Distributed-based energy collaborative scheduling method and system for multiple service areas of expressway

Abstract

The application relates to a kind of based on distributed expressway multi-service area energy collaborative scheduling method and system, including the energy system based on each service area, establish the global optimization model of multi-service area collaborative scheduling;Using distributed optimization algorithm, the global optimization model is decomposed into multiple local sub-problems capable of parallel solving, each local sub-problem corresponds to a service area, and the global constraint condition is decoupled into each local sub-problem by introducing decoupling parameter;Based on current coordination information, independently solve local sub-problems and report local boundary information, after summarizing local boundary information, according to global optimization model, update the coordination information, and distribute the updated coordination information to each service area until the optimal local decision variable obtained by each service area, according to the optimal local decision variable, generate respective energy scheduling plan and execute.The application can realize the efficient, real-time, robust collaborative scheduling of multi-service area.

Description

Technical Field

[0001] This invention belongs to the field of transportation and energy integration technology, specifically relating to a distributed energy collaborative scheduling method and system for multiple service areas on highways. Background Technology

[0002] With the increasing popularity of electric vehicles, the charging demand at highway service areas is growing rapidly. Many service areas have already deployed photovoltaic, energy storage, and charging piles, forming small-scale photovoltaic-storage-charging microgrids. Currently, energy management in most service areas is still in a "lone wolf" stage, with little consideration given to the complementary capabilities between adjacent service areas and the coupling constraints of power grid lines.

[0003] Existing multi-service area collaborative scheduling mostly adopts centralized optimization, which involves uploading detailed models, photovoltaic forecast data, load data, and energy storage status of all service areas to a central cloud platform, where the cloud platform calculates the globally optimal solution. This approach encounters several problems in practical engineering: First, operators of individual service areas are often unwilling to share detailed energy consumption data with third parties due to data privacy concerns; second, there may be hundreds of service areas along a highway, resulting in a large volume of data being uploaded, placing a significant burden on communication networks, especially in remote areas with poor fiber optic coverage; third, the computational load of the global optimization problem increases dramatically with the number of service areas, leading to excessively long solution times that cannot meet the requirements of real-time scheduling; fourth, if the cloud platform fails, the entire scheduling system collapses. Summary of the Invention

[0004] To address the challenge of effectively utilizing the complementary capabilities of adjacent service areas and satisfying the coupling constraints of power grid lines in the coordinated scheduling of photovoltaic-storage-charging microgrids in highway service areas, while protecting the privacy of energy consumption data in each service area, reducing communication network bandwidth pressure, minimizing the computational burden on the central cloud platform, and avoiding single-point failure risks, this invention provides a distributed energy coordinated scheduling method and system for multiple highway service areas.

[0005] In a first aspect, the present invention provides a distributed energy collaborative scheduling method for multiple service areas on highways, comprising the following steps: Based on the energy systems of each service area, a global optimization model for coordinated scheduling of multiple service areas is established. The global optimization model takes the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization objective, and sets global constraints involving the coupling relationship between multiple service areas. The global optimization model is decomposed into multiple local subproblems that can be solved in parallel using a distributed optimization algorithm. Each local subproblem corresponds to a service area. The global constraints are decoupled to each local subproblem by introducing decoupling parameters. The following operations are performed iteratively until the convergence condition is met: Each service area independently solves its local subproblem based on the current coordination information and reports its local boundary information; after summarizing the local boundary information of all service areas, the coordination information is updated according to the global optimization model, and the updated coordination information is sent to each service area; based on the optimal local decision variables finally obtained by each service area, its own energy scheduling plan is generated and executed.

[0006] Furthermore, the iterative execution and coordination information update process includes: under the current decoupling parameters, each service area independently and in parallel solves its own local subproblem to obtain the current local decision variables and local boundary variable estimates; based on the local boundary variable estimates reported by each service area, the global constraint-related information is updated; based on the updated global constraint-related information, the decoupling parameters are updated, and the updated global constraint-related information and the decoupling parameters are sent to each service area as new coordination information.

[0007] Furthermore, the method also includes: establishing an energy system model for each service area along the highway, wherein the energy system model includes local decision variables and local operating indicators; wherein the local decision variables are a set of operating parameters that are autonomously controlled within each service area and obtained by solving local sub-problems, and the local operating indicators are quantitative standards for the operation of the energy system of each service area.

[0008] Furthermore, each service area independently and in parallel solves its own local sub-problems, including: the local control unit of each service area independently and in parallel solves the corresponding local sub-problems according to the current coordination information issued by the central coordination unit, and outputs the operation plan of each device in the service area and the global coupling boundary information related to the service area.

[0009] Furthermore, updating the decoupling parameters includes: cumulatively correcting the decoupling parameters corresponding to each service area based on the deviation between the estimated local boundary variables reported by each service area and the updated global constraint information, so that the solution of the local subproblem in the next iteration tends to reduce the deviation.

[0010] Furthermore, the global constraints include at least one of the following: an upper limit constraint on the sum of the power exchanged between each service area and the upper-level power grid, a capacity constraint on the power transmission of the tie lines between adjacent service areas, and a voltage consistency constraint at the boundary nodes.

[0011] Furthermore, the distributed optimization algorithm is the alternating direction multiplier method, the decoupling parameters include auxiliary variables and Lagrange multipliers, and the local boundary information includes at least one of the following: the interactive power estimate of each service area, the tie-line power estimate, and the boundary node voltage estimate.

[0012] Furthermore, the local operating indicators include at least one of the following: electricity cost, voltage fluctuation range, renewable energy absorption rate, and energy storage state of charge deviation; and different service areas can independently set their own local operating indicators and their weights.

[0013] Secondly, the present invention also provides a distributed highway multi-service area energy collaborative scheduling system, comprising: The first processing module is used to establish a global optimization model for coordinated scheduling of multiple service areas based on the energy systems of each service area. The global optimization model takes the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization target, and sets global constraints involving the coupling relationship between multiple service areas. The second processing module is used to decompose the global optimization model into multiple local subproblems that can be solved in parallel using a distributed optimization algorithm. Each local subproblem corresponds to a service area, and the global constraints are decoupled to each local subproblem by introducing decoupling parameters. The third processing module is used to iteratively perform the following operations until the convergence condition is met: enabling each service area to independently solve its own local subproblems based on the current coordination information and report its local boundary information; after summarizing the local boundary information of all service areas, updating the coordination information according to the global optimization model, and distributing the updated coordination information to each service area; and generating and executing the respective energy scheduling plan based on the optimal local decision variables finally obtained by each service area.

[0014] Furthermore, the third processing module is specifically used to: under the current decoupling parameters, enable each service area to independently and in parallel solve its own local subproblems to obtain the current local decision variables and local boundary variable estimates; update the global constraint-related information based on the local boundary variable estimates reported by each service area; update the decoupling parameters based on the updated global constraint-related information, and send the updated global constraint-related information and the decoupling parameters as new coordination information to each service area.

[0015] Furthermore, the first processing module is also used to: establish an energy system model for each service area along the highway, wherein the energy system model includes local decision variables and local operating indicators; wherein the local decision variables are a set of operating parameters that are autonomously controlled within each service area and obtained by solving local sub-problems, and the local operating indicators are quantitative standards for the operation of the energy system in each service area.

[0016] Furthermore, the third processing module is used to enable each service area to solve its own local sub-problems independently and in parallel, including: enabling the local control unit of each service area to solve the corresponding local sub-problems independently and in parallel according to the current coordination information issued by the central coordination unit, and outputting the operation plan of each device in the service area and the global coupling boundary information related to the service area.

[0017] Furthermore, updating the decoupling parameters includes: cumulatively correcting the decoupling parameters corresponding to each service area based on the deviation between the estimated local boundary variables reported by each service area and the updated global constraint information, so that the solution of the local subproblem in the next iteration tends to reduce the deviation.

[0018] Furthermore, the global constraints include at least one of the following: an upper limit constraint on the sum of the power exchanged between each service area and the upper-level power grid, a capacity constraint on the power transmission of the tie lines between adjacent service areas, and a voltage consistency constraint at the boundary nodes.

[0019] Furthermore, the distributed optimization algorithm is the alternating direction multiplier method, the decoupling parameters include auxiliary variables and Lagrange multipliers, and the local boundary information includes at least one of the following: the interactive power estimate of each service area, the tie-line power estimate, and the boundary node voltage estimate.

[0020] Furthermore, the local operating indicators include at least one of the following: electricity cost, voltage fluctuation range, renewable energy absorption rate, and energy storage state of charge deviation; and different service areas can independently set their own local operating indicators and their weights.

[0021] Thirdly, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the distributed optimization-based energy collaborative scheduling method for multiple service areas of highways as described in the first aspect.

[0022] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the distributed highway multi-service area energy collaborative scheduling method described in the first aspect.

[0023] The distributed energy coordination scheduling method for multiple service areas on highways provided by this invention requires each service area to report only its local boundary information, without uploading detailed internal data such as photovoltaic forecasts, load curves, and energy storage status, effectively addressing operators' concerns about data privacy. Distributed optimization involves exchanging only a small amount of coordination information, resulting in a significantly smaller communication data volume compared to centralized solutions, making it suitable for practical engineering conditions such as insufficient fiber optic coverage and limited bandwidth along highways.

[0024] The global optimization problem is decomposed into multiple local subproblems that can be solved in parallel. Each service area solves the problem independently and in parallel. The computational load increases linearly with the number of service areas rather than exponentially, and the solution time is greatly shortened, which can meet the real-time scheduling requirements.

[0025] Employing a distributed architecture, the central coordination unit is only responsible for information aggregation and coordination parameter updates, while the local control units in each service area can independently solve sub-problems. Even if the central unit or individual service areas fail, the remaining service areas can still operate normally, avoiding the risk of system paralysis due to a single point of failure in a centralized solution.

[0026] By introducing decoupling parameters and iterative coordination mechanisms, global constraints involving multiple service areas can be effectively handled. These constraints include the upper limit constraint on the sum of power interactions between each service area and the upper-level grid, the capacity constraint on power transmission through tie lines between adjacent service areas, and the voltage consistency constraint at boundary nodes. This ensures that coordinated dispatch meets the actual physical limitations of the power grid. Different service areas can independently set their own local operating indicators and their weights, fully reflecting the autonomous decision-making preferences of each service area operator.

[0027] By employing mature distributed optimization algorithms such as the alternating direction multiplier method, and by accumulating and correcting the decoupling parameters, the deviation between local boundary variables and global constraints is gradually reduced during the iterative process, eventually converging to a globally optimal or near-optimal cooperative scheduling scheme. Attached Figure Description

[0028] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the distributed multi-service area energy collaborative scheduling method for highways provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating a distributed multi-service area energy collaborative scheduling method for highways provided in another embodiment of the present invention. Figure 3 This is a schematic diagram of a distributed highway multi-service area energy collaborative scheduling system provided in another embodiment of the present invention; Figure 4 It is an electronic device. Detailed Implementation

[0029] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0031] With the continuous increase in the number of electric vehicles, the charging demand at highway service areas has experienced explosive growth. To address this trend, many service areas have successively deployed distributed photovoltaic (PV) systems, energy storage systems, and DC fast charging piles, forming typical small-scale PV-storage-charging microgrids. However, in terms of actual operation, the energy management of most service areas remains in a "separate management" stage. Each service area independently formulates its dispatch plan based solely on its own PV output, load demand, and energy storage status, rarely considering the synergistic capabilities between adjacent service areas in terms of power complementarity and energy storage mutual support, and failing to incorporate the voltage, power, and other coupling constraints resulting from different service areas connecting to the same distribution network line into a unified dispatch framework.

[0032] To address the aforementioned shortcomings, existing technologies have proposed using a centralized optimization method to achieve collaborative scheduling across multiple service areas. The basic idea is to upload all detailed models of all service areas, including photovoltaic module parameters, energy storage charging and discharging characteristics, charging pile load models, high-resolution photovoltaic forecast data, real-time and historical load data, energy storage state of charge, and other extensive information, to a central cloud platform. The cloud platform then constructs a large-scale optimization problem based on the global data, solves it to obtain the globally optimal scheduling scheme, and then distributes it to each service area for execution. However, this centralized architecture faces a series of unavoidable engineering challenges in real-world highway scenarios: First, data privacy issues are prominent. The operators of different service areas are often different business entities, and their detailed energy consumption data is considered core business information. Handing over this data entirely to a third-party cloud platform not only lacks a foundation of trust but may also violate data security protection regulations. In practice, many service area operators explicitly refuse to participate in collaborative scheduling projects that require the uploading of detailed operational data, resulting in significant obstacles to the implementation of centralized solutions.

[0033] Secondly, the communication network bears a heavy burden. There are often hundreds of service areas along highways. If each service area uploads multi-dimensional time-series data at a refresh rate of minutes, the daily data volume can reach hundreds of megabytes. For service areas located in remote sections, with insufficient fiber optic coverage, or relying solely on 4G / 5G wireless communication, this continuous uploading of large amounts of data will cause serious communication bottlenecks, not only increasing operating costs but also potentially leading to data packet loss and increased latency during severe weather or peak holiday periods, directly threatening the real-time performance and reliability of dispatch instructions.

[0034] Third, the computational burden of global optimization explodes, making it difficult to meet real-time scheduling requirements. As the number of service areas increases, the dimensionality of decision variables and the number of constraints in centralized optimization problems grow non-linearly. When the number of service areas reaches tens or even hundreds, the solution time for the constructed mixed-integer programming or non-linear programming problems will rise sharply, often requiring several minutes or even tens of minutes to complete a single global optimization. However, photovoltaic power output and charging loads have strong randomness and volatility, and scheduling instructions typically need to be updated on a second- to minute-by-minute basis to effectively cope with rapid changes. The excessively long solution time makes centralized solutions unable to meet the time window requirements of real-time scheduling, ultimately causing the optimization scheme to lag behind actual operating conditions and significantly reducing the collaborative effect.

[0035] Fourth, the system is susceptible to single-point-of-failure risks. The centralized architecture heavily relies on the stable operation of the central cloud platform. If the cloud platform fails due to network attacks, software malfunctions, hardware damage, or human error, the entire multi-service area collaborative scheduling system will immediately lose its command center: each service area will be unable to obtain globally optimal scheduling instructions, forced to revert to a "lone wolf" local mode, and may even experience safety hazards such as power backflow and voltage exceeding limits due to a lack of coordination. This vulnerability of "single-point failure leading to global failure" is unacceptable for highway energy infrastructure requiring high reliability.

[0036] In summary, how to fully utilize the complementary capabilities of adjacent service areas and take into account the coupling constraints of power grid lines, while protecting the data privacy of each service area, reducing communication bandwidth pressure, and controlling computational complexity, to achieve efficient, real-time, and robust collaborative scheduling of multiple service areas, has become a key technical problem that urgently needs to be solved in the field of highway photovoltaic-storage-charging microgrids.

[0037] To address the problems of privacy leakage risks, heavy communication burden, computational complexity, and poor fault tolerance in existing centralized optimization methods for energy dispatching across multiple service areas on highways, this invention provides a collaborative dispatching method and system based on distributed optimization. This invention, while protecting the data privacy of each service area and reducing the communication burden, controls computational complexity to linear growth through distributed parallel solution, meeting real-time dispatching requirements. Simultaneously, it avoids the single-point-of-failure risk of centralized architectures, enhancing system robustness; and effectively handles grid coupling constraints between multiple service areas. Furthermore, each service area can flexibly set local differentiated operating indicators, and the algorithm guarantees convergence to the global optimum.

[0038] Figure 1 This is a flowchart illustrating the distributed multi-service area energy collaborative scheduling method for highways provided in an embodiment of the present invention. Figure 1 As shown, it includes the following steps: Step 1: Establish a global optimization model.

[0039] In this embodiment, a global optimization model for coordinated scheduling of multiple service areas is established based on the energy systems of each service area. This model uses the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization objective, and sets global constraints involving the coupling relationships between multiple service areas. Specifically, the local decision variables are the set of operating parameters autonomously controlled within each service area and obtained by solving local optimization sub-problems, such as energy storage charging and discharging power, photovoltaic dispatching power, and power interaction with the grid.

[0040] Local operating indicators are quantitative standards for the operation of the energy system in each service area, including one or more of the following: electricity cost, voltage fluctuation range, renewable energy absorption rate, or deviation of energy storage state of charge. Different service areas can independently set their own local operating indicators and their weights.

[0041] Global constraints include one or more of the following: upper limit constraint on the sum of power exchanged between each service area and the upper-level power grid, capacity constraint on power transmission through tie lines between adjacent service areas, and voltage consistency constraint at boundary nodes.

[0042] Step 2: Decompose into local subproblems.

[0043] In this embodiment, a distributed optimization algorithm is used to decompose the global optimization model into multiple local subproblems that can be solved in parallel. Each local subproblem corresponds to a service area, and the global constraints are decoupled to each local subproblem by introducing decoupling parameters. Preferably, the distributed optimization algorithm is the Alternating Direction Multiplier Method (ADMM), and the decoupling parameters include auxiliary variables and Lagrange multipliers.

[0044] Step 3: Distributed iterative solution.

[0045] In this embodiment of the application, each service area independently solves its local subproblem and reports its local boundary information based on the current coordination information. After summarizing the local boundary information, the coordination information is updated according to the global optimization model, and the updated coordination information is sent to each service area until each service area obtains the optimal local decision variables. Based on the optimal local decision variables, each service area generates its own energy scheduling plan and executes it.

[0046] Specifically, this step includes iteratively performing the following operations until the convergence condition is met: (a) Under the current decoupling parameters, each service area independently and in parallel solves its own local subproblems to obtain the current local decision variables and local boundary variable estimates.

[0047] Specifically, given the global coordination information issued by the central coordination unit, the local control units of each service area independently and in parallel solve their own part of the optimization problem, producing two types of output results: the operation plan of each device within the service area, and the information related to the global coupling constraints of the service area, namely the estimated value of the local boundary variables.

[0048] (b) Update global constraint-related information, such as global variables, based on the local boundary variable estimates for each of the service areas.

[0049] (c) Update the decoupling parameters based on the global constraint information, and distribute the updated global constraint information and the decoupling parameters to the service areas. Specifically, updating the decoupling parameters includes: cumulatively correcting the decoupling parameters for each service area based on the deviation between the estimated local boundary variables reported by each service area and the updated global constraint information, so that the solution of the local subproblem in the next iteration is more inclined to reduce the deviation.

[0050] The convergence condition can be that both the original residual and the dual residual are less than a preset threshold, or that the number of iterations reaches a preset maximum value.

[0051] Step 4: Execute the scheduling plan.

[0052] In the embodiments of this application, after iterative convergence, each service area generates and executes an energy storage charging and discharging plan, a photovoltaic dispatching plan, and an interaction plan with the power grid based on the finally obtained optimal local decision variables.

[0053] Figure 2 This is a flowchart illustrating the distributed multi-service area energy collaborative scheduling method for highways provided in an embodiment of the present invention. Figure 2 As shown, it includes the following steps: Step S1: Build the model.

[0054] In this embodiment, energy system models are established for each service area along the highway. Each service area is equipped with a photovoltaic power generation system, an energy storage system, a charging pile system, and a local energy management system (EM). The service areas are connected together via 10kV or 0.4kV power grid lines and can exchange power with the upper-level power grid through a common junction point.

[0055] Step S2: Establish a global optimization model.

[0056] In this embodiment, a global optimization model for multi-service area collaborative scheduling is established. Optimization variables include the energy storage charging and discharging power, photovoltaic dispatch power, power interaction with the grid, and actual output power of charging piles for each service area. The local objective function can be selected as minimum electricity cost, minimum voltage fluctuation, maximum renewable energy absorption, or a weighted combination of these objectives, as needed.

[0057] Among them, the global constraints may also include that the total power exchanged between all service areas and the upper-level power grid cannot exceed the demand limit given by the upper-level power grid, that is, the power transmitted by the tie line between adjacent service areas cannot exceed the thermal stability limit of the line; and that the voltage at the boundary nodes is consistent, that is, the voltage amplitude at both ends of the tie line should be equal.

[0058] Step S3: ADMM problem decomposition.

[0059] In this embodiment, the alternating direction multiplier method (ADMM) is used to decompose the global optimization model into multiple local subproblems that can be solved in parallel, with each subproblem corresponding to a service area. Auxiliary variables are introduced. Indicates service area The boundary variables could be, for example, the target value of the power interacting with the grid, and the Lagrange multipliers. The global constraints are decoupled to each local subproblem through the augmented Lagrangian function.

[0060] Step S4: Distributed iterative solution.

[0061] The cloud-side coordinator and the edge controllers of each service area work together to repeatedly execute the following sub-steps until convergence: Sub-step S41: The side controllers of each service area fix the current Lagrange multiplier. and global variables In this case, solve your own local subproblems independently and in parallel:

[0062] Obtain the local optimal decision variables and local boundary variable estimates .

[0063] in, It is a service area index, indicating the first... Service areas , It is an iteration count index; It is the first The service area is in the first The values ​​of local decision variables in each iteration, such as the output power of each device, the charging and discharging power of energy storage, and the power of electricity purchased and sold at the electricity price.

[0064] It is the first The local objective function for each service area, such as the operating cost function, electricity cost, maintenance cost, etc.

[0065] It is the first The coefficient matrix of each service area maps local decision variables to boundary variables related to the global coupling constraints of that service area.

[0066] It is the first The service area is in the first The estimated local boundary variables are calculated in the next iteration.

[0067] It is a penalty parameter that controls the step size of the Lagrange multiplier update, and is usually a positive number.

[0068] It is the first In the next iteration, with the first Lagrange multiplier vectors associated with the boundary constraints of each service area;

[0069] Sub-step S42: Each service area encrypts its local boundary variable estimates and uploads them to the cloud coordinator.

[0070] Sub-step S43: The cloud-side coordinator solves the following global variable update problem:

[0071] in, It is the first In the nth iteration Global variables for each service area.

[0072] It is the first The overall vector is formed by concatenating the global variables of all service areas in the next iteration.

[0073] This problem usually has an analytical solution, or can be solved with very little computation. Taking the global constraint "the sum of the interaction power of each service area does not exceed the demand limit" as an example, the update formula is:

[0074] in, It is the first In the nth iteration The estimated values ​​of boundary variables calculated locally for each service area.

[0075] Sub-step S44: The cloud-side coordinator updates the Lagrange multipliers according to the following formula:

[0076] Then the updated and The announcement was broadcast to all service areas.

[0077] Repeat the above iterations until the original residual is found. Both the dual residual and the dual residual are less than the preset threshold, or the maximum number of iterations is reached.

[0078] Step S5: Plan generation and execution.

[0079] In this embodiment of the application, after convergence, each service area generates an energy storage charging and discharging plan, a photovoltaic scheduling plan, and an interaction plan with the power grid based on the finally obtained local optimal decision variables, and then sends them to the energy storage converter, photovoltaic inverter, and charging pile controller for execution.

[0080] Compared to existing centralized methods, this invention has the following advantages: each service area only needs to upload the estimated values ​​of boundary variables, without exposing detailed internal data such as photovoltaic forecasts, load forecasts, and energy storage SOC, effectively protecting the commercial privacy of each operating entity.

[0081] Each iteration uploads very little data to each service area, reducing the total amount of communication data by more than 90% compared to the centralized method, which is fully adaptable to the actual situation of 4G / 5G networks along highways.

[0082] Each local subproblem can be solved in complete parallel, and the computation time does not increase significantly with the increase in the number of service areas, which can support the coordinated scheduling of hundreds of service areas.

[0083] If a communication failure occurs in a certain service area or on the cloud side, the remaining service areas can continue to collaborate or switch to local autonomous mode. A problem at a single point will not cause the entire system to crash.

[0084] Different service areas can independently set the weights of their local objective functions according to their own operational preferences. For example, some service areas value voltage stability more, while others value green electricity consumption more. This does not require uniformity across the entire network and is more in line with the diversity in actual operation.

[0085] Each service area only needs to upload estimated values ​​of boundary variables, such as interactive power and tie-line power, without needing to expose detailed internal data such as photovoltaic forecasts, load forecasts, and energy storage SOC, effectively protecting the commercial privacy of each operator.

[0086] Each iteration uploads very little data to each service area, reducing the total amount of communication data by more than 90% compared to the centralized method, which is fully adaptable to the actual situation of 4G / 5G networks along highways.

[0087] Each local subproblem can be solved in complete parallel, and the computation time does not increase significantly with the increase in the number of service areas, which can support the coordinated scheduling of hundreds of service areas.

[0088] If a communication failure occurs in a certain service area or on the cloud side, the remaining service areas can continue to collaborate or switch to local autonomous mode. A problem at a single point will not cause the entire system to crash.

[0089] Different service areas can independently set the weights of their local objective functions according to their own operational preferences (for example, some service areas value voltage stability more, while others value green electricity consumption more), without needing to be uniform across the entire network, which is more in line with the diversity in actual operation.

[0090] The present invention will be further described below with reference to the accompanying drawings and specific examples. It should be noted that the embodiments given herein are merely for the purpose of aiding understanding and do not constitute a limitation on the scope of the present invention.

[0091] The following is another embodiment of the distributed highway multi-service area energy collaborative scheduling system provided by the present invention. The highway multi-service area energy collaborative scheduling system includes: The cloud-side coordination unit, deployed on the cloud platform, includes a global variable update module, a decoupling parameter update module, a convergence judgment module, and a communication module. Multiple edge-side control units are deployed in each service area, each containing a local optimization solution module, a data acquisition module, an instruction execution module, and a communication module. A communication network connects the cloud-side coordination unit to each edge-side control unit. Each service area is equipped with a photovoltaic power generation system, an energy storage system, a charging pile system, and a local energy management system. Service areas are interconnected via grid interconnections and can exchange power with the upper-level grid through a point of common coupling.

[0092] The cloud-side coordination unit includes a global variable update module, a decoupling parameter update module, a convergence judgment module, and a communication module. Each edge-side control unit includes a local optimization solution module, a data acquisition module, an instruction execution module, and a communication module. A communication network (such as 4G / 5G, fiber optic) connects the cloud-side coordination unit to each edge-side control unit.

[0093] The distributed iterative solution process in this embodiment is as follows: Initialization: The cloud-side coordination unit sets initial decoupling parameters (e.g., Lagrange multipliers are zero vectors), initial global variables (e.g., the difference between predicted load and photovoltaic output in each service area), and sets penalty parameters. Convergence threshold And the maximum number of iterations, Kmax.

[0094] Iterative process (where k is the iteration index, k=0,1,2,…): Local parallel solution: The local control unit of each service area receives the current decoupling parameters (such as...) ) and global variables (such as Independent and parallel solution of local subproblems:

[0095] Obtain the local optimal decision variables and local boundary variable estimates .

[0096] Upload boundary information: Each service area will Uploaded to the cloud-side coordination unit.

[0097] Update global variables: The cloud-side coordination unit solves the least squares problem based on global constraints (such as total power demand limits) and updates the global variables. .

[0098] Update decoupling parameters: The cloud-side coordination unit updates the Lagrange multipliers using the following formula:

[0099] Broadcast distribution: The cloud-side coordination unit will update the global variables. and decoupling parameters The broadcast was sent to all service areas.

[0100] Convergence criterion: Calculate the first... The original residual of the next iteration and the The dual residual of the next iteration .like If the iteration fails, stop; otherwise, continue.

[0101] in, It is the maximum allowed number of iterations. It is the convergence threshold. Convergence is determined when both the original residual and the dual residual are less than this value.

[0102] It is a service area index, indicating the first... Service areas

[0103] in, It is a service area index, indicating the first... Service areas , It is an iteration count index; It is the first The service area is in the first The values ​​of local decision variables in each iteration, such as the output power of each device, the charging and discharging power of energy storage, and the power of electricity purchased and sold at the electricity price.

[0104] It is the first The local objective function for each service area, such as the operating cost function, electricity cost, maintenance cost, etc.

[0105] It is the first The coefficient matrix of each service area maps local decision variables to boundary variables related to the global coupling constraints of that service area.

[0106] It is the first The service area is in the first The estimated local boundary variables are calculated in the next iteration.

[0107] It is a penalty parameter that controls the step size of the Lagrange multiplier update, and is usually a positive number.

[0108] It is the first In the next iteration, with the first Lagrange multiplier vectors associated with the boundary constraints of each service area;

[0109] Plan Execution: After convergence, each service area will proceed according to the final... Generate a scheduling plan and send it to the local device for execution.

[0110] Local parallel solution: The local control unit of each service area receives the current decoupling parameters (such as...) ) and global variables (such as Independent and parallel solution of local subproblems:

[0111] Obtain the local optimal decision variables and local boundary variable estimates .

[0112] Upload boundary information: Each service area will Uploaded to the cloud-side coordination unit.

[0113] Update global variables: The cloud-side coordination unit solves the least squares problem based on global constraints (such as total power demand limits) and updates the global variables. .

[0114] Update decoupling parameters: The cloud-side coordination unit updates the Lagrange multipliers using the following formula:

[0115] Broadcast distribution: The cloud-side coordination unit will update the global variables. and decoupling parameters The broadcast was sent to all service areas.

[0116] Convergence criterion: Calculate the first... The original residual of the next iteration and the The dual residual of the next iteration .like and ,or If the iteration fails, stop; otherwise, continue.

[0117] Plan Execution: After convergence, each service area will proceed according to the final... Generate a scheduling plan and send it to the local device for execution.

[0118] The above embodiments effectively address operators' concerns about data privacy by requiring each service area to report only local boundary information, such as interactive power estimates, without uploading detailed internal data such as photovoltaic forecasts, load curves, and energy storage status. Distributed optimization exchanges only a small amount of coordination information, decoupling parameters and boundary variables. The amount of communication data is far less than that of centralized solutions, making it suitable for practical engineering conditions along highways, especially in remote sections where fiber optic coverage is insufficient and bandwidth is limited.

[0119] The global optimization problem is decomposed into multiple local subproblems that can be solved in parallel. Each service area solves the problem independently and in parallel. The computational load increases linearly with the number of service areas rather than exponentially, and the solution time is greatly shortened, which can meet the real-time scheduling requirements.

[0120] Employing a distributed architecture, the central coordination unit is only responsible for information aggregation and coordination parameter updates, while the local control units in each service area can independently solve sub-problems. Even if the central unit or individual service areas fail, the remaining service areas can still operate normally, avoiding the risk of "system paralysis due to a single point of failure" in centralized solutions.

[0121] By introducing decoupling parameters, such as Lagrange multipliers and iterative coordination mechanisms, global constraints involving multiple service areas can be effectively handled, including the upper limit constraint of the sum of the power interaction between each service area and the upper-level grid, the capacity constraint of the power transmission of tie lines between adjacent service areas, and the voltage consistency constraint of boundary nodes, ensuring that coordinated scheduling meets the actual physical limitations of the power grid.

[0122] Different service areas can independently set their own local operating indicators, such as electricity cost, voltage fluctuation, renewable energy absorption rate, and energy storage state of charge deviation, along with their weights, fully reflecting the autonomous decision-making preferences of each service area operator. Mature distributed optimization algorithms, such as the alternating direction multiplier method, are employed. By accumulating and correcting decoupling parameters, the iterative process gradually reduces the deviation between local boundary variables and global constraints, ultimately converging to a globally optimal or near-optimal collaborative scheduling scheme.

[0123] Figure 3This is a schematic diagram of a distributed highway multi-service area energy collaborative scheduling system according to another embodiment of the present invention. The distributed optimized highway multi-service area energy collaborative scheduling system includes: The first processing module is used to establish a global optimization model for coordinated scheduling of multiple service areas based on the energy systems of each service area. It takes the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization target, and sets global constraints involving the coupling relationship between multiple service areas. The second processing module is used to decompose the global optimization model into multiple local subproblems that can be solved in parallel using a distributed optimization algorithm. Each local subproblem corresponds to a service area, and the global constraints are decoupled to each local subproblem by introducing decoupling parameters. The third processing module is used to obtain the current coordination information of each service area, independently solve the local sub-problem and report the local boundary information, summarize the local boundary information, update the coordination information according to the global optimization model, and send the updated coordination information to each service area until each service area obtains the optimal local decision variable, and generates and executes its own energy scheduling plan according to the optimal local decision variable.

[0124] Furthermore, the third processing module is used to iteratively perform the following operations until the convergence condition is met: Under the current decoupling parameters, each service area independently and in parallel solves its own local subproblems to obtain the current local decision variables and local boundary variable estimates. Update global constraint information based on the estimated local boundary variables of each service area; Based on the global constraint information, the decoupling parameters are updated, and the updated global constraint information and the decoupling parameters are sent to the service area.

[0125] Furthermore, the first processing module is used to establish an energy system model for each server along the highway. The energy system model includes local decision variables and local operating indicators. The local decision variables are a set of operating parameters that are autonomously controlled within each service area and obtained by solving local optimization sub-problems. The local operating indicators are quantitative standards for the operation of the energy system of each server.

[0126] Furthermore, the third processing module, given the global coordination information issued by the central coordination unit, enables the local control units of each service area to independently and in parallel solve their respective optimization problems, thereby generating two types of output results: This refers to the operation plans of each device within the service area and the information related to the coupling constraints between the service area and the global environment.

[0127] Furthermore, the updated decoupling parameters include: based on the deviation between the estimated local boundary variables reported by each service area and the updated global constraint information, the decoupling parameters corresponding to each service area are cumulatively corrected, so that the solution of the local subproblem in the next iteration is more inclined to reduce the deviation.

[0128] Furthermore, the global constraints include one or more of the following: an upper limit constraint on the sum of the power exchanged between each service area and the upper-level power grid, a capacity constraint on the power transmission of tie lines between adjacent service areas, and a voltage consistency constraint at boundary nodes.

[0129] Furthermore, the distributed optimization algorithm is the alternating direction multiplier method, the decoupling parameters include auxiliary variables and Lagrange multipliers, and the local boundary information includes one or more of the following: interactive power estimates of each service area, tie-line power estimates, or boundary node voltage estimates.

[0130] Furthermore, the local operating indicators include one or more of the following: electricity cost, voltage fluctuation range, renewable energy absorption rate, or deviation of energy storage state of charge; different service areas can independently set their own local operating indicators and their weights.

[0131] Furthermore, embodiments of the present invention include a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the distributed multi-service area energy collaborative scheduling of highways as described in any of the above technical solutions.

[0132] This invention also includes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the distributed multi-service area energy collaborative scheduling of highways as described in any of the above technical solutions.

[0133] Those skilled in the art will readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is implemented in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the present invention.

[0134] It should be noted that, Figure 3 The division of modules / units is illustrative and represents only one logical functional division; in actual implementation, other division methods are possible. For example, two or more functions can be integrated into a single data acquisition module. The integrated modules described above can be implemented either in hardware or as software functional modules.

[0135] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the topology discovery method of an optical switch in any of the above-mentioned optoelectronic converged networks. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the distributed highway multi-service area energy collaborative scheduling method shown in any embodiment of the present invention by calling the computer program.

[0136] In one alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0137] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0138] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0139] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0140] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0141] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0142] It should be noted that, Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0143] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned distributed optimization-based highway multi-service area energy collaborative scheduling methods.

[0144] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0145] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned distributed optimization-based highway multi-service area energy collaborative scheduling method.

[0146] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0147] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0148] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0149] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the distributed optimization-based highway multi-service area energy collaborative scheduling method shown in the above embodiments.

[0150] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0151] It should be noted that the terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of the invention described herein can be implemented in an order other than that shown or described.

[0152] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0153] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A distributed energy collaborative scheduling method for multiple service areas on highways, characterized in that, Includes the following steps: Based on the energy systems of each service area, a global optimization model for coordinated scheduling of multiple service areas is established. The global optimization model takes the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization objective, and sets global constraints involving the coupling relationship between multiple service areas. The global optimization model is decomposed into multiple local subproblems that can be solved in parallel using a distributed optimization algorithm. Each local subproblem corresponds to a service area. The global constraints are decoupled to each local subproblem by introducing decoupling parameters. The following operations are performed iteratively until the convergence condition is met: each service area independently solves its local subproblem based on the current coordination information and reports its local boundary information; after summarizing the local boundary information of all service areas, the coordination information is updated according to the global optimization model, and the updated coordination information is sent to each service area; Based on the optimal local decision variables obtained by each service area, an energy dispatch plan is generated and executed for each service area.

2. The method according to claim 1, characterized in that, The iterative execution and coordination information update process includes: Under the current decoupling parameters, each service area independently and in parallel solves its own local subproblem to obtain the current local decision variables and local boundary variable estimates. Update the global constraint information based on the estimated local boundary variables reported by each service area; Based on the updated global constraint information, the decoupling parameters are updated, and the updated global constraint information and the decoupling parameters are sent to each service area as new coordination information.

3. The method according to claim 1, characterized in that, Also includes: An energy system model is established for each service area along the highway. The energy system model includes local decision variables and local operating indicators. The local decision variables are the set of operating parameters that are autonomously controlled within each service area and obtained by solving local sub-problems. The local operating indicators are the quantitative standards for the operation of the energy system in each service area.

4. The method according to claim 2, characterized in that, Each service area independently and in parallel solves its own local subproblems, including: Each service area's local control unit independently and in parallel solves the corresponding local sub-problems based on the current coordination information issued by the central coordination unit, and outputs the operation plan of each device in the service area as well as the global coupling boundary information related to the service area.

5. The method according to claim 2, characterized in that, The updating of the decoupling parameters includes: Based on the deviation between the estimated local boundary variables reported by each service area and the updated global constraint information, the decoupling parameters corresponding to each service area are cumulatively corrected.

6. The method according to claim 1, characterized in that, The global constraints include at least one of the following: The upper limit constraint on the sum of the power exchanged between each service area and the upper-level power grid, the capacity constraint on the power transmission of the tie lines between adjacent service areas, and the voltage consistency constraint at the boundary nodes.

7. The method according to claim 1, characterized in that, The distributed optimization algorithm is the alternating direction multiplier method, the decoupling parameters include auxiliary variables and Lagrange multipliers, and the local boundary information includes at least one of the following: the interactive power estimate of each service area, the tie-line power estimate, and the boundary node voltage estimate.

8. The method according to claim 1, characterized in that, The local operating metrics include at least one of the following: Electricity cost, voltage fluctuation range, renewable energy absorption rate, and deviation of energy storage charge status are all considered. Different service areas independently set their own local operation indicators and their weights.

9. A distributed highway multi-service area energy collaborative dispatching system, characterized in that, include: The first processing module is used to establish a global optimization model for coordinated scheduling of multiple service areas based on the energy systems of each service area. The global optimization model takes the local decision variables of each service area as the optimization object, the local operating indicators of each service area as the optimization target, and sets global constraints involving the coupling relationship between multiple service areas. The second processing module is used to decompose the global optimization model into multiple local subproblems that can be solved in parallel using a distributed optimization algorithm. Each local subproblem corresponds to a service area, and the global constraints are decoupled to each local subproblem by introducing decoupling parameters. The third processing module is used to iteratively perform the following operations until the convergence condition is met: enable each service area to independently solve its local subproblems based on the current coordination information and report its local boundary information; after summarizing the local boundary information of all service areas, update the coordination information according to the global optimization model, and send the updated coordination information to each service area. And to generate and execute energy dispatch plans for each service area based on the optimal local decision variables obtained at the end.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distributed highway multi-service area energy collaborative scheduling method as described in any one of claims 1 to 8.

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