Multi-load-side resource participation micro-grid frequency modulation method and device based on cloud-side cooperation

Abstract

The invention relates to the technical field of power system frequency modulation, and discloses a multi-load-side resource participation micro-grid frequency modulation method and device based on cloud edge collaboration, and the method comprises the steps: building a system frequency modulation frame based on a cloud edge collaboration architecture, and enabling a temperature control load, an electric vehicle load and other various load-side resources to participate in frequency modulation together, communication system faults are considered, the limitation that frequency modulation only depends on a generator set traditionally is effectively solved, the spinning reserve capacity of the generator is reduced, carbon emission is further reduced, meanwhile, the user satisfaction degree is improved, and efficient and stable operation of an electric power system is achieved; the technical problem of how to improve the operation stability of a power system in which multi-load-side resources participate is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency regulation, and in particular to a method and device for microgrid frequency regulation involving multiple load-side resources based on cloud-edge collaboration. Background Art

[0002] Microgrid refers to a microgrid, which is a power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, related loads and monitoring and protection devices. Frequency regulation is one of the important means to maintain the stability of the microgrid power system. It achieves supply and demand balance by adjusting the power generation or load-side resources to cope with grid frequency fluctuations and ensure the safe and reliable operation of the system. The frequency regulation of traditional power systems mainly relies on generator sets. However, with the integration of a large number of renewable energy sources into the power grid, the system's rotational inertia decreases and the uncertainty of power supply increases. Therefore, the frequency regulation of the power grid faces increasing pressure. First, the frequency regulation response speed of the generator set is slow, and it is difficult to effectively cope with the rapid changes in power load. Specifically, when the load increases suddenly, the ramp speed of the generator set may limit its ability to quickly adjust the grid frequency, causing the grid frequency to fluctuate beyond the safe range. In addition, relying on generator frequency regulation is usually accompanied by continuous fossil fuel consumption, which not only reduces energy utilization efficiency, but also brings higher carbon emissions and environmental costs. Therefore, introducing more flexible and efficient frequency regulation methods to cope with the dynamic changes of the power system has become an urgent problem to be solved.

[0003] The diversification of load-side resources provides a more flexible and efficient frequency regulation alternative for power systems than traditional generators. Load-side resources such as temperature-controlled loads and electric vehicle loads have been widely used in frequency regulation due to their high flexibility, but there are still some issues that need to be addressed. Most existing studies rely on centralized computing, where the central node directly sends frequency modulation signals to each load-side resource, but this approach has certain limitations. First, large amounts of data transmission are prone to communication congestion delays, making it difficult to respond to frequency fluctuations in the power system in real time, affecting frequency regulation efficiency and system stability. Second, centralized processing requires all load data to be uploaded to the central server for calculation, increasing the risk of data privacy leakage. In addition, the failure of the central node may cause the system to crash, lacking robustness and reliability support. Therefore, this model is difficult to cope with the needs of diversification and distributed development of load-side resources in future power systems, and there is an urgent need for a frequency regulation solution that improves the operational stability of power systems with the participation of multiple load-side resources. Summary of the invention

[0004] The present invention provides a method and device for microgrid frequency regulation with the participation of multiple load-side resources based on cloud-edge collaboration, which solves the technical problem of how to improve the operational stability of a power system with the participation of multiple load-side resources.

[0005] A first aspect of the present invention provides a method for multi-load side resource participation in microgrid frequency regulation based on cloud-edge collaboration, comprising:

[0006] Based on the distribution of virtual power plants and associated load-side resource clusters within the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is built;

[0007] Through the communication system of the multi-load side resource cluster architecture, a frequency modulation instruction communication system of the multi-load side resource cluster under cloud-edge collaboration is constructed;

[0008] Constructing a temperature control load frequency modulation model of a microgrid, a tram load frequency modulation model, and a random fault model based on the frequency modulation command communication system;

[0009] Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a coordinated frequency regulation model, and frequency regulation constraints are set;

[0010] Based on the frequency regulation constraint condition, the coordinated frequency regulation model is solved using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid;

[0011] The optimal frequency regulation instruction quantity is sent to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and the frequency regulation operation is performed.

[0012] Optionally, the multi-load side resource cluster system architecture includes a cloud center, the cloud center is communicatively connected to a plurality of virtual power plant control units, and each of the virtual power plant control units is respectively connected to a load side resource cluster.

[0013] Optionally, the frequency modulation command communication system includes an uplink communication layer, an edge terminal layer, a downlink communication layer, a local control layer and a load side resource layer;

[0014] The cloud center is communicatively connected with the edge terminal layer via the uplink communication layer, and the edge terminal layer includes a plurality of the virtual power plant control units;

[0015] The edge terminal layer is communicatively connected to the local control layer via the downlink communication layer, and the local control layer includes different types of control terminals;

[0016] The local control layer is communicatively connected to the load-side resource layer, and the load-side resource layer includes different types of load-side resource clusters.

[0017] Optionally, the expression of the temperature control load frequency modulation model is:

[0018]

[0019] In the formula, express The indoor temperature at the moment, express The outdoor temperature at the time, Indicated in Cooling / heating capacity at the moment, when When the air conditioner is in cooling mode, When the air conditioner is in heating working state, represents the equivalent thermal resistance, represents the equivalent heat capacity, express The outdoor temperature at the moment.

[0020] Optionally, the constraints of the temperature control load frequency modulation model include:

[0021] The cooling / heating constraints of the air conditioning load are as follows:

[0022]

[0023]

[0024] When the air conditioning load is in cooling mode, the working state constraints of the air conditioning load are as follows:

[0025]

[0026] When the air conditioning load is in heating mode, the working state constraints of the air conditioning load are as follows:

[0027]

[0028] In the formula, Indicates Virtual power plant control unit The air conditioning load is Cooling / heating capacity at all times, Indicates Virtual power plant control unit The energy efficiency conversion coefficient of each air conditioning load is Indicates Virtual power plant control unit The air conditioning load is The power of the moment, Indicates Virtual power plant control unit The air conditioning load is The working status at the moment, where 1 indicates the running state and 0 indicates the closed state. Indicates Virtual power plant control unit Rated power of each air conditioning load;

[0029] The set temperature value constraints of the air conditioning load are as follows:

[0030]

[0031]

[0032] In the formula, Indicates Virtual power plant control unit The minimum allowable indoor temperature for each air conditioning load. Indicates Virtual power plant control unit The allowable temperature deviation of each air conditioning load is Indicates Virtual power plant control unit The maximum allowable indoor temperature for each air conditioning load is Indicates the set temperature value of the air conditioning load;

[0033] The specific constraints of aggregate temperature control load are:

[0034]

[0035]

[0036]

[0037] In the formula, Indicates A virtual power plant control unit is Aggregate temperature control load at all times, Indicates The air conditioning load group of the virtual power plant control unit is aggregated. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times.

[0038] Optionally, the expression of the tram load frequency regulation model is:

[0039]

[0040] In the formula, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates the time interval, Indicates the charging efficiency, Indicates Virtual power plant control unit Electric vehicles in Charging power at the moment, Indicates Virtual power plant control unit Electric vehicles in The discharge power at the moment, represents the discharge efficiency, Indicates Virtual power plant control unit The capacity of an electric car.

[0041] Optionally, the constraints of the tram load frequency regulation model include:

[0042] The charging / discharging power constraints of electric vehicles are specifically:

[0043]

[0044]

[0045]

[0046]

[0047] In the formula, Indicates Virtual power plant control unit The minimum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum charging power of an electric vehicle is Indicates Virtual power plant control unit The maximum discharge power of an electric vehicle is Indicates Virtual power plant control unit Electric vehicles in The charging status at the moment, where 1 indicates the charging state and 0 indicates the uncharging state. Indicates Virtual power plant control unit Electric vehicles in The discharge state at the moment, where 1 indicates the discharge state and 0 indicates the non-discharge state;

[0048] The specific constraints for aggregated electric vehicle loads are:

[0049]

[0050]

[0051]

[0052] In the formula, Indicates A virtual power plant control unit is Aggregate EV charging load at any given moment, Indicates A collection of electric vehicle charging stations in a virtual power plant control unit, Indicates A virtual power plant control unit is The upper frequency regulation capacity when electric vehicle load participates in frequency regulation at all times. Indicates A virtual power plant control unit is The lower frequency regulation capacity when electric vehicle load participates in frequency regulation at all times.

[0053] Optionally, the random fault model based on the frequency modulation instruction communication system includes:

[0054] The expression of the random failure model of the uplink communication layer is:

[0055]

[0056] The expression of the random fault model of the edge terminal layer is:

[0057]

[0058] In the formula, A binary variable representing the uplink communication layer failure status, where Indicates that the uplink communication layer is Failures occur all the time. Indicates that the uplink communication layer is No failure occurred at any time. Indicates the average rate at which communication failures occur in the uplink communication layer. Indicates the time interval, A binary variable representing the fault status of the edge terminal layer, where Indicates that the edge terminal layer is Failures occur all the time. Indicates that the edge terminal layer is No failure occurred at any time. Indicates the average rate at which communication failures occur at the edge terminal layer.

[0059] Optionally, the objective function of the coordinated frequency modulation model is specifically:

[0060]

[0061]

[0062]

[0063]

[0064] In the formula, represents the cost of electricity generation, and Both represent the power generation cost coefficient, Indicates that at time t the node The active power of the generator, Indicates the cost of decreased user satisfaction, express time, represents the set of nodes in the microgrid, Indicates the total number of nodes, represents the unit cost of deviation from the user set temperature, Indicates Virtual power plant control unit The set temperature value of each air conditioning load, Indicates A virtual power plant control unit, Indicates load-side resource clusters, express The indoor temperature at the moment, represents the penalty cost of frequency modulation failure, represents the unit cost of frequency regulation failure, Represents the node at time t The remaining capacity when frequency modulation fails. represents the carbon emission cost, represents carbon tax, Representation Node The carbon emission factor of the generator, Represents the total frequency regulation cost.

[0065] Optionally, the frequency modulation constraint condition includes:

[0066] The active power and reactive power balance constraints of microgrid nodes with multiple load-side resources are as follows:

[0067]

[0068]

[0069]

[0070] In the formula, express Time from node Flow to Node The active power, express Time from node Flow to Node The active power, express Time Node The active base load, express Time from node Flow to Node The reactive power, Indicated in Time Node The reactive power of the generator, express Time from node The reactive power flowing to node h, express Time Node The reactive base load, express The frequency modulation instruction quantity of automatic power generation control at the moment, express Time Node Frequency modulation power change command;

[0071] The power generation constraints of the generator are as follows:

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, Representation Node The minimum active power of the generator at Representation Node The maximum active power of the generator at Representation Node The minimum reactive power of the generator at Representation Node The maximum reactive power of the generator at Indicated in Time Node The active power of the generator, Representation Node The maximum ramp rate of the generator at

[0077] The constraints of microgrid node voltage and power flow are as follows:

[0078]

[0079]

[0080]

[0081] In the formula, Representation Node exist The square of the voltage amplitude at that moment, Representation Node exist The square of the voltage amplitude at that moment, Representation Node With Node The resistance of the transmission line between Representation Node With Node The reactance of the transmission line between Representation Node The minimum value of the square of the voltage amplitude at Representation Node The maximum value of the square of the voltage amplitude at Indicates line The maximum apparent power.

[0082] Optionally, before the step of solving the coordinated frequency regulation model based on the frequency regulation constraint condition and using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid, the step further includes:

[0083] The big M method is used to discretize the working state constraints of the air conditioning load.

[0084] A second aspect of the present invention provides a microgrid frequency regulation device with multiple load-side resources participating based on cloud-edge collaboration, comprising:

[0085] The architecture building module is used to build a multi-load-side resource cluster system architecture under cloud-edge collaboration based on the distribution of virtual power plants and associated load-side resource clusters in the microgrid;

[0086] A system construction module, used to construct a frequency modulation instruction communication system of multiple load-side resource clusters under cloud-edge collaboration through the communication system of the multiple load-side resource cluster architecture;

[0087] A model building module, used to build a temperature control load frequency regulation model of a microgrid, a tram load frequency regulation model, and a random fault model based on the frequency regulation instruction communication system;

[0088] A model coupling module is used to take the minimization of the total frequency regulation cost of the microgrid as the optimization goal, couple the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model, construct a coordinated frequency regulation model, and set frequency regulation constraints;

[0089] A model solving module, used to solve the coordinated frequency regulation model based on the frequency regulation constraint conditions and using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid;

[0090] The frequency regulation module is used to send the optimal frequency regulation instruction amount to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and perform frequency regulation operations.

[0091] A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of the above items.

[0092] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of the above items.

[0093] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of the above items.

[0094] It can be seen from the above technical solutions that the present invention has the following advantages:

[0095] In the present invention, based on the distribution of each virtual power plant and the associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is constructed. Through the communication system of the multi-load-side resource cluster system architecture, a frequency regulation instruction communication system of the multi-load-side resource cluster under cloud-edge collaboration is constructed, and a temperature control load frequency regulation model, a tram load frequency regulation model and a random fault model based on the frequency regulation instruction communication system of the microgrid are constructed. Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model, and frequency regulation constraints are set. Based on the frequency regulation constraints, the operation control data of the microgrid is used to coordinate The frequency regulation model is solved to obtain the optimal frequency regulation instruction amount of the microgrid, and the optimal frequency regulation instruction amount is issued to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture to perform frequency regulation operations; the present invention builds a system frequency regulation framework based on the cloud-edge collaborative architecture, and utilizes various load side resources such as temperature control loads and electric vehicle loads to jointly participate in frequency regulation, and takes into account communication system failures, effectively solving the limitations of traditional frequency regulation that only relies on generator sets, reducing the rotating standby capacity of generators, thereby reducing carbon emissions, and at the same time improving user satisfaction, thereby achieving efficient and stable operation of the power system; it solves the technical problem of how to improve the operating stability of the power system with the participation of multiple load side resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0097] Figure 1 A flowchart of the steps of a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration provided in Embodiment 1 of the present invention;

[0098] Figure 2 A flowchart of the steps of a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration provided in Embodiment 2 of the present invention;

[0099] Figure 3 A schematic diagram of a three-layer multi-load-side resource cluster system architecture based on a cloud-edge collaborative architecture where load-side resources participate in microgrid frequency regulation;

[0100] Figure 4 A schematic diagram of a five-layer FM command communication system for FM command transmission;

[0101] Figure 5 This is a schematic diagram of the frequency modulation instructions of the AGC;

[0102] Figure 6 A structural block diagram of a microgrid frequency regulation device with multiple load-side resources participating based on cloud-edge collaboration provided in Embodiment 3 of the present invention;

[0103] Figure 7 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0104] The embodiments of the present invention provide a method and device for microgrid frequency regulation with the participation of multiple load-side resources based on cloud-edge collaboration, which are used to solve the technical problem of how to improve the operational stability of a power system with the participation of multiple load-side resources.

[0105] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0106] In order to solve the problems existing in the existing technology, a load-side resource frequency regulation model based on the cloud-edge collaborative architecture is proposed. After the introduction of the cloud-edge collaborative architecture, computing tasks are assigned to edge nodes, which improves the computing speed and the responsiveness of the system frequency regulation, and avoids the occurrence of system failure after a single point failure. However, the existing attention to carbon emissions and user satisfaction is still insufficient. Traditional frequency regulation schemes often ignore the high carbon emissions caused by the use of fossil energy. In addition, users may face the problem of reduced comfort such as temperature control equipment during the frequency regulation process, and existing studies rarely consider how to balance the user's demand for comfort and the system frequency regulation requirements, which may reduce the user's willingness to participate in the frequency regulation response.

[0107] Based on this, the present invention proposes a solution for multiple load-side resources to participate in microgrid frequency regulation based on cloud-edge collaboration, making full use of load-side resources to improve frequency regulation efficiency. First, a cloud-edge collaborative frequency regulation framework is designed to reasonably decompose the frequency regulation tasks and effectively utilize the edge computing capabilities of the virtual power plant. Based on the characteristics of flexible resources on the load side, a frequency regulation model is further constructed, focusing on two main types of load resources: temperature control loads and electric vehicle charging stations, and fully considering user satisfaction. In view of the possible transmission failure risks of cloud-edge communication systems, the present invention proposes an optimized frequency regulation strategy from multiple dimensions such as frequency regulation costs, carbon emissions, and user satisfaction, aiming to improve the stability and efficiency of the system.

[0108] See also Figure 1 , Figure 1A flowchart of the steps of a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration provided in Example 1 of the present invention.

[0109] The present invention provides a method for multi-load side resource participation in microgrid frequency regulation based on cloud-edge collaboration, comprising:

[0110] Step 101: Based on the distribution of virtual power plants and associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is built.

[0111] Microgrid refers to a microgrid, which is a power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, related loads and monitoring and protection devices. It can operate independently (island mode) or in parallel with the large power grid.

[0112] A virtual power plant refers to the aggregation of different types of distributed energy resources (including distributed generation, controllable loads, energy storage, etc.) into a virtual "power plant" through information technology and software systems, so as to achieve coordinated and optimized control of these resources and participate in the electricity market and grid operation and dispatching.

[0113] The load-side resource cluster refers to a group formed by the concentrated combination of multiple resources located on the load side in the power system, including but not limited to distributed energy resources (including distributed power generation equipment, such as small solar photovoltaic power generation systems, micro wind turbines, etc.), controllable loads (referring to electrical equipment that is controlled according to demand. For example, smart air-conditioning systems, smart refrigerators, adjustable motors in industry, etc.) and energy storage equipment (such as batteries, supercapacitors and other energy storage devices).

[0114] The multi-load side resource cluster system architecture refers to a three-layer framework for load side resources to participate in microgrid frequency regulation based on the cloud-edge collaborative architecture, including the cloud center (top layer), the virtual power plant VPP control unit (middle layer) and the load side resource cluster (bottom layer).

[0115] In an embodiment of the present invention, the hierarchical structure of the architecture is first determined. The present invention is preferably set to three layers, dividing the entire microgrid into several areas, each area containing one or more virtual power plants and their associated load-side resource clusters, and then according to the geographical location distribution of the virtual power plants and load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is built in layers, including a cloud center (top layer), a virtual power plant VPP control unit (middle layer) and a load-side resource cluster (bottom layer).

[0116] Step 102: Build a frequency modulation instruction communication system for multiple load-side resource clusters under cloud-edge collaboration through the communication system of the multiple load-side resource cluster architecture.

[0117] The communication system refers to the communication system of the multi-load side resource cluster system architecture, which refers to the collection of communication networks, communication protocols, and related equipment and technologies used to realize information interaction and data transmission between various components in the multi-load side resource cluster system.

[0118] The frequency modulation command communication system refers to a communication system that realizes the effective transmission of frequency modulation commands between various related components under the cloud-edge collaborative architecture.

[0119] In an embodiment of the present invention, a frequency modulation instruction communication system of multiple load side resource clusters under cloud-edge collaboration is constructed through the communication system of the multi-load side resource cluster architecture. The frequency modulation instruction communication system includes an uplink communication layer, an edge terminal layer, a downlink communication layer, a local control layer and a load side resource layer.

[0120] Step 103: construct a temperature control load frequency regulation model of the microgrid, a tram load frequency regulation model, and a random fault model based on the frequency regulation instruction communication system.

[0121] The temperature-controlled load frequency regulation model refers to a mathematical model used to describe the participation of temperature-controlled loads (such as air conditioners, electric water heaters, etc.) in the frequency regulation of the power system. Taking the air conditioning load as an example, a model for the participation of temperature-controlled loads in frequency regulation is established, and the thermal inertia of the temperature-controlled load is used to participate in auxiliary frequency regulation.

[0122] The electric vehicle load frequency regulation model refers to a mathematical model used to describe the participation of electric vehicles (EVs) as adjustable loads in the frequency regulation of microgrid power systems, so that the electric vehicle loads can choose to charge or discharge according to the frequency regulation requirements to assist in frequency regulation.

[0123] The random fault model means that the occurrence of faults in the frequency modulation command communication system is random, and the time of occurrence, fault type, and fault impact range are all random. This randomness is based on a variety of uncertain factors, such as random hardware failures of communication equipment, random external interference of communication links (such as electromagnetic interference, weather effects, etc.), and random vulnerabilities or errors in the software system. Therefore, a communication system fault model is established to consider the impact of communication system faults on frequency modulation performance.

[0124] In the embodiment of the present invention, a temperature control load frequency regulation model of the microgrid, a tram load frequency regulation model, and a random fault model based on the frequency regulation instruction communication system are constructed respectively.

[0125] It should be noted that the multi-load side resource cluster in the present invention refers to a cluster of various types of load side resources, including but not limited to temperature control loads, electric vehicle loads, battery loads and other different load side resources. The present invention only selects temperature control loads and electric vehicle loads as two load side resources to construct a frequency regulation model. Specifically, different types of loads can be constructed to participate in the frequency regulation model as needed.

[0126] Step 104: Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a coordinated frequency regulation model, and frequency regulation constraints are set.

[0127] It should be noted that the total frequency regulation cost is composed of power generation cost, user satisfaction reduction cost, frequency regulation failure penalty cost and carbon emission cost. Therefore, it is necessary to consider the optimization goals of minimizing power generation cost, minimizing user satisfaction reduction cost, minimizing frequency regulation failure penalty cost and minimizing carbon emission cost during the frequency regulation process of the microgrid, on the premise of meeting the frequency stability requirements of the microgrid, so as to minimize the sum of the above costs and comprehensively consider the goal setting of microgrid operation economy and reliability.

[0128] The collaborative frequency regulation model refers to a collaborative frequency regulation optimization model for multiple load resources that takes into account carbon emissions and user satisfaction. The model calculates the carbon emissions of generators and the impact of indoor temperature deviation from the set temperature on user satisfaction during the frequency regulation process. It achieves the overall optimal frequency regulation effect by coordinating the characteristics and advantages of different resources, and sets safe frequency regulation constraints such as voltage and flow of the power system.

[0129] In an embodiment of the present invention, with the minimum power generation cost, the minimum user satisfaction reduction cost, the minimum frequency regulation failure penalty cost and the minimum carbon emission cost as the optimization objectives, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model of multiple load resources considering carbon emissions and user satisfaction, and frequency regulation constraints are set.

[0130] Step 105: Based on the frequency regulation constraint conditions, the coordinated frequency regulation model is solved using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid.

[0131] The optimal frequency regulation instruction quantity refers to the quantitative value of the optimal frequency regulation task allocation determined by solving the collaborative frequency regulation model by the solver to solve the resources on the frequency regulation load side under the frequency regulation constraint condition.

[0132] In an embodiment of the present invention, under the frequency regulation constraint condition, the cooperative frequency regulation model is solved by a solver using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid.

[0133] Step 106: Send the optimal frequency regulation instruction to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and perform frequency regulation operations.

[0134] In an embodiment of the present invention, the optimal frequency regulation instruction quantity of the microgrid is solved and sent to each virtual power plant in the microgrid through the constructed multi-load side resource cluster system architecture to perform frequency regulation operations, thereby optimizing the frequency regulation performance of the entire power system, and thus realizing efficient and stable operation of the microgrid power system.

[0135] In the present invention, based on the distribution of each virtual power plant and the associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is constructed. Through the communication system of the multi-load-side resource cluster system architecture, a frequency regulation instruction communication system of the multi-load-side resource cluster under cloud-edge collaboration is constructed, and a temperature control load frequency regulation model, a tram load frequency regulation model and a random fault model based on the frequency regulation instruction communication system of the microgrid are constructed. Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model, and frequency regulation constraints are set. Based on the frequency regulation constraints, the operation control data of the microgrid is used to coordinate The frequency regulation model is solved to obtain the optimal frequency regulation instruction amount of the microgrid, and the optimal frequency regulation instruction amount is issued to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture to perform frequency regulation operations; the present invention builds a system frequency regulation framework based on the cloud-edge collaborative architecture, and utilizes various load side resources such as temperature control loads and electric vehicle loads to jointly participate in frequency regulation, and takes into account communication system failures, effectively solving the limitations of traditional frequency regulation that only relies on generator sets, reducing the rotating standby capacity of generators, thereby reducing carbon emissions, and at the same time improving user satisfaction, thereby achieving efficient and stable operation of the power system; it solves the technical problem of how to improve the operating stability of the power system with the participation of multiple load side resources.

[0136] See also Figure 2 , Figure 2 A flowchart of the steps of a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration provided in Example 2 of the present invention.

[0137] The present invention provides a method for multi-load side resource participation in microgrid frequency regulation based on cloud-edge collaboration, comprising:

[0138] Step 201: Based on the distribution of virtual power plants and associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is built.

[0139] For further information, see Figure 3 , Figure 3 This is a schematic diagram of a three-layer multi-load side resource cluster system architecture in which load side resources participate in microgrid frequency regulation based on a cloud-edge collaborative architecture. The multi-load side resource cluster system architecture includes a cloud center, and the cloud center is connected to multiple virtual power plant control units in communication. Each virtual power plant control unit is connected to a load side resource cluster.

[0140] In the embodiment of the present invention, the top layer is the cloud center. This layer is the dispatching center of the power system and is located in the cloud. The cloud center comprehensively analyzes the system operation data through the automatic generation control (AGC) system and issues frequency modulation instructions to the virtual power plant (VPP) control unit.

[0141] Middle layer: VPP control unit. As the communication medium between the cloud center and the load cluster, each VPP control unit receives frequency modulation instructions from the cloud center, and further decomposes these instructions into specific operation commands based on the real-time status, frequency regulation capability and other information uploaded by the load-side resources.

[0142] The lowest layer: load-side resource clusters. This layer includes temperature control load clusters, energy storage load clusters, electric vehicle load clusters, and other types of load clusters. These resource clusters are at the very end of frequency regulation execution, directly receiving frequency regulation instructions from the VPP control unit and responding to the frequency regulation needs of the power system by adjusting their own power output.

[0143] Step 202: Build a frequency modulation instruction communication system for multiple load-side resource clusters under cloud-edge collaboration through the communication system of the multiple load-side resource cluster architecture.

[0144] For further information, see Figure 4 , Figure 4 A schematic diagram of a five-layer frequency modulation command communication system for frequency modulation command transmission, wherein the frequency modulation command communication system includes an uplink communication layer, an edge terminal layer, a downlink communication layer, a local control layer, and a load side resource layer;

[0145] The cloud center is connected to the edge terminal layer through the uplink communication layer, and the edge terminal layer includes multiple virtual power plant control units;

[0146] The edge terminal layer is connected to the local control layer through the downlink communication layer, and the local control layer includes different types of control terminals;

[0147] The local control layer is communicatively connected with the load side resource layer, and the load side resource layer includes different types of load side resource clusters.

[0148] In the embodiment of the present invention, the uplink communication layer: the uplink communication layer Ethernet passive optical network and wireless network link the cloud center and each VPP control unit, which is responsible for feeding back the unit frequency modulation potential to the cloud center and transmitting the unit control signal to each VPP control unit;

[0149] Edge terminal layer: The edge terminal layer is composed of each VPP control unit. This layer collects data from local terminals, evaluates the cluster's adjustment capabilities, and reports this information to the cloud center through the uplink communication layer. When the cloud center sends each cluster control signal to each VPP, the VPP control unit needs to quickly decompose the instructions and transmit them to each control terminal;

[0150] Downlink communication layer: The downlink communication layer is responsible for wired and wireless short-range communication between edge terminals and local control terminals. The most commonly used communication strategies include high-speed power line carrier and high-speed radio frequency;

[0151] Local control layer and load-side resource layer: The local control layer is mainly responsible for executing the frequency regulation instructions from the upper-level VPP control unit, and directly managing and regulating the operating status of various physical devices. The equipment at this level usually includes smart inverters, smart switches, building control centers, industrial facilities, etc. The load-side resource layer is the physical object of frequency regulation, which responds to the frequency regulation needs of the power system by adjusting its own power output.

[0152] Step 203: construct a temperature control load frequency regulation model of the microgrid, a tram load frequency regulation model, and a random fault model based on the frequency regulation instruction communication system.

[0153] It should be noted that the modeling of air conditioning load usually uses a first-order equivalent thermal parameter model to describe the temperature change of the air-conditioned room. The first-order differential equation of the equivalent thermal parameter model is as follows:

[0154]

[0155] In the formula, express The indoor temperature at the moment.

[0156] By discretizing the first-order differential equation of the equivalent thermal parameter model, the first-order recursive formula of the indoor ambient temperature can be obtained, which is also the expression of the temperature control load frequency modulation model.

[0157] Furthermore, the expression of the temperature control load frequency modulation model is:

[0158]

[0159] In the formula, express The indoor temperature at the moment, express The outdoor temperature at the time, Indicated in Cooling / heating capacity at the moment, when When the air conditioner is in cooling mode, When the air conditioner is in heating working state, represents the equivalent thermal resistance, represents the equivalent heat capacity, express The outdoor temperature at the moment.

[0160] The constraints of the temperature control load frequency modulation model include:

[0161] The cooling / heating constraints of the air conditioning load are as follows:

[0162]

[0163]

[0164] Taking the air conditioning cooling mode as an example, when the air conditioning is turned off, the indoor temperature will gradually rise; when the air conditioning is running, the indoor temperature will gradually drop. and In the interval, the on-off state is continuously switched. Taking the cooling state as an example, when the air-conditioning load is in the cooling mode, the working state constraint of the air-conditioning load is specifically as follows:

[0165]

[0166] When the air conditioning load is in heating mode, the working state constraints of the air conditioning load are as follows:

[0167]

[0168] In the formula, Indicates Virtual power plant control unit The air conditioning load is Cooling / heating capacity at all times, Indicates Virtual power plant control unit The energy efficiency conversion coefficient of each air conditioning load is Indicates Virtual power plant control unit The air conditioning load is The power of the moment, Indicates Virtual power plant control unit The air conditioning load is The working status at the moment, where 1 indicates the running state and 0 indicates the closed state. Indicates Virtual power plant control unit Rated power of each air conditioning load;

[0169] Assume that the set temperature value of the air conditioning load set by the user is , the air conditioner will change its power to keep the indoor temperature within the allowed range. The set temperature value constraint of the air conditioner load is as follows:

[0170]

[0171]

[0172] In the formula, Indicates Virtual power plant control unit The minimum allowable indoor temperature for each air conditioning load. Indicates Virtual power plant control unit The allowable temperature deviation of each air conditioning load is Indicates Virtual power plant control unit The maximum allowable indoor temperature for each air conditioning load is Indicates the set temperature value of the air conditioning load;

[0173] The specific constraints of aggregate temperature control load are:

[0174]

[0175]

[0176]

[0177] In the formula, Indicates A virtual power plant control unit is Aggregate temperature control load at all times, Indicates The air conditioning load group of the virtual power plant control unit is aggregated. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times.

[0178] It is worth mentioning that the constraints of the temperature control load frequency modulation model involve Variable-related constraints that participate in the objective function represents the cost item of decreased user satisfaction, among which the aggregate temperature control load constraint involves Variable, which acts on the active power and reactive power balance constraints of microgrid nodes containing multiple load-side resources in the frequency regulation constraints, and then acts on the objective function represents the cost of electricity generation and Represents the cost of carbon emissions.

[0179] It should be noted that electric vehicles can not only be charged from the power grid, but also input electricity into the power grid using the bidirectional charging mode (vehicle to grid, V2G).

[0180] In the present invention, the electric vehicle load in the V2G mode is mainly considered.

[0181] The present invention divides the electric vehicle load into three states: 1. charging state; 2. discharging state; 3. idle state.

[0182] An electric vehicle can only be in one state at any time and can be transformed between different states. Taking a single electric vehicle as an example, the modeling of electric vehicle load participating in frequency regulation is as follows:

[0183] Furthermore, the electric vehicle power state changes with time, and the expression of the electric vehicle load frequency regulation model is:

[0184]

[0185] In the formula, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates the time interval, Indicates the charging efficiency, Indicates Virtual power plant control unit Electric vehicles in Charging power at the moment, Indicates Virtual power plant control unit Electric vehicles in The discharge power at the moment, represents the discharge efficiency, Indicates Virtual power plant control unit The capacity of an electric car.

[0186] The constraints of the tram load frequency regulation model include:

[0187] The charging / discharging power constraints of electric vehicles are specifically:

[0188] The following constraints represent the upper and lower limits of the electric vehicle's state of charge:

[0189]

[0190] The following constraints limit the charging and discharging power of electric vehicles:

[0191]

[0192]

[0193] The following constraints mean that electric vehicles cannot be charged and discharged at the same time:

[0194]

[0195] In the formula, Indicates Virtual power plant control unit The minimum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum charging power of an electric vehicle is Indicates Virtual power plant control unit The maximum discharge power of an electric vehicle is Indicates Virtual power plant control unit Electric vehicles in The charging status at the moment, where 1 indicates the charging state and 0 indicates the uncharging state. Indicates Virtual power plant control unit Electric vehicles in The discharge state at the moment, where 1 indicates the discharge state and 0 indicates the non-discharge state;

[0196] The specific constraints for aggregated electric vehicle loads are:

[0197] The following constraints represent the VPP control unit The load of Zhongju electric vehicles is (normally in charging state):

[0198]

[0199] The following constraint represents the upper frequency regulation capacity of electric vehicle load participating in frequency regulation: and down-modulation capacity :

[0200]

[0201]

[0202] In the formula, Indicates A virtual power plant control unit is Aggregate EV charging load at any given moment, Indicates A collection of electric vehicle charging stations in a virtual power plant control unit, Indicates A virtual power plant control unit is The upper frequency regulation capacity when electric vehicle load participates in frequency regulation at all times. Indicates A virtual power plant control unit is The lower frequency regulation capacity when electric vehicle load participates in frequency regulation at all times.

[0203] It is worth mentioning that the constraints of the tram load frequency regulation model involve Variable, which acts on the active power and reactive power balance constraints of microgrid nodes containing multiple load-side resources in the frequency regulation constraints, and then acts on the objective function represents the cost of electricity generation and Represents the cost of carbon emissions.

[0204] Furthermore, the random failure model based on the frequency modulation instruction communication system includes:

[0205] It should be noted that uplink communication failure: In the load-side resource frequency modulation system based on the cloud-edge collaborative architecture, the uplink communication layer is mainly responsible for transmitting frequency modulation signals and data from the cloud center to the edge terminal layer. When the uplink communication layer fails, it may cause delays or interruptions in data transmission, thereby affecting the effective execution of frequency modulation instructions. In order to analyze the impact of uplink communication layer failures on system performance, the present invention establishes a failure model of the uplink communication layer.

[0206] The occurrence of uplink communication layer failure can be modeled by a Poisson process. The expression of the uplink communication layer random failure model is:

[0207]

[0208] Among them, if When all VPP control units connected to the uplink cannot participate in frequency modulation control, then:

[0209]

[0210] like When , all VPPs connected to the uplink can participate in frequency modulation control, then:

[0211]

[0212]

[0213] It should be noted that edge terminal failure: Similar to uplink communication failure, when an edge terminal fails, the frequency modulation signal cannot be sent from each VPP control unit to each flexible side resource cluster. The failure can also be modeled by a Poisson process. The expression of the random failure model of the edge terminal layer is:

[0214]

[0215] Among them, if When the corresponding load-side resource cluster connected to the VPP control unit cannot participate in frequency regulation control, then:

[0216]

[0217] like When , all VPPs connected to the edge terminal can participate in frequency modulation control, then:

[0218]

[0219]

[0220] In the formula, A binary variable representing the uplink communication layer failure status, where Indicates that the uplink communication layer is Failures occur all the time. Indicates that the uplink communication layer is No failure occurred at any time. Indicates the average rate at which communication failures occur in the uplink communication layer. Indicates the time interval, A binary variable representing the fault status of the edge terminal layer, where Indicates that the edge terminal layer is Failures occur all the time. Indicates that the edge terminal layer is No failure occurred at any time. Indicates the average rate at which communication failures occur at the edge terminal layer.

[0221] In an embodiment of the present invention, a temperature-controlled load frequency regulation model, a tram load frequency regulation model and a random fault model based on a frequency modulation instruction communication system of a microgrid are respectively constructed, and model constraints corresponding to the temperature-controlled load frequency regulation model, the tram load frequency regulation model and the random fault model based on a frequency modulation instruction communication system of the microgrid are respectively set.

[0222] Step 204: Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a coordinated frequency regulation model, and frequency regulation constraints are set.

[0223] It should be noted that the objective function of the coordinated frequency regulation model of multiple load-side resources considering carbon emissions and user satisfaction includes four parts: power generation cost, , Decreased user satisfaction costs , Penalty cost for frequency modulation failure and carbon emission costs , the objective function of the coordinated frequency modulation model is:

[0224]

[0225]

[0226]

[0227]

[0228] In the formula, represents the cost of electricity generation, and Both represent the power generation cost coefficient, Indicates that at time t the node The active power of the generator, Indicates the cost of decreased user satisfaction, express time, represents the set of nodes in the microgrid, Indicates the total number of nodes, represents the unit cost of deviation from the user set temperature, Indicates Virtual power plant control unit The set temperature value of each air conditioning load, Indicates A virtual power plant control unit, Indicates load-side resource clusters, express The indoor temperature at the moment, represents the penalty cost of frequency modulation failure, represents the unit cost of frequency regulation failure, Represents the node at time t The remaining capacity when frequency modulation fails. represents the carbon emission cost, represents carbon tax, Representation Node The carbon emission factor of the generator, Represents the total frequency regulation cost.

[0229] It should be noted that the penalty cost for frequency regulation failure is In the case of random failures, the variables are directly affected and , these two variables indirectly affect the frequency regulation failure penalty cost according to the active power and reactive power balance constraints described below, and therefore do not need to be reflected in the random fault model.

[0230] Frequency modulation constraints include:

[0231] The active power and reactive power balance constraints of microgrid nodes with multiple load-side resources are as follows:

[0232]

[0233]

[0234]

[0235] In the formula, express Time from node Flow to Node The active power, express Time from node Flow to Node The active power, express Time Node The active base load, express Time from node Flow to Node The reactive power, Indicated in Time Node The reactive power of the generator, express Time from node The reactive power flowing to node h, express Time Node The reactive base load, express The frequency modulation instruction quantity of automatic power generation control at the moment, express Time Node Frequency modulation power change command;

[0236] The power generation constraints of the generator are as follows:

[0237] The following constraints limit the upper and lower limits of the active and reactive output of the generator:

[0238]

[0239]

[0240] The following constraint limits the ramp rate of the generator:

[0241]

[0242]

[0243] In the formula, Representation Node The minimum active power of the generator at Representation Node The maximum active power of the generator at Representation Node The minimum reactive power of the generator at Representation Node The maximum reactive power of the generator at Indicated in Time Node The active power of the generator, Representation Node The maximum ramp rate of the generator at

[0244] The linearized Distflow model is used to represent the power flow equation of the power system. Without considering the network loss, the constraints of the microgrid node voltage and power flow are as follows:

[0245]

[0246]

[0247]

[0248] In the formula, Representation Node exist The square of the voltage amplitude at that moment, Representation Node exist The square of the voltage amplitude at that moment, Representation Node With Node The resistance of the transmission line between Representation Node With Node The reactance of the transmission line between Representation Node The minimum value of the square of the voltage amplitude at Representation Node The maximum value of the square of the voltage amplitude at Indicates line The maximum apparent power.

[0249] In an embodiment of the present invention, the minimization of the total frequency regulation cost of the microgrid is taken as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model, and frequency regulation constraints are set.

[0250] Step 205: Use the Big M method to discretize the working state constraints of the air conditioning load.

[0251] In the embodiment of the present invention, since the working state constraint of the air-conditioning load is a discrete binary variable and is non-convex, the model is difficult to solve directly using a solver. Therefore, it is necessary to convert the non-convex constraint into a mixed integer linear constraint using the big M method.

[0252] The steps to transform the working state constraints of air conditioning load from nonlinear constraints to mixed integer linear constraints by using the big M method are as follows:

[0253] First, introduce three auxiliary binary variables , , To represent the indicator variable of indoor temperature. The relationship between the auxiliary variable and temperature is as follows:

[0254]

[0255]

[0256]

[0257] In the formula, represents a very large number, used to relax the constraints, , , Both represent auxiliary binary variables.

[0258] Then the three auxiliary variables and the operating status of the air conditioning load are Establish contact as follows:

[0259]

[0260]

[0261]

[0262] The working state constraints of air-conditioning load are transformed from nonlinear constraints to mixed integer linear constraints through the big M method, so that the model can be solved directly using commercial solvers.

[0263] Step 206: Based on the frequency regulation constraint conditions, the coordinated frequency regulation model is solved using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid.

[0264] Operation control data, including the nodes, lines, and generator parameters of the power system; the number of temperature control loads and electric vehicle loads included in each VPP control unit and their corresponding parameters; and the system's frequency modulation instructions, etc.

[0265] In an embodiment of the present invention, under the frequency regulation constraint condition, the cooperative frequency regulation model is solved by a solver using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid.

[0266] Step 207: Send the optimal frequency regulation instruction to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and perform frequency regulation operations.

[0267] The following is an application example of the present invention:

[0268] Step 1: Input the initial data, including the nodes, lines, and generator parameters of the power system; the number of temperature control loads and electric vehicle loads included in each VPP control unit and their corresponding parameters; and the system's frequency modulation instructions, etc.

[0269] Step 2: Establish a coordinated frequency regulation model for multiple load-side resources that takes into account carbon emissions and user satisfaction. The model includes a frequency regulation model for temperature-controlled loads, a frequency regulation model for electric vehicle loads, a random failure model for the communication system, and basic constraints on the power system, such as power flow, voltage, and generation constraints.

[0270] Step 3: Since the original multiple load-side resource coordinated frequency regulation model considering carbon emissions contains logical judgment constraints, it is difficult to solve it directly using the existing solver. Therefore, the big M method is used to convert the nonlinear logical constraints of the temperature control load into mixed integer linear constraints. The converted model can be directly called by the solver for solution.

[0271] Step 4: The converted final model is solved by calling the Gurobi optimization solver using the YALMIP toolbox in MATLAB to determine whether the preset termination condition, i.e., the solver error threshold, is met.

[0272] Step 5: If the error is less than the preset error threshold, the solver is considered to meet the termination condition, the calculation stops and the optimal frequency modulation result is output.

[0273] In summary, the present invention provides a brand-new solution for the frequency regulation of microgrid power systems. This method not only relies on the frequency regulation capability of traditional generator sets, but also breaks through the limitations of traditional single reliance on generator sets by introducing a variety of load-side resources to collaboratively participate in frequency regulation. In the frequency regulation process, the factors of carbon emissions and user satisfaction are fully considered, and the impact of frequency regulation on the environment and user experience is scientifically quantified. In addition, the present invention also proposes solutions to possible failures in the communication system, making the frequency regulation strategy more realistic and robust. In general, the present invention provides a reference with innovative and practical value for the frequency regulation of power systems, and has broad potential for promotion and application.

[0274] The following are the advantages of the present invention:

[0275] 1. The present invention uses a cloud-edge collaborative architecture to distribute the frequency modulation tasks to various virtual power plants, thereby effectively reducing the pressure of cloud computing, improving the reliability of the system, and better protecting the privacy of users.

[0276] 2. In view of the communication system failure that may occur when the frequency modulation instruction is issued, the present invention designs a fault-tolerant mechanism, uses the unaffected communication lines to execute the frequency modulation response, significantly reduces the frequency modulation failure caused by communication failure, and improves the robustness of the system.

[0277] 3. The present invention establishes a model in which temperature control loads and electric vehicles participate in frequency modulation. Virtual energy storage is achieved by utilizing the thermal inertia of the temperature control loads to assist in frequency modulation; at the same time, the electric vehicle loads can choose to charge or discharge according to the frequency modulation requirements, thereby assisting in frequency modulation. This multi-resource participation approach overcomes the limitations of frequency modulation that relies solely on temperature control loads, and enhances the flexibility and effectiveness of frequency modulation.

[0278] 4. The present invention proposes a coordinated frequency modulation optimization model for multiple load-side resources that comprehensively considers carbon emissions and user satisfaction, covering temperature control loads and electric vehicle charging loads. According to the model optimization results, the AGC frequency modulation instructions issued by the cloud center can be reasonably allocated to each VPP control unit. This not only effectively reduces the spinning reserve capacity of the generator and reduces carbon emissions, but also improves user satisfaction and helps achieve efficient and environmentally friendly operation of the power system.

[0279] In the embodiment of the present invention, the optimal frequency modulation instruction is sent to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and the frequency modulation operation is performed. The cloud center of the microgrid power system sends the frequency modulation instruction to each VPP control unit through the AGC, such as Figure 5As shown, Figure 5 Figure 1 is a schematic diagram of the frequency regulation instructions of the AGC. Specifically, these frequency regulation instructions indicate the power that each VPP control unit needs to increase or decrease at a specific moment. Through the optimization model proposed by the invention, the fault status of each communication line can be considered under the overall AGC frequency regulation instruction, and the frequency regulation power that each load aggregator should bear can be accurately determined according to the current status and capabilities of each load aggregator. In addition, this method enables a more efficient allocation of frequency regulation responsibilities, reducing reliance on traditional generator sets, thereby reducing the rotating reserve capacity of generators. Ultimately, this not only improves the frequency regulation efficiency of the power system, but also reduces the overall carbon emissions of the system, improves user satisfaction, and promotes the efficient and stable operation of the power system.

[0280] In the present invention, based on the distribution of each virtual power plant and the associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is constructed. Through the communication system of the multi-load-side resource cluster system architecture, a frequency regulation instruction communication system of the multi-load-side resource cluster under cloud-edge collaboration is constructed, and a temperature control load frequency regulation model, a tram load frequency regulation model and a random fault model based on the frequency regulation instruction communication system of the microgrid are constructed. Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model, and frequency regulation constraints are set. Based on the frequency regulation constraints, the operation control data of the microgrid is used to coordinate The frequency regulation model is solved to obtain the optimal frequency regulation instruction amount of the microgrid, and the optimal frequency regulation instruction amount is issued to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture to perform frequency regulation operations; the present invention builds a system frequency regulation framework based on the cloud-edge collaborative architecture, and utilizes various load side resources such as temperature control loads and electric vehicle loads to jointly participate in frequency regulation, and takes into account communication system failures, effectively solving the limitations of traditional frequency regulation that only relies on generator sets, reducing the rotating standby capacity of generators, thereby reducing carbon emissions, and at the same time improving user satisfaction, thereby achieving efficient and stable operation of the power system; it solves the technical problem of how to improve the operating stability of the power system with the participation of multiple load side resources.

[0281] See also Figure 6 , Figure 6 A structural block diagram of a microgrid frequency regulation device with multiple load-side resources participating based on cloud-edge collaboration provided in Example 3 of the present invention.

[0282] The present invention provides a microgrid frequency regulation device with multiple load-side resources participating based on cloud-edge collaboration, comprising:

[0283] The architecture building module 301 is used to build a multi-load side resource cluster system architecture under cloud-edge collaboration based on the distribution of each virtual power plant and the associated load side resource cluster in the microgrid;

[0284] The system construction module 302 is used to construct a frequency modulation instruction communication system of multiple load-side resource clusters under cloud-edge collaboration through the communication system of the multiple load-side resource cluster architecture;

[0285] Model building module 303, used to build a temperature control load frequency regulation model of the microgrid, a tram load frequency regulation model and a random fault model based on the frequency regulation instruction communication system;

[0286] The model coupling module 304 is used to take the minimization of the total frequency regulation cost of the microgrid as the optimization goal, couple the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model, construct a coordinated frequency regulation model, and set frequency regulation constraints;

[0287] The model solving module 305 is used to solve the coordinated frequency regulation model based on the frequency regulation constraint conditions and the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid;

[0288] The frequency regulation module 306 is used to send the optimal frequency regulation instruction amount to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and perform frequency regulation operations.

[0289] Furthermore, the multi-load side resource cluster system architecture includes a cloud center, and the cloud center is communicatively connected to multiple virtual power plant control units, and each virtual power plant control unit is respectively connected to a load side resource cluster.

[0290] Further, the frequency modulation command communication system includes an uplink communication layer, an edge terminal layer, a downlink communication layer, a local control layer, and a load side resource layer;

[0291] The cloud center is connected to the edge terminal layer through the uplink communication layer, and the edge terminal layer includes multiple virtual power plant control units;

[0292] The edge terminal layer is connected to the local control layer through the downlink communication layer, and the local control layer includes different types of control terminals;

[0293] The local control layer is communicatively connected with the load side resource layer, and the load side resource layer includes different types of load side resource clusters.

[0294] Furthermore, the expression of the temperature control load frequency modulation model is:

[0295]

[0296] In the formula, express The indoor temperature at the moment, express The outdoor temperature at the time, Indicated in Cooling / heating capacity at the moment, when When the air conditioner is in cooling mode, When the air conditioner is in heating working state, represents the equivalent thermal resistance, represents the equivalent heat capacity, express The outdoor temperature at the moment.

[0297] Furthermore, the constraints of the temperature control load frequency modulation model include:

[0298] The cooling / heating constraints of the air conditioning load are as follows:

[0299]

[0300]

[0301] When the air conditioning load is in cooling mode, the working state constraints of the air conditioning load are as follows:

[0302]

[0303] When the air conditioning load is in heating mode, the working state constraints of the air conditioning load are as follows:

[0304]

[0305] In the formula, Indicates Virtual power plant control unit The air conditioning load is Cooling / heating capacity at all times, Indicates Virtual power plant control unit The energy efficiency conversion coefficient of each air conditioning load is Indicates Virtual power plant control unit The air conditioning load is The power of the moment, Indicates Virtual power plant control unit The air conditioning load is The working status at the moment, where 1 indicates the running state and 0 indicates the closed state. Indicates Virtual power plant control unit Rated power of each air conditioning load;

[0306] The set temperature value constraints of the air conditioning load are as follows:

[0307]

[0308]

[0309] In the formula, Indicates Virtual power plant control unit The minimum allowable indoor temperature for each air conditioning load. Indicates Virtual power plant control unit The allowable temperature deviation of each air conditioning load is Indicates Virtual power plant control unit The maximum allowable indoor temperature for each air conditioning load is Indicates the set temperature value of the air conditioning load;

[0310] The specific constraints of aggregate temperature control load are:

[0311]

[0312]

[0313]

[0314] In the formula, Indicates A virtual power plant control unit is Aggregate temperature control load at all times, Indicates The air conditioning load group of the virtual power plant control unit is aggregated. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times.

[0315] Furthermore, the expression of the tram load frequency regulation model is:

[0316]

[0317] In the formula, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates the time interval, Indicates the charging efficiency, Indicates Virtual power plant control unit Electric vehicles in Charging power at the moment, Indicates Virtual power plant control unit Electric vehicles in The discharge power at the moment, represents the discharge efficiency, Indicates Virtual power plant control unit The capacity of an electric car.

[0318] Furthermore, the constraints of the tram load frequency regulation model include:

[0319] The charging / discharging power constraints of electric vehicles are specifically:

[0320]

[0321]

[0322]

[0323]

[0324] In the formula, Indicates Virtual power plant control unit The minimum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum charging power of an electric vehicle is Indicates Virtual power plant control unit The maximum discharge power of an electric vehicle is Indicates Virtual power plant control unit Electric vehicles in The charging status at the moment, where 1 indicates the charging state and 0 indicates the uncharging state. Indicates Virtual power plant control unit Electric vehicles in The discharge state at the moment, where 1 indicates the discharge state and 0 indicates the non-discharge state;

[0325] The specific constraints for aggregated electric vehicle loads are:

[0326]

[0327]

[0328]

[0329] In the formula, Indicates A virtual power plant control unit is Aggregate EV charging load at any given moment, Indicates A collection of electric vehicle charging stations in a virtual power plant control unit, Indicates A virtual power plant control unit is The upper frequency regulation capacity when electric vehicle load participates in frequency regulation at all times. Indicates A virtual power plant control unit is The lower frequency regulation capacity when electric vehicle load participates in frequency regulation at all times.

[0330] Furthermore, the random failure model based on the frequency modulation instruction communication system includes:

[0331] The expression of the random failure model of the uplink communication layer is:

[0332]

[0333] The expression of the random fault model of the edge terminal layer is:

[0334]

[0335] In the formula, A binary variable representing the uplink communication layer failure status, where Indicates that the uplink communication layer is Failures occur all the time. Indicates that the uplink communication layer is No failure occurred at any time. Indicates the average rate at which communication failures occur in the uplink communication layer. Indicates the time interval, A binary variable representing the fault status of the edge terminal layer, where Indicates that the edge terminal layer is Failures occur all the time. Indicates that the edge terminal layer is No failure occurred at any time. Indicates the average rate at which communication failures occur at the edge terminal layer.

[0336] Furthermore, the objective function of the coordinated frequency modulation model is specifically:

[0337]

[0338]

[0339]

[0340]

[0341] In the formula, represents the cost of electricity generation, and Both represent the power generation cost coefficient, Indicates that at time t the node The active power of the generator, Indicates the cost of decreased user satisfaction, express time, represents the set of nodes in the microgrid, Indicates the total number of nodes, represents the unit cost of deviation from the user set temperature, Indicates Virtual power plant control unit The set temperature value of each air conditioning load, Indicates A virtual power plant control unit, Indicates load-side resource clusters, express The indoor temperature at the moment, represents the penalty cost of frequency modulation failure, represents the unit cost of frequency regulation failure, Represents the node at time t The remaining capacity when frequency modulation fails. represents the carbon emission cost, represents carbon tax, Representation Node The carbon emission factor of the generator, Represents the total frequency regulation cost.

[0342] Furthermore, the frequency modulation constraints include:

[0343] The active power and reactive power balance constraints of microgrid nodes with multiple load-side resources are as follows:

[0344]

[0345]

[0346]

[0347] In the formula, express Time from node Flow to Node The active power, express Time from node Flow to Node The active power, express Time Node The active base load, express Time from node Flow to Node The reactive power, Indicated in Time Node The reactive power of the generator, express Time from node The reactive power flowing to node h, express Time Node The reactive base load, express The frequency modulation instruction quantity of automatic power generation control at the moment, express Time Node Frequency modulation power change command;

[0348] The power generation constraints of the generator are as follows:

[0349]

[0350]

[0351]

[0352]

[0353] In the formula, Representation Node The minimum active power of the generator at Representation Node The maximum active power of the generator at Representation Node The minimum reactive power of the generator at Representation Node The maximum reactive power of the generator at Indicated in Time Node The active power of the generator, Representation Node The maximum ramp rate of the generator at

[0354] The constraints of microgrid node voltage and power flow are as follows:

[0355]

[0356]

[0357]

[0358] In the formula, Representation Node exist The square of the voltage amplitude at that moment, Representation Node exist The square of the voltage amplitude at that moment, Representation Node With Node The resistance of the transmission line between Representation Node With Node The reactance of the transmission line between Representation Node The minimum value of the square of the voltage amplitude at Representation Node The maximum value of the square of the voltage amplitude at Indicates line The maximum apparent power.

[0359] Furthermore, based on the frequency regulation constraint condition, the coordinated frequency regulation model is solved by using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid, and the following is also included before the step:

[0360] The big M method is used to discretize the working state constraints of the air conditioning load.

[0361] In the present invention, based on the distribution of each virtual power plant and the associated load-side resource clusters in the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is constructed. Through the communication system of the multi-load-side resource cluster system architecture, a frequency regulation instruction communication system of the multi-load-side resource cluster under cloud-edge collaboration is constructed, and a temperature control load frequency regulation model, a tram load frequency regulation model and a random fault model based on the frequency regulation instruction communication system of the microgrid are constructed. Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a collaborative frequency regulation model, and frequency regulation constraints are set. Based on the frequency regulation constraints, the operation control data of the microgrid is used to coordinate The frequency regulation model is solved to obtain the optimal frequency regulation instruction amount of the microgrid, and the optimal frequency regulation instruction amount is issued to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture to perform frequency regulation operations; the present invention builds a system frequency regulation framework based on the cloud-edge collaborative architecture, and utilizes various load side resources such as temperature control loads and electric vehicle loads to jointly participate in frequency regulation, and takes into account communication system failures, effectively solving the limitations of traditional frequency regulation that only relies on generator sets, reducing the rotating standby capacity of generators, thereby reducing carbon emissions, and at the same time improving user satisfaction, thereby achieving efficient and stable operation of the power system; it solves the technical problem of how to improve the operating stability of the power system with the participation of multiple load side resources.

[0362] See also Figure 7 , Figure 7 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention.

[0363] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402, wherein the memory 402 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as in any of the above embodiments.

[0364] The memory 401 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 401 has a storage space 403 for a program code 413 for executing any method step in the above method. For example, the storage space 403 for the program code may include individual program codes 413 for implementing the various steps in the above method, respectively. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program code may be compressed, for example, in an appropriate form. When these codes are run by a computing and processing device, the computing and processing device performs the various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program code may be compressed, for example, in an appropriate form. When these codes are executed by a computing and processing device, they cause the computing and processing device to execute the various steps of the above-described method for multi-load-side resource participation in microgrid frequency regulation based on cloud-edge collaboration.

[0365] Embodiment 5 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as in any of the above embodiments is implemented.

[0366] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes a method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as in any of the above embodiments.

[0367] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0368] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0369] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0370] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0371] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0372] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for frequency modulation of microgrids with multi-load side resources participating in cloud-edge collaboration, characterized in that: include: Based on the distribution of virtual power plants and associated load-side resource clusters within the microgrid, a multi-load-side resource cluster system architecture under cloud-edge collaboration is built; Through the communication system of the multi-load side resource cluster architecture, a frequency modulation instruction communication system of the multi-load side resource cluster under cloud-edge collaboration is constructed; Constructing a temperature control load frequency modulation model of a microgrid, a tram load frequency modulation model, and a random fault model based on the frequency modulation command communication system; Taking the minimization of the total frequency regulation cost of the microgrid as the optimization goal, the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model are coupled to construct a coordinated frequency regulation model, and frequency regulation constraints are set; Based on the frequency regulation constraint condition, the coordinated frequency regulation model is solved using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid; The optimal frequency regulation instruction quantity is sent to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and the frequency regulation operation is performed.

2. The method for frequency modulation of microgrids with multi-load side resources participating in cloud-edge collaboration according to claim 1 is characterized in that: The multi-load side resource cluster system architecture includes a cloud center, the cloud center is communicatively connected to a plurality of virtual power plant control units, and each of the virtual power plant control units is respectively connected to a load side resource cluster.

3. The method for multi-load side resource participation in microgrid frequency modulation based on cloud-edge collaboration according to claim 2 is characterized in that: The frequency modulation command communication system includes an uplink communication layer, an edge terminal layer, a downlink communication layer, a local control layer and a load side resource layer; The cloud center is communicatively connected with the edge terminal layer via the uplink communication layer, and the edge terminal layer includes a plurality of the virtual power plant control units; The edge terminal layer is communicatively connected to the local control layer via the downlink communication layer, and the local control layer includes different types of control terminals; The local control layer is communicatively connected to the load-side resource layer, and the load-side resource layer includes different types of load-side resource clusters.

4. The method for multi-load side resource participation in microgrid frequency modulation based on cloud-edge collaboration according to claim 1 is characterized in that: The expression of the temperature control load frequency modulation model is: In the formula, express The indoor temperature at the moment, express The outdoor temperature at the time, Indicated in Cooling / heating capacity at the moment, when When the air conditioner is in cooling mode, When the air conditioner is in heating working state, represents the equivalent thermal resistance, represents the equivalent heat capacity, express The outdoor temperature at the moment.

5. The method for multi-load side resource participation in microgrid frequency modulation based on cloud-edge collaboration according to claim 4 is characterized in that: The constraints of the temperature control load frequency modulation model include: The cooling / heating constraints of the air conditioning load are as follows: When the air conditioning load is in cooling mode, the working state constraints of the air conditioning load are as follows: When the air conditioning load is in heating mode, the working state constraints of the air conditioning load are as follows: In the formula, Indicates Virtual power plant control unit The air conditioning load is Cooling / heating capacity at all times, Indicates Virtual power plant control unit The energy efficiency conversion coefficient of each air conditioning load is Indicates Virtual power plant control unit The air conditioning load is The power of the moment, Indicates Virtual power plant control unit The air conditioning load is The working status at the moment, where 1 indicates the running state and 0 indicates the closed state. Indicates Virtual power plant control unit Rated power of each air conditioning load; The set temperature value constraints of the air conditioning load are as follows: In the formula, Indicates Virtual power plant control unit The minimum allowable indoor temperature for each air conditioning load, Indicates Virtual power plant control unit The allowable temperature deviation of each air conditioning load is Indicates Virtual power plant control unit The maximum allowable indoor temperature for each air conditioning load is Indicates the set temperature value of the air conditioning load; The specific constraints of aggregate temperature control load are: In the formula, Indicates A virtual power plant control unit is Aggregate temperature control load at all times, Indicates The air conditioning load group of the virtual power plant control unit is aggregated. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times. Indicates A virtual power plant control unit is The temperature control load participates in the frequency regulation capacity of the system at all times.

6. The method for multi-load side resource participation in microgrid frequency modulation based on cloud-edge collaboration according to claim 5 is characterized in that: The expression of the tram load frequency regulation model is: In the formula, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates Virtual power plant control unit Electric vehicles in The state of charge at the moment, Indicates the time interval, Indicates the charging efficiency, Indicates Virtual power plant control unit Electric vehicles in Charging power at the moment, Indicates Virtual power plant control unit Electric vehicles in The discharge power at the moment, represents the discharge efficiency, Indicates Virtual power plant control unit The capacity of an electric car.

7. The method for multi-load side resource participation in microgrid frequency modulation based on cloud-edge collaboration according to claim 6 is characterized in that: The constraints of the tram load frequency regulation model include: The charging / discharging power constraints of electric vehicles are specifically: In the formula, Indicates Virtual power plant control unit The minimum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum state of charge of an electric vehicle, Indicates Virtual power plant control unit The maximum charging power of an electric vehicle is Indicates Virtual power plant control unit The maximum discharge power of an electric vehicle is Indicates Virtual power plant control unit Electric vehicles in The charging status at the moment, where 1 indicates the charging state and 0 indicates the uncharging state. Indicates Virtual power plant control unit Electric vehicles in The discharge state at the moment, where 1 indicates the discharge state and 0 indicates the non-discharge state; The specific constraints for aggregated electric vehicle loads are: In the formula, Indicates A virtual power plant control unit is Aggregate EV charging load at any given moment, Indicates A collection of electric vehicle charging stations in a virtual power plant control unit, Indicates A virtual power plant control unit is The upper frequency regulation capacity when electric vehicle load participates in frequency regulation at all times. Indicates A virtual power plant control unit is The lower frequency regulation capacity when electric vehicle load participates in frequency regulation at all times.

8. The method for frequency modulation of microgrids with multi-load side resources participating in cloud-edge collaboration according to claim 7 is characterized in that: The random fault model based on the frequency modulation instruction communication system includes: The expression of the random failure model of the uplink communication layer is: The expression of the random fault model of the edge terminal layer is: In the formula, A binary variable representing the uplink communication layer failure status, where Indicates that the uplink communication layer is Failures occur all the time. Indicates that the uplink communication layer is No failure occurred at any time. Indicates the average rate at which communication failures occur in the uplink communication layer. Indicates the time interval, A binary variable representing the fault status of the edge terminal layer, where Indicates that the edge terminal layer is Failures occur all the time. Indicates that the edge terminal layer is No failure occurred at any time. Indicates the average rate at which communication failures occur at the edge terminal layer.

9. The method for frequency modulation of microgrids with multi-load side resources participating in cloud-edge collaboration according to claim 1 is characterized in that: The objective function of the coordinated frequency modulation model is specifically: In the formula, represents the cost of electricity generation, and Both represent the power generation cost coefficient, Indicates that at time t the node The active power of the generator, Indicates the cost of decreased user satisfaction, express time, represents the set of nodes in the microgrid, Indicates the total number of nodes, represents the unit cost of deviation from the user set temperature, Indicates Virtual power plant control unit The set temperature value of each air conditioning load, Indicates A virtual power plant control unit, Indicates load-side resource clusters, express The indoor temperature at the moment, represents the penalty cost of frequency modulation failure, represents the unit cost of frequency regulation failure, Represents the node at time t The remaining capacity in case of frequency modulation failure, represents the carbon emission cost, represents carbon tax, Representation Node The carbon emission factor of the generator, Represents the total frequency regulation cost.

10. The method for frequency modulation of microgrids with multiple load-side resources participating in cloud-edge collaboration according to claim 9 is characterized in that: The frequency modulation constraint conditions include: The active power and reactive power balance constraints of microgrid nodes with multiple load-side resources are as follows: In the formula, express Time from node Flow to Node The active power, express Time from node Flow to Node The active power, express Time Node The active base load, express Time from node Flow to Node The reactive power, Indicated in Time Node The reactive power of the generator, express Time from node The reactive power flowing to node h, express Time Node The reactive base load, express The frequency modulation instruction quantity of automatic power generation control at the moment, express Time Node Frequency modulation power change command; The power generation constraints of the generator are as follows: In the formula, Representation Node The minimum active power of the generator at Representation Node The maximum active power of the generator at Representation Node The minimum reactive power of the generator at Representation Node The maximum reactive power of the generator at Indicated in Time Node The active power of the generator, Representation Node The maximum ramp rate of the generator at The constraints of microgrid node voltage and power flow are as follows: In the formula, Representation Node exist The square of the voltage amplitude at that moment, Representation Node exist The square of the voltage amplitude at that moment, Representation Node With Node The resistance of the transmission line between Representation Node With Node The reactance of the transmission line between Representation Node The minimum value of the square of the voltage amplitude at Representation Node The maximum value of the square of the voltage amplitude at Indicates line The maximum apparent power.

11. The method for frequency modulation of microgrids with multiple load-side resources participating in cloud-edge collaboration according to claim 5, characterized in that: Before the step of solving the coordinated frequency regulation model based on the frequency regulation constraint condition and using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid, the method further includes: The big M method is used to discretize the working state constraints of the air conditioning load.

12. A device for frequency regulation of microgrid with multi-load side resources participating in cloud-edge collaboration, characterized in that: include: The architecture building module is used to build a multi-load-side resource cluster system architecture under cloud-edge collaboration based on the distribution of virtual power plants and associated load-side resource clusters in the microgrid; A system construction module, used to construct a frequency modulation instruction communication system of multiple load-side resource clusters under cloud-edge collaboration through the communication system of the multiple load-side resource cluster architecture; A model building module, used to build a temperature control load frequency regulation model of a microgrid, a tram load frequency regulation model, and a random fault model based on the frequency regulation instruction communication system; A model coupling module is used to take the minimization of the total frequency regulation cost of the microgrid as the optimization goal, couple the temperature control load frequency regulation model, the tram load frequency regulation model and the random fault model, construct a coordinated frequency regulation model, and set frequency regulation constraints; A model solving module, used to solve the coordinated frequency regulation model based on the frequency regulation constraint conditions and using the operation control data of the microgrid to obtain the optimal frequency regulation instruction amount of the microgrid; The frequency regulation module is used to send the optimal frequency regulation instruction amount to each virtual power plant in the microgrid through the multi-load side resource cluster system architecture and perform frequency regulation operations.

13. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, it implements the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of claims 1-11.

15. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the method for multi-load side resources participating in microgrid frequency regulation based on cloud-edge collaboration as described in any one of claims 1-11.

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