Forced power flow control method for improving new energy absorption capability through flexible direct current

Abstract

The invention discloses a forced power flow control method for improving new energy consumption capability through flexible direct current. The method comprises the following steps: step 1), establishing a steady-state equivalent model of each element of an alternating-current and direct-current hybrid power grid and an alternating-current system admittance matrix; 2) constructing a new energy-containing cross-regional AC / DC hybrid power grid power flow optimization model and solving the model; 3) if the established model has no solution, adjusting limit value setting of related constraints in the model or system state parameters, and performing solution again; 4) if the established model has no solution and the transmission power of the key channel line does not exceed the limit, solving again to obtain an optimization regulation and control decision in the time period; 5) if the established model has no solution and the transmission power of the key channel line is out of limit, reconstructing an AC / DC hybrid power grid power flow optimization model, and solving the optimization problem; and step 6), performing inspection to obtain an optimization regulation and control decision in the time period. According to the invention, flexible regulation and control of the power system are realized, and effective utilization of scarce power transmission channels and key power transmission channels is ensured.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a forced power flow control method for improving the new energy consumption capacity of flexible direct current. Background Art

[0002] The installed capacity of renewable energy is increasing year by year, and the pressure on power transmission channels from renewable energy gathering areas to load gathering areas has increased sharply. Due to the randomness and uncertainty of renewable energy output, it is easy to cause transmission line blockage under the AC system flow distribution, resulting in cross-regional transmission channels being blocked and local power grid congestion. This poses a severe test for the optimal utilization of scarce transmission resources such as cross-river channels and key channels of the transmission network that undertake cross-regional transmission tasks.

[0003] At the same time, the backbone transmission network has gradually transformed from the traditional AC power grid to the new AC / DC hybrid power grid. In this context, how to improve the new energy consumption capacity is the current problem. Summary of the invention

[0004] In view of the above problems, the present invention provides a forced power flow control strategy for flexible direct current to improve the new energy absorption capacity.

[0005] The technical solution of the present invention is: a forced power flow control method for improving the new energy consumption capacity of flexible direct current, comprising the following steps:

[0006] Step 1): Establish a steady-state equivalent model of each component of the AC / DC hybrid power grid and an AC system admittance matrix;

[0007] Step 2): With the goal of minimizing the power generation cost, a cross-regional AC / DC hybrid power grid power flow optimization model containing new energy sources is constructed and solved;

[0008] Step 3): If there is no solution for the model established in step 2), identify the constraint conditions that have exceeded the limit in the optimization model, adjust the limit settings of the relevant constraints or system state parameters in the model and solve again;

[0009] Step 4): If there is no solution to the model established in step 3) and the transmission power of the key channel line does not exceed the limit, on the basis of maintaining the modified constraint conditions in step 3), re-solve with the goal of maximizing the consumption of new energy to obtain the optimal control decision for this period;

[0010] Step 5): If there is no solution to the model established in step 3) and the transmission power of the key channel line exceeds the limit, on the basis of maintaining the modified constraint conditions in step 3), with the goal of maximizing the consumption of new energy, according to different DC control modes, add DC control balance constraints, and reconstruct the AC / DC hybrid power grid power flow optimization model for this period; solve the optimization problem and record the number of actions of each flexible DC control means;

[0011] Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operation requirements; if all nodes satisfy the inequality, the optimal control decision for this period is obtained; if the i-th node does not meet the action number requirement, the power of the DC node is corrected, and step 5) is repeated to obtain the optimal control decision for this period.

[0012] In step 2),

[0013] The objective function of the power flow optimization model of the inter-regional AC / DC hybrid power grid containing new energy is expressed as:

[0014]

[0015] In the formula, F(P Gi ) is the i-th thermal power node with P Gi The power generation cost during power generation; X1 is the column vector of optimization variables; Ng is the number of thermal power nodes in the system.

[0016] The optimization variables include:

[0017]

[0018] In the formula, is the column vector of optimization variables; is the system node voltage column vector; N is the number of system nodes, and the system heavy node 1 is assumed to be the system balance node, are the node voltages from the 1st node to the Nth node of the system respectively; is the system node phase angle column vector; They are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the thermal power nodes in the system; Ng is the number of thermal power nodes in the system, Each element is the active power from the 1st thermal power node to the Ngth thermal power node in the system; It is the reactive power column vector of the thermal power units in the system; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive power column vector of the system’s new energy nodes; Nr is the number of new energy nodes in the system, Each element is the reactive power of the system's 1st new energy node to the Nrth new energy node.

[0019] The equality constraints in the optimization model are active and reactive balance equations between nodes in the system. The specific expressions are:

[0020]

[0021] Where P Gi ,QGi are respectively the active and reactive outputs of the generator at the i-th node; P Li ,Q Li are the active and reactive load demands of the i-th node respectively; G ij ,B ij are the node admittance matrix elements Y ij The real and imaginary parts of

[0022] The inequality constraints in the optimization model are the voltage constraints and phase angle constraints of each node; the upper and lower limit constraints of active and reactive power of the power generation node, and the transmission capacity constraints of the AC line. The specific expressions are:

[0023]

[0024] Where U imin ,U imax are the upper and lower limits of the voltage of the i-th node respectively; θ imin ,θ imax are the upper and lower limits of the phase angle of the ith node respectively; P Gjmin ,P Gjmax are the upper and lower limits of the active output of the jth node respectively; Q Gjmin ,Q Gjmax are the upper and lower limits of reactive power output of the jth node respectively; Q rekmin ,Q rekmax are the upper and lower limits of reactive power output of the kth new energy node respectively; S stmax is the thermal power limit of the AC line between node s and node t; α is the transmission power limit relaxation factor of the AC line between the sth node and the tth node.

[0025] In step 3), when there is no solution in step 2), the constraint conditions that have exceeded the limit in the optimization model are identified, and the relationship between the constraint conditions that have exceeded the limit and the system control variables is determined according to the situation. The specific method for adjusting the limit setting of the relevant constraints in the model or the system state parameters is:

[0026] Combined with the actual optimization calculation results, the constraints that restrict the optimization model from having a solution are identified, which are specifically divided into the following situations:

[0027] 3.1) If the constraint factor is the transmission power limit of the critical channel line, increase the relaxation factor α of the critical channel transmission power limit, substitute the new relaxation factor α and repeat step 2) to optimize the solution;

[0028] 3.2) If the constraint factor is the voltage amplitude range constraint, adjust the reactive power output level in the vicinity of the corresponding voltage-exceeding node and repeat step 2) to optimize the solution;

[0029] 3.3) If the constraint is the transmission power limit of the ordinary transmission line, the active power injection of the strongly correlated nodes of the corresponding line is modified based on the power transmission distribution factor matrix and step 2) is repeated to optimize the solution.

[0030] In step 3.3), the power transmission allocation factor matrix M PTDF for:

[0031]

[0032] Where N and L are the number of nodes and AC lines respectively, and P ln is the injection power transmission allocation coefficient of the nth node on the lth line, 1≤n≤N, 1≤l≤L.

[0033] In step 4), the optimization goal is to maximize the consumption of new energy, without changing the DC system control mode, and the specific method is re-solved as follows:

[0034] The optimization variable of the new optimization model in this step is the active power of the new energy node added to the original optimization variable, expressed as:

[0035]

[0036] In the formula, is the optimization variable column vector of the optimization model in step 4); is the column vector of optimization variables of the optimization model in step 2); is the active power column vector of the new energy node; Each element is the active output of the 1st to Nrth new energy nodes;

[0037] The optimization objective function is changed to:

[0038]

[0039] Where P rek is the active power of the kth new energy node; P rek0 Contribute to the planned active power of the kth new energy node;

[0040] The equality constraints of the new optimization model are consistent with the corresponding constraints of the optimization model in step 2), and the inequality constraints add the upper and lower limits of the active power of the new energy node, and the expression is:

[0041] 0≤P rek ≤P rek0 ,1≤k≤Nr

[0042] The new optimization model is solved to obtain the result of the optimization problem.

[0043] In step 5), with the goal of maximizing the consumption of new energy, according to different DC control modes, the specific construction method of the AC / DC hybrid power grid power flow optimization model for this period is as follows:

[0044] The optimization variables of the new optimization model are based on the optimization variables of the optimization model in step 4) and the control variables and parameters of the flexible straight line are added. The expression is:

[0045]

[0046] In the formula, is the column vector of optimization variables of the optimization model in step 5); is the optimization variable column vector of the optimization model in step 4); They are respectively the active and reactive column vectors injected into the DC system by the AC nodes connected to the converter; Inject active and reactive power of the DC system into the 1st to Ndcth AC nodes connected to the converter; is the active power injected into the DC node from the converter; is the active power of the 1st to Ndc DC nodes; is the DC node voltage column vector excluding the constant voltage controlled DC node; The DC node voltages of the 1st to Ndcth DC nodes without the constant voltage control; is the column vector of DC node current; is the current of the 1st to Ndcth DC nodes; the objective function is consistent with the objective function modified in step 4);

[0047] The constraint equation conditions of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraint conditions. The specific constraint condition expressions are:

[0048]

[0049] Where P si ,Q si are respectively the active and reactive power injected into the converter for the i-th AC node; P dci is the active power absorbed by the ith DC node from the converter; R i ,X i is the equivalent resistance and inductance of the converter; U dci ,I dci are the voltage and current of the ith DC node respectively; Y dcij is the conductance of the DC line between the i-th DC node and the j-th DC node;

[0050] Inequality constraints Based on the inequality constraints of the optimization model in step 2), the power constraints of the DC nodes are added. The specific expression is:

[0051]

[0052] Where P dcimin ,P dcimax ,Q dcimin ,Q dcimax are the upper and lower limits of active and reactive power of the i-th DC node respectively.

[0053] Based on the above optimization model, the optimization result of the AC / DC hybrid power grid power flow optimization model in step 5) is calculated.

[0054] According to the optimization results, the number of converter actions at each DC node is recorded respectively. The specific calculation method is:

[0055]

[0056] Where P dci ,P d ' ci are the target DC power and the current DC power respectively, ΔP is the step size of the DC converter power adjustment, [x] is the floor rounding function, N si is the estimated number of actions of the i-th node converter in this action.

[0057] In step 6), the method for evaluating the number of actions of the DC control means is:

[0058] For the number of actions recorded by each DC converter, the sum of the number of actions of all previous decisions from the day to the scheduling decision period and the number of actions required for this optimization is calculated to be less than the number of actions allowed by the converter on that day, that is, the number of actions not used in the previous period is allowed to be reserved for use in this period. The constraint is expressed as:

[0059]

[0060] Where N si N is the estimated number of actions of the converter at the ith node. sik is the number of actions of the i-th node converter in the k-th time period; N0 is the average number of allowed actions of the converter in a single time period; w is the time period;

[0061] If all nodes satisfy the inequality, the decision is made according to the optimization result; if the i-th node does not meet the action number requirement, the DC power of the node is set to be constant, removed from the optimization variable, and step 5) is repeated to obtain the optimization decision for this regulation.

[0062] In operation, the present invention can construct a cross-regional AC / DC hybrid power grid power flow optimization model containing new energy sources during the optimization and control period, flexibly correct the objective function and constraint boundary of the optimization model according to the constraints that take effect in the optimization process, flexibly add DC control means according to whether the transmission constraints of key channels are restricted, and reasonably determine the optimization space, thereby realizing flexible control of the optimized power system and ensuring the effective use of scarce transmission channels and key transmission channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The present invention is a flow chart of a forced power flow control strategy for improving the new energy consumption capacity. DETAILED DESCRIPTION

[0064] According to the present invention, a forced power flow control method for improving the new energy consumption capacity of flexible direct current includes the following steps:

[0065] Step 1): Establish a steady-state equivalent model of each component of the AC / DC hybrid power grid and an AC system admittance matrix;

[0066] Step 2): In the single period of active power optimization and control, with the goal of minimizing the power generation cost, the DC system control mode is not changed, the transmission power limit relaxation factor of the key channel is set, and a cross-regional AC / DC hybrid power grid power flow optimization model containing new energy is constructed and the nonlinear optimization model is solved;

[0067] Step 3): If there is no solution for the model established in step 2), identify the constraint conditions that have exceeded the limit in the optimization model, determine the relationship between the constraint conditions that have exceeded the limit and the system control variables according to the situation, adjust the limit settings of the relevant constraints in the model or the system state parameters and solve again;

[0068] Step 4): If there is no solution to the model established in step 3) and the transmission power of the key channel line does not exceed the limit, on the basis of maintaining the modified constraint conditions in step 3), with the goal of maximizing the consumption of new energy, without changing the DC system control mode, re-solve and obtain the optimal control decision for this period;

[0069] Step 5): If there is no solution to the model established in step 3) and the transmission power of the key channel line exceeds the limit, on the basis of maintaining the modified constraints in step 3), with the goal of maximizing the consumption of new energy, add DC control balance constraints according to different DC control modes, and rebuild the AC / DC hybrid power grid power flow optimization model for this period. Solve the optimization problem and record the number of actions of each flexible DC control means;

[0070] Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operation requirements; if all nodes satisfy the inequality, the optimal control decision for this period is obtained; if the i-th node does not meet the action number requirement, the power of the DC node is corrected, and step 5) is repeated to obtain the optimal control decision for this period.

[0071] In the work, in order to improve the new energy consumption capacity and ensure the transmission capacity of scarce transmission channels, the present invention firstly designs an optimization control model with the goal of minimizing the power generation cost and the AC control method as the adjustment means. Secondly, when the optimization space of the model is limited or has no solution, the objective function of the optimization problem is changed, and the feasible domain range is adjusted by adjusting the key constraints; further, if the transmission capacity of the key transmission channel exceeds the limit, the scarce flexible DC control means are continued to be added to increase the dimension of the control variable, so as to realize the solvability of the optimal scheduling of the new AC / DC hybrid power grid under the goal of maximizing the consumption of new energy, and ensure the stability and economic operation of the AC / DC hybrid power grid.

[0072] In step 1), an equivalent model of an AC / DC hybrid power grid is established based on the steady-state equivalent circuit models of components such as transmission lines, transformers, and VSCs in the AC / DC hybrid power grid, and an AC system admittance matrix is ​​obtained.

[0073] In step 1), this part is the "basic component parameter calculation" part of the power system flow analysis, which is a conventional method.

[0074] The method for constructing the power flow optimization model of the cross-regional AC / DC hybrid power grid containing new energy in step 2) is as follows:

[0075] For the power flow optimization model of the cross-regional AC / DC hybrid power grid containing new energy without changing the DC control mode, the optimization variables include:

[0076]

[0077] In the formula, is the column vector of optimization variables; is the system node voltage column vector; N is the number of system nodes, and the system heavy node 1 is assumed to be the system balance node, are the node voltages from the 1st node to the Nth node of the system respectively; is the system node phase angle column vector; They are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the thermal power nodes in the system; Ng is the number of thermal power nodes in the system, Each element is the active power from the 1st thermal power node to the Ngth thermal power node in the system; It is the reactive power column vector of the thermal power units in the system; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive power column vector of the system’s new energy nodes; Nr is the number of new energy nodes in the system, Each element is the reactive power of the system's 1st new energy node to the Nrth new energy node.

[0078] At this time, the objective function of minimizing the power generation cost is expressed as:

[0079]

[0080] In the formula, F(P Gi ) is the i-th thermal power node with P Gi The cost of electricity generation when it is generated.

[0081] The equality constraints in the optimization model are active and reactive balance equations between nodes in the system. The specific expressions are:

[0082]

[0083] Where P Gi ,Q Gi are respectively the active and reactive outputs of the generator at the i-th node; P Li ,Q Li are the active and reactive load demands of the i-th node respectively; G ij ,B ij are the node admittance matrix elements Y ij The real and imaginary parts of .

[0084] The inequality constraints in the optimization model are the voltage constraints and phase angle constraints of each node; the upper and lower limit constraints of active and reactive power of the power generation node, and the transmission capacity constraints of the AC line. The specific expressions are:

[0085]

[0086] Where U imin ,U imax are the upper and lower limits of the voltage of the i-th node respectively; θ imin ,θ imax are the upper and lower limits of the phase angle of the ith node respectively; P Gjmin ,P Gjmax are the upper and lower limits of the active output of the jth node respectively; Q Gjmin ,Q Gjmax are the upper and lower limits of reactive power output of the jth node respectively; Q rekmin ,Q rekmax are the upper and lower limits of reactive power output of the kth new energy node respectively; S stmaxis the thermal power limit of the AC line between node s and node t; α is the transmission power limit relaxation factor of the AC line between the sth node and the tth node.

[0087] For AC systems, the optimal solution can be obtained using the interior point method based on the above nonlinear optimization model.

[0088] In step 3), when there is no solution in step 2), the constraint conditions that have exceeded the limit in the optimization model are identified, and the relationship between the constraint conditions that have exceeded the limit and the system control variables is determined according to the situation. The specific method for adjusting the limit setting of the relevant constraints in the model or the system state parameters is as follows:

[0089] Combined with the actual optimization calculation results, the constraints that restrict the optimization model from having no solution are identified, which can be divided into the following three cases:

[0090] 3.1) If the constraint factor is the transmission power limit of the critical channel line, then appropriately increase the relaxation factor α (α≤1) of the critical channel transmission power limit to reduce the thermal stability limit requirement of the line, substitute the new relaxation factor α and repeat step 2) for optimization solution;

[0091] 3.2) If the constraint factor is the voltage amplitude range constraint, adjust the reactive power output level in the vicinity of the corresponding voltage-exceeding node and repeat step 2) to optimize the solution;

[0092] 3.3) If the constraint factor is the transmission power limit of the common transmission line, then based on the Power Transfer Distribution Factor (PTDF) matrix, modify the active power injection of the strongly correlated nodes of the corresponding line and repeat step 2) to optimize the solution;

[0093] For other over-limit situations, go to step 5) or step 6) and try a new round of optimization problem solving by modifying the objective function.

[0094] In step 3.3), the PTDF matrix based on which the active power injection of the strongly correlated nodes of the corresponding line is modified is calculated as follows:

[0095] In the AC / DC hybrid power grid, the active power balance equation of the AC node is expressed as:

[0096]

[0097] Where P i is the net active power injection of the ith node; U i , U j are the voltage amplitudes of the i-th and j-th nodes respectively, G ij , B ij are the Y in the AC system admittance matrix ijThe real and imaginary parts of the elements, Y is the admittance matrix of the AC system, δ ij is the voltage phase difference between the i-th node and the j-th node;

[0098] For AC transmission network, based on the assumption that R<<X, and assuming that the voltage of each node U i ≈1. The node power balance equation is simplified to:

[0099]

[0100] In the formula, δ i , δ j are the voltage phase angles of the i-th and j-th nodes, respectively, and x ij is the impedance value of the AC line between the i-th node and the j-th node;

[0101] The DC power flow equation further converted into matrix form is:

[0102]

[0103] make

[0104]

[0105] Where B is the system node admittance matrix, is the system node voltage phase angle column vector, is the system node power column vector, and the system refers to the AC power grid system;

[0106] The DC power flow equation is expressed as:

[0107]

[0108] For the DC power flow equation obtained, remove the rows and columns where the balance nodes in the line are located, and we get:

[0109]

[0110] Where B′ is the system node admittance matrix without the equilibrium node, is the system node voltage phase angle column vector excluding the balance node, is the system node power column vector excluding the balancing node;

[0111] Unit active power is injected into the kth node of the unbalanced node, and the power is balanced through the balanced node. The power change of each line when the kth node injects unit power is calculated. The calculation method is:

[0112] make

[0113]

[0114] Among them, P i 'for The system node power of the i-th node in;

[0115] Depend on get:

[0116]

[0117] Then we get:

[0118]

[0119] In the formula, is the voltage difference between the i-th node and the j-th node, is the current flowing from the i-th node to the j-th node, is the voltage of the ith node, y ij is the admittance of the AC line between the ith node and the jth node, is the voltage of the jth node, g ij is the conductance of the AC line between the i-th node and the j-th node, δ ij ′The voltage phase difference between the i-th node and the j-th node, b ij is the susceptance of the AC line between the i-th node and the j-th node, δ i ′ is the voltage phase angle of the i-th node, δ j ′ is the voltage phase angle of the jth node.

[0120] When the kth node has unit power injected, the active power flowing through the AC line between the i-th node and the j-th node increases by P ij , P ij represents the transmission allocation coefficient of the injected power of the kth node on the AC line between the i-th node and the j-th node;

[0121] In summary, the power transmission allocation factor matrix M is obtained PTDF :

[0122]

[0123] Where N and L are the number of nodes and AC lines respectively, and P ln is the injection power transmission allocation coefficient of the nth node on the lth line, 1≤n≤N, 1≤l≤L.

[0124] In step 4), the optimization goal is to maximize the consumption of new energy, without changing the DC system control mode, and the specific method is re-solved as follows:

[0125] The optimization variable of the new optimization model in this step is to add the active power of the new energy node to the original optimization variable, which is expressed as

[0126]

[0127] In the formula, is the optimization variable column vector of the optimization model in step 4); is the column vector of optimization variables of the optimization model in step 2); is the active power column vector of the new energy node; Each element is the active output of the 1st to Nrth new energy nodes.

[0128] The optimization objective function is changed to:

[0129]

[0130] Where P rek is the active power of the kth new energy node; P rek0 Contribute to the plan of the kth new energy node.

[0131] The equality constraints of the new optimization model are consistent with the corresponding constraints of the optimization model in step 2), and the inequality constraints add the upper and lower limits of the active power of the new energy node, and the expression is:

[0132] 0≤P rek ≤P rek0 ,1≤k≤Nr

[0133] The new optimization model is solved to obtain the result of the optimization problem.

[0134] In step 5), the goal is to maximize the consumption of new energy. According to different flexible DC control modes, the specific construction method of the AC / DC hybrid power grid power flow optimization model for this period is as follows:

[0135] The optimization variables of the new optimization model are based on the optimization variables of the optimization model in step 4) and the control variables and parameters of the flexible straight line are added. The expression is:

[0136]

[0137] In the formula, is the column vector of optimization variables of the optimization model in step 5); is the optimization variable column vector of the optimization model in step 4); They are respectively the active and reactive column vectors injected into the DC system by the AC nodes connected to the converter; Inject active and reactive power of the DC system into the 1st to Ndcth AC nodes connected to the converter; is the active power injected into the DC node from the converter; is the active power of the 1st to Ndc DC nodes; is the DC node voltage column vector excluding the constant voltage controlled DC node; The DC node voltages of the 1st to Ndcth DC nodes without the constant voltage control; is the column vector of DC node current; is the current of the 1st to Ndcth DC nodes; the objective function is consistent with the objective function modified in step 4).

[0138] The constraint equation conditions of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraint conditions. The specific constraint condition expressions are:

[0139]

[0140] Where P si ,Q si are respectively the active and reactive power injected into the converter for the i-th AC node; P dci is the active power absorbed by the ith DC node from the converter; R i ,X i is the equivalent resistance and inductance of the converter; U dci ,I dci are the voltage and current of the ith DC node respectively; Y dcij is the conductance of the DC line between the i-th DC node and the j-th DC node.

[0141] Inequality constraints Based on the inequality constraints of the optimization model in step 2), the power constraints of the DC nodes are added. The specific expression is:

[0142]

[0143] Where P dcimin ,P dcimax ,Q dcimin ,Q dcimax are the upper and lower limits of active and reactive power of the i-th DC node respectively.

[0144] Based on the above optimization model, the optimization result of the AC / DC hybrid power grid power flow optimization model in step 5) is calculated.

[0145] According to the optimization results, the number of converter actions at each DC node is recorded respectively. The specific calculation method is:

[0146]

[0147] Where P dci ,P d ' ci are the target DC power and the current DC power respectively, ΔP is the step size of the DC converter power adjustment, [x] is the floor rounding function, Nsi is the estimated number of actions of the i-th node converter in this action.

[0148] In step 5), when the previous optimization model has no solution, the system's new energy consumption is maximized by adjusting the DC transmission capacity.

[0149] Flexible DC control modes mainly include: constant voltage control and constant active power control;

[0150] The calculation here does not distinguish between control modes, and all are quantities to be determined in the optimization model. The distinction is made during actual control after the optimization solution.

[0151] Limited by the total number of adjustments during the life cycle of the DC control equipment, the DC control means action number evaluation method in step 6) is designed based on the calculated daily allowable converter action number, specifically:

[0152] For the number of actions recorded by each DC converter, the sum of the number of actions of all previous decisions (k = 1, 2, ..., w-1) from the day to the scheduling decision period (set as the wth period) and the number of actions required for this optimization is calculated to be less than the number of actions allowed by the converter on the day, that is, the number of actions not used in the previous period is allowed to be used in this period. The constraint is expressed as:

[0153]

[0154] Where N si N is the estimated number of actions of the converter at the ith node. sik is the number of actions of the i-th node converter in the k-th time period; N0 is the average allowed number of actions of the converter in a single time period.

[0155] If all nodes satisfy the inequality, the decision is made according to the optimization result; if the i-th node does not meet the action number requirement, the DC power of the node is set to be constant, removed from the optimization variable, and step 5) is repeated to obtain the optimization decision for this regulation.

[0156] Here, the average total number of daily operations of the converter is considered for evaluation, which can meet the operation number requirements of the converter for long-term operation. At the same time, since the daily operation number of the converter will not reach the upper limit in most cases, there is a certain margin for the daily operation number.

[0157] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A forced power flow control method for flexible direct current to improve the new energy consumption capacity, characterized in that: The following steps are involved: Step 1): Establish a steady-state equivalent model of each component of the AC / DC hybrid power grid and an AC system admittance matrix; Step 2): With the goal of minimizing the power generation cost, a cross-regional AC / DC hybrid power grid power flow optimization model containing new energy sources is constructed and solved; Step 3): If there is no solution for the model established in step 2), identify the constraint conditions that have exceeded the limit in the optimization model, adjust the limit settings of the relevant constraints or system state parameters in the model and solve again; Step 4): If there is no solution to the model established in step 3) and the transmission power of the key channel line does not exceed the limit, on the basis of maintaining the modified constraint conditions in step 3), re-solve with the goal of maximizing the consumption of new energy to obtain the optimal control decision for this period; Step 5): If there is no solution to the model established in step 3) and the transmission power of the key channel line exceeds the limit, on the basis of maintaining the modified constraint conditions in step 3), with the goal of maximizing the consumption of new energy, according to different DC control modes, add DC control balance constraints, and reconstruct the AC / DC hybrid power grid power flow optimization model for this period; solve the optimization problem and record the number of actions of each flexible DC control means; Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operation requirements; if all nodes satisfy the inequality, the optimal control decision for this period is obtained; if the i-th node does not meet the action number requirement, the power of the DC node is corrected, and step 5) is repeated to obtain the optimal control decision for this period.

2. According to claim 1, a forced power flow control method for improving the new energy consumption capacity of flexible direct current is characterized in that: In step 2), The objective function of the power flow optimization model of the inter-regional AC / DC hybrid power grid containing new energy is expressed as: In the formula, F(P Gi ) is the i-th thermal power node with P Gi the cost of electricity generation when it is generated; is the column vector of optimization variables; Ng is the number of thermal power nodes in the system.

3. According to claim 1, a forced power flow control method for improving the new energy consumption capacity of flexible direct current is characterized in that: The optimization variables include: In the formula, is the column vector of optimization variables; is the system node voltage column vector; N is the number of system nodes, and the system heavy node 1 is assumed to be the system balance node, are the node voltages from the 1st node to the Nth node of the system respectively; is the system node phase angle column vector; They are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the thermal power nodes in the system; Ng is the number of thermal power nodes in the system, Each element is the active power of the system's first thermal power node to the Ngth thermal power node; Q G It is the reactive power column vector of the thermal power units in the system; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive power column vector of the system’s new energy nodes; Nr is the number of new energy nodes in the system, Each element is the reactive power of the system's 1st new energy node to the Nrth new energy node.

4. According to claim 1, a forced power flow control method for improving the new energy consumption capacity of flexible direct current is characterized in that: The equality constraints in the optimization model are active and reactive balance equations between nodes in the system. The specific expressions are: Where P Gi ,Q Gi are respectively the active and reactive outputs of the generator at the i-th node; P Li ,Q Li are the active and reactive load demands of the i-th node respectively; G ij ,B ij are the node admittance matrix elements Y ij The real and imaginary parts of The inequality constraints in the optimization model are the voltage constraints and phase angle constraints of each node; the upper and lower limit constraints of active and reactive power of the power generation node, and the transmission capacity constraints of the AC line. The specific expressions are: Where U imin ,U imax are the upper and lower limits of the voltage of the i-th node respectively; θ imin ,θ imax are the upper and lower limits of the phase angle of the ith node respectively; P Gjmin ,P Gjmax are the upper and lower limits of the active output of the jth node respectively; Q Gjmin ,Q Gjmax are the upper and lower limits of reactive power output of the jth node respectively; Q rekmin ,Q rekmax are the upper and lower limits of reactive power output of the kth new energy node respectively; S stmax is the thermal power limit of the AC line between node s and node t; α is the transmission power limit relaxation factor of the AC line between the sth node and the tth node.

5. According to claim 1, a forced power flow control method for improving the new energy consumption capacity of flexible direct current is characterized in that: In step 3), when there is no solution in step 2), the constraint conditions that have exceeded the limit in the optimization model are identified, and the relationship between the constraint conditions that have exceeded the limit and the system control variables is determined according to the situation. The specific method for adjusting the limit setting of the relevant constraints in the model or the system state parameters is: Combined with the actual optimization calculation results, the constraints that restrict the optimization model from having a solution are identified, which are specifically divided into the following situations: 3.1) If the constraint factor is the transmission power limit of the critical channel line, increase the relaxation factor α of the critical channel transmission power limit, substitute the new relaxation factor α and repeat step 2) to optimize the solution; 3.2) If the constraint factor is the voltage amplitude range constraint, adjust the reactive power output level in the vicinity of the corresponding voltage-exceeding node and repeat step 2) to optimize the solution; 3.3) If the constraint is the transmission power limit of the ordinary transmission line, the active power injection of the strongly correlated nodes of the corresponding line is modified based on the power transmission distribution factor matrix and step 2) is repeated to optimize the solution.

6. A forced power flow control method for improving the new energy consumption capacity of flexible direct current according to claim 5, characterized in that: In step 3.3), the power transmission allocation factor matrix M PTDF for: Where N and L are the number of nodes and AC lines respectively, and P ln is the injection power transmission allocation coefficient of the nth node on the lth line, 1≤n≤N, 1≤l≤L.

7. The forced power flow control method for improving the new energy consumption capacity of flexible direct current according to claim 1 is characterized in that: In step 4), the optimization goal is to maximize the consumption of new energy, without changing the DC system control mode, and the specific method is re-solved as follows: The optimization variable of the new optimization model in this step is the active power of the new energy node added to the original optimization variable, expressed as: In the formula, is the optimization variable column vector of the optimization model in step 4); is the column vector of optimization variables of the optimization model in step 2); is the active power column vector of the new energy node; Each element is the active output of the 1st to Nrth new energy nodes; The optimization objective function is changed to: Where P rek is the active power of the kth new energy node; P rek0 Contribute to the planned active power of the kth new energy node; The equality constraints of the new optimization model are consistent with the corresponding constraints of the optimization model in step 2), and the inequality constraints add the upper and lower limits of the active power of the new energy node, and the expression is: 0≤P rek ≤P rek0 ,1≤k≤Nr The new optimization model is solved to obtain the result of the optimization problem.

8. The forced power flow control method for improving the new energy consumption capacity of flexible direct current according to claim 1 is characterized in that: In step 5), with the goal of maximizing the consumption of new energy, according to different DC control modes, the specific construction method of the AC / DC hybrid power grid power flow optimization model for this period is as follows: The optimization variables of the new optimization model are based on the optimization variables of the optimization model in step 4) and the control variables and parameters of the flexible straight line are added. The expression is: In the formula, is the column vector of optimization variables of the optimization model in step 5); is the optimization variable column vector of the optimization model in step 4); They are respectively the active and reactive column vectors injected into the DC system by the AC nodes connected to the converter; Inject active and reactive power of the DC system into the 1st to Ndcth AC nodes connected to the converter; P dc is the active power injected into the DC node from the converter; is the active power of the 1st to Ndc DC nodes; is the DC node voltage column vector excluding the constant voltage controlled DC node; The DC node voltages of the 1st to Ndcth DC nodes without the constant voltage control; is the column vector of DC node current; is the current of the 1st to Ndcth DC nodes; the objective function is consistent with the objective function modified in step 4); The constraint equation conditions of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraint conditions. The specific constraint condition expressions are: Where P si ,Q si are respectively the active and reactive power injected into the converter for the i-th AC node; P dci is the active power absorbed by the ith DC node from the converter; R i ,X i is the equivalent resistance and inductance of the converter; U dci ,I dci are the voltage and current of the ith DC node respectively; Y dcij is the conductance of the DC line between the i-th DC node and the j-th DC node; Inequality constraints Based on the inequality constraints of the optimization model in step 2), the power constraints of the DC nodes are added. The specific expression is: Where P dcimin ,P dcimax ,Q dcimin ,Q dcimax are the upper and lower limits of active and reactive power of the i-th DC node respectively. Based on the above optimization model, the optimization result of the AC / DC hybrid power grid power flow optimization model in step 5) is calculated.

9. The forced power flow control method for improving the new energy consumption capacity of flexible direct current according to claim 1 is characterized in that: According to the optimization results, the number of converter actions at each DC node is recorded respectively. The specific calculation method is: Where P dci ,P d ' ci are the target DC power and the current DC power respectively, ΔP is the step size of the DC converter power adjustment, [x] is the floor rounding function, N si is the estimated number of actions of the i-th node converter in this action.

10. The forced power flow control method for improving the new energy consumption capacity of flexible direct current according to claim 1 is characterized in that: In step 6), the method for evaluating the number of actions of the DC control means is: For the number of actions recorded by each DC converter, the sum of the number of actions of all previous decisions from the day to the scheduling decision period and the number of actions required for this optimization is calculated to be less than the number of actions allowed by the converter on that day, that is, the number of actions not used in the previous period is allowed to be reserved for use in this period. The constraint is expressed as: Where N si N is the estimated number of actions of the converter at the ith node. sik is the number of actions of the i-th node converter in the k-th time period; N0 is the average allowed number of actions of the converter in a single time period; w is the time period; If all nodes satisfy the inequality, the decision is made according to the optimization result; if the i-th node does not meet the action number requirement, the DC power of the node is set to a constant, removed from the optimization variable, and step 5) is repeated to obtain the optimization decision for this regulation.

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