CN107508280A - A kind of reconstruction method of power distribution network and system - Google Patents
A kind of reconstruction method of power distribution network and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The present invention discloses a kind of reconstruction method of power distribution network and system.This method includes：Establish the interval model of load and distributed energy；Active power loss expense is established respectively, it is expected to lack delivery and the first object majorized function of switching manipulation expense；Determine the constraints of the first object majorized function；According to the weight of the interval model of the load and distributed energy, the constraints and each first object majorized function, the first object majorized function is normalized, obtains the second objective optimization function；Set the node of the power distribution network switches on-off initial value；The second objective optimization function is solved using harmonic search algorithm；Initial value is switched on-off according to the node of the power distribution network corresponding to the solution of the second objective optimization function of minimum, carries out power distribution network reconfiguration.The uncertainty that the present invention has considered load, the random fluctuation of distributed energy is brought, there is the uniformity of height with actual state, there is higher accuracy.
Description
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a power distribution network reconstruction method and system.
Background
The reconstruction of the power distribution network is a process of switching and adjusting the on-off state of the tie switch and the section switch in the network. Through network reconstruction, the new topology network can reduce the network loss and improve the operation reliability. In the reconstruction process, timing effects and environmental factors will inevitably cause uncertainty in the data. These uncertainties are typically manifested as fluctuations in load, fluctuations in equipment maintenance parameters, and so forth. For example, load uncertainty is an example, and an uncertain value interval is often required in load prediction to describe the load for a period of time in the future. Such values are more objective. And the prediction result obtained by using the uncertain value is more reliable and scientific in the aspects of power grid planning, risk analysis, reliability evaluation and the like.
The traditional power distribution network reconstruction method has limited consideration of reconstruction factors, so that the generated reconstruction result has certain deviation.
Disclosure of Invention
The embodiment of the invention provides a power distribution network reconstruction method and system, and aims to solve the problem that reconstruction factors considered by a reconstruction method in the prior art are limited, so that a reconstruction result has certain deviation.
In a first aspect, a power distribution network reconstruction method is provided, including: establishing an interval model of load and distributed energy; respectively establishing a first target optimization function of active network loss cost, expected power shortage amount and switch operation cost; determining constraints of the first objective optimization function; according to the load and distributed energy interval model, the constraint conditions and the weight of each first objective optimization function, carrying out normalization processing on the first objective optimization function to obtain a second objective optimization function; setting initial on-off values of switches of nodes of the power distribution network; solving the second objective optimization function by using a harmony search algorithm; and reconstructing the power distribution network according to the initial on-off value of the switch of the node of the power distribution network corresponding to the solution of the minimum second objective optimization function.
In a second aspect, a power distribution network reconfiguration system is provided, which includes: the first establishing module is used for establishing an interval model of the load and the distributed energy; the second establishing module is used for respectively establishing a first target optimization function of active network loss cost, expected power shortage amount and switch operation cost; a determining module, configured to determine a constraint condition of the first objective optimization function; the normalization module is used for performing normalization processing on the first objective optimization function according to the load and distributed energy interval model, the constraint conditions and the weight of each objective optimization function to obtain a second objective optimization function; the setting module is used for setting an initial on-off value of a switch of a node of the power distribution network; the solving module is used for solving the second objective optimization function by using a harmony search algorithm; and the reconstruction module is used for reconstructing the power distribution network according to the initial on-off value of the switch of the node of the power distribution network corresponding to the minimum solution of the second objective optimization function.
Therefore, in the embodiment of the invention, the uncertainty caused by the random fluctuation of the load and the distributed energy is comprehensively considered, the consistency with the actual condition is high, and the accuracy is higher; the power distribution network reconstruction multi-target model is designed, the operation network loss, the power supply reliability and the switching operation cost are calculated, the optimization target coverage is comprehensive, the optimization of the power supply path of the power distribution network is facilitated, the operation efficiency of the power distribution network is improved, and the purposes of reducing the network loss, eliminating overload and load balancing, improving the voltage quality, improving the power supply reliability and economic benefit and the like are achieved; directly reflects the consideration factors in the process of the reconstruction decision-making, and has stronger practicability; the multi-target and nonlinear power distribution network reconstruction model with interval uncertainty calculation is solved based on the harmony search algorithm, the solving performance is good, the convergence is fast, and the precision is high.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of a power distribution network reconfiguration method according to an embodiment of the present invention;
FIG. 2 is a schematic view of the operating characteristics of an induction motor;
FIG. 3 is a schematic illustration of a force curve of a wind turbine;
FIG. 4 is a schematic representation of the topology change of the network generated by the harmonic search algorithm of an embodiment of the present invention;
FIG. 5 is a flowchart illustrating steps for solving a second objective optimization function using a harmonic search algorithm, in accordance with an embodiment of the present invention;
fig. 6 is a block diagram of a power distribution network reconfiguration system according to an embodiment of the present invention;
FIG. 7 is a schematic representation of an IEEE33 node power distribution network prior to reconfiguration;
fig. 8 is a schematic diagram of an IEEE33 node distribution network reconstructed by using the power distribution network reconstruction method according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a power distribution network reconstruction method. As shown in fig. 1, the method comprises the following steps:
step S10: and establishing an interval model of load and distributed energy.
Step S20: and respectively establishing a first target optimization function of active network loss cost, expected power shortage amount and switch operation cost.
Step S30: constraints of the first objective optimization function are determined.
Step S40: according to the load, the interval model of the distributed energy, the constraint conditions and the weight of each objective optimization function, performing normalization processing on the first objective optimization function to obtain a second objective optimization function;
step S50: and setting initial on-off values of switches of nodes of the power distribution network.
Step S60: and solving the second objective optimization function by adopting a harmony search algorithm.
Step S70: and reconstructing the power distribution network according to the initial on-off value of the switch of the node of the power distribution network corresponding to the solution of the minimum second objective optimization function.
Specifically, in step S10:
the load and distributed energy interval model comprises the following steps: the system comprises a load uncertainty model, a wind power generation active power interval model, a wind power generation reactive power interval model, a photovoltaic power generation active power interval model and a photovoltaic power generation reactive power interval model.
(1) Load uncertainty model
The load uncertainty model is:
wherein [ S ]]Which represents the fluctuation interval of the actual complex power S of the induction motor. [ P ]]Representing the fluctuation interval of the actual active power P of the induction motor. [ Q ]]The fluctuation range of the actual reactive power Q of the induction motor is shown. Representing power factor of induction motorThe fluctuation interval of (2).
The load of the distribution network with the largest proportion is the induction motor. Parameters characterizing the operating characteristics of induction motors are mainly efficiency eta, power factorAnd a rotational speed n. These parameters are each a function of the induction motor load factor beta. As shown in fig. 2, a diagram of the operating characteristics of an induction motor is shown, where M and s are the electromagnetic torque and slip of the induction motor, respectively. Power factor of induction motorDecreases with decreasing load factor beta, and the lower the load factor, the power factorThe faster the drop. From the viewpoint of efficiency and power factor, the load factor beta of the induction motor is in the range of 0.75-0.85, which is the optimum operation condition. The induction motor can be set to operate normally in a load factor beta of 0.3 to 0.9, depending on the actual load variation. When the load factor beta of the induction motor varies within this range, the power factor can be seen from the power factor characteristic of the induction motorThe variation of (c) is small and can be considered as approximately fluctuating within + -1%.
Thus, when the rated active power P of the induction motor is given N And power factorIn time, it can be assumed that its actual active power P is in the interval [0.3P ] N ，0.9P N ]Fluctuation within range, denoted as [ P ]](ii) a And power factorIn thatWithin range of variation, denoted asThe actual complex power S of the induction motor can be considered to be in the interval S]And (4) up to the upper limit.
(2) Wind power generation active power interval model
Wind power generation is a technology that converts conventional wind energy into electrical energy. Wind is the prime mover of the wind turbine. The power output of the fan is closely related to the wind speed. Wind speed is random, and the wind speed is described by a typical Weibull distribution. The weibull distribution is specifically as follows:
wherein k is a shape coefficient, the value of which is generally between 1.8 and 2.3 and is reflected on the curve shape of the Weibull distribution probability density function. Mu is a scale coefficient and represents the average wind speed of the observed region in a certain time period.
It is generally accepted that the power output of a wind turbine is related to the third power of the wind speed at the height of the hub of the wind turbine,
namely, it is
Wherein, P R The rated output is the rated output of the wind generating set. P is W (v) The power output of the wind generating set when the wind speed is v. N is the number of wind driven generators. ρ is the air density. C p To energy conversion efficiency. R is the radius of the swept area of the rotor. v. of R Is the rated wind speed. v. of ci Is the cut-in wind speed. v. of co To cut out the wind speed.
When the actual wind speed is greater than the cut-in wind speed v ci When the wind power generator is started, the wind power generator generates electric energy; when the actual wind speed is greater than the rated wind speed v R When the wind driven generator is used, the rated power output is kept; when the wind speed is less than the cut-in wind speed v ci Or above cut-out wind speed v co And when the wind driven generator is in failure, the wind driven generator is disconnected from the power grid. The output curve of the wind turbine is substantially as shown in figure 3.
Interval modeling of wind generator output, namely, assigning an interval [ P w down ,P w up ]So that it satisfies the following two conditions: 1) The output of the wind-driven generator has a sufficiently large probability of being in the interval [ P ] w down ,P w up ]Inner; 2) Interval [ P ] w down ,P w up ]Should be as small as possible, at least it is an interval of limited width, not an infinite range. Therefore, the requirements required to be met by the wind power generation active power interval model can be represented by the probability form described by the wind power generation active power interval model.
Specifically, the wind power generation active power interval model is as follows:
wherein,representing eventsIs true probability, epsilon w Representing a parameter, P, related to the probability that the wind generator's contribution is in the interval model W Representing wind power active power, P w down And P w up Respectively the lower limit and the upper limit of the active power of the wind power generation.
(3) Wind power generation reactive power interval model
The wind driven generator generates active power and reactive power at the same time, and generally, the power factor of the wind driven generator is determined, and then an active power interval model [ P ] of wind power generation is carried out w down ,P w up ]Can calculate to obtain a reactive power interval model (Q) w down ,Q w up ]。
Specifically, the wind power generation reactive power interval model is as follows:
wherein cos θ w Is the power factor, Q, of the wind generator w down And Q w up Respectively the lower limit and the upper limit of the wind power generation reactive power.
(4) Photovoltaic power generation active power interval model
For photovoltaic power generation, the output power of a photovoltaic power generation system is closely related to the intensity of sunlight, so that the randomness of the change of the intensity of the sunlight brings randomness to the output power of the photovoltaic power generation system. In the embodiment of the invention, the illumination intensity is calculated by adopting the beta distribution, and the probability density function is as follows:
wherein, s is the sunlight intensity, the value range of a is not less than s and not more than b, and a and b are respectively the minimum value and the maximum value of the sunlight intensity in a certain period of time in a certain place.Γ is the Gamma function.
Based on the solar illumination intensity, the output power of the photovoltaic power generation system can be calculated according to the following formula:
P PV ＝ξ×cosθ×η m ×A p ×η p 。
where ξ is the solar illumination intensity. And theta is the incident angle of sunlight. Eta m MPPT (Maximum Power Point Tracking) efficiency. A. The p Is the area of the photovoltaic cell panel. Eta p Is the conversion efficiency of the photovoltaic cell.
Using the interval [ P PV down ,P PV up ]And characterizing the output power of the photovoltaic power generation system, wherein the interval needs to meet the requirements of an active power interval model of photovoltaic power generation.
Specifically, the photovoltaic power generation active power interval model is as follows:
wherein,representing eventsIs a true probability of ε PV Representing a parameter, P, related to the probability that the output of the photovoltaic power generation system is in the interval model PV Represents the active power of the photovoltaic power generation,andrespectively the lower limit and the upper limit of the active power of the photovoltaic power generation.
For a determined epsilon PV Due to the parameter P PV down And P PV up The probability distribution of the solar illumination intensity is difficult to obtain exactly, so that the interval model of the output power of the photovoltaic power generation system can be obtained by a Monte Carlo simulation method according to the probability density function of the solar illumination intensity.
(5) Photovoltaic power generation reactive power interval model
The method is similar to wind power, generally, the power factor of a photovoltaic grid-connected power generation system is determined, and then a photovoltaic power generation active power interval model [ P ] is used w down ,P w up ]The reactive power interval model can be obtained by calculation
Specifically, the photovoltaic power generation reactive power interval model is as follows:
wherein cos θ PV Is the power factor of the photovoltaic power generation system,andrespectively the lower limit and the upper limit of the reactive power of the photovoltaic power generation.
Specifically, the load uncertainty model, the wind power generation active power interval model, the wind power generation reactive power interval model, the photovoltaic power generation active power interval model and the photovoltaic power generation reactive power interval model can be obtained by adopting a forward-backward flow calculation method.
Specifically, the power uncertainty interval calculation formula is as follows:
where { x } and { y } represent two sets. The interval calculation can be replaced by an upper and lower limit calculation, i.e.x， y，In the embodiment of the invention, interval calculation is applied to forward-backward substitution load flow calculation of the power distribution network. Wherein the iteration is divided into two steps:
1) And (5) a forward pushing process. The calculation is started from the node farthest from the power supply point to the head end, the voltage of the whole network is set as the rated voltage, the power distribution of each section of line is calculated in sequence against the power transmission direction, and the voltage drop is not calculated. Firstly, the injection current of each node is calculated by adopting the following formula:
in the formula: i is ja 、I jb 、I jc Indicates the injected current, S, of each phase at node j ja 、S jb 、S jc Representing the load power, U, of each phase of node j ja 、U jb 、U jc Representing the voltages of each phase at node j, Y ja 、Y jb 、Y jc Indicating the respective relative admittances of the nodes j and k the number of iterations.
Then, the branch current is calculated: calculating the current of each branch according to KCL (kirchhoff current law) from the tail end of the feeder line and against the direction of the power flow, and solving the current of a root node:
in the formula: I.C. A ja 、I jb 、I jc Representing the injected current of each phase at node j, M representing the set of all the lower branches directly connected to node j, I ma 、I mb 、I mc Representing the current of each lower leg m.
2) And (4) performing a back substitution process. And calculating the voltage drop of each section of line from the starting end to the tail end section by section along the power transmission direction from the power supply point by using the given starting end voltage and the power distribution obtained in the first step to obtain the voltage of each node, but not calculating the power loss again at this time. Starting from the head end of a feeder line, calculating each phase voltage of a node j according to KVL (kirchhoff voltage law) by using the known root node voltage and the obtained three-phase current along the direction of the power flow:
in the formula: u shape ia 、U ib 、U ic Representing the voltages at node i.Representing a nodal impedance matrix, I la 、I lb 、I lc Representing the current at each node l.
Through the two processes of forward pushing and backward substitution, one round of iterative calculation is completed. In the next round of forward iteration, the node voltage obtained by the previous round of iteration process can be used for calculating the power loss. In each forward iteration, the power flow distribution is obtained by the network voltage; and calculating the voltage distribution by the power distribution in the back-substitution iteration, and iterating for several times until the modulus of the node voltage difference of the two iterations meets the precision requirement, and stopping the iteration.
There are times when it is desirable to compare the merits between the intervals. Two intervals [ E ] and [ F ] are taken, and when the [ E ] and the [ F ] have no overlapping region, the advantages and the disadvantages of the two intervals can be distinguished only by comparing the upper limit and the lower limit. For example, [0.1,0.2] the minimum value 0.5 in the latter variation range is larger than the maximum value 0.2 in the former variation range than [0.5,0.8], and therefore, the latter is larger.
When [ E ]]And [ F]When there is an overlap region, the following comparison method is required: assume that from an interval obeying normal distribution [ E ]]And [ F]Respectively taking a random variable xi E ,ξ F . Xi is recorded E ＞ξ F For event P, its probability of occurrence represents the slave [ E]Median value is superior to [ F]Is a probability of [ E ]]Is superior to [ F]。
For example, [0.3,0.8] is compared in size with [0.4,0.7] and sampled 100 times. Taking a ratio from the two intervals every time of sampling, recording the probability that the number taken from the first interval is larger than the number taken from the second interval as P, if P is larger than 0.5, the first interval is considered to be large, otherwise, the reverse is performed.
Specifically, in step S20:
(1) The first objective optimization function for the active network loss cost is:
f 1 (x)＝P tloss d。
wherein f is 1 (x) Representing the net loss cost. d is unit electricity price.
And is
Wherein, P tloss Representing the total loss of the network. P loss,k And Q loss,k Respectively the active loss and the reactive loss before the node k. U shape k Is the node voltage. P is k And Q k The active outgoing power and the reactive outgoing power of the node are distinguished. R is k Representing the resistance of the downstream branch. N represents the number of nodes.
The power relationship of two adjacent nodes is:
wherein, P L,k And Q L,k The active injection power and the reactive injection power of the node are respectively.
In the invention, a Forward Backward Substitution Method (FBSM) is adopted to calculate the power flow, wherein the iterative equations of forward and backward substitution processes are as follows:
wherein,andrespectively representing the node injection current and the node voltage in the kth iteration.Andrespectively representing the branch current and voltage vectors in the kth iteration. Y and Z are the admittance and impedance matrices, respectively. f represents a function with a voltage vector as an independent variable and represents the node injection current caused by a constant load. A denotes an incidence matrix submatrix that contains all nodes associated with the calculated node.
(2) The first objective optimization function for the expected starved power is:
wherein, f 2 (x) Indicating the desired amount of starved power. P a,i Represents the average load at node i, T i Represents the average annual outage time, anm is the total number of devices at node i. λ is the mean annual failure rate of the device. And gamma is the average overhaul time of the equipment.
(3) The first objective optimization function for the switching operation cost is:
f 3 (x)＝N op ×q。
wherein q is the cost of a single switch operation. The cost of the switch operation involves a variety of costs. The operation itself will cause losses to the switch, reducing its lifetime. Meanwhile, the connection switch is closed, so that the network can instantly enter a ring network running state, and extra cost generated in the process needs to be counted in the operation cost. Therefore, these costs described above are quantified as a single switch operation cost q. N is a radical of op Is the number of operations.
Specifically, in step S30:
the constraint conditions include: the method comprises the following steps of power distribution network topological structure constraint, power flow balance constraint and voltage deviation constraint.
(1) The topological structure constraint of the power distribution network comprises the following steps:
g∈G。
wherein g is the reconstructed topology. G is the set of all topologies that satisfy the constraint.
(2) The power flow balance constraint comprises:
wherein, P i And Q i Respectively the active load and the reactive load of the node i. P DG,i And Q DG,i Respectively the active output and the reactive output of the distributed power supply. U shape i Is the voltage at node i. G ij And B ij The real and imaginary parts of the admittance matrix, respectively. Delta ij Is the power factor angle of node i.
(3) The voltage deviation constraints include:
wherein, U i min Andrespectively, the upper and lower limits of the voltage at node i.The maximum current flowing at node i.The maximum active output of the distributed power supply at node i.
Specifically, in step S40:
the second objective optimization function is obtained by:
the above-described topology constraint of the power distribution network is ensured by the connectivity discrimination notability in step S603 described below. The above-mentioned load flow balance constraint is ensured by load flow calculation. And correcting the multi-objective optimization model through the voltage deviation and branch limit power constraints by a penalty function. Therefore, by adding penalty factors to the equality and inequality constraints and integrating the penalty factors into the objective function, the original optimization problem with constraint conditions can be converted into an unconstrained optimization problem, as shown below:
after considering the load and the interval value of the interval model of the distributed energy in step S10, the second objective optimization function is as follows:
wherein, w 1 、w 2 And w 3 And weight factors of the first objective optimization function of the active network loss cost, the first objective optimization function of the expected power shortage amount and the first objective optimization function of the switch operation cost are respectively represented. w is a 4 And w 5 Which represent the penalty factors for the voltage and current constraints, respectively.AndU i respectively representing the upper and lower limits of the voltage interval value of the node i.Represents the upper bound of the current interval value of the node i.
Specifically, as shown in fig. 4, step S60 includes the following steps:
step S601: and constructing a harmony objective function, and initializing harmony memory and related parameters.
Wherein, the relevant parameters include: the number of iterations, the number of instruments (i.e. switches), the sum-of-acoustic-memory-bank HM, the sum-of-acoustic-memory-bank retention probability HMCR, and the bank disturbance probability PAR. The iteration number can be set according to the calculation amount and the actual requirement.
The sum acoustic objective function is a second objective optimization function.
The initialized harmony memory comprises: at least one harmonic. Each harmony includes a harmony solution vector. Each harmony solution vector represents a preset initial on-off value of a switch of a node of the power distribution network (namely, a preset value is assigned to an instrument). For example, the off initial value is 0 and the on initial value is 1.
Step S602: and creating new harmony.
And the harmony solution vector corresponding to the new harmony sound is an initial on-off value of the switch of the node of the power distribution network, which is different from the initial on-off value of the switch of the node of the power distribution network represented by the harmony solution vector in the harmony memory.
Step S603: and carrying out topology structure verification on the harmony solution vector corresponding to the new harmony.
And through the verification of the topological structure, whether the initial on-off values of the switches of the nodes of the power distribution network, which are newly corresponding to the sound and represented by the harmonic solution vector, enable the power distribution network to meet the constraint conditions of radiativity and connectivity or not can be determined.
The topological structure is verified as follows: and detecting whether the network generated by the sound contains a looped network or an isolated island. Because the particle generation process of the harmony search algorithm determines that the generated network cannot contain a ring network, topology analysis only needs to detect whether the network contains an island or not.
For a network generated by the harmony search algorithm, the node association matrix is recorded as B. Each row of the matrix represents the connection relationship between one node and other nodes: 1 represents linked and 0 represents unconnected. The sum of each row (column) element is called the connectivity of the node, and represents how closely the node is connected to other nodes. The degree of the isolated node is 0, the degree of the first node and the last node of the network is 1, and the degrees of other nodes are not less than 2.
Taking the system shown in fig. 5 as an example, the topology analysis method according to the embodiment of the present invention is briefly described. The system node association matrix of fig. 5 (a) in the normal state is as follows:
the topology analysis steps of the embodiment of the invention are as follows:
1) And calculating the connectivity of all nodes in the matrix B. If the node with the degree of 0 exists, the network contains an isolated node, namely the network is not feasible and cannot pass the verification of the topological structure.
2) If the network does not contain the node with the degree of 0, deleting the node with the degree of 1 and the maximum number in the network, namely deleting the row and the column corresponding to the node, and then returning to the step 1) until only two nodes are left in the system.
3) If the degrees of the two remaining nodes of the system are both 1, the network does not contain an island, is feasible and can be verified through a topological structure; otherwise, it is not feasible and can not pass the verification of the topology.
The circulant matrix B is scaled down by one step each time. After 4 cycles, for example, the matrix B will be reduced to the form shown below:
in the reduced matrix B, the rows (columns) with degree 1 are 1, 5 and 7, corresponding to nodes 1, 5 and 7 in the original system. Therefore, step 2) only reduces the size of the original system and does not destroy the topological characteristics. As the process continues, eventually matrix B will leave nodes 1 and 2, both of which have degrees 1. This shows that the original system passes the topology verification, i.e. does not contain an island.
If the verification is passed, go to step S604; otherwise, go to step S602.
Step S604: and obtaining a solution of the harmony objective function corresponding to the new harmony by adopting a load flow calculation method.
Step S605: and judging whether the solution of the harmony objective function of the new harmony is better than the solution of the harmony objective function corresponding to the worst harmony in the harmony memory.
If yes, go to step S606; otherwise, step S602 is performed.
Step S606: the new harmony is substituted for the worst harmony in the harmony memory.
Step S607: and judging whether the maximum iteration number is reached.
If the maximum iteration number is reached, go to step S608; otherwise, step S602 is performed, and the number of iterations is increased once.
Step S608: output and acoustic memory.
Specifically, in step S70:
the output harmony memory includes a solution to at least one second objective optimization function. Among these solutions, the smallest solution is obtained. If there are multiple minimum solutions, one of the minimum solutions can be selected according to actual conditions. And reconstructing the power distribution network by adopting the initial on-off value of the switch of the node of the power distribution network corresponding to the minimum solution.
In conclusion, the power distribution network reconstruction method comprehensively considers the uncertainty caused by the random fluctuation of the load and the distributed energy, has high consistency with the actual condition, and has higher accuracy; the power distribution network reconstruction multi-target model is designed, the operation network loss, the power supply reliability and the switching operation cost are calculated, the optimization target coverage is comprehensive, the optimization of the power supply path of the power distribution network is facilitated, the operation efficiency of the power distribution network is improved, and the purposes of reducing the network loss, eliminating overload and load balancing, improving the voltage quality, improving the power supply reliability and economic benefit and the like are achieved; directly reflects the consideration factors in the process of the reconstruction decision-making, and has stronger practicability; the multi-target and nonlinear power distribution network reconstruction model with interval uncertainty calculation is solved based on the harmony search algorithm, the solving performance is good, the convergence is fast, and the precision is high.
The embodiment of the invention also provides a power distribution network reconstruction system. As shown in fig. 6, the system for reconstructing a power distribution network includes:
the first establishing module 601 is configured to establish an interval model of the load and the distributed energy.
A second establishing module 602, configured to establish a first objective optimization function of the active network loss cost, the expected reactive power amount, and the switching operation cost, respectively.
A determining module 603 configured to determine constraints of the first objective optimization function.
And a normalization module 604, configured to perform normalization processing on the first objective optimization function according to the load and the interval model of the distributed energy, the constraint condition, and the weight of each objective optimization function, so as to obtain a second objective optimization function.
And a setting module 605, configured to set an initial on-off value of a switch of a node of the power distribution network.
And a solving module 606, configured to solve the second objective optimization function by using a harmony search algorithm.
And the reconstruction module 607 is configured to perform power distribution network reconstruction according to the initial on-off value of the switch of the node of the power distribution network corresponding to the solution of the minimum second objective optimization function.
In conclusion, the power distribution network reconstruction system comprehensively considers the uncertainty caused by random fluctuation of the load and the distributed energy, has high consistency with the actual condition, and has higher accuracy; a power distribution network reconstruction multi-objective model is designed, running network loss, power supply reliability and switching operation cost are calculated, optimization objective coverage is comprehensive, optimization of a power supply path of a power distribution network is facilitated, the running efficiency of the power distribution network is improved, and the purposes of reducing network loss, eliminating overload, balancing load, improving voltage quality, improving power supply reliability and economic benefit and the like are achieved; directly reflects the consideration factors in the process of the reconstruction decision-making, and has stronger practicability; the multi-target and nonlinear power distribution network reconstruction model with interval uncertainty calculation is solved based on the harmony search algorithm, the solving performance is good, the convergence is fast, and the precision is high.
The technical solution of the present invention is further explained by a specific application example.
As shown in fig. 7, the IEEE33 node network with distributed power supplies is shown, where DG1 to DG3 use photovoltaic power generation, and DG4 to DG5 use fans to generate power. The interval fluctuation is [0.3,0.9 ]]The upper limit and the lower limit of the distributed energy are [0.3P ] rated ,0.9P rated ]Namely DG1-The fluctuation interval of the distributed photovoltaic and wind power of the DG5 is 30% -90% of the rated power value. The load fluctuation takes into account a fluctuation of 10%, and therefore, the interval is [0.9P ] L ,1.1P L ]That is, the fluctuation range of the load is 90% -110% of the rated value. The IEEE33 node network is a 12.66kV standard radiation distribution network and comprises 33 tie switches and 5 section switches. Fig. 8 is a schematic diagram of an IEEE33 node distribution network reconstructed by using the power distribution network reconstruction method according to the embodiment of the present invention. The dashed lines in fig. 7 represent the tie lines, and there is a closed line connection between the two load nodes, and also a normally open line connection, and the line of the normally open switch can be selectively closed if necessary to change the power supply path. As shown in table 1, are the relevant device reliability parameters. As shown in table 2, the results before and after reconstruction are compared.
TABLE 1 associated equipment reliability parameters
| Type of device | λ×r(h/year) |
| Overhead line | [0.282,0.358] |
| Circuit breaker | [0.0418,0.0508] |
| Switch with a switch body | [0.036,0.044] |
According to different equipment types, different lambda multiplied by r reliability parameters can be obtained, and further the annual average power-off time T can be obtained i And according to T i Can be calculated to obtain
Table 2 comparison of results before and after reconstruction of IEEE33 node system including distributed energy
From table 1, it can be seen that the network loss before reconstruction is [108.85,266.06], the expected power shortage amount is [2438.5,2880.3], the minimum voltage per unit value is [0.8718,0.8178], which are all significantly larger than the network loss after reconstruction [75.41,161.33], the expected power shortage amount [2425.1,2864.5], and the minimum voltage per unit value is [0.8282,0.8536], thus proving the effectiveness of the reconstruction method of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A power distribution network reconstruction method is characterized by comprising the following steps:
establishing an interval model of load and distributed energy;
respectively establishing a first target optimization function of active network loss cost, expected power shortage amount and switch operation cost;
determining constraints of the first objective optimization function;
according to the load and distributed energy interval model, the constraint conditions and the weight of each first objective optimization function, carrying out normalization processing on the first objective optimization function to obtain a second objective optimization function;
setting initial on-off values of switches of nodes of the power distribution network;
solving the second objective optimization function by using a harmony search algorithm;
and reconstructing the power distribution network according to the initial on-off value of the switch of the node of the power distribution network corresponding to the minimum solution of the second objective optimization function.
2. The method of claim 1, wherein the load and distributed energy interval model comprises: the system comprises a load uncertainty model, a wind power generation active power interval model, a wind power generation reactive power interval model, a photovoltaic power generation active power interval model and a photovoltaic power generation reactive power interval model.
3. The method according to claim 2, wherein the load uncertainty model, the wind power active power interval model, the wind power reactive power interval model, the photovoltaic power active power interval model and the photovoltaic power reactive power interval model are obtained by a forward-backward substitution power flow calculation method.
4. The method of claim 2,
the load uncertainty model is:wherein [ S ]]Represents the fluctuation interval of the actual complex power S of the induction motor P]Represents the fluctuation interval of the actual active power P of the induction motor, [ Q ]]Representing the fluctuation interval of the actual reactive power of the induction motor,[cosφ]a fluctuation section representing a power factor cos phi of the induction motor;
the wind power generation active power interval model is as follows:wherein,representing eventsIs a true probability of ε w Representing a parameter, P, related to the probability that the wind generator's contribution is in the interval model W The wind power generation active power is represented,andrespectively the lower limit and the upper limit of the active power of the wind power generation;
the wind power generation reactive power interval model is as follows:wherein cos θ w Is the power factor of the wind turbine,andrespectively is the lower limit and the upper limit of the wind power generation reactive power;
the photovoltaic power generation active power interval model is as follows:wherein,representing eventsIs true probability, epsilon PV Representing the output of the photovoltaic power generation system in an interval modelIs a probability-related parameter, P PV Represents the active power of the photovoltaic power generation,andrespectively is the lower limit and the upper limit of the active power of the photovoltaic power generation;
the photovoltaic power generation reactive power interval model is as follows:wherein cos θ PV Is the power factor of the photovoltaic power generation system,andrespectively the lower limit and the upper limit of the reactive power of the photovoltaic power generation.
5. The method of claim 1,
the first objective optimization function of the active network loss cost is as follows: f. of 1 (x)＝P tloss d, wherein, f 1 (x) Representing the loss of power network, d is the unit price of electricity, P tloss represents the total loss of the network, P loss，k And Q loss，k Respectively active and reactive losses, U, in front of node k k Is the node voltage, P k And Q k Respectively the active outflow power and the reactive outflow power of the node; r k The resistance of the downstream branch is represented, and N represents the number of nodes; the power relation of two adjacent nodes is：Wherein, P L，k And Q L，k Respectively active injection power and reactive injection power of the node;
the first objective optimization function of the expected power shortage amount is as follows:wherein, f 2 (x) Indicates the desired amount of starved power, P a，i Represents the average load at node i, T i Represents the average annual outage time, anm is the total number of equipment at the node i, lambda is the annual average failure rate of the equipment, and gamma is the average overhaul time of the equipment;
the first objective optimization function of the switching operation cost is: f. of 3 (x)＝N op X q, wherein f 3 (x) Represents the cost of switching operation, q is the cost of single switching operation, N op Is the number of operations.
6. The method of claim 1, wherein the constraints comprise: the method comprises the following steps of power distribution network topological structure constraint, power flow balance constraint and voltage deviation constraint.
7. The method of claim 6,
the power distribution network topology constraints include: g belongs to G, wherein G is a reconstructed topological structure, and G is a set of all topological structures meeting constraint conditions;
the power flow balance constraint comprises:wherein, P i And Q i Active and reactive loads, P, respectively, of node i DG，i And Q DG，i Are respectively distributed power supplyActive and reactive output of U i Is the voltage of node i, G ij And B ij Respectively the real and imaginary parts, delta, of the admittance matrix ij Power factor angle for node i;
the voltage deviation constraints include:wherein,andrespectively the upper and lower limit of the voltage at node i,is the maximum current flowing out of the node i,the maximum active output of the distributed power supply at node i.
8. The method of claim 5, wherein the second objective optimization function is:
wherein, w 1 、w 2 And w 3 Weight factors, w, of a first objective optimization function representing active network loss costs, a first objective optimization function of expected starved power amounts and a first objective optimization function of switching operation costs, respectively 4 And w 5 Penalty factors representing voltage and current constraints respectively,andU i respectively represent the upper limit and the lower limit of the voltage interval value of the node i,represents the upper limit of the current interval value of the node i.
9. The method of claim 1, wherein the step of solving the second objective optimization function using a harmonic search algorithm comprises:
constructing a harmony objective function, and initializing harmony memory and related parameters; wherein the sum-sound objective function is a second objective optimization function; the harmony memory comprises: each harmony comprises harmony solution vectors, and each harmony solution vector of each harmony represents a preset on-off initial value of a switch of a node of the power distribution network;
creating new harmony;
carrying out topology structure verification on the harmony solution vector corresponding to the new harmony;
if the new harmony objective function passes the verification, obtaining a solution of the harmony objective function corresponding to the new harmony by adopting a load flow calculation method; otherwise, returning to the step of creating new harmony;
judging whether the solution of the new harmony objective function is better than the solution of the worst harmony objective function corresponding to the new harmony sound in the harmony memory;
if so, replacing the worst harmony sound in the harmony sound memory with the new harmony sound; otherwise, returning to the step of creating new harmony;
replacing the worst harmony voice in the harmony voice memory with the new harmony voice;
judging whether the maximum iteration times is reached;
if the maximum iteration times is reached, outputting a harmony memory; otherwise, returning to the step of creating the new harmony sound, and increasing the iteration times for one time.
10. A system for reconfiguring a power distribution network, comprising:
the first establishing module is used for establishing an interval model of load and distributed energy;
the second establishing module is used for respectively establishing a first target optimization function of active network loss cost, expected power shortage amount and switch operation cost;
a determining module, configured to determine a constraint condition of the first objective optimization function;
the normalization module is used for performing normalization processing on the first objective optimization function according to the load and distributed energy interval model, the constraint condition and the weight of each objective optimization function to obtain a second objective optimization function;
the setting module is used for setting an initial on-off value of a switch of a node of the power distribution network;
the solving module is used for solving the second objective optimization function by using a harmony search algorithm;
and the reconstruction module is used for reconstructing the power distribution network according to the initial on-off value of the switch of the node of the power distribution network corresponding to the minimum solution of the second objective optimization function.
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