CN110880771B - Transmission and distribution network reactive power optimization method and device - Google Patents
Transmission and distribution network reactive power optimization method and device Download PDFInfo
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Abstract
The application discloses a transmission and distribution network reactive power optimization method and device, wherein the method comprises the following steps: a two-stage robust reactive power optimization model for a transmission and distribution network considering uncertainty is constructed, and steps and processes for decomposing the model into main problems and sub problems and solving the main problems and the sub problems by adopting a benders decomposition method are provided according to the structural characteristics of the constructed model. According to the method, the disturbance resistance of the reactive power optimization decision result is enhanced by effectively considering the uncertainty of new energy power generation and load prediction, various reactive power compensation and reactive power support elements of the power transmission and distribution network are comprehensively considered by the method system, and meanwhile, the model and the solving method provided by the invention not only can ensure that the obtained result is an optimal solution, but also have higher calculation efficiency.
Description
Technical Field
The application relates to the technical field of power grid power transmission and distribution control, in particular to a power transmission and distribution network reactive power optimization method and device.
Background
Under traditional electric wire netting pattern, electricity generation, transmission of electricity, distribution, power consumption have clear and definite hierarchical structure, along with distributed generator inserts the distribution network in a large number, the operating condition of distribution network is complicated day by day, possesses the ability that changes self operating condition to a certain extent, but new problems such as voltage out of limit, output fluctuation are big appear easily. Meanwhile, reactive voltage regulation means of the power distribution network are richer, all levels of reactive voltage of the power transmission and distribution network are in closer contact, the power transmission network and the power distribution network have certain mutual supporting effect on reactive power, the low-voltage power distribution network needs the high-voltage power transmission network to strengthen reactive support, and the high-voltage power distribution network also needs to fully utilize the active and reactive power regulation capacity of the low-voltage power distribution network. Moreover, the proportion of new energy power generation in the current power grid is continuously increased, and the new energy power generation is easily influenced by the external environment, so that the output of the new energy power generation is relatively uncertain, and therefore the uncertainty of the output of the new energy power generation, such as wind power and the like, needs to be calculated in a reactive power optimization problem. In addition, because the reactive load is difficult to predict accurately, the uncertainty of the load also needs to be considered so as to enhance the uncertainty of the reactive voltage operation scheme for the new energy power generation output and the load prediction.
At present, the following disadvantages exist in the prior art solutions: the existing method only for optimizing the reactive voltage of the power transmission network or the power distribution network cannot fully utilize the respective regulating capacity of the power transmission network and the power distribution network, so that the maximum utilization of the regulating and controlling means can be realized only by comprehensively analyzing and deciding the power transmission network and the power distribution network and improving the deep reactive resource optimal configuration capacity. In addition, the existing layered automatic voltage control carries out reactive power optimization on the transmission and distribution network respectively by setting gateway power or voltage amplitude, bidirectional interaction among different voltage levels of the distribution network is not fully realized, and the layered automatic voltage control is not applicable any more along with the enhancement of the connection degree among the power grids.
On the other hand, most of the existing uncertainty-considering reactive power optimization methods are only applicable to power distribution networks and cannot be applied to power transmission networks, so that the existing uncertainty-considering reactive power optimization methods for power distribution networks cannot be directly applied to the problem of reactive power optimization of power transmission and distribution networks, and the main reason is that reactive voltage cannot be calculated by linear power flow equations of the power transmission networks. In addition, in the aspects of constructing and solving the active and reactive power combined optimization model of the transmission and distribution network, the traditional active optimization and reactive power optimization are usually carried out separately, so that the research on the active and reactive power combined optimization is relatively less. Moreover, because the active and reactive combined optimization model usually includes a mixed integer nonlinear programming problem of discrete variables and continuous variables, that is, belongs to the NP-hard problem, the large-scale solution of such a problem is very difficult, and therefore, a special research needs to be performed on the model solution method in order to efficiently solve the problem.
Disclosure of Invention
The embodiment of the application provides a transmission and distribution network reactive power optimization method and device, so that the uncertainty of new energy power generation and load prediction is considered, the disturbance resistance of a reactive power optimization decision result is enhanced, the result can be guaranteed to be an optimal solution, and the calculation efficiency is high.
In view of the above, a first aspect of the present application provides a transmission and distribution network reactive power optimization method, including:
constructing a two-stage robust reactive power optimization model for the transmission and distribution network, wherein uncertainty is taken into account;
and decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and solving the main problem and the sub problem.
Optionally, the building of the uncertainty-considered two-stage robust reactive power optimization model of the transmission and distribution network specifically includes:
predicting according to the transmission and distribution network load and the historical data of the new energy output to obtain the whole load result of the transmission and distribution network and the new energy output result;
optimizing a target function of an optimization model on the premise of not considering load and new energy output uncertainty according to the whole load result of the transmission and distribution network, the new energy output result, the configuration condition of a capacitor and a reactor and main transformer model parameters;
determining the capacitance of the transmission and distribution network, the reactance switching mode and the tap gear of the main transformer according to the optimized result;
according to the capacitance, the reactance switching mode and the tap gear of the main transformer, on the premise of considering the uncertainty of the distributed power supply and the load, the continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or reactive power support element are optimized through calculating an objective function of an optimization model;
and respectively iterating and optimizing the obtained decision results of the continuous variable and the discrete variable until the convergence condition of the benders decomposition method is met, and determining the result of the whole transmission and distribution network reactive power optimization model.
Optionally, the objective function in performing reactive power optimization according to the power transmission and distribution network overall load prediction result and the parameters of the capacitor, the reactor and the main transformer model specifically includes:
wherein T is a time set; f is a branch set; d is a capacitor bank and a reactor bank set; s is a transformer set; y isd,tRepresenting the switching times of the capacitor and the reactance; tau iss,tRepresenting the number of transformer tap changes; r isij[(Pij,t)2+(Qij,t)2]Characterizing the active loss of the line, rijIs a line resistance; pij,tAnd Qij,tRespectively the active and reactive power of the line; II typeg,ΠlRespectively representing the wind power and load uncertainty; pgThe active power output of the wind power is realized; plActive power for the load; i and j represent the ith and jth nodes;
optionally, the constraint conditions of the objective function include: the method comprises the following steps of linear power flow equation constraint for a power transmission and distribution network reactive power optimization problem, constraint representing active and reactive corresponding relation in a set of wind power output and load prediction uncertainty, a tap adjustable transformer model (comprising adjustable gear and adjustment frequency constraint), SVC or STATCOM continuous reactive power adjustment range constraint, switchable group number and switching frequency constraint of a group switching capacitor reactor, node voltage amplitude upper and lower limit constraint, generator set reactive power output constraint, distributed power supply output constraint and energy storage device charging and discharging and charging state constraint.
Optionally, the linear power flow equation constraint for the transmission and distribution network reactive power optimization problem specifically includes:
p represents active power of the node, Q represents reactive power of the node, B' is an susceptance array formed by neglecting parallel susceptances of the susceptance array in the node admittance array, theta is a phase angle difference array between the nodes, B and G are the susceptance array and the conductance array between the nodes respectively, and U represents a voltage array of the node.
Optionally, the constraint of the active and reactive corresponding relationship in the set representing the wind power output and the load prediction uncertainty is specifically:
in the formula IIg,ΠlRespectively representing the wind power and load uncertainty; pgAnd QgThe active and reactive power output of the wind power is realized; plAnd QlActive and reactive power for the load; theta1And theta2Respectively corresponding power factor angles of wind power and load; the superscript ^ represents a predicted value, and the superscript ^ represents a fluctuation amount; it is generally believed that there will be ideal compensation in the wind turbine, so that the wind farm operates at a certain power factor; the method is also suitable for loads, generally has the requirement of minimum power factor for users, and therefore, the uncertain relation of representing active power and reactive power by adopting the constant power factor model has certain practical significance.
Optionally, a reactive power optimization model is established as a mixed integer nonlinear programming model according to the objective function and the constraint condition:
Min cTx+fTy
s.t.Ax+By≥b
y∈{0,1}
x≥0
in the formula, c is a coefficient array of a continuous variable x in the objective function, f is a coefficient array of a discrete variable y in the objective function, and A and B are coefficient arrays related to variables on the left side in the inequality constraint respectively; b is an inequality constraint right coefficient array.
Optionally, the main problem and the sub problem in decomposing the model into the main problem and the sub problem and solving the main problem and the sub problem by using the benders decomposition method are respectively:
the main problems are as follows:
the sub-problems are:
the second aspect of the present application provides a transmission and distribution network reactive power optimization device, the device includes:
the model building unit is used for building a two-stage robust reactive power optimization model of the transmission and distribution network considering uncertainty;
and the model solving unit is used for decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and carrying out iterative solution.
Optionally, the model building unit further includes:
the load prediction unit is used for predicting to obtain the whole load result of the transmission and distribution network and the new energy output result within a period of time in the future according to the transmission and distribution network load and the new energy output historical data;
the reactive power optimization unit is used for carrying out reactive power optimization according to the whole load result of the transmission and distribution network, the new energy output result and the parameters of the capacitor, the reactor and the main transformer model;
the fixing unit is used for fixing the transmission and distribution network capacitor, the reactance switching mode and the main transformer tap gear according to the result of reactive power optimization;
the original element optimization unit is used for optimizing continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or the reactive power support element according to the capacitance, the reactance switching mode and the tap gear of the main transformer;
and the model determining unit is used for respectively optimizing the obtained decision results of the continuous variable and the discrete variable, and determining the result of the whole transmission and distribution network reactive power optimization model when the convergence requirement of the second decomposition method is met. According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a transmission and distribution network reactive power optimization method, which comprises the following steps: constructing a two-stage robust reactive power optimization model for the transmission and distribution network, wherein uncertainty is taken into account; and decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and solving the main problem and the sub problem.
According to the method, the disturbance resistance of the reactive power optimization decision result is enhanced by effectively considering the uncertainty of new energy power generation and load prediction, various reactive power compensation and reactive power support elements of the power transmission and distribution network are comprehensively considered by the method system, and meanwhile, the model and the solving method provided by the invention not only can ensure that the obtained result is an optimal solution, but also have higher calculation efficiency.
Drawings
Fig. 1 is a flowchart of a method of an embodiment of a transmission and distribution network reactive power optimization method of the present application;
fig. 2 is a flowchart of a method of another embodiment of a transmission and distribution network reactive power optimization method of the present application;
fig. 3 is a schematic structural diagram of an embodiment of a transmission and distribution network reactive power optimization device according to the present application;
fig. 4 is a schematic diagram of simple structural division of a transmission and distribution network according to the present application;
FIG. 5 is a schematic diagram illustrating a corresponding relationship of active and reactive powers of an uncertain wind power set in an embodiment of the application;
fig. 6 is a flowchart of solving the robust reactive power optimization model by the Benders decomposition method in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. 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 application.
Referring to fig. 1, fig. 1 is a flowchart of a method of an embodiment of a transmission and distribution network reactive power optimization method according to the present application, as shown in fig. 1, where fig. 1 includes:
101. and constructing a two-stage robust reactive power optimization model for the transmission and distribution network, which takes uncertainty into account.
It should be noted that, in the application, a two-stage robust reactive power optimization model for the transmission and distribution network considering uncertainty is constructed, and firstly, the whole load result of the transmission and distribution network and the new energy output result can be obtained according to the load of the transmission and distribution network and the new energy output prediction; then, performing reactive power optimization according to the overall load result, the new energy output result and the parameters of the capacitor, the reactor and the main transformer model, and solving an optimal value according to a preset objective function and constraint conditions thereof during optimization so as to determine the parameters of the capacitor, the reactor and the main transformer model; fixing the transmission and distribution network capacitance, a reactance switching mode and a main transformer tap gear according to a reactive power optimization result; according to the capacitance, the reactance switching mode and the tap gear of the main transformer, continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or the reactive power support element are optimized; and respectively optimizing the obtained decision results of the continuous variable and the discrete variable, and determining the result of the whole transmission and distribution network reactive power optimization model, wherein SVC and STATCOM are reactive power compensation devices.
102. And decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and solving the main problem and the sub problem.
It should be noted that the method can adopt a benders decomposition method to solve continuous variables and discrete variables in an objective function, and the problems in the model are divided into main problems and sub-problems to be repeatedly solved in an iterative manner, so that the final optimal solution of the two-stage robust reactive power optimization model of the transmission and distribution network is obtained.
Benders' decomposition algorithm was first proposed in 1962 by j.f. Benders, and is intended to solve the problem of mixed integer programming, i.e. the problem of extreme values that occur simultaneously for continuous and integer variables. The Benders decomposition algorithm is a very common algorithm used to compute difficult computational problems such as the minimum integer nonlinear programming problem and the stochastic programming problem.
According to the method, the disturbance resistance of the reactive power optimization decision result is enhanced by effectively considering the uncertainty of new energy power generation and load prediction, various reactive power compensation and reactive power support elements of the power transmission and distribution network are comprehensively considered by the method system, and meanwhile, the model and the solving method provided by the invention not only can ensure that the obtained result is an optimal solution, but also have higher calculation efficiency.
For easy understanding, please refer to fig. 2, fig. 2 is a flowchart of a method of another embodiment of the present invention for a transmission and distribution network reactive power optimization method, and as shown in fig. 2, the method specifically includes:
201. and predicting according to the transmission and distribution network load and the historical data of the new energy output to obtain the whole load result and the new energy output result of the transmission and distribution network.
It should be noted that, in the application, the overall load condition of the future transmission and distribution network can be predicted according to the historical data of the load of the existing transmission and distribution network and the historical data of the new energy output prediction, so that the load trend of the transmission and distribution network in a period of time in the future can be predicted. In addition, the whole load result of the transmission and distribution network can be obtained by load prediction according to a load prediction function module in the distribution scheduling system, and can also be obtained by prediction by adopting any existing load prediction method.
202. And optimizing the objective function of the optimization model on the premise of not considering the load and the uncertainty of the new energy output according to the whole load prediction result of the transmission and distribution network, the new energy output result, the capacitor, the reactor and the main transformer model parameters.
It should be noted that the optimization refers to optimization of a main problem of the benders decomposition method on the premise of not considering uncertainty of the load and the new energy output, that is, optimization of an objective function is performed according to a prediction result of the load and the new energy output, and an optimization result of discrete variables such as the number of groups of switched capacitors and reactors and tap gears of a main transformer is obtained.
According to the method and the device, the objective function in reactive power optimization can be established according to the parameters of the capacitor, the reactor and the main transformer model, a plurality of constraint conditions exist for the objective function, and the objective function is solved according to the whole load prediction result of the power transmission and distribution network and the constraint conditions, so that the optimal parameters of the capacitor, the reactor and the main transformer model are obtained.
The objective function is specifically as follows:
wherein T is a time set; f is a branch set; d is a capacitor bank and a reactor bank set; s is a transformer set; y isd,tRepresenting the switching times of the capacitor and the reactance; tau iss,tRepresenting the number of transformer tap changes; r isij[(Pij,t)2+(Qij,t)2]Characterizing the active loss of the line, rijIs a line resistance; pij,tAnd Qij,tRespectively the active and reactive power of the line; II typeg,ΠlRespectively representing the wind power and load uncertainty; pgThe active power output of the wind power is realized; plActive power for the load; i and j represent the ith and jth nodes;
the constraint conditions include: the method comprises the following steps of linear power flow equation constraint for a transmission and distribution network reactive power optimization problem, constraint representing an active and reactive corresponding relation in a set of wind power output and load prediction uncertainty, a tap adjustable transformer model (comprising adjustable gear and adjustment frequency constraint), SVC or STATCOM continuous reactive power adjustment range constraint, switchable group number and switching frequency constraint of a group switching capacitor reactor, node voltage amplitude upper and lower limit constraint, generator set reactive power output constraint, distributed power output constraint and charging and discharging and charging state constraint of an energy storage device;
the linear power flow equation constraint for the reactive power optimization problem of the transmission and distribution network is specifically as follows:
p represents active power of the node, Q represents reactive power of the node, B' is an susceptance array formed by neglecting parallel susceptances of the susceptance array in the node admittance array, theta is a phase angle difference array between the nodes, B and G are the susceptance array and the conductance array between the nodes respectively, and U represents a voltage array of the node.
The specific derivation process is as follows:
firstly, as shown in fig. 4, which is a schematic diagram of simple structure division of the transmission and distribution network of the present application, polar coordinate expression forms of active power and reactive power are as follows:
in the formula, PiAnd QiRespectively the active power and the reactive power of the node i; u shapeiAnd UiVoltage amplitudes of the node i and the node j are respectively; gijAnd BijRespectively the conductance and susceptance between node i and node j; thetaijIs the phase angle difference between node i and node j.
The expansion form of the active power flow equation is:
in the formula, gii is the self-conductance of the node i; gij is the mutual conductance of the node i and the node j; bijIs the mutual susceptance of node i and node j. Suppose gijUi(Ui-Ujcosθij)≈gijUi(Ui-Uj) Another thetaij＝θi-θjThen there are:
in the formula, Bi′jThe electric nano array formed by neglecting parallel electric nano in the electric nano array in the node electric nano array.
For the reactive power flow, the equation is similar to the equation, and the parallel conductance is negligible compared with the parallel susceptance, so that the equation is converted into the following equation:
the reactive power flow equation and the active power flow equation are combined to obtain a matrix form:
p represents active power of the node, Q represents reactive power of the node, B' is an susceptance array formed by neglecting parallel susceptances of the susceptance array in the node admittance array, theta is a phase angle difference array between the nodes, B and G are the susceptance array and the conductance array between the nodes respectively, and U represents a voltage array of the node.
According to the types of PQ, PV and V theta nodes (nodes are generally divided into three types in an electric power system, namely PQ nodes, PV nodes, V nodes, G nodes and R nodes, the PQ nodes, the PV nodes, PV nodes and V nodes are known in active power and voltage amplitude, the PV nodes and the V nodes are required to be required; g refers to Generator, a Generator node, i.e., a PV node; r refers to a balanced node, i.e., a V theta node. Then, according to the known quantity and the quantity to be solved, and according to the block matrix theory, the linear power flow equation in the matrix form can be expressed as the following block matrix form:
the matrix expression is the linear power flow equation constraint for the reactive power optimization problem of the transmission and distribution network.
The constraint of the active and reactive corresponding relations in the set representing the wind power output and load prediction uncertainty is characterized in that a schematic diagram representing the active and reactive corresponding relations in the uncertain set is shown in fig. 5, and specifically comprises the following steps:
in the formula IIg,ΠlRespectively representing the wind power and load uncertainty; pgAnd QgThe active and reactive power output of the wind power is realized; plAnd QlActive and reactive power for the load; theta1And theta2Respectively corresponding power factor angles of wind power and load; the superscript ^ represents a predicted value, and the superscript ^ represents a fluctuation amount; it is generally believed that there will be ideal compensation in the wind turbine, so that the wind farm operates at a certain power factor; the method is also suitable for loads, generally has the requirement of minimum power factor for users, and therefore, the uncertain relation of representing active power and reactive power by adopting the constant power factor model has certain practical significance.
The tap adjustable transformer model comprises adjustable gears and adjustment times, and the restriction specifically comprises the following steps:
for a branch with a tunable transformer, the voltage relationship across the branch can be represented by:
Ui,t＝as,t 2⊙Uj,t,(i,j)∈v,s∈S,t∈T
in the formula:
representing a set of branches on which the adjustable transformer is installed; s is an adjustable transformer set; i is the primary side of the adjustable transformer, and j is the secondary side of the adjustable transformer; u shapei,t,Uj,tThe voltages of the primary side and the secondary side of the adjustable transformer respectively correspond to the t time period; a iss,tRepresenting the corresponding transformation ratio of the adjustable transformer in the t period;
the tap variable of the adjustable transformer is a value in { -16, -15, …, +15, +16 }.
The SVC or STATCOM continuous reactive power regulation range constraint specifically comprises:
because the SVC or STATCOM can both inject reactive power into the network and absorb reactive power from the network, and the reactive power output can be continuously adjusted, the model can be simply expressed as:
in the formula: qs,tPositive means that reactive power is absorbed from the network, and vice versa means that reactive power compensation is performed on the network.
The constraints of the switchable group number and switching times of the grouped switched capacitor reactor are as follows:
ycr,t＝yc,t+yr,t
in the formula:
respectively representing the corresponding capacitor compensation capacity and the corresponding reactor compensation capacity; qC，QRReactive compensation power which can be provided by a single capacitor bank and a single reactor bank;
respectively inputting group number variables of the three-phase capacitor bank, the values of which are less than or equal to the number of the installed switchable capacitor or reactor groups
yc,t,yr,tThe number of adjustments of the capacitor bank and the reactor bank are indicated, respectively.
The node voltage amplitude upper and lower limit constraints are specifically as follows:
in the formula: u shapei minAnd Ui maxThe upper limit and the lower limit of the voltage amplitude of the node i are respectively 0.95pu and 1.05 pu.
The reactive output constraint of the generator set is specifically as follows:
because the generator in the distribution network is equivalent to the high-voltage power grid, active power and reactive power can be injected into the network and absorbed from the network, and the active power and reactive power output can be continuously adjusted, so that the model can be expressed as follows:
in the formula: pg,t,Qg,tThe active and reactive outputs of the generator are respectively represented, the positive values of the active and reactive outputs represent the injection of active and reactive power into the network, and otherwise the absorption of active and reactive power from the network.
The output constraint of the distributed power supply is specifically as follows:
in the formula: w represents a distributed power supply set;
and
the maximum value and the minimum value of the active output power of the distributed power supply are respectively; lambda [ alpha ]w minA minimum power factor for operation of the distributed power supply; sw maxIs the rated capacity of the distributed power supply. The constraints described above indicate that the output power of the distributed power supply can be regulated within its power factor constraints and capacity constraint limits.
The charging and discharging and charge state constraint of the energy storage device is as follows:
generally, the operating constraints of the energy storage device are mainly defined by charge-discharge constraints, state-of-charge constraints, and capacity constraints, which can be represented by the following mathematical model.
In the formula: e is a set formed by energy storage devices;
the maximum charge-discharge power of the energy storage device; epsiloncAnd epsilondThe charging and discharging efficiencies of the energy storage device are respectively 0.9 and 1.11; SOCe minAnd SOCe maxThe upper limit and the lower limit of the energy storage device charge state constraint can be respectively 10 percent and 90 percent of the energy storage device capacity;
is the capacity of the energy storage device.
203. And determining the capacitance of the transmission and distribution network, the reactance switching mode and the tap gear of the main transformer according to the optimized result.
It should be noted that, according to the optimal result of the solution, the optimal parameters of the transmission and distribution network capacitance, the reactance switching mode, and the tap gear of the main transformer can be obtained, wherein 203 is the optimization result of discrete variables such as the group number of the group switching capacitors and reactors, the tap gear of the main transformer, and the like, which are determined according to the optimization result.
204. And according to the capacitance, the reactance switching mode and the tap gear of the main transformer, on the premise of considering the uncertainty of the distributed power supply and the load, optimizing the continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or reactive power support element by calculating an objective function of an optimization model.
It should be noted that the optimization in step 204, that is, the optimization corresponding to the neutron problem in the bender decomposition method, is to optimize the reactive power output of continuous variables such as the generator set, the SVC, the STATCOM, the distributed power supply, the energy storage reactive power source or the reactive power support element by optimizing the objective function under the constraint condition when the result of obtaining the discrete result variable by the optimization of the main problem is known. The specific implementation scheme is seen in the subproblems of the implementation process of the benders decomposition method. Namely, under the condition that the result of obtaining the discrete result variable by the optimization of the main problem is known, the reactive power output results of continuous variables such as generator sets, SVCs, STATCOMs, distributed power supplies, energy storage reactive power sources or reactive power support elements are optimized.
205. And respectively iterating and optimizing the obtained decision results of the continuous variable and the discrete variable until the convergence condition of the benders decomposition method is met, thereby determining the result of the whole transmission and distribution network reactive power optimization model.
It should be noted that the continuous variables in the present application correspond to the optimization variables occurring in the render decomposition method problem, and mainly include continuous variables such as a generator set, an SVC, a STATCOM, a distributed power supply, an energy storage reactive power source or a reactive support element; the discrete variables correspond to optimized variables appearing in the main problems of the benders decomposition method, and mainly comprise discrete variables such as grouping switched capacitors, the number of groups of reactors, tap gears of main transformers and the like. Step 205 is obtained by optimizing the neutron problem in the bender decomposition method, and also by optimizing the objective function under the condition of satisfying the constraint, and the specific implementation flows are given in both the model solution and the bender decomposition method flow.
Firstly, a robust reactive power optimization model which is constructed by considering an objective function and constraints and takes uncertainty into account is a mixed integer nonlinear programming (MINLP) model, and the robust reactive power optimization model can be simplified and expressed into a standard matrix form as follows:
Min cTx+fTy
s.t.Ax+By≥b
y∈{0,1}
x≥0
in the formula, c is a coefficient array of a continuous variable x in the objective function, f is a coefficient array of a discrete variable y in the objective function, and A and B are coefficient arrays related to variables on the left side in the inequality constraint respectively; b is an inequality constraint right coefficient array.
To solve the MINLP problem shown in equation (10), the value of the {0-1} variable y can be fixed to be y, and the problem becomes a linear programming problem with respect to the continuous variable x. And solving the linear programming problem, so that a better solution of the variable y can be obtained.
The value of the fixed variable y being
The above problem becomes the following linear programming problem:
x≥0
the inner layer minimization linear programming problem is further converted into the following maximization problem through a dual theory:
the formula is as follows:
converting into:
thus, the main problem and the sub problem in the Benders decomposition algorithm can be obtained as follows:
the main problems are as follows:
the sub-problems are:
the main problem and the sub problems are repeatedly and iteratively solved, and the final optimal solution x of the two-stage robust reactive power optimization model of the transmission and distribution network can be obtained*And y*The specific solving flow chart can be referred to fig. 6.
The above is an embodiment of the method of the present application, and the present application further provides an embodiment of a transmission and distribution network reactive power optimization device, as shown in fig. 3, including:
the model building unit 301 is configured to build a two-stage robust reactive power optimization model for the transmission and distribution network, which accounts for uncertainty.
And the model solving unit 302 is used for decomposing the model into the main problem and the sub-problems by adopting a benders decomposition method and carrying out iterative solution.
The model member unit further includes:
and the load prediction unit is used for predicting the whole load of the transmission and distribution network and the new energy output result in a period of time in the future according to the transmission and distribution network load and the new energy output historical data.
And the reactive power optimization unit is used for performing reactive power optimization according to the whole load result of the transmission and distribution network, the new energy output result, the capacitor, the reactor and the main transformer model parameters.
And the fixing unit is used for fixing the transmission and distribution network capacitor, the reactance switching mode and the main transformer tap gear according to the result of the reactive power optimization.
And the original element optimization unit is used for optimizing the continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or the reactive power support element according to the capacitance, the reactance switching mode and the tap gear of the main transformer.
And the model determining unit is used for respectively optimizing the obtained decision results of the continuous variable and the discrete variable, and determining the result of the whole transmission and distribution network reactive power optimization model when meeting the convergence requirement of the benders decomposition method.
The method adopts the linear power flow equations suitable for both the transmission and distribution network to optimize problem modeling, so that the method provided by the invention has higher calculation and solution efficiency and can realize the reactive power coordination optimization of the transmission and distribution network; in addition, the method takes the minimum of capacitance, reactance switching times, transformer tap changing times and system network loss as an objective function, can effectively ensure the service life of equipment, and improves the economical efficiency and safety of system operation. When the reactive power optimization model is constructed, a two-stage robust optimization method is adopted, two-stage division is carried out according to discrete variables and continuous variables needing to be decided, the strong coupling relation among all the time stages in the reactive power optimization can be simplified, and the model solution is made to be simple. When a decision-making target is optimized, the system comprehensively considers the number of groups which can be put into the capacitors and the reactances, the adjustable tap position of the transformer, the reactive power of the generator set, the compensation capability of reactive compensation equipment such as SVC and STATCOM, the constraints of a power flow equation, the voltage amplitude value and the like, and the constraints of elements which can provide reactive support such as a distributed power supply and energy storage. In model solving, the method converts the extracted model into a main problem and a subproblem by adopting a benders decomposition method to carry out iterative solving, provides steps and flows for model conversion solving, and has higher calculation efficiency.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (9)
1. A transmission and distribution network reactive power optimization method is characterized by comprising the following steps:
constructing a two-stage robust reactive power optimization model for the transmission and distribution network, wherein uncertainty is taken into account;
the objective function of the optimization model is specifically as follows:
wherein T is a time set; f is a branch set; d is a capacitor bank and a reactor bank set; s is a transformer set; y isd,tRepresenting the switching times of the capacitor and the reactance; tau iss,tRepresenting the number of transformer tap changes; r isij[(Pij,t)2+(Qij,t)2]Characterizing the active loss of the line, rijIs a line resistance; pij,tAnd Qij,tRespectively the active and reactive power of the line; II typeg,ΠlRespectively representing the wind power and load uncertainty; pgThe active power output of the wind power is realized; plActive power for the load; i and j represent the ith and jth nodes;
and decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and solving the main problem and the sub problem.
2. The transmission and distribution network reactive power optimization method according to claim 1, wherein the constructed uncertainty-considered transmission and distribution network two-stage robust reactive power optimization model is specifically:
predicting according to the transmission and distribution network load and the historical data of the new energy output to obtain the whole load result of the transmission and distribution network and the new energy output result;
optimizing a target function of an optimization model on the premise of not considering load and new energy output uncertainty according to the whole load result of the transmission and distribution network, the new energy output result, the configuration condition of a capacitor and a reactor and main transformer model parameters;
determining the capacitance of the transmission and distribution network, the reactance switching mode and the tap gear of the main transformer according to the optimized result;
according to the capacitance, the reactance switching mode and the tap gear of the main transformer, on the premise of considering the uncertainty of the distributed power supply and the load, the continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or reactive power support element are optimized through calculating an objective function of an optimization model;
and respectively iterating and optimizing the obtained decision results of the continuous variable and the discrete variable until the convergence condition of the benders decomposition method is met, and determining the result of the whole transmission and distribution network reactive power optimization model.
3. The transmission and distribution network reactive power optimization method according to claim 1, wherein the constraints of the objective function include: the method comprises the following steps of linear power flow equation constraint for a power transmission and distribution network reactive power optimization problem, constraint representing active and reactive corresponding relation in a set of wind power output and load prediction uncertainty, a tap adjustable transformer model, SVC or STATCOM continuous reactive power regulation range constraint, switchable group number and switching frequency constraint of a group switching capacitor reactor, node voltage amplitude upper and lower limit constraint, generator set reactive power output constraint, distributed power output constraint and charging and discharging and charging state constraint of an energy storage device.
4. The transmission and distribution network reactive power optimization method according to claim 3, wherein the linear power flow equation constraints for the transmission and distribution network reactive power optimization problem are specifically:
p represents active power of the node, Q represents reactive power of the node, B' is an susceptance array formed by neglecting parallel susceptances of the susceptance array in the node admittance array, theta is a phase angle difference array between the nodes, B and G are the susceptance array and the conductance array between the nodes respectively, and U represents a voltage array of the node.
5. The transmission and distribution network reactive power optimization method according to claim 3, wherein the constraints characterizing the active and reactive correspondence relationship in the set of wind power output and load prediction uncertainty are specifically:
in the formula IIg,ΠlRespectively representing the wind power and load uncertainty; pgAnd QgThe active and reactive power output of the wind power is realized; plAnd QlActive and reactive power for the load; theta1And theta2Respectively corresponding power factor angles of wind power and load; the superscript ^ represents a predicted value, and the superscript ^ represents a fluctuation amount.
6. The transmission and distribution network reactive power optimization method according to claim 3, wherein a reactive power optimization model is established as a mixed integer nonlinear programming model according to the objective function and constraint conditions:
Min cTx+fTy
s.t.Ax+By≥b
y∈{0,1}
x≥0
in the formula, c is a coefficient array of a continuous variable x in the objective function, f is a coefficient array of a discrete variable y in the objective function, and A and B are coefficient arrays related to variables on the left side in the inequality constraint respectively; b is an inequality constraint right coefficient array.
7. The transmission and distribution network reactive power optimization method according to claim 6, wherein the main problem and the sub problem in decomposing the model into the main problem and the sub problem and solving by using the benders decomposition method are respectively:
the main problems are as follows:
the sub-problems are:
8. a transmission and distribution network reactive power optimization device is characterized by comprising:
the model building unit is used for building a two-stage robust reactive power optimization model of the transmission and distribution network considering uncertainty;
the objective function of the optimization model is specifically as follows:
wherein T is a time set; f is a branch set; d is a capacitor bank and a reactor bank set; s is a transformer set; y isd,tRepresenting the switching times of the capacitor and the reactance; tau iss,tRepresenting the number of transformer tap changes;
characterizing the active loss of the line, rijIs a line resistance; pij,tAnd Qij,tRespectively the active and reactive power of the line; II typeg,ΠlRespectively representing the wind power and load uncertainty; pgThe active power output of the wind power is realized; plActive power for the load; i and j represent the ith and jth nodes;
and the model solving unit is used for decomposing the model into a main problem and a sub problem by adopting a benders decomposition method and carrying out iterative solution.
9. The transmission and distribution network reactive power optimization device of claim 8, wherein the model building unit further comprises:
the load prediction unit is used for predicting to obtain the whole load result of the transmission and distribution network and the new energy output result within a period of time in the future according to the transmission and distribution network load and the new energy output historical data;
the reactive power optimization unit is used for carrying out reactive power optimization according to the whole load result of the transmission and distribution network, the new energy output result and the parameters of the capacitor, the reactor and the main transformer model;
the fixing unit is used for fixing the transmission and distribution network capacitor, the reactance switching mode and the main transformer tap gear according to the result of reactive power optimization;
the original element optimization unit is used for optimizing continuous reactive power output results of the generator set, the SVC, the STATCOM, the distributed power supply and the energy storage reactive power source or the reactive power support element according to the capacitance, the reactance switching mode and the tap gear of the main transformer;
and the model determining unit is used for respectively optimizing the obtained decision results of the continuous variable and the discrete variable, and determining the result of the whole transmission and distribution network reactive power optimization model when the convergence requirement of the second decomposition method is met.
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