New energy power grid coordinated scheduling method, apparatus and device, and storage medium

Abstract

The invention discloses a new energy power grid coordinated dispatching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the uncertainty coupling analysis of a power distribution network and a power transmission network in a new energy power grid, and generating an interval coupling relation which comprises binary variable constraints; respectively generating a combined fuzzy set of the power distribution network and the power transmission network based on the power instruction of the power transmission and distribution network and the uncertainty mapping analysis; based on the interval coupling relationship, performing coupling collaborative analysis according to the two-stage optimization scheduling models of the power distribution network and the power transmission network, and generating a collaborative optimization scheduling model; and solving the collaborative optimization scheduling model by adopting an alternating direction multiplier method and a distributed robust optimization algorithm to obtain a new energy power grid collaborative scheduling scheme. The technical problems that in the prior art, due to the fact that a transmission and distribution network deterministic interaction relation is depended on, solving is conservative, efficiency and accuracy are low, the calculation scale is large, and the new energy power grid collaborative scheduling requirement in an actual scene is difficult to meet can be solved.

Description

Technical Field

[0001] This application relates to the field of power grid dispatching, and in particular to a method, apparatus, equipment and storage medium for coordinated dispatching of new energy power grids. Background Technology

[0002] The large-scale integration of renewable energy sources poses challenges to the safe operation of the power system, with both the transmission and distribution sides facing issues of renewable energy absorption and safe operation. As distributed power sources, energy storage, and active loads are increasingly integrated into the distribution system, the operation of the distribution network is becoming more flexible and dynamic. Traditional distribution networks are gradually transforming into active distribution networks, and the interaction between the transmission and distribution networks is becoming increasingly close. Through transmission and distribution coordination optimization, the auxiliary regulation capabilities of the distribution network can be fully released, enhancing the renewable energy absorption capacity of the entire network and ensuring the safe operation of the entire system.

[0003] Existing technologies typically first establish separate optimal scheduling models for transmission and distribution networks, then combine these models with their power transmission to form a coordinated transmission-distribution optimal scheduling model. However, existing technologies assume that the power interaction between the transmission and distribution networks is deterministic, failing to adjust this interaction power in real time, thus hindering the complementary adjustment capabilities of the two networks. Furthermore, the solutions to their respective problems are generally based on robust optimization, which is relatively conservative, or they use linear strategies to approximate their robust optimizations, further impacting scheduling optimality. Moreover, current methods involve centralized solutions to the coordinated transmission-distribution optimal scheduling model, resulting in a massive computational scale. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for coordinated scheduling of new energy power grids, which addresses the technical problem that existing technologies rely on deterministic interaction relationships in transmission and distribution networks, and the solutions are relatively conservative, resulting in low efficiency and accuracy. In addition, the computational scale is large, making it difficult to meet the requirements of coordinated scheduling of new energy power grids in practical scenarios.

[0005] In view of this, the first aspect of this application provides a method for coordinated dispatch of new energy power grids, including:

[0006] Uncertainty coupling analysis is performed on the distribution network and transmission network in the new energy power grid to generate interval coupling relationships, which include binary variable constraints.

[0007] Based on power command and uncertainty mapping analysis of the power transmission and distribution network, joint fuzzy sets of the distribution network and the transmission network are generated respectively.

[0008] Based on the aforementioned interval coupling relationship, a coupling and collaborative analysis is performed on the two-stage optimization scheduling model of the distribution network and the transmission network to generate a collaborative optimization scheduling model. The two-stage optimization scheduling model is constructed based on the joint fuzzy set.

[0009] The cooperative optimization scheduling model is solved using the alternating direction multiplier method and the distributed robust optimization algorithm to obtain a coordinated scheduling scheme for the new energy power grid.

[0010] Preferably, the uncertainty coupling analysis of the distribution network and transmission network in the new energy power grid generates interval coupling relationships, which include binary variable constraints, including:

[0011] A first external characteristic interval is defined in the distribution network of the new energy power grid, the first external characteristic interval including a first uncertain interval and a first adjustable interval;

[0012] Configure binary variable constraints for the first external characteristic interval;

[0013] The uncertainty coupling relationship between the distribution network and the transmission network is analyzed based on the first external characteristic interval, and a second external characteristic interval of the transmission network is generated. The second external characteristic interval includes a second uncertain interval and a second adjustable interval.

[0014] The first external characteristic interval and the second external characteristic interval are used to describe the interval coupling relationship.

[0015] Preferably, the step of generating a collaborative optimization scheduling model based on the two-stage optimization scheduling model of the distribution network and transmission network according to the interval coupling relationship further includes:

[0016] Based on the joint fuzzy set, a two-stage optimal scheduling model for the distribution network and transmission network is constructed according to the scheduling cost and the adjustment cost. The two-stage optimal scheduling model includes multiple constraints.

[0017] Preferably, the step of solving the cooperative optimization scheduling model using the alternating direction multiplier method and the distributed robust optimization algorithm to obtain the coordinated scheduling scheme for the new energy power grid includes:

[0018] The cooperative optimization scheduling model is decomposed using the alternating direction multiplier method to obtain independent optimization problems;

[0019] Based on the distributed robust optimization algorithm and the dual theory of wireless planning, the independent optimization problem is simplified into a mixed integer linear programming problem, and the solution is obtained to obtain the coordinated scheduling scheme of the new energy power grid.

[0020] The second aspect of this application provides a new energy power grid coordinated dispatch device, comprising:

[0021] The coupling analysis unit is used to perform uncertainty coupling analysis on the distribution network and transmission network in the new energy power grid, and generate interval coupling relationships, which include binary variable constraints.

[0022] The fuzzy analysis unit is used to generate joint fuzzy sets for the distribution network and the transmission network based on power command and uncertainty mapping analysis of the power transmission and distribution network, respectively.

[0023] The collaborative modeling unit is used to perform coupled collaborative analysis based on the interval coupling relationship and the two-stage optimization scheduling model of the distribution network and the transmission network to generate a collaborative optimization scheduling model. The two-stage optimization scheduling model is constructed based on the joint fuzzy set.

[0024] The scheduling solution unit is used to solve the cooperative optimization scheduling model using the alternating direction multiplier method and the distributed robust optimization algorithm to obtain the coordinated scheduling scheme of the new energy power grid.

[0025] Preferably, the coupling analysis unit is specifically used for:

[0026] A first external characteristic interval is defined in the distribution network of the new energy power grid, the first external characteristic interval including a first uncertain interval and a first adjustable interval;

[0027] Configure binary variable constraints for the first external characteristic interval;

[0028] The uncertainty coupling relationship between the distribution network and the transmission network is analyzed based on the first external characteristic interval, and a second external characteristic interval of the transmission network is generated. The second external characteristic interval includes a second uncertain interval and a second adjustable interval.

[0029] The first external characteristic interval and the second external characteristic interval are used to describe the interval coupling relationship.

[0030] Preferably, it further includes:

[0031] The cost modeling unit is used to construct a two-stage optimized scheduling model for the distribution network and transmission network based on the joint fuzzy set and according to the scheduling cost and adjustment cost. The two-stage optimized scheduling model includes multiple constraints.

[0032] Preferably, the scheduling solution unit is specifically used for:

[0033] The cooperative optimization scheduling model is decomposed using the alternating direction multiplier method to obtain independent optimization problems;

[0034] Based on the distributed robust optimization algorithm and the dual theory of wireless planning, the independent optimization problem is simplified into a mixed integer linear programming problem, and the solution is obtained to obtain the coordinated scheduling scheme of the new energy power grid.

[0035] A third aspect of this application provides a new energy power grid coordination and dispatching device, the device including a processor and a memory;

[0036] The memory is used to store program code and transmit the program code to the processor;

[0037] The processor is used to execute the new energy power grid coordination and scheduling method described in the first aspect according to the instructions in the program code.

[0038] The fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the new energy grid coordinated dispatch method described in the first aspect.

[0039] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0040] This application provides a method for coordinated scheduling of new energy power grids, comprising: performing uncertainty coupling analysis on the distribution network and transmission network in the new energy power grid to generate interval coupling relationships, the interval coupling relationships including binary variable constraints; generating joint fuzzy sets for the distribution network and transmission network based on power commands and uncertainty mapping analysis of the transmission and distribution networks respectively; performing coupling and collaborative analysis based on the interval coupling relationships and a two-stage optimization scheduling model of the distribution network and transmission network to generate a collaborative optimization scheduling model, the two-stage optimization scheduling model being constructed based on the joint fuzzy sets; and solving the collaborative optimization scheduling model using the alternating direction multiplier method and a distributed robust optimization algorithm to obtain a coordinated scheduling scheme for the new energy power grid.

[0041] The new energy power grid coordinated dispatch method provided in this application fully considers the energy uncertainties of the transmission and distribution network, especially the uncertain interactions between transmission and distribution networks. It generates a coordinated optimization dispatch model through joint modeling, quantifying the uncertainties of the transmission and distribution network into a model-solving problem. Furthermore, using the alternating direction multiplier method and distributed robust optimization algorithms to solve this model significantly simplifies the coupled coordinated problem model, transforming it into simpler subproblems, thereby reducing the solution complexity. While ensuring the accuracy of the model solution, it also ensures the efficiency of model computation, meeting the needs of practical applications. Therefore, this application solves the technical problem that existing technologies rely on deterministic interactions in the transmission and distribution network, and their solutions are relatively conservative, resulting in low efficiency and accuracy. Coupled with the large computational scale, these technologies are unable to meet the requirements of coordinated dispatch of new energy power grids in practical scenarios. Attached Figure Description

[0042] Figure 1 A flowchart illustrating a new energy power grid coordinated dispatch method provided in this application embodiment;

[0043] Figure 2 A schematic diagram of the structure of a new energy power grid coordination and dispatching device provided in this application embodiment;

[0044] Figure 3This is a schematic diagram illustrating the process of solving the collaborative optimization scheduling model using the alternating direction multiplier method and the distributed robust optimization algorithm, as provided in this lightweight embodiment. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0046] For easier understanding, please refer to Figure 1 This application provides an embodiment of a new energy power grid coordinated dispatch method, which includes:

[0047] Step 101: Perform uncertainty coupling analysis on the distribution network and transmission network in the new energy power grid to generate interval coupling relationships, which include binary variable constraints.

[0048] Further, step 101 includes:

[0049] Define the first external characteristic interval of the distribution network in the new energy power grid, which includes a first uncertain interval and a first adjustable interval;

[0050] Configure binary variable constraints for the first external characteristic interval;

[0051] The uncertainty coupling relationship between the distribution network and the transmission network is analyzed based on the first external characteristic interval, and the second external characteristic interval of the transmission network is generated. The second external characteristic interval includes the second uncertain interval and the second adjustable interval.

[0052] The first external characteristic interval and the second external characteristic interval are used to describe the interval coupling relationship.

[0053] It should be noted that, typically, a system includes one transmission network and multiple distribution networks; for the distribution network, its external characteristics can be defined by a first uncertainty interval. and the first adjustable range This is expressed as the first external characteristic range. For a distribution network, it can only be uncertain or adjustable, so this embodiment also needs to introduce binary variable constraints for it:

[0054]

[0055]

[0056] in, Let be a positive real number, and its value be sufficiently large. Represents a binary variable.

[0057] Since the distribution network and transmission network are coupled, the first uncertainty interval is equivalent to the distribution network transmitting uncertainty to the transmission network. Therefore, the transmission network needs to provide the distribution network with a second adjustable interval. Similarly, the first adjustable range is equivalent to the transmission network transferring uncertainty to the distribution network, while the transmission network provides the distribution network with a second uncertain range. Therefore, the second external characteristic interval of the transmission network corresponds to the first external characteristic interval of the distribution network, which can be specifically expressed as:

[0058]

[0059]

[0060] Step 102: Generate joint fuzzy sets for the distribution network and the transmission network based on power command and uncertainty mapping analysis of the power transmission and distribution network.

[0061] For distribution networks In terms of power commands issued by the external transmission network It is uncertain, and its internal new energy sources fluctuate. It is also uncertain, because The upper and lower boundaries are variables, and nonlinear terms will be generated in the process of dealing with uncertainties. Therefore, this embodiment will... Uncertainty mapped to normalization ,in, , Therefore, the specific mapping relationship can be expressed as:

[0062]

[0063] Based on this mapping, the distribution network The joint fuzzy set of internal and external uncertainties can be expressed as:

[0064]

[0065] in The joint fuzzy set of the distribution network represents the feasible region of the probability distribution of internal and external uncertainties. This represents the set of internal and external uncertainties, that is, the scope of scenarios with internal and external uncertainties. Let f(u,v) represent the probability distribution under internal and external uncertainties, and f(u,v) represent the probability density of scenario (u,v). The first row indicates that the sum of all probability densities is 1, and the second row indicates that the expected value of the new energy fluctuation u is 1. The third line indicates the expected value of the power command v issued by the transmission network to the distribution network. The fourth line indicates that the 1-norm of new energy fluctuations in each time period does not exceed a threshold. ; h, g, For each row of constraints, there is a dual variable.

[0066] Distribution network Joint uncertainty set The scope of scenarios involving internal and external uncertainties is expressed as follows:

[0067]

[0068] and They represent the distribution network. The lower and upper bounds of internal new energy uncertainty uα and external power command uncertainty vα.

[0069] Similarly, the uncertainty of the transmission network also includes the power command uncertainty and internal renewable energy fluctuations of the distribution network, so its joint fuzzy set can also be expressed in a similar way as described above. One transmission network is connected to multiple distribution networks; in this embodiment, subscripts are used. =0 indicates the power transmission network, subscript This refers to the distribution network. Additionally, there are external uncertainties in the transmission network. Where N is the number of distribution networks, including the power command uncertainty of all connected distribution networks. Therefore, the joint fuzzy set of the transmission network is represented as:

[0070]

[0071] Due to the uncertainty of new energy sources within the power transmission network, Distribution network connected to the transmission network Uncertainty regarding power command for the transmission network, and These represent the lower and upper bounds of the two uncertainty conditions, respectively.

[0072] Step 103: Based on the interval coupling relationship, perform coupling and collaborative analysis according to the two-stage optimization scheduling model of the distribution network and the transmission network to generate a collaborative optimization scheduling model. The two-stage optimization scheduling model is constructed based on joint fuzzy sets.

[0073] Furthermore, step 103, preceding the following, also includes:

[0074] Based on joint fuzzy sets, a two-stage optimal scheduling model for distribution and transmission networks is constructed according to scheduling cost and adjustment cost. The two-stage optimal scheduling model includes multiple constraints.

[0075] In this embodiment, the two-stage optimal scheduling model of the transmission network mainly optimizes the sum of the expected values ​​of the pre-scheduling cost in the first stage and the adjustment cost in the second stage:

[0076]

[0077] The first formula is the objective function, and the first two terms of the objective function represent the unit standby cost. and The lower and upper reserve cost coefficients are used. The reference output for unit i, and The third term represents the lower and upper limits of the standby capacity for unit i, and f is the base generation cost of the unit. i,t () represents a quadratic function of the power generation cost of unit i, with the fourth term being the expected value of the second-stage adjustment cost. The second formula represents the output constraint of unit i. and The upper and lower bounds of the output of unit i are defined, and the third and fourth formulas constrain the unit's ramp-up constraint. and The fifth formula represents the maximum downward and upward ramp rates for unit i, and it also represents the benchmark output constraint for new energy sources. To provide the benchmark output for new energy k, Its available output prediction value, the sixth formula is the line transmission power constraint, g l,j D is the power transfer factor, representing the impact of power injection at node j on the transmission power of branch l. j,t Let j be the load of node j. For the reference power transmission with the distribution network α, the seventh formula is the power balance constraint.

[0078] For the second stage of regulation cost optimization, the transmission network can adjust the output of generating units, the output of new energy sources, and the interaction power with the distribution network in a timely manner to cope with real-time fluctuations in new energy sources. Therefore, the regulation cost Q(x,u,v) in the second stage can be expressed as:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] The first formula is the objective function, and the first term of the objective function is the unit adjustment cost. and These represent the upward and downward power adjustments for unit i, respectively. and The first item represents an increase or decrease in cost coefficients, while the second item represents a penalty cost reduction for new energy vehicles. z is the penalty cost coefficient. k,t For new energy power stations k in fluctuating scenarios u k,t The actual output, the third item is the distribution network reserve cost, r 0,α,t The standby power output provided for distribution network α, d 0,α,t This is the standby cost coefficient. The second formula represents the actual output p of unit i. i,t The first formula equals the sum of the baseline output and the adjustment value. The second formula represents the actual output constraint of the new energy source k. The third formula represents the output of the transmission and distribution networks. Interactive power constraints, v 0,α,t To the distribution network The transmitted power is uncertain. The fifth formula is the line transmission power constraint, and the sixth formula is the power balance constraint.

[0085] The two-stage optimal scheduling model for the distribution network mainly optimizes the sum of the expected values ​​of the pre-scheduling cost in the first stage and the adjustment cost in the second stage.

[0086]

[0087] in, For the distribution network Penalty coefficient for the uncertainty interval This is the reward coefficient for the first adjustable interval.

[0088] For the second phase of adjustment cost optimization, the distribution network can adjust the output of micro gas turbines, renewable energy sources, and the interaction power with the transmission network in a timely manner to cope with real-time renewable energy fluctuations.

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] The first formula is the objective function, and the first term of the objective function is the electricity purchase cost of the distribution network, c. t For time-of-use pricing, r a,t The second term represents the reserve power provided to the power transmission network, and the reduction penalty cost of new energy n is ηn, where ηn is the penalty cost coefficient, Z n,t u is the predicted value of new energy n. n,t For fluctuations, For actual output, y represents the total set of decision variables in the second stage. The second formula represents the active power balance constraint of node i, where Ai and Bi are the upstream and downstream node sets of node i, respectively, and p ij,t p represents the active power transmitted by line ij. i,t The third formula represents the active power injection at node i, and the third formula represents the reactive power balance constraint at node i. ij,t The fourth formula represents the active power transmitted in branch ij, and the fifth formula represents the voltage drop constraint at the two ends of the branch. i,t Let e0 be the voltage at node i, and r be the voltage reference value. ij and x ij These represent the resistance and reactance of line ij, respectively. The fifth formula represents the composition of active power injection at the node, including the active power output of the micro gas turbine m. The active power output of new energy n The active load of node i and the power of interaction with the transmission network. δi indicates whether node i is a bus; it equals 1 if it is, and 0 if it is not. The sixth formula represents the composition of reactive power injection at the node, including the reactive power output of the micro gas turbine m. The active power output of new energy n The reactive load of node i and reactive power interacting with the transmission network. The seventh formula is the upper and lower bound constraints for the active and reactive power output of the micro gas turbine m, and the eighth formula is the upper and lower bound constraints for the active and reactive power output of the new energy n.

[0098] Since both transmission and distribution networks are linear, their problem forms are consistent. Once the coupling variables are determined, the two-stage optimal scheduling models for both transmission and distribution networks can be described in a unified and compact form as follows:

[0099]

[0100] in, Indicates the power transmission network. This represents the distribution network. Since the models for transmission and distribution networks are similar, they are distinguished only by the different values ​​of their subscripts. For the pre-scheduling cost of the first phase, x αLet X represent the set of decision variables for the first stage, and f be the decision variables for the first stage. α Its cost coefficient. To adjust costs in the second phase, P represents the expected value. α Let be the probability distribution under uncertain conditions.

[0101] The compact form of the second-stage cost adjustment is as follows:

[0102]

[0103] Among them, y α For the second-stage decision variable, a α b α d α Let A be a constant vector. α B α C α D is a constant matrix. α [x α [Regarding x] α A linear function.

[0104] Based on the above model and interval coupling relationships, a collaborative optimization scheduling model for the power transmission and distribution network can be constructed. The interval coupling relationships are described by corresponding expressions formed by specific first and second external characteristic intervals; coupling is based on the reference transmission power. A compact expression for the coupling relationship can be determined:

[0105]

[0106] Among them, T 0,α For the boundary variables of the transmission network, specifically T α For the boundary variables of the distribution network, specifically .

[0107] Therefore, the compact form of the coordinated optimization scheduling problem of the power transmission and distribution network is:

[0108]

[0109] Step 104: Solve the collaborative optimization scheduling model using the alternating direction multiplier method and the distributed robust optimization algorithm to obtain the coordinated scheduling scheme for the new energy power grid.

[0110] Further, step 104 includes:

[0111] The cooperative optimization scheduling model is decomposed into independent optimization problems by using the alternating direction multiplier method.

[0112] Based on the distributed robust optimization algorithm and the dual theory of wireless planning, the independent optimization problem is simplified into a mixed integer linear programming problem, and the solution is obtained to obtain the coordinated scheduling scheme of the new energy power grid.

[0113] Please see Figure 3 Since the computational scale of the transmission and distribution coordinated optimization problem is large, the outer iterative solution based on the alternating direction multiplier method in this embodiment is to solve the coordinated optimization scheduling model in a decentralized manner, and to decompose the problem into independent transmission network and distribution network problems for separate calculation; in addition, since both transmission and distribution networks are uncertain problems, this embodiment uses a distributed robust optimization algorithm to solve each model separately.

[0114] The solution process based on the alternating direction multiplier method in this embodiment is as follows: Initialize boundary variable T 0,α T α and Lagrange multipliers Let the iteration step size be Then, the collaborative optimization scheduling model of the transmission and distribution network is decomposed to obtain independent optimization problems for the transmission and distribution networks:

[0115]

[0116]

[0117] in, Given the update step size of the Lagrange multipliers, the objective values ​​of each subsystem are added together to obtain the overall objective of the power transmission and distribution network optimization problem, expressed as: .

[0118] Lagrange multipliers update:

[0119]

[0120] The algorithm converges and outputs the optimal decomposition decision x if the following conditions are met. α Otherwise, update the iteration step size. Return to the decomposition operation process:

[0121]

[0122]

[0123] , It is a very small positive real number.

[0124] Since the independent optimization problems of each power transmission and distribution network are solved using distributed robust optimization algorithms and wireless planning duality theory, this embodiment only describes the solution process using the distribution network as an example. For the aforementioned generated independent optimization problems, the expected adjustment cost in the second stage can be expressed as:

[0125]

[0126] Given the uncertainty of probability distributions, its essence is an infinite programming problem. Therefore, according to the duality theory of infinite programming, it can be transformed into a dual problem:

[0127]

[0128]

[0129] The constraints must hold for any scenario; since the scenario size is infinite, extreme scenarios can be extracted. When the constraints are satisfied in the extreme scenarios, they can be considered to hold for any scenario; therefore, once all extreme scenarios are found, the independent optimization problem can be transformed into a deterministic expression:

[0130]

[0131]

[0132] Among them, (u α,n ,v α,n Let ) represent the nth extreme scenario for distribution network α. This is a collection of extreme scenarios. That is, introducing the decision variable y n This leads to the issue of adjusting costs in the second phase.

[0133] The key to deterministic transformation lies in identifying the extreme scenarios that have the greatest impact on the constraints. The problem of finding extreme scenarios can be represented as a subproblem that minimizes the feasibility of the constraints:

[0134]

[0135] because It is a minimization problem, which utilizes the duality principle to reduce Q. α Perform the transformation:

[0136]

[0137] Substituting the dual form of into the formula for the subproblem that minimizes the constraint feasibility, the subproblem becomes:

[0138]

[0139] The optimal value of a subproblem must occur in uncertainty (u α ,v α At the vertices of ), binary variables can be introduced to linearize the bilinear terms. At the same time, the 1-norm If linearized, the subproblem can eventually be transformed into a mixed-integer linear programming problem.

[0140] The specific subsystem problem-solving process is as follows: Initialize the extreme scenario set N, then solve the main problem, i.e., the independent optimization problem, to obtain the decision variable x. α and dual variables Next, we solve the subproblem expressed by the above formula to obtain an extreme scenario (u). α ,v α The objective value of the subproblem is defined as f. sub If the following conditions are met, the algorithm converges and outputs the boundary variable T. α Otherwise, regenerate the extreme scenarios and add them to set N, then continue solving the main problem and subproblems.

[0141]

[0142] in, It is a very small positive real number.

[0143] It should be noted that as the alternating direction multiplier method iterates, the boundary variables of adjacent iterations tend to be consistent. Therefore, in solving transmission and distribution network problems, the extreme scenario set of the current iteration can be initialized using the extreme scenario set formed in the previous iteration to improve the algorithm's solution efficiency.

[0144] Based on the above, it can be seen that the embodiments of this application, through transmission and distribution coordinated optimization, can fully assess the adjustability potential of the distribution network and support the consumption of new energy across the entire network by flexibly adjusting the exchange power between the transmission and distribution networks. Simultaneously, the distributed optimization algorithm fully considers all extreme cases of uncertainties in the transmission and distribution networks, ensuring the safe operation of the system. The distributed optimization algorithm decouples the overall system problem into independent optimization problems for the transmission and distribution networks, effectively reducing the optimization difficulty for the dispatch center.

[0145] The new energy power grid coordinated dispatch method provided in this application fully considers the energy uncertainties of the transmission and distribution network, especially the uncertain interactions between transmission and distribution networks. It generates a coordinated optimization dispatch model through joint modeling, quantifying the uncertainties of the transmission and distribution network into a model-solving problem. Furthermore, using the alternating direction multiplier method and distributed robust optimization algorithm to solve this model significantly simplifies the coupled coordinated problem model, transforming it into simpler subproblems, thereby reducing the solution complexity. While ensuring the accuracy of the model solution, it also ensures the efficiency of model computation, meeting the needs of practical applications. Therefore, this application can solve the technical problem that existing technologies rely on deterministic interactions in the transmission and distribution network, and the solutions are relatively conservative, resulting in low efficiency and accuracy. Coupled with the large computational scale, these technologies are unable to meet the requirements of coordinated dispatch of new energy power grids in practical scenarios.

[0146] For easier understanding, please refer to Figure 2 This application provides an embodiment of a new energy power grid coordinated dispatch device, comprising:

[0147] The coupling analysis unit 201 is used to perform uncertainty coupling analysis on the distribution network and transmission network in the new energy power grid, and generate interval coupling relationships, which include binary variable constraints.

[0148] The fuzzy analysis unit 202 is used to generate joint fuzzy sets for the distribution network and the transmission network based on the power command and uncertainty mapping analysis of the power transmission and distribution network, respectively.

[0149] The collaborative modeling unit 203 is used to perform coupled collaborative analysis based on the interval coupling relationship and the two-stage optimal scheduling model of the distribution network and the transmission network to generate a collaborative optimal scheduling model. The two-stage optimal scheduling model is constructed based on joint fuzzy sets.

[0150] The scheduling solution unit 204 is used to solve the collaborative optimization scheduling model using the alternating direction multiplier method and the distributed robust optimization algorithm to obtain the coordinated scheduling scheme of the new energy power grid.

[0151] Furthermore, the coupling analysis unit 201 is specifically used for:

[0152] Define the first external characteristic interval of the distribution network in the new energy power grid, which includes a first uncertain interval and a first adjustable interval;

[0153] Configure binary variable constraints for the first external characteristic interval;

[0154] The uncertainty coupling relationship between the distribution network and the transmission network is analyzed based on the first external characteristic interval, and the second external characteristic interval of the transmission network is generated. The second external characteristic interval includes the second uncertain interval and the second adjustable interval.

[0155] The first external characteristic interval and the second external characteristic interval are used to describe the interval coupling relationship.

[0156] Furthermore, it also includes:

[0157] Cost modeling unit 205 is used to construct a two-stage optimal scheduling model for distribution networks and transmission networks based on joint fuzzy sets and scheduling costs and adjustment costs. The two-stage optimal scheduling model includes multiple constraints.

[0158] Furthermore, the scheduling solution unit 204 is specifically used for:

[0159] The cooperative optimization scheduling model is decomposed into independent optimization problems by using the alternating direction multiplier method.

[0160] Based on the distributed robust optimization algorithm and the dual theory of wireless planning, the independent optimization problem is simplified into a mixed integer linear programming problem, and the solution is obtained to obtain the coordinated scheduling scheme of the new energy power grid.

[0161] This application also provides a new energy grid coordination and dispatching device, which includes a processor and a memory;

[0162] The memory is used to store program code and transfer the program code to the processor;

[0163] The processor is used to execute the new energy grid coordination and scheduling method in the above method embodiment according to the instructions in the program code.

[0164] This application also provides a computer-readable storage medium for storing program code for executing the new energy grid coordination and dispatch method in the above method embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0169] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A new energy grid coordinated dispatching method, characterized in that, The method comprises the following steps: performing uncertainty coupling analysis on the distribution network and the transmission network in the new energy power grid to generate an interval coupling relationship, wherein the interval coupling relationship comprises binary variable constraints; generating joint fuzzy sets of the distribution network and the transmission network based on power instructions and uncertainty mapping analysis of the distribution and transmission networks; performing coupling and collaborative analysis on the two-stage optimal scheduling models of the distribution network and the transmission network based on the interval coupling relationship to generate a collaborative optimal scheduling model, wherein the two-stage optimal scheduling models are constructed based on the joint fuzzy sets; solving the collaborative optimal scheduling model by using an alternating direction multiplier method and a distributed robust optimization algorithm to obtain a new energy power grid coordinated scheduling scheme.

2. The new energy grid coordinated dispatching method according to claim 1, characterized in that, The uncertainty coupling analysis on the distribution network and the transmission network in the new energy power grid to generate an interval coupling relationship, wherein the interval coupling relationship comprises binary variable constraints, comprises the following steps: defining a first external characteristic interval of the distribution network in the new energy power grid, wherein the first external characteristic interval comprises a first uncertainty interval and a first adjustable interval; configuring binary variable constraints for the first external characteristic interval; analyzing the uncertainty coupling relationship of the distribution network and the transmission network according to the first external characteristic interval, and generating a second external characteristic interval of the transmission network, wherein the second external characteristic interval comprises a second uncertainty interval and a second adjustable interval; describing the interval coupling relationship by using the first external characteristic interval and the second external characteristic interval.

3. The new energy grid coordinated dispatching method according to claim 1, characterized in that, The coupling and collaborative analysis on the two-stage optimal scheduling models of the distribution network and the transmission network based on the interval coupling relationship to generate a collaborative optimal scheduling model further comprises the following steps: constructing the two-stage optimal scheduling models of the distribution network and the transmission network according to scheduling costs and adjustment costs based on the joint fuzzy sets, wherein the two-stage optimal scheduling models comprise a plurality of constraint conditions.

4. The new energy grid coordinated dispatching method of claim 1, wherein, The solving of the collaborative optimal scheduling model by using an alternating direction multiplier method and a distributed robust optimization algorithm to obtain a new energy power grid coordinated scheduling scheme comprises the following steps: optimally decomposing the collaborative optimal scheduling model by using an alternating direction multiplier method to obtain independent optimization problems; simplifying the independent optimization problems into a mixed integer linear programming problem based on a distributed robust optimization algorithm and a wireless programming dual theory, and solving to obtain a new energy power grid coordinated scheduling scheme.

5. A new energy grid coordinated dispatching device, characterized in that, The method comprises the following steps: a coupling analysis unit is configured to perform uncertainty coupling analysis on the distribution network and the transmission network in the new energy power grid to generate an interval coupling relationship, wherein the interval coupling relationship comprises binary variable constraints; a fuzzy analysis unit is configured to generate joint fuzzy sets of the distribution network and the transmission network based on power instructions and uncertainty mapping analysis of the distribution and transmission networks; a collaborative modeling unit is configured to perform coupling and collaborative analysis on the two-stage optimal scheduling models of the distribution network and the transmission network based on the interval coupling relationship to generate a collaborative optimal scheduling model, wherein the two-stage optimal scheduling models are constructed based on the joint fuzzy sets; a scheduling solving unit is configured to solve the collaborative optimal scheduling model by using an alternating direction multiplier method and a distributed robust optimization algorithm to obtain a new energy power grid coordinated scheduling scheme.

6. The new energy grid coordinated dispatching device according to claim 5, characterized in that, The coupling analysis unit is specifically configured to: defining a first outer characteristic interval of a distribution network in a new energy power grid, the first outer characteristic interval comprising a first uncertain interval and a first adjustable interval; configuring a binary variable constraint for the first outer characteristic interval; analyzing an uncertain coupling relationship between the distribution network and the transmission network according to the first outer characteristic interval, and generating a second outer characteristic interval of the transmission network, the second outer characteristic interval comprising a second uncertain interval and a second adjustable interval; describing an interval coupling relationship by using the first outer characteristic interval and the second outer characteristic interval.

7. The new energy grid coordinated dispatching device according to claim 5, characterized in that, Further comprising: a cost modeling unit configured to construct a two-stage optimal scheduling model of the distribution network and the transmission network according to a scheduling cost and a regulation cost based on the joint fuzzy set, the two-stage optimal scheduling model comprising a plurality of constraint conditions. 8.The new energy grid coordinated dispatching device according to claim 5, characterized in that, The scheduling solving unit is specifically configured to: perform optimal decomposition on the collaborative optimal scheduling model by using an alternating direction multiplier method to obtain independent optimization problems; simplify the independent optimization problems into a mixed integer linear programming problem based on a distributed robust optimization algorithm and a wireless programming duality theory, and solve to obtain a new energy power grid coordinated scheduling scheme.

9. A new energy power grid coordinated dispatching device, characterized in that, The device comprises a processor and a memory; The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the new energy power grid coordinated scheduling method according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store program code, and the program code is configured to execute the new energy power grid coordinated scheduling method.

Images