A data-driven distributed and robust joint opportunity-constrained dispatching method for AC / DC hybrid power systems

Abstract

The present invention discloses a data-driven distributed-rod joint opportunity-constrained scheduling method for an AC / DC hybrid power system, comprising the following steps: constructing a flexible consumption model for a DC interconnection line; constructing a data-driven distributed-rod joint opportunity-constrained scheduling model for the AC / DC hybrid system based on the flexible consumption model for the DC interconnection line; reconstructing the data-driven distributed-rod joint opportunity-constrained scheduling model based on the optimal conditional value-at-risk approximation (OCA), and solving the reconstructed data-driven distributed-rod joint opportunity-constrained scheduling model through an iterative sequential convex programming (SCP) algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a data-driven distributed robust joint opportunity-constrained scheduling method for an AC / DC hybrid power system. Background Art

[0002] Global wind power generation has grown rapidly over the past few decades. However, due to the remote locations of some wind farms, interregional power transmission is crucial for accommodating large-scale wind power. Currently, high-voltage direct current (HVDC) and high-voltage alternating current (HVAC) technologies provide sufficient technical support for large-scale renewable energy transmission between remote locations. HVDC also reduces losses during long-distance transmission and enhances grid controllability. HVDC transmission technology, with its flexible power adjustment capabilities, interconnects AC grids via high-voltage DC interconnectors, making the operation of the entire AC / DC hybrid system more economical and efficient.

[0003] Currently, modeling methods such as stochastic optimization, two-stage robust optimization, and data-driven distributed robust opportunity constraints are commonly used to address the uncertainty of high renewable energy output. Stochastic optimization methods struggle to obtain accurate probability distributions for uncertain variables, while two-stage robust optimization solutions are overly conservative. Due to the difficulty in obtaining a suitable probability distribution to describe uncertainty and the availability of historical data, data-driven distributed robust opportunity constraint modeling methods have gained increasing attention in recent years. They eliminate the reliance of stochastic optimization on accurate probability distributions and address the conservatism of robust optimization solutions. However, current distributed robust scheduling models ignore the spatiotemporal correlations among the uncertain variables in renewable energy output, resulting in overly conservative solutions. Furthermore, the strong non-convexity of the distributed robust joint opportunity constraint model makes it difficult to solve and computationally challenging. Therefore, there is an urgent need to develop efficient and advanced approximate reconstruction methods for data-driven distributed robust joint opportunity constraint scheduling models that consider the spatiotemporal correlations of uncertain variables, thereby enabling data-driven distributed robust joint opportunity constraint scheduling for AC / DC hybrid power systems. Summary of the Invention

[0004] In view of this, the present invention provides a data-driven distributed robust joint opportunity constrained scheduling method for an AC / DC hybrid power system, which is used to at least solve the problem that the model in the prior art is difficult to solve.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A data-driven distributed robust joint opportunity-constrained scheduling method for an AC / DC hybrid power system comprises the following steps:

[0007] Construct a flexible consumption model for DC interconnection lines;

[0008] A data-driven distributed and blue-robust joint opportunity-constrained scheduling model is constructed based on the flexible consumption model of DC tie lines;

[0009] The data-driven distributed robust joint opportunity-constrained scheduling model is reconstructed and solved, including:

[0010] S1. Rewrite the data-driven distributed robust joint chance-constrained scheduling model into a compact form:

[0011] Objective function:

[0012] Joint opportunity constraints:

[0013] Where c 0,1 、c 0,2 and C0 represent the coefficient matrix of the objective function, and the elements in C0 are 0 or 1, the matrix and Represents the power regulation factor of the thermal power unit at time t, and t∈T, T represents the total number of scheduling periods; the matrix and Indicates the addition of α t Other decision variables besides represents the probability distribution of wind power output, represents the set of all possible probability distributions of wind power output, represents the expected value, ξ represents the wind power prediction error, Indicates the The risk level of the joint opportunity constraint is joint opportunity constraints, and No. The coefficient matrix of the joint opportunity constraints;

[0014] S2. Based on the optimal conditional value-at-risk (OCA) approach, the compact data-driven distributed robust joint opportunity-constrained scheduling model is reconstructed to obtain the reconstructed model Φ(Δ), where the objective function represents the sum of the power generation cost under the wind power forecast scenario and the power generation adjustment cost under the wind power error scenario:

[0015]

[0016] Where, For the The newly introduced scaling parameter is constrained by a joint chance constraint, ∈ is the risk level of the joint opportunity constraint, τ is the auxiliary variable, and L(·) represents the loss function;

[0017] S3. Find the optimal solution to equation (3) through the iterative sequential convex programming SCP algorithm:

[0018] S31. Initialize iterative residual Constant φ>0, let For the The number of constraints in a joint opportunity constraint;

[0019] S32. Introduce slack variables to reconstruct Equation (3) into Equation (4), and iteratively solve Equation (4):

[0020]

[0021] Where, For the Auxiliary slack variables introduced by joint opportunity constraints, and ω is the penalty coefficient; A represents the expected term in the objective function (1) After the equivalent convex cone programming problem, B represents the convex cone constraint after the joint opportunity constraint (2) is processed by the optimal conditional risk value approximation OCA;

[0022] S33. If satisfied Or if the preset maximum number of iterations is reached, the current solution (x,Λ,ν) is the optimal solution, otherwise proceed to S34;

[0023] S34. Update by solving (5) and returning to S32

[0024]

[0025] Where δ is the scaling parameter, Π ++ represents the relative interior of the probability simplex.

[0026] Preferably, the flexible consumption model of the DC tie line includes:

[0027] DC tie line power limit:

[0028]

[0029] DC tie line power adjustment rate constraints:

[0030]

[0031] DC tie line power adjustment direction constraints:

[0032]

[0033] Limitation on the number of DC tie line power adjustments:

[0034]

[0035] DC tie line power adjustment direction restrictions:

[0036]

[0037] Step-by-step operation characteristics of DC tie-line power transmission:

[0038]

[0039] Where, represents the power of the ith DC tie line at time t, P i DC and denote the lower and upper limits of the transmission power of the ith DC tie line, represents the set of DC tie lines, Represents a set of time periods, and They represent the upward and downward power regulation status of the i-th DC tie line at time t, and denote the upward and downward power regulation rates of the i-th DC tie line, κ DC Indicates the number of power adjustments allowed for the DC tie line, M + and M - represents a positive number, τ represents the shortest duration after the DC tie line adjusts the power once, and Indicates whether the i-th DC tie line starts and ends power regulation at time t.

[0040] Preferably, the data-driven distributed robust joint opportunity-constrained scheduling model is:

[0041]

[0042] The objective function is to minimize the expected power generation cost of the AC regional power grid under the worst case of wind power:

[0043]

[0044] (18)-(19)(31)

[0045] Among them, (21) represents the AC power balance constraint, (22) represents the power injection limit of the DC tie line, (23) represents the power injection limit of the AC tie line, (24)-(25) represent the minimum start-stop time constraint of the thermal power unit, (26)-(27) represent the start-stop action constraint of the thermal power unit, and (28)-(30) are the joint opportunity constraints.

[0046] Where, and They represent the output of thermal power unit i at time t after the uncertainty occurs and in the prediction scenario, respectively, it represents the power regulation factor of thermal power unit i at time t, ξ jt represents the prediction error of wind farm j at time t, and They represent the load, thermal power unit, line, wind farm, DC tie line node and AC tie line node set in AC grid a respectively. represents the AC power grid, a i 、b i and c i They represent the consumption characteristic coefficient of thermal power unit i, u it 、v it and w it They represent the start-up and shutdown status, start-up action and shutdown action of thermal power unit i at time t respectively. represents the startup cost of thermal power unit i, d it represents the output adjustment cost coefficient of thermal power unit i, represents the predicted value of wind farm i at time t, represents the load of node i at time t, represents the power of the ith AC tie line at time t, P i AC and They represent the lower and upper limits of the transmission power of the i-th AC tie line, MU i and MD i are the minimum start-up time and minimum shutdown time of thermal power unit i, P i G 、 Indicates the lower and upper limits of the output of thermal power unit i, R i represents the ramp rate limit of thermal power unit i, ∈1, ∈2 and ∈3 represent the risk levels of the joint opportunity constraint, represents the transmission power upper limit of line l, w represents the wind farm, d represents the load in AC grid a, g represents the thermal power unit, j represents the jth AC tie line node, M gl 、M wl 、M dl 、M il and M jl They represent the power transfer distribution factors of thermal power units, wind farms, loads, DC interconnection lines, and AC interconnection lines respectively.

[0047] Preferably, the wind power prediction error ξ is constrained by a polyhedron constraint Ω, and Ω=ξ:Lξ≤l, L and l represent the parameters constituting the polyhedron Ω, and the expected term in the objective function (1) is It is equivalent to solving the convex cone programming problem A:

[0048]

[0049] Where θ represents the Wasserstein radius, N represents the number of samples of wind power prediction error, represents the sample value of the i-th wind power prediction error, κ o 、 All are auxiliary variables.

[0050] Preferably, the specific content of S2 includes:

[0051] The joint opportunity constraint (2) is reconstructed using the optimal conditional value at risk approximation (OCA) method:

[0052] The joint opportunity constraint (2) can be further expressed as:

[0053]

[0054] Among them, C k and c k Respectively and The kth row of

[0055] Reconstruct (33) as:

[0056]

[0057] Using the conditional value-at-risk approximation (34), we get:

[0058]

[0059] according to For a given scaling parameter δ, Equation (35) is further transformed into the following convex cone programming form, namely B:

[0060]

[0061] Where, κ c , σ c , β c , γ c is an auxiliary variable;

[0062] Then (1)-(2) are rewritten into the form based on OCA reconstruction shown in formula (3).

[0063] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a data-driven distributed and robust joint opportunity-constrained scheduling method for an AC / DC hybrid power system, which has the following beneficial effects:

[0064] (1) The present invention makes full use of the flexible adjustment capability of the DC interconnection line and proposes a flexible consumption model for the high-voltage DC interconnection line, thereby realizing the joint optimization of the DC interconnection line transmission power and the output of the power grid generator set, and promoting the cross-regional consumption of a high proportion of new energy.

[0065] (2) A data-driven distributed robust joint opportunity constraint scheduling model is established to cope with the uncertainty of renewable energy output and the spatiotemporal correlation of prediction errors. An optimized conditional value at risk algorithm (OCA) method is proposed to reconstruct the distributed robust joint opportunity constraint model that takes into account the spatiotemporal correlation of renewable energy prediction errors.

[0066] (3) A sequential convex programming (SCP) algorithm with an iterative optimization strategy is proposed to solve the distributed robust joint chance-constrained scheduling model reconstructed by OCA, which reduces the burden of model solution and the conservatism of the distributed robust model. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0068] Figure 1 A topological diagram of an AC / DC hybrid power system with a high proportion of renewable energy provided by an embodiment of the present invention;

[0069] Figure 2 Wind power and load output curve diagram provided by the embodiment of the present invention;

[0070] Figure 3 Flexible absorption curve of DC tie line provided by the embodiment of the present invention;

[0071] Figure 4 The operating costs of the distributed robust joint chance-constrained scheduling model based on BA reconstruction provided in the embodiment of the present invention and the distributed robust joint chance-constrained scheduling model based on OCA reconstruction and solved by the SCA algorithm are compared. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0073] The present invention provides a data driven distributed blue stick joint opportunity constraint scheduling method for AC / DC hybrid power system, which is suitable for objects such as Figure 1 As shown in FIG, AC grid A containing a high proportion of renewable energy transmits large-scale wind power within AC grid A to AC grids B and C over long distances through two high-voltage DC interconnection lines.

[0074] 1. Build a flexible DC tie line consumption model

[0075] In order to fully utilize the flexible adjustment capability of DC interconnection lines and promote the long-distance cross-regional consumption of high-proportion renewable energy, a DC interconnection line flexible consumption model is established. (6) represents the power limit of the DC interconnection line, (7)-(8) represent the power adjustment rate of the DC interconnection line, (9)-(11) represent the power adjustment direction of the DC interconnection line, and ensure that the transmission power of the DC interconnection line cannot be reversed in adjacent time periods, (12) represents the number of power adjustments of the DC interconnection line, (13)-(14) represent the power adjustment direction limit of the DC interconnection line, and (15)-(17) represent the step-by-step operation characteristics of the DC interconnection line transmission power.

[0076] DC tie line power limit:

[0077]

[0078] DC tie line power adjustment rate constraints:

[0079]

[0080] DC tie line power adjustment direction constraints:

[0081]

[0082] Limitation on the number of DC tie line power adjustments:

[0083]

[0084] DC tie line power adjustment direction restrictions:

[0085]

[0086] Step-by-step operation characteristics of DC tie-line power transmission:

[0087]

[0088]

[0089] Where, represents the set of DC tie lines, represents the time period set, κ DC Indicates the number of power adjustments allowed by the DC tie line, P i DC 、 Indicates the lower and upper limits of the transmission power of the DC tie line, M + 、M - Represents a large positive number, τ represents the shortest duration after the DC tie line adjusts the power once, Indicates the upward and downward power regulation rate of the DC tie line, represents the power of the DC tie line at time t, Indicates the upward and downward power regulation status of the DC tie line at time t, Indicates whether the DC tie line starts or ends power regulation at time t.

[0090] 2. Build a data-driven distributed robust joint opportunity-constrained scheduling model

[0091] In order to alleviate the impact of wind power forecast errors on power generation plans, a data-driven distributed robust joint chance-constrained scheduling model is constructed, and the affine adjustable strategy shown in Equation (6) is used to adjust the power of thermal power units to ensure that the forecast errors can be fully absorbed.

[0092]

[0093] The objective function (20) represents the minimization of the expected power generation cost of the AC regional power grid under the worst case scenario of wind power, (21) represents the power balance constraint of the AC power grid, (22) represents the power injection limit of the DC interconnection line, (23) represents the power injection limit of the AC interconnection line, (24)-(25) represent the minimum start-stop time constraint of the thermal power unit, (26)-(27) represent the start-stop action constraint of the thermal power unit, and the joint opportunity constraints (28)-(30) ensure that even under the worst probability distribution of wind power, the maximum / minimum output limit, climbing / slipping rate and line transmission capacity constraint of the thermal power unit can be met.

[0094]

[0095]

[0096] (18)-(19)(31)

[0097] Where, represents the set of loads, thermal power units, lines, wind farms, DC tie line nodes, and AC tie line nodes in the AC power grid a. represents the AC power grid, R i represents the ramp rate limit of thermal power unit i, represents the load of node i at time t, P i G 、 Indicates the lower and upper limits of the output of thermal power unit i, u it 、v it 、w it They represent the start-up and shutdown status, start-up action, and shutdown action of thermal power unit i at time t, respectively. represents the startup cost of thermal power unit i, MU i ,MD i are the minimum start-up time and minimum shutdown time of thermal power unit i, represents the predicted value of wind farm i at time t, ∈ 1-3 represents the risk level of the joint opportunity constraint, M gl 、M wl 、M dl 、M il 、M jl It represents the power transfer distribution factor of thermal power units, wind farms, loads, DC tie lines, and AC tie lines. represents the upper limit of the transmission power of line l, a i 、b i 、c i They represent the consumption characteristic coefficient of thermal power unit i, d it represents the output adjustment cost coefficient of thermal power unit i, They represent the output of thermal power unit i at time t after the uncertainty occurs and in the prediction scenario, respectively, it represents the power regulation factor of thermal power unit i at time t, ξ jt represents the prediction error of wind farm j at time t.

[0098] 3. Data-driven distributed robust joint opportunity-constrained scheduling model reconstruction

[0099] First, the data-driven distributed robust joint opportunity-constrained scheduling problem is written into a compact form as shown in Equation (10). The model considers different nodes and different times Wind power prediction error ξ j,t The spatiotemporal correlation of wind power forecast errors can be eliminated without assuming that the wind power forecast errors are independent and identically distributed.

[0100] Objective function:

[0101] Joint opportunity constraints:

[0102] Where c 0,1 、c 0,2 and C0 represent the coefficient matrix of the objective function, and the elements in C0 are 0 or 1, the matrix and Represents the power regulation factor of the thermal power unit at time t, and t∈T, T represents the total number of scheduling periods; the matrix and Indicates the addition of α t Other decision variables besides represents the probability distribution of wind power output, represents the set of all possible probability distributions of wind power output, represents the expected value, ξ represents the wind power prediction error, Indicates the The risk level of the joint opportunity constraint is joint opportunity constraints, and No. The coefficient matrix of the joint opportunity constraints.

[0103] Without loss of generality, assume that the wind power prediction error ξ is subject to a polyhedron constraint Ω, and Ω=ξ:Lξ≤l, L and l represent the parameters constituting the polyhedron Ω, then the expected term in the objective function (1) is It is equivalent to solving the following convex cone programming problem A:

[0104]

[0105] Where θ represents the Wasserstein radius, N represents the number of samples of wind power prediction error, represents the sample value of the i-th wind power prediction error, κ o 、 All are auxiliary variables.

[0106] The distributed robust joint opportunity constraint (2) can be further expressed as:

[0107]

[0108] Among them, C k and c k are the k-th row of the coefficient matrices C and c, respectively.

[0109] A classic method for dealing with the distributed robust joint chance constraint shown in Equation (33) is to use Bonferroni approximation (BA) to transform the joint chance constraint into multiple independent chance constraints as follows:

[0110]

[0111] Then, the worst-case value at risk is used to express the individual chance constraints:

[0112]

[0113] in, is defined as:

[0114] However, in the above classic BA processing method, ∈ k The value of will significantly affect the accuracy of the distributed robust joint chance constraint model, so it is necessary to choose a suitable k But ∈ k It is very difficult to choose the value of However, this value will make the model results overly conservative.

[0115] To this end, the present invention proposes an optimized conditional value at risk (OCA) processing method to transform the distributed robust joint chance constraints (28)-(30) into a single tractable form.

[0116] First, reconstruct (33) as:

[0117]

[0118] Where δ is the scaling parameter.

[0119] Using the conditional value-at-risk approximation (34), we get:

[0120]

[0121] When the scaling parameter δ is chosen optimally, the approximation of Equation (35) becomes very accurate, and thus the worst-case CVaR constraint can be effectively evaluated.

[0122] according to For a given scaling parameter δ, Equation (35) can be further transformed into the following convex cone programming form, namely B:

[0123]

[0124] Where, κ c , σ c , β c , γ c is an auxiliary variable.

[0125] Therefore, the data-driven distributed robust joint chance-constrained scheduling problem (10) considering the uncertainty of wind power output and the spatiotemporal correlation of prediction errors can be written as the OCA-based reconstruction form shown in Equation (3).

[0126]

[0127] Problem (3) provides an upper bound for the distributed robust joint chance-constrained scheduling problem (10) by optimizing the scaling parameter δ∈Π ++ To find the optimal solution to problem (3), it is:

[0128]

[0129] However, when the scaling parameter δ is a variable, problem (4) becomes a non-convex optimization problem. To this end, this paper further proposes an iterative sequential convex programming (SCP) algorithm to handle non-convex problems by sequentially optimizing (x, Λ) and Δ in alternating directions. The iterative process of the SCP algorithm is shown below.

[0130] Step 1: Initialize the iterative residual A large constant φ>0, let the scaling parameter For the The number of constraints in a joint opportunity constraint;

[0131] Step 2: Solve the following problem:

[0132]

[0133] (13), (19)

[0134] Step 3: If If the condition is satisfied or the maximum number of iterations set by the SCP algorithm is reached, the optimal solution (x,Λ,ν) is returned, otherwise go to step 4;

[0135] Step 4: Update by solving (5) And return to step 2.

[0136]

[0137] In the SCP algorithm, an auxiliary slack variable ν ≥ 0 is introduced in problem (4) and penalized by the penalty coefficient ω in the objective function to ensure the feasibility of the algorithm when the initialization value of the scaling parameter is poorly selected.

[0138] The effect of the present invention will be verified through test examples below.

[0139] In this embodiment, the test environment is carried out in a personal computer equipped with Intel Core i7-12650H, 2.3GHz, 16GB RAM, and 10 cores. The parallel computing toolbox is called in the MATLAB R2021a platform, and Gurobi10.0 is used for solving.

[0140] The test case consists of three 6-node AC grids A, B, and C. AC grid A is connected to AC grids B and C via two DC tie lines, and AC grid B is connected to AC grid C via one AC tie line. In AC grid A, two wind farms are located at nodes 2 and 4, respectively. The output of each wind farm and the load of AC grid A are shown in the following figure. Figure 2 The transmission capacity upper and lower limits of the DC tie line are [50, 150] MW, the transmission capacity upper and lower limits of the AC tie line are [0, 100] MW, the power adjustment rate of the DC tie line is [10, 30] MW, the number of power adjustments allowed per day is 6, and the minimum power adjustment duration interval is 2 hours.

[0141] (1) Effectiveness of the flexible consumption model of DC tie lines

[0142] The role of the proposed DC interconnection line flexible consumption model in promoting the cross-regional consumption of high-proportion renewable energy is as follows: Figure 3 As shown, the transmission power of DC tie lines AB and AC matches the changes in wind power output in AC grid A. During periods 2 to 9, as wind power output increases, the DC tie line transmission power also increases, delivering more power to AC grids B and C. Similarly, during periods 21 to 24, when the AC grid load increases, the DC tie line transmission power increases to support the load demands of AC grids B and C. This demonstrates that the proposed DC tie line flexible accommodation model can achieve a joint optimization of DC tie line transmission power and AC grid generator output, better adapting to fluctuations in large-scale wind power output.

[0143] (2) The effectiveness of the data-driven distributed robust joint opportunity-constrained scheduling model considering the uncertainty of renewable energy output and the spatiotemporal correlation of prediction errors

[0144] The proposed distributed robust model based on OCA reconstruction and iterative solution using SCP algorithm is compared with the classic distributed robust model based on BA. The system operation cost comparison under different sample numbers N is shown in Figure 2. Figure 4 As shown in Figure 2, it can be found that the size of the system operation cost is negatively correlated with the size of the sample size N, that is, when the sample size N is larger, the system operation cost is smaller. Figure 4 It can be seen from the above that under a certain sample size N, the system operation cost of the classic BA-based distributed robust model is higher than that of the proposed distributed robust model based on OCA reconstruction and SCP solution, that is, the proposed OCA reconstruction model can reduce the conservatism of the distributed robust scheduling model. Moreover, when the Wasserstein radius θ is close to 10 -3 When θ is larger, the operating cost of the BA-based system increases dramatically. It can be concluded that when the Wasserstein radius θ is larger, the solution obtained by BA is more conservative than the proposed model based on OCA reconstruction and SCP solution. In addition, when the Wasserstein radius θ is less than 10 -2 When , the operating cost of the proposed model reconstructed based on OCA and solved by SCP does not change significantly, which means that the proposed distributed robust scheduling model based on OCA reconstruction, which considers uncertainty and spatiotemporal correlation of prediction errors, has a critical Wasserstein radius θ, which corresponds to better solution performance.

[0145] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A data-driven distributed robust joint opportunity-constrained scheduling method for AC / DC hybrid power systems, characterized in that: The following steps are involved: Construct a flexible consumption model for DC interconnection lines; A data-driven distributed and blue-robust joint opportunity-constrained scheduling model is constructed based on the flexible consumption model of DC tie lines; The data-driven distributed robust joint opportunity-constrained scheduling model is reconstructed and solved, including: S1. Rewrite the data-driven distributed robust joint chance-constrained scheduling model into a compact form: Objective function: Joint opportunity constraints: Where c 0,1 、c 0,2 and C0 represent the coefficient matrix of the objective function, and the elements in C0 are 0 or 1, the matrix and Represents the power regulation factor of the thermal power unit at time t, and t∈T, T represents the total number of scheduling periods; the matrix and Indicates the addition of α t Other decision variables besides represents the probability distribution of wind power output, represents the set of all possible probability distributions of wind power output, represents the expected value, ξ represents the wind power prediction error, ∈ l represents the risk level of the lth joint opportunity constraint. For the lth joint opportunity constraint, C l (·) and c l (·) The coefficient matrix of the lth joint opportunity constraint; S2. Based on the optimal conditional value-at-risk (OCA) approach, the compact data-driven distributed robust joint opportunity-constrained scheduling model is reconstructed to obtain the reconstructed model Φ(Δ), where the objective function represents the sum of the power generation cost under the wind power forecast scenario and the power generation adjustment cost under the wind power error scenario: Where, δ l The newly introduced scaling parameter for the l-th joint opportunity constraint, ∈ is the risk level of the joint opportunity constraint, τ is the auxiliary variable, and L(·) represents the loss function; S3. Find the optimal solution to equation (3) through the iterative sequential convex programming SCP algorithm: S31. Initialize iterative residual Constant φ>0, let For the The number of constraints in a joint opportunity constraint; S32. Introduce slack variables to reconstruct Equation (3) into Equation (4), and iteratively solve Equation (4): Where, ν l is the auxiliary slack variable introduced for the lth joint opportunity constraint, and ν l ≥0, ω is the penalty coefficient; A represents the expected term in the objective function (1) After the equivalent convex cone programming problem, B represents the convex cone constraint after the joint opportunity constraint (2) is processed by the optimal conditional risk value approximation OCA; S33. If satisfied Or if the preset maximum number of iterations is reached, the current solution (x,Λ,ν) is the optimal solution, otherwise proceed to S34; S34. Update δ by solving (5) and returning to S32 l ; Where δ is the scaling parameter, Π ++ represents the relative interior of the probability simplex.

2. The data-driven distributed robust joint opportunity-constrained scheduling method for AC / DC hybrid power systems according to claim 1, characterized in that: The flexible consumption model of DC tie lines includes: DC tie line power limit: DC tie line power adjustment rate constraints: DC tie line power adjustment direction constraints: Limitation on the number of DC tie line power adjustments: DC tie line power adjustment direction restrictions: Step-by-step operation characteristics of DC tie-line power transmission: Where, represents the power of the ith DC tie line at time t, P i DC and denote the lower and upper limits of the transmission power of the ith DC tie line, represents the set of DC tie lines, Represents a set of time periods, and They represent the upward and downward power regulation status of the i-th DC tie line at time t, and denote the upward and downward power regulation rates of the i-th DC tie line, κ DC Indicates the number of power adjustments allowed for the DC tie line, M + and M - represents a positive number, τ represents the shortest duration after the DC tie line adjusts the power once, and Indicates whether the i-th DC tie line starts and ends power regulation at time t.

3. The data-driven distributed robust joint opportunity-constrained scheduling method for AC / DC hybrid power systems according to claim 2, characterized in that: The data-driven distributed robust joint opportunity-constrained scheduling model is: The objective function is to minimize the expected power generation cost of the AC regional power grid under the worst case of wind power: Among them, (21) represents the AC power balance constraint, (22) represents the power injection limit of the DC tie line, (23) represents the power injection limit of the AC tie line, (24)-(25) represent the minimum start-stop time constraint of the thermal power unit, (26)-(27) represent the start-stop action constraint of the thermal power unit, and (28)-(30) are the joint opportunity constraints. Where, and They represent the output of thermal power unit i at time t after the uncertainty occurs and in the prediction scenario, respectively, it represents the power regulation factor of thermal power unit i at time t, ξ jt represents the prediction error of wind farm j at time t, and They represent the load, thermal power unit, line, wind farm, DC tie line node and AC tie line node set in AC grid a respectively. represents the AC power grid, a i 、b i and c i They represent the consumption characteristic coefficient of thermal power unit i, u it 、v it and w it They represent the start-up and shutdown status, start-up action and shutdown action of thermal power unit i at time t respectively. represents the startup cost of thermal power unit i, d it represents the output adjustment cost coefficient of thermal power unit i, represents the predicted value of wind farm i at time t, represents the load of node i at time t, represents the power of the ith AC tie line at time t, P i AC and They represent the lower and upper limits of the transmission power of the i-th AC tie line, MU i and MD i are the minimum start-up time and minimum shutdown time of thermal power unit i, P i G 、 Indicates the lower and upper limits of the output of thermal power unit i, R i represents the ramp rate limit of thermal power unit i, ∈1, ∈2 and ∈3 represent the risk levels of the joint opportunity constraint, represents the transmission power upper limit of line l, w represents the wind farm, d represents the load in AC grid a, g represents the thermal power unit, j represents the jth AC tie line node, M gl 、M wl 、M dl 、M il and M jl They represent the power transfer distribution factors of thermal power units, wind farms, loads, DC interconnection lines, and AC interconnection lines respectively.

4. The data-driven distributed robust joint opportunity-constrained scheduling method for AC / DC hybrid power systems according to claim 1, characterized in that: The wind power prediction error ξ is subject to a polyhedron constraint Ω, and Ω=ξ:Lξ≤l, L and l represent the parameters constituting the polyhedron Ω. The expected term in the objective function (1) is It is equivalent to solving the convex cone programming problem A: Where θ represents the Wasserstein radius, N represents the number of samples of wind power prediction error, represents the sample value of the i-th wind power prediction error, κ o 、 All are auxiliary variables.

5. The data-driven distributed robust joint opportunity-constrained scheduling method for AC / DC hybrid power systems according to claim 1, characterized in that: The specific contents of S2 include: The joint opportunity constraint (2) is reconstructed using the optimal conditional value at risk approximation (OCA) method: The joint opportunity constraint (2) can be further expressed as: Among them, C k and c k Represents C l (·) and c l The kth row of (·); Reconstruct (33) as: Using the conditional value-at-risk approximation (34), we get: according to For a given scaling parameter δ, Equation (35) is further transformed into the following convex cone programming form, namely B: Where, κ c , σ c , β c , γ c is an auxiliary variable; Then (1)-(2) are rewritten into the form based on OCA reconstruction shown in formula (3).

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