A forced power flow control method for flexible DC to improve the capacity of renewable energy absorption

Abstract

The present invention discloses a forced power flow control method for flexible direct current to enhance the capacity of absorbing new energy, comprising the following steps: Step 1): Establishing a steady-state equivalent model of each component of an AC / DC hybrid power grid and an AC system admittance matrix; Step 2): Constructing and solving a cross-regional AC / DC hybrid power grid power flow optimization model containing new energy; Step 3): If the established model has no solution, adjusting the limit settings of relevant constraints or system state parameters in the model and resolving; Step 4): If the established model has no solution and the transmission power of the key channel line does not exceed the limit, resolving and obtaining the optimal control decision for this period; Step 5): If the established model has no solution and the transmission power of the key channel line exceeds the limit, reconstructing the AC / DC hybrid power grid power flow optimization model and solving the optimization problem; Step 6): Verifying and obtaining the optimal control decision for this period. The present invention realizes flexible control of the power system and ensures the effective utilization of scarce transmission channels and key transmission channels.

Description

Technical Field

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

[0002] The installed capacity of renewable energy is increasing year by year, and the pressure on transmission network channels from renewable energy gathering areas to load-concentrated 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 current distribution, resulting in obstruction of cross-regional transmission channels and power congestion in local power grids. 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 grid has been gradually transforming and upgrading from traditional AC power grids to new AC / DC hybrid power grids. Against this backdrop, the challenge currently faced is how to improve the capacity to absorb new energy. Summary of the Invention

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

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

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

[0007] Step 2): With the goal of minimizing power generation costs, a cross-regional AC / DC hybrid power grid power flow optimization model containing renewable energy 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 in the model or the system state parameters, and re-solve;

[0009] Step 4): If there is no solution for 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 constraints 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 critical 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, based on different DC control modes, add DC control balance constraints, and rebuild the AC / DC hybrid power flow optimization model for this period; solve this optimization problem, and record the number of times each flexible DC control method operates;

[0011] Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operating 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 with renewable energy is expressed as:

[0014]

[0015] Where, 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] Where, is the column vector of optimization variables; is the column vector of system node voltage; 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; are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the system thermal power nodes; 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; is the reactive power column vector of the system's thermal power units; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive column vector of the system’s new energy nodes; Nr is the number of the system’s new energy nodes, 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 the active and reactive balance equations between the 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 generation node; and the transmission capacity constraint 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 i-th node respectively; P Gjmin ,P Gjmax are the upper and lower limits of the active power 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 settings of the relevant constraints in the model or the system state parameters is as follows:

[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 cases:

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

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

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

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

[0031]

[0032] Among them, 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. 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, which is expressed as:

[0035]

[0036] Where, is the column vector of optimization variables 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 k-th 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). The inequality constraints add the upper and lower limits of the active power of the new energy node, which are expressed as follows:

[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 AC / DC hybrid power grid power flow optimization model for this period is specifically constructed 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] Where, is the column vector of optimization variables of the optimization model in step 5); is the column vector of optimization variables 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; Injecting 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 Ndcth 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 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 equations of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraints. The specific constraint expressions are:

[0048]

[0049] Where, P si ,Q si are the active and reactive power injected into the converter for the i-th AC node respectively; 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 node 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 rounding function, N si is the expected number of actions of the converter at the i-th node.

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

[0058] For the number of actions recorded for 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 expected number of actions of the converter at the i-th node; sik is the number of times the converter of the i-th node operates in the k-th time period; N0 is the average number of operations allowed for 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 constant, removed from the optimization variable, and step 5 is repeated to obtain the optimization decision for this control.

[0062] During operation, the present invention can construct a cross-regional AC / DC hybrid power grid flow optimization model containing new energy sources during the optimization and control period, flexibly modify the objective function and constraint boundary of the optimization model according to the constraints in effect during the optimization process, and flexibly add DC control measures 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 utilization of scarce transmission channels and key transmission channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a forced current control strategy for improving the new energy absorption capacity. DETAILED DESCRIPTION

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

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

[0066] Step 2): In a single period of active power optimization and control, with the goal of minimizing power generation costs, without changing the DC system control mode, a relaxation factor for the transmission power limit of key channels is set, and a power flow optimization model for a cross-regional AC / DC hybrid power grid containing renewable 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 re-solve;

[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 constraints 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 critical channel line exceeds the limit, maintain the modified constraints in step 3) and, with the goal of maximizing renewable energy consumption, add DC control balance constraints based on different DC control modes and rebuild the AC / DC hybrid power flow optimization model for this period. Solve this optimization problem and record the number of times each flexible DC control method operates.

[0070] Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operating 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 of this invention, to enhance the capacity to absorb new energy and ensure the transmission capacity of scarce transmission channels, first, an optimization control model is designed with the goal of minimizing power generation costs and AC control as the regulation method. Secondly, when the optimization space of the model is limited or unsolvable, the objective function of the optimization problem is changed, and the feasible domain is adjusted by adjusting key constraints. Furthermore, if the transmission capacity of the key transmission channel exceeds the limit, the scarce flexible DC control method is further added to increase the dimension of the control variable, thereby achieving the solvability of the optimized scheduling of the new AC / DC hybrid power grid under the goal of maximizing new energy absorption, and ensuring the stable and economic operation of the AC / DC hybrid power grid.

[0072] In step 1), an equivalent model of the AC / DC hybrid power grid is established based on the steady-state equivalent circuit models of the transmission lines, transformers, VSCs and other components in the AC / DC hybrid power grid, and the 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 cross-regional AC / DC hybrid power grid power flow optimization model containing renewable energy without changing the DC control mode, the optimization variables include:

[0076]

[0077] Where, is the column vector of optimization variables; is the column vector of system node voltage; 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; are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the system thermal power nodes; 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; is the reactive power column vector of the system's thermal power units; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive column vector of the system’s new energy nodes; Nr is the number of the system’s new energy nodes, 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 power generation cost is expressed as:

[0079]

[0080] Where, 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 the active and reactive balance equations between the 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 generation node; and the transmission capacity constraint 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 i-th node respectively; P Gjmin ,P Gjmax are the upper and lower limits of the active power 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), 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, and adjust the limit settings of the relevant constraints in the model or the system state parameters. The specific method is as follows:

[0089] Based on the actual optimization calculation results, the constraints that restrict the optimization model from having a solution are identified, which can be divided into the following three cases:

[0090] 3.1) If the limiting factor is the critical channel transmission power limit, appropriately increase the critical channel transmission power limit relaxation factor α (α≤1) to reduce the line thermal stability limit requirement. Substitute the new relaxation factor α and repeat step 2) to optimize the solution.

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

[0092] 3.3) If the constraint is the transmission power limit of a common transmission line, modify the active power injection of the corresponding strongly correlated nodes based on the Power Transfer Distribution Factor (PTDF) matrix 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 lines is modified is calculated as follows:

[0095] In an 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 i-th node; U i 、U j are the voltage amplitudes of the i-th and j-th nodes respectively, G ij 、B ij They are the Y in the admittance matrix of the AC system 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] Where, δ i , δ j are the voltage phase angles of the i-th and j-th nodes, 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 is further converted into matrix form:

[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 system refers to the AC power grid system;

[0106] Then the DC power flow equation is expressed as:

[0107]

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

[0109]

[0110] Where B′ is the system node admittance matrix excluding the balance node, is the system node voltage phase angle column vector excluding the balance node, is the system node power column vector excluding the balance 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 as follows:

[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] Where, 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 i-th node, y ij is the admittance of the AC line between the i-th node and the j-th 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 angle 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 injection, 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 power transfer distribution coefficient 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] Among them, 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. The specific method is re-solved as follows:

[0125] 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, which is expressed as

[0126]

[0127] Where, is the column vector of optimization variables 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 k-th 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). The inequality constraints add the upper and lower limits of the active power of the new energy node, which are expressed as follows:

[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), with the goal of maximizing the consumption of new energy, according to different flexible DC control modes, the AC / DC hybrid power grid power flow optimization model for this period is specifically constructed 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] Where, is the column vector of optimization variables of the optimization model in step 5); is the column vector of optimization variables 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; Injecting 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 Ndcth 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 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 equations of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraints. The specific constraint expressions are:

[0139]

[0140] Where, P si ,Q si are the active and reactive power injected into the converter for the i-th AC node respectively; 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 node 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 rounding function, Nsi is the expected number of actions of the converter at the i-th node.

[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. They are all quantities to be determined in the optimization model, and the distinction is made during actual control after the optimization solution.

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

[0152] For the number of actions recorded for each DC converter, the sum of the number of previous decision actions (k = 1, 2, ..., w-1) from the current 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 that day. That is, the number of actions not used in the previous period is allowed to be used in the current period. The constraint is expressed as:

[0153]

[0154] Where N si N is the expected number of actions of the converter at the i-th node; sik is the number of operations of the converter at the i-th node in the k-th time period; N0 is the average allowed number of operations 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 constant, removed from the optimization variable, and step 5 is repeated to obtain the optimization decision for this control.

[0156] Here, the evaluation is based on the average total number of daily operations of the converter, which can meet the operation number requirements for long-term operation of the converter. 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 embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A forced power flow control method for flexible direct current to enhance the capacity of absorbing new energy, characterized in that: The following steps are involved: Step 1): Establish the steady-state equivalent model of each component of the AC / DC hybrid power grid and the AC system admittance matrix; Step 2): With the goal of minimizing power generation costs, a cross-regional AC / DC hybrid power grid power flow optimization model containing renewable energy 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 in the model or the system state parameters, and re-solve; Step 4): If there is no solution for 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 constraints 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 critical 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, based on different DC control modes, add DC control balance constraints, and reconstruct the AC / DC hybrid power flow optimization model for this period; solve the reconstructed AC / DC hybrid power flow optimization model, and record the number of times each flexible DC control method operates; Step 6): Check whether the number of actions of each flexible DC control means obtained in step 5) meets the operating 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, correct the power of the i-th node and repeat step 5) to obtain the optimal control decision for this period.

2. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 1 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 with renewable energy is expressed as: Where, 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. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 1 is characterized in that: The optimization variables include: Where, is the column vector of optimization variables; is the column vector of system node voltage; 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; are the node phase angles from the 2nd node to the Nth node of the system respectively; is the active column vector of the system thermal power nodes; 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; is the reactive power column vector of the system's thermal power units; Each element is the reactive power from the 1st thermal power node to the Ngth thermal power node in the system; is the reactive column vector of the system’s new energy nodes; Nr is the number of the system’s new energy nodes, Each element is the reactive power of the system's 1st new energy node to the Nrth new energy node.

4. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 1 is characterized in that: The equality constraints in the optimization model are the active and reactive balance equations between the 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 generation node; and the transmission capacity constraint of the AC line. The specific expressions are: Where U imax ,U imin are the upper and lower limits of the voltage of the i-th node respectively; θ imax ,θ imin are the upper and lower limits of the phase angle of the i-th node respectively; P Gjmax ,P Gjmin are the upper and lower limits of the active power output of the jth node respectively; Q Gjmax ,Q Gjmin are the upper and lower limits of reactive power output of the jth node respectively; Q rekmax ,Q rekmin 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. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 1 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 settings of the relevant constraints in the model or the system state parameters is as follows: 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 cases: 3.1) If the constraint is the critical channel line transmission power limit, increase the critical channel transmission power limit relaxation factor α, substitute the new relaxation factor α and repeat step 2) to optimize the solution; 3.2) If the constraint is the voltage amplitude range, adjust the reactive power output level in the vicinity of the corresponding voltage-exceeding node and repeat step 2) for the optimization solution; 3.3) If the constraint is the transmission power limit of the ordinary transmission line, then modify the active power injection of the strongly correlated nodes of the corresponding line based on the power transmission distribution factor matrix and repeat step 2) to optimize the solution.

6. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 5 is characterized in that: In step 3.3), the power transmission allocation factor matrix M PTDF for: Among them, 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 flexible direct current to enhance the new energy absorption capacity 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. 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, which is expressed as: Where, is the column vector of optimization variables 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 k-th new energy node; The equality constraints of the new optimization model are consistent with the corresponding constraints of the optimization model in step 2). The inequality constraints add the upper and lower limits of the active power of the new energy node, which are expressed as follows: 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 flexible direct current to enhance the new energy absorption capacity 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 AC / DC hybrid power grid power flow optimization model for this period is specifically constructed 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: Where, is the column vector of optimization variables of the optimization model in step 5); is the column vector of optimization variables 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; Injecting 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 Ndcth 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 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 equations of the new optimization model need to modify the AC power equation and add additional DC line equation constraints based on the original constraints. The specific constraint 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 P si ,Q si are the active and reactive power injected into the converter for the i-th AC node respectively; 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 node are added. The specific expression is: Where, P dcimax ,P dcimin ,Q dcimax ,Q dcimin 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 flexible direct current to enhance the new energy absorption capacity 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′ dci 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 rounding function, N si is the expected number of actions of the converter at the i-th node.

10. The forced power flow control method for flexible direct current to enhance the new energy absorption capacity according to claim 1, characterized in that: In step 6), the method for evaluating the number of DC control means actions is: For the number of actions recorded for 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 expected number of actions of the converter at the i-th node; sik is the number of times the converter of the i-th node operates in the k-th time period; N0 is the average number of operations allowed for 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 constant, removed from the optimization variable, and step 5 is repeated to obtain the optimization decision for this control.

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