CN107317361B - active power distribution network global optimization scheduling method considering regional autonomous capacity - Google Patents
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Abstract
the invention belongs to the technical field of optimal scheduling of an electric power system, and particularly relates to an active power distribution network global optimal scheduling method considering regional autonomous capacity, which comprises the following 4 steps of 1: establishing a global optimization scheduling model by taking the minimum total cost as a target; step 2: setting the regional tie line power obtained through global optimization as a base value, and establishing a regional autonomous model by taking the minimum cost of the regional tie line power variation as a target when wind power and photovoltaic fluctuate; and step 3: generating a Lagrange function aiming at the regional autonomous model, and solving a partial derivative of the power variation of the tie line to obtain a measurement index of autonomous capacity based on price; and 4, step 4: and iteratively updating the regional tie line power and autonomy capacity measurement indexes until the regional tie line power is updated and does not change any more, so as to obtain the optimal scheduling scheme of the global optimization of the active power distribution network considering regional autonomy capacity.
Description
Technical Field
the invention relates to the technical field of optimal scheduling of an electric power system, in particular to a global optimal scheduling method of an active power distribution network considering regional autonomous capacity.
background
with the continuous development of power systems, a large number of Distributed Generation (Distributed Generation DG) mainly using photovoltaic and wind power are connected to a power Distribution network to form an Active Distribution Grid (ADG). While economic and environmental benefits are brought, the inherent uncertainty of the distributed power supply also brings great challenges to the optimized operation of the active power distribution network.
When a large number of distributed power sources are accessed, the traditional optimized operation of the whole level of the power distribution network can cause the increase of network communication pressure, the solving process is complex, and the speed is difficult to meet the real-time requirement, so that the current active power distribution network mostly adopts a scheduling method of global optimization and regional autonomous hierarchical partitioning, a day-ahead plan is made at the global level, and real-time autonomous adjustment is carried out in a region.
for an active power distribution network comprising a plurality of areas, when global optimization is carried out, the optimization coordination targets mainly comprise: the total cost of the power distribution network is minimum, the network loss is minimum, the utilization rate of renewable energy sources is maximum, and the like. In actual operation, different optimization targets are selected according to different requirements. Most of the targets are the key points of traditional power distribution network scheduling concerns, but for an active power distribution network, in addition to the concerns, the influence caused by renewable energy uncertainty is more important to consider.
most of the existing documents use methods such as multi-scene and robust optimization to process the uncertainty of renewable energy sources, global optimization is performed after equivalence, but the influence of fluctuation of the renewable energy sources on the areas is not considered, and hierarchical partition scheduling is performed only in the areas after a day-ahead plan is made, if the autonomous capacity of some areas is low, the fluctuation of wind power and photovoltaic can cause the optimal target to deviate greatly from the optimal target, autonomy is difficult to realize, a conservative or impersonable decision scheme is obtained, the meaning of hierarchical partition is lost, and finally the economic safety of the whole power distribution network is threatened.
For the optimal scheduling of the active power distribution network, a modeling and solving method for the global optimal scheduling of the active power distribution network considering the regional autonomous ability is lacked at present.
Disclosure of Invention
aiming at the problems, the invention provides a global optimization scheduling method of an active power distribution network, which considers the regional autonomy.
the method comprises 4 steps:
Step 1: taking the minimum total cost as a target, considering distributed power supply, energy storage and network security constraints, establishing a global optimization scheduling model, and obtaining the power distribution of each regional tie line, wherein the total cost comprises the change cost of the tie line power representing regional autonomous capability besides the electricity purchase cost, the cost of a micro gas turbine and the energy storage cost, so as to adjust the power of the regional tie lines and coordinate the regional autonomous capability;
Step 2: according to the regional tie line power obtained in the step 1, establishing a regional autonomy model by taking the minimum cost of the regional tie line power variation when the renewable energy source generates fluctuation as a target;
And step 3: generating a Lagrange function for solving aiming at the regional autonomous model in the step 2, and solving a partial derivative of the regional tie line power variation to obtain an autonomous ability measurement index;
And 4, step 4: and iteratively updating the regional tie line power and autonomy capacity measurement indexes in the global optimization scheduling mathematical model and the regional autonomy model of the active power distribution network until the regional tie line power is not changed any more, and obtaining the global optimization optimal scheduling scheme of the active power distribution network considering the regional autonomy capacity.
The overall optimization scheduling mathematical model of the active power distribution network in the step 1 is as follows:
An objective function:
in the formula:respectively the electricity price for purchasing the electricity by the superior electric network, the unit power electricity price of the micro gas turbine and the unit power electricity price of the stored energy,respectively injecting active power, micro gas turbine power, energy storage discharge power and energy storage charging power beta into the superior power grid at a node j at the time tl,tthe cost per unit change in power of the regional links, Δ P, representing region ll,tRepresents the power variation of the local tie line, T represents the time period, Ngridrepresenting the number of connections to the superordinate grid, NMTrepresenting the number of micro gas turbines, NBSrepresenting the number of energy storage battery groups, and L representing the number of areas;
constraint conditions are as follows:
a) Line active power balance constraint:
In the formula: pij,t、Iij,t、rijThe active power, the current and the resistance of a line from the node i to the node j at the time t respectively; pjk,tThe active power of other lines connected with the node j at the moment t; pj,m,tThe renewable energy source network access power at the node j at the time t;the active load of the node j at the moment t is shown, the node k is a tail end node of a line connected with the node j, and delta (j) is a tail end node set of the line connected with the node j; the node i is a head end node of a line connected with the node j, and pi (j) is a head end node set of the line connected with the node j;
Area link power P for area ll,tthen modify it toFor regional tie line power reference value, Δ P, at regional autonomyl,tIs the amount of change in power of the local tie line, toAs the area link power of each area,
b) Line reactive power balance constraint:
in the formula: qij,t、xijThe reactive power and the line reactance of the line from the node i to the node j at the time t respectively; qjk,tthe reactive power of other lines connected by the node j at the time t; vj,tThe voltage amplitude of the node j at time t;injecting reactive power into a superior power grid at a node j at the time t;Reactive load at time t for node j，bjIs susceptance at node j, Iij,tAt the time t, the line from the node i to the node j has current;
c) Voltage constraint:
d) line tide:
e) upper level grid injection power constraint
in the formula:Respectively the minimum value of injected active power, the maximum value of active power, the minimum value of reactive power, the maximum value of reactive power, Vi.tthe voltage amplitude of the node i at time t;
f) Micro gas turbine constraint
in the formula:Is the maximum power output of the micro gas turbine,respectively is the minimum value of the climbing constraint and the maximum value of the climbing constraint,
g) Restraint of stored energy
In the formula:Are respectively the switch variable of the energy storage,in order to store the capacity of the energy at time t, And C0Maximum, minimum and initial values of the energy storage capacity, eta, respectivelyjin order to achieve a high charging efficiency,The maximum value of the stored energy discharging power and the charging power is obtained;
h) Photovoltaic, wind power constraints
in the formula:for the predicted value of the power generated by the renewable energy source at the moment t,
i) Network security constraints
In the formula: vj,maxAnd Vj,minRespectively, the maximum value of the voltage and the minimum value of the voltage, Iij,maxIs the maximum value of the current.
And (3) performing convex relaxation treatment on the line power flow equation (5) to treat model nonlinearity:
Introducing intermediate variablesAndeliminating the original square term, equation (2) is equivalent to:
The equation (3) is equivalent as follows:
The equation (4) is equivalent as follows:
equation (5) is transformed to a standard second order cone:
The safety constraint (10) is equivalent as follows:
the regional autonomous model in said step 2 is as follows,
an objective function:
Constraint conditions are as follows:
similar to the formulas (6) - (8), (10) - (17), the injection work of the region is adjustedrate of changeModified into modified intowherein,regional tie power, Δ P, for global optimizationl,t、ΔQl,tIs the amount of change in regional tie line power, Cl,tThe area link power unit change cost represents area i.
in the step 3, a lagrangian function is generated for the regional autonomous model, and the power variation delta P of the regional tie line is calculatedl,tThe deviation is calculated and the deviation is calculated,
generating a Lagrangian function Ll(delta P, lambda, alpha, pi, eta, omega), wherein the delta P is the increment of the injection power of the tie line, and the delta P is more than or equal to 0; lambda, alpha, pi, eta, omega refer to lagrange multipliers corresponding to corresponding constraint conditions,
Will betal,tDefined as an impact indicator of the autonomy of an area, when the area can autonomy when the renewable energy fluctuates, betal,tWhen the area cannot be autonomous, β is 0l,t＜0。
In the regional autonomous model in the step 2, the fluctuation amount of the renewable energy source is set to be the worst scene, that is, the maximum output is the minimum value, and the fluctuation amount is set to beIn the worst scenario, the power generation of renewable energy isNamely:
The predicted value of the resource m at the time t is
The invention has the beneficial effects that:
(1) a measure of autonomy based on price is presented. When wind power and photovoltaic are fluctuated, the cost of the tie line power variation is minimum, an area autonomous model is established, a Lagrange function is generated, the tie line power variation is subjected to partial derivation, a measure index of the area autonomous capacity is obtained, the measure index can reasonably measure the influence of the tie line power variation on the area autonomous capacity, is given in a cost mode, and is convenient to introduce into a global optimization model;
(2) and generating an active power distribution network global optimization scheduling method considering the regional autonomous ability. Compared with the traditional optimization method, the method provided by the invention considers the influence of the regional autonomous ability, measures the regional autonomous ability by using the provided index, adds the regional autonomous ability into the objective function, adjusts the power of the connecting line, comprehensively considers the cost and the autonomous ability, and obtains an optimal solution.
drawings
FIG. 1 is a schematic flow chart of an active power distribution network global optimization scheduling method considering regional autonomous capacity;
fig. 2 is a schematic diagram of an active power distribution network structure.
Detailed Description
the embodiments are described in detail below with reference to the accompanying drawings.
as shown in fig. 1, the active distribution network global optimization scheduling method considering regional autonomous capability includes the following steps:
step 1: and (3) with the minimum total cost as a target, considering constraints such as distributed power supplies, energy storage, network safety and the like, establishing a global optimization scheduling model, and solving the power distribution of each regional tie line. The total cost comprises the change cost of the call wire power representing the regional autonomous ability besides the conventional cost, so that the regional call wire power is adjusted to coordinate the autonomous ability of each region; the schematic diagram of the network structure of the active power distribution network is shown in fig. 2.
step 2: setting the regional tie line power obtained through global optimization as a base value, establishing a regional autonomy model by taking the minimum cost of the regional tie line power variation as a target when wind power and photovoltaic are fluctuated, and measuring the autonomy of each region;
And step 3: generating a Lagrange function aiming at the regional autonomous model, solving a partial derivative of the tie line power variation to obtain a price-based autonomous capacity measurement index to measure the influence of the tie line power variation on the regional autonomous capacity;
and 4, step 4: and introducing the indexes into a global optimization model, and iteratively updating the regional tie line power and autonomy measurement indexes according to the global optimization scheduling model and the regional autonomy model until no change occurs, so as to obtain the optimal scheduling scheme of the global optimization of the active power distribution network considering the regional autonomy.
in step 1, a global optimization scheduling model is established by taking the minimum total cost as a target and considering constraints such as distributed power supplies, energy storage, network safety and the like.
In consideration of economy, the global optimization aims at minimizing the total cost, including the electricity purchase cost, the micro gas turbine cost and the energy storage cost. In order to make full use of renewable energy, the costs of renewable energy are not considered here.
meanwhile, the relation between the upper layer and the lower layer is mainly the tie line power, and the regional autonomous capability of the lower layer is also related to the tie line power, so in order to consider the influence of the regional autonomous capability, the tie line power change cost representing the regional autonomous capability should be added into the objective function.
the objective function is as follows:
in the formula:the price of electricity purchased by a superior power grid, the price of electricity per unit power of the micro gas turbine and the price of electricity per unit power of stored energy are respectively.active power, micro gas turbine power, energy storage discharge power and energy storage charging power are injected into the upper-level power grid at a node j at the time t. Beta is al,tthe unit change cost of the power of the connecting line of the representative area I is calculated by the steps 2 and 3, and delta Pl,tRepresenting the amount of change in tie line power, T representing the time period, NgridRepresenting the number of connections to the superordinate grid, NMTRepresenting the number of micro gas turbines, NBSrepresenting the number of energy storage battery groups, and L representing the number of areas.
constraint conditions:
a) Line active power balance constraint:
in the formula: pij,t、Iij,t、rijthe active power, the current and the resistance of a line from the node i to the node j at the time t respectively; pjk,tThe active power of other lines connected with the node j at the moment t; pj,m,tThe renewable energy source network access power at the node j at the time t;The active load of the node j at the moment t is shown, the node k is a tail end node of a line connected with the node j, and delta (j) is a tail end node set of the line connected with the node j; node i is the head end node of the line connected with node j, and pi (j) is the line connected with node jA set of head-end nodes of the way.
tie line power P for region ll,tthen modify it tofor regional autonomy time tie line power reference value, Δ Pl,tFor the amount of tie line power variation, and finallyand the power of the tie line serving as each area is sent to the lower autonomous area.
b) line reactive power balance constraint:
In the formula: qij,t、xijThe reactive power and the line reactance of the line from the node i to the node j at the time t respectively; qjk,tthe reactive power of other lines connected by the node j at the time t; vj,tThe voltage amplitude of the node j at time t;Injecting reactive power into a superior power grid at a node j at the time t;for reactive load at node j at time t, bjis susceptance at node j, Iij,tAt time t, the line from node i to node j has current.
c) Voltage constraint:
d) line tide:
e) upper level grid injection power constraint
In the formula:respectively the minimum value of injected active power, the maximum value of active power, the minimum value of reactive power, the maximum value of reactive power, Vi.tThe voltage amplitude of the node i at time t;
f) micro gas turbine constraint
in the formula:Is the maximum power output of the micro gas turbine,Respectively is the minimum value of the climbing constraint and the maximum value of the climbing constraint,
g) restraint of stored energy
in the formula:Are respectively the switch variable of the energy storage,in order to store the capacity of the energy at time t, and C0Respectively, maximum value of energy storage capacityMinimum and initial values, ηjIn order to achieve a high charging efficiency,The maximum value of the stored energy discharging power and the charging power is obtained.
h) Photovoltaic, wind power constraints
in the formula:And predicting the power generation power of the renewable energy source at the time t.
i) network security constraints
in the formula: vj,maxAnd Vj,minrespectively, a maximum and a minimum of the voltage, Iij,maxIs the maximum value of the current.
Second order cone equivalent deformation:
because the original problem is difficult to solve due to the equations (2) - (5), the load flow equation in the original problem is subjected to convex relaxation treatment, the original problem is converted into a mixed integer second-order cone optimization problem which can be effectively solved, the optimal result can be easily obtained through an algorithm packet, the SOC relaxation is strict for a radiation network, and the obtained solution is the optimal solution of the original problem.
And (3) processing model nonlinearity:
introducing variablesAndEliminating the original square term, equation (2) is equivalent to:
the equation (3) is equivalent as follows:
The equation (4) is equivalent as follows:
Equation (5) is transformed to a standard second order cone:
The safety constraint (10) is equivalent as follows:
and 2, establishing a regional autonomy model according to the obtained regional tie line power and aiming at the minimum change of the tie line power when the renewable energy source generates fluctuation, and measuring the regional autonomy.
An objective function:
The strength of the autonomous ability of the area is represented by whether controllable resources in the area are enough to balance the power fluctuation when the renewable energy power changes, namely whether the power of the tie line changes. So as to be measured by delta Pl,tThe minimum is an objective function which can be well embodiedThe autonomous capability of the area.
The target of the regional autonomous model is modified into the cost C of the power fluctuation of the tie line because the index is considered by substituting the global optimization model, and the cost of the global optimization target is generally minimuml,tΔPl,tAnd minimum.
Constraint conditions:
the injection power of the region is determined by the same equations (6) - (8), (10) - (17)Modified into Modified intoWherein,Orderwire power, Δ P, for global optimizationl,t、ΔQl,tcorresponding to the amount of fluctuation.
Meanwhile, the scene with the worst fluctuation amount of the renewable energy sources is considered, namely the maximum output is the lower limit, so that the influence of the fluctuation of the renewable energy sources on the self-control capability is measured. Let the predicted value of resource m at time t beAssuming an amount of fluctuation ofTherefore, in the worst situation, the power generation amount of the renewable energy source isNamely:
For the regional autonomous model in the step 3, a Lagrange function is generated to solve, and the power variation delta P of the tie line is calculatedl,tAnd (5) obtaining a partial derivative to obtain a measurement index of the regional autonomy.
generating a Lagrangian function Ll(Δ P, λ, α, π, η, ω), one can obtain:
Delta P is the injected power increment of the tie line, and the delta P is more than or equal to 0; lambda, alpha, pi, eta and omega refer to corresponding Lagrange multipliers of constraint conditions, and beta is obtainedl,tThe method is defined as an influence index of the power fluctuation of the call wire of the ith area on the autonomous ability of the area, is equivalent to the cost of the change of the call wire, is equivalent to the cost in the global optimization, and can participate in the global optimization scheduling.
When the renewable energy fluctuates, if the area can be autonomous, betal,tNot autonomous, beta, 0l,tIf the power of the tie line is less than 0, the autonomous ability of the area is measured by using the index, the global optimization scheduling is participated, and the power of the tie line is adjusted.
And 4, iteratively updating the tie line power and regional autonomous capacity measurement indexes in the global optimization scheduling mathematical model and the regional autonomous model of the active power distribution network until no change occurs, and obtaining an optimal scheduling scheme.
And (3) an iterative process:
let k be 1 and then,calculating to obtain the power of each regional tie line according to the model in the step 1
② willSubstituting the model in the step 2, and generating a Lagrangian function according to the step 3 to obtainMeasurement index of regional autonomy
③ willSubstituting the model in the step 1, and calculating to obtain new tie line power
fourthly ifare all provided withThe iteration is stopped, otherwise k is k +1, and the process returns to step (c).
the present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. An active power distribution network global optimization scheduling method considering regional autonomous capability is characterized by comprising 4 steps of:
step 1: taking the minimum total cost as a target, considering distributed power supply, energy storage and network security constraints, establishing a global optimization scheduling model, and obtaining the power distribution of each regional tie line, wherein the total cost comprises the change cost of the tie line power representing regional autonomous capability besides the electricity purchase cost, the cost of a micro gas turbine and the energy storage cost, so as to adjust the power of the regional tie lines and coordinate the regional autonomous capability;
step 2: establishing a regional autonomous model by taking the minimum cost of regional tie line power variation as a target when renewable energy fluctuates;
and step 3: generating a Lagrange function for solving aiming at the regional autonomous model in the step 2, and solving a partial derivative of the regional tie line power variation to obtain an autonomous ability measurement index;
and 4, step 4: iteratively updating regional tie line power and autonomous capacity measurement indexes in the active power distribution network global optimization scheduling mathematical model and the regional autonomous model until the regional tie line power is not changed any more, and obtaining an active power distribution network global optimization optimal scheduling scheme considering regional autonomous capacity;
The overall optimization scheduling mathematical model of the active power distribution network in the step 1 is as follows:
an objective function:
In the formula:Respectively the electricity price for purchasing the electricity by the superior electric network, the unit power electricity price of the micro gas turbine and the unit power electricity price of the stored energy,Respectively injecting active power, micro gas turbine power, energy storage discharge power and energy storage charging power beta into the superior power grid at a node j at the time tl,tthe cost per unit change in power of the regional links, Δ P, representing region ll,tRepresents the power variation of the local tie line, T represents the time period, NgridRepresenting the number of connections to the superordinate grid, NMTrepresenting the number of micro gas turbines, NBSRepresenting the number of energy storage battery groups, and L representing the number of areas;The active power injected for the node at time t,The active power of the micro gas turbine power is the t moment node;
Constraint conditions are as follows:
a) Line active power balance constraint:
In the formula: pij,t、Iij,t、rijthe active power, the current and the resistance of a line from the node i to the node j at the time t respectively; pjk,tThe active power of other lines connected with the node j at the moment t; pj,m,tthe renewable energy source network access power at the node j at the time t;The active load of the node j at the moment t is shown, the node k is a tail end node of a line connected with the node j, and delta (j) is a tail end node set of the line connected with the node j; the node i is a head end node of a line connected with the node j, and pi (j) is a head end node set of the line connected with the node j;
Area link power P for area ll,tThen modify it to For regional tie line power reference value, Δ P, at regional autonomyl,tis the amount of change in power of the local tie line, toas area link power of each area;
b) line reactive power balance constraint:
In the formula: qij,t、xijThe reactive power and the line reactance of the line from the node i to the node j at the time t respectively; qjk,tThe reactive power of other lines connected by the node j at the time t; vj,tThe voltage amplitude of the node j at time t;Injecting reactive power into a superior power grid at a node j at the time t;For reactive load at node j at time t, bjis susceptance at node j, Iij,tAt the time t, the line from the node i to the node j has current;
c) Voltage constraint:
d) line tide:
e) upper level grid injection power constraint
In the formula:respectively the minimum value of injected active power, the maximum value of active power, the minimum value of reactive power, the maximum value of reactive power, Vi.tThe voltage amplitude of the node i at time t;the active power injected for the node at time t,Injecting reactive power for a node at the time t;
f) Micro gas turbine constraint
in the formula:is the maximum power output of the micro gas turbine,The minimum value of the climbing constraint and the maximum value of the climbing constraint are respectively;
g) restraint of stored energy
in the formula:Are respectively the switch variable of the energy storage,In order to store the capacity of the energy at time t, And C0respectively a maximum value, a minimum value and an initial value of the energy storage capacity; etajin order to achieve a high charging efficiency,the maximum value of the stored energy discharging power and the charging power is obtained;
h) photovoltaic, wind power constraints
In the formula:For the predicted value of the power generated by the renewable energy source at the moment t,
i) Network security constraints
in the formula: vj,maxand Vj,minRespectively, the maximum value of the voltage and the minimum value of the voltage, Iij,maxis the maximum value of the current.
2. The global optimization scheduling method for the active power distribution network considering the regional autonomous ability of claim 1 is characterized in that the line power flow equation (5) in the step 1 is subjected to a convex relaxation process, and model nonlinearity is processed:
Introducing intermediate variablesAndEliminating the original square term, equation (2) is equivalent to:
The equation (3) is equivalent as follows:
the equation (4) is equivalent as follows:
Equation (5) is transformed to a standard second order cone:
The safety constraint (10) is equivalent as follows:
3. the active distribution network global optimization scheduling method considering regional autonomous capability of claim 1, wherein the regional autonomous model in the step 2 is as follows,
An objective function:
constraint conditions are as follows:
The injection power of the region is determined by the same equations (6) - (8), (10) - (17)Modified into modified intowherein,Regional tie power, Δ P, for global optimizationl,t、ΔQl,tis the amount of change in regional tie line power, Cl,tthe area link power unit change cost represents area i.
4. the active power distribution network global optimization scheduling method considering regional autonomous capability as claimed in claim 1, wherein in the step 3, a lagrangian function is generated for a regional autonomous model, and power variation Δ P of regional tie lines is calculatedl,tCalculating a partial derivative, wherein delta P is the injected power increment of the tie line and is more than or equal to 0; lambda, alpha, pi, eta, omega refer to lagrange multipliers corresponding to corresponding constraint conditions,
Generating a Lagrangian function Ll(ΔP,λ,α,π,η,ω)，
Will betal,tdefined as an impact indicator of the autonomy of an area, when the area can autonomy when the renewable energy fluctuates, betal,twhen the area cannot be autonomous, β is 0l,t＜0。
5. The active power distribution network global optimization scheduling method considering regional autonomous capability of claim 1, wherein in the regional autonomous model in step 2, the fluctuation amount of renewable energy is set to be the worst scenario, that is, the maximum output is the minimum value, and the fluctuation amount is set to beIn the worst scenario, the power generation of renewable energy isnamely:
the predicted value of the resource m at the time t is
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