Bidirectional aging perception multi-energy information physical system scheduling method, system and device

Abstract

The invention relates to a multi-energy information physical system scheduling method, system and device with bidirectional aging perception, and belongs to the technical field of telecommunication. The method comprises the following steps: firstly, constructing a physical domain multi-energy system model and an information domain multi-energy bidirectional timeliness model based on an information physical fusion multi-energy system; secondly, constructing an information physical fusion multi-energy system scheduling model considering the multi-energy bidirectional timeliness to minimize the global scheduling cost of the system; bidirectional timeliness constraint is converted through a virtual queue, and decoupling of short-term decision and long-term constraint is achieved. On the basis, model learning at an offline stage and extrinsic matching optimization at an online stage are combined, an information interaction sequence and a consistency variable weight are dynamically adjusted, information domain interaction and physical domain regulation and control are coordinated, and through distributed iterative optimization, the calculation complexity is effectively reduced, and the calculation efficiency is improved. The convergence speed of the consistency variable and the system regulation and control efficiency and stability are improved, and the system scheduling cost is reduced.

Description

Technical Field

[0001] The present invention relates to a scheduling method, system, and device for a bidirectional aging-aware multi-energy cyber-physical system, belonging to the field of electric communication technology. Background Art

[0002] Currently, with the rapid development of emerging fields such as new energy storage technologies, renewable energy, and electric vehicles, the upgrade of the energy system is moving towards the direction of green and low-carbon. The multi-energy system provides key support for achieving this goal by integrating various energy forms such as electric energy, thermal energy, hydrogen energy, and natural gas. Due to the heterogeneous energy demands and complex multi-energy coupling characteristics involved in the multi-energy system, it is particularly important to enhance the scalability and robustness of the multi-energy system by combining electric energy storage technology. Considering the volatility of new energy output and load demand, how to optimize the scheduling of electric energy storage to reduce operating costs and meet load demand has become an important problem to be solved urgently.

[0003] The consensus algorithm is widely used in energy storage scheduling due to its flexibility and scalability. Its core principle is to ensure that each agent converges towards consistency during the state update process through local state information interaction and consensus mechanism, thereby achieving global convergence and optimization of distributed scheduling. However, the traditional distributed scheduling methods still have the following problems. First, the existing distributed scheduling models ignore the strong coupling effect between the stability of physical domain regulation and the timeliness of information interaction in the information domain, resulting in information delay being easily diffused to the physical domain through the communication network, causing system control oscillation. There is a lack of a timeliness model that reflects the round-trip closed-loop information interaction of multi-energy subsystems, making it difficult to effectively quantify the loss of timeliness in the consensus iteration closed-loop and unable to provide accurate state information for physical domain regulation optimization. Second, the traditional regulation methods rely on fixed weights and one-way delay models, ignoring the two-way closed-loop characteristics of information interaction and the dynamic external influence between subsystems, resulting in slow consensus convergence speed, high distributed scheduling cost, and even control oscillation. In addition, the weight allocation does not consider the difference in subsystem influence, which will cause uneven resource allocation and response delay. Therefore, a scheduling method, device, and system for a bidirectional aging-aware multi-energy cyber-physical system are needed to achieve low-complexity distributed economic scheduling optimization of the multi-energy cyber-physical system.

[0004] In view of the above defects, the present invention aims to create a scheduling method, system, and device for a bidirectional aging-aware multi-energy cyber-physical system, making it more valuable for industrial use. Summary of the Invention

[0005] To solve the above technical problems, the object of the present invention is to provide a scheduling method, system, and device for a bidirectional aging-aware multi-energy cyber-physical system.

[0006] A two-way aging-aware scheduling method for a multi-energy cyber-physical system of the present invention, and the specific scheduling steps are as follows:

[0007] First, construct a physical-domain multi-energy system model; secondly, construct an information-domain multi-energy two-way information timeliness model, propose two-way information age, and efficiently characterize the entire life cycle of the generation, transmission, reception, processing, waiting, and feedback of consistent information; finally, construct an information-physical fusion multi-energy system scheduling model considering multi-energy two-way timeliness, consider the coupling between the information domain and the physical domain, and minimize the global scheduling cost of the system by jointly optimizing the information-domain interaction order, consistent variable weights, and the output power of multi-energy subsystems in the physical domain, providing support for the distributed economic scheduling optimization of the multi-energy cyber-physical system;

[0008] Based on the above constructed model, decouple short-term decisions and long-term constraints by transforming two-way timeliness constraints through virtual queues.

[0009] In the offline stage, use K-means clustering and radial basis function fitting to map the relationship between two-way timeliness and scheduling cost, guide the adjustment of dynamic weights, and complete the learning of the influence of multi-energy subsystems;

[0010] Subsequently, based on the information interaction optimization method of online externality matching, optimize the interaction order through externality matching in the online stage;

[0011] Finally, combine the virtual queue to adjust the update process of consistent variables to adapt to real-time constraints and achieve the economic distributed optimal scheduling of the information-physical fusion multi-energy system.

[0012] Furthermore, the physical-domain multi-energy system model includes:

[0013] Define K physical-domain multi-energy subsystems, including K DG distributed power sources, K EB electric boilers, and K ES electric energy storages, and the set is represented as

[0014] 1) Distributed power source model: The index of the distributed power source is k = 1, 2,..., K DG ; The regulation cost C k of the distributed power source k is expressed as:

[0015]

[0016] In the formula, P k represents the output power of the distributed power source k; and are the quadratic, primary, and constant cost coefficients of the distributed power source k respectively;

[0017] The output power P of the distributed power source kk should not exceed its own maximum output power It is expressed as:

[0018]

[0019] 2) Electric boiler model; its index is k = K DG +1, K DG +2,..., K DG +K EB ; The heating power of electric boiler k It is expressed as:

[0020]

[0021] In the formula, is the conversion efficiency of electric boiler k; The regulation cost C of electric boiler k k It is expressed as:

[0022]

[0023] In the formula, χ EB and κ EB are the secondary, primary and constant cost coefficients of electric boiler regulation respectively;

[0024] The electric power P of electric boiler k k shall not be greater than its own maximum power It is expressed as:

[0025]

[0026] 3) Electric energy storage model; the index of electric energy storage is k = K DG +K EB +1, K DG +K EB +2,..., K; The regulation cost of the kth electric energy storage is expressed as:

[0027]

[0028] In the formula, η k ES 、χ ES and κ ES are the secondary, primary and constant cost coefficients of electric energy storage regulation respectively; P k <0 indicates the discharge power of electric energy storage k, P k >0 indicates the charging power of electric energy storage k;

[0029] The electric energy storage power constraint is expressed as

[0030] -P ES,ch,max ≤Pk ≤P ES,dch,max (7)

[0031] In the formula, P ES,ch,max , P ES,dch,max are the maximum charging power and the maximum discharging power of the electrical energy storage respectively;

[0032] The capacity constraint of the electrical energy storage is expressed as:

[0033]

[0034] In the formula, and are the upper and lower limits of the capacity of the electrical energy storage respectively, is the capacity of the k-th electrical energy storage in the t-th regulation time slot.

[0035] Furthermore, the information domain multi-energy bidirectional information timeliness model includes four key links: the sending end k' transmits the consistency information to the receiving end k, the receiving end k processes and updates the received information, the receiving end k waits for the information to be returned, and the feedback consistency information is returned to the sending end k';

[0036] Define the bidirectional information age model of the multi-energy system as Γ = {Ψ, I}, where, represents the influence set of the multi-energy subsystem, represents the set of bidirectional information ages of the consistency model;

[0037] Define the order of the k-th multi-energy subsystem to transmit the updated consistency variable as the information interaction order; the interaction order set is where, s k,k′ = i, i = 0 means that there is no connection between k and k' and information interaction cannot be carried out; s k,k′ = i, i ≠ 0 means that the interaction order of the consistency variable of k' is i;

[0038] The information ages of the four stages of bidirectional information transmission are specifically as follows:

[0039] 1) The sending end k' transmits the consistency information to the receiving end k;

[0040] For the receiving end k, when receiving the consistency variable sent by the subsystem k' in the d-th iteration, the age of the forward information represents the time required for the subsystem k' to generate the information and transmit the consistency variable to the receiving end k, and this quantity is known;

[0041] 2) The receiving end k processes and updates the received information;

[0042] When the receiving end k receives the consistency variable It can be updated immediately; the delay of the receiving end k in processing the consistency variable information is expressed as

[0043]

[0044] In the formula, Y k′,k represents the CPU execution cycles required for the subsystem k′ to process data, and f k,t represents the computing power of k in the t-th time slot;

[0045] 3) The receiving end k waits for the information to be sent back:

[0046] The waiting delay for the consistency information to be sent back is expressed as:

[0047]

[0048] 4) Send the feedback consistency information back to the sending end k′;

[0049] The round-trip transmission delay of the consistency variable is expressed as:

[0050]

[0051] In the formula, represents the size of the consistency variable transmitted from k to k′ in the d-th iteration; P k is the transmit power, B represents the available channel bandwidth, represents the channel gain, and σ 2 represents the noise power, is the electromagnetic interference;

[0052] The two-way information transfer includes two parts: the forward and the return trips; the information transmitted by k′ ends when the message sent back by k arrives; the two-way information age of k′ and k, that is, I k′,k is expressed as:

[0053]

[0054] First, k′ goes through to transmit the consistency information to the subsystem k; second, k goes through to process and update the received information to obtain new information Then, k follows the interaction sequence table S k ={s k,1 , s k,2 , s k,3 , s k,4}={3, 1, 4, 2} and interacts with subsystems 3, 2, 4, and 1 in sequence; when k′ = 3, the round-trip waiting delay between the subsystem k and k′ is The round-trip transmission delay is Obviously, the interaction order has a significant impact on the two-way timeliness and consistency convergence. An unreasonable order may lead to a more serious deterioration of the information age of some subsystems, affect the consistency iterative convergence, and cause oscillations.

[0055] Furthermore, the cyber-physical integrated multi-energy system scheduling model considering multi-energy two-way timeliness. The distributed economic scheduling of the physical-domain multi-energy system is to coordinate the outputs of each multi-energy subsystem through the deep integration of the physical domain and the information domain, and minimize the global scheduling cost under the condition of meeting the load demand. Specifically, the IoT terminals in the information domain collect the operation status information and load demand information of each multi-energy subsystem, and through edge-end collaboration, summarize the information to the gateways of each multi-energy subsystem. Through edge-edge collaboration among the multi-energy subsystem gateways, the optimal scheduling plan is solved based on consistency iteration, and the scheduling instructions are sent to the physical domain. The physical-domain multi-energy subsystems adjust their own output powers according to the scheduling instructions to achieve economic scheduling.

[0056] The multi-energy system operator purchases electricity to make up for the deficit power to achieve the balance of energy supply and demand. The electricity purchase cost C of the multi-energy system Pur is expressed as

[0057] C Pur = ζP Pur (13)

[0058] In the formula, ζ is the electricity purchase price, and P Pur is the electricity purchase power.

[0059] The electric power balance of the multi-energy system is expressed as

[0060]

[0061] In the formula, P EL is the electric load power. The left side of the equal sign represents the total power supply of the system, and the right side of the equal sign represents the total electrical load of the system.

[0062] The thermal power balance of the multi-energy system is expressed as

[0063]

[0064] The total scheduling cost of the multi-energy system includes the electricity purchase cost and the scheduling costs of each multi-energy subsystem. The total scheduling cost is expressed as

[0065]

[0066] In the scheduling of the multi-energy cyber-physical system, the interaction order of the multi-energy subsystems will have a certain impact on both the two-way timeliness and the consistency convergence. Assume For two-way timeliness constraints, if the two-way information age of subsystem k exceeds this threshold, it will cause information distortion, which will in turn lead to a consistency scheduling deviation, seriously affecting the stable operation of the system. The optimization of the interaction order needs to consider both the current two-way information age and the link conditions of the subsystems, and give priority to interacting with the subsystems with a large two-way information age and a small information age reliability threshold to avoid causing control oscillations.

[0067] Based on the above analysis, considering the physical constraints, power balance constraints and two-way timeliness reliability constraints of multi-energy subsystems, by jointly optimizing the interaction order in the information domain, the weights of consistency variables and the output power of multi-energy subsystems in the physical domain, the global scheduling cost of the system is minimized. The scheduling modeling of the cyber-physical fusion multi-energy system considering multi-energy two-way timeliness is

[0068]

[0069] In the formula, is the two-way timeliness reliability indication variable at the d-th regulation, is the two-way timeliness constraint; C1 is the scheduling output and capacity constraint of each multi-energy subsystem, C2 is the electro-thermal power balance constraint of the multi-energy system; C3 is the multi-energy two-way timeliness constraint.

[0070] Furthermore, the method of converting the two-way timeliness constraint through the virtual queue is as follows:

[0071] The virtual queue Z of the two-way information timeliness constraint k (d) The update formula is expressed as:

[0072]

[0073] When the virtual queue Z of the two-way information timeliness constraint k (d) is stable, the long-term two-way information timeliness indication variable will not exceed Thus, the constraint condition C3 is satisfied; the short-term decision-making and long-term constraints are decoupled; P1 can be transformed into:

[0074]

[0075] In the formula, V is the weight, which is used to balance the minimization of the global scheduling cost of the multi-energy system in the physical domain and the minimization of the deviation of the two-way information age reliable constraint in the information domain.

[0076] Furthermore, the method of using K-means clustering and radial basis function to fit the mapping relationship between two-way timeliness and scheduling cost to guide dynamic weight adjustment and complete the influence learning of multi-energy subsystems is as follows:

[0077] First, construct the set of multi-energy subsystem k and adjacent subsystems The set of feasible topologies, denoted as For all under each topology Define the step size as ΔI k,k′ , gradually increase the two-way information age between k and any k', obtain the global scheduling cost of the multi-energy system with respect to the two-way information age between subsystem k and k', and define the number of samples as Q k , obtain the dataset Ω of the global scheduling cost of the multi-energy system varying with the two-way information age between subsystems k and k' k , denoted as:

[0078]

[0079] In the formula, Ω k is the dataset obtained by subsystem k under the nth topology, is the global scheduling cost of the multi-energy system obtained at the qth sampling between subsystems k and k', is the number of times of information age increase;

[0080] Secondly, after obtaining the dataset Ω k , obtain the influence function based on the fitting method of K-means and radial basis function Describes the influence of multi-energy system k when the two-way information age is I k ; The specific steps are as follows:

[0081] 1) Calculation of the optimal clustering center based on Kmeans

[0082] Randomly select k data from the dataset Ω to construct the initial clustering center Randomly obtain training samples from the dataset Ω each iteration k u is the number of iterations, The category of is defined as the category of the closest clustering center, denoted as:

[0083]

[0084] In the formula, is the category index of the training sample ;

[0085] The positions of each clustering center change according to the newly added training sample , and the update process is expressed as:

[0086]

[0087] After the algorithm converges, obtain the optimal clustering center

[0088] 2) Influence function fitting based on radial basis function

[0089] Obtain the data set Ω k Cluster center After that, construct a set of radial basis functions {Φ k (I k -f n )}, where Φ k is a Gaussian distribution function; the influence function can be obtained through a radial basis neural network and is expressed as:

[0090]

[0091] Furthermore, the specific steps of the information interaction optimization method for online external matching are as follows:

[0092] Definition 1: Define ι as the mapping of the one-to-one matching relationship between the set of subsystems and the set of their interaction orders in the d-th round of iteration, and

[0093]

[0094] where, when |ι(k,d,i)| = 1, it means that the i-th interaction object of subsystem k in the d-th round has been matched; when |ι(k,d,i)| = 0, it means that the i-th interaction object of subsystem k has not been matched;

[0095] Definition 2: Define ι as the mapping of the one-to-one matching relationship between the set of subsystems and the adjacency matrix in the d-th round of iteration, and

[0096]

[0097] where, when |ι(k,d,k′)| = 1, it means that the interaction object of subsystem k in the d-th round is; when |ι(k,d,i)| = 0, it means that subsystem k has no interaction object at this time;

[0098] Definition 3: ι(k,d,k′) and ι(k,d,i) satisfy the following equivalence relationship:

[0099]

[0100] This ensures that each system can only interact with one object at the same time;

[0101] In the d-th iteration, for the sender k, the cost function for the receiver k' and the interaction order i to match is expressed as:

[0102]

[0103] It means that at the d-th iteration, the two-way information age of k taking k' as the i-th interaction object is It means that when the information age is the influence of subsystem k';

[0104] At the d-th iteration, the cost function of the interaction order i matching the receiving terminal k' is expressed as:

[0105]

[0106] Definition 4 (exchange matching): Given a matching ι and two subsystem-order matching pairs (k', i) and That is, ι(k, d, k') = i and And and i ≠ i', If the condition That is:

[0107]

[0108] Definition (bilateral exchange stable matching): When there does not exist ι is a bilateral exchange stable matching;

[0109] The external matching algorithm based on information timeliness perception mainly includes two stages: the initialization stage and the exchange matching stage;

[0110] In the initialization stage, a matching relationship is randomly established between subsystems, and at the same time, it is ensured that is the information age boundary value for system regulation instability; secondly, each subsystem establishes its cost function for the interaction order according to formula (27), and constructs a preference list through descending order;

[0111] In the exchange matching stage, the subsystem k' currently matched with i sends a matching request to its most preferred ranking i'; for the subsystem currently matched with i' If the following three conditions are simultaneously met:

[0112]

[0113] Then the original matching relationship ι is replaced with Otherwise, ι remains unchanged; i' is removed from the preference list of subsystem k', and the above matching process is repeated until there is no exchange matching.

[0114] Furthermore, the method for economic distributed optimal scheduling of the cyber-physical fusion multi-energy system is:

[0115] During the d-th iteration of the consensus variable, when the multi-energy subsystem k receives the consensus variable transmitted by the subsystem k', its corresponding age of information is a known quantity; by substituting into the mapping function formula (23) in the offline data-driven stage, the influence of the subsystem k' can be calculated On this basis, using the online externality matching algorithm, the interaction cost is calculated according to formulas (27) and (28), and the preference list of the subsystem k' is constructed accordingly; through the analysis of the preference list, it is judged whether the current interaction order is optimal; if not, the exchange matching process is carried out for optimization; after the matching optimization is completed, the multi-energy subsystem k will preferentially select the subsystem k' with the largest influence and the lowest age of information for interaction, so as to accelerate the convergence speed of the consensus variable; based on this, the weights of the subsystems k and k' in the d-th iteration

[0116]

[0117] Because So its normalization is:

[0118]

[0119] Combined with the weight of the consensus variable and the age of information The update process of the consensus variable in the d-th iteration can be expressed as:

[0120]

[0121] In the (d + 1)-th iteration, the multi-energy subsystem, through further calculates the optimized power output and adjusts it in combination with the upper and lower limit constraints to ensure that the power dispatch meets the physical domain constraints; at the same time, this iterative process dynamically integrates the two-way timeliness of the information domain and the subsystem influence, realizing the economic distributed optimal dispatch of the cyber-physical integrated multi-energy system.

[0122] A two-way timeliness-aware multi-energy cyber-physical system scheduling system includes:

[0123] Physical domain: including multi-energy subsystems such as distributed power sources, electric boilers, and electrical energy storage; the distributed power source is a distributed photovoltaic, the electric boiler generates heat by consuming electrical energy, and the electrical energy storage realizes the transfer of electrical energy in time by storing and releasing energy, and the multi-energy subsystems meet the load demand of the system through coordinated scheduling;

[0124] Information domain: It includes IoT terminals deployed on multi-energy subsystems and a multi-energy cyber-physical fusion control device with two-way timeliness perception; among them, the IoT terminals collect temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge and discharge power, and electrical load power information, and upload it to the cyber-physical fusion control device with two-way timeliness perception; the multi-energy cyber-physical fusion control devices with two-way timeliness perception exchange status information through edge-edge cooperation, realize iterative consistency variables, calculate the optimal output power of each subsystem, and send the corresponding scheduling instructions to the physical domain; the consistency information interaction between the sender and the receiver can adopt a two-way timeliness model, including four links: information sending, information processing and updating, waiting for information feedback, and information feedback.

[0125] A scheduling device for a multi-energy cyber-physical system with two-way timeliness perception, deployed in the information domain, includes:

[0126] Communication module: It is used to receive the multi-energy subsystem information of temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge and discharge power, and electrical load power uploaded by the IoT terminal, exchange status information with other multi-energy cyber-physical fusion control devices with two-way timeliness perception, perform consistency iteration, and at the same time be responsible for sending scheduling instructions to the physical domain;

[0127] Scheduling model construction module: It is used to construct a multi-energy system model in the physical domain, a two-way timeliness model, and a cyber-physical fusion multi-energy system scheduling model considering multi-energy two-way timeliness; and decouple long-term constraints and short-term decisions based on Lyapunov theory;

[0128] Dataset construction module: It is used to construct a dataset for the two-way timeliness and scheduling cost obtained by the scheduling model construction module for fitting the influence function of the offline process;

[0129] Optimal clustering center calculation module based on Kmeans: It is used to adopt the Kmeans algorithm, calculate the optimal clustering center according to the offline dataset, and transmit the result to the influence function fitting module based on the radial basis function;

[0130] Influence function fitting module based on radial basis function: It is used to fit the influence function with a radial basis neural network according to the optimal clustering center, and transmit the result to the importance weight calculation module;

[0131] Matching cost function calculation module: It is used to calculate the cost function according to the current interaction order, and construct a preference list through descending order, and transmit the result to the exchange matching module;

[0132] Exchange matching module: used to send matching requests to subsystems that meet the interaction matching relationship. If the request is satisfied, the preference list is updated, and the above matching process is repeated until there is no exchange matching, and the result is transmitted to the interaction order adjustment module;

[0133] Interaction order adjustment module: used to optimize the result according to the interaction order and adjust the information interaction order of subsystems in the consistency iteration;

[0134] Importance weight calculation module: used to calculate the importance weight of the current subsystem consistency variable according to the influence function and the consistency information age, and transmit the result to the consistency variable update module;

[0135] Consistency variable update module: used to update the current consistency variable according to the importance weight, and transmit the result to the convergence judgment module and the scheduling power output module;

[0136] Convergence judgment module: used to judge whether the difference between the consistency variables before and after iteration meets the convergence condition. If it is satisfied, the convergence value of the consistency variable is output; otherwise, the next iteration is performed;

[0137] Scheduling power output module: used to calculate the scheduling power according to the convergence value of the consistency variable, and send the scheduling instruction to the physical domain via the communication module.

[0138] By the above solution, the present invention has at least the following advantages:

[0139] (1) The present invention proposes a method for constructing a scheduling model of a multi-energy cyber-physical system with bidirectional timeliness awareness. First, based on the cyber-physical integrated multi-energy system, a physical domain multi-energy system model is constructed, including multi-energy subsystems such as distributed power sources, electric boilers, and electrical energy storage. Secondly, an information domain multi-energy bidirectional timeliness model is constructed, and bidirectional information age is proposed to characterize the entire life cycle of consistency information generation, transmission, reception, processing, waiting, and feedback. Finally, a cyber-physical integrated multi-energy system scheduling model considering multi-energy bidirectional timeliness is constructed. By jointly optimizing the interaction order in the information domain, the weight of the consistency variable, and the output power of the multi-energy subsystems in the physical domain, the global scheduling cost of the system is minimized.

[0140] (2) Information-Physical Fusion Multi-Energy System Consistency Scheduling Method Based on Two-Way Information Timeliness and Information Interaction Optimization: The present invention proposes an information-physical fusion multi-energy system consistency scheduling method based on two-way information timeliness and information interaction optimization. By transforming two-way timeliness constraints through virtual queues, decoupling of short-term decision-making and long-term constraints is achieved. On this basis, combined with model learning in the offline stage and externality matching optimization in the online stage, the information interaction order and consistency variable weights are dynamically adjusted to coordinate information domain interaction and physical domain regulation. Through distributed iterative optimization, the computational complexity is effectively reduced, the convergence speed of consistency variables, the system regulation efficiency and stability are improved, and the system scheduling cost is reduced.

[0141] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it in accordance with the content of the specification, the following takes the preferred embodiments of the present invention and describes them in detail with reference to the accompanying drawings as follows. Brief Description of the Drawings

[0142] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0143] Figure 1 is a schematic structural diagram of the multi-energy information-physical system with two-way timeliness perception of the present invention;

[0144] Figure 2 is a schematic structural diagram of the information-physical fusion control device with two-way timeliness perception of the present invention;

[0145] Figure 3 is a flowchart of the scheduling method of the multi-energy information-physical system with two-way timeliness perception of the present invention. Detailed Description of the Embodiments

[0146] The following further describes the specific embodiments of the present invention in detail with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0147] Multi-Energy Information-Physical System with Two-Way Timeliness Perception

[0148] The scheduling system of the multi-energy information-physical system with two-way timeliness perception proposed by the present invention is as Figure 1 shown. It includes a physical domain and an information domain. Through the deep fusion of information and physics, unified scheduling of various electrical and thermal resources can be achieved, the energy utilization structure can be optimized, and the economic benefits and operation efficiency of the multi-energy system can be improved. The specific introduction is as follows:

[0149] Physical domain: It includes multiple energy subsystems such as distributed power sources, electric heating boilers, and electrical energy storage. The distributed power source is mainly distributed photovoltaic. The electric heating boiler generates heat by consuming electrical energy. The electrical energy storage realizes the transfer of electrical energy over time by storing and releasing energy. The multiple energy subsystems meet the load demand of the system through coordinated scheduling.

[0150] Information domain: It includes IoT terminals deployed on multiple energy subsystems and an information - physical fusion control device with two - way timeliness perception. Among them, the IoT terminals collect information such as temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge - discharge power, and electrical load power, and upload it to the information - physical fusion control device with two - way timeliness perception. The information - physical fusion control devices with two - way timeliness perception exchange status information through edge - to - edge cooperation, realize the iteration of consistency variables, calculate the optimal output power of each subsystem, and send the corresponding scheduling instructions to the physical domain. The consistency information interaction between the sender and the receiver can adopt a two - way timeliness model, including four links: information sending, information processing and updating, waiting for information feedback, and information feedback.

[0151] Information - physical fusion control device with two - way timeliness perception

[0152] The present invention proposes an information - physical fusion control device with two - way timeliness perception, which is deployed in the information domain. As Figure 2 shown, the proposed device includes a communication module, a scheduling model construction module, a dataset construction module, an optimal clustering center calculation module based on Kmeans, an influence function fitting module based on radial basis function, a matching cost function calculation module, an exchange matching module, an interaction order adjustment module, an importance weight calculation module, a consistency variable update module, a convergence judgment module, and a scheduling power output module, which are specifically introduced as follows:

[0153] Communication module: It is responsible for receiving information of multiple energy subsystems such as temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge - discharge power, and electrical load power uploaded by IoT terminals, exchanging status information with other information - physical fusion control devices with two - way timeliness perception for consistency iteration, and is also responsible for sending scheduling instructions to the physical domain.

[0154] Scheduling model construction module: It is responsible for constructing a multi - energy system model of the physical domain, a two - way timeliness model, and an information - physical fusion multi - energy system scheduling model considering multi - energy two - way timeliness. And decouple the long - term constraints and short - term decisions based on Lyapunov theory.

[0155] Dataset construction module: Based on the two - way timeliness and scheduling cost obtained by the scheduling model construction module, construct a dataset for fitting the influence function in the offline process.

[0156] Optimal Clustering Center Calculation Module Based on Kmeans: Using the Kmeans algorithm, calculate the optimal clustering center according to the offline dataset, and transmit the result to the Influence Function Fitting Module Based on Radial Basis Function.

[0157] Influence Function Fitting Module Based on Radial Basis Function: According to the optimal clustering center, use a radial basis neural network to fit the influence function, and transmit the result to the Importance Weight Calculation Module.

[0158] Matching Cost Function Calculation Module: Calculate the cost function according to the current interaction order, and construct a preference list through descending order, and transmit the result to the Exchange Matching Module.

[0159] Exchange Matching Module: Send a matching request to the subsystems that meet the interaction matching relationship. If the request is satisfied, update the preference list, and repeat the above matching process until there is no exchange matching, and transmit the result to the Interaction Order Adjustment Module.

[0160] Interaction Order Adjustment Module: According to the optimization result of the interaction order, adjust the information interaction order of the subsystems in the consistency iteration.

[0161] Importance Weight Calculation Module: Calculate the importance weight of the current subsystem consistency variable according to the influence function and the age of the consistency information, and transmit the result to the Consistency Variable Update Module.

[0162] Consistency Variable Update Module: Update the current consistency variable according to the importance weight, and transmit the result to the Convergence Judgment Module and the Scheduling Power Output Module.

[0163] Convergence Judgment Module: Judge whether the difference between the consistency variables before and after iteration meets the convergence condition. If it meets, output the convergence value of the consistency variable; otherwise, perform the next iteration.

[0164] Scheduling Power Output Module: Calculate the scheduling power according to the convergence value of the consistency variable, and issue the scheduling instruction to the physical domain via the communication module.

[0165] Bi - directional Time - effect - aware Scheduling Method for Multi - energy Cyber - physical Systems

[0166] The present invention proposes a bi - directional time - effect - aware scheduling method for multi - energy cyber - physical systems, as Figure 3 shown, including a method for constructing a bi - directional time - effect - aware scheduling model for multi - energy cyber - physical systems and a method for consistent scheduling of cyber - physical fusion multi - energy systems with bi - directional time - effect - awareness and information interaction optimization, which are introduced as follows:

[0167] (1) Method for Constructing a Bi - directional Time - effect - aware Scheduling Model for Multi - energy Cyber - physical Systems

[0168] The present invention proposes a method for constructing a scheduling model of a two-way aging-aware multi-energy cyber-physical system. Based on the cyber-physical integrated multi-energy system, first, a physical-domain multi-energy system model is constructed, which includes multi-energy subsystems such as distributed power sources, electric boilers, and electrical energy storage. Secondly, a two-way multi-energy aging model in the information domain is constructed, and a two-way information age is proposed to characterize the entire life cycle of the generation, transmission, reception, processing, waiting, and feedback of consistent information. Finally, a cyber-physical integrated multi-energy system scheduling model considering two-way multi-energy aging is constructed. By jointly optimizing the interaction order in the information domain, the weight of the consistency variable, and the output power of the multi-energy subsystems in the physical domain, the global scheduling cost of the system is minimized.

[0169] Step 1: Construction of the physical-domain multi-energy system model

[0170] Considering K physical-domain multi-energy subsystems, including K DG distributed power sources, K EB electric boilers, and K ES electrical energy storage, which are represented by the set

[0171] 1) Distributed power source model. The index of the distributed power source is k = 1, 2, …, K DG . The scheduling cost of the distributed power source can be represented by a multi-segment linear function or a quadratic function. In this patent, a quadratic function is selected to model the scheduling cost of the traditional distributed power source. The regulation cost C k of the distributed power source k is expressed as

[0172]

[0173] where P k represents the output power of the distributed power source k. and are the quadratic, linear, and constant cost coefficients of the distributed power source k, respectively.

[0174] The output power P k of the distributed power source k should not exceed its maximum output power which is expressed as

[0175]

[0176] 2) Electric boiler model. The electric boiler converts electrical energy into heat energy, and its index is k = K DG +1, K DG +2,..., K DG +K EB . The heating power of the electric boiler k is expressed as

[0177]

[0178] In the formula, is the conversion efficiency of the electric boiler k. The regulation cost C of the electric boiler k k is expressed as

[0179]

[0180] In the formula, χ EB and κ EB are the secondary, primary and constant cost coefficients for the regulation of the electric boiler respectively.

[0181] The electric power P of the electric boiler k k shall not be greater than its own maximum power is expressed as

[0182]

[0183] 3) Electric energy storage model. The index of the electric energy storage is k = K DG +K EB +1, K DG +K EB +2,..., K. The regulation cost of the k-th electric energy storage is expressed as

[0184]

[0185] In the formula, χ ES and κ ES are the secondary, primary and constant cost coefficients for the regulation of the electric energy storage respectively. P k < 0 represents the discharge power of the electric energy storage k, and P k > 0 represents the charging power of the electric energy storage k.

[0186] The electric energy storage power constraint is expressed as

[0187] -P ES,ch,max ≤P k ≤P ES,dch,max (7)

[0188] In the formula, P ES,ch,max , P ES,dch,max are the maximum charging power and the maximum discharge power of the electric energy storage respectively.

[0189] The electric energy storage capacity constraint is expressed as

[0190]

[0191] In the formula, and are the upper and lower limits of the capacity of the electric energy storage respectively, is the capacity of the k-th electric energy storage in the t-th regulation time slot.

[0192] Step 2: Construction of the Information Domain Multi - functional Two - way Information Timeliness Model

[0193] To measure the information timeliness of the consistency variable interaction between multi - functional subsystems, this patent proposes the two - way information age, which characterizes the entire life cycle of the generation, transmission, reception, processing, waiting, and feedback of consistency information. Specifically, it includes four key links: the sending end k'transmits the consistency information to the receiving end k, the receiving end k processes and updates the received information, the receiving end k waits for the information to be fed back, and the feedback consistency information is fed back to the sending end k'. Each link will directly affect the degree of deterioration of the two - way information age.

[0194] Define the two - way information age model of the multi - functional system as Γ = {Ψ, I}, where, represents the set of influences of the multi - functional subsystems, represents the set of two - way information ages of the consistency model.

[0195] Define the order of the receiving end k of the multi - functional subsystem k to feed back the updated consistency variable as the information interaction order. The interaction order set is where, s k,k′ = i, i = 0 indicates that there is no connection relationship between k and k' and information interaction cannot be carried out; s k,k′ = i, i ≠ 0 indicates that the interaction order of the consistency variable of k' is i.

[0196] The information ages of the four stages of two - way information transfer are specifically introduced as follows.

[0197] 1) The sending end k'transmits the consistency information to the receiving end k.

[0198] For the receiving end k, when receiving the consistency variable sent by the subsystem k' in the d - th round of iteration, the age of the forward - journey information represents the time required for the subsystem k' to generate the information and transmit the consistency variable to the receiving end k, and this quantity is known.

[0199] 2) The receiving end k processes and updates the received information.

[0200] When the receiving end k receives the consistency variable sent by the subsystem k', it can be updated. The time delay for the receiving end k to process the consistency variable information is expressed as

[0201]

[0202] In the formula, Y k′,k represents the CPU execution cycles required for the subsystem k' to process data, and f k,t represents the computing power of k at the t - th time slot.

[0203] 3) The receiving end k waits for the information to be sent back.

[0204] Before subsystem k sends back the consistency information to subsystem k′, it needs to complete the interactive transmission of the consistency variables with other subsystems in sequence according to the interaction sequence table S k This sequence results in subsystem k having to wait for the previous interaction to complete before it can send back the consistency information to k′, leading to additional waiting latency. Therefore, the waiting latency for sending back the consistency information is expressed as

[0205]

[0206] 4) Send back the feedback consistency information to the sending end k′.

[0207] The round-trip transmission latency of the consistency variable is expressed as

[0208]

[0209] In the formula, represents the size of the consistency variable passed from k to k′ in the d-th iteration. P k is the transmission power, B represents the available channel bandwidth, represents the channel gain, σ 2 represents the noise power, is the electromagnetic interference.

[0210] The two-way information transfer includes the forward and return trips. When the message sent back by k arrives, the information sent out by k′ ends. The two-way information age of k′ and k, that is, I k′,k is expressed as

[0211]

[0212] First, k′ goes through to transmit the consistency information to subsystem k. Second, k goes through to process and update the received information to obtain new information Then, k follows the interaction sequence table S k ={s k,1 , s k,2 , s k,3 , s k,4}={3, 1, 4, 2} and interacts with subsystems 3, 2, 4, and 1 in sequence. When k′ = 3, the return waiting latency between subsystem k and k′ is The round-trip transmission latency is Obviously, the interaction order has a significant impact on two-way timeliness and consistency convergence. An unreasonable order may lead to a more serious deterioration of the information age of some subsystems, affecting the consistency iterative convergence and causing oscillations.

[0213] Step 3: Construction of an information-physical fusion multi-energy system scheduling model considering multi-energy two-way timeliness

[0214] 1) Distributed economic scheduling of the multi-energy system in the physical domain.

[0215] Distributed economic scheduling of the multi-energy system in the physical domain is to minimize the global scheduling cost by deeply integrating the physical domain and the information domain and coordinately scheduling the output of each multi-energy subsystem under the condition of meeting the load demand. Specifically, the IoT terminals in the information domain collect the operation status information and load demand information of each multi-energy subsystem, and through edge-end collaboration, summarize the information to the gateway of each multi-energy subsystem. Through edge-edge collaboration among the gateways of multi-energy subsystems, the optimal scheduling plan is solved based on consistency iteration, and the scheduling instruction is sent to the physical domain. The multi-energy subsystems in the physical domain adjust their own output power according to the scheduling instruction to achieve economic scheduling.

[0216] The multi-energy system operator makes up for the deficit power by purchasing electricity to achieve the balance of energy supply and demand. The electricity purchase cost C of the multi-energy system Pur is expressed as

[0217] C Pur = ζP Pur (13)

[0218] In the formula, ζ is the electricity purchase price, and P Pur is the electricity purchase power.

[0219] The power balance of the multi-energy system is expressed as

[0220]

[0221] In the formula, P EL is the electrical load power. The left side of the equal sign represents the total power supply of the system, and the right side represents the total electrical load of the system.

[0222] The thermal power balance of the multi-energy system is expressed as

[0223]

[0224] The total scheduling cost of the multi-energy system includes the electricity purchase cost and the scheduling cost of each multi-energy subsystem. The total scheduling cost is expressed as

[0225]

[0226] 2) Scheduling of the information-physical fusion multi-energy system.

[0227] In the scheduling of multi - energy cyber - physical systems, the interaction order of multi - energy subsystems has a certain impact on both two - way timeliness and consistency convergence. Suppose is the two - way timeliness constraint. If the two - way information age of subsystem k exceeds this threshold, it will cause information distortion, and then lead to consistency scheduling deviation, seriously affecting the stable operation of the system. The optimization of the interaction order needs to consider both the current two - way information age and the link conditions of the subsystems, and give priority to interacting with the subsystems with a large two - way information age and a small information - age reliability threshold to avoid causing control oscillation.

[0228] Based on the above analysis, this patent proposes an information - physical fusion multi - energy system scheduling model considering multi - energy two - way timeliness. Considering the physical constraints, power balance constraints and two - way timeliness reliability constraints of multi - energy subsystems, by jointly optimizing the interaction order in the information domain, the weights of consistency variables and the output power of multi - energy subsystems in the physical domain, the global scheduling cost of the system is minimized. The scheduling of the information - physical fusion multi - energy system considering multi - energy two - way timeliness is modeled as

[0229]

[0230] In the formula, is the two - way timeliness reliability indicator variable at the d - th regulation, is the two - way timeliness constraint. C1 is the scheduling output and capacity constraint of each multi - energy subsystem, C2 is the electric - power balance constraint between the electric power of the multi - energy system. C3 is the multi - energy two - way timeliness constraint.

[0231] (2) A consistency scheduling method for information - physical fusion multi - energy systems based on two - way information timeliness and information interaction optimization

[0232] The present invention proposes a consistency scheduling method for information - physical fusion multi - energy systems based on two - way information timeliness and information interaction optimization. First, the two - way timeliness constraint is transformed through a virtual queue to decouple short - term decision - making and long - term constraints. Secondly, in the offline stage, K - means clustering and radial basis function are used to fit the mapping relationship between two - way timeliness and scheduling cost to guide the adjustment of dynamic weights. Finally, in the online stage, the interaction order is optimized through externality matching, and the update process of consistency variables is adjusted in combination with the virtual queue to adapt to real - time constraints. This method combines offline and online optimization, coordinates information - domain interaction and physical - domain regulation, and reduces the computational complexity through distributed iterative optimization, improving the convergence speed of consistency variables and the system regulation efficiency.

[0233] Step 1: Problem transformation based on virtual queue

[0234] This patent introduces the concept of a virtual queue to transform the two - way information timeliness constraint into a queue stability constraint. The virtual queue Z of the two - way information timeliness constraintk (d) is updated as follows

[0235]

[0236] When the virtual queue Z for the timeliness constraint of two-way information k is stable, the long-term two-way information timeliness indicator variable will not exceed thus satisfying the constraint condition C3. This decouples the short-term decision-making from the long-term constraints.

[0237] P1 can be transformed into

[0238]

[0239] where V is the weight, used to balance the minimization of the global scheduling cost of the physical-domain multi-energy system and the minimization of the deviation of the reliable constraint of the two-way information age in the information domain.

[0240] Step 2: Offline data-driven learning of the influence of multi-energy subsystems

[0241] In the scheduling optimization of the multi-energy cyber-physical system, the main purpose of the offline-driven stage is to learn and predict the influence of multi-energy subsystems through historical data and simulation data. The influence of multi-energy subsystems can reflect the degree of influence of each subsystem on the overall system scheduling decision. Accurately predicting the influence of multi-energy subsystems can provide support for decision-making in the online stage, thus more reasonably allocating resources and improving energy utilization efficiency. In the offline case, a dataset of the global scheduling cost of the multi-energy system varying with the two-way information age between subsystems is constructed, and an influence function is obtained based on the fitting method of K-means and radial basis functions. The radial basis function can effectively handle nonlinear problems, and solving the selection problem of its center parameters through Kmeans clustering can greatly improve the fitting accuracy.

[0242] First, construct the set of feasible topologies of multi-energy subsystem k and the set of adjacent subsystems , denoted as For all under each topology Define the step size as ΔI k,k′ , gradually increase the two-way information age between k and any k', and obtain the global scheduling cost of the multi-energy system varying with the two-way information age between subsystem k and k'. Define the number of samples as Q k , and obtain the dataset Ω of the global scheduling cost of the multi-energy system varying with the two-way information age between subsystem k and k' k , denoted as

[0243]

[0244] where Ω kThe data set obtained by subsystem k under the nth topology, is the global scheduling cost of the multi - energy system obtained during the qth sampling between subsystems k and k'. is the number of times of the age - of - information increase.

[0245] Secondly, after obtaining the data set Ω k the influence function is obtained based on the fitting method of K - means and radial basis function describes the influence of multi - energy system k when the two - way age - of - information is I k The specific steps are as follows.

[0246] 1) Calculation of the optimal clustering center based on Kmeans

[0247] Randomly select k from the data set Ω data to construct the initial clustering center Each iteration randomly obtains training samples k from the data set Ω u is the number of iterations, the class of

[0248]

[0249] is defined as the class of the nearest clustering center, denoted as where is the class index of the training sample

[0250] The position of each clustering center changes according to the newly added training sample The update process is expressed as

[0251]

[0252] After the algorithm converges, the optimal clustering center

[0253] 2) Fitting of the influence function based on the radial basis function

[0254] After obtaining the clustering center k of the data set Ω construct the radial basis function set {Φ k (I k -f n )}, where Φ k is the Gaussian distribution function. The influence function can be obtained through the radial basis neural network, denoted as

[0255]

[0256] Step 3: Optimization of Information Interaction Based on Online Externality Matching

[0257] P2 is a non-convex combinatorial optimization problem. Due to the complex coupling relationship of the information interaction order and influence among subsystems, the time complexity of the traditional exhaustive algorithm grows exponentially and it is difficult to be applied in practice. In addition, there is an externality among subsystems, that is, the interaction choice of one subsystem will dynamically affect the order and preference of other subsystems, making it difficult for traditional matching algorithms to adapt to this dynamic change.

[0258] To solve this problem, the present invention proposes an information interaction optimization method based on online externality matching. This method dynamically improves the preference affected by externality during the matching process by introducing swap matching, so as to enhance the efficiency and stability of the consistent iteration. Specifically, the original optimization problem aims to minimize the system operation cost, models the matching preference as the matching cost, and optimizes the interaction order by minimizing the matching cost, thereby improving the convergence performance of the consistent variable. This problem is modeled as a reverse swap matching problem, in which each subsystem independently executes the matching process as the sending end to achieve distributed parallel optimization of the interaction order. Through the above improved algorithm, it is possible to effectively optimize the information interaction order in a multi-energy system with significant externality, meet the two-way information timeliness requirements, reduce the overall scheduling cost of the system, and improve the convergence performance of the consistent variable.

[0259] Definition 1: Define ι as the set of subsystems and its interaction order set the one-to-one matching relationship mapping in the d-th round of iteration, and

[0260]

[0261] where, when |ι(k,d,i)| = 1, it means that the i-th interaction object of subsystem k in the d-th round has completed the matching; when |ι(k,d,i)| = 0, it means that the i-th interaction object of subsystem k has not been matched yet.

[0262] Definition 2: Define ι as the set of subsystems and the adjacency matrix the one-to-one matching relationship mapping in the d-th round of iteration, and

[0263]

[0264] where, when |ι(k,d,k′)| = 1, it means that the interaction object of subsystem k in the d-th round is; when |ι(k,d,i)| = 0, it means that subsystem k has no interaction object at this time.

[0265] Definition 3: ι(k,d,k′) and ι(k,d,i) satisfy the following equivalence relationship:

[0266]

[0267] This ensures that each system can interact with only one object at the same time.

[0268] In the d-th iteration, for the sender k, the cost function for the receiver k' and the interaction order i to match is expressed as

[0269]

[0270] Denote the two-way age of information when k takes k' as the i-th interaction object at the d-th iteration, Denote when the age of information is the influence of subsystem k'.

[0271] At the d-th iteration, the cost function for the interaction order i to match with the receiving terminal k′ is expressed as

[0272]

[0273] Definition 4 (Exchange matching): Given a matching ι and two subsystem-order matching pairs (k′, i) and That is, ι(k, d, k′) = i and And And i ≠ i′, If the condition That is:

[0274]

[0275] Definition (Bilateral exchange stable matching): When there does not exist ι is a bilateral exchange stable matching.

[0276] The external matching algorithm based on information timeliness perception mainly includes two stages: the initialization stage and the exchange matching stage.

[0277] In the initialization stage, a matching relationship is randomly established between subsystems, while ensuring that is the age of information boundary value for system regulation instability. Secondly, each subsystem establishes its cost function for the interaction order according to Equation (27) and constructs a preference list through descending order.

[0278] In the exchange matching stage, the subsystem k′ currently matched with i sends a matching request to its most preferred ranking i'. For the subsystem currently matched with i' If the following three conditions are simultaneously satisfied:

[0279]

[0280] Then replace the original matching relationship ι with Otherwise, keep ι unchanged. Remove i' from the preference list of subsystem k', and repeat the above matching process until there is no exchange matching.

[0281] Step 4: Economic Distributed Scheduling of Cyber-Physical Fusion Multi-Energy System

[0282] During the d-th iteration of the consistency variable, when multi-energy subsystem k receives the consistency variable transmitted by subsystem k′, its corresponding age of information is a known quantity. By substituting into the mapping function formula (23) in the offline data-driven stage, the influence of subsystem k′ can be calculated On this basis, using the online externality matching algorithm, the interaction cost is calculated according to formulas (27) and (28), and the preference list of subsystem k′ is constructed accordingly. Through the analysis of the preference list, it is judged whether the current interaction order is optimal; if not, the exchange matching process is carried out for optimization. After the matching optimization is completed, multi-energy subsystem k will preferentially select the subsystem k′ with the largest influence and the lowest age of information for interaction, so as to accelerate the convergence speed of the consistency variable. Based on this, the weights of subsystems k and k′ in the d-th iteration are

[0283]

[0284] Because So it is normalized to

[0285]

[0286] Combining the weight of the consistency variable and the age of information The update process of the consistency variable in the d-th iteration can be expressed as

[0287]

[0288] In the (d + 1)-th iteration, the multi-energy subsystem further calculates the optimized power output through and adjusts it in combination with the upper and lower limit constraints to ensure that the power scheduling meets the physical domain constraints. At the same time, this iterative process dynamically integrates the two-way timeliness of the information domain and the subsystem influence, realizing the economic distributed optimal scheduling of the cyber-physical fusion multi-energy system.

[0289] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A two-way aging-aware scheduling method for a multi-energy cyber-physical system, characterized in that The specific scheduling steps are as follows: First, construct a physical-domain multi-energy system model; second, construct an information-domain multi-energy two-way information timeliness model, propose two-way information age, and efficiently characterize the entire life cycle of the generation, transmission, reception, processing, waiting, and feedback of consistent information; finally, construct an information-physical fusion multi-energy system scheduling model considering multi-energy two-way timeliness, consider the coupling between the information domain and the physical domain, and minimize the global scheduling cost of the system by jointly optimizing the information-domain interaction order, consistent variable weights, and the output power of the physical-domain multi-energy subsystems, providing support for the distributed economic scheduling optimization of multi-energy information-physical systems; Based on the above constructed model, decouple short-term decisions and long-term constraints by converting two-way timeliness constraints through virtual queues; In the offline stage, use K-means clustering and radial basis function fitting to map the relationship between two-way timeliness and scheduling cost, guide the adjustment of dynamic weights, and complete the influence learning of multi-energy subsystems; Subsequently, based on the information interaction optimization method of online externality matching, optimize the interaction order through externality matching in the online stage; Finally, adjust the consistent variable update process in combination with virtual queues to adapt to real-time constraints and achieve the economic distributed optimal scheduling of the information-physical fusion multi-energy system.

2. A scheduling method for a two-way time-aware multi-energy cyber-physical system according to claim 1, characterized in that: The physical-domain multi-energy system model includes: Define K physical-domain multi-energy subsystems, including K DG distributed power sources, K EB electric boilers, and K ES electric energy storages, which are collectively denoted as 1) Distributed power source model: The index of the distributed power source is k = 1, 2, …, K DG ; The regulation cost C of the distributed power source k k is expressed as: Wherein, P k represents the output power of the distributed power source k; and are respectively the quadratic, primary, and constant cost coefficients of the distributed power source k; Output power P of distributed power source k k should not exceed its own maximum output power which is expressed as: 2) Electric boiler model; its index is k = K DG +1, K DG +2,..., K DG +K EB ; The heating power of electric boiler k is expressed as: In the formula, is the conversion efficiency of the electric boiler k; the regulation cost C of the electric boiler k k is expressed as: In the formula, χ EB and κ EB are the secondary, primary, and constant cost coefficients for the regulation of the electric heating boiler, respectively; The electric power P of the electric boiler k k shall not be greater than its own maximum power It is expressed as: 3) Electrical energy storage model; the index of electrical energy storage is k = K DG +K EB +1, K DG +K EB +2,..., K; the regulation cost of the k-th electrical energy storage is expressed as: In the formula, χ ES and κ ES are respectively the secondary, primary and constant cost coefficients of electric energy storage regulation; P k < 0 indicates the discharge power of electric energy storage k, and P k > 0 indicates the charging power of electric energy storage k; The electrical energy storage power constraint is expressed as -P ES,ch,max ≤P k ≤P ES,dch,max (7) Wherein, P ES,ch,max , P ES,dch,max are respectively the maximum charging power and the maximum discharging power of the electrical energy storage; The electrical energy storage capacity constraint is expressed as: wherein, and are respectively the upper and lower limits of the capacity of the electrical energy storage, is the capacity of the k-th electrical energy storage in the t-th regulation time slot.

3. A two-way aging-aware multi-energy cyber-physical system scheduling method according to claim 1, characterized in that: The information-domain multi-energy two-way information timeliness model includes four key links: the sender k′ transmits consistent information to the receiver k, the receiver k processes and updates the received information, the receiver k waits for the information to be feedback, and the feedback consistent information is transmitted back to the sender k′; Define the two-way information age model of the multi-energy system as Γ = {Ψ, I}, where, represents the set of influences of the multi-energy subsystems, represents the set of two-way information ages of the consistency model; Define the order of the consistency variables after the k-th feedback update of the multi-energy subsystem as the information interaction order; the set of interaction orders is where s k,k′ = i, and i = 0 indicates that there is no connection relationship between k and k' and information interaction cannot be performed; s k,k′ = i, i ≠ 0 indicates that the interaction order of the consistency variables of k' is i; The information age of the four stages of two-way information transmission is as follows: 1) The sender k′ transmits consistent information to the receiver k; For the receiving end \(k\), when receiving the consensus variable sent by the subsystem \(k'\) in the \(d\)-th iteration, the age of information in the upstream represents the time required for the subsystem \(k'\) to generate the information and transmit the consensus variable to the receiving end \(k\), and this quantity is known; 2) The receiver k processes and updates the received information; When the receiving end k receives the consistency variable sent by the subsystem k′ it can be updated; the delay of the receiving end k in processing the consistency variable information is expressed as Where Y k′,k represents the CPU execution cycles required for subsystem k′ to process data, and f k,t represents the computing power of k at the t-th time slot; 3) The receiver k waits for the information to be feedback: Consistency information feedback waiting delay Expressed as: 4) The feedback consistent information is transmitted back to the sender k′; Round-trip transmission delay of the consistency variable It is expressed as: wherein, represents the size of the consistency variable passed from k to k' in the d-th iteration; P k is the transmit power, B represents the available channel bandwidth, represents the channel gain, σ 2 represents the noise power, is the electromagnetic interference; Two-way information transfer consists of a forward journey and a return journey; the information transmitted by k' ends only when the message transmitted by k in the reverse direction arrives; the two-way information ages of k' and k, namely I k′,k are expressed as: First, k' goes through transmit the consistency information to subsystem k; Second, k goes through process and update the received information to obtain new information Then, k follows the interaction sequence table S k ={s k,1 , s k,2 , s k,3 , s k,4}={3, 1, 4, 2}, and interacts with subsystems 3, 2, 4, and 1 in sequence; when k' = 3, the round-trip waiting delay between subsystem k and k' is The round-trip transmission delay is Obviously, the interaction order has a significant impact on the two-way timeliness and consistency convergence. An unreasonable order may lead to aggravated deterioration of the information age of some subsystems, affect the consistency iterative convergence, and cause oscillations.

4. A two-way aging-aware multi-energy cyber-physical system scheduling method according to claim 1, characterized in that: The distributed economic scheduling of the physical-domain multi-energy system is to minimize the global scheduling cost by deeply integrating the physical domain and the information domain and coordinately scheduling the output of each multi-energy subsystem under the condition of meeting the load demand. Specifically, the IoT terminals in the information domain collect the operation status information and load demand information of each multi-energy subsystem, and through edge-end collaboration, summarize the information to the gateways of each multi-energy subsystem. The gateways of the multi-energy subsystems perform edge-edge collaboration, solve the optimal scheduling plan based on consistency iteration, and send the scheduling instructions to the physical domain. The physical-domain multi-energy subsystems adjust their own output power according to the scheduling instructions to achieve economic scheduling. The multi - energy system operator purchases electricity to make up for the deficit power and achieve the balance of energy supply and demand. The electricity purchase cost C of the multi - energy system Pur is expressed as C Pur = ζP Pur (13) where ζ is the electricity purchase price and P Pur is the electricity purchase power. The electrical power balance of the multi-energy system is expressed as where P EL is the electrical load power, the left side of the equal sign represents the total power supply of the system, and the right side of the equal sign represents the total electrical load of the system. The thermal power balance of the multi-energy system is expressed as The total scheduling cost of the multi-energy system includes the electricity purchase cost and the scheduling costs of each multi-energy subsystem. The total scheduling cost is expressed as In the scheduling of multi - energy cyber - physical systems, the interaction order of multi - energy subsystems has a certain impact on both two - way timeliness and consistency convergence. Suppose is the two - way timeliness constraint. If the two - way age of information of subsystem k exceeds this threshold, it will cause information distortion, and then lead to a consistency scheduling deviation, seriously affecting the stable operation of the system. The optimization of the interaction order needs to consider both the current two - way age of information and the link conditions of the subsystems, and give priority to interacting with the subsystems with a large two - way age of information and a small reliability threshold of the age of information to avoid causing control oscillations. Based on the above analysis, considering the physical constraints, power balance constraints, and two-way timeliness reliability constraints of multi-energy subsystems, the global scheduling cost of the system is minimized by jointly optimizing the information-domain interaction order, consistent variable weights, and the output power of the physical-domain multi-energy subsystems. The scheduling modeling of the information-physical fusion multi-energy system considering multi-energy two-way timeliness is Wherein, is the two-way timeliness reliability indication variable at the d-th regulation, is the two-way timeliness constraint; C1 is the dispatching output and capacity constraint of each multi-energy subsystem, C2 is the electro-thermal power balance constraint of the multi-energy system; C3 is the multi-energy two-way timeliness constraint.

5. A two-way aging-aware multi-energy cyber-physical system scheduling method according to claim 1, characterized in that: The method for converting two-way timeliness constraints through a virtual queue is as follows: Virtual Queue Z with Two-Way Information Timeliness Constraint k (d)'s update formula is expressed as: When the virtual queue Z of the two-way information timeliness constraint k (d) When it is stable, the long-term two-way information timeliness indicator variable will not exceed Thus, the constraint condition C3 is satisfied; the short-term decision-making and long-term constraints are decoupled; P1 can be transformed into: In the formula, V is the weight, which is used to balance the minimization of the global scheduling cost of the physical-domain multi-energy system and the minimization of the deviation of the two-way information age reliable constraint in the information domain.

6. A two-way aging-aware multi-energy cyber-physical system scheduling method according to claim 1, characterized in that: The method for adopting K-means clustering and radial basis function fitting to map two-way timeliness and scheduling cost, guiding dynamic weight adjustment, and completing the influence learning of multi-energy subsystems is as follows: First, construct the feasible topology sets of the multi - energy subsystem k and the set of adjacent subsystems denoted as For all under each topology Define the step size as ΔI k,k′ , gradually increase the two - way age of information between k and any k', obtain the global scheduling cost of the multi - energy system with respect to the two - way age of information between subsystem k and k', define the number of samples as Q k , obtain the data set Ω of the global scheduling cost of the multi - energy system varying with the two - way age of information between subsystem k and k' k denoted as: where, Ω k is the dataset obtained by subsystem k under the nth topology, is the global scheduling cost of the multi - energy system obtained at the qth sampling between subsystem k and k', is the number of times of the age - of - information increase; Secondly, after obtaining the dataset Ω k the influence function is obtained based on the fitting method of K-means and radial basis function describes the influence of the multi-energy system k when the two-way information age is I k ; the specific steps are as follows: 1) Calculation of the optimal clustering center based on Kmeans Randomly select from the data set Ω k and construct the initial clustering centers by randomly selecting Each time in iteration, randomly obtain training samples from the data set Ω k Let u be the number of iterations, and the class of is defined as the class of the nearest clustering center, expressed as: wherein, is the training sample category index; The positions of the cluster centers change according to the newly added training samples and the update process is expressed as: After the algorithm converges, the optimal clustering centers are obtained 2) Fitting of the influence function based on the radial basis function Obtain the data set Ω k Cluster center After that, construct the radial basis function set {Φ k (I k -f n )}, where Φ k is a Gaussian distribution function; the influence function can be obtained through a radial basis neural network and is expressed as:

7. A scheduling method for a two-way aging-aware multi-energy cyber-physical system according to claim 1, characterized in that: The specific steps of the information interaction optimization method for online externality matching are as follows: Definition 1: Define ι as the set of subsystems and the set of their interaction orders a one-to-one matching relationship mapping in the d-th round of iteration, and Among them, when |ι(k, d, i)| = 1, it means that the i-th interaction object of subsystem k in the d-th round has completed the matching; when |ι(k, d, i)| = 0, it means that the i-th interaction object of subsystem k has not been matched yet; Definition 2: Define ι as the set of subsystems and the adjacency matrix is the one-to-one matching relationship mapping in the d-th round of iteration, and Among them, when |ι(k, d, k′)| = 1, it means that the interaction object of subsystem k in the d-th round is; when |ι(k, d, i)| = 0, it means that subsystem k has no interaction object at this time; Definition 3: ι(k, d, k′) and ι(k, d, i) satisfy the following equivalence relationship: This ensures that each system can only interact with one object at the same time; In the d-th iteration, for the sending end k, the cost function for matching the receiving end k' and the interaction order i is expressed as: Denote that at the d-th iteration, the two-way information age of k taking k' as the i-th interaction object, Denote that when the information age is the influence of subsystem k'; In the d-th iteration, the cost function for matching the interaction order i with the receiving terminal k′ is expressed as: Definition 4 (Exchange Matching): Given a matching ι and two subsystem-sequential matching pairs (k′, i) and i.e., ι(k, d, k′) = i and and and i ≠ i′,[ If the conditions are met i.e.: Definition (bilateral exchange stable matching): When there does not exist ι is a bilateral exchange stable matching; The externality matching algorithm based on information timeliness perception mainly includes two stages: the initialization stage and the exchange matching stage; In the initialization phase, matching relationships are randomly established between subsystems while ensuring that the information age boundary value for system regulation instability is satisfied; secondly, each subsystem establishes its cost function for the interaction order according to Equation (27) and constructs a preference list by sorting in descending order; In the exchange matching stage, the subsystem k' currently matching with i sends a matching request for its most preferred ranking i'; for the subsystem currently matching with i' If the following three conditions are simultaneously satisfied: Then replace the original matching relationship ι with Otherwise, keep ι unchanged; remove i' from the preference list of subsystem k', and repeat the above matching process until there is no swap matching.

8. A scheduling method for a two-way aging-aware multi-energy cyber-physical system according to claim 1, characterized in that: The method for economic distributed optimal scheduling of the cyber-physical fusion multi-energy system is: During the d-th iteration of the consistency variable, when the multi-energy subsystem k receives the consistency variable transmitted by the subsystem k′, its corresponding age of information is a known quantity; by substituting into the mapping function formula (23) in the offline data-driven stage, the influence of the subsystem k′ can be calculated On this basis, using the online externality matching algorithm, the interaction cost is calculated according to formulas (27) and (28), and the preference list of the subsystem k′ is constructed accordingly; through the analysis of the preference list, it is judged whether the current interaction order is optimal; if not, the exchange matching process is carried out for optimization; after the matching optimization is completed, the multi-energy subsystem k will preferentially select the subsystem k′ with the largest influence and the lowest age of information to interact, so as to accelerate the convergence speed of the consistency variable; based on this, the weights of the subsystems k and k′ in the d-th iteration are: Because So its normalization is as follows: Combined weight of consistency variables and age of information The update process of the consistency variable in the d-th iteration can be expressed as: In the (d + 1)-th iteration, the multi-energy subsystem, through further calculation, obtains the optimized power output and adjusts it in combination with the upper and lower bound constraints to ensure that the power dispatch meets the physical domain constraints. At the same time, this iterative process dynamically integrates the two-way timeliness and subsystem influence in the information domain, realizing the economic distributed optimal dispatch of the cyber-physical integrated multi-energy system.

9. A two-way aging-aware multi-energy cyber-physical system scheduling system, characterized in that Including: Physical domain: including multi-energy subsystems such as distributed power sources, electric boilers, and electrical energy storage; The distributed power source is distributed photovoltaics. The electric boiler generates heat by consuming electric energy. The electrical energy storage realizes the transfer of electric energy in time by storing and releasing energy. The multi-energy subsystems meet the load demand of the system through coordinated scheduling; Information domain: including IoT terminals deployed on multi-energy subsystems and a cyber-physical fusion control device for two-way timeliness perception; among them, the IoT terminals collect temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge and discharge power, and electrical load power information, and upload it to the cyber-physical fusion control device for two-way timeliness perception; the cyber-physical fusion control devices for two-way timeliness perception exchange status information through edge-edge collaboration, realize iterative consistency variables, calculate the optimal output power of each subsystem, and send the corresponding scheduling instructions to the physical domain; the consistency information interaction between the sending end and the receiving end can adopt a two-way timeliness model, including four links: information sending, information processing and updating, waiting for information feedback, and information feedback.

10. A two-way age-aware multi-energy cyber-physical system scheduling device, deployed in the information domain, characterized in that Including: Communication module: used to receive multi-energy subsystem information such as temperature, humidity, active power, reactive power, voltage, current, heating power, energy storage charge and discharge power, and electrical load power uploaded by IoT terminals, exchange status information with other cyber-physical fusion control devices for two-way timeliness perception, perform consistency iteration, and at the same time be responsible for sending scheduling instructions to the physical domain; Scheduling Model Construction Module: Used to construct a physical domain multi - energy system model, a two - way timeliness model, and an information - physical fusion multi - energy system scheduling model considering two - way multi - energy timeliness; and decouple long - term constraints and short - term decisions based on Lyapunov theory; Dataset Construction Module: Used to construct a dataset for the fitting of the offline process influence function based on the two - way timeliness and scheduling cost obtained by the Scheduling Model Construction Module; Optimal Clustering Center Calculation Module Based on Kmeans: Used to calculate the optimal clustering center according to the offline dataset using the Kmeans algorithm and transmit the result to the Influence Function Fitting Module Based on Radial Basis Function; Influence Function Fitting Module Based on Radial Basis Function: Used to fit the influence function using a radial basis neural network according to the optimal clustering center and transmit the result to the Importance Weight Calculation Module; Matching Cost Function Calculation Module: Used to calculate the cost function according to the current interaction order and construct a preference list through descending order, and transmit the result to the Exchange Matching Module; Exchange Matching Module: Used to send matching requests to subsystems that meet the interaction matching relationship. If the request is satisfied, update the preference list and repeat the above matching process until there is no exchange matching, and transmit the result to the Interaction Order Adjustment Module; Interaction Order Adjustment Module: Used to adjust the information interaction order of subsystems in the consistency iteration according to the optimized result of the interaction order; Importance Weight Calculation Module: Used to calculate the importance weight of the current subsystem's consistency variable according to the influence function and the consistency information age, and transmit the result to the Consistency Variable Update Module; Consistency Variable Update Module: Used to update the current consistency variable according to the importance weight and transmit the result to the Convergence Judgment Module and the Scheduling Power Output Module; Convergence Judgment Module: Used to judge whether the difference in consistency variables before and after iteration meets the convergence condition. If it meets, output the convergence value of the consistency variable; otherwise, perform the next iteration; Scheduling Power Output Module: Used to calculate the scheduling power according to the convergence value of the consistency variable and issue the scheduling instruction to the physical domain via the communication module.

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