuator can be configured as a multi-antenna array. By measuring the phase difference or time difference when the synthesized pheromone signal arrives at different antennas, this unit can calculate the angle of arrival of the signal. Since the received signal is a superposition of signals from multiple pheromone generators, its combined angle of arrival approximately points to the "centroid" of the signal energy, thus serving as an effective estimate of the pheromone concentration gradient direction. Simultaneously, by comparing the intensity differences of the received signals on different antennas, the magnitude of the gradient can be estimated.

[0051] As another feasible implementation, in some application scenarios, multiple adjacent pheromone sensors and actuators can interact via a very low-bandwidth local communication link. Each actuator shares its measured pheromone concentration value with its nearest neighbors. Through these spatially discrete concentration sampling points, each actuator can calculate the local concentration gradient at its location using numerical difference methods.

[0052] In this way, each pheromone sensor and actuator deployed in distributed flexible resources can independently and autonomously acquire complete field characteristic information of its surroundings: that is, the pheromone concentration representing the intensity of local demand. And the pheromone concentration gradient representing the spatial orientation and rate of urgency of global demand. These two physical quantities together constitute all the external inputs for subsequent power regulation calculations, enabling the response decisions of flexible resources to take into account both the local state and the global objective.

[0053] S4: The pheromone sensor and actuator calculates the power adjustment amount based on the field characteristics and the state of the flexible resource through a preset response vector spectrum. In this embodiment, the process of how the pheromone sensor and actuator calculates the power adjustment amount based on the field characteristics and the state of the flexible resource through a preset response vector spectrum after acquiring the field characteristics is described in detail.

[0054] This calculation process is a core step in enabling the pheromone sensor and actuator to achieve its autonomous decision-making function. Its purpose is to combine perceived, multi-dimensional environmental information with the internal state of resources to generate a specific, executable power regulation command.

[0055] Specifically, the pheromone sensing and actuator internally stores or configures a response vector map. Functionally, this response vector map is a pre-defined function model or rule set. It defines a precise mapping relationship, whose inputs are external field characteristics and internal resource states, and whose output is a mapping for the first... Power regulation required for grid flexibility .

[0056] The input to this calculation process is multi-dimensional, including at least: firstly, the local pheromone concentration sensed by the pheromone sensor and actuator in the previous stage. Secondly, the magnitude of the local pheromone concentration gradient sensed by the pheromone sensor and actuator in the previous stage. Third, the current state of flexible resources. .

[0057] The inherent state of flexible resources This is a key endogenous constraint variable that ensures that while providing grid support services, the resource's fundamental functions and physical constraints are respected. For example, when the flexible resource is a battery energy storage system, its own state... That is, its state of charge; when the resource is a smart temperature-controlled load, It can show the relationship between the current indoor temperature and the user's set comfort range.

[0058] Based on the above input, the power regulation amount The calculation process can be functionally described by the following formula: ; For ease of implementation and configuration, the response vector graph function... It can preferably be decomposed into the product of two interacting functional modules, i.e., a state-dependent response gain function. A pheromone response function Its specific form is: ; State-dependent response gain function It is primarily used to reflect the responsiveness and capability of flexible resources. The value range of this function is typically between 0 and 1. It depends on the internal state of the resource. The amplitude of its response is dynamically adjusted. Taking a battery energy storage system as an example, its function... It is configured such that when its SOC is within a preset healthy operating range (e.g., 20% to 80%), the function value is close to or equal to 1, indicating that it has sufficient responsiveness; while when its SOC is close to the full charge or discharge limit, the function value smoothly approaches 0. The purpose of this design is to naturally avoid overcharging or over-discharging the battery by reducing the response gain, thereby protecting its operational safety and cycle life.

[0059] Pheromones response function This defines the resource response mode to external grid demand. It transforms the sensed field characteristics, pheromone concentration, and concentration gradient into a basic power regulation command. As a preferred implementation, this function can be designed as a linear weighted sum of the two: ; In this expression, and These are pre-configured weighting coefficients. By configuring these two weighting coefficients differently, different types of flexible resources can be assigned different response characteristics. For example, for resources with fast response speed and strong adjustment capability, a relatively large gradient weight can be configured. This makes it more sensitive to the most urgent needs across the entire network; while for resources with slower response times and thermal inertia, a relatively large concentration weight can be configured. This allows it to primarily respond to local, sustained, and significant needs.

[0060] Therefore, the final calculated power regulation amount is obtained in this way. This is a rational decision result that integrates the intensity and spatial orientation of external power grid demand with the internal state constraints of the resources themselves. It not only determines whether to respond and how much to respond, but also indirectly determines the priority of the response by considering the gradient, thereby realizing implicit coordination based on environmental information among massive heterogeneous resources without explicit communication.

[0061] S5: Based on the power adjustment amount, control the flexible resources to adjust the power output.

[0062] This embodiment elaborates on the process by which the pheromone sensor and actuator, after calculating the power adjustment amount, specifically controls the flexible resources to adjust the power output based on this adjustment amount. This step is the final execution stage of the control method of this invention, which transforms the abstract decision formed in the previous step into a concrete action that has a real physical impact on the power grid.

[0063] The power regulation amount for one or more types of grid flexibility requirements k is calculated by pheromone sensing and actuators. Next, it is necessary to determine an overall power adjustment amount. In the presence of multiple pheromone gradient fields, the pheromone sensor and actuator algebraically superimpose the power adjustment amounts of all responses to form a power adjustment amount at the current moment. Total power regulation The calculation method is as follows: ; This total power regulation This is not the resource's final output power, but rather an increment or decrement relative to its baseline operating power. Therefore, the next step is to calculate the final power setpoint. The pheromone sensor and actuator will regulate the total power. Superimposed on the reference power of flexible resources without pheromone influence Above. Reference power This refers to the power curve of a resource under normal operating conditions, such as the maximum power point tracking output power of a photovoltaic inverter, or the charging power planned by an electric vehicle charging station according to user presets or time-of-use pricing strategies. Therefore, the initial power setpoint is calculated as follows: .

[0064] However, to ensure the safe and stable operation of flexible resources and strictly adhere to their physical and operational constraints, the calculated power setpoints must be limited to their permissible operating range. Each flexible resource has a minimum operating power. and a maximum operating power .

[0065] To this end, the pheromone sensing and actuator includes a constraint enforcement module. This module limits the initially calculated power setpoint to ensure that the final command does not exceed the physical capacity of the resources. The generation process is described by the following formula: ; in, It is a limiting function whose function is: if the input value Below the lower limit Then output ;like Above the upper limit Then output ;like If it falls between the two, then output This step is crucial in ensuring the robustness and security of this method in practical applications.

[0066] After generating the final power setpoint that satisfies all constraints Subsequently, the pheromone sensor and actuator, through its execution interface, transforms the setpoint into specific control commands and sends them to the underlying controller of the flexible resource it is bound to, such as an inverter, charging controller, or building automation system. Preferably, this communication can employ a standard industrial communication protocol.

[0067] Upon receiving this instruction, the underlying controller of the flexible resource immediately adjusts its power output to operate at... At the same level, because massive amounts of distributed flexible resources are autonomously regulating power in this way, their collective, convergent power response will directly affect the power grid, thereby changing the physical state of the grid. For example, when the response frequency drops, a large number of resources increase their power output, collectively injecting active power into the grid, thereby suppressing further frequency drops and causing it to recover.

[0068] This control execution process is not an open-loop action, but rather the final stage of the entire self-consistent closed-loop control system. When the power regulation behavior of massive resources causes deviations in the power grid state... When the pheromone concentration decreases, the pheromone generators deployed in the power grid will correspondingly reduce the concentration of pheromone they generate, resulting in a weakening of the overall pheromone gradient field strength. After the pheromone sensor and actuator detect the weakening of the field strength, they will calculate the power regulation amount. It will also decrease naturally. This process forms a negative feedback regulation mechanism based on environmental information that does not require central coordination, until the power grid state returns to the preset normal range, the pheromone gradient field disappears, all resource regulation actions automatically stop, and the system reaches stability at a new equilibrium point.

[0069] This embodiment achieves decentralized, rapid, and self-stabilizing collaborative control by constructing a physical information field to guide autonomous response. Furthermore, it improves the spatial accuracy of the response through gradient perception, effectively addressing the flexibility challenges of renewable energy supply.

[0070] Corresponding to the method described, such as Figure 2 As shown, this embodiment also provides a renewable energy supply flexibility control system for source-grid-load interaction, including: Multiple pheromone generation modules are deployed at different nodes of the power grid to obtain the power grid state deviation of their respective nodes in real time, and generate and broadcast synthetic pheromones based on the power grid state deviation to form a dynamic, non-uniform pheromone gradient field in the power grid. Multiple autonomous response modules, each deployed on a distributed flexible resource, each autonomous response module includes: The field feature sensing module is used to sense the field features of the pheromone gradient field at its location. The field features include pheromone concentration and pheromone concentration gradient. The power regulation calculation module is used to calculate the power regulation amount based on the field characteristics and the state of the flexible resources through a preset response vector spectrum. The power control execution module is used to control the flexible resources to adjust the power output according to the power adjustment amount.

[0071] Example 2: This embodiment provides a renewable energy supply flexibility control system for source-grid-load interaction, including: The data acquisition module is configured to acquire relevant physical information of multiple nodes in the power grid. The pheromone gradient field determination module is configured to: obtain the power grid state deviation based on relevant physical information; generate and broadcast synthetic pheromones based on the power grid state deviation to obtain the pheromone gradient field; The field feature extraction module is configured to: extract field features of the pheromone gradient field at the location of the distributed flexible resource; the field features include pheromone concentration and pheromone concentration gradient; The power adjustment amount determination module is configured to: calculate the power adjustment amount based on the field characteristics and the state of the flexible resource through a preset response vector spectrum; The control module is configured to control the flexible resource to adjust its power output according to the power adjustment amount.

[0072] The operating method of the system is the same as that of the renewable energy supply flexibility control method of source-grid-load interaction in Example 1, and will not be repeated here.

[0073] Example 3: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the renewable energy supply flexibility control method for source-grid-load interaction described in Embodiment 1.

[0074] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements the steps of the renewable energy supply flexibility control method for source-grid-load interaction described in Embodiment 1.

[0075] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the source-grid-load interaction renewable energy supply flexibility control method described in Embodiment 1.

[0076] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for controlling the flexibility of renewable energy supply through source-grid-load interaction, characterized in that, include: Obtain relevant physical information of multiple nodes in the power grid; Based on relevant physical information, the power grid state deviation is obtained; based on the power grid state deviation, pheromones are generated and broadcast to obtain the pheromone gradient field. Extract field features of the pheromone gradient field at the location of the distributed flexible resource; the field features include pheromone concentration and pheromone concentration gradient; Based on the field characteristics and the state of the flexible resource itself, the power adjustment amount is calculated through a preset response vector spectrum; The power output of the flexible resource is controlled to adjust according to the power adjustment amount.

2. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 1, characterized in that, The relevant physical information includes operating frequency and voltage amplitude; The power grid condition deviation is the difference between the operating frequency and the rated frequency, and the difference between the voltage amplitude and the rated voltage.

3. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 1, characterized in that, Generate and broadcast synthetic pheromones based on power grid state deviations, including: according to the first Grid state deviations corresponding to grid flexibility requirements Through the preset transfer function Calculate the first The pheromone generator generates the first... Pheromones concentration : 。 4. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 3, characterized in that, The transfer function For nonlinear functions including response dead zone and saturation region: ； in, This is the absolute value of the input power grid state deviation; This is the preset response dead zone threshold; The response saturation threshold; The gain coefficient in the linear response region; This represents the maximum permissible concentration of pheromones.

5. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 1, characterized in that, During the field feature extraction, multiple pheromone sensors and actuators pre-deployed in the distributed flexible resources are used to sense the field features of the pheromone gradient field at the location of the pheromone sensors and actuators. Specifically, the pheromone concentration at the location of the pheromone sensors and actuators, representing the intensity of demand, is measured, and the pheromone concentration gradient at the location of the pheromone sensors and actuators, representing the spatial orientation and rate of change of demand, is estimated.

6. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 1, characterized in that, The calculation of the power adjustment amount includes: adjusting the pheromone concentration corresponding to the k-th type of pheromone. The magnitude of the pheromone concentration gradient and the state of the flexible resource itself. As input, the power regulation amount for the k-th type of grid flexibility demand is calculated through the response vector map. : ； in, This is a state-dependent response gain function, used to characterize the flexible resource in its own state. Next to the The ability to respond to similar requirements; This is the pheromone response function.

7. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 6, characterized in that, When the flexible resource is an energy storage device, its own state The state of charge (SOC) of the energy storage device; the state-dependent response gain function. It is configured such that when the state of charge (SSOC) is within a preset intermediate range, the response gain function value is close to or equal to 1, and when the SSOC is close to the full charge or discharge limit, the response gain function approaches zero; the pheromone response function is a weighted sum of the pheromone concentration and the magnitude of the pheromone concentration gradient.

8. The renewable energy supply flexibility control method based on source-grid-load interaction as described in claim 1, characterized in that, The synthetic pheromone is distinguished by the physical characteristics of the signal, and the type corresponds to the preset power grid flexibility requirements, which include frequency support requirements or voltage support requirements. Alternatively, the distributed flexible resources may include at least one of battery energy storage systems, photovoltaic inverters, electric vehicle charging piles, intelligent temperature-controlled loads, and building energy management systems; Alternatively, the response vector map is characterized by being a preset set of rules or a function model.

9. A renewable energy supply flexibility control system with source-grid-load interaction, characterized in that, include: The data acquisition module is configured to acquire relevant physical information of multiple nodes in the power grid. The pheromone gradient field determination module is configured to: obtain the power grid state deviation based on relevant physical information; generate and broadcast synthetic pheromones based on the power grid state deviation to obtain the pheromone gradient field; The field feature extraction module is configured to: extract field features of the pheromone gradient field at the location of the distributed flexible resource; the field features include pheromone concentration and pheromone concentration gradient; The power adjustment amount determination module is configured to: calculate the power adjustment amount based on the field characteristics and the state of the flexible resource through a preset response vector spectrum; The control module is configured to control the flexible resource to adjust its power output according to the power adjustment amount.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the renewable energy supply flexibility control method for source-grid-load interaction as described in any one of claims 1-6.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the steps of the renewable energy supply flexibility control method for source-grid-load interaction as described in any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the source-grid-load interaction renewable energy supply flexibility control method as described in any one of claims 1-8.

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