AC / DC power grid optimal power flow calculation method and system considering source and load uncertainty

Abstract

The present invention discloses a method and system for calculating the optimal power flow of an AC / DC power grid taking into account the uncertainty of sources and loads. The constructed optimal power flow model of an AC / DC power grid containing back-to-back flexible DC / DC takes into account the uncertainty of new energy and loads, can effectively take into account the influence of source and load uncertainty, and also provides linear power flow equation constraints for modeling optimization problems, so that the method proposed in the present invention has high computational efficiency and can take into account the influence of reactive voltage and system network loss, thereby improving the solution accuracy of the model. It solves the technical problems that the existing AC power flow model is difficult and time-consuming to calculate, the DC power flow model is difficult to take into account reactive voltage and is prone to large calculation errors due to ignoring network losses, and the uncertainty of new energy power generation and load is not taken into account in the optimal power flow calculation, and the optimization results cannot cover the uncertain scenarios of actual operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for calculating optimal power flow in AC and DC power grids taking into account source and load uncertainties. Background Art

[0002] The optimal power flow (OPF) problem involves optimizing the regulation of various controllable devices in a power system from an optimization perspective, minimizing an objective function while satisfying the constraints of node power balance and the safety of various devices. The objective function can be categorized into two types, depending on whether the optimization problem is focused on active power or reactive power. The objective function for active power optimization is typically to minimize the overall system power generation and operating cost, while the objective for reactive power optimization is typically voltage regulation, which can be set to minimize network losses or the sum of squared voltage deviations at each node. The OPF problem is a nonlinear problem and is typically solved using nonlinearization methods or intelligent search techniques, such as the Newton method, gradient method, interior point method, particle swarm optimization, and genetic algorithm. While these methods can solve OPF, they often suffer from computational complexity and long computation times. Alternatively, methods have been proposed for OPF calculation based on quadratic programming, but these are based on the DC power flow equation, which makes it difficult to account for reactive power and voltage issues and often ignores network losses, resulting in large calculation errors.

[0003] With growing attention to environmental protection, renewable energy is rapidly developing on the power supply side, and the proportion of renewable energy in the current power grid is constantly increasing. However, renewable energy is easily affected by the external environment, resulting in a large uncertainty in its output. Therefore, it is necessary to account for the uncertainty of renewable energy output, such as wind power and solar power, in the optimal power flow problem. In addition, on the load side, due to the drastic changes in the climate environment, the accuracy of load forecasting is low, especially during extreme weather conditions, such as typhoons, sustained high temperatures, or continuous cold waves. Therefore, it is also necessary to consider load uncertainty. Summary of the Invention

[0004] The present invention provides a method and system for calculating the optimal power flow of AC and DC power grids that takes into account source and load uncertainties. The method is used to solve the technical problems that the existing AC power flow model is difficult and time-consuming to calculate, the DC power flow model has difficulty in taking reactive voltage into account and is prone to large calculation errors due to ignoring network losses, and the uncertainty of renewable energy power generation and load is not taken into account in the optimal power flow calculation, resulting in the optimization results being unable to cover the uncertain scenarios of actual operation.

[0005] In view of this, a first aspect of the present invention provides a method for calculating optimal power flow in AC and DC power grids taking into account source and load uncertainties, comprising:

[0006] Taking the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps and the power generation cost of generator sets as the minimum, the objective function of the optimal power flow model of the back-to-back flexible DC / DC grid is constructed. The objective function is:

[0007]

[0008] Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, y c,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state quantity of the generator set g in period t, δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, ne The uncertainty of new energy output is a collection of l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator set g in period t;

[0009] The constraints of the optimal power flow model of the back-to-back flexible DC / DC power grid are constructed and linearized. The constraints include power flow constraints, uncertainty set constraints, back-to-back flexible DC operation constraints, active power balance constraints, reactive power balance constraints, line transmission capacity constraints, tap-adjustable transformer model constraints, reactive power regulation range constraints of continuous reactive compensation devices, switching group number and switching frequency constraints of discrete reactive compensation devices, node voltage amplitude constraints, generator set active output constraints, generator set reactive processing constraints, generator set ramp rate constraints, renewable energy active output constraints, and renewable energy reactive output constraints. The power flow constraints are:

[0010]

[0011]

[0012]

[0013]

[0014] Among them, P i is the active power of node i, Q i is the reactive power of node i, Pij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, g ij is the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j;

[0015] The C&CG algorithm is used to solve the optimal power flow model of the AC / DC power grid containing back-to-back flexible DC and obtain the optimal power flow calculation results of the AC / DC power grid.

[0016] Optionally, the uncertainty set constraint includes a set constraint of renewable energy output uncertainty and a set constraint of load uncertainty, and the set constraint of renewable energy output uncertainty is divided into a set constraint of renewable energy output uncertainty under a constant power factor control mode and a set constraint of renewable energy output uncertainty under an inverter reactive power optimization control mode;

[0017] The collective constraint of the uncertainty of renewable energy output under constant power factor control is:

[0018]

[0019] Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active power output fluctuation of the renewable energy in period t, θ1 is the constant power factor angle corresponding to the renewable energy;

[0020] The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is:

[0021]

[0022] The collective constraints of load uncertainty are:

[0023]

[0024] Among them, P l,t is the active power of the load, Q l,t is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

[0025] Optionally, the back-to-back flexible direct current operation constraints include power relationship constraints between the transmitting and receiving ends, transmission power constraints, power regulation rate constraints, power regulation direction constraints in adjacent time periods, all-day power transmission constraints, and active power and reactive power transmission constraints;

[0026] The power relationship constraint at the receiving end is:

[0027]

[0028] Among them, d is the back-to-back flexible DC unit, D is the back-to-back flexible DC unit set, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other;

[0029] The transmission power constraint is:

[0030]

[0031]

[0032]

[0033]

[0034] Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, α d,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t It is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit;

[0035] The power regulation rate constraint is:

[0036]

[0037]

[0038]

[0039]

[0040] in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for the downward adjustment of the power of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit;

[0041] The power adjustment direction constraints in adjacent time periods are:

[0042]

[0043]

[0044]

[0045] Among them, z d,t It is the state variable indicating whether the power of the back-to-back flexible direct current unit is 0;

[0046] The power transmission constraint for the whole day is:

[0047]

[0048] Among them, Q total The amount of power delivered by back-to-back flexible direct current throughout the day;

[0049] The active power and reactive power transmission constraints are:

[0050]

[0051] in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

[0052] Optionally, the C&CG algorithm is used to solve the optimal power flow model of the AC / DC power grid including back-to-back flexible DC and DC power grids to obtain the optimal power flow calculation results of the AC / DC power grid, including:

[0053] S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future;

[0054] S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0;

[0055] S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable.

[0056] S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable;

[0057] S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage;

[0058] S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result;

[0059] S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8;

[0060] S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3;

[0061] S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

[0062] Optionally, the active power balance constraint, the reactive power balance constraint and the line transmission capacity constraint are linearized using an inner polygon approximation method.

[0063] A second aspect of the present invention provides an AC / DC power grid optimal power flow calculation system taking into account source and load uncertainties, comprising:

[0064] The objective function construction module is used to construct the objective function of the optimal power flow model of the back-to-back flexible DC / DC power grid with the goal of minimizing the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps, and the power generation cost of generator sets. The objective function is:

[0065]

[0066] Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, yc,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state quantity of the generator set g in period t, δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, ne The uncertainty of new energy output is a collection of l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator set g in period t;

[0067] The constraint condition construction module is used to construct the constraint conditions of the optimal power flow model of the back-to-back flexible DC / AC power grid and linearize the constraint conditions. The constraint conditions include power flow constraint, uncertainty set constraint, back-to-back flexible DC operation constraint, active power balance constraint, reactive power balance constraint, line transmission capacity constraint, tap-adjustable transformer model constraint, reactive power regulation range constraint of continuous regulation reactive compensation device, number of switching groups and switching times constraint of discrete regulation reactive compensation device, node voltage amplitude constraint, generator set active output constraint, generator set reactive processing constraint, generator set ramp rate constraint, new energy active output constraint and new energy reactive output constraint. Among them, the power flow constraint is:

[0068]

[0069]

[0070]

[0071]

[0072] Among them, P i is the active power of node i, Q i is the reactive power of node i, P ij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, g ijis the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j;

[0073] The solution module is used to solve the optimal power flow model of the AC / DC power grid containing back-to-back flexible DC and DC power grids using the C&CG algorithm to obtain the optimal power flow calculation results of the AC / DC power grid.

[0074] Optionally, the uncertainty set constraint includes a set constraint of renewable energy output uncertainty and a set constraint of load uncertainty, and the set constraint of renewable energy output uncertainty is divided into a set constraint of renewable energy output uncertainty under a constant power factor control mode and a set constraint of renewable energy output uncertainty under an inverter reactive power optimization control mode;

[0075] The collective constraint of the uncertainty of renewable energy output under constant power factor control is:

[0076]

[0077] Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active power output fluctuation of the renewable energy in period t, θ1 is the constant power factor angle corresponding to the renewable energy;

[0078] The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is:

[0079]

[0080] The collective constraints of load uncertainty are:

[0081]

[0082] Among them, P l,t is the active power of the load, Q l,t is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

[0083] Optionally, the back-to-back flexible direct current operation constraints include power relationship constraints between the transmitting and receiving ends, transmission power constraints, power regulation rate constraints, power regulation direction constraints in adjacent time periods, all-day power transmission constraints, and active power and reactive power transmission constraints;

[0084] The power relationship constraint at the receiving end is:

[0085]

[0086] Among them, d is the back-to-back flexible DC unit, D is the set of back-to-back flexible DC units, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other;

[0087] The transmission power constraint is:

[0088]

[0089]

[0090]

[0091]

[0092] Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, α d,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t It is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit;

[0093] The power regulation rate constraint is:

[0094]

[0095]

[0096]

[0097]

[0098] in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for the downward adjustment of the power of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit;

[0099] The power adjustment direction constraints in adjacent time periods are:

[0100]

[0101]

[0102]

[0103] Among them, z d,t It is the state variable indicating whether the power of the back-to-back flexible direct current unit is 0;

[0104] The power transmission constraint for the whole day is:

[0105]

[0106] Among them, Q total The amount of power delivered by back-to-back flexible direct current throughout the day;

[0107] The active power and reactive power transmission constraints are:

[0108]

[0109] in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

[0110] Optionally, the solution module is specifically used to:

[0111] S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future;

[0112] S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0;

[0113] S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable.

[0114] S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable;

[0115] S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage;

[0116] S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result;

[0117] S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8;

[0118] S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3;

[0119] S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

[0120] Optionally, the active power balance constraint, the reactive power balance constraint and the line transmission capacity constraint are linearized using an inner polygon approximation method.

[0121] From the above technical solutions, it can be seen that the optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention has the following advantages:

[0122] The present invention provides an optimal power flow calculation method for AC / DC power grids that takes into account source and load uncertainties. The constructed optimal power flow model of the back-to-back flexible DC / AC power grid takes into account the uncertainty of new energy and loads, can effectively take into account the influence of source and load uncertainties, and also provides linear power flow equation constraints for optimization problem modeling, so that the method proposed in the present invention has high computational efficiency and can take into account the influence of reactive voltage and system network loss, thereby improving the solution accuracy of the model. It solves the technical problems that the existing AC power flow model is difficult and time-consuming to calculate, the DC power flow model is difficult to take into account reactive voltage and is prone to large calculation errors due to ignoring network losses, and the uncertainty of new energy power generation and load is not taken into account in the optimal power flow calculation, and the optimization results cannot cover the uncertain scenarios of actual operation.

[0123] At the same time, the optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention adopts the C&CG algorithm to convert the model into a two-stage robust optimization problem for iterative solution, and divides the model into two stages according to the discrete variables and continuous variables to be optimized. This can simplify the strong coupling relationship between each time period in the optimization model and make the model solution easier.

[0124] The optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention takes into account the operation cost of discrete reactive power compensation equipment in addition to the power generation operation cost in the optimization objective function, thereby ensuring the service life of the equipment while achieving economical operation of the system.

[0125] The optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention comprehensively considers constraints such as the capacitance of discrete reactive compensation devices, the number of reactance groups that can be put into operation, the adjustable gears of transformer taps, the active and reactive output of generator sets, the flexible operation capability of back-to-back flexible direct current (FDC), the compensation capability of reactive compensation equipment, the power flow equation of linear transformation, and the voltage amplitude when optimizing decision-making objectives. This ensures that the voltage level of the system is reasonable while ensuring the optimal optimization result of active power. BRIEF DESCRIPTION OF THE DRAWINGS

[0126] 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 related drawings can be obtained based on these drawings without paying any creative work.

[0127] Figure 1 A flow chart of a method for calculating optimal power flow in AC and DC power grids taking into account source and load uncertainty provided by the present invention;

[0128] Figure 2 This is a schematic diagram of the active and reactive output of renewable energy under constant power factor control;

[0129] Figure 3 A schematic diagram of the active and reactive output of new energy corresponding to the reactive power of new energy, which is optimized and adjusted by the inverter within its rated capacity range;

[0130] Figure 4 This is a schematic diagram of back-to-back flexible direct current power transmission;

[0131] Figure 5 This is an equivalent diagram of the back-to-back flexible straight model;

[0132] Figure 6 A schematic diagram of the process of solving the optimal power flow model of a back-to-back flexible DC / AC power grid using the C&CG algorithm provided in the present invention;

[0133] Figure 7 The diagram is a structural diagram of the AC / DC power grid optimal power flow calculation system taking into account source and load uncertainties provided in the present invention. DETAILED DESCRIPTION

[0134] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0135] For easier understanding, see Figure 1 The present invention provides an embodiment of a method for calculating an optimal power flow in an AC / DC power grid taking into account source and load uncertainty, comprising:

[0136] Step 101: With the goal of minimizing the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps, and the power generation cost of generator sets, an objective function of an optimal power flow model including a back-to-back flexible DC / DC grid is constructed.

[0137] It should be noted that in the present invention, the objective function of the optimal power flow model for the back-to-back flexible DC / DC power grid, which is constructed with the goal of minimizing the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps, and the power generation cost of the generator set, is:

[0138]

[0139] Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, y c,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state of generator set g in period t, u g,t is a 0-1 variable, u g,t Determined by the optimization of the day-ahead unit commitment problem, it is 1 when it is turned on, otherwise it is 0, and δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, ne The uncertainty of new energy output is a collection of l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator set g in period t.

[0140] Step 102: Construct the constraints of the optimal power flow model of the back-to-back flexible DC / DC power grid and linearize the constraints. The constraints include power flow constraints, uncertainty set constraints, back-to-back flexible DC operation constraints, active power balance constraints, reactive power balance constraints, line transmission capacity constraints, tap-adjustable transformer model constraints, reactive power regulation range constraints of continuous reactive compensation devices, switching group number and switching frequency constraints of discrete reactive compensation devices, node voltage amplitude constraints, generator set active output constraints, generator set reactive processing constraints, generator set ramp rate constraints, renewable energy active output constraints, and renewable energy reactive output constraints.

[0141] It should be noted that the AC power flow equation and line transmission power based on polar coordinates can be expressed as:

[0142]

[0143]

[0144]

[0145] Among them, P i is the active power of node i, Q i is the reactive power of node i, P ij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, gi j is the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j.

[0146] In power system flow calculations, the flat start method is usually used. Flat start means setting the initial voltage amplitude to 1 and the initial phase angle to 0. Under the flat start method, the first-order Taylor series expansion is used to make the following approximation:

[0147]

[0148]

[0149]

[0150] The first-order Taylor series expression corresponding to the function involved in formula (2) is derived as follows:

[0151] First, the quadratic function f(x,y) at point (x k ,y k ) is the first-order Taylor series expansion:

[0152]

[0153] Where f′ x (x k ,y k ) and f′ y (x k ,y k ) is the binary function f(x,y) at the initial point (x k ,y k ). Secondly, the first-order Taylor series expansion of the sine and cosine functions sinx and cosx is:

[0154]

[0155] Therefore, corresponding to v in formula (2) i v j The binary function form is f(v i ,v j )=v i v j , at this time at the flat start point (v i0 =v j0 =1.0) and perform a first-order Taylor series expansion to obtain:

[0156]

[0157] At this point, the first approximate condition in formula (2) can be obtained.

[0158] Similarly, for sinθ in formula (2) ij ,cosθ ij The function can be obtained by directly applying the first-order Taylor technical expansion shown in formula (4) So far, the second approximate condition in formula (2) can be obtained.

[0159] Similarly, similar to the binary function, v i v j θ ij Consider it as a ternary function, use the first-order Taylor series expansion of the ternary function, and substitute v i0 =v j0 =1.0,θ ij0 =θ i0 -θ j0 =0, we can get the third approximate condition in formula (2).

[0160] At this point, substituting the three approximate conditions in formula (2) into formula (1), we can obtain:

[0161]

[0162] Among them, U j is the voltage amplitude at node j, θ j is the voltage phase angle at node j, B′ ij is the susceptance matrix formed after ignoring the parallel susceptance in the node susceptance matrix.

[0163] Furthermore, the formulas of line active power and reactive power in formula (5) are further simplified, and the line active power P ij The expression is used as an example to deduce the g in formula (6). ij v i (v i -v j ) term is approximated, mainly based on the assumption that the amplitude of the node voltage is often around the per-unit value 1.0pu. Usually, the node voltage amplitude in the transmission network system does not deviate much from this voltage value, that is, it is assumed that v i =(1+Δv i ),v j =(1+Δv j ), where Δv i ,Δv j The deviation from the stable voltage by 1.0 pu is negligible. i (Δv i -Δv j ) corresponding to the higher-order terms. Thus, we can deduce:

[0164]

[0165] At this point, substitute the result of formula (6) into formula (5) to get P ij =g ij (v i -v j )-b ij θ ij Since the DC power flow in this formula does not take into account the power loss on the line, in order to improve the approximation accuracy, the branch loss approximation method is used to approximate the active power loss of the line as the square of the phase angle difference between the two ends of the line. Therefore, the line loss is generally

[0166] At this time, the active power term in the line power can be obtained (The corresponding line loss is Since the acquisition of the reactive power term is completely consistent with the above derivation process, it will not be repeated here, and the power flow equation used in the present invention is directly given as follows:

[0167]

[0168] The line active and reactive power flow terms P in formula (7) are ij , Q ij There is a quadratic term in The polygonal approximation method can be used for linearization. Similarly, the quadratic function in the objective function The polygonal inner approximation method can also be used for linearization. Considering the accuracy requirements in practical applications, a regular dodecagon inscribed in a circle is used to approximate the circular constraint.

[0169] Uncertainty set constraints include set constraints of renewable energy output uncertainty and set constraints of load uncertainty. The set constraints of renewable energy output uncertainty are divided into set constraints of renewable energy output uncertainty under constant power factor control mode and set constraints of renewable energy output uncertainty under inverter reactive power optimization control mode. Taking renewable energy as an example, assuming that the rated capacity of the rectifier / inverter is S ne , the available active power forecast output is λ ne Indicates the corresponding power factor, then the active and reactive output diagram of new energy under constant power factor control mode is as follows Figure 2 shown.

[0170] The collective constraint of the uncertainty of renewable energy output under constant power factor control is:

[0171]

[0172] Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active output fluctuation of the renewable energy in period t, and θ1 is the constant power factor angle corresponding to the renewable energy.

[0173] If the new energy adopts reactive power and the inverter optimizes and adjusts the control mode within its rated capacity, the corresponding active and reactive output diagram of the new energy is as follows: Figure 3 shown.

[0174] The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is:

[0175]

[0176] For loads, users generally have a minimum power factor requirement, so using a constant power factor model to characterize the uncertain relationship between active and reactive power has certain practical significance. The set constraint of load uncertainty is:

[0177]

[0178] Among them, P l,t is the active power of the load, Q l,t is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

[0179] Back-to-back flexible direct current transmission schematic diagram Figure 4 As shown, the equivalent diagram of the back-to-back flexible direct current model is as follows Figure 5 Assuming that the loss of back-to-back flexible DC is very small and can be ignored, a virtual generator can be used at each of the two AC systems to perform the equivalent operation. During operation, it is necessary to meet the power relationship constraints between the sending and receiving ends, the transmission power constraints, the power regulation rate constraints, the power regulation direction constraints in adjacent time periods, the full-day transmission capacity constraints, and the active power and reactive power transmission constraints.

[0180] The power relationship constraint at the receiving end is:

[0181]

[0182] Among them, d is the back-to-back flexible DC unit, D is the back-to-back flexible DC unit set, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other;

[0183] The transmission power constraint is:

[0184]

[0185]

[0186]

[0187]

[0188] Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, αd,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t α is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit. d,t and β d,t are all 0-1 variables. When the power transmission is 0, α d,t =0,β d,t =0.

[0189] The power regulation rate constraint is:

[0190]

[0191]

[0192]

[0193]

[0194] in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for downward power regulation of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit. and are 0-1 variables, Indicates that the power in period t+1 is adjusted downward compared to period t. Indicates that the power of time period t+1 is adjusted upward compared to time period t. When the power level at time t+1 does not change compared to time t, the corresponding

[0195] The power adjustment direction constraints in adjacent time periods are:

[0196]

[0197]

[0198]

[0199] Among them, z d,t is the state variable of whether the power of the back-to-back flexible direct current unit is 0; z d,t If it is 0, it means the power of the back-to-back flexible DC unit is 0. If the power of the back-to-back flexible DC unit is not 0, then zd,t The value is 1.

[0200] The power transmission constraint for the whole day is:

[0201]

[0202] Among them, Q total The amount of power delivered by back-to-back flexible direct current throughout the day;

[0203] The active power and reactive power transmission constraints are:

[0204]

[0205] in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

[0206] Active power balance constraints, reactive power balance constraints, line transmission capacity constraints, tap-adjustable transformer model constraints, reactive power adjustment range constraints of continuously adjustable reactive compensation devices, constraints on the number of switching groups and switching times of discretely adjustable reactive compensation devices, node voltage amplitude constraints, generator set active output constraints, generator set reactive power processing constraints, generator set ramp rate constraints, new energy active output constraints and new energy reactive output constraints all use existing technologies and will not be elaborated here.

[0207] Step 103: Use the C&CG algorithm to solve the optimal power flow model of the AC / DC power grid including back-to-back flexible DC and obtain the optimal power flow calculation result of the AC / DC power grid.

[0208] It should be noted that the constraints established in step 102 can be divided into two stages of optimization based on discrete variables and continuous variables, and solved using the C&CG algorithm through a two-stage robust optimization method. The solution process is as follows: Figure 6 Specifically, the solution process is:

[0209] S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future;

[0210] S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0;

[0211] S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable.

[0212] S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable;

[0213] S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage;

[0214] S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result;

[0215] S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8;

[0216] S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3;

[0217] S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

[0218] The present invention provides an optimal power flow calculation method for AC / DC power grids that takes into account source and load uncertainties. The constructed optimal power flow model of the back-to-back flexible DC / AC power grid takes into account the uncertainty of new energy and loads, can effectively take into account the influence of source and load uncertainties, and also provides linear power flow equation constraints for optimization problem modeling, so that the method proposed in the present invention has high computational efficiency and can take into account the influence of reactive voltage and system network loss, thereby improving the solution accuracy of the model. It solves the technical problems that the existing AC power flow model is difficult and time-consuming to calculate, the DC power flow model is difficult to take into account reactive voltage and is prone to large calculation errors due to ignoring network losses, and the uncertainty of new energy power generation and load is not taken into account in the optimal power flow calculation, and the optimization results cannot cover the uncertain scenarios of actual operation.

[0219] At the same time, the optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention adopts the C&CG algorithm to convert the model into a two-stage robust optimization problem for iterative solution, and divides the model into two stages according to the discrete variables and continuous variables to be optimized. This can simplify the strong coupling relationship between each time period in the optimization model and make the model solution easier.

[0220] The optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention takes into account the operation cost of discrete reactive power compensation equipment in addition to the power generation operation cost in the optimization objective function, thereby ensuring the service life of the equipment while achieving economical operation of the system.

[0221] The optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided by the present invention comprehensively considers constraints such as the capacitance of discrete reactive compensation devices, the number of reactance groups that can be put into operation, the adjustable gears of transformer taps, the active and reactive output of generator sets, the flexible operation capability of back-to-back flexible direct current (FDC), the compensation capability of reactive compensation equipment, the power flow equation of linear transformation, and the voltage amplitude when optimizing decision-making objectives. This ensures that the voltage level of the system is reasonable while ensuring the optimal optimization result of active power.

[0222] For easier understanding, see Figure 7 The present invention provides an embodiment of an AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty, comprising:

[0223] The objective function construction module is used to construct the objective function of the optimal power flow model of the back-to-back flexible DC / DC power grid with the goal of minimizing the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps, and the power generation cost of generator sets. The objective function is:

[0224]

[0225] Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, y c,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state quantity of the generator set g in period t, δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, ne The uncertainty of new energy output is a collection of l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator set g in period t;

[0226] The constraint condition construction module is used to construct the constraint conditions of the optimal power flow model of the back-to-back flexible DC / AC power grid and linearize the constraint conditions. The constraint conditions include power flow constraint, uncertainty set constraint, back-to-back flexible DC operation constraint, active power balance constraint, reactive power balance constraint, line transmission capacity constraint, tap-adjustable transformer model constraint, reactive power regulation range constraint of continuous regulation reactive compensation device, number of switching groups and switching times constraint of discrete regulation reactive compensation device, node voltage amplitude constraint, generator set active output constraint, generator set reactive processing constraint, generator set ramp rate constraint, new energy active output constraint and new energy reactive output constraint. Among them, the power flow constraint is:

[0227]

[0228]

[0229]

[0230]

[0231] Among them, P i is the active power of node i, Q i is the reactive power of node i, P ij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, g ij is the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j;

[0232] The solution module is used to solve the optimal power flow model of the AC / DC power grid containing back-to-back flexible DC and DC power grids using the C&CG algorithm to obtain the optimal power flow calculation results of the AC / DC power grid.

[0233] Uncertainty set constraints include set constraints on renewable energy output uncertainty and set constraints on load uncertainty. The set constraints on renewable energy output uncertainty are divided into set constraints on renewable energy output uncertainty under constant power factor control mode and set constraints on renewable energy output uncertainty under inverter reactive power optimization control mode.

[0234] The collective constraint of the uncertainty of renewable energy output under constant power factor control is:

[0235]

[0236] Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active power output fluctuation of the renewable energy in period t, θ1 is the constant power factor angle corresponding to the renewable energy;

[0237] The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is:

[0238]

[0239] The collective constraints of load uncertainty are:

[0240]

[0241] Among them, P l,t is the active power of the load, Q l,t is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

[0242] Back-to-back flexible direct current operation constraints include power relationship constraints between the transmitter and receiver, transmission power constraints, power regulation rate constraints, power regulation direction constraints in adjacent time periods, all-day power transmission constraints, and active power and reactive power transmission constraints.

[0243] The power relationship constraint at the receiving end is:

[0244]

[0245] Among them, d is the back-to-back flexible DC unit, D is the set of back-to-back flexible DC units, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other;

[0246] The transmission power constraint is:

[0247]

[0248]

[0249]

[0250]

[0251] Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, α d,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t It is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit;

[0252] The power regulation rate constraint is:

[0253]

[0254]

[0255]

[0256]

[0257] in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for the downward adjustment of the power of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit;

[0258] The power adjustment direction constraints in adjacent time periods are:

[0259]

[0260]

[0261]

[0262] Among them, z d,t It is the state variable indicating whether the power of the back-to-back flexible direct current unit is 0;

[0263] The power transmission constraint for the whole day is:

[0264]

[0265] Among them, Q totalThe amount of power delivered by back-to-back flexible direct current throughout the day;

[0266] The active power and reactive power transmission constraints are:

[0267]

[0268] in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

[0269] The solver module is specifically used for:

[0270] S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future;

[0271] S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0;

[0272] S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable.

[0273] S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable;

[0274] S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage;

[0275] S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result;

[0276] S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8;

[0277] S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3;

[0278] S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

[0279] The active power balance constraint, reactive power balance constraint and line transmission capacity constraint are linearized using the polygonal inner approximation method.

[0280] The optimal power flow calculation system for AC and DC power grids taking into account source and load uncertainty provided in the present invention is used to execute the optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided in the present invention. Its principles and technical effects are the same as those of the optimal power flow calculation method for AC and DC power grids taking into account source and load uncertainty provided in the present invention, and will not be repeated here.

[0281] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, 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 embodiments of the present invention.

Claims

1. A method for calculating the optimal power flow of AC and DC power grids taking into account source and load uncertainty, characterized in that: include: Taking the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps and the power generation cost of generator sets as the minimum, the objective function of the optimal power flow model of the back-to-back flexible DC / DC grid is constructed. The objective function is: Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, y c,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state quantity of the generator set g in period t, δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, Π ne is the set of uncertainties in the output of new energy, Π l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator set g in period t; The constraints of the optimal power flow model of the back-to-back flexible DC / DC power grid are constructed and linearized. The constraints include power flow constraints, uncertainty set constraints, back-to-back flexible DC operation constraints, active power balance constraints, reactive power balance constraints, line transmission capacity constraints, tap-adjustable transformer model constraints, reactive power regulation range constraints of continuous reactive compensation devices, switching group number and switching frequency constraints of discrete reactive compensation devices, node voltage amplitude constraints, generator set active output constraints, generator set reactive processing constraints, generator set ramp rate constraints, renewable energy active output constraints, and renewable energy reactive output constraints. The power flow constraints are: Among them, P i is the active power of node i, Q i is the reactive power of node i, P ij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, g ij is the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j; The C&CG algorithm is used to solve the optimal power flow model of the AC / DC power grid containing back-to-back flexible DC and obtain the optimal power flow calculation results of the AC / DC power grid.

2. The method for calculating the optimal power flow of AC and DC power grids taking into account source and load uncertainty according to claim 1, characterized in that: Uncertainty set constraints include set constraints on renewable energy output uncertainty and set constraints on load uncertainty. The set constraints on renewable energy output uncertainty are divided into set constraints on renewable energy output uncertainty under constant power factor control mode and set constraints on renewable energy output uncertainty under inverter reactive power optimization control mode. The collective constraint of the uncertainty of renewable energy output under constant power factor control is: Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active power output fluctuation of the renewable energy in period t, θ1 is the constant power factor angle corresponding to the renewable energy; The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is: The collective constraints of load uncertainty are: Among them, P l,t is the active power of the load, Q l,t is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

3. The method for calculating the optimal power flow of AC and DC power grids taking into account source and load uncertainty according to claim 2, characterized in that: Back-to-back flexible direct current operation constraints include power relationship constraints between the transmitter and receiver, transmission power constraints, power regulation rate constraints, power regulation direction constraints in adjacent time periods, all-day power transmission constraints, and active power and reactive power transmission constraints. The power relationship constraint at the receiving end is: Among them, d is the back-to-back flexible DC unit, D is the back-to-back flexible DC unit set, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other; The transmission power constraint is: Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, α d,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t It is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit; The power regulation rate constraint is: in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for the downward adjustment of the power of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit; The power adjustment direction constraints in adjacent time periods are: Among them, z d,t It is the state variable indicating whether the power of the back-to-back flexible direct current unit is 0; The power transmission constraint for the whole day is: Among them, Q total The amount of power delivered by back-to-back flexible direct current throughout the day; The active power and reactive power transmission constraints are: in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

4. The method for calculating the optimal power flow of AC and DC power grids taking into account source and load uncertainty according to claim 3, characterized in that: The C&CG algorithm is used to solve the optimal power flow model of the AC / DC power grid with back-to-back flexible DC and DC power grids, and the optimal power flow calculation results of the AC / DC power grid are obtained, including: S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future; S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0; S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable. S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable; S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage; S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result; S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8; S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3; S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

5. The method for calculating the optimal power flow of AC and DC power grids taking into account source and load uncertainty according to claim 3, characterized in that: The active power balance constraint, reactive power balance constraint and line transmission capacity constraint are linearized using the polygonal inner approximation method.

6. An AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty, characterized in that: include: The objective function construction module is used to construct the objective function of the optimal power flow model of the back-to-back flexible DC / DC power grid with the goal of minimizing the cost of switching times of discrete reactive compensation devices, the cost of changing transformer taps, and the power generation cost of generator sets. The objective function is: Where T is the time set, G is the generator set, C is the discrete reactive compensation device capacitor bank and reactor bank set, S is the transformer set, y c,t is the number of switching times of capacitance and reactance, τ s,t is the number of transformer tap changes, u g,t is the start and stop state quantity of the generator set g in period t, δ is y c,t The cost of times, ψ is τ s,t The cost coefficient, P ne For the active contribution of new energy, P l is the active power of the load, ne The uncertainty of new energy output is a collection of l is the set of load uncertainties, a g 、b g 、c g are power generation cost coefficients, P g,t is the power output value of generator group g in period t; The constraint condition construction module is used to construct the constraint conditions of the optimal power flow model of the back-to-back flexible DC / AC power grid and linearize the constraint conditions. The constraint conditions include power flow constraint, uncertainty set constraint, back-to-back flexible DC operation constraint, active power balance constraint, reactive power balance constraint, line transmission capacity constraint, tap-adjustable transformer model constraint, reactive power regulation range constraint of continuous regulation reactive compensation device, number of switching groups and switching times constraint of discrete regulation reactive compensation device, node voltage amplitude constraint, generator set active output constraint, generator set reactive processing constraint, generator set ramp rate constraint, new energy active output constraint and new energy reactive output constraint. Among them, the power flow constraint is: Among them, P i is the active power of node i, Q i is the reactive power of node i, P ij is the active power of line ij, Q ij is the reactive power of line ij, v i is the voltage amplitude of node i, v j is the voltage amplitude of node j, n is the number of nodes, G ij is the conductance between node i and node j in the node admittance matrix, B ij is the susceptance between node i and node j in the node admittance matrix, g ij is the conductance of the transmission line ij, b ij is the susceptance of transmission line ij, θ ij is the phase angle difference between node i and node j; The solution module is used to solve the optimal power flow model of the AC / DC power grid containing back-to-back flexible DC and DC power grids using the C&CG algorithm to obtain the optimal power flow calculation results of the AC / DC power grid.

7. The AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty according to claim 6, characterized in that: Uncertainty set constraints include set constraints on renewable energy output uncertainty and set constraints on load uncertainty. The set constraints on renewable energy output uncertainty are divided into set constraints on renewable energy output uncertainty under constant power factor control mode and set constraints on renewable energy output uncertainty under inverter reactive power optimization control mode. The collective constraint of the uncertainty of renewable energy output under constant power factor control is: Among them, P ne,t is the active power output of renewable energy in period t, Q ne,t is the reactive power output of renewable energy in period t, is the predicted active power output of renewable energy in period t, is the active power output fluctuation of the renewable energy in period t, θ1 is the constant power factor angle corresponding to the renewable energy; The collective constraint of renewable energy output uncertainty under the inverter reactive power optimization control mode is: The collective constraints of load uncertainty are: Among them, P lt is the active power of the load, Q lt is the reactive power of the load, θ2 is the constant power factor angle corresponding to the load, is the predicted active power of the load, is the active power fluctuation of the load.

8. The AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty according to claim 7, characterized in that: Back-to-back flexible direct current operation constraints include power relationship constraints between the transmitter and receiver, transmission power constraints, power regulation rate constraints, power regulation direction constraints in adjacent time periods, all-day power transmission constraints, and active power and reactive power transmission constraints. The power relationship constraint at the receiving end is: Among them, d is the back-to-back flexible DC unit, D is the back-to-back flexible DC unit set, is the active power of the virtual generator set on the first AC system side during period t, is the active power of the virtual generator set on the second AC system side during period t, with the first AC system side and the second AC system side acting as the transmitter and receiver of each other; The transmission power constraint is: Among them, P d,min P is the lower limit of the regulating power of a single valve group of the back-to-back flexible direct current unit. d,max is the upper limit of the regulating power of a single valve group of the back-to-back flexible direct current unit, f d,t is the minimum number of valve groups operating on the first AC system side and the second AC system side during the back-to-back flexible DC unit period t, α d,t is the forward power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit, β d,t It is the reverse power transmission state variable of the first AC system side virtual machine group of the back-to-back flexible DC unit; The power regulation rate constraint is: in, The upper limit of the downward adjustment rate of the back-to-back flexible direct current unit, It is the lower limit of the downward adjustment rate of the back-to-back flexible direct current unit. The upper limit of the upward adjustment rate of the back-to-back flexible direct current unit. It is the lower limit of the upward adjustment rate of the back-to-back flexible direct current unit. is the state variable for upward power regulation of the back-to-back flexible DC unit, is the state variable for the downward adjustment of the power of the back-to-back flexible DC unit, and M1 is a constant greater than the maximum power transmission capacity of the back-to-back flexible DC unit; The power adjustment direction constraints in adjacent time periods are: Among them, z d,t It is the state variable indicating whether the power of the back-to-back flexible direct current unit is 0; The power transmission constraint for the whole day is: Among them, Q total The amount of power delivered by back-to-back flexible direct current throughout the day; The active power and reactive power transmission constraints are: in, is the reactive power of the virtual generator set on the first AC system side during period t, S d is the rated capacity of the back-to-back flexible DC converter.

9. The AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty according to claim 8, characterized in that: The solver module is specifically used for: S1. Obtain the forecast results of renewable energy output and load for a certain period of time in the future; S2. Initialize the upper and lower limits of the number of iterations and set the current number of iterations to 0; S3. Optimize the objective function of the optimal power flow model for the AC / DC grid containing back-to-back flexible DC based on the load, capacitors, reactors, transformers, and back-to-back flexible DC model parameters to obtain optimization results for discrete variables. The discrete variables include the number of capacitor and reactor switching groups, the main transformer tap position, and the back-to-back flexible DC power adjustment variable. S4. Update the lower limit of the number of iterations according to the optimization results of discrete variables. The update formula is: Where LB is the lower limit of the number of iterations, χ is the first auxiliary variable; S5. Based on known discrete variables and constraints, comprehensively optimize the continuous active power output and continuous reactive power output of the generator set, continuously regulating reactive power compensation device, new energy power source and energy storage; S6. Update the upper limit of the number of iterations based on the optimization results of continuous active output and continuous reactive output. The update formula is: Among them, UB is the upper limit of the number of iterations, W is the second auxiliary variable, and W takes the inner layer of the objective function The optimal result; S7. Determine whether UB-LB is less than or equal to ε, where ε is the difference threshold. If so, jump to step S9; if not, jump to step S8; S8. Assign the second auxiliary variable W to infinity, increase the current number of iterations by 1, and add a cut constraint: Return to step S3; S9. According to the discrete variables and continuous variables obtained by optimization, the optimal power flow calculation results of the AC and DC power grids are obtained.

10. The AC / DC power grid optimal power flow calculation system taking into account source and load uncertainty according to claim 8, characterized in that: The active power balance constraint, reactive power balance constraint and line transmission capacity constraint are linearized using the polygonal inner approximation method.

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