CN115912466A - Active power distribution network island division method and system based on information gap decision theory - Google Patents
Active power distribution network island division method and system based on information gap decision theory Download PDFInfo
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Abstract
The invention discloses an active power distribution network island division method and system based on an information gap decision theory, which comprises the following steps: 1. acquiring historical data of a distributed power supply and loads of all nodes in a power distribution network, and predicting wind power, photovoltaic output and load requirements of all nodes in an island power supply period; 2. constructing a power distribution network islanding model, substituting predicted values of wind power, photovoltaic and load into the solution, and obtaining an islanding scheme and power failure loss based on a deterministic model; 3. constructing an uncertainty model considering wind, light and load fluctuation based on an information gap decision theory; 4. selecting a corresponding uncertainty model according to different risk management strategies to solve; 5. and obtaining a final island division scheme. The island division scheme obtained by the method can bear the fluctuation of the distributed power supply output and the load within a certain range, the stability of the operation of each island in the island power supply period is ensured, and the minimization of the power failure loss is realized.
Description
Technical Field
The invention belongs to the field of power system scheduling, and particularly relates to an active power distribution network island division method and system based on an information gap decision theory.
Background
In recent years, with the frequent occurrence of some extreme weather, black start and island division of a power distribution network are more and more emphasized by power enterprises. The wide application of distributed power supply in the distribution network provides the possibility of distribution network isolated island operation, and when some lines of the distribution network break down or are maintained and the main network power supply is lost, the distributed power supply in the distribution network is fully utilized to communicate the load around for isolated island power supply, so that the operation of important loads is guaranteed, and the method has important significance for reducing economic loss caused by power failure and improving power supply reliability. Because distributed generator power generation capacity is limited in the distribution network, and does not possess the powerful voltage frequency regulation ability of main network power supply, consequently need fully consider the power supply balance relation in the island when carrying out the island division, the volatility of wind-powered electricity generation, photovoltaic, load all can bring the risk for island electric wire netting steady operation.
The method needs a large amount of historical data as support, solves the probability distribution of uncertainty variables, adopts a large amount of typical prediction scenes to represent the possible output of the uncertainty variables, and is generally low in solving efficiency. The other type is that the model is subjected to robust optimization by constructing an uncertainty variable output set, and the results obtained by the method are usually over conservative and have low economy.
In addition, in an island division model, because factors such as a switch state, load switching, network power flow constraint and the like need to be considered, the problem is a mixed integer nonlinear programming problem, and the traditional method utilizes a group optimization intelligent algorithm to solve the problem, which is generally low in efficiency, long in solving time and easy to fall into a local optimal solution.
Disclosure of Invention
The invention aims to solve the technical problems in the background technology and provides an active power distribution network island division method and system based on an information gap decision theory, wind-solar output and probability distribution with accurate load requirements are not needed, an island division model is modeled into a mixed integer convex optimization model, a commercial solver is used for solving, and the solving speed is high and the solving precision is high.
In order to solve the technical problem, the technical scheme of the invention is as follows:
an active power distribution network island division method based on an information gap decision theory comprises the following steps:
forecasting wind power, photovoltaic output and load requirements of each node in an island power supply period by using historical data of a distributed power supply and loads of each node in a power distribution network to obtain the demand forecasting values of the wind power, the photovoltaic and the loads;
constructing a power distribution network island division model by taking the minimum economic loss of power failure of a power distribution network in the island power supply period as a target function;
inputting the demand predicted value into a power distribution network island division model for solving to obtain power failure loss based on a deterministic model;
an islanding method based on the power failure loss of a deterministic model and an information gap decision theory calculates an uncertain variable to obtain a deviation coefficient, and constructs an uncertain model considering wind, light and load fluctuation;
selecting a decision strategy, setting a deviation factor, and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and solving the robust model or the opportunity model, and determining a final island division scheme.
Further, the final islanding scheme includes: each island division range, node load recovery state, branch switch state and distributed power supply output condition in each time period.
Further, the objective function of the power distribution network islanding model is as follows:
in the formula, the first term represents the economic loss of the load cut off, T is the total power supply time interval of the island, N is the load node set, and omega i Represents the economic loss of the node i in unit load electricity power failure, P load,i For the active load demand of node i, Y i,t The recovery state of the load of the node i in the t time interval is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit time interval; the second term represents the network loss of the distribution network, P loss And the total network loss of the power distribution network in the current time period is represented.
Further, the constraint conditions of the power distribution network islanding model comprise: network power flow constraint, wind-solar constraint, energy storage constraint, network topology constraint and operation constraint;
the network flow constraints are as follows:
in the formula u (j) Is the set of all nodes upstream of the node j; v. of (j) Is the set of all nodes downstream of node j; p is ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t time period; p is DER,j,t And Q DER,j,t Respectively the total active power and the total reactive power output by the distributed power supply at the node j in the period t; p D,j,t And Q D,j,t Actual active and reactive power of the load at node j during the period t; r ij And X ij The resistance and reactance of branch ij are respectively; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is open when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for time period t; i is sqr,ij,t Is the square of the current of branch ij during time t; m is a sufficiently large positive number. The formula (2) represents a node power balance equation, the formula (3) represents an adjacent node voltage relational expression, and the formula (4) represents a branch power definition expression relaxed by a second-order cone;
the wind-solar constraints are as follows:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ (6)
in the formula, P pw,t And Q pw,t Representing the actual active and reactive power of the wind and light in the t period, P pw,max,t Representing the wind-light output upper limit in the t period, and gamma representing the wind-light adjustable maximum power factor angle;
the energy storage constraints are as follows:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η (9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P, respectively ch,max And P dis,max Respectively, the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charge-discharge efficiency of the stored energy;
the network topology constraints are as follows:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variable, 1 represents that node i belongs to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The open-close state of the branch circuit ij is represented, the branch circuit is closed when the open-close state is 1, the branch circuit is opened when the open-close state is 0, and the number of the power distribution network islands is represented by | S |. Watch of the formula (10)Each node of the power distribution network only belongs to one island; the expression (11) indicates that when one line ij belongs to a certain island, nodes i and j at two ends of the line must also belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; the relation between the number of nodes of the power distribution network and the number of branches is expressed by the formula (13) to meet the radial requirement; bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
the operating constraints are as follows:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage of node i, U, during a period of t max 、U min Represents the upper and lower limit values of the node voltage, I ij,t Representing the current, I, of branch ij during time t max 、I min Representing the upper and lower branch current limits.
Further, the calculating the uncertainty variable deviation coefficient specifically includes:
determining a net load P as a difference value between the power demand of a user and the wind-solar output, wherein the fluctuation range of the net load P is as follows:
in the formula (I), the compound is shown in the specification,
the predicted value of the net load is represented, xi is the deviation coefficient of the net load, 0<ξ<1；
Determining deviation coefficients corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation coefficient is tau 1 、τ 2 、τ 3 And satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1 (19)
measuring the fluctuation range of each uncertain variable by using the maximum standard deviation of historical data, thereby determining the deviation coefficient corresponding to each uncertain variable;
in the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
represents the average of the historical values of the variable over a period T of 30 days, delta max The data of each 30 days of the variable are respectively taken as standard deviation, then the maximum value of the standard deviation is taken as the delta of each uncertain variable max The value is used as the basis for determining the deviation coefficient;
if the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively, a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
the combined formulas (18) and (19) are used for solving the deviation coefficient xi corresponding to wind power, photovoltaic and load wind 、ξ pv 、ξ load 。
Further, the construction of the uncertainty model considering wind, light and load fluctuation based on the information gap decision theory specifically comprises:
robust model:
in the formula, ξ represents an uncertainty parameterAmplitude of fluctuation of f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted values, f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 Degree of deviation of (a). Xi shape wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind power, photovoltaic and load, P wind 、P pv 、P load Respectively representing wind power, photovoltaic and load actual values, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load D) the function represents an islanding division power failure economic loss objective function when the wind, light and load uncertain variables are taken as fixed values; />
Opportunity model:
in the formula (f) r Representing the minimum blackout economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 The degree of deviation of (a).
Active power distribution network islanding system based on information gap decision theory, the system includes:
the prediction module is used for predicting wind power, photovoltaic output and load requirements of each node in an island power supply period by using historical data of a distributed power supply and loads of each node in a power distribution network to obtain the demand prediction values of the wind power, the photovoltaic and the loads;
the power distribution network island division model building module is used for building a power distribution network island division model by taking the minimum power failure economic loss of a power distribution network in an island power supply period as a target function;
the first solving module is used for inputting the demand predicted value into a power distribution network island division model for solving to obtain power failure loss based on a deterministic model;
the calculation construction module is used for calculating an uncertain variable to obtain a deviation coefficient and constructing an uncertainty model considering wind, light and load fluctuation based on an island division method of a power failure loss and information gap decision theory of a certainty model;
the determining module is used for selecting a decision strategy, setting a deviation factor and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and the second solving module is used for solving the robust model or the opportunity model and determining a final island division scheme.
Further, the building module includes: constructing a subunit;
the construction subunit is used for constructing an island division model of the power distribution network by taking network tide, wind and light, energy storage, network topology and operation as constraints and taking the minimum power failure economic loss of the power distribution network in the island power supply period as a target function;
the target function of the power distribution network islanding model is as follows:
in the formula, the first term represents the economic loss of the removed load, T is the total island power supply time interval, N is the load node set, and omega i Represents the economic loss of the node i in the power failure of unit load electricity quantity, P load,i Is the active load demand of node i, Y i,t The recovery state of the load of the node i in the period t is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit period; the second term represents the network loss of the distribution network, P loss Representing the total network loss of the power distribution network in the current time period;
the network flow constraints are as follows:
in the formula u (j) Is the set of all nodes upstream of node j; v. of (j) Is the set of all nodes downstream of node j; p is ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t period; p DER,j,t And Q DER,j,t Respectively obtaining total active power and total reactive power output by the distributed power supply at a node j in a time period t; p D,j,t And Q D,j,t Actual active and reactive power of the load at node j during the period t; r ij And X ij The resistance and reactance of branch ij are respectively; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is disconnected when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for time period t; I.C. A sqr,ij,t Is the square of the current of branch ij during period t; m is a sufficiently large positive number. The formula (2) represents a node power balance equation, the formula (3) represents an adjacent node voltage relational expression, and the formula (4) represents a branch power definitional expression relaxed by a second-order cone;
the wind-solar constraints are as follows:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ (6)
in the formula, P pw,t And Q pw,t Representing the actual active and reactive power of the wind and light in the t period, P pw,max,t Representing the wind-light output upper limit in the t period, and gamma representing the wind-light adjustable maximum power factor angle;
the energy storage constraints are as follows:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η (9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P ch,max And P dis,max Respectively, the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charge-discharge efficiency of the stored energy;
the network topology constraints are as follows:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variable, 1 represents that node i belongs to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The open-close state of the branch circuit ij is represented, the branch circuit is closed when the open-close state is 1, the branch circuit is opened when the open-close state is 0, and the number of the power distribution network islands is represented by | S |. The expression (10) shows that each node of the power distribution network only belongs to one island; watch of formula (11)When a line ij belongs to a certain island, nodes i and j at two ends of the line must belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; the relation between the number of nodes of the power distribution network and the number of branches is expressed by the formula (13) to meet the radial requirement; bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
the operating constraints are as follows:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage of node i, U, during a period of t max 、U min Represents the upper and lower limit values of the node voltage, I ij,t Representing the current, I, of branch ij during time t max 、I min Representing the upper and lower branch current limits.
Further, the calculation building module comprises: the calculating subunit is used for calculating deviation coefficients of wind, light and load uncertain variables, and specifically comprises:
determining a net load P as a difference value between the power demand of a user and the wind-solar output, wherein the fluctuation range of the net load P is as follows:
in the formula (I), the compound is shown in the specification,
the predicted value of the net load is represented, xi is the deviation coefficient of the net load, 0<ξ<1；
Determining deviation coefficients corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation coefficient is tau 1 、τ 2 、τ 3 And satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1 (19)
measuring the fluctuation range of each uncertain variable by using the maximum standard deviation of historical data, thereby determining the deviation coefficient corresponding to each uncertain variable;
in the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
represents the average of the historical values of the variable over a period T of 30 days, delta max Indicating that the standard deviation is taken for each hour of the variable for 30 days, then the maximum value of the standard deviation is taken, and the delta of each uncertain variable is used max The value is used as the basis for determining the deviation coefficient;
if the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively having a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
the combined formulas (18) and (19) are used for solving the deviation coefficient xi corresponding to wind power, photovoltaic and load wind 、ξ pv 、ξ load 。
Further, the calculation building module further includes: the first building unit and the second building unit are used for building an uncertainty model considering wind, light and load fluctuation, and specifically comprise the following steps:
the first construction unit is used for constructing a robust model:
where xi represents the fluctuation width of the uncertain parameter, f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted values, f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 Degree of deviation of (a). Xi wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind power, photovoltaic and load, P wind 、P pv 、P load Respectively representing wind power, photovoltaic and load actual values, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load D) the function represents an islanding division power failure economic loss objective function when the wind, light and load uncertain variables are taken as fixed values;
the second building unit is used for the opportunity model:
in the formula, f r Representing the minimum blackout economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 Degree of deviation of (a).
A computer storage medium having stored thereon computer executable instructions for performing the method of any one of the above when executed by a processor.
Compared with the prior art, the invention has the advantages that:
1. in the traditional island division scheme, uncertainty of a distributed power supply and load in a power distribution network is not considered, all parameters of a model are solved under a determined condition, and the obtained scheme is likely to be unrealized when the distributed power supply and the load fluctuate greatly, so that extra economic loss is generated.
2. Compared with a scene method, the method provided by the invention does not need to obtain the probability distribution of uncertain parameters and does not need a large amount of historical data as data support.
3. Compared with the traditional robust optimization scheme, the active power distribution network island division method based on the information gap decision theory can preset a target value, so that the final decision scheme is not worse than the preset minimum acceptable result in an uncertain variable fluctuation range. The over-conservative decision of the traditional robust optimization scheme is avoided, and meanwhile, the fluctuation of uncertain variables to a certain degree can be coped with.
Drawings
FIG. 1 is a schematic flow diagram of an active power distribution network islanding method based on an information gap decision theory;
FIG. 2 is a topological diagram of a test distribution network structure;
fig. 3 is a diagram of a power distribution network islanding result.
Detailed Description
The following describes embodiments of the present invention with reference to examples:
it should be noted that the structures, proportions, sizes, and other elements shown in the specification are included for the purpose of understanding and reading only, and are not intended to limit the scope of the invention, which is defined by the claims, and any modifications of the structures, changes in the proportions and adjustments of the sizes, without affecting the efficacy and attainment of the same.
In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are used for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms may be changed or adjusted without substantial change in the technical content.
Example 1:
the invention provides an active power distribution network island division method based on an information gap decision theory, which comprises the following specific steps of:
step 1: the method comprises the steps of constructing a power distribution network island division model by taking the minimum economic loss of power failure of a power distribution network in an island power supply period as an objective function and taking network power flow constraint, wind and light constraint, energy storage constraint, network topology constraint, operation constraint and the like as constraint conditions, wherein the model is a mixed integer second-order cone optimization model. Wind power, photovoltaic and load predicted values and distribution network structure data are brought into a model to be solved, and distribution network power failure economic loss f based on the wind power, photovoltaic and load predicted values is obtained 0 。
And 2, step: and constructing an uncertainty model considering wind, light and load fluctuation based on an information gap decision theory. The robust model and the opportunity model can be classified according to different risk management strategies. The robust model corresponds to a risk avoidance strategy, and the opportunity model corresponds to a risk preference strategy.
And 3, step 3: and (4) selecting a decision strategy by a decision maker, setting a deviation factor, and determining the bearable maximum power failure economic loss (a robust model) or the pursued minimum power failure economic loss (an opportunity model).
And 4, step 4: solving the robust/opportunity model, and determining an island division scheme which comprises each island division range, a node load recovery state, a branch switch state, the output condition of the distributed power supply in each time period and the like.
in the formula, a first term of the formula represents the economic loss of the load removal, T is the total power supply time interval of the island, N is a load node set, and omega represents the total power supply time interval of the island i Represents the economic loss of the node i in unit load electricity power failure, P load,i Is the active load demand of node i, Y i,t The recovery state of the load of the node i in the period t is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit period; the second term represents the network loss of the distribution network, P loss And the total network loss of the power distribution network in the current time period is represented.
and (3) network power flow constraint:
in the formula u (j) Is the set of all nodes upstream of node j; v. of (j) Is the set of all nodes downstream of the node j; p ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t period; p DER,j,t And Q DER,j,t Respectively obtaining total active power and total reactive power output by the distributed power supply at a node j in a time period t; p is D,j,t And Q D,j,t Actual active and reactive power of the load at node j during the period t; r ij And X ij The resistance and reactance of branch ij are respectively; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is disconnected when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for time period t; i is sqr,ij,t Is the square of the current of branch ij during period t; m is a sufficiently large positive number. Equation (2) represents a node power balance equation, equation (3) represents an adjacent node voltage relation, and equation (4) represents a branch power definitional equation relaxed by a second-order cone.
Wind and light restraint:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ (6)
in the formula, P pw,t And Q pw,t Representing t-period wind-light actual active and reactive power, P pw,max,t And the upper limit of wind-light output in the t period is shown, and gamma represents the angle of the wind-light adjustable maximum power factor.
Energy storage restraint:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η (9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P, respectively ch,max And P dis,max Respectively, the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charge-discharge efficiency of the stored energy.
And (3) network topology constraint:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variables, 1 representing node i belonging to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The opening and closing states of the branch circuit ij are represented, the branch circuit is closed when the number of the branch circuits is 1, the branch circuit is opened when the number of the branch circuits is 0, and the number of the power distribution network islands is | S |. The formula (10) shows that each node of the power distribution network only belongs to one island; the expression (11) indicates that when one line ij belongs to a certain island, nodes i and j at two ends of the line must belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; and the formula (13) shows that the relation between the number of the nodes of the power distribution network and the number of the branches meets the radial requirement. Bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
and (4) operation constraint:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage of node i, U, during a period of t max 、U min Represents the upper and lower limit values of the node voltage, I ij,t Representing the branch ij current, I, during the period t max 、I min Representing the upper and lower branch current limits.
And 2, constructing an island division model based on an information gap decision theory, wherein uncertain variables in the model comprise wind power, photovoltaic output and load. Defining the net load P as the difference value between the electricity demand of the user and the wind-solar output, and setting the fluctuation range of the net load P as follows:
in the formula (I), the compound is shown in the specification,
representing the predicted value of the payload, ξ being the coefficient of variation of the payload, 0<ξ<1。
The invention defines the deviation coefficient corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation factor is tau 1 、τ 2 、τ 3 And satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1 (19)
according to the invention, through historical data, the fluctuation range of each uncertain variable is measured by utilizing the maximum standard deviation of each item of data, so that the deviation coefficient corresponding to each uncertain variable is determined.
In the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
represents the average of the historical values of the variable over a period T of 30 days, delta max The data of each 30-day variable is expressed by taking the standard deviation of the data and then taking the maximum value of the standard deviation. By δ of each uncertain variable max The value is used as the basis for determining the deviation coefficient.
If the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively, a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
wind power, photovoltaic and load pairs can be obtained by combining the formulas (18) and (19)Corresponding deviation coefficient xi wind 、ξ pv 、ξ load 。
robust model:
where xi represents the fluctuation width of the uncertain parameter, f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted values, f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 Degree of deviation of (a). Xi wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind power, photovoltaic and load, P wind 、P pv 、P load Respectively representing wind power, photovoltaic and load actual values, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load And d) the function represents an islanding power failure economic loss objective function when uncertain variables such as wind, light, load and the like are taken as fixed values.
An opportunity model:
in the formula (f) r Representing the minimum blackout economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 Degree of deviation of (a).
Example 2:
in the embodiment, a simulation test is performed on a distribution network 69 node system of the company PG & E in the united states as a test example, and the system structure is shown in fig. 2. Distributed power supplies exist at the nodes 5, 19, 36 and 62, the power supplies of the nodes 5 and 36 are wind storage combined systems, the power supplies of the nodes 19 and 62 are light storage combined systems, and each power supply has certain voltage and frequency regulation capacity and can serve as an island main power supply. The distributed power supply and the energy storage parameters in the power distribution network are shown in table 1.
TABLE 1 wind and solar Power Generation parameters
| Type of power supply | Maximum active power/kW | Maximum reactive power/kvar | Minimum power factor |
| Wind power generation | 500 | / | 0.9 |
| Photovoltaic system | 400 | / | 0.9 |
TABLE 2 energy storage System parameters
| Maximum charge/discharge power/kW | Energy storage capacity/kWh | Minimum/maximum SOC/pu | Charge and discharge efficiency |
| 500 | 1000 | 0.05/0.95 | 0.95 |
Setting a scene:
when the power distribution network is disconnected with the main network and the power supply of the main network is lost, the distributed power supply in the power distribution network is utilized to carry out island power supply in short time, and the island power supply time length is set to be 6h. Considering the coordination and coordination problem of different distributed power supply control strategies, the number of the pre-divided islands is set to be 4, 4 distributed power supplies are respectively used as main power supplies for each island, and island fusion situations are not considered.
Wind-solar output and load predicted values are brought into the island model constructed in the step 1 to perform island division under the deterministic condition, the division result is shown in fig. 3, and the economic loss of power failure caused by island power supply under the deterministic condition is 13879
An island division method based on an information gap decision theory comprises the following steps:
firstly, the maximum standard variance of wind power, photovoltaic and load is calculated according to historical data, and the corresponding deviation coefficient xi is determined wind 、ξ pv 、ξ load . In this example, xi is calculated wind ＝0.48ξ、ξ pv ＝0.42ξ、ξ load ＝0.1ξ。
Robust model: setting a robust deviation factor
And if the maximum acceptable power failure economic loss is 0.2, 13879 x (1 + 0.2) =16654.8, the maximum acceptable power failure economic loss is introduced into a robust model to be solved, and xi =0.3226 is obtained, namely, the maximum fluctuation range of the bearable net load of the power distribution network is 32.26%.
Opportunity model: setting an opportunity bias factor
And when the minimum power failure economic loss is 0.2, 13879 x (1-0.2) =11103.2 is pursued, the minimum power failure economic loss is put into an opportunity model to be solved, and xi =0.433 is obtained, namely when the net load of the power distribution network changes towards a favorable direction, the minimum fluctuation range is 43.3%.
In order to embody the superiority of the active power distribution network island division method based on the information gap decision theory, the traditional robust method is adopted to carry out island division comparison on the power distribution network, according to the new energy prediction error requirement standard in China, the actual wind power and photovoltaic output fluctuation deviation is assumed to be 15% of the predicted value, the load fluctuation range is 5%, the island division scheme is solved in the worst scene, and the power failure economic loss result is shown in table 3.
TABLE 3
As can be seen from table 3, the active power distribution network islanding method based on the information gap decision theory provided by the invention can select the power failure economic loss in the bearable range by setting the robust/opportunity deviation factor, obtain the response intervals of the uncertain variables such as wind, light, load and the like, more accurately depict the fluctuation range of the uncertain variables, and avoid the disadvantage of over-conservative traditional robust optimization schemes, thereby providing a decision basis for the scheduling personnel.
Example 3:
in order to better implement the above method, the present embodiment provides an active power distribution network islanding system based on an information gap decision theory;
for example, an active power distribution network islanding system based on an information gap decision theory, the system includes:
the prediction module is used for predicting wind power, photovoltaic output and load requirements of each node in an island power supply period by using historical data of a distributed power supply and loads of each node in a power distribution network to obtain the demand prediction values of the wind power, the photovoltaic and the loads;
the power distribution network island division model building module is used for building a power distribution network island division model by taking the minimum power failure economic loss of a power distribution network in an island power supply period as a target function;
the first solving module is used for inputting the demand predicted value into a power distribution network islanding model for solving to obtain the power failure loss based on a deterministic model;
the calculation construction module is used for calculating an uncertain variable to obtain a deviation coefficient and constructing an uncertainty model considering wind, light and load fluctuation based on an island division method of a power failure loss and information gap decision theory of a certainty model;
the determining module is used for selecting a decision strategy, setting a deviation factor and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and the second solving module is used for solving the robust model or the opportunity model and determining a final island division scheme.
Further, the building module includes: constructing a subunit;
the construction subunit is used for constructing an island division model of the power distribution network by taking network tide, wind and light, energy storage, network topology and operation as constraints and taking the minimum power failure economic loss of the power distribution network in the island power supply period as a target function;
the target function of the power distribution network islanding model is as follows:
in the formula, the first term represents the economic loss of the load being cut off, and T is used for island supplyTotal time of electricity, N is the set of load nodes, ω i Represents the economic loss of the node i in unit load electricity power failure, P load,i Is the active load demand of node i, Y i,t The recovery state of the load of the node i in the period t is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit period; the second term represents the network loss of the distribution network, P loss Representing the total network loss of the power distribution network in the current time period;
the network flow constraints are as follows:
in the formula u (j) Is the set of all nodes upstream of node j; v. of (j) Is the set of all nodes downstream of node j; p ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t period; p DER,j,t And Q DER,j,t Respectively obtaining total active power and total reactive power output by the distributed power supply at a node j in a time period t; p D,j,t And Q D,j,t Actual active and reactive power of the load at node j during the period t; r ij And X ij The resistance and reactance of branch ij are respectively; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is open when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for the period t; i is sqr,ij,t Is the square of the current of branch ij during period t; m is a sufficiently large positive number. The formula (2) represents a node power balance equation, the formula (3) represents an adjacent node voltage relational expression, and the formula (4) represents a branch power definitional expression relaxed by a second-order cone;
the wind-solar constraints are as follows:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ (6)
in the formula, P pw,t And Q pw,t Representing the actual active and reactive power of the wind and light in the t period, P pw,max,t Representing the wind-light output upper limit in the t period, and gamma representing the wind-light adjustable maximum power factor angle;
the energy storage constraints are as follows:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η (9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P, respectively ch,max And P dis,max Respectively, the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charge-discharge efficiency of the stored energy;
the network topology constraints are as follows:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variables, 1 representing node i belonging to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The opening and closing states of the branch circuit ij are represented, the branch circuit is closed when the number of the branch circuits is 1, the branch circuit is opened when the number of the branch circuits is 0, and the number of the power distribution network islands is | S |. The expression (10) shows that each node of the power distribution network only belongs to one island; the expression (11) indicates that when one line ij belongs to a certain island, nodes i and j at two ends of the line must also belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; the relation between the number of nodes of the power distribution network and the number of branches is expressed by the formula (13) to meet the radial requirement; bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
the operating constraints are as follows:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage of node i, U, during a period of t max 、U min Represents the upper and lower limit values of the node voltage, I ij,t Representing the current, I, of branch ij during time t max 、I min Representing the upper and lower branch current limits.
Further, the calculation building module comprises: the calculating subunit is used for calculating deviation coefficients of wind, light and load uncertain variables, and specifically comprises:
determining a net load P as a difference value between the power demand of a user and the wind-solar output, wherein the fluctuation range of the net load P is as follows:
in the formula (I), the compound is shown in the specification,
representing the predicted value of the payload, ξ being the coefficient of variation of the payload, 0<ξ<1；
Determining deviation coefficients corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation coefficient is tau 1 、τ 2 、τ 3 And satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1 (19)
measuring the fluctuation range of each uncertain variable by using the maximum standard variance of the historical data so as to determine the deviation coefficient corresponding to each uncertain variable;
in the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
represents the average of the historical values of the variable over a period T of 30 days, delta max The data of each 30 days of the variable are respectively taken as standard deviation, then the maximum value of the standard deviation is taken as the delta of each uncertain variable max The value is used as the basis for determining the deviation coefficient;
if the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively having a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
the combined formulas (18) and (19) are used for solving the deviation coefficient xi corresponding to wind power, photovoltaic and load wind 、ξ pv 、ξ load 。
Further, the calculation building module further includes: the first building unit and the second building unit are used for building an uncertainty model considering wind, light and load fluctuation, and specifically comprise the following steps:
the first construction unit is used for constructing a robust model:
where xi represents the fluctuation width of the uncertain parameter, f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted value f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 The degree of deviation of (a). Xi shape wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind power, photovoltaic and load, P wind 、P pv 、P load Respectively representing wind power, photovoltaic and load actual values, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load D) the function represents an islanding division power failure economic loss objective function when the wind, light and load uncertain variables are taken as fixed values;
the second construction unit is used for the opportunity model:
in the formula (f) r Representing the minimum blackout economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 Degree of deviation of (a).
Example 4:
it will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present invention provides a storage medium, where a plurality of instructions are stored, where the instructions can be loaded by a processor to execute steps in any active power distribution network islanding method based on an information gap decision theory provided in an embodiment of the present invention.
For example, the instructions may perform the steps of:
an active power distribution network island division method based on an information gap decision theory comprises the following steps:
forecasting wind power, photovoltaic output and load requirements of each node in an island power supply period by using historical data of a distributed power supply and loads of each node in a power distribution network to obtain the demand forecasting values of the wind power, the photovoltaic and the loads;
constructing a power distribution network island division model by taking the minimum power failure economic loss of a power distribution network in an island power supply period as a target function;
inputting the demand predicted value into a power distribution network islanding model for solving to obtain power failure loss based on a deterministic model;
an island dividing method based on the power failure loss and information gap decision theory of a deterministic model calculates an uncertain variable to obtain a deviation coefficient, and constructs an uncertain model considering wind, light and load fluctuation;
selecting a decision strategy, setting a deviation factor, and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and solving the robust model or the opportunity model, and determining a final island division scheme.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Many other changes and modifications can be made without departing from the spirit and scope of the invention. It is to be understood that the invention is not to be limited to the specific embodiments, but only by the scope of the appended claims.
Claims (10)
1. An active power distribution network island division method based on an information gap decision theory is characterized by comprising the following steps:
forecasting wind power, photovoltaic output and load demands of each node in an island power supply period by using a distributed power supply in a power distribution network and historical data of loads of each node to obtain demand forecasting values of the wind power, the photovoltaic and the loads;
constructing a power distribution network island division model by taking the minimum economic loss of power failure of a power distribution network in the island power supply period as a target function;
inputting the demand predicted value into a power distribution network island division model for solving to obtain power failure loss based on a deterministic model;
an island dividing method based on the power failure loss and information gap decision theory of a deterministic model calculates the deviation coefficient of an uncertain variable, and constructs an uncertain model considering wind, light and load fluctuation;
selecting a decision strategy, setting a deviation factor, and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and solving the robust model or the opportunity model, and determining a final island division scheme.
2. The active power distribution network islanding method based on the information gap decision theory as claimed in claim 1, wherein the final islanding scheme includes: each island is divided into a range, a node load recovery state, a branch switch state and the output condition of each time period of the distributed power supply.
3. The active power distribution network islanding method based on the information gap decision theory as claimed in claim 1, wherein the objective function of the power distribution network islanding model is as follows:
in the formula, the first term represents the economic loss of the removed load, T is the total island power supply time interval, N is the load node set, and omega i Represents the economic loss of the node i in unit load electricity power failure, P load,i For the active load demand of node i, Y i,t The recovery state of the load of the node i in the t time interval is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit time interval; the second term represents the network loss of the distribution network, P loss And the total network loss of the power distribution network in the current time period is represented.
4. The active power distribution network islanding method based on the information gap decision theory as claimed in claim 1, wherein the constraint conditions of the power distribution network islanding model include: network power flow constraint, wind-solar constraint, energy storage constraint, network topology constraint and operation constraint;
the network flow constraints are as follows:
in the formula u (j) Is the set of all nodes upstream of node j; v. of (j) Is the set of all nodes downstream of node j; p ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t period; p is DER,j,t And Q DER,j,t Respectively obtaining total active power and total reactive power output by the distributed power supply at a node j in a time period t; p is D,j,t And Q D,j,t Actual active and reactive power of the load at node j in the period t; r ij And X ij Resistance and electricity of branch ij respectivelyResisting; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is open when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for the period t; I.C. A sqr,ij,t Is the square of the current of branch ij during period t; m is a sufficiently large positive number. The formula (2) represents a node power balance equation, the formula (3) represents an adjacent node voltage relational expression, and the formula (4) represents a branch power definitional expression relaxed by a second-order cone;
the wind-solar constraints are as follows:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ(6)
in the formula, P pw,t And Q pw,t Representing t-period wind-light actual active and reactive power, P pw,max,t Representing the wind-light output upper limit in the t period, and gamma representing the wind-light adjustable maximum power factor angle;
the energy storage constraints are as follows:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η(9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P ch,max And P dis,max Respectively the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charge-discharge efficiency of the stored energy;
the network topology constraints are as follows:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variables, 1 representing node i belonging to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The opening and closing states of the branch circuit ij are represented, the branch circuit is closed when the number of the branch circuits is 1, the branch circuit is opened when the number of the branch circuits is 0, and the number of the power distribution network islands is | S |. The expression (10) shows that each node of the power distribution network only belongs to one island; the expression (11) indicates that when one line ij belongs to a certain island, nodes i and j at two ends of the line must also belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; the relation between the number of nodes of the power distribution network and the number of branches is expressed by the formula (13) to meet the radial requirement; bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
the operating constraints are as follows:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage of node i, U, during a period of t max 、U min Display sectionUpper and lower limit values of point voltage, I ij,t Representing the current, I, of branch ij during time t max 、I min Representing the upper and lower branch current limits.
5. The active power distribution network islanding method based on the information gap decision theory, as claimed in claim 1, wherein the calculating an uncertainty variable deviation coefficient specifically includes:
determining a net load P as a difference value between the power demand of a user and the wind-solar output, wherein the fluctuation range of the net load P is as follows:
in the formula (I), the compound is shown in the specification,
representing the predicted value of the payload, ξ being the coefficient of variation of the payload, 0<ξ<1；
Determining deviation coefficients corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation coefficient is tau 1 、τ 2 、τ 3 Satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1(19)
measuring the fluctuation range of each uncertain variable by using the maximum standard variance of the historical data so as to determine the deviation coefficient corresponding to each uncertain variable;
in the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
represents the average of the historical values of the variable over a period T of 30 days, delta max Indicating that the standard deviation is taken for each hour of the variable for 30 days, then the maximum value of the standard deviation is taken, and the delta of each uncertain variable is used max The value is used as the basis for determining the deviation coefficient;
if the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively, a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
the combined formulas (18) and (19) are used for solving the deviation coefficient xi corresponding to wind power, photovoltaic and load wind 、ξ pv 、ξ load 。
6. The active power distribution network island division method based on the information gap decision theory according to claim 1, wherein the uncertainty model considering wind, light and load fluctuation is constructed based on the information gap decision theory, and specifically comprises the following steps:
robust model:
where xi represents the fluctuation width of the uncertain parameter, f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted value f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 The degree of deviation of (a). Xi shape wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind electricity, photovoltaic and load, P wind 、P pv 、P load Respectively representing windActual value of electricity, photovoltaic, load, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load D) the function represents an islanding division power failure economic loss objective function when the wind, light and load uncertain variables are taken as fixed values;
opportunity model:
in the formula (f) r Representing the minimum blackout economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 The degree of deviation of (a).
7. Active power distribution network islanding system based on information gap decision theory, its characterized in that, the system includes:
the prediction module is used for predicting wind power, photovoltaic output and load requirements of each node in an island power supply period by using historical data of a distributed power supply and loads of each node in a power distribution network to obtain the demand prediction values of the wind power, the photovoltaic and the loads;
the power distribution network island division model is constructed by taking the minimum economic loss of power failure of the power distribution network in the island power supply period as a target function;
the first solving module is used for inputting the demand predicted value into a power distribution network island division model for solving to obtain power failure loss based on a deterministic model;
the calculation construction module is used for calculating an uncertain variable to obtain a deviation coefficient and constructing an uncertainty model considering wind, light and load fluctuation based on an island division method of a power failure loss and information gap decision theory of a certainty model;
the determining module is used for selecting a decision strategy, setting a deviation factor and determining an uncertainty model considering wind, light and load fluctuation as a robust model or an opportunity model;
and the second solving module is used for solving the robust model or the opportunity model and determining a final island division scheme.
8. The active power distribution network islanding system based on the information gap decision theory as claimed in claim 7, wherein the building module comprises: constructing a subunit;
the construction subunit is used for constructing an island division model of the power distribution network by taking network tide, wind and light, energy storage, network topology and operation as constraints and taking the minimum power failure economic loss of the power distribution network in the island power supply period as a target function;
the target function of the power distribution network islanding model is as follows:
in the formula, the first term represents the economic loss of the removed load, T is the total island power supply time interval, N is the load node set, and omega i Represents the economic loss of the node i in the power failure of unit load electricity quantity, P load,i Is the active load demand of node i, Y i,t The recovery state of the load of the node i in the t time interval is represented, the value of the load recovery power supply is 1, otherwise, the value is 0, and delta t represents the unit time interval; the second term represents the network loss of the distribution network, P loss Representing the total network loss of the power distribution network in the current time period;
the network flow constraints are as follows:
in the formula u (j) Is the set of all nodes upstream of the node j; v. of (j) Is the set of all nodes downstream of node j; p ij,t And Q ij,t Respectively the active power and the reactive power of the branch ij in the t period; p DER,j,t And Q DER,j,t Respectively obtaining total active power and total reactive power output by the distributed power supply at a node j in a time period t; p D,j,t And Q D,j,t Actual active and reactive power of the load at node j during the period t; r ij And X ij The resistance and reactance of branch ij are respectively; alpha is alpha ij The open-close state of the branch ij is represented, the branch is closed when the open-close state is 1, and the branch is open when the open-close state is 0; u shape sqr,i,t Is the square of the voltage at node i for time period t; i is sqr,ij,t Is the square of the current of branch ij during period t; m is a sufficiently large positive number. The formula (2) represents a node power balance equation, the formula (3) represents an adjacent node voltage relational expression, and the formula (4) represents a branch power definitional expression relaxed by a second-order cone;
the wind-solar constraints are as follows:
0≤P pw,t ≤P pw,max,t (5)
-P pw,max,t tanγ≤Q pw,t ≤P pw,max,t tanγ(6)
in the formula, P pw,t And Q pw,t Representing t-period wind-light actual active and reactive power, P pw,max,t Representing the wind-light output upper limit in the t period, and gamma representing the wind-light adjustable maximum power factor angle;
the energy storage constraints are as follows:
SOC min ≤SOC t ≤SOC max (8)
SOC t+1 ＝SOC t +ηP ch (t)Δt-P dis (t)Δt/η(9)
in the formula, P ch (t) and P dis (t) stored energy charging/discharging power, P ch,max And P dis,max Respectively, the maximum charge/discharge power of the stored energy, s ch,t And s dis,t Respectively, energy storage charging/discharging marks, wherein 1 represents that the energy storage is in a charging/discharging state, SOC t 、SOC min 、SOC max The current, minimum and maximum charge quantities of the stored energy are respectively, and eta is the charging and discharging efficiency of the stored energy;
the network topology constraints are as follows:
in the formula, S represents a power distribution network island set, E represents a power distribution network branch set, and N represents the total number of nodes of the power distribution network; c. C is Representing node islanding variable, 1 represents that node i belongs to islanding s, l s ij Representing branch islanding variables, 1 representing branch ij belonging to islanding s, alpha ij The opening and closing states of the branch circuit ij are represented, the branch circuit is closed when the number of the branch circuits is 1, the branch circuit is opened when the number of the branch circuits is 0, and the number of the power distribution network islands is | S |. The formula (10) shows that each node of the power distribution network only belongs to one island; the expression (11) indicates that when one line ij belongs to a certain island, nodes i and j at two ends of the line must also belong to the island at the same time; equation (12) indicates that when the line ij does not belong to any island, the line ij is in an open state; the number of nodes and branches of the distribution network are represented by the formula (13)The quantitative relation of (a) and (b) meets the radial requirement; bilinear terms exist in the formula (11), and are linearly transformed into the formula (14):
the operating constraints are as follows:
U min ≤U i,t ≤U max (15)
I min ≤I ij,t ≤I max (16)
in the formula of U i,t Representing the voltage at node i, U, during the period t max 、U min Represents the upper and lower limit values of the node voltage, I ij,t Representing the current, I, of branch ij during time t max 、I min Representing the upper and lower branch current limits.
9. The active power distribution network islanding system based on the information gap decision theory as claimed in claim 7, wherein the calculation building module comprises: the calculating subunit is used for calculating deviation coefficients of wind, light and load uncertain variables, and specifically comprises:
determining a net load P as a difference value between the power demand of a user and the wind-solar output, wherein the fluctuation range of the net load P is as follows:
in the formula (I), the compound is shown in the specification,
the predicted value of the net load is represented, xi is the deviation coefficient of the net load, 0<ξ<1；
Determining deviation coefficients corresponding to wind power, photovoltaic and load as xi wind 、ξ pv 、ξ load The weight corresponding to the deviation coefficient is tau 1 、τ 2 、τ 3 And satisfies the following conditions:
τ 1 +τ 2 +τ 3 ＝1(19)
measuring the fluctuation range of each uncertain variable by using the maximum standard deviation of historical data, thereby determining the deviation coefficient corresponding to each uncertain variable;
in the formula, x i,T Representing the historical value of the variable at the ith day during the T period,
means, δ, representing the average of the historical values of the variable over a period T of 30 days max The data of each 30 days of the variable are respectively taken as standard deviation, then the maximum value of the standard deviation is taken as the delta of each uncertain variable max The value is used as the basis for determining the deviation coefficient;
if the delta of wind power, photovoltaic and load in the distribution network is obtained max Respectively having a value of delta 1 、δ 2 、δ 3 Then, there are:
τ 1 :τ 2 :τ 3 ＝δ 1 :δ 2 :δ 3 (21)
the combined formulas (18) and (19) are used for obtaining deviation coefficients xi corresponding to wind power, photovoltaic and load wind 、ξ pv 、ξ load 。
10. The active power distribution network islanding system based on the information gap decision theory as claimed in claim 9, wherein the calculation building module further includes: the first building unit and the second building unit are used for building an uncertainty model considering wind, light and load fluctuation, and specifically comprise the following steps:
the first construction unit is used for constructing a robust model:
in the formula, xi represents the fluctuation range of the uncertain parameter, f 0 Representing the economic loss of power failure caused by island division according to the wind-solar output and load predicted value f c Representing the maximum outage economic loss that the decision maker can accept,
is a deviation factor, representing f c And f 0 Degree of deviation of (a). Xi shape wind 、ξ pv 、ξ load Respectively the deviation coefficients of wind power, photovoltaic and load, P wind 、P pv 、P load Respectively representing wind power, photovoltaic and load actual values, P 0 wind 、P 0 pv 、P 0 load Is a predicted value. d represents the decision variable in the model, f (P) wind ，P pv ，P load D) the function represents an islanding division power failure economic loss objective function when the wind, light and load uncertain variables are taken as fixed values;
the second building unit is used for the opportunity model:
in the formula (f) r Representing the minimum outage economic loss sought by the decision maker,
is a deviation factor, representing f r And f 0 Degree of deviation of (a). />
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| CN116937629A (en) * | 2023-07-21 | 2023-10-24 | 国网黑龙江省电力有限公司电力科学研究院 | Information decision theory-based random optimal scheduling method for multi-transformer micro-grid |
| CN117277392A (en) * | 2023-11-22 | 2023-12-22 | 国网山西省电力公司经济技术研究院 | Emergency resource optimal configuration method for elastic lifting of power distribution system |
| CN117767305A (en) * | 2023-12-29 | 2024-03-26 | 四川大学 | Power distribution network protection method considering demand response and dynamic reconstruction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116937629A (en) * | 2023-07-21 | 2023-10-24 | 国网黑龙江省电力有限公司电力科学研究院 | Information decision theory-based random optimal scheduling method for multi-transformer micro-grid |
| CN117277392A (en) * | 2023-11-22 | 2023-12-22 | 国网山西省电力公司经济技术研究院 | Emergency resource optimal configuration method for elastic lifting of power distribution system |
| CN117277392B (en) * | 2023-11-22 | 2024-04-09 | 国网山西省电力公司经济技术研究院 | Emergency resource optimal configuration method for elastic lifting of power distribution system |
| CN117767305A (en) * | 2023-12-29 | 2024-03-26 | 四川大学 | Power distribution network protection method considering demand response and dynamic reconstruction |
| CN117767305B (en) * | 2023-12-29 | 2024-06-11 | 四川大学 | Power distribution network protection method considering demand response and dynamic reconstruction |
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