Multi-region power distribution network source and network load cooperative control method and system based on dynamic optimization

Abstract

The invention relates to a multi-region power distribution network source and network load cooperative control method and system based on dynamic optimization, and the method comprises the following steps: deploying a cloud center, and deploying region edge ends and terminal equipment in each distributed power distribution network sub-region; the method comprises the following steps: constructing a global optimization model considering inter-region source network load cooperative multiple targets through a cloud center, solving the global optimization model through an improved epsilon-constraint method to generate a Pareto leading edge, and issuing a cooperative solving instruction to a region edge end; the region side end solves the local optimization sub-problem according to the received collaborative solving instruction and issues a decision instruction to the terminal equipment in the corresponding power distribution network sub-region according to the solving result; and the terminal equipment executes the decision instruction and feeds back the operation data to the cloud center, and the cloud center updates the collaborative solving instruction according to the received operation data.

Description

TECHNICAL FIELD

[0001] The application relates to a multi-region power distribution network source-network-load collaborative control method and system based on dynamic optimization, and belongs to the technical field of multi-region power distribution network collaborative optimization of power systems. BACKGROUND

[0002] With the in-depth promotion of new power system construction, multi-region power distribution network collaborative optimization technology has become a key support for energy transformation. This technology coordinates cross-regional power dispatching, network reconstruction and load response, which has strategic significance for improving new energy consumption capacity, reducing system loss and ensuring operation safety. Especially under the background of high proportion of wind power and photovoltaic power, the power interaction and constraint coupling between regions are increasingly close, and an efficient collaborative mechanism that takes into account economy, safety and environmental protection is urgently needed. Global power grid operators are listing cross-regional collaborative optimization as a core direction of smart grid development, which is crucial for breaking through to achieve the low-carbon target.

[0003] However, existing optimization methods face significant bottlenecks when dealing with large-scale regional coupled systems. The centralized optimization architecture needs to solve a large number of coupled constraint equations at the same time, resulting in excessive computational burden and difficulty in meeting the time requirements of real-time dispatching. Although the traditional distributed scheme can improve local computing efficiency, it lacks an effective boundary constraint coordination mechanism, and is prone to cause power imbalance between regions when new energy output fluctuates, causing power grid safety risks. The deeper problem is the lack of multi-objective collaboration capability: the current method relies on artificial preset target weights and cannot dynamically adapt to changes in operating scenarios, resulting in optimization results deviating from actual needs and forming a dilemma of continuous decline in decision effectiveness.

[0004] Although the existing patent technology has made certain progress in multi-region collaborative optimization, it has not yet solved the core contradiction of dynamic coupling constraints and multi-objective real-time balance. For example, the Chinese invention patent with publication number "CN114142532B" discloses a "method and system for distributed photovoltaic participation in source network load storage coordination control", which proposes a source network load storage coordination strategy based on the principle of hierarchical zoning, and realizes cross-regional scheduling through comparison of predicted adjustable capacity and actual data. However, this method has significant limitations: the "hierarchical zoning" strategy improves local response efficiency, but does not establish a global coordination mechanism for dynamic coupling constraints, and regional power mutual aid still relies on preset thresholds, which cannot adapt to the real-time reconstruction demand of power flow caused by wind and light fluctuations; and the optimization target focuses on photovoltaic consumption and energy storage economy, without considering the coordination of key targets such as network loss and mutual aid cost, leading to imbalance in multi-objective game. Another Chinese invention patent with publication number "CN113642793A" discloses a "multi-region power grid collaborative optimization scheduling method based on cooperative game", which designs a multi-region scheduling model based on cooperative game and VCG mechanism, and realizes joint optimization of cost through contribution degree allocation. The defects of this scheme are: its cooperative game relies on static contribution degree allocation rules and does not introduce a rolling feedback mechanism to correct regional strategy bias, which is prone to cause boundary power flow mismatch when new energy output suddenly changes; at the same time, the model takes cost optimization as the single core target and does not include renewable energy consumption rate and voltage stability in the Pareto frontier solution, making it difficult to support "economic-safety-green" multi-dimensional balanced decision-making. SUMMARY

[0005] To solve the problems of distributed decision-making conflict and low cross-regional resource mutual aid efficiency in the existing technology of multi-region distribution network collaborative control, the present application proposes a multi-region distribution network source network load collaborative control method and system based on dynamic optimization.

[0006] The technical scheme of the present application is as follows:

[0007] On the one hand, the present application proposes a multi-region distribution network source network load collaborative control method based on dynamic optimization, which includes the following steps:

[0008] Deploy a cloud center and deploy regional edge and terminal devices in each distributed distribution network sub-region;

[0009] Construct a global optimization model considering multi-objective source network load coordination between regions through the cloud center, solve the global optimization model to generate the Pareto frontier through the improved ε-constraint method, and then issue collaborative solving instructions to the regional edge;

[0010] The regional edge solves the local optimization sub-problem according to the received collaborative solving instructions and issues decision-making instructions to the terminal devices in the corresponding distribution network sub-region according to the solving results;

[0011] The terminal device executes the decision instruction and feeds back operation data to the cloud center, and the cloud center updates the collaborative solving instruction according to the received operation data.

[0012] As a preferred embodiment, in the global optimization model considering the inter-regional source-network-load coordination multi-objective, the multi-objective specifically includes:

[0013] The inter-regional power mutual assistance cost, the total network loss, and the renewable energy consumption rate.

[0014] As a preferred embodiment, the method for generating the Pareto front by solving the global optimization model by the improved ε-constraint method specifically includes:

[0015] Selecting any two objectives in the inter-regional power mutual assistance cost, the total network loss, and the renewable energy consumption rate as secondary objectives, and converting the secondary objectives into constraints;

[0016] Adaptively adjusting the boundary of the constraint by using the golden section method, and performing boundary contraction by using the historical maximum and minimum values of the secondary objectives;

[0017] Solving the global optimization model to generate the Pareto front based on the contracted boundary of the constraint.

[0018] As a preferred embodiment, the step of solving the local optimization sub-problem by the regional edge end according to the received collaborative solving instruction is implemented based on the regional collaborative solving mechanism of the alternating direction multiplier method ADMM.

[0019] As a preferred embodiment, the regional collaborative solving mechanism based on the alternating direction multiplier method ADMM specifically includes:

[0020] The cloud center issues the collaborative solving instruction containing the coupling constraint variable, and the coupling constraint variable specifically includes:

[0021]

[0022] Wherein, z (t) represents the coupling constraint variable; A i represents the coupling constraint matrix of the power distribution network region i; represents the decision variable of the power distribution network region i at the previous time; ρ represents the penalty parameter; λ (t-1) represents the Lagrange multiplier vector at the previous time;

[0023] The regional edge end solves the local optimization sub-problem in parallel to obtain the decision variable:

[0024]

[0025] Wherein, f i (X i) represents the local optimization objective function for distribution network region i;

[0026] Update the Lagrange multiplier vector:

[0027]

[0028] Where b represents the power balance constant vector;

[0029] Repeat the above steps iteratively until the convergence condition is met:

[0030]

[0031] Where ξ represents the convergence threshold.

[0032] In a preferred embodiment, the step of the region edge solving the local optimization subproblem according to the received collaborative solution instruction includes:

[0033] When solving local optimization subproblems, the regional edge considers local power output constraints, grid power flow safety constraints, and load response constraints.

[0034] In a preferred embodiment, the step of updating the collaborative solving instructions by the cloud center based on the received runtime data includes:

[0035] If the deviation between the actual value of the running data received by the cloud center and the preset planned value is greater than the preset threshold, the global optimization model is reconstructed based on the actual running data, and a new global optimization model is solved to generate the Pareto front. Then, collaborative solution instructions are sent to the regional edge.

[0036] On the other hand, this invention also proposes a multi-regional distribution network source-grid-load coordinated control system based on dynamic optimization, comprising:

[0037] A cloud-based central hub is deployed at the regional edges and terminal equipment of each distributed distribution network sub-region; among which:

[0038] The cloud center is used to construct a global optimization model that considers multiple objectives of source-network-load coordination between regions. The improved ε-constraint method is used to solve the global optimization model to generate the Pareto front, and then collaborative solution instructions are sent to the edge of the region.

[0039] The edge of the region solves the local optimization sub-problem according to the received collaborative solving instructions and issues decision instructions to the terminal equipment in the corresponding distribution network sub-region based on the solution results;

[0040] The terminal device executes decision instructions and feeds back operating data to the cloud center, which then updates the collaborative solution instructions based on the received operating data.

[0041] In still another aspect, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for dynamic optimization-based multi-region power distribution network source-network-load collaborative control as described in any of the embodiments of the present application.

[0042] In still another aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method for dynamic optimization-based multi-region power distribution network source-network-load collaborative control as described in any of the embodiments of the present application.

[0043] The present application has the following beneficial effects:

[0044] The present application provides a method and system for dynamic collaborative optimization-based multi-region power distribution network source-network-load collaborative control, which constructs a three-level dynamic architecture of "cloud global optimization-regional parallel decision-terminal feedback execution", performs distributed collaborative decomposition, significantly reduces the complexity of large-scale system solving, and effectively improves the optimization timeliness to support real-time scheduling requirements.

[0045] The present application also innovatively designs a multi-objective adaptive balancing mechanism, taking regional mutual aid cost, network loss, and renewable energy consumption rate as core targets, dynamically tracks the Pareto optimal solution set through improved ε-constraint method, eliminates the subjective limitations of artificial weight setting, and realizes collaborative optimization decision of economy, safety, and greenness.

[0046] Additional aspects and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. Furthermore, various aspects and advantages of the present application can be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The method flowchart of the embodiment one of the present application is shown in the figure.

[0048] Figure 2 The system framework schematic diagram of the embodiment two of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0050] It should be understood that the step numbers used herein are only for the convenience of description and are not limited to the execution sequence of the steps.

[0051] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] The terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0053] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations thereof, and includes these combinations.

[0054] Embodiment one:

[0055] Referring to Figure 1 The embodiment provides a multi-region power distribution network source-network-load collaborative control method based on dynamic optimization, specifically comprising the following steps:

[0056] S100, deploying a cloud center in a provincial power distribution network control cloud platform; deploying a regional edge industrial server in each distributed power distribution network sub-region; arranging terminal devices including distributed power supply controllers, load control terminals, and intelligent monitoring devices, etc. in the field of the power distribution network sub-region.

[0057] S200, constructing a global optimization model considering regional inter-source network load collaborative multi-objective through the cloud center, solving the global optimization model to generate a Pareto frontier through an improved ε-constraint method, and then issuing a collaborative solution instruction to the regional edge.

[0058] S300, the regional edge solves the local optimization sub-problem according to the received collaborative solution instruction and issues a decision instruction to the terminal devices in the corresponding power distribution network sub-region according to the solving result.

[0059] S400, the terminal devices execute the decision instruction and feed back operation data to the cloud center, and the cloud center updates the collaborative solution instruction according to the received operation data.

[0060] As a preferred embodiment of the present embodiment, in the global optimization model considering regional inter-source network load collaborative multi-objective, the multi-objective specifically includes:

[0061] The inter-regional power mutual aid cost, the total network loss, and the renewable energy consumption rate, and the specific objective function is:

[0062] minF = [f1(X), f2(X), -f3(X)];

[0063] Wherein, f1(X) represents the inter-regional power mutual aid cost (unit: yuan); f2(X) represents the network total loss (unit: kW); f3(X) represents the renewable energy consumption rate (unit: %); X represents the decision variable matrix; Specifically, the inter-regional power mutual aid cost is calculated, which is expressed in the formula as:

[0064]

[0065] In the formula, c ij represents the mutual aid price of region i to j (yuan / kWh); P ij (t) represents the transmission power of region i to j at time period t (kW); T represents the number of time periods in the scheduling period; Δt represents the time period length; N represents the total number of regions participating in mutual aid;

[0066] The network total loss is calculated, which is expressed in the formula as:

[0067]

[0068] In the formula, I l (t) represents the current of branch l at time period t (A); R l represents the resistance of branch l (Ω); L represents the total number of branches;

[0069] The renewable energy consumption rate is calculated, which is expressed in the formula as:

[0070]

[0071] In the formula, P RE,k (t) represents the actual output of renewable energy k at time period t (kW); represents the maximum output of renewable energy k at time period t (kW); k represents the total number of renewable energies.

[0072] As a preferred embodiment of the present embodiment, the method for solving the global optimization model to generate the Pareto front by the improved ε-constraint method is specifically:

[0073] Select any two of the inter-regional power mutual aid cost, the network total loss, and the renewable energy consumption rate as the secondary target, and convert the secondary target into a constraint; For example, select the inter-regional power mutual aid cost as the primary target, and select the network total loss and the renewable energy consumption rate as the secondary target, then convert the network total loss and the renewable energy consumption rate into constraints:

[0074]

[0075] wherein ε2 and ε3 are the second and third secondary target constraints, respectively;

[0076] The boundary of the constraint is adaptively adjusted using the golden section method, and the boundary is shrunk through the historical maximum and minimum values of the secondary target, which is expressed in a formula as follows:

[0077]

[0078] wherein φ represents the golden section ratio (about 0.618); represents the historical minimum network loss value; represents the historical maximum consumption rate; k represents the iteration number.

[0079] Based on the shrunk constraint boundary, the global optimization model is solved to generate the Pareto frontier.

[0080] As a preferred embodiment of the present embodiment, the step of solving the local optimization sub-problem according to the received collaborative solving instruction is implemented based on a regional collaborative solving mechanism of the alternating direction multiplier method (ADMM), and the regional collaborative solving mechanism of the alternating direction multiplier method (ADMM) specifically iteratively performs the following steps:

[0081] The cloud center issues a collaborative solving instruction containing a coupling constraint variable, and the coupling constraint variable is specifically:

[0082]

[0083] wherein z (t) represents the coupling constraint variable; A i represents the coupling constraint matrix of the power distribution network region i; represents the decision variable of the power distribution network region i at the previous time; ρ represents a penalty parameter; λ (t-1) represents the Lagrange multiplier vector at the previous time;

[0084] The regional edge end solves the local optimization sub-problem in parallel to obtain the decision variable:

[0085]

[0086] wherein f i (X i ) represents the local optimization objective function of the power distribution network region i;

[0087] The Lagrange multiplier vector is updated as follows:

[0088]

[0089] wherein b represents a power balance constant vector;

[0090] The above steps are iteratively performed until a convergence condition is reached:

[0091]

[0092] wherein ξ represents a convergence threshold.

[0093] As a preferred embodiment of the present embodiment, the regional edge end, according to the received collaborative solving instruction, solves the local optimization sub-problem in the step of:

[0094] The regional edge end considers the local power output constraint, the grid power flow safety constraint and the load response constraint when solving the local optimization sub-problem, specifically:

[0095] The power output constraint is expressed in a formula as:

[0096]

[0097] In the formula, P DG,i represents the output of the distributed power i;

[0098] The grid power flow safety constraint is expressed in a formula as:

[0099] |I l (t)|≤I l ;

[0100] V min ≤V k (t)≤V max ;

[0101] In the formula, I l represents the current of branch l; V k represents the voltage of node k;

[0102] The load response constraint is expressed in a formula as:

[0103]

[0104] In the formula, δP DR,m represents the reduction of load m.

[0105] As a preferred embodiment of the present embodiment, the step of updating the collaborative solving instruction according to the received operation data by the cloud center comprises:

[0106] The terminal device uploads real-time data such as actual power output, node voltage and load power to the cloud center.

[0107] The cloud center calculates the deviation between the actual value of the received operation data and the preset planned value, and if the deviation is greater than a preset threshold, a correction process is triggered:

[0108] The global optimization model is reconstructed based on actual operation data, and a new global optimization model is solved to generate a Pareto frontier, and the ADMM mechanism is restarted to issue collaborative solving instructions to the regional edge ends.

[0109] The terminal device uploads real-time operation data, which is expressed by a formula as follows:

[0110]

[0111] In the formula, represents the actual power output; represents the actual node voltage; represents the actual load power;

[0112] The cloud center calculates the deviation, which is expressed by a formula as follows:

[0113] ΔD = ‖D real -D pred ‖2;

[0114] In the formula, D pred represents the predicted value;

[0115] The trigger correction condition is expressed by a formula as follows:

[0116] ΔD ≥ δ th ;

[0117] In the formula, δ th represents a preset threshold value.

[0118] Embodiment Two:

[0119] Referring to Figure 2 , the embodiment provides a multi-regional power distribution network source-network-load collaborative control system based on dynamic optimization, which comprises:

[0120] a cloud center deployed on a provincial power distribution network regulation and control cloud platform, and a regional edge end industrial server distributed in each power distribution network sub-region, and a terminal device arranged on site, the terminal device comprising a distributed power source controller (PLC), a load control terminal (RTU), an intelligent monitoring device (sensor network), etc.; the cloud center is in communication connection with the regional edge end industrial server through a fiber optic private network, the regional edge end industrial server is connected with the distributed power source controller (PLC) and the load control terminal (RTU) through an industrial Ethernet, the regional edge end industrial server is connected with the intelligent monitoring device (sensor network) through an RS485 bus, and the terminal device is in communication connection with the cloud center through 5G or an optical fiber; wherein:

[0121] The cloud center is configured to build a global optimization model considering inter-regional source-network-load coordination multi-objective, solve the global optimization model by an improved ε-constraint method to generate a Pareto frontier, and then issue a coordination solving instruction to the regional edge end; the cloud center is configured to perform the corresponding functions in Embodiment 1, and details are not repeated here.

[0122] The regional edge end is configured to solve a local optimization sub-problem according to the received coordination solving instruction, and issue a decision instruction to a terminal device in a corresponding power distribution network sub-region according to a solving result; the regional edge end is configured to perform the corresponding functions in Embodiment 1, and details are not repeated here.

[0123] The terminal device is configured to execute the decision instruction and feed back operation data to the cloud center, and the cloud center is configured to update the coordination solving instruction according to the received operation data; the terminal device is configured to perform the corresponding functions in Embodiment 1, and details are not repeated here.

[0124] Embodiment 3

[0125] The embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the dynamic optimization-based multi-regional power distribution network source-network-load coordination control method according to any one of the embodiments.

[0126] Embodiment 4

[0127] The embodiment provides a computer readable storage medium, which stores a computer program executable by a processor to implement the dynamic optimization-based multi-regional power distribution network source-network-load coordination control method according to any one of the embodiments.

[0128] In the embodiments of the present application, “at least one” refers to one or more, and “multiple” refers to two or more. “And / or” describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Wherein A and B can be singular or plural. The character “ / ” generally represents an “or” relationship between the front and rear associated objects. “At least one of the following” and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, and c can be single or multiple.

[0129] Those skilled in the art can clearly understand that the units and algorithm steps described in the embodiments disclosed herein can be realized by electronic hardware, computer software and a combination of the two. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0131] In several embodiments provided in the present application, any function realized in the form of a software function unit and sold or used as an independent product can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0132] The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent flow transformation, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization, characterized in that, Includes the following steps: Deploy a cloud center and deploy regional edge and terminal equipment in each distributed distribution network sub-area; A global optimization model considering multiple objectives of source-grid-load coordination between regions is constructed through a cloud center. The Pareto front is generated by solving the global optimization model using an improved ε-constraint method, and then collaborative solution instructions are sent to the edge of the region. The regional edge solves the local optimization sub-problem based on the received collaborative solution instructions and issues decision instructions to the terminal equipment in the corresponding distribution network sub-region based on the solution results; The terminal device executes the decision-making instructions and feeds back the operating data to the cloud center. The cloud center updates the collaborative solution instructions based on the received operating data.

2. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 1, characterized in that, In the global optimization model considering inter-regional source-grid-load coordination with multiple objectives, the multiple objectives specifically include: Inter-regional power exchange costs, total network losses, and renewable energy integration rate.

3. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 2, characterized in that, The method for generating the Pareto front by solving the global optimization model using the improved ε-constraint method is as follows: Select any two of the following objectives—inter-regional power exchange cost, total network loss, and renewable energy absorption rate—as secondary objectives, and transform these secondary objectives into constraints. The boundary of the constraint is adaptively adjusted using the golden section method, and the boundary is shrunk by using the historical maximum and minimum values ​​of the secondary objective. Based on the contracted constraint boundary, the Pareto front is generated by solving the global optimization model.

4. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 1, characterized in that: The steps for solving the local optimization subproblems at the edge of the region according to the received collaborative solution instructions are implemented based on the regional collaborative solution mechanism of the Alternating Direction Multiplier Method (ADMM).

5. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 4, characterized in that, The specific regional collaborative solution mechanism based on the Alternating Direction Multiplier Method (ADMM) is as follows: The cloud center issues a collaborative solution instruction containing coupled constraint variables, which are specifically: Among them, z (t) A represents the coupling constraint variable; i Represents the coupling constraint matrix of distribution network region i; ρ represents the decision variable of distribution network region i at the previous time step; ρ represents the penalty parameter; λ represents the decision variable of distribution network region i at the previous time step. (t-1) Represents the Lagrange multiplier vector of the previous time step; Parallel solution of the local optimization subproblem at the region edge yields the decision variables: Among them, f i (X i ) represents the local optimization objective function for distribution network region i; Update the Lagrange multiplier vector: Where b represents the power balance constant vector; Repeat the above steps iteratively until the convergence condition is met: Where ξ represents the convergence threshold.

6. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 1, characterized in that, In the step where the edge of the region solves the local optimization subproblem according to the received collaborative solution instructions: When solving local optimization subproblems, the regional edge considers local power output constraints, grid power flow safety constraints, and load response constraints.

7. The method for coordinated control of power generation, grid, and load in a multi-regional distribution network based on dynamic optimization according to claim 1, characterized in that, The steps by which the cloud center updates the collaborative solving instructions based on the received runtime data include: If the deviation between the actual value of the running data received by the cloud center and the preset planned value is greater than the preset threshold, the global optimization model is reconstructed based on the actual running data, and a new global optimization model is solved to generate the Pareto front. Then, collaborative solution instructions are sent to the regional edge.

8. A multi-regional power distribution network source-grid-load coordinated control system based on dynamic optimization, characterized in that, include: A cloud-based central hub is deployed at the regional edges and terminal equipment in each distributed sub-region of the distribution network; among which: The cloud center is used to construct a global optimization model that considers multiple objectives of source-network-load coordination between regions. The improved ε-constraint method is used to solve the global optimization model to generate the Pareto front, and then collaborative solution instructions are sent to the edge of the region. The edge of the region solves the local optimization sub-problem according to the received collaborative solving instructions and issues decision instructions to the terminal equipment in the corresponding distribution network sub-region based on the solution results; The terminal device executes decision instructions and feeds back operating data to the cloud center, which then updates the collaborative solution instructions based on the received operating data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-regional power distribution network source-grid-load coordinated control method based on dynamic optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multi-regional power distribution network source-grid-load coordinated control method based on dynamic optimization as described in any one of claims 1 to 7.

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