A multi-microgrid active power distribution network collaborative optimization method based on optical quantum acceleration

Abstract

The application belongs to the technical field of power system coordinated optimization, and relates to a multi-microgrid active distribution network collaborative optimization method based on optical quantum acceleration, comprising the following steps: 1, constructing a multi-microgrid active distribution network collaborative optimization framework based on optical quantum acceleration according to an optimization target and an actual situation; 2, constructing an optical quantum acceleration collaborative optimization model for the multi-microgrid active distribution network; 3, discretizing the multi-microgrid active distribution network collaborative operation model, and then establishing a quantum interpretable model on the basis; 4, decoupling the discretized model, using transmission line power deviation as the coupling relationship between the upper and lower subjects, and then proposing an accuracy adjustment method for the precision problem of the optical quantum computer, so as to identify the adaptive solver of the problem; the application not only retains the modeling flexibility of mixed integer optimization, but also fully develops the potential parallelism and acceleration capacity of quantum computing in processing large-scale combinatorial optimization problems.

Description

Technical Field

[0001] This invention belongs to the field of power system coordination and optimization technology, and relates to a multi-microgrid active distribution network coordination and optimization method based on quantum optical acceleration. Background Technology

[0002] With the large-scale integration of renewable energy into the power system, constructing multi-microgrid active distribution systems (MMADS) composed of multiple microgrids has become an important path to improve system flexibility and resilience. To achieve coordinated optimization among multiple microgrids and with the distribution system, the academic community has extensively studied distributed coordinated optimization methods, such as the Alternating Direction Multiplier Method (ADMM), the Consensus Algorithm (CA), and the Objective Concatenation Algorithm (ATC). Among these, the ATC algorithm is widely used in the optimization of multi-level, multi-agent systems due to its good convergence, simplified parameter settings, and parallel solution capabilities. Existing research has constructed coordinated optimization models between microgrids and active distribution systems based on the ATC framework, achieving good computational efficiency in various planning and operation scenarios. However, with the expansion of system scale and the increase in uncertainties, traditional algorithms face problems such as heavy computational burden and low solution efficiency when solving complex mixed integer problems. Therefore, in recent years, efforts have begun to explore the application of the parallel acceleration characteristics of quantum computing in the field of power system optimization. Hybrid quantum-classical (HQC) algorithms combine the advantages of quantum and classical optimization and have shown promising application prospects in various power optimization problems, such as unit combination and distribution network planning. Under the HQC framework, large-scale problems are decomposed and coordinated, providing a feasible path for the practical application of quantum computing in power systems.

[0003] However, existing MMADS coordinated optimization methods generally rely on classical optimization algorithms. When faced with large-scale, strongly coupled mixed-integer linear programming problems, they are prone to problems such as excessive computation time, getting trapped in local optima, and convergence failure, making it difficult to meet the needs of efficient decision-making in practical engineering. Although some studies have attempted to introduce quantum computing technology, the limited number of qubits and high noise levels in current quantum hardware limit their application to solving only small-scale problems and make it difficult to directly use them for the optimization of complex power systems.

[0004] Therefore, a method is needed to solve the above technical problems that is compatible with current optical quantum hardware with moderate noise levels while maintaining high computational performance. Summary of the Invention

[0005] The technical solution adopted by this invention to solve the technical problem is: a collaborative optimization method for multi-microgrid active distribution networks based on quantum optical acceleration, comprising the following steps: Step 1: Based on the optimization objectives and actual conditions, construct a collaborative optimization framework for multi-microgrid active distribution networks based on quantum computing, and clarify the tasks under the quantum computing environment; Step 2: Construct a quantum-accelerated collaborative optimization model for multi-microgrid active distribution networks. The multi-layer modeling includes the objective function, resource operation constraints, power balance constraints, and safety operation constraints of the active distribution network and each microgrid (MG). Step 3: Discretize and simplify the multi-microgrid active distribution network collaborative operation model to reduce the complexity of the model and the number of qubits used. Then, establish a quantum interpretable model based on this model to match the processing scale and format of current optical quantum devices. Step 4: Decouple the problem for the discretized model, using the transmission line power deviation as the coupling relationship between the upper and lower main components. Then, addressing the accuracy problem of optical quantum computers, propose an accuracy adjustment method to identify the suitable solver for the problem. The process is constructed using a quantum target cascade analysis algorithm, which is based on the basic steps of the target cascade algorithm and incorporates quantum algorithms.

[0006] Preferably, in step 1, the multi-microgrid active distribution network collaborative optimization framework based on quantum acceleration collects the predicted output data of each load in real time through measurement devices and information systems, and transmits the predicted output data synchronously to the control center of the distribution network (ADS) and multi-microgrid (MMG); the control center carries out multi-level collaborative optimization based on multi-subject characteristics and generates adjustment commands; within the range allowed by operating constraints, the control center formulates the optimal operation strategy of the distribution network.

[0007] Even better, after acquiring the forecast information, the control center of the multi-microgrid autonomously completes the internal resource optimization operation based on its own optimization goals and interaction deviations, implements a two-way collaborative control mechanism between the distribution network and the multi-microgrid, and promotes the operation of the distributed energy system.

[0008] Preferably, in step 2, the operating model of the active distribution network includes: (1) (2) (3) In equations (1) to (3), This indicates an improvement in the system's energy allocation efficiency. This indicates the energy consumption of MT during operation. This indicates the power balance adjustment term caused by energy interaction with multiple microgrids; Indicates the duration of the operation; Indicates the power of MT within the ADS range; n Indicates the total number of MTs; and These represent the natural gas characteristic parameters related to the MT operating status; This indicates the power generation efficiency of MT; and m Indicates tie-line power and total number of tie-lines; This indicates an external power guidance signal set based on a time series. The model constraints for active distribution networks include: (4) (5) In equations (4) and (5), , This represents the load forecast value within ADS. and These represent the upper and lower limits of power, respectively. This represents the power of A within the ADS range.

[0009] Preferably, in step 2, the microgrid's operating model includes: (6) (7) (8) (9) (10) (11) In equations (6) to (11), This indicates an optimization of the operating efficiency of multi-source systems. This indicates the energy conversion consumption of MT. This represents the energy exchange items with the distribution network. This indicates the charge and discharge loss of the ESS. This indicates the impact of load shedding regulation response. This indicates the control effect of DR adjustable load; and These represent the natural gas characteristic parameters related to the MT operating status; This indicates the power generation efficiency of MT. This indicates an external power guidance signal based on a time series setting. and These represent the power of the MT and the tie line within the MG, respectively. and Corresponding to the unit operating coefficient and power of ESS within MG, and This indicates the load shedding factor and load shedding power. and These represent the operating coefficient and power of the DR, respectively; The model constraints for microgrids include: (12) (13) (14) (15) (16) (17) (18) In equations (12) to (18), , and These represent the upper and lower limits of power within the MG, respectively. Indicates the power within Φ; , , and These represent the ESS's capacity, initial capacity, upper capacity limit, and lower capacity limit, respectively. and These represent the total demand response load and the maximum load shedding load, respectively.

[0010] Preferably, step 3 specifically includes the following sub-steps: Step 3-1: Model Discretization and Simplification. Given the accuracy limitations of medium-scale quantum hardware in noisy environments, a partially linear discretization method is used to balance model fidelity and computational feasibility. Step 3-2: Establishment of the quantum interpretable model and conversion of the scheduling discrete model based on ADS.

[0011] Preferably, step 4 specifically includes the following sub-steps: Step 4-1: Model decoupling. ADS and MMG are coupled through tie lines, connecting their respective optimization processes and preventing independent optimization. Step 4-2: Accuracy adjustment, controlling model accuracy to ensure computational feasibility; Step 4-3: Quantum target cascade analysis. Integrating quantum computing into the ATC framework, fully leveraging the advantages of parameter tuning efficiency and parallel computing, enhances the solution process for collaborative optimization of multi-microgrid active distribution networks.

[0012] More preferably, step 4-2 specifically includes the following sub-steps: Step 4-2-1: Calculate the maximum and minimum absolute values ​​of all non-zero elements in the QUBO matrix: (45) (46) if If violated, the matrix is ​​considered infeasible, and the program terminates. Indicates the modeling limit; Step 4-2-2: Transform the QUBO matrix into an equivalent Ising matrix. ;make and frequency function Defined as The number of occurrences; then, quantization resolution is introduced. And determine it as follows: (47) (48) in, This represents the minimum modeling step size. for Integer multiples of, and These are the upper and lower limits for matrix processing in CPQC, respectively. Step 4-2-3: Let Set frequency threshold and proportional threshold If equation (49) is violated, the model is considered infeasible and the process terminates; (49) Step 4-2-4: After excluding incompatible elements and zero elements, the absolute value Ising matrix ( )exist There are internal boundaries, and... Smooth out incompatible elements within the same element: (50) (51) in, It is a symbolic function; Step 4-2-5: By introducing a scaling factor ( ),Will through Transformation yields: (52) Step 4-2-6: Evaluate whether the matrix meets the required accuracy criteria: (53) In the formula, This represents the floor function. and These represent the upper and lower bounds of the rounding, respectively; if the constraints are met, the Ising matrix can be imported into an optical quantum computer; otherwise, its precision is insufficient.

[0013] More preferably, step 4-3 specifically includes the following sub-steps: Step 4-3-1: Initialize the running parameters and maximum number of iterations for each system component. Initial value of the penalty function multiplier and Precision adjustment parameters , , and coupling variables and Set the number of iterations Start the QATC algorithm; Step 4-3-2: Based on the coupled variable data, the distribution network control center constructs a QUBO problem with the objective function and the deviation from MMG as the objective.

[0014] Step 4-3-3: To ensure that the accuracy requirements are met, an accuracy adjustment strategy is implemented; if the accuracy requirements are not met, an optimized solver is first used to obtain a reliable solution; if the accuracy requirements are met, a quantum computer is used to solve the problem. Step 4-3-4: The multi-microgrid control center establishes a QUBO model and implements a precision adjustment strategy to guide the selection of a suitable solution method, and accelerates the processing speed by performing parallel computing across MGs; Step 4-3-5: Evaluate the convergence criteria; Convergence criterion I ensures that the power flow deviation on the connection line between the two subjects meets the specified requirements, and convergence criterion II ensures that the difference in the MMADS objective function between two consecutive iterations meets the specified conditions. Convergence criterion I is: (54) Convergence criterion II is: (55) In equations (54) and (55), This represents the overall optimization objective of MMADS. and Indicates the convergence limit; Step 4-3-6: Output the result when all conditions in Step 4-3-5 are met simultaneously; otherwise, update the penalty function multiplier, setting... And return to step 4-3-2; the principle of updating the penalty function multiplier is: (56) In equation (56), This represents the update coefficient of the quadratic term of the penalty function multiplier.

[0015] The beneficial effects of this invention are: 1. This invention addresses the shortcomings of traditional optimization methods, such as low computational efficiency and poor resource coordination, in handling large-scale discrete decision-making problems by constructing a unified quantum-accelerated collaborative optimization model for multi-microgrid active distribution networks. It proposes a hierarchical modeling and decoupling optimization strategy oriented towards a quantum computing architecture. Specifically, under a unified modeling framework, this patent collaboratively describes the objective functions and constraints of the distribution network and multi-microgrids, and divides them into multiple sub-problems with decoupling characteristics based on the problem structure. For sub-problems satisfying accuracy constraints, they are transformed into quadratic unconstrained optimization (QUBO) forms and solved in parallel on a quantum computing platform. For sub-problems not satisfying the embedding conditions, solvers such as GUROBI or CPLEX are used for efficient processing in a classical environment. This method not only retains the modeling flexibility of mixed-integer optimization but also fully leverages the potential parallelism and acceleration capabilities of quantum computing in handling large-scale combinatorial optimization problems.

[0016] 2. The "quantum-classical co-optimization" mechanism proposed in this invention can effectively alleviate the problem of exponential growth in computational complexity caused by the increase in problem size. At the same time, with the continuous improvement of quantum computing resources, this method has stronger scalability and foresight, providing a feasible solution with engineering application prospects for achieving efficient coordination and optimization of distributed resources in new power systems. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the photonic quantum acceleration framework for a multi-microgrid active distribution network collaborative optimization method based on photonic quantum acceleration, as described in this invention. Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0018] The related technologies of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figures 1-2 The multi-microgrid active distribution network collaborative optimization method based on quantum acceleration in this embodiment includes the following steps: Step 1: Based on the optimization objectives and actual conditions, construct a collaborative optimization framework for multi-microgrid active distribution networks based on quantum computing acceleration, and clarify the tasks under the quantum computing environment. (This framework includes the decomposition calculation principle of a hybrid quantum-classical algorithm.) like Figure 1 As shown, this framework collects real-time forecasted power output data of new energy sources and various loads through measurement devices and information systems, and transmits it synchronously to the control centers of the distribution network (ADS) and multiple microgrids (MMG). After obtaining the forecasted power output information of distributed power sources and loads, the control centers carry out multi-level collaborative optimization based on multi-entity characteristics: the distribution network control center comprehensively considers the power interaction between local operation and multiple microgrids, coordinates resources such as photovoltaic (PV), wind power (WG), energy storage systems (ESS), and gas turbines (MT), generates regulation commands, and formulates the optimal operation strategy of the distribution network within the allowable range of operational constraints to achieve overall operational efficiency. At the same time, after obtaining forecast information, the multiple microgrid control center also autonomously completes internal resource optimization operation based on its own optimization objectives and interaction deviations, realizing a two-way collaborative control mechanism between the distribution network and multiple microgrids, and promoting the efficient operation of the distributed energy system.

[0020] To fully leverage the potential advantages of quantum computing in the collaborative optimization of multi-microgrid active distribution networks, a distributed solution framework based on a target cascade analysis algorithm is proposed. This method decomposes and discretizes the original model, transforming it into a set of coupled hybrid binary subproblems. Subproblems satisfying quantum hardware precision constraints are solved in parallel within an optical quantum computing environment; subproblems not meeting precision requirements are solved efficiently using a classical computing environment. The subproblems are interconnected through power coupling relationships via transmission lines, and multiple rounds of iterative coordinated computation yield the optimal solution satisfying the global convergence condition, thus achieving an efficient integration of quantum computing and classical optimization in the collaborative optimization of multi-microgrid active distribution networks.

[0021] For subproblems that do not meet the precision constraints of quantum computing, mature optimization solvers (such as Gurobi or CPLEX) can be directly invoked to obtain the global optimal solution in mixed-integer linear programming (MILP) mode. For subproblems that meet the precision requirements, they need to be reconstructed into a form that can be embedded and deployed on quantum computing hardware, such as a quadratic unconstrained optimization problem (QUBO). During the quantum solution phase, users interact with the coherent optical quantum computing platform through a web user interface (UI). This platform consists of optical and electrical modules that work together to handle tasks such as modulation, interference, and measurement of photons, respectively. The system guides the evolution of the photon Hamiltonian towards the lowest energy state, ultimately forming a stable output represented by phase states (i.e., 0 state and π state), where the phase 0 state and π state correspond to the spin states {-1, +1} of the photon qubit, respectively. This establishes a mapping relationship between the decision variables {0, 1} and the photon qubit, achieving structural docking between the Ising model and the QUBO model, thereby obtaining the solution set of the optimization problem.

[0022] Step 2: Construct a quantum-accelerated collaborative optimization model for a multi-microgrid active distribution network. Multi-layer modeling includes objective functions, resource operation constraints, power balance constraints, and safety operation constraints for both the active distribution network and each microgrid (MG). At the active distribution network level: the objective function is to improve the system's energy allocation efficiency, including the energy consumption of the local gas turbine (MT) and the power balance adjustment term for energy interaction among multiple microgrids; it also considers power balance constraints and safety operation constraints such as component / line power limit exceedances. At the microgrid level: the optimization objective is the operating efficiency of the multi-source system, covering the energy conversion consumption of the MT, the energy exchange term with the distribution network, the charging and discharging losses of energy storage (ESS), the impact of load shedding response, and the control impact of adjustable loads (DR) when necessary; it also considers power balance constraints, power limit exceedance constraints, and the operation constraints of various distributed resources.

[0023] (1) Active distribution network operation model The optimization goal of active distribution network operation is to improve the system's energy allocation efficiency. This includes the energy consumption of MT (Metal Transport Management System). ) and the power balance adjustment term caused by energy interaction with multiple microgrids ( ), such as (1)-(3): (1) (2) (3) In the formula, Indicates the duration of the operation; Indicates the power of MT within the ADS range; nIndicates the total number of MTs; and These represent the natural gas characteristic parameters related to the MT operating status; This indicates the power generation efficiency of MT; and m Indicates tie-line power and total number of tie-lines; This indicates an external power guidance signal set based on a time series. Model constraints include power balance constraints and tie lines ( ) and MT power over-limit constraints, such as (4)-(5): (4) (5) In the formula, , This represents the load forecast value within ADS. and These represent the upper and lower limits of power, respectively.

[0024] (2) Microgrid operation model The operational goal in MG is to optimize the operational efficiency of multi-source systems. ), including the energy conversion consumption of MT ( ), and power exchange items with the distribution network ( ), ESS charging and discharging losses ( ), the impact of load shedding regulation response ( ) and the control effects of DR adjustable load ( As shown in the following formula. (6) (7) (8) (9) (10) (11) In the formula, and These represent the power of the MT and the tie line within the MG, respectively; and Corresponding to the unit operating coefficient and power of the ESS within the MG; and This indicates the load shedding factor and load shedding power; and These represent the operating coefficient and power of the DR, respectively.

[0025] The constraints of the model are shown in (12)-(18), including ESS, DR, MT, tie line operation constraints, power balance constraints and load shedding constraints.

[0026] (12) (13) (14) (15) (16) (17) (18) In the formula, , and Represents the upper and lower limits of power within the MG; , , and These represent the ESS's capacity, initial capacity, upper capacity limit, and lower capacity limit, respectively. and These represent the total demand response load and the maximum load shedding load, respectively.

[0027] In fact, this model also supports considering more factors, such as electric vehicles, mobile energy storage, or other distributed power sources, simply by replacing the corresponding constraints in this model.

[0028] Step 3: Discretize and simplify the multi-microgrid active distribution network collaborative operation model to reduce the complexity of the model and the number of qubits used. Then, establish a quantum interpretable model based on this model to match the processing scale and format of current optical quantum devices.

[0029] Step 3-1: Model Discretization and Simplification Given the accuracy limitations of noisy intermediate-scale (NISQ) quantum hardware, a partially linear discretization method is employed to balance model fidelity and computational feasibility. Within the ADS framework, and The discretization is as follows: (19) (20) In the formula, This represents the discretization process, while , , These are binary variables introduced to facilitate the discretization of variables within ADS. , and This indicates the step size associated with the corresponding binary variable.

[0030] Within the MG framework, variables , , , and Discretization is as follows: (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In the formula, , , , , and These are binary variables introduced to facilitate the discretization of the internal variables of MG. , , , , , and This indicates the step size associated with the corresponding binary variable. This corresponds to the initial capacity after discretization.

[0031] At this stage, a discretized multi-microgrid active distribution network collaborative operation model was established, and the coupling between ADS and multi-microgrid (MMG) was realized through tie-line power exchange. To simplify the model, a redundancy constraint identification method was introduced, in which the tie-line power limit in ADS is determined by (5) and (19), as shown below: (27) (28) (29) Redundant constraints are eliminated by comparing the upper and lower limits at each time step, typically without the need for slack variables. This method allows for the adjustment or omission of constraints related to DR, MT, and tie lines, simplifying the model without affecting the optimal solution.

[0032] Step 3-2: Establishment of the quantum interpretable model To formulate the problem in the QUBO format compatible with quantum computers, the ADS-based scheduling discrete model transformation process is as follows: (30) (31) (32) (33) In the formula, , , And represent the corresponding penalty factors; , , and Corresponding to the Hamiltonian; and This represents the remaining time set after eliminating redundant constraints; and These are slack variables; and This represents the length of the corresponding slack variable; and This indicates the maximum conservatism of the corresponding constraint.

[0033] Similarly, the quantum interpretable model of MG is defined as follows: (34) (35) (36) (37) (38) (39) (40) (41) The terminology remains consistent with previous explanations. This stage established a quantum-interpretable model of MMADS, laying the foundation for the subsequent implementation of the HQC algorithm.

[0034] Step 4: To decouple the problem in the discretized model, transmission line power deviation is used as the coupling relationship between the upper and lower main components. Then, addressing the accuracy problem of optical quantum computers, an effective accuracy adjustment method is proposed to identify the suitable solver for the problem. The construction process of the Quantum Target Cascade Analysis (QATC) algorithm is based on the basic steps of the target cascade algorithm, with improvements and the embedding of quantum algorithms.

[0035] Step 4-1: Model Decoupling ADS and MMG are coupled via tie lines, connecting their respective optimization processes and hindering their independent optimization. To accommodate the ATC framework, tie line power is modeled as a virtual load in ADS (…). In MMG, it is modeled as virtual power ( This achieves decoupling optimization and convergence through iterative coordination. An augmented Lagrangian penalty term is introduced into the ADS objective to address the discrepancy between virtual load and power. The resulting quantum interpretable model is as follows: (42) In the formula, and These represent the linear and quadratic coefficients of the penalty function, respectively. This penalty function modifies the optimization process by introducing linear and quadratic biases into the objective function.

[0036] Similarly, the resulting quantum interpretable model for MG is as follows: (43) Therefore, equation (42) characterizing the operation of ADS and equation (43) describing the operation of MG are reformulated to be compatible with quantum computing architecture and can be solved in an alternating iterative manner.

[0037] Step 4-2, Precision Adjustment Optical quantum computers only support [ The Ising matrix, an 8-bit integer within the range of [127, 128], requires strict control of model accuracy to ensure computational feasibility. For ease of subsequent discussion, the generalized QUBO matrix corresponding to the quantum interpretable model is shown below: (44) Step 4-2-1: Calculate the maximum and minimum absolute values ​​of all non-zero elements in the QUBO matrix: (45) (46) if If violated, the matrix is ​​considered infeasible, and the program terminates. This indicates the modeling limit.

[0038] Step 4-2-2: Transform the QUBO matrix into an equivalent Ising matrix ( ).make and frequency function Defined as The number of times it appears. Then, quantization resolution is introduced. And determine it as follows: (47) (48) in, This represents the minimum modeling step size. Usually chosen as Integer multiples of , determined by appropriate enumeration. and These are the upper and lower limits for matrix processing in CPQC. Step 4-2-3: Let Let the frequency threshold be ( ) and proportional threshold ( If the following is violated, the model is considered infeasible and the process is terminated.

[0039] (49) Step 4-2-4: After excluding incompatible elements and zero elements, the absolute value Ising matrix ( )exist It is internally bounded, as defined in equations (50) and (51). Subsequently, for... The corresponding incompatible elements are smoothed out, as shown below: (50) (51) in, It is a symbolic function.

[0040] Step 4-2-5: By introducing a scaling factor ( ),Will through The transformation yields the following relationship: (52) Step 4-2-6: Evaluate whether the matrix meets the required accuracy criteria, as shown below. (53) In the formula, This represents the floor function. and These represent the upper and lower bounds of the rounding, respectively. If the constraints are met, the Ising matrix can be imported into an optical quantum computer; otherwise, its precision is insufficient.

[0041] Step 4-3: Quantum Target Cascade Analysis Method Integrating quantum computing into the ATC framework fully leverages its advantages in parameter tuning efficiency and parallel computing, thereby enhancing the solution process for collaborative optimization of multi-microgrid active distribution networks. The specific implementation steps are as follows: Step 4-3-1: Initialize the running parameters and maximum number of iterations for each system component. ), the initial value of the penalty function multiplier ( and ), precision adjustment parameters ( , , ) and coupling variables ( and Set the number of iterations. Start the QATC algorithm.

[0042] Step 4-3-2: Based on the coupled variable data, the distribution network control center constructs a QUBO problem in the form of equation (42) with the objective function and the deviation from MMG as the objective.

[0043] Step 4-3-3: To ensure the accuracy requirements are met, the accuracy adjustment strategy proposed in the previous section is adopted. If the accuracy requirements are not met, an optimized solver is used to obtain a reliable solution. Conversely, if the accuracy requirements are met, a quantum computer is used to solve the problem.

[0044] Step 4-3-4: The multi-microgrid control center establishes the QUBO model based on formula (43), executes the accuracy adjustment strategy to guide the selection of the appropriate solution method, and accelerates the processing speed by performing parallel calculations across MGs.

[0045] Step 4-3-5: Evaluate the convergence criteria. Convergence criterion I (defined in formula (54)) ensures that the power flow deviation on the connection line between the two subjects meets the specified requirements. Convergence criterion II (defined in formula (55)) ensures that the difference in the MMADS objective function between two consecutive iterations meets the specified conditions.

[0046] (54) (55) In the formula, This represents the overall optimization objective of MMADS. and This represents the convergence limit.

[0047] Step 4-3-6: Output the result when all conditions in Step 4-3-5 are met simultaneously; otherwise, update the penalty function multiplier, setting... Then return to step 4-3-2. The principle of updating the penalty function multiplier is as follows: (56) In the formula, This represents the update coefficient of the quadratic term of the penalty function multiplier.

[0048] In summary, this invention collaboratively describes the objective functions and constraints of distribution networks and multiple microgrids within a unified modeling framework, and divides the problem into multiple sub-problems with decoupling characteristics based on the problem structure. For sub-problems that satisfy accuracy constraints, they are transformed into quadratic unconstrained optimization (QUBO) forms and solved in parallel on a quantum computing platform. For sub-problems that do not satisfy the embedding conditions, solvers such as GUROBI or CPLEX are used to efficiently process them in a classical environment. This invention not only retains the modeling flexibility of mixed integer optimization, but also fully leverages the potential parallelism and acceleration capabilities of quantum computing in handling large-scale combinatorial optimization problems.

[0049] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A collaborative optimization method for multi-microgrid active distribution networks based on quantum optical acceleration, characterized in that, Includes the following steps: Step 1: Based on the optimization objectives and actual conditions, construct a collaborative optimization framework for multi-microgrid active distribution networks based on quantum computing, and clarify the tasks under the quantum computing environment; Step 2: Construct a quantum-accelerated collaborative optimization model for the active distribution network of multiple microgrids. The multi-layer modeling includes the objective function, resource operation constraints, power balance constraints, and safety operation constraints of the active distribution network and each microgrid. Step 3: Discretize and simplify the multi-microgrid active distribution network collaborative operation model to reduce the complexity of the model and the number of qubits used. Then, establish a quantum interpretable model based on this model to match the processing scale and format of current optical quantum devices. Step 4: Decouple the problem for the discretized model, using the transmission line power deviation as the coupling relationship between the upper and lower main components. Then, addressing the accuracy problem of optical quantum computers, propose an accuracy adjustment method to identify the suitable solver for the problem. The process is constructed using a quantum target cascade analysis algorithm, which is based on the basic steps of the target cascade algorithm and incorporates quantum algorithms.

2. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 1, characterized in that, In step 1, the multi-microgrid active distribution network collaborative optimization framework based on quantum acceleration collects the predicted output data of each load in real time through measurement devices and information systems, and transmits the predicted output data synchronously to the control center of the distribution network and the multi-microgrid; the control center carries out multi-level collaborative optimization based on multi-subject characteristics and generates adjustment commands; within the range allowed by operating constraints, the control center formulates the optimal operation strategy of the distribution network.

3. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum optical acceleration according to claim 2, characterized in that, After acquiring forecast information, the control center of the multi-microgrid autonomously completes internal resource optimization operation based on its own optimization goals and interaction deviations, implements a two-way collaborative control mechanism between the distribution network and the multi-microgrid, and promotes the operation of the distributed energy system.

4. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 1, characterized in that, In step 2, the operating model of the active distribution network includes: (1) (2) (3) In equations (1) to (3), This indicates an improvement in the system's energy allocation efficiency. This indicates the energy consumption of MT during operation. This indicates the power balance adjustment term caused by energy interaction with multiple microgrids; Indicates the duration of the operation; Indicates the power of MT within the ADS range; n Indicates the total number of MTs; and These represent the natural gas characteristic parameters related to the MT operating status; This indicates the power generation efficiency of MT; and m Indicates tie-line power and total number of tie-lines; This indicates an external power guidance signal set based on a time series. The model constraints for the active distribution network include: (4) (5) In equations (4) and (5), , This represents the load forecast value within ADS. and These represent the upper and lower limits of power, respectively. This represents the power of A within the ADS range.

5. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 1, characterized in that, In step 2, the operating model of the microgrid includes: (6) (7) (8) (9) (10) (11) In equations (6) to (11), This indicates an optimization of the operating efficiency of multi-source systems. Indicates the energy conversion consumption of MT. This represents the energy exchange items with the distribution network. This indicates the charge and discharge loss of the ESS. This indicates the impact of load shedding regulation response. This indicates the control effect of DR adjustable load; and These represent the natural gas characteristic parameters related to the MT operating status; This indicates the power generation efficiency of MT. This indicates an external power guidance signal based on a time series setting. and These represent the power of the MT and the tie line within the MG, respectively. and Corresponding to the unit operating coefficient and power of ESS within MG, and This indicates the load shedding factor and load shedding power. and These represent the operating coefficient and power of the DR, respectively. The model constraints for the microgrid include: (12) (13) (14) (15) (16) (17) (18) In equations (12) to (18), , and These represent the upper and lower limits of power within the MG, respectively. Indicates the power within Φ; , , and These represent the ESS's capacity, initial capacity, upper capacity limit, and lower capacity limit, respectively. and These represent the total demand response load and the maximum load shedding load, respectively.

6. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 1, characterized in that, Step 3 specifically includes the following sub-steps: Step 3-1: Model Discretization and Simplification. Given the accuracy limitations of medium-scale quantum hardware in noisy environments, a partially linear discretization method is used to balance model fidelity and computational feasibility. Step 3-2: Establishment of the quantum interpretable model and conversion of the scheduling discrete model based on ADS.

7. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 1, characterized in that, Step 4 specifically includes the following sub-steps: Step 4-1: Model decoupling. ADS and MMG are coupled through tie lines, connecting their respective optimization processes and preventing independent optimization. Step 4-2: Accuracy adjustment, controlling model accuracy to ensure computational feasibility; Step 4-3: Quantum target cascade analysis. Integrating quantum computing into the ATC framework, fully leveraging the advantages of parameter tuning efficiency and parallel computing, enhances the solution process for collaborative optimization of multi-microgrid active distribution networks.

8. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum acceleration according to claim 7, characterized in that, Step 4-2 specifically includes the following sub-steps: Step 4-2-1: Calculate the maximum and minimum absolute values ​​of all non-zero elements in the QUBO matrix: (45) (46) if If violated, the matrix is ​​considered infeasible, and the program terminates. Indicates the modeling limit; Step 4-2-2: Transform the QUBO matrix into an equivalent Ising matrix. ;make and frequency function Defined as The number of occurrences; then, quantization resolution is introduced. And determine it as follows: (47) (48) in, This represents the minimum modeling step size. for Integer multiples of, and These are the upper and lower limits for matrix processing in CPQC, respectively. Step 4-2-3: Let Set frequency threshold and proportional threshold If equation (49) is violated, the model is considered infeasible and the process terminates; (49) Step 4-2-4: After excluding incompatible elements and zero elements, the absolute value Ising matrix ( )exist There are internal boundaries, and... Smooth out incompatible elements within the same element: （50） （51） in, It is a symbolic function; Step 4-2-5: By introducing a scaling factor ( ),Will through Transformation yields: (52) Step 4-2-6: Evaluate whether the matrix meets the required accuracy criteria: (53) In the formula, This represents the floor function. and These represent the upper and lower bounds of the rounding, respectively; if the constraints are met, the Ising matrix can be imported into an optical quantum computer; otherwise, its precision is insufficient.

9. The method for collaborative optimization of multi-microgrid active distribution networks based on quantum optical acceleration according to claim 7, characterized in that, Step 4-3 specifically includes the following sub-steps: Step 4-3-1: Initialize the running parameters and maximum number of iterations for each system component. Initial value of the penalty function multiplier and Precision adjustment parameters , , and coupling variables and Set the number of iterations Start the QATC algorithm; Step 4-3-2: Based on the coupled variable data, the distribution network control center constructs a QUBO problem with the objective function and the deviation from MMG as the objectives; Step 4-3-3: To ensure that the accuracy requirements are met, an accuracy adjustment strategy is implemented; if the accuracy requirements are not met, an optimized solver is first used to obtain a reliable solution; if the accuracy requirements are met, a quantum computer is used to solve the problem. Step 4-3-4: The multi-microgrid control center establishes a QUBO model and implements a precision adjustment strategy to guide the selection of a suitable solution method, and accelerates the processing speed by performing parallel computing across MGs; Step 4-3-5: Evaluate the convergence criteria; Convergence criterion I ensures that the power flow deviation on the connection line between the two subjects meets the specified requirements, and convergence criterion II ensures that the difference in the MMADS objective function between two consecutive iterations meets the specified conditions. Convergence criterion I is: (54) The convergence criterion II is as follows: (55) In equations (54) and (55), This represents the overall optimization objective of MMADS. and Indicates the convergence limit; Step 4-3-6: Output the result when all conditions in step 4-3-5 are met simultaneously; Otherwise, update the penalty function multiplier, let... And return to step 4-3-2; the principle of updating the penalty function multiplier is as follows: (56) In equation (56), This represents the update coefficient of the quadratic term of the penalty function multiplier.

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