CN110492493B - Reactive compensation configuration optimization method for power system - Google Patents
Reactive compensation configuration optimization method for power system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
- H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
- H02J3/1871—Methods for planning installation of shunt reactive power compensators
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Abstract
The invention provides a reactive power compensation configuration optimization method for an electric power system, and belongs to the field of reactive power compensation configuration of the electric power system. Firstly, establishing an electromechanical transient simulation model of a power system to be optimized, and acquiring load flow data and short-circuit current of each node in the power system to be optimized; after the nodes are classified, the node load rate is considered, initial candidate nodes for configuring reactive compensation are obtained, the action of a reactive voltage source and multi-feed-in interaction factors are considered for aggregation, and finally the reactive compensation candidate nodes of the power system to be optimized are obtained; and optimizing the reactive capacity required to be configured for each reactive compensation alternative node by adopting a genetic algorithm to obtain a reactive capacity optimization compensation result of each reactive compensation alternative node. The method is easy to realize, has high accuracy and is a practical method for optimizing the reactive power compensation configuration of the power system.
Description
Technical Field
The invention relates to a reactive power compensation configuration optimization method for an electric power system, and belongs to the field of reactive power compensation configuration of the electric power system.
Background
Due to uneven resource distribution and unbalanced regional development, after trans-regional alternating current and direct current transmission is developed within a period of time, a plurality of multi-direct current drop point load areas are formed. In an ac/dc power transmission system, an ac/dc converter whose topology is constantly changed, whether in rectification or inversion operation, becomes a nonlinear load of an ac system because it consumes a large amount of reactive power. I.e. for an ac system, the dc transmission system is a reactive load. The failure or continuous reduction of the voltage of the alternating current system can cause multiple commutation failures or even locking of the multi-circuit direct current transmission system. The essential problem is that the system reactive support does not match the reactive demand. Therefore, it is necessary to provide a certain amount of reactive power compensation in the power system.
At present, the research adopts an improved particle swarm algorithm to optimize the reactive power compensation of an alternating current and direct current power grid, and the provided method adopts multiple times of load flow calculation to select points so as to avoid overcompensation, but increases a certain amount of calculation; in addition, the capacity configuration aspect is implemented by adopting a packet optimization mode, and the effect of global compensation optimization is not necessarily achieved.
However, there are many nodes in the ac/dc transmission system, and in order to properly equip the reactive power compensation device, an effective reactive power compensation configuration optimization method is needed, including node selection and capacity optimization of the reactive power configuration.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a reactive compensation configuration optimization method for an electric power system. On one hand, after the candidate nodes are preliminarily screened through the load rate, the nodes with strong interaction in the candidate nodes with the multi-feed interaction factors (MIIF) are aggregated, meanwhile, the effect of combining a reactive voltage source-a generator and the MIIF is considered, one node with the strongest interaction with other nodes in the aggregation is selected as the candidate node, the system node network is contracted, and the efficiency of screening the candidate nodes is improved; on the other hand, the traditional optimization method for the reactive power configuration capacity of the single node is overcome, the genetic algorithm is adopted to carry out unified processing on the optimization of the global alternative node, and multiple times of load flow calculation are carried out to obtain a more optimized compensation value, so that an effective method is provided for solving the problem of reactive power optimization (voltage stability) of the alternating current and direct current power transmission system.
The invention provides a reactive compensation configuration optimization method for an electric power system, which is characterized by comprising the following steps of:
1) based on an electromechanical transient simulation program, establishing an electromechanical transient simulation model of the electric power system to be optimized by adopting a lumped parameter model;
2) by passingThe electromechanical transient program respectively carries out load flow calculation and short circuit current calculation on the simulation model established in the step 1), and load flow data of each node in the power system to be optimized is obtained, and the method comprises the following steps: real part of voltage e of each nodeiImaginary part of voltage per node fiPower per node and short circuit current;
3) selecting reactive compensation alternative nodes of the power system to be optimized; the method comprises the following specific steps:
3-1) dividing nodes in the power system to be optimized into load nodes, generator nodes and converter nodes, wherein the load nodes and the generator nodes form alternating current system nodes, and the converter nodes are direct current system nodes;
3-2) respectively calculating the load rate of each load node in the alternating current system nodes and the load rate of each node in the direct current system;
the calculation expression of the load rate of the load nodes in the alternating current system nodes is as follows:
wherein S isiIs the apparent power of node i, QliLoad reactive power for node i, l represents the load;
showing the voltage generation delta U of the node i after the disturbance occurs at the node iiCorresponding to the amount of change in reactive power;
the calculation expression of the node load rate of the direct current system is as follows:
3-3) judging each alternating current system load node and each direct current system node according to the calculation result of the step 3-2):
if the load rate of any one of the AC system load node or the DC system node is higher than the average load rate of the system and the load rate of the node is ranked in the first third of the load rate of the same type of node of the system, taking the node as an initial alternative node for configuring reactive compensation;
3-4) screening all the initial alternative nodes obtained in the step 3-3); the method comprises the following specific steps:
3-4-1) calculating the multi-feed interaction factor MIIF of each initial alternative node and all other nodes in the systemij；
Where MIIF between node i and node jijThe calculation expression of (a) is:
wherein Z isijRepresenting the mutual impedance between node i and node j, ZiiRepresents the self-impedance of node i;
3-4-2) using the calculation results of step 3-4-1) to extract all MIIFsij>0.3, aggregating the initial alternative nodes; selecting nodes with the multi-feed interaction factor value larger than 0.3 with all other nodes in the set from each aggregated node set to obtain screened initial candidate nodes;
3-5) when the node i is a generator node, calculating the multi-feed interaction factor MIIF of each generator node and all other nodes in the systemij(ii) a The MIIF in the calculation resultij>0.3 corresponding node j is aggregated, if the node belongs to the initial candidate node screened in the step 3-4) in the result obtained by aggregation, the node is deleted from the screened initial candidate node, and finally the number m of reactive compensation candidate nodes and the serial number of each candidate node are obtained;
4) optimizing the reactive capacity required to be configured for each reactive compensation alternative node obtained in the step 3) by adopting a genetic algorithm to obtain a reactive capacity optimization compensation result of each reactive compensation alternative node; the method comprises the following specific steps:
4-1) obtaining parameters and variables of the power system to be optimized, wherein the parameters and variables comprise: number of all nodes, real part of voltage e of each nodeiAnd imaginary part fiNode power and the connection relationship between nodes;
4-2) configuring reactive capacity codes required by each reactive compensation alternative node of the power system to be optimized, calculating the load flow of the system, and acquiring the reactive power value Q of each reactive compensation alternative node in the system according to the result of the step 4-1)i[k]As the initial reactive power value of the node, and assigning qi[k]＝Qi[k](ii) a Make each alternative node compensate quantity capacity to be QCi[k]And assigning an initial value QCi[k]Updating the reactive power Q of the alternative node at 0i[k]＝Qi[k]+QCi[k]Wherein Q isi[k]Representing the value of reactive power, Q, at the kth reactive power compensation candidate nodeCi[k]The reactive compensation capacity of the kth reactive compensation candidate node is represented, k represents a candidate node number, and k is 1 and 2 … m;
4-3) randomly generating an initial population;
4-4) carrying out iterative calculation on the system load flow, and updating the node voltage and the node power in the system after reactive compensation;
4-5) determining a system optimization target, taking the minimum voltage deviation as an objective function:
f＝min(f△V)
wherein the content of the first and second substances,
wherein Δ V represents a voltage variation amount, V0Is a voltage reference value, ViIs the voltage value of node i, VimaxAnd ViminRespectively representing the upper and lower voltage amplitude limits of a node i, wherein i represents a node number, i is 1,2 … N, and N is the total number of system nodes;
the constraint conditions include:
and (3) constraint of an equation:
wherein the content of the first and second substances,
the active power and the reactive power of the PQ node i are respectively; delta Pi、△QiRespectively representing the deviation amount of active power and reactive power of a node i; gijAnd BijRespectively the real part and the imaginary part of the ith row and jth column element of the admittance matrix; qCiThe capacity of the reactive power compensation device at the node i is calculated; pdi、QdiRespectively, the active power and the reactive power at the AC/DC connection node i;
the inequality constrains:
Vimin≤Vi≤Vimax,i∈N
QCimin≤QCi≤QCimax,i∈NC
wherein N isCCompensating the number for the reactive device; vimaxAnd ViminRespectively representing the upper limit and the lower limit of the voltage amplitude of the node i; qCimax、QCiminRespectively representing the upper limit and the lower limit of the compensation capacity;
4-6) converting the objective function of the step 4-5) into a fitness function, and sequentially carrying out selection, crossing and variation processes according to a genetic algorithm to obtain the fitness value of each reactive compensation candidate node and the compensation capacity correction value delta q of the nodeCi[k]Voltage real part correction quantity delta e of each nodeiAnd imaginary part correction quantity delta fi(ii) a The fitness function is the reciprocal of the objective function;
4-7) respectively correcting the real parts and the imaginary parts of all node voltages:
ei＝ei+△ei，fi＝fi+△fi
4-8) judging whether the adaptability value of each reactive compensation alternative node is larger than the reciprocal of the objective function value:
if yes, calculating reactive compensation quantity delta Q of each reactive compensation alternative nodeCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]The optimization is finished as an optimization compensation result of the reactive capacity required to be configured for each reactive compensation alternative node;
if not, correcting the value delta q according to the compensation capacity of each reactive compensation alternative nodeCi[k]Update QCi[k]＝QCi[k]+△qCi[k]And then returning to the step 4-4) again until the adaptability values of all the reactive compensation alternative nodes are larger than the reciprocal of the objective function value, and calculating the reactive compensation quantity delta Q of each reactive compensation alternative nodeCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]And finishing the optimization as an optimization compensation result of the reactive capacity required to be configured by each reactive compensation alternative node.
The invention has the characteristics and beneficial effects that:
the method comprises the steps of primarily screening load nodes and direct current system nodes in alternating current system nodes through node load rates, and fully considering reactive dynamic changes of the alternating current system nodes; the method comprises the steps that multi-feed interaction factors (MIIF) are adopted to aggregate nodes with large interaction in a system, namely if MIIF between a certain node and other nodes is larger, the node can be selected as a standby node without compensating each corresponding node; in addition, the potential standby value of the reactive voltage source is combined with the multi-feed interaction factor, nodes with strong interaction with reactive voltage source nodes are aggregated, and the aggregated nodes are excluded from the range of the alternative nodes. By considering the node load rate and carrying out aggregation through MIIF, on one hand, the range and the configuration number of reactive configuration selection points in the system are reduced, on the other hand, the node network of the system can be effectively contracted, and the node selection efficiency is improved; the traditional optimization method for the reactive power configuration capacity of the single node is overcome, the genetic algorithm is adopted to carry out unified processing on the optimization of the global alternative nodes, and multiple times of load flow calculation are carried out to obtain a more optimized compensation value. The method provides an effective method for solving the problem of reactive power optimization (voltage stabilization) of the AC/DC power transmission system.
The load flow calculation, the short circuit current calculation and the genetic algorithm mentioned in the method are mature methods, are easy to realize and have high accuracy, and are practical methods for reactive compensation configuration optimization of the power system.
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FIG. 1 is a general flow diagram of the process of the present invention.
Detailed Description
The invention provides a reactive power compensation configuration optimization method for an electric power system, which is further described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a reactive compensation configuration optimization method for an electric power system, the whole flow is shown in figure 1, and the method comprises the following steps:
1) based on an electromechanical transient simulation program (such as BPA) and adopting a lumped parameter model to establish an electromechanical transient simulation model of the power system to be optimized;
2) respectively carrying out load flow calculation and short circuit current calculation on the simulation model established in the step 1) through an electromechanical transient program to obtain load flow data of each node in the power system to be optimized, wherein the load flow data comprises the following steps: real part of voltage e of each nodeiImaginary part of voltage per node fiPower per node and short circuit current;
3) selecting reactive compensation alternative nodes of the power system to be optimized; the method comprises the following specific steps:
3-1) the nodes in the power system can be divided into load nodes, generator nodes and converter nodes, and the power system nodes are classified according to an alternating current and direct current system and divided into alternating current system nodes (load nodes and generator nodes) and direct current system nodes (converter nodes);
3-2) respectively calculating the load rate of each load node in the alternating current system nodes and the load rate of each node in the direct current system;
the calculation expression of the load rate of the load nodes in the alternating current system nodes is as follows:
wherein S isiIs the apparent power of node i, QliLoading reactive power for node i (l represents load);
represents the voltage delta of the node i after the disturbance at the node iUiCorresponding to the amount of change in reactive power;
the calculation expression of the node load rate of the direct current system is as follows:
3-3) according to the calculation result of the step 3-2), respectively judging each alternating current system load node and each direct current system node:
the higher the load rate of a load node in an alternating current system or a direct current system node (if the load rate of the node is higher than the average load rate of the system and the load rate ranks in the first third of the load rate of the same type of nodes in the system), the higher the probability that the node needs reactive power (the higher the probability of generating a voltage stability problem), the node can be regarded as a weak link of the power system and can be selected as an initial candidate node for configuring reactive power compensation;
3-4) for the initial alternative node obtained in the step 3-3), utilizing a multi-feed interaction factor (MIIF)ij) Calculating the interaction size between each node in the initial candidate nodes and all other nodes in the system, taking the interaction size as a reactive power compensation point distribution selection factor in the alternating current-direct current transmission system, and considering the aggregation of the nodes with strong multi-feed interaction (when the MIIF is usedij>0.3, the interaction between node i and node j is considered strong). Selecting nodes with strong interaction with other nodes in the set from each aggregated node set as screened reactive power compensation alternative nodes (when MIIF)ij>0.3, the interaction between the node i and the node j is considered to be strong) to contract the node network of the system to be optimized, and simultaneously, the calculation efficiency in the reactive compensation capacity optimization process is improved.
Where MIIF between node i and node jijThe calculation expression of (a) is:
wherein Z isijRepresenting the mutual impedance between node i and node j，ZiiRepresenting the self-impedance of node i. When MIIFij>0.3, the interaction between node i and node j is considered strong.
3-5) the synchronous generator in the system is an important reactive power supply, so when the reactive compensation configuration alternative node in the system is considered, the multi-feed interaction factor of the generator node is also calculated; when node i is a generator node, there will be a strong interaction with the generator node (MIIF)ij>0.3) carrying out aggregation on the node j, and deleting the overlapping part of the result obtained by aggregation and the result screened in the step 3-4) to obtain the number m of the reactive compensation candidate nodes needing to be configured with reactive power and the serial number of each candidate node;
4) optimizing the reactive capacity required to be configured for each reactive compensation alternative node obtained in the step 3) by adopting a genetic algorithm to obtain a reactive capacity optimization compensation result of each reactive compensation alternative node; the method comprises the following specific steps:
4-1) obtaining parameters and variables of the power system to be optimized, wherein the parameters and variables comprise: number of all nodes, voltage of each node (including real part e)iImaginary part fi) Node power (including reactive power and active power) and the connection relationship between nodes;
4-2) coding the compensation value possibly existing in the reactive power capacity of each reactive power compensation alternative node of the power system to be optimized (the genetic algorithm needs to code the solution of the research problem into a character string form, wherein the 'problem' corresponds to the possibly existing reactive power compensation capacity of each alternative node of the system to be optimized), calculating the load flow of the system, and acquiring the reactive power value Q of each reactive power compensation alternative node in the system according to the result of the step 4-1)i[k]As the initial reactive power value of the node, and assigning qi[k]＝Qi[k](ii) a Make each alternative node compensate quantity capacity to be QCi[k]And assigning an initial value QCi[k]0, reactive power Q of the alternative nodei[k]＝Qi[k]+QCi[k]Wherein Q isi[k]Representing the value of reactive power, Q, at the kth reactive power compensation candidate nodeCi[k]Denotes the reactive compensation capacity of the kth reactive compensation candidate node, k denotes the candidate node number，k＝1,2…m；
4-3) randomly generating initial group seeds (the size and the number of the group seeds can be set according to the scale of the system);
4-4) carrying out iterative calculation on the system load flow again, and updating the node voltage and the node power in the system after reactive compensation;
4-5) determining a system optimization target: under the condition of meeting various constraint conditions, the voltage stability is improved to the maximum extent, the voltage quality is improved, and the system network loss is reduced with the minimum reactive investment. Here, the voltage deviation minimum is taken as an objective function:
f＝min(f△V)
wherein the content of the first and second substances,
wherein, DeltaV represents voltage variation, N is total number of nodes of the power system, and V0Is a voltage reference value, ViIs the voltage value of node i, VimaxAnd ViminThe voltage amplitude upper limit and the voltage amplitude lower limit of the node i are respectively represented, i represents a node number, i is 1,2 … N, and N is the total number of the system nodes.
The constraint conditions are as follows:
equality constraint (node power constraint):
wherein the content of the first and second substances,
the active power and the reactive power of the PQ node i are respectively; delta Pi、△QiRespectively representing the deviation amount of active power and reactive power of a node i; gijAnd BijRespectively the real part and the imaginary part of the ith row and jth column element of the admittance matrix; qCiThe capacity of the reactive power compensation device at the node i is calculated; pdi、QdiRespectively, the active power and the reactive power at the AC/DC connection node i;
the inequality constrains:
Vimin≤Vi≤Vimax,i∈N
QCimin≤QCi≤QCimax,i∈NC
wherein N isCCompensating the number for the reactive device; vimaxAnd ViminRespectively representing the upper limit and the lower limit (0.95-1.05) of the voltage amplitude of the node i; qCimax、QCiminRespectively representing the upper and lower limits of the compensation capacity.
4-6) converting the objective function in the step 4-5) into a fitness function (reciprocal of the objective function), and sequentially carrying out selection, crossing and variation processes according to a genetic algorithm to obtain the fitness value of each reactive compensation alternative node and the compensation capacity correction value delta q of the nodeCi[k]Voltage real part correction quantity delta e of each nodeiAnd imaginary part correction quantity delta fi；
4-7) respectively correcting the real parts and the imaginary parts of all node voltages: e.g. of the typei＝ei+△ei，fi＝fi+△fi，eiAnd fiRespectively a real part and an imaginary part of the ith row of the node voltage matrix;
4-8) judging whether the adaptability value of each reactive compensation alternative node is larger than the set adaptability value index (the reciprocal of the objective function value):
if yes, the reactive compensation quantity delta Q of each reactive compensation alternative node can be calculatedCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]The optimization is finished as an optimization compensation result of the reactive capacity required to be configured for each reactive compensation alternative node; if not, update QCi[k]＝QCi[k]+△qCi[k]And then returning to the step 4-4) again until the adaptability values of all the reactive compensation alternative nodes are larger than the adaptability value indexes, and calculating the reactive compensation quantity delta Q of each reactive compensation alternative nodeCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]And finishing the optimization as an optimization compensation result of the reactive capacity required to be configured by each reactive compensation alternative node.
Claims (1)
1. A reactive compensation configuration optimization method for a power system is characterized by comprising the following steps:
1) based on an electromechanical transient simulation program, establishing an electromechanical transient simulation model of the electric power system to be optimized by adopting a lumped parameter model;
2) respectively carrying out load flow calculation and short circuit current calculation on the simulation model established in the step 1) through an electromechanical transient program to obtain load flow data of each node in the power system to be optimized, wherein the load flow data comprises the following steps: real part of voltage e of each nodeiImaginary part of voltage per node fiPower per node and short circuit current;
3) selecting reactive compensation alternative nodes of the power system to be optimized; the method comprises the following specific steps:
3-1) dividing nodes in the power system to be optimized into load nodes, generator nodes and converter nodes, wherein the load nodes and the generator nodes form alternating current system nodes, and the converter nodes are direct current system nodes;
3-2) respectively calculating the load rate of each load node in the alternating current system nodes and the load rate of each node in the direct current system;
the calculation expression of the load rate of the load nodes in the alternating current system nodes is as follows:
wherein S isiIs the apparent power of node i, QliLoad reactive power for node i, l represents the load;
showing the voltage generation delta U of the node i after the disturbance occurs at the node iiCorresponding to the amount of change in reactive power;
the calculation expression of the node load rate of the direct current system is as follows:
3-3) judging each alternating current system load node and each direct current system node according to the calculation result of the step 3-2):
if the load rate of any one of the AC system load node or the DC system node is higher than the average load rate of the system and the load rate of the node is ranked in the first third of the load rate of the same type of node of the system, taking the node as an initial alternative node for configuring reactive compensation;
3-4) screening all the initial alternative nodes obtained in the step 3-3); the method comprises the following specific steps:
3-4-1) calculating the multi-feed interaction factor MIIF of each initial alternative node and all other nodes in the systemij；
Where MIIF between node i and node jijThe calculation expression of (a) is:
wherein Z isijRepresenting the mutual impedance between node i and node j, ZiiRepresents the self-impedance of node i;
3-4-2) using the calculation results of step 3-4-1) to extract all MIIFsij>0.3, aggregating the initial alternative nodes; selecting nodes with the multi-feed interaction factor value larger than 0.3 with all other nodes in the set from each aggregated node set to obtain screened initial candidate nodes;
3-5) when the node i is a generator node, calculating the multi-feed interaction factor MIIF of each generator node and all other nodes in the systemij(ii) a The MIIF in the calculation resultij>0.3 corresponding node j is aggregated, if the node belongs to the initial candidate node screened in the step 3-4) in the result obtained by aggregation, the node is deleted from the screened initial candidate node, and finally the number m of reactive compensation candidate nodes and the serial number of each candidate node are obtained;
4) optimizing the reactive capacity required to be configured for each reactive compensation alternative node obtained in the step 3) by adopting a genetic algorithm to obtain a reactive capacity optimization compensation result of each reactive compensation alternative node; the method comprises the following specific steps:
4-1) obtaining parameters and variables of the power system to be optimized, wherein the parameters and variables comprise: number of all nodes, real part of voltage e of each nodeiAnd imaginary part fiNode power and the connection relationship between nodes;
4-2) configuring reactive capacity codes required by each reactive compensation alternative node of the power system to be optimized, calculating the load flow of the system, and acquiring the reactive power value Q of each reactive compensation alternative node in the system according to the result of the step 4-1)i[k]As the initial reactive power value of the node, and assigning qi[k]＝Qi[k](ii) a Make each alternative node compensate quantity capacity to be QCi[k]And assigning an initial value QCi[k]Updating the reactive power Q of the alternative node at 0i[k]＝Qi[k]+QCi[k]Wherein Q isi[k]Representing the value of reactive power, Q, at the kth reactive power compensation candidate nodeCi[k]The reactive compensation capacity of the kth reactive compensation candidate node is represented, k represents a candidate node number, and k is 1 and 2 … m;
4-3) randomly generating an initial population;
4-4) carrying out iterative calculation on the system load flow, and updating the node voltage and the node power in the system after reactive compensation;
4-5) determining a system optimization target, taking the minimum voltage deviation as an objective function:
wherein the content of the first and second substances,
wherein Δ V represents a voltage variation amount, V0Is a voltage reference value, ViIs the voltage value of node i, VimaxAnd ViminRespectively representing the upper and lower voltage amplitude limits of a node i, wherein i represents a node number, i is 1,2 … N, and N is the total number of system nodes;
the constraint conditions include:
and (3) constraint of an equation:
wherein, Pi SP、
The active power and the reactive power of the PQ node i are respectively; delta Pi、△QiRespectively representing the deviation amount of active power and reactive power of a node i; gijAnd BijRespectively the real part and the imaginary part of the ith row and jth column element of the admittance matrix; qCiThe capacity of the reactive power compensation device at the node i is calculated; pdi、QdiRespectively, the active power and the reactive power at the AC/DC connection node i;
the inequality constrains:
Vimin≤Vi≤Vimax,i∈N
QCimin≤QCi≤QCimax,i∈NC
wherein N isCCompensating the number for the reactive device; vimaxAnd ViminRespectively representing the upper limit and the lower limit of the voltage amplitude of the node i; qCimax、QCiminRespectively representing the upper limit and the lower limit of the compensation capacity;
4-6) converting the objective function of the step 4-5) into a fitness function, and sequentially carrying out selection, crossing and variation processes according to a genetic algorithm to obtain the fitness value of each reactive compensation candidate node and the compensation capacity correction value delta q of the nodeCi[k]Voltage real part correction quantity delta e of each nodeiAnd imaginary part correction quantity delta fi(ii) a The fitness function is the reciprocal of the objective function;
4-7) respectively correcting the real parts and the imaginary parts of all node voltages:
ei＝ei+△ei，fi＝fi+△fi
4-8) judging whether the adaptability value of each reactive compensation alternative node is larger than the reciprocal of the objective function value:
if yes, calculating reactive compensation quantity delta Q of each reactive compensation alternative nodeCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]The optimization is finished as an optimization compensation result of the reactive capacity required to be configured for each reactive compensation alternative node; if not, correcting the value delta q according to the compensation capacity of each reactive compensation alternative nodeCi[k]Update QCi[k]＝QCi[k]+△qCi[k]And then returning to the step 4-4) again until the adaptability values of all the reactive compensation alternative nodes are larger than the reciprocal of the objective function value, and calculating the reactive compensation quantity delta Q of each reactive compensation alternative nodeCi[k]＝Qi[k]-qi[k]Output of Δ QCi[k]And finishing the optimization as an optimization compensation result of the reactive capacity required to be configured by each reactive compensation alternative node.
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