Power distribution network robust optimization scheduling method based on line loss and new energy output uncertainty

Abstract

The invention belongs to the technical field of power grids, and relates to a power distribution network robust optimization scheduling method based on line loss and new energy output uncertainty, which comprises the following steps: S1, obtaining historical data of a power distribution network, and obtaining a power distribution network load curve and a new energy output interval through a prediction model; s2, establishing a power distribution network double-layer robust optimization scheduling model by taking power distribution network line loss minimization and new energy output uncertainty as targets; s3, converting the double-layer robust optimization scheduling model into a mixed integer linear programming model through a KKT optimality condition and a linear programming dual theorem; and S4, solving the mixed integer linear programming model to obtain a load excitation setting and scheduling scheme, and completing robust optimization scheduling control of the power distribution network. The method is suitable for reducing the line loss in the operation and maintenance of the power distribution network, especially for a scene with a high proportion of new and new energy output, the load use condition is adjusted in real time, the peak-valley difference of the load is reduced, the uncertainty of new energy output is resisted, and the power supply reliability of the power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power grids, and in particular relates to a robust optimization dispatching method for a distribution network based on line loss and uncertainty of new energy output. Background Art

[0002] In traditional power systems, excessive line loss is a common problem in the power transmission process, which directly affects the economy and operation efficiency of the power system. With the expansion of the scale of the power grid and the increase in power demand, how to effectively consider and reduce line loss in scheduling has become a research hotspot. Generally, line loss increases with the increase of transmission distance and load fluctuation. Therefore, in the optimal scheduling of the power system, line loss needs to be included in the power transmission planning to ensure the practical execution of the scheduling plan.

[0003] With the large-scale access of renewable energy, the uncertainty in the power system has increased significantly. Renewable energy has significant volatility and uncertainty. Its output fluctuates with environmental factors and is difficult to accurately predict. Traditional dispatching methods are difficult to adapt to the fluctuations of renewable energy, which often leads to output that cannot meet load demand or grid unbalance. Therefore, in order to ensure the stable operation of the system and reduce uncertainty risks, it is necessary to incorporate the uncertainty of renewable energy into the dispatching model.

[0004] Robust optimization is a common uncertainty optimization method, which is widely used in optimization problems under uncertainty, especially for power system dispatching with high access rate of renewable energy. By introducing robust optimization method, the dispatching scheme can be optimized under the condition of uncertainty of renewable energy output, and the dispatching deviation caused by volatility can be reduced. Under the premise of considering line loss and uncertainty of renewable energy output, robust optimization method can improve the stability and feasibility of dispatching scheme, thereby ensuring efficient operation and power supply reliability of power system.

[0005] As the challenges brought by line loss and uncertainty of renewable energy output in power systems are gradually increasing, traditional dispatching methods are difficult to meet the requirements of modern power systems for safety, economy and efficiency. Therefore, it is of great practical significance and application prospects to develop a robust optimization dispatching method that combines line loss and uncertainty of renewable energy output. This method will provide a reliable dispatching strategy for future power grids with a high proportion of renewable energy access, and improve the adaptability and risk resistance of power systems. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a robust optimization scheduling method for distribution networks based on line losses and uncertainty in renewable energy output, which is suitable for reducing line losses in distribution network operation and maintenance, especially in scenarios where the output of renewable energy accounts for a high proportion. It can adjust load usage in real time, reduce load peak-to-valley differences, resist the uncertainty of renewable energy output, and improve the power supply reliability of the distribution network.

[0007] To achieve the above object, the present invention discloses a robust optimization dispatching method for a distribution network based on line loss and uncertainty of new energy output, which comprises:

[0008] S1: Obtain historical data of the distribution network, and obtain the distribution network load curve and new energy output range through the prediction model;

[0009] S11: Obtain the historical load incentive output L and the historical output data N of the low-voltage distribution network through the data acquisition and monitoring control system;

[0010] S12: Establish a historical load incentive output prediction model for low-voltage distribution network based on BP neural network;

[0011] S13: Establish a new energy historical output data prediction model based on ARIMA time series;

[0012] S14: Input the historical load incentive output L of the low-voltage distribution network and the historical output data N of new energy into the prediction model to calculate the load incentive output P at the next time node ld and new energy output range [P ne- ,P ne+ ];

[0013] S2: Establish a two-layer robust optimization dispatch model for distribution network with the goal of minimizing distribution network line losses and uncertainty in renewable energy output;

[0014] The minimization of the distribution network line loss is the first-level optimization model; the uncertainty of the new energy output is the second-level optimization model; the two-level robust optimization scheduling model of the distribution network is obtained as follows:

[0015]

[0016] Where: (i, j) is the line between the first node i and the second node j; i is the first node number; j is the second node number; r ij is the resistance of line (i, j); P ij,t is the active power of line (i, j) at time t; Q ij,t is the reactive power of line (i, j) at time t; (i, j)=>i is the set of lines flowing to the first node i; i=>(i, j) is the set of lines flowing out of the first node i; V i,t is the voltage of the first node i at time t; x ij is the reactance of line (i, j); V j,t is the voltage of the second node j at time t; P i ld+ is the upper limit of load excitation output of the first node i; P ild- is the lower limit of load excitation output of the first node i; P i ne+ is the upper limit of the new energy output of the first node i; P i ne- is the lower limit of the new energy output of the first node i; The load incentive output minimum optimization target of the first node i; is the maximum optimization target of the new energy output of the first node i; t is the time parameter; T is the total number of time parameters; V i,t is the voltage of the first node i at time t; P i ne is the new energy output of the first node i; P i ld is the load excitation output of the first node i; The reactive power of the load excitation for the first node i;

[0017] S3: The two-level robust optimization scheduling model in step S2 is transformed into a mixed integer linear programming model through the KKT optimality condition and the linear programming duality theorem:

[0018]

[0019] in: is the first dual variable of the first node i; is the first dual variable of the second node j; is the second dual variable of the first node i; is the second dual variable of the second node j; is the third dual variable of the node; is the first dual variable of the first node i inequality; is the second dual variable of the first node i inequality; is the dual variable Boolean variable corresponding to the constraint relaxation; is the dual variable Boolean variable corresponding to the constraint relaxation; θ i,t is the power factor angle of the first node i at time t; M is a constant parameter;

[0020] S4: Use the next time node load obtained in step S1 to stimulate the output P ld and new energy output range [P ne- ,P ne + ] is used as a constraint condition to solve the mixed integer linear programming model described in step S3, obtain the load incentive setting and scheduling plan for the next time node, and complete the robust optimization scheduling control of the distribution network.

[0021] Preferably, the historical load incentive output L prediction model of the low-voltage distribution network based on the BP neural network in step S12 includes an input layer, a hidden layer, and an error calculation and back propagation layer of the weight adjustment rule, and the specific model is:

[0022]

[0023] Where: x i is the i-th input; w ij is the weight between the input layer and the hidden layer; b j is the input layer bias; f(·) is the activation function; h j is the output of the jth hidden node; v jk is the weight between the hidden layer and the output layer; c k is the output layer bias; g(·) is the output layer activation function; y k Output value of the kth BP neural network model; is the actual value of the kth BP neural network model; E is the loss function; m is the total number of second nodes; n is the total number of first nodes; j is the index of the hidden layer node; k is the index of the output layer node; p is the total number of output nodes.

[0024] Preferably, the prediction model of new energy historical output data N based on ARIMA time series in step S13 is specifically:

[0025]

[0026] Where: y t is the predicted value at time t; c is the constant parameter; is the coefficient of the first autoregressive term; is the coefficient of the second autoregressive term; is the coefficient of the pth autoregressive term; ε t is the white noise at time t; θ2 is the coefficient of the second moving average term; θ3 is the coefficient of the third moving average term; θ q is the coefficient of the qth moving average term.

[0027] Preferably, the first-level optimization model in step S2 takes minimizing the line loss of the distribution network as the optimization goal, specifically:

[0028]

[0029] Where: x i,t is the elastic coefficient of the first node i at time t; Load before implementing demand response; The load after implementing demand response; is the load incentive variable of node i at time t before implementing demand response; is the load incentive variable of node i at time t after implementing demand response; r peak is the load excitation peak value; r valley is the load excitation valley; T peak is the load excitation peak period; T valley It is the load excitation valley period; The upper limit after the implementation of demand-side response; It is the lower limit after implementing demand-side response.

[0030] Preferably, the second-level optimization model in step S2 determines the next node load curve data P according to the upper and lower limits of the new energy output peak and valley. ld The adjustment range [P ld- ,P ld+ ]for:

[0031]

[0032] in, is the load incentive upper limit of the first node i at time t; is the predicted load size of the first node i at time t; The lower limit of incentives after implementing demand-side response; To provide incentives before implementing demand-side response; i,t Correction parameters for load excitation; is the load excitation lower limit of the first node i at time t; It is the incentive cap after implementing demand-side response.

[0033] Preferably, the KKT optimality condition in step S3 is a constraint condition that the first-layer optimization model can be transformed into a second-layer optimization model by Lagrangian partial derivative, specifically:

[0034]

[0035] By transforming the dual conditions, with the help of the dual variables The constraints are transformed into:

[0036]

[0037] Preferably, the linear programming dual theorem in step S3 is specifically:

[0038] At the optimal solution, the objective function values ​​of the original problem and the dual problem are equal, which is the sum of the products of the dual variables and the constant terms in the original first-level optimization model, and the equation is:

[0039]

[0040] Preferably, in step S4, the mixed integer linear programming model in step S3 is solved by a branch and bound algorithm, specifically:

[0041] S41: Node splitting, splitting integer variables, dividing the search space, generating sub-problems, and ensuring that each sub-problem contains a solution space;

[0042] S42: Relaxed solution: for the generated sub-problems, the problem is converted into a linear programming problem by relaxing the integer constraints and solving it, and the upper and lower bounds of the optimal solution of the sub-problems are obtained;

[0043] S43: Optimality judgment, when checking whether the relaxation solution of the current node meets the optimality condition or the relaxation solution cannot improve the global optimal solution; if the condition is met, stop splitting and record the optimal solution;

[0044] S44: Pruning operation, directly pruning nodes that do not meet the constraints or whose objective function values ​​are inferior to the current optimal solution, reducing the amount of calculation and improving the solution efficiency;

[0045] S45: Output the algorithm solution result, output the optimal scheduling solution solved by the branch and bound algorithm, and the key parameters include the node load scheduling power, the voltage level of each node and the line power flow.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention can effectively reduce line losses in the power transmission process by establishing a robust optimization scheduling model with the minimum line loss as the optimization goal. This optimization method takes into account various uncertain factors in actual operation, ensuring that the optimal scheduling scheme can still be achieved when the output of new energy sources fluctuates, thereby improving the economy and efficiency of the power system.

[0048] (2) The present invention uses a neural network or time series method to predict node load curves and new energy output intervals, and combines it with an electricity price incentive demand response model to adjust user loads in real time and enhance the distribution network's ability to resist the uncertainty of new energy output. This method enables users to actively respond to changes in electricity prices, thereby balancing loads, reducing load peak-to-valley differences, improving the power supply reliability of the distribution network, and ensuring the stability of power supply when a high proportion of new energy is connected.

[0049] (3) The application of the present invention in scenarios with a high proportion of new energy promotes the effective use of renewable energy. By optimizing the scheduling scheme, it is possible to better integrate and utilize new energy resources such as wind power and photovoltaics, reduce dependence on traditional fossil energy, and thus promote the development of green energy and achieve sustainable power supply. At the same time, based on the demand response mechanism of electricity prices, users are encouraged to use new energy at appropriate times, further improving the absorption capacity of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the robust optimization dispatching method of the distribution network based on line loss and uncertainty of new energy output of the present invention;

[0051] Figure 2 The network structure of the distribution network in the embodiment of the present invention is a standard IEEE 33 node structure diagram;

[0052] Figure 3 It is a normalized graph of the load curve data of the distribution network and the output interval of new energy sources of the present invention;

[0053] Figure 4 The worst new energy output curve diagram obtained by solving the present invention;

[0054] Figure 5 The curves before and after the load dispatching of the distribution network of the present invention are shown in the figure;

[0055] Figure 6 It is a comparison diagram of line loss rate before and after load scheduling of the distribution network of the present invention. DETAILED DESCRIPTION

[0056] The exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0057] The embodiment of the present invention provides a distribution network robust optimization scheduling method based on line loss and uncertainty of renewable energy output, such as Figure 1 As shown, the historical data of the distribution network is obtained, and the distribution network load curve and the new energy output range are obtained through the prediction model; a two-layer robust optimization scheduling model of the distribution network is established with the goal of minimizing the line loss of the distribution network and the uncertainty of the new energy output; the two-layer robust optimization scheduling model is converted into a mixed integer linear programming model through the KKT optimality condition and the linear programming duality theorem; the mixed integer linear programming model is solved to obtain the load incentive setting and scheduling plan, and the robust optimization scheduling control of the distribution network is completed; it includes:

[0058] Step S1: Obtain historical data of the distribution network, and obtain the distribution network load curve and new energy output range through the prediction model.

[0059] like Figure 2The network structure of the distribution network used in the embodiment of the present invention is shown as a standard IEEE 33 node, which is composed of 33 grid nodes to form a low-voltage distribution system. The new energy access location and capacity are shown in Table 1, indicating the new energy capacity of the corresponding node access, which is used to reflect the impact of the uncertainty of new energy output on the distribution network optimization process. Among them, new energy refers to renewable energy developed and utilized on the basis of new technologies, including wind power, photovoltaic power generation, tidal power generation, etc.

[0060] Table 1 New energy access location and capacity

[0061] Node Number New energy access capacity Node Number New energy access capacity 3 250.54KVA 21 217.75KVA 6 264.47KVA 22 126.91KVA 7 932.15KVA 23 840.12KVA 13 708.69KVA 24 827.24KVA 17 521.22KVA 28 271.59KVA 18 246.16KVA 29 941.72KVA 19 109.70KVA 30 239.17KVA 20 217.75KVA 31 697.65KVA

[0062] Step S11: Obtain the historical load incentive output L and the new energy historical output data N of the low-voltage distribution network through a supervisory control and data acquisition (SCADA) system. The data time period can be set to one week or one month.

[0063] Step S12: Establish a historical load incentive output prediction model for low-voltage distribution network based on BP neural network, including an input layer, a hidden layer, and an error calculation and back propagation layer of weight adjustment rules. The specific model is:

[0064]

[0065] Where: x i is the i-th input; w ij is the weight between the input layer and the hidden layer; b j is the input layer bias; f(·) is the activation function; h j is the output of the jth hidden node; v jk is the weight between the hidden layer and the output layer; c k is the output layer bias; g(·) is the output layer activation function; y k Output value of the kth BP neural network model; is the actual value of the kth BP neural network model; E is the loss function; m is the total number of second nodes; n is the total number of first nodes; j is the index of the hidden layer node; k is the index of the output layer node; p is the total number of output nodes.

[0066] Step S13: Establish a new energy historical output data prediction model based on ARIMA time series, specifically:

[0067]

[0068] Where: y t is the predicted value at time t; c is the constant parameter; is the coefficient of the first autoregressive term; is the coefficient of the second autoregressive term; is the coefficient of the pth autoregressive term; ε t is the white noise at time t; θ2 is the coefficient of the second moving average term; θ3 is the coefficient of the third moving average term; θ q is the coefficient of the qth moving average term.

[0069] Step S14: Input the historical load incentive output L of the low-voltage distribution network and the historical output data N of new energy into the prediction model to calculate the load incentive output P at the next time node. ld and new energy output range [P ne- ,P ne+ ], the normalized result is as follows Figure 3 As shown, it shows the relationship between the load forecast curve and the output range of renewable energy during the operation of the distribution network, and depicts the upper and lower limits of the renewable energy output range.

[0070] Step S2: Establish a two-layer robust optimization scheduling model for the distribution network with the goal of minimizing the line loss of the distribution network and the uncertainty of the output of new energy.

[0071] The minimization of distribution network line loss is the first-level optimization model, which is as follows:

[0072]

[0073] Where: x i,t is the elastic coefficient of the first node i at time t; Load before implementing demand response; The load after implementing demand response; is the load incentive variable of node i at time t before implementing demand response; is the load incentive variable of node i at time t after implementing demand response; r peak is the load excitation peak value; r valley is the load excitation valley; T peak is the load excitation peak period; T valley It is the load excitation valley period; The upper limit after the implementation of demand-side response; It is the lower limit after implementing demand-side response.

[0074] The uncertainty of new energy output is the second-level optimization model, which determines the next node load curve data P according to the upper and lower limits of new energy output peaks and valleys. ld The adjustment range [P ld- ,P ld+ ]for:

[0075]

[0076] in, is the load incentive upper limit of the first node i at time t; is the predicted load size of the first node i at time t; The lower limit of incentives after implementing demand-side response; To provide incentives before implementing demand-side response; i,t Correction parameters for load excitation; is the load excitation lower limit of the first node i at time t; It is the incentive cap after implementing demand-side response.

[0077] The two-layer robust optimization dispatch model of distribution network is obtained as follows:

[0078]

[0079] Where: (i, j) is the line between the first node i and the second node j; i is the first node number; j is the second node number; r ij is the resistance of line (i, j); P ij,t is the active power of line (i, j) at time t; Q ij,t is the reactive power of line (i, j) at time t; (i, j)=>i is the set of lines flowing to the first node i; i=>(i, j) is the set of lines flowing out of the first node i; V i,t is the voltage of the first node i at time t; x ij is the reactance of line (i, j); V j,t is the voltage of the second node j at time t; P i ld+ is the upper limit of load excitation output of the first node i; P i ld- is the lower limit of load excitation output of the first node i; P i ne+ is the upper limit of the new energy output of the first node i; P i ne- is the lower limit of the new energy output of the first node i; The load incentive output minimum optimization target of the first node i; is the maximum optimization target of the new energy output of the first node i; t is the time parameter; T is the total number of time parameters; V i,t is the voltage of the first node i at time t; P i ne is the new energy output of the first node i; P i ld is the load excitation output of the first node i; The reactive power is excited for the load of the first node i.

[0080] Step S3: Using the KKT optimality condition is to transform the first-layer optimization model into the constraint condition of the second-layer optimization model through Lagrangian partial derivative, specifically:

[0081]

[0082] By transforming the dual conditions, with the help of the dual variables The constraints are transformed into:

[0083]

[0084] The use of the duality theorem of linear programming makes the objective function values ​​of the original problem and the dual problem equal at the optimal solution, which is the sum of the products of the dual variables and the constant terms in the original first-level optimization model, and the equation is:

[0085]

[0086] The two-level robust optimization scheduling model in step S2 is transformed into a mixed integer linear programming model through the KKT optimality condition and the linear programming duality theorem:

[0087]

[0088] in: is the first dual variable of the first node i; is the first dual variable of the second node j; is the second dual variable of the first node i; is the second dual variable of the second node j; is the third dual variable of the node; is the first dual variable of the first node i inequality; is the second dual variable of the first node i inequality; is the dual variable Boolean variable corresponding to the constraint relaxation; is the dual variable Boolean variable corresponding to the constraint relaxation; θ i,t is the power factor angle of the first node i at time t; M is a constant parameter.

[0089] Step S4: Use the load output P obtained in step S1 to stimulate the next time node ld and new energy output range [P ne- ,P ne+ ] is the constraint condition to solve the mixed integer linear programming model in step S3 by using the branch and bound algorithm, specifically:

[0090] Step S41: Node splitting, splitting the integer variables, dividing the search space, generating sub-problems, and ensuring that each sub-problem contains a solution space.

[0091] Step S42: Relaxed solution: for the generated sub-problems, the integer constraints are relaxed, the problem is converted into a linear programming problem for solution, and the optimal upper and lower bounds of the sub-problems are obtained.

[0092] Step S43: Optimality judgment, when checking whether the relaxed solution of the current node meets the optimality condition or the relaxed solution cannot improve the global optimal solution; if the condition is met, stop splitting and record the optimal solution.

[0093] Step S44: Pruning operation, directly pruning nodes that do not meet the constraints or whose objective function values ​​are inferior to the current optimal solution, reducing the amount of calculation and improving the solution efficiency.

[0094] Step S45: Output the algorithm solution result, output the optimal dispatching solution solved by the branch and bound algorithm, and the key parameters include the node load dispatching power, the voltage level of each node and the line power flow. Obtain the node load incentive setting and dispatching solution for the next time, and complete the robust optimization dispatching control of the distribution network.

[0095] like Figure 4 The figure shows the output curve of new energy in the worst scenario obtained by the solution of the present invention. This scenario is the result generated by the present invention based on the extreme assumption of the uncertainty factor of new energy output, which shows that no matter how the new energy output changes, the scheduling optimization result brought by the load incentive setting and scheduling scheme at the next time node will be better than that in the worst scenario, that is, the result calculated by the model is the most conservative solution, and the actual situation will even perform better than the calculated result.

[0096] like Figure 5 The curves before and after the load dispatch of the distribution network of the present invention are shown. It corresponds to the curve of the new energy output in the worst scenario, indicating that under this load incentive and dispatch result, the dispatch strategy under the new energy output in the worst scenario is optimal.

[0097] Finally, by scheduling the load before the day, the impact of new energy access and its output uncertainty on line loss optimization is reduced. The line loss rate before and after load scheduling is compared. Figure 6 The total antenna loss rate before load scheduling is 3.9378%, and the total antenna loss rate after load scheduling is 3.7659%. The loss reduction effect is obvious, and the worst new energy output scenario is taken into account, so it has strong robustness.

[0098] The beneficial effects of the present invention are as follows: the present invention provides a method for robust optimization scheduling of distribution networks based on line loss and uncertainty of new energy output. By establishing a robust optimization scheduling model with minimum line loss as the optimization target, the line loss in the power transmission process can be effectively reduced. By using a neural network or time series method to predict the node load curve and the new energy output interval, combined with an incentive-based demand response model, the user load can be adjusted in real time to enhance the distribution network's ability to resist the uncertainty of new energy output. The embodiment of the present invention takes into account various uncertain factors in actual operation, ensuring that the optimal scheduling scheme can still be achieved in the case of fluctuations in the output of new energy, thereby improving the economy and efficiency of the power system; enabling users to actively respond to incentive changes, thereby balancing the load, reducing the peak-to-valley difference in the load, and improving the power supply reliability of the distribution network, ensuring the stability of power supply when a high proportion of new energy is connected, and optimizing the scheduling scheme to better integrate and utilize new energy resources such as wind power and photovoltaics, reduce dependence on traditional fossil energy, thereby promoting the development of green energy and achieving sustainable power supply.

[0099] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A robust optimization dispatching method for distribution network based on line loss and uncertainty of renewable energy output, characterized in that: It includes: S1: Obtain historical data of the distribution network, and obtain the distribution network load curve and new energy output range through the prediction model; S11: Obtain the historical load incentive output L and the historical output data N of the low-voltage distribution network through the data acquisition and monitoring control system; S12: Establish a historical load incentive output prediction model for low-voltage distribution network based on BP neural network; S13: Establish a new energy historical output data prediction model based on ARIMA time series; S14: Input the historical load incentive output L of the low-voltage distribution network and the historical output data N of new energy into the prediction model to calculate the load incentive output P at the next time node ld and new energy output range [P ne- ,P ne+ ]; S2: Establish a two-layer robust optimization dispatch model for distribution network with the goal of minimizing distribution network line losses and uncertainty in renewable energy output; The minimization of the distribution network line loss is the first-level optimization model; the uncertainty of the new energy output is the second-level optimization model; the two-level robust optimization scheduling model of the distribution network is obtained as follows: Where: (i, j) is the line between the first node i and the second node j; i is the first node number; j is the second node number; r ij is the resistance of line (i, j); P ij,t is the active power of line (i, j) at time t; Q ij,t is the reactive power of line (i, j) at time t; (i, j)=>i is the set of lines flowing to the first node i; i=>(i, j) is the set of lines flowing out of the first node i; V i,t is the voltage of the first node i at time t; x ij is the reactance of line (i, j); V j,t is the voltage of the second node j at time t; P i ld+ is the upper limit of load excitation output of the first node i; P i ld- is the lower limit of load excitation output of the first node i; P i ne + is the upper limit of the new energy output of the first node i; P i ne- is the lower limit of the new energy output of the first node i; The load incentive output minimum optimization target of the first node i; is the maximum optimization target of the new energy output of the first node i; t is the time parameter; T is the total number of time parameters; V i,t is the voltage of the first node i at time t; P i ne is the new energy output of the first node i; P i ld is the load excitation output of the first node i; The reactive power of the load excitation for the first node i; S3: The two-level robust optimization scheduling model in step S2 is transformed into a mixed integer linear programming model through the KKT optimality condition and the linear programming duality theorem: in: is the first dual variable of the first node i; is the first dual variable of the second node j; is the second dual variable of the first node i; is the second dual variable of the second node j; is the third dual variable of the node; is the first dual variable of the first node i inequality; is the second dual variable of the first node i inequality; is the dual variable Boolean variable corresponding to the constraint relaxation; is the dual variable Boolean variable corresponding to the constraint relaxation; θ i,t is the power factor angle of the first node i at time t; M is a constant parameter; S4: Use the next time node load obtained in step S1 to stimulate the output P ld and new energy output range [P ne- ,P ne+ ] is used as a constraint condition to solve the mixed integer linear programming model described in step S3, obtain the load incentive setting and scheduling plan for the next time node, and complete the robust optimization scheduling control of the distribution network.

2. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: The historical load incentive output L prediction model of the low-voltage distribution network based on the BP neural network in step S12 includes an input layer, a hidden layer, and an error calculation and back propagation layer of the weight adjustment rule. The specific model is: Where: x i is the i-th input; w ij is the weight between the input layer and the hidden layer; b j is the input layer bias; f(·) is the activation function; h j is the output of the jth hidden node; v jk is the weight between the hidden layer and the output layer; c k is the output layer bias; g(·) is the output layer activation function; y k Output value of the kth BP neural network model; is the actual value of the kth BP neural network model; E is the loss function; m is the total number of second nodes; n is the total number of first nodes; j is the index of the hidden layer node; k is the index of the output layer node; p is the total number of output nodes.

3. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: The prediction model of new energy historical output data N based on ARIMA time series in step S13 is specifically: Where: y t is the predicted value at time t; c is the constant parameter; is the coefficient of the first autoregressive term; is the coefficient of the second autoregressive term; is the coefficient of the pth autoregressive term; ε t is the white noise at time t; θ2 is the coefficient of the second moving average term; θ3 is the coefficient of the third moving average term; θ q is the coefficient of the qth moving average term.

4. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: The first-level optimization model in step S2 takes minimizing the line loss of the distribution network as the optimization goal, specifically: Where: x i,t is the elastic coefficient of the first node i at time t; Load before implementing demand response; The load after implementing demand response; is the load incentive variable of node i at time t before implementing demand response; is the load incentive variable of node i at time t after implementing demand response; r peak is the load excitation peak value; r valley is the load excitation valley; T peak is the load excitation peak period; T valley It is the load excitation valley period; The upper limit after the implementation of demand-side response; It is the lower limit after implementing demand-side response.

5. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: The second-level optimization model in step S2 determines the next node load curve data P according to the upper and lower limits of the new energy output peak and valley. ld The adjustment range [P ld- ,P ld+ ]for: in, is the load incentive upper limit of the first node i at time t; is the predicted load size of the first node i at time t; The lower limit of incentives after implementing demand-side response; To provide incentives before implementing demand-side response; i,t Correction parameters for load excitation; is the load excitation lower limit of the first node i at time t; It is the incentive cap after implementing demand-side response.

6. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of renewable energy output according to claim 1 is characterized in that: The KKT optimality condition in step S3 is a constraint condition that the first-layer optimization model can be transformed into the second-layer optimization model through Lagrangian partial derivative, specifically: By transforming the dual conditions, with the help of the dual variables The constraints are transformed into:

7. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: The linear programming dual theorem in step S3 is specifically: At the optimal solution, the objective function values ​​of the original problem and the dual problem are equal, which is the sum of the products of the dual variables and the constant terms in the original first-level optimization model, and the equation is:

8. The method for robust optimization and dispatching of distribution networks based on line loss and uncertainty of new energy output according to claim 1 is characterized in that: In step S4, the mixed integer linear programming model described in step S3 is solved by a branch and bound algorithm, specifically: S41: Node splitting, splitting integer variables, dividing the search space, generating sub-problems, and ensuring that each sub-problem contains a solution space; S42: Relaxed solution: for the generated sub-problems, the problem is converted into a linear programming problem by relaxing the integer constraints and solving it, and the upper and lower bounds of the optimal solution of the sub-problems are obtained; S43: Optimality judgment, when checking whether the relaxation solution of the current node meets the optimality condition or the relaxation solution cannot improve the global optimal solution; if the condition is met, stop splitting and record the optimal solution; S44: Pruning operation, directly pruning nodes that do not meet the constraints or whose objective function values ​​are inferior to the current optimal solution, reducing the amount of calculation and improving the solution efficiency; S45: Output the algorithm solution result, output the optimal scheduling solution solved by the branch and bound algorithm, and the key parameters include the node load scheduling power, the voltage level of each node and the line power flow.

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