An optimization scheduling method, device and system for a power system

Abstract

The present invention discloses an optimal scheduling method, device and system for a power system, belonging to the technical field of electrical engineering. The method includes: establishing a centralized optimal scheduling model of the power system based on the Bellman optimality principle by using the technical parameters of each component in the power system; splitting the centralized optimal scheduling model into sub-optimal scheduling models corresponding to each component based on the Lagrangian equation and the consensus theory; applying a power limit factor to each sub-optimal scheduling model to improve each sub-optimal scheduling model; solving the improved sub-optimal scheduling models by using the power system consensus distributed algorithm with first-order cost function components to obtain a convergent optimal solution, and implementing scheduling by using the distributed optimal scheduling scheme corresponding to the convergent optimal solution. The present invention can overcome the problem of unstable convergence of traditional consensus optimization algorithms when optimizing a power system with first-order cost function components, and ensure the feasibility and optimality of the solution.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical engineering, and more specifically, relates to an optimal scheduling method, device and system for a power system. Background Art

[0002] With the development of distributed generation technology and demand-side response, the structure of the power system has changed greatly, and the types of components it contains are also rich and diverse. The traditional optimal scheduling method for power systems is a centralized scheduling method. This type of scheduling method has a centralized controller, and all components transmit information to the centralized controller, and the centralized controller makes scheduling decisions uniformly. However, the centralized control method has the disadvantages of high communication and computing costs, poor privacy, security and scalability. Therefore, distributed optimal scheduling methods have received extensive attention in recent years.

[0003] The distributed algorithm based on consensus is a very popular type in distributed algorithms. However, the traditional consensus-based optimization algorithm has the phenomenon of unstable convergence when solving a power system containing first-order cost coefficients, and cannot guarantee the feasibility and optimality of scheduling decisions. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides an optimal scheduling method, device and system for a power system, the purpose of which is to improve the power limit factor in each sub-optimal scheduling model corresponding to each component; then use the consensus-based distributed algorithm for a power system containing components with first-order cost functions to solve, and implement scheduling using the obtained distributed optimal scheduling scheme corresponding to the convergent optimal solution, thereby solving the technical problem that the consensus-based distributed algorithm cannot guarantee the feasibility and optimality of scheduling decisions.

[0005] To achieve the above object, according to one aspect of the present invention, an optimal scheduling method for a power system is provided, including:

[0006] S1: Based on the Bellman optimality principle, establish a centralized optimal scheduling model of the power system by using the technical parameters of each component in the power system; the objective function of the centralized optimal scheduling model is to minimize the sum of the costs of multiple components, and the components include: fossil fuel power generation units, external power grids, wind power, photovoltaic power and energy storage;

[0007] S2: Based on the Lagrange equation and the consensus theory, split the centralized optimal scheduling model of the power system into sub-optimal scheduling models corresponding to each component;

[0008] S3: Apply the power limit factor to each sub-optimal scheduling model corresponding to each component to improve each sub-optimal scheduling model;

[0009] S4: Solve the improved sub-optimal scheduling models using the power system consistency distributed algorithm with a first-order cost function element to obtain the convergent optimal solution, and implement the scheduling using the distributed optimal scheduling scheme corresponding to the convergent optimal solution.

[0010] In one embodiment, the Lagrangian equation in S2 is:

[0011]

[0012] The sub-optimal scheduling model corresponding to the element includes:

[0013] Sub-optimal scheduling model of fossil fuel power generation units Expressed as:

[0014]

[0015] Sub-optimal scheduling model of external power grid Is:

[0016] Sub-optimal scheduling model of wind power Is:

[0017] Sub-optimal scheduling model of photovoltaic power Is:

[0018] Sub-optimal scheduling model of energy storage Is:

[0019] Where, G is the set of fossil fuel power generation units with a quadratic function of cost in the power system, a g , b g , c g Are the cost coefficients of element g, P g Is the output of element g; E is the set of external power grids in the power system, p is the electricity price, P e Is the exchange power between the power system and external power grid e; W is the set of wind turbines in the power system, C w Is the penalty cost for wind curtailment, Is the theoretical output of the wind turbines in the power system, P w Is the output of wind turbine w in the power system; S is the set of photovoltaic units in the power system, P s Is the output of photovoltaic unit s in the power system, Is the theoretical output of photovoltaic unit s in the power system, C s Is the penalty cost for PV curtailment; B is the set of energy storage units in the power system; is the optimal decision cost of energy storage SOC under the framework of Bellman optimality principle; λ is the Lagrange multiplier of equality constraint; l is the load, μ is the Lagrange multiplier of inequality constraint, is the Lagrange multiplier associated with the lower limit constraint on fossil fuel output; is the Lagrange multiplier associated with the upper limit constraint on fossil fuel output, and The lower and upper output limits of fossil fuel generators; is the Lagrange multiplier related to the lower limit constraint of wind power output, is the Lagrange multiplier related to the upper limit constraint of wind power output, The upper limit of wind power output; is the Lagrange multiplier related to the photovoltaic output lower limit constraint, is the Lagrange multiplier related to the upper limit constraint of photovoltaic output, The upper limit of photovoltaic output; is the Lagrange multiplier related to the lower limit constraint of the external power grid exchange power, is the Lagrange multiplier related to the upper limit constraint of the external power grid exchange power, The upper limit of power exchange between the power system and the external power grid; is the Lagrange multiplier related to the lower limit constraint of energy storage output, is the Lagrange multiplier related to the upper limit constraint of energy storage output, The lower and upper output limits of the energy storage unit; is the marginal cost on the fossil fuel side; is the marginal cost on the external grid side; is the marginal cost of wind power; is the marginal cost on the photovoltaic side; is the marginal cost of energy storage; is the initial capacity of energy storage, and are the minimum and maximum capacity of energy storage, is the theoretical maximum power of energy storage.

[0020] In one embodiment, the power limit factor in S3 refers to the optimal power or maximum power of a component having a first-order cost function; the improved sub-optimal scheduling model includes:

[0021] The improved sub-optimal dispatch model of the external power grid is:

[0022] The improved sub-optimal dispatch model of wind power is:

[0023] The improved sub-optimal scheduling model of photovoltaic is:

[0024] The sub-optimal scheduling model after energy storage improvement is as follows:

[0025] Wherein, is the power limit factor of component i on the side of component j.

[0026] In one embodiment, the S4 includes:

[0027] S41: Initialize the power limit factor κ 0 to 1, the marginal cost λ 0 , the power P of each component 0 and the power mismatch D 0 ; i = g, e, w, l, s, b, λ = [λ g ; λ e ; λ e ; λ l ; λ s ; λ b , P = [P g ; P e ; P w ; P l ; P s ; P b , D = [D g ; D e ; D w ; D l ; D s ; D b ; P 0 and D 0 satisfy:

[0028]

[0029] S42: Use the formula to update the marginal cost λ and the power limit factor κ, A is the weight matrix of the system, and ∈ is the feedback gain;

[0030] S43: Use the formula to solve the improved sub-optimal scheduling problem corresponding to each component, and update the power P and the power mismatch D of each component;

[0031] S44: Determine whether the solution algorithm converges. If the algorithm converges, output the optimal solution; if the algorithm does not converge, return to S42 to continue iteration until a convergent optimal solution is obtained; the optimal solution corresponds to the distributed optimization scheduling scheme,

[0032] S45: Use the distributed optimization scheduling scheme to implement the scheduling of the power system.

[0033] In one embodiment, determining whether the algorithm in S44 converges includes:

[0034] If ||λ n+1 - λ n ||2 ≤ ν λ and ||D ns+1 ||2 ≤ ν d then it is regarded as converged, where ν λ and ν d are the allowed error ranges.

[0035] In one embodiment, the objective function of the centralized optimal scheduling model of the power system in S1 is:

[0036]

[0037] In one embodiment, the constraints of the centralized optimal scheduling model of the power system include:

[0038] Power balance constraint:

[0039] Upper and lower limits of the output of fossil fuel generating units:

[0040] Upper and lower limits of the exchanged power between the power system and the external power grid:

[0041] Upper and lower limits of the output of wind power:

[0042] Upper and lower limits of the output of photovoltaic power:

[0043] Upper and lower limits of the charge and discharge power of energy storage:

[0044] According to another aspect of the present invention, an optimal scheduling device for a power system is provided, including:

[0045] A modeling module, configured to establish the centralized optimal scheduling model of the power system based on the Bellman optimality principle and using the technical parameters of each component in the power system; the objective function of the centralized optimal scheduling model is to minimize the sum of the costs of multiple said components, and the components include: fossil fuel generating units, external power grids, wind power, photovoltaic power, and energy storage;

[0046] A splitting module, configured to split the centralized optimal scheduling model of the power system into sub-optimal scheduling models corresponding to each said component based on the Lagrangian equation and the consistency theory;

[0047] An application module, configured to apply a power limit factor to the sub-optimal scheduling models corresponding to each of the components, so as to improve each of the sub-optimal scheduling models;

[0048] A solution module, configured to use a power system consistency distributed algorithm with first-order cost function components to solve the improved sub-optimal scheduling models to obtain a converged optimal solution, and implement scheduling by using the distributed optimal scheduling scheme corresponding to the converged optimal solution.

[0049] According to another aspect of the present invention, there is provided an optimal scheduling system for a power system, including a memory and a processor, where the memory stores a computer program, and it is characterized in that when the processor executes the computer program, the steps of the method are implemented.

[0050] According to another aspect of the present invention, there is provided a computer-readable storage medium, where when the computer program is executed by a processor, the steps of the method are implemented.

[0051] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0052] 1. The present invention provides an optimal scheduling method for a power system, which establishes a centralized optimal scheduling model of the power system according to the technical parameters of each component in the power system and the Bellman optimality principle; and based on the Lagrange equation and the consistency theory, the centralized optimal model is split into multiple sub-optimal scheduling models that are only related to each component itself, the concept of a power limit factor is proposed and applied to the sub-optimal scheduling models to promote the convergence of the sub-optimal scheduling models, and a power system consistency distributed algorithm with first-order cost function components is used to solve to obtain a distributed optimal scheduling scheme. The present invention can overcome the problem of unstable convergence of traditional consistency optimization algorithms when optimizing a power system with first-order cost function components, and ensure the feasibility and optimality of the solution.

[0053] 2. The optimal scheduling method for a power system provided by the present invention ensures the distributed communication between each component during the power operation process, reduces the communication and calculation costs in the actual system, and enhances the privacy, security and scalability of the power system itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flowchart of the optimal scheduling method for a power system in an embodiment of the present invention;

[0055] Figure 2 is a power structure diagram applied to the optimal scheduling method for a power system in an embodiment of the present invention;

[0056] Figure 3a and Figure 3bA simulation diagram of the optimal solution that cannot be converged to by traditional distributed optimization methods in the prior art;

[0057] Figure 4a , Figure 4b and Figure 4c A simulation diagram of the optimal solution that can be successfully converged to by the optimal power flow method of the power system provided by the present invention. Detailed implementation manners

[0058] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0059] As Figure 1 shown, the present invention provides an optimal power flow method for a power system, including:

[0060] S1: Based on the Bellman optimality principle, a centralized optimal power flow model of the power system is established by using the technical parameters of each component in the power system; the objective function of the centralized optimal power flow model is to minimize the sum of the costs of multiple components, and the components include: fossil fuel generator sets, external power grids, wind power, photovoltaic power, and energy storage;

[0061] S2: Based on the Lagrangian equation and the consensus theory, the centralized optimal power flow model of the power system is split into sub-optimal power flow models corresponding to each component;

[0062] S3: Apply the power limit factor to the sub-optimal power flow models corresponding to each component to improve each sub-optimal power flow model;

[0063] S4: Use the consensus distributed algorithm of the power system with first-order cost function components to solve the improved sub-optimal power flow models to obtain the converged optimal solution, and implement the dispatch using the distributed optimal power flow scheme corresponding to the converged optimal solution.

[0064] In order to more clearly illustrate an optimal power flow method proposed by the present invention, it will be described in detail below with reference to specific embodiments:

[0065] Taking a 6-node power system as an example for analysis, as Figure 2 shown. This system has 1 distributed generator set (denoted as g), 1 node connected to the external power grid (denoted as e), 1 wind turbine generator (denoted as w), 1 load node (denoted as l), 1 photovoltaic generator set (denoted as s), and 1 energy storage unit (denoted as b).

[0066] First, collect the technical parameters of each component in the power system; among them, each component of the power system includes distributed generating units, external power grids, wind turbines, loads, photovoltaic units, and energy storage units.

[0067] Specifically, the technical parameters of each component specifically include:

[0068] 1) The number of nodes N in the power system bus , the number of lines N in the power system branch , and the node numbers at the beginning and end of the line;

[0069] 2) The node number where the distributed generating unit is located, the upper and lower limits of the output of the distributed generating unit and and the cost coefficient a of the distributed generating unit g , and c g ;

[0070] 3) The node number where the external power grid unit is located, the upper limit of the exchange power between the power system and the external power grid

[0071] 4) The nodes where the wind turbine, load, and photovoltaic unit are located, and the available output of the wind and photovoltaic units at the current moment and the load l at the current moment;

[0072] 5) The node where the energy storage unit is located, the theoretical maximum power of the energy storage the upper and lower limits of the capacity of the energy storage unit and and the initial power of the energy storage unit

[0073] In this embodiment, the upper and lower limits of the output of the distributed generating unit are 80 kW and 20 kW, and the cost coefficients are 0.0005 $ / kWh^2, 0.0397 $ / kWh, and 0.4 $ respectively; the upper limit of the exchange power between the power system and the external power grid is 20 kW; the available outputs of the wind and photovoltaic units are 23.75 kW and 0 kW; the load is 53.2 kW; the theoretical maximum power of the energy storage unit is 15 kW, the upper and lower limits of the capacity of the energy storage unit are 60 kWh and 0 kWh, and the initial capacity of the energy storage unit is 52.16 kWh.

[0074] This application provides an optimal scheduling method for a power system, which specifically includes the following steps:

[0075] S1. According to the technical parameters of each component in the power system and the Bellman optimality principle, establish a centralized optimal scheduling model for the power system:

[0076] Specifically, the objective function of the optimal scheduling model of the power system under the Bellman optimality principle is:

[0077]

[0078] Among them, J represents the objective function of the optimal power system dispatch model; G is the set of fossil fuel generating units in the power system whose cost is a quadratic function, a g , b g , c g are the cost coefficients of component g, and P g is the output of component g; E is the set of external power grids in the power system, p is the electricity price, and P e is the exchange power between the power system and external power grid e; W is the set of wind turbines in the power system, and C w is the penalty cost for curtailed wind, is the theoretical output of the wind turbines in the power system, and P w is the output of wind turbine w in the power system; S is the set of photovoltaic units in the power system, and P s is the output of photovoltaic unit s in the power system, is the theoretical output of photovoltaic unit s in the power system, and C s is the penalty cost for curtailed light; B is the set of energy storage units in the power system; is the optimal decision cost affected by the energy storage SOC under the framework of the Bellman optimality principle.

[0079] The constraints of the centralized optimal dispatch model of the power system include:

[0080] Power balance constraint:

[0081] Output upper and lower limit constraints of fossil fuel generating units:

[0082] Output upper and lower limit constraints of the exchange power between the power system and external power grids:

[0083] Output upper and lower limit constraints of wind power:

[0084] Output upper and lower limit constraints of photovoltaics:

[0085] Output upper and lower limit constraints of energy storage charge and discharge power:

[0086] S2. Based on the Lagrangian equation and the consistency theory, the centralized optimal dispatch model of the power system is split into multiple sub-optimal dispatch models that are only related to each component itself;

[0087] Specifically, the Lagrangian equation of the constructed centralized optimization model is as follows:

[0088]

[0089] Among them, λ is the Lagrange multiplier of the equality constraint, μ is the Lagrange multiplier of the inequality constraint, and l is the load. and are the lower and upper limits of the output of the fossil fuel power generation unit. is the upper limit of the power exchanged between the power system and the external power grid. and are the lower and upper limits of the output of the energy storage unit.

[0090] The sub-optimal scheduling model includes the sub-optimal scheduling model of the fossil fuel power generation unit, the sub-optimal scheduling model of the external power grid, the sub-optimal scheduling model of the wind power, the sub-optimal scheduling model of the photovoltaic power, and the sub-optimal scheduling model of the energy storage:

[0091] Sub-optimal scheduling model of the fossil fuel power generation unit:

[0092]

[0093] Sub-optimal scheduling model of the external power grid:

[0094] Sub-optimal scheduling model of the wind power:

[0095] Sub-optimal scheduling model of the photovoltaic power:

[0096] Sub-optimal scheduling model of the energy storage:

[0097] Among them, is the initial power of the energy storage. and are the minimum and maximum powers of the energy storage. is the theoretical maximum power of the battery.

[0098] S3. Propose the concept of the power limit factor and apply the power limit factor to the sub-optimal scheduling model to improve the sub-optimal scheduling model of the component with the first-order cost function.

[0099] Specifically, the physical meaning of the proposed power limit factor refers to the optimal power / maximum power of the component with the first-order cost function (such as the external power grid, wind power, photovoltaic power, or energy storage, etc.). Apply the power limit factor to the sub-optimal scheduling model to improve the sub-optimal scheduling model of the component with the first-order cost function. The improved sub-optimal scheduling models are respectively:

[0100]

[0101]

[0102]

[0103]

[0104] Among them, is the power limit factor of component i on the side of component j, is the theoretical maximum power of energy storage.

[0105] S4. Propose an iterative optimization framework for the consensus distributed algorithm of the power system with first-order cost function components to ensure that the algorithm can converge to the optimal solution.

[0106] Specifically, the first step of the proposed iterative optimization framework for the consensus distributed algorithm of the power system with first-order cost function components is to initialize the power limit factor κ 0 to 1, the marginal cost λ 0 , the power P 0 of each component and the power mismatch D 0 . Among them, λ = [λ g ; λ e ; λ e ; λ l ; λ s ; λ b , P = [P g ; P e ; P w ; P l ; P s ; P b , D = [D g ; D e ; D w ; D l ; D s ; D b . To ensure the convergence of the distributed algorithm, P 0 and D 0 should satisfy:

[0107]

[0108] The second step of the proposed iterative optimization framework for the consensus distributed algorithm of the power system with first-order cost function components is to update the marginal cost λ and the power limit factor κ:

[0109]

[0110] Among them, A is the weight matrix of the system, and ∈ is the feedback gain.

[0111] The third step of the iterative optimization framework of the proposed power system consensus distributed algorithm with first-order cost function elements is to solve the sub-optimal scheduling problems of each element and update the power P and the power mismatch D:

[0112]

[0113] The fourth step of the iterative optimization framework of the proposed power system consensus distributed algorithm with first-order cost function elements is to determine whether the algorithm converges:

[0114] ||λ n+1 -λ n ||2≤ν λ and||D ns+1 ||2≤ν d ;

[0115] If the algorithm converges, output the optimal solution; if the algorithm does not converge, return to the second step for continued iteration. ν λ and ν d are the allowable error ranges.

[0116] To further illustrate the effectiveness of an optimal scheduling method for a power system of the present invention, the proposed distributed optimization method is compared with traditional distributed optimization methods, Figure 3a and Figure 3b it is shown that the traditional distributed optimization method cannot converge in this system. It can be seen that neither the marginal cost nor the power mismatch can converge; while Figure 4a and Figure 4b it is proved that the proposed distributed optimization method can successfully converge, the marginal cost converges to a fixed value, and the power mismatch is reduced to 0, Figure 4c indicating that an optimal scheduling method for a power system of the present invention can successfully converge to the optimal solution of the centralized optimization algorithm.

[0117] It is easy for those skilled in the art to understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimal scheduling method for a power system, characterized in that Including: S1: Based on the Bellman optimality principle, establish a centralized optimal scheduling model of the power system by using the technical parameters of each component in the power system; The objective function of the centralized optimal scheduling model is to minimize the sum of the costs of multiple components, and the components include: fossil fuel power generation units, external power grids, wind power, photovoltaic power, and energy storage; S2: Based on the Lagrange equation and the consensus theory, split the centralized optimal scheduling model of the power system into sub-optimal scheduling models corresponding to each component; S3: Apply the power limit factor to the sub-optimal scheduling models corresponding to each component to improve each sub-optimal scheduling model; S4: Use the power system consensus distributed algorithm with components containing first-order cost functions to solve the improved sub-optimal scheduling models to obtain a convergent optimal solution, and implement scheduling using the distributed optimal scheduling scheme corresponding to the convergent optimal solution; The objective function of the centralized optimal scheduling model of the power system in S1 is: The constraints of the centralized optimal scheduling model of the power system include: Power balance constraint: Output upper and lower limits constraints of fossil fuel power generation units: Upper and lower limit constraints on the exchange power between the power system and the external power grid: Lower and upper limits constraints on the output of wind power: Upper and lower limits constraints on the output of photovoltaic power: Constraints on the upper and lower limits of energy storage charging and discharging power: Let \(G\) be the set of fossil fuel power generation units with quadratic cost functions in the power system, \(a\) g , \(b\) g , \(c\) g be the cost coefficients of component \(g\), \(P\) g be the output of component \(g\); \(p\) is the electricity price, \(P\) e is the exchange power between the power system and the external power grid \(e\); \(W\) is the set of wind turbines in the power system, \(C\) w is the curtailment penalty cost of wind power, is the theoretical output of wind turbines in the power system, \(P\) w is the output of wind turbine \(w\) in the power system; \(S\) is the set of photovoltaic units in the power system, \(P\) s is the output of photovoltaic unit \(s\) in the power system, is the theoretical output of photovoltaic unit \(s\) in the power system, \(C\) s is the curtailment penalty cost of photovoltaic power; \(B\) is the set of energy storage units in the power system; is the optimal decision cost affected by the energy storage SOC under the framework of the Bellman optimality principle; \(l\) is the load, and are the lower and upper limits of the output of fossil fuel power generation units; is the upper limit of the output of wind power; is the upper limit of the output of photovoltaic power; is the upper limit of the exchange power between the power system and the external power grid; and are the lower and upper limits of the output of energy storage units; \(P\) b is the output of the energy storage unit.

2. The optimization scheduling method of the power system according to claim 1, characterized in that, The Lagrange equation in S2 is: The sub-optimal scheduling models corresponding to the components include: Sub-optimal Scheduling Model for Fossil Fuel Power Generation Units Expressed as: Optimal Scheduling Model for External Power Grid It is as follows: Wind power electronic optimal scheduling model Namely: Photovoltaic Sub-optimization Scheduling Model is as follows: Energy storage sub-optimal scheduling model Namely: Among them, \(E\) is the set of external power grids in the power system, \(\lambda\) is the Lagrange multiplier of the equality constraint, and \(\mu\) is the Lagrange multiplier of the inequality constraint. is the Lagrange multiplier related to the lower limit constraint of fossil fuel output; is the Lagrange multiplier related to the upper limit constraint of fossil fuel output, is the Lagrange multiplier related to the lower limit constraint of wind power output, is the Lagrange multiplier related to the upper limit constraint of wind power output, is the Lagrange multiplier related to the lower limit constraint of PV output, is the Lagrange multiplier related to the upper limit constraint of PV output, is the Lagrange multiplier related to the lower limit constraint of the exchanged power with the external power grid, is the Lagrange multiplier related to the upper limit constraint of the exchanged power with the external power grid, is the Lagrange multiplier related to the lower limit constraint of energy storage output, is the Lagrange multiplier related to the upper limit constraint of energy storage output, is the marginal cost on the fossil fuel side; is the marginal cost on the external power grid side; is the marginal cost on the wind power side; is the marginal cost on the PV side; is the marginal cost on the energy storage side; is the initial power of the energy storage, and are the minimum and maximum powers of the energy storage, is the theoretical maximum power of the energy storage.

3. The optimal scheduling method for the power system according to claim 2, wherein, The power limit factor in S3 refers to the optimal power or maximum power of components with first-order cost functions; the improved sub-optimal scheduling models include: The sub-optimal scheduling model after the improvement of the external power grid is as follows: The sub-optimal scheduling model after improvement for wind power is as follows: The sub-optimal scheduling model after improvement for photovoltaic power is as follows: The sub-optimal scheduling model after energy storage improvement is as follows: Among them, is the power limit factor of component i on the side of component j.

4. The optimal scheduling method for the power system according to claim 3, wherein S4 includes: S41: Initialize the power limit factor κ 0 to 1, the marginal cost λ 0 , the power P 0 of each component and the power mismatch D 0 ; i = g, e, w, l, s, b, P = [P g ; P e ; P w ; P l ; P s ; P b , D = [D g ; D e ; D w ; D l ; D s ; D b ; P 0 and D 0 satisfy: S42: Use the formula to update the marginal cost λ and the power limit factor κ, where A is the weight matrix of the system and ∈ is the feedback gain; S43: Using the formula to solve the improved sub-optimal scheduling problems corresponding to the respective components, and update the power P and the power mismatch D of each component; S44: Determine whether the solution algorithm converges. If the algorithm converges, output the optimal solution; if the algorithm does not converge, return to S42 to continue iterating until a convergent optimal solution is obtained; the distributed optimal scheduling scheme corresponding to the optimal solution, S45: Use the distributed optimal scheduling scheme to implement the scheduling of the power system.

5. The optimal scheduling method for the power system according to claim 4, characterized in that, Determining whether the algorithm converges in S44 includes: If ||λ n+1 -λ n ||2 ≤ ν λ and ||D ns+1 ||2 ≤ ν d then it is regarded as convergent, where ν λ and ν d are the allowable error ranges.

6. An optimized scheduling device for a power system, characterized in that, For implementing the optimal scheduling method of the power system according to any one of claims 1-5, including: A modeling module for establishing a centralized optimal scheduling model of the power system based on the Bellman optimality principle by using the technical parameters of each component in the power system; the objective function of the centralized optimal scheduling model is to minimize the sum of the costs of multiple components, and the components include: fossil fuel power generation units, external power grids, wind power, photovoltaic power, and energy storage; A splitting module for splitting the centralized optimal scheduling model of the power system into sub-optimal scheduling models corresponding to each component based on the Lagrange equation and the consensus theory; An application module for applying the power limit factor to the sub-optimal scheduling models corresponding to each component to improve each sub-optimal scheduling model; A solving module for using the power system consensus distributed algorithm with components containing first-order cost functions to solve the improved sub-optimal scheduling models to obtain a convergent optimal solution, and implementing scheduling using the distributed optimal scheduling scheme corresponding to the convergent optimal solution.

7. An optimal scheduling system for a power system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. 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 steps of the method according to any one of claims 1 to 5.

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