CN112803422B - Power transmission network voltage double-layer control method based on active and reactive power coordinated optimization - Google Patents
Power transmission network voltage double-layer control method based on active and reactive power coordinated optimization Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
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Abstract
The invention relates to the technical field of power system operation control, and discloses a power transmission network voltage double-layer control method based on active and reactive power coordination optimization, which comprises the steps of constructing an active and reactive power coupling model of schedulable resources of a power transmission network; constructing an alternating current power flow equation of the line network frame, and converting a non-convex power flow constraint equation into a second-order cone optimization problem which can be solved by a second-order cone relaxation method; combining the output limit of the constructed active reactive power coupling model with the line alternating current power flow limit obtained by relaxation, minimizing the voltage deviation of each node and the scheduling cost to form a target function, and solving the mixed integer second-order cone optimization problem of the multi-target function to obtain the output value of each device; and constructing a double-layer voltage control model to realize the control of the voltage of the power transmission network. Compared with the prior art, the method expands the second-order cone relaxation method of the alternating current power flow into the voltage control of the power transmission network, and obtains a more accurate scheduling result through the combination of the day-ahead look-ahead scheduling and the in-day rolling optimization.
Description
Technical Field
The invention relates to the technical field of power system operation control, in particular to a power transmission network voltage double-layer control method based on active and reactive power coordinated optimization.
Background
With the high-speed development of economy in China, the economic structure increasingly shows the characteristics of speed change, structure optimization, power conversion and the like. The aim of China is to build a developed economic body, so the economic development mode needs transformation. In response to the national call for the stable transformation and development of economy, we should focus on the problem of economic waste in the energy field. The power generation and transmission link is used as the basis of the national power supply service, and the research on reasonable planning and scheduling of the power generation and transmission link is particularly important under the new economic situation.
For the active and reactive combined dispatching method, some research achievements have been accumulated for years at home and abroad. According to various research results, various application patents related to reactive and reactive combined dispatching exist. Patent CN111416395A "a multi-level power grid nested decomposition coordination active and reactive power joint scheduling method" proposes a multi-level power grid nested decomposition coordination active and reactive power joint scheduling method. Firstly, an active and reactive power combined dispatching model of multi-level power grid cooperation is provided, the active and reactive power combined dispatching model of each level power grid cooperation is solved in a nested decomposition coordination mode among all levels of power grids, and active and reactive power combined dispatching is carried out on all regional power grids of all levels based on an optimal solution. The key steps are as follows: the method comprises the steps of decomposing, coordinating and calculating an optimal solution of active and reactive power joint dispatching of a regional power grid and a subordinate power grid in a certain level, and calculating an optimal secant plane and an approximate projection function of the regional power grid in the certain level, wherein the two steps are called recursively, so that the decomposition, coordination and calculation of the cooperative active and reactive power joint dispatching model of the power grids at all levels are realized.
Patent CN109274134B, "an active power distribution network robust active and reactive power coordination optimization method based on time series scene analysis" proposes an active power distribution network robust active and reactive power coordination optimization method based on time series scene analysis. Firstly, establishing an active and reactive power coordination optimization certainty model of an active power distribution network; then analyzing the uncertain factors and generating scenes, and clustering similar scenes by using a clustering method; establishing a two-stage robust optimization model, converting an original problem into a single objective function model only containing a main problem, and solving; and performing coordination control on the active power distribution network by adopting a two-stage robust optimization model. Uncertainty factors caused by distributed power output and load fluctuation in the active power distribution network are considered in the modeling process, system uncertainty is represented in an uncertain set mode, and stability and reliability of system operation are improved.
The patent CN106953359B 'an active and reactive power coordinated optimization control method for a distribution-type photovoltaic power distribution network', provides a method for performing active and reactive power coordinated optimization control on the power distribution network based on model predictive control. Firstly, starting from different time scales, dividing a control process of a power distribution network system into a fast time scale and a slow time scale, and establishing a fast time scale optimization model and a slow time scale optimization model according to control characteristics of different devices; and then converting the optimization control problems of the two time scales into a second-order cone planning problem according to the relaxation of a second-order cone, wherein the fast time scale optimization control adopts multi-step rolling optimization to solve the active and reactive power output of each controllable device, and the slow time scale optimization control adopts the solution result of the long-time scale optimization control model as a reference value to continuously roll and solve the output increment value of each device.
However, the above conventional method has the following drawbacks:
1. the existing reactive coupling dispatching is mainly applied to a power distribution network at present, and the existing reactive coupling dispatching is rarely considered in a power transmission network.
2. The control objects consider the characteristics of safety, economy and the like, and part of the control objects conflict with each other. In the aspect of decision, the contradiction between active and reactive power dispatching cannot be solved well.
3. The active and reactive combined dispatching of the single-layer model can not only give consideration to the economy and the safety of an optimization target well, but also can not coordinate various devices with different time constants in the power system.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a transmission network voltage double-layer control method based on active and reactive power coordinated optimization.
The invention provides a transmission network voltage double-layer control method based on active and reactive power coordinated optimization, which comprises the following steps:
s101, constructing an active and reactive power coupling model of schedulable resources of the power transmission network, and analyzing active and reactive power output characteristics of renewable energy sources, energy storage equipment and reactive power compensation devices;
s102, constructing an alternating current power flow equation of the line network frame, and converting non-convex power flow constraint into second-order cone constraint which can be solved by a second-order cone relaxation method;
s103, combining the active reactive power coupling output limit of the schedulable resource constructed in the S101 with the line alternating current power flow limit obtained by relaxation in the S102, minimizing the voltage deviation of each node and the scheduling cost into a target function, and solving a mixed integer second-order cone optimization problem of the target function to obtain the output value of each device;
and S104, considering the response time characteristic of each scheduling resource, constructing a multi-time scale double-layer voltage control model, and realizing the control of the voltage of the power transmission network by combining the forward-looking scheduling and the rolling optimization in the day.
Further, the active-reactive coupling model in step S101 includes: constructing an active reactive coupling model of wind power photovoltaic; constructing an active reactive coupling model of the reactive compensation device; and constructing an active and reactive power coupling model of the energy storage equipment.
Further, the specific step of S102 constructing the ac power flow equation constraint includes: 1) establishing a BFM model of the alternating current power flow; 2) converting the non-convex limit by a second-order cone relaxation method; 3) the uniqueness of the tidal current solution is guaranteed by the relaxed phase angle constraint.
Further, the objective function of the grid voltage control in S103 is as follows:
wherein, alpha, beta and gamma are cost coefficients, U j Is the voltage of node j, U ref Is the reference voltage of the node; c e As a unit cost function; c bess For the cost of energy storage devices, C Q The cost of the reactive power compensation device; the constraint conditions comprise generator set output constraint, energy storage equipment output constraint, power flow constraint, safe operation constraint, reactive compensation constraint and unit combination constraint.
Further, the specific content of the multi-time scale double-layer voltage control model constructed in S104 is as follows: the upper-layer control model aims at economic optimization, solves the economic optimization problem under the multi-objective function on a slow time scale, ensures the economy of the output of the unit and the energy storage equipment, and realizes day-ahead pre-scheduling; the lower control model is a voltage control layer, the voltage deviation of a control area on a fast time scale is minimum, the control deviation of a control quantity and the control deviation of the upper control model are minimum, the unit and the energy storage equipment are modified according to the real-time load and the output of renewable energy, and the voltage safety is guaranteed.
Further, the time resolution of the upper control model is 1h, and an objective function of the upper control model is as follows:
wherein, C e As a unit cost function; c bess For the cost of energy storage devices, C Q The cost of the reactive power compensation device. Alpha is the voltage deviation coefficient, U j Is the voltage value of the central bus j, U ref Is a reference value of voltage; the upper layer constraint conditions comprise generator set output constraint and energy storage equipment output constraintPower flow constraint and safe operation constraint.
Further, the lower layer control model is optimized by rolling a time window within a day, a model predictive control method is adopted, an optimized objective function is that the voltage deviation is minimum and the active and reactive power regulating quantity of the control equipment is minimum, the time resolution of the lower layer control model is 5min, and the objective function of the lower layer control model is as follows:
wherein, W p Is a weight coefficient of voltage deviation, U n Is the voltage of each node; delta P gen，i ，ΔQ gen，i The output variation of the active power and the reactive power of the generator set compared with the output variation of a day-ahead scheduling layer is represented by PT which is the size of a control time domain time window; the lower layer constraint conditions comprise voltage range constraint, generator output variation constraint, energy storage equipment output variation constraint and power variation balance constraint, the rest constraints are the same as the upper layer control model, and the machine set only participates in voltage adjustment in the day without influencing the start and stop of the machine set; the reactive compensation equipment does not participate in the in-day model.
Further, the specific rolling optimization steps of the lower control model are as follows:
1) solving the lower-layer voltage control problem in the [ T, T + PT-1] time period at the moment T, so that the voltage is as close to the reference voltage as possible, and the variation of the active and reactive power output is as small as possible;
2) outputting the active and reactive distribution quantity of the predicted time domain moment T;
3) updating system data, including current machine set active and inactive output values, load fluctuation and renewable energy output fluctuation predicted values;
4) let T ═ T +1, repeat step 1) to step 3).
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention expands the second-order cone relaxation method of the alternating current power flow equation into the power transmission network to realize the voltage control of the power transmission network.
(2) The invention considers the problem of unit combination, combines a reactive power compensation device with an alternating current power flow equation, constructs a mixed integer second-order cone optimization problem and solves the problem.
(3) The invention considers the response time characteristic of each scheduling device, and realizes the voltage control under multiple time scales through the double-layer active and reactive power combined optimization of forward looking and real-time rolling in the day.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flowchart of the algorithm for voltage bi-level control according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in a flow chart 1, a transmission network voltage double-layer control method based on active and reactive power coordinated optimization deals with uncertainty of load and renewable energy output through double-layer progressive optimization, and controls voltage within a reasonable range, and mainly includes the following steps:
step S101, an active and reactive power coupling model of the schedulable resource of the power transmission network is built, and active and reactive power output characteristics of the renewable energy source, the energy storage device and the reactive power compensation device are analyzed. As follows:
1. generator set output model
Wherein the content of the first and second substances,
is the maximum apparent power of the generator set i; q gen，i The reactive power is generated by the generator set.
2. Energy storage equipment output model
D ch，i (t)+D dis，i (t)≤1
In the formula: p ch，i Representing the charging power of the energy storage device; p dis，i Representing the discharge power of the energy storage device; d ch，i To represent the state of charge process 0-1 variables: when the value is 1, the stored energy is in a charging process; d dis，i When the value is 1, the stored energy is in the discharging process; the energy storage device cannot be in both a charging and a discharging state at the same time;
and
respectively representing the maximum charging power and the maximum discharging power of the energy storage system; e SOC，i Indicating the state of charge of the energy storage device at the present time,
then the maximum capacity of the energy storage device is indicated; eta ch，i And η dis，i Respectively representing the charging point efficiency and the discharging efficiency of stored energy, and delta T represents the time interval of charging and discharging of stored energy.
Due to the limitation of inverter capacity, apparent power constraints are introduced.
Wherein Q is ESS，i Reactive power is injected into the energy storage device at node i,
the maximum apparent power of the energy storage inverter at node i.
And S102, constructing an alternating current power flow equation of the line network frame, and converting the non-convex power flow constraint into a second-order cone constraint which can be solved by a second-order cone relaxation method. The second-order cone is relaxed, the second-order cone conversion can convert a complex optimization model into a cone model, variables with complex relations are expressed by a cone set with a special structure, and the solution of the original model is simplified. In this step, the second order cone approximation is performed mainly for two types of constraints.
The standard form of the second order cone programming is
In the formula: (x) is an objective function; the variable x ∈ R N Coefficient constant b ∈ R M ；A M×N ∈R M×N (ii) a C is a second order cone or a rotating second order cone of the form.
1) Apparent power constrained second order cone relaxation:
for the ac optimal power flow equation, two constraints are included, namely power balance and ohm's law:
first of all by z ij ＝r ij +ix ij ，S ij ＝P ij +iQ ij Separating real and imaginary parts, and then replacing with variables, i.e. using
And
the square of the node voltage amplitude and the square of the branch current amplitude are respectively represented, namely:
after the replacement, the power flow equation is as follows.
And finally, convex relaxation treatment is carried out, and the equal sign in the (6) relaxation in the system power flow constraint formula is changed into a sign greater than or equal to:
and performing equivalent deformation to form a standard second-order cone:
2) power coupling constrained second order cone relaxation
The active and reactive power output coupling of the generator and the energy storage equipment is constrained to a standard second-order cone form, which is shown as follows:
step S103, multi-objective optimization realizes voltage control of the power transmission network: and combining the active and reactive power coupling output limit of the schedulable resource constructed in the step S101 with the line alternating current power flow limit obtained by relaxation in the step S102, minimizing the voltage deviation of each node and the scheduling cost into a target function, and solving a mixed integer second-order cone optimization problem of the multi-target function to obtain the output value of each device.
The total cost of the generator set is as follows:
wherein G is the total number of generators, a i ，b i ，c i As cost factor of the generator, P gen，i And (t) is the power generation amount of the ith unit at the time of t.
The total energy storage cost is as follows:
where Gs is the total number of energy storage devices, a i ，b i ，c i For cost factors of energy storage devices, P bess，i And (t) is the charging and discharging amount of the ith energy storage device at the moment t.
The reactive compensation cost is as follows:
wherein, c q Is the unit reactive compensation cost; q M And M is the total number of the reactive compensation devices.
The objective function of grid voltage control is to minimize the cost of the generator, energy storage equipment and reactive compensation equipment, and the voltage deviation at each node:
wherein, alpha, beta and gamma are cost coefficients, U j Is the voltage of node j, U ref Is the reference voltage for that node. The constraint conditions comprise generator set output constraint, energy storage equipment output constraint, power flow constraint, safe operation constraint, reactive compensation constraint and unit combination constraint.
1. Flow restraint
The MISOCP power flow model optimized by the second-order cone is shown as the following formula.
In the formula, P ij，t ，Q ij，t Respectively the active power and the reactive power of the head end of the branch ij at the moment t; p jk，t ，Q jk，t Net active power and net reactive power injected into node j, respectively; p d，j，t And Q d，j，t Respectively an active load and a reactive load of a node j;
is the voltage amplitude at node j;
current for branch ij; r is ij And x ij The resistance and reactance of branch ij are respectively; p ch，j，t Represents the charging power of the energy storage device at node j, P dis，j，t Representing the energy storage device discharge power at node j; q ESS，j，t Representing the reactive power emitted by the energy storage equipment at the node j; q M，j，t The output of the reactive power compensation device. The above formula is applicable to radial grids, and for closed transmission grids, the voltage phase angle limitation needs to be considered.
Wherein, theta i Is the phase angle of the voltage at node i, P ij ，Q ij Respectively the active and reactive power on the branch ij,
is the voltage at node i, j, which is generally considered constant in order to ensure the linearity of the optimization problem.
2. Safe operation constraint
In the operation process of the power system, the system needs to meet node voltage safety constraint, branch current safety constraint and unit active power output maximum and minimum value constraint.
In the formula (I), the compound is shown in the specification,
and
respectively representing the maximum value and the minimum value of the voltage amplitude of the node n;
the maximum value of the current flowing through branch i.
3. Unit combination constraint
In the formula, r g,i The standby power of each generator set; r t Is a total reserve power constraint; v. of g,i The variable is 0-1, which indicates that the generator set is in a starting state; u. of g,i The variable is 0-1, and the generator set is in the running state; w is a g,t The variable is 0-1, which indicates that the generator set is in a shutdown state; RU (RU) g Is the maximum climbing power; RD g Is the maximum downhill power; p is a radical of g,t And the output value of the generator set is obtained. TU (tunnel junction transistor) g And TD g Respectively, the minimum online time and the minimum offline time of the unit.
4. Reactive power compensator restraint
The reactive compensation device comprises a parallel reactive compensation capacitor, a switching reactor and the like, and the maximum and minimum values of the reactive compensation device are constrained as follows:
upper and lower limits of the number of actions of the parallel capacitor/reactor group are constrained:
in the formula:
the upper limit of the number of actions of the capacitor/reactor bank at node i.
Because the constraint containing the absolute value cannot directly call CPLEX to solve, the constraint is processed by adopting an equivalent transformation method:
0≤Z i，t -(y i，t -y i，(t+1) )≤Mδ i1，t
0≤Z i，t -(y i，(t+1) -y i，t )≤Mδ i2，t
δ i1，t +δ i2，t ≤1
introducing 3 auxiliary variables respectively as Z i,t ，δ i1,t ，δ i2,t Wherein Z is i,t Is an integer variable, δ i1,t And delta i2,t Is a variable from 0 to 1. Z i,t Representing the action times of the reactive equipment from the time t to the time (t +1), and if the number of groups of the reactive equipment switched from the time t to the time t +1 changes, Z i,t The number of the switching groups is represented, and if the number of the switching groups is not changed, Z is i,t When M is 0, M is greater than or equal to Y i max 。
δ i1,t And delta i2,t And y i,t And y i,t+1 The following relationships exist:
the start-stop plan of the unit in each hour is reasonably arranged through the control of the voltage control layer of the power transmission network, and sufficient standby power is reserved for coping with load disturbance and fluctuation of renewable energy sources.
Step S104, a multi-time scale double-layer active and reactive power combined scheduling model: and (4) considering the response time characteristic of each scheduling resource, constructing a multi-time scale double-layer voltage control model, and realizing the control of the voltage of the power transmission network by combining the forward-looking scheduling and the rolling optimization in the day.
The upper-layer control model takes economic optimization as a target, solves the economic optimization problem under the multi-objective function on a slow time scale, and ensures the output economy of the unit and the energy storage equipment; the lower control model is a voltage control layer, the voltage deviation of a control area on a fast time scale is minimum, and meanwhile, the control deviation of a control quantity and the control deviation of the upper control model are minimum, so that the running safety is guaranteed.
The upper control objective is that the power generation cost is minimum, the voltage deviation amount is minimum, the time resolution of the upper control model is 1h, and the objective function is as follows:
wherein, C e As a unit cost function; c bess For the cost of energy storage devices, C Q The cost of the reactive power compensation device. Alpha is the voltage deviation coefficient, U j Is the voltage value of the central bus i, U ref Is a reference value of voltage.
The upper layer constraint conditions include generator set output constraint, energy storage device output constraint, power flow constraint and safe operation constraint, which are given in S102.
The lower control model is the optimization of rolling time windows in the day, a model predictive control method is adopted, and the optimized objective function is the minimum voltage deviation and the minimum active and reactive power regulating quantity of the control equipment. The time resolution of the lower control model is 5min, and the lower control objective function is as follows:
wherein, W p Is a weight coefficient of voltage deviation, U n Is the voltage of each node; delta P gen，i ，ΔQ gen，i And PT is the size of a time window of a control time domain.
The lower layer constraints are as follows:
voltage range safety constraints
U n，min ≤U n (k+l|k)≤U n，max
Wherein, U n，min And U n，max Is the maximum and minimum voltage at node n, U n And (k + l | k) indicates that the voltage of each node meets the safety constraint in the range from k to k + l during the optimization.
Generator output and energy storage equipment output variable quantity constraint
ΔP gen，i，min ≤ΔP gen，i (k+l|k)≤ΔP gen，i，max
ΔQ gen，i，min ≤ΔQ gen，i (k+l|k)≤ΔQ gen，i，max
ΔP bess，i，min ≤ΔP bess，i (k+l|k)≤ΔP bess，i，max
ΔQ bess，i，min ≤ΔQ bess，i (k+l|k)≤ΔQ bess，i，max
Wherein, Δ P gen，i ，ΔQ gen，i Comparing the active power and the reactive power of the generator set with the output variable quantity delta P of a day-ahead scheduling layer bess，i ，ΔQ bess，i The output variation quantity of the active power and the reactive power of the energy storage equipment is compared with that of a day-ahead scheduling layer.
Power delta balance constraint
Wherein, Δ P d And Δ Q d Delta P for the change of active versus reactive of the load in the day compared to the scheduling horizon in the day w ，ΔQ w The active and reactive variable quantity of the wind turbine generator is obtained.
The other constraint conditions are the same as those of the upper model, and the unit only participates in voltage adjustment in the day, so that the start and stop of the unit are not influenced; the reactive compensation equipment does not participate in the in-day model.
Optimizing the lower layer by rolling once every 5min, wherein the predicted time domain PT is 5, the controlled time domain MT is 5, and the steps of the rolling optimization are as follows: 1. solving the lower-layer voltage control problem in the [ T, T + PT-1] time period at the moment T, so that the voltage is as close to the reference voltage as possible, and the variation of the active and reactive power output is as small as possible; 2. outputting the active and reactive distribution quantity of the predicted time domain moment T; 3. updating system data, including current machine set active and inactive output values, load fluctuation and renewable energy output fluctuation predicted values; 4. let T be T +1 and repeat steps 1 to 3.
Thereby forming a voltage double-layer control method: the upper layer is an economic distribution layer, the economic optimal problem containing voltage control under the multi-constraint condition is solved on a slow time scale, and the output economy of each device is guaranteed. The lower layer is a voltage control layer, the output of each device is adjusted on a fast time scale to cope with power disturbance, and the voltage of the power system is guaranteed to be within a safe range.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (4)
1. A transmission network voltage double-layer control method based on active and reactive power coordination optimization is characterized by comprising the following steps:
s101, constructing an active and reactive power coupling model of schedulable resources of the power transmission network, and analyzing active and reactive power output characteristics of renewable energy sources, energy storage equipment and reactive power compensation devices;
s102, constructing an alternating current power flow equation of the line network frame, and converting non-convex power flow constraint into second-order cone constraint which can be solved by a second-order cone relaxation method;
s103, combining the active reactive power coupling output limit of the schedulable resource constructed in the S101 with the line alternating current power flow limit obtained by relaxation in the S102, minimizing the voltage deviation of each node and the scheduling cost into a target function, and solving a mixed integer second-order cone optimization problem of the target function to obtain the output value of each device;
the objective function of the grid voltage control in S103 is as follows:
wherein, alpha, beta and gamma are cost coefficients, U j Is the voltage of node j, U ref Is the reference voltage of the node; c e As a unit cost function; c bess For the cost of energy storage devices, C Q The cost of the reactive power compensation device; the constraint conditions comprise generator set output constraint, energy storage equipment output constraint, power flow constraint, safe operation constraint, reactive compensation constraint and generator set combination constraint;
s104, considering the response time characteristic of each scheduling resource, constructing a multi-time scale double-layer voltage control model, and realizing the control of the voltage of the power transmission network by combining the forward-looking schedule and the rolling optimization in the day;
the specific content of the multi-time scale double-layer voltage control model constructed in the step S104 is as follows: the upper control model takes economic optimization as a target, solves the economic optimization problem under the multi-objective function on a slow time scale, and realizes day-ahead pre-scheduling; the lower control model is a voltage control layer, the voltage deviation of a control area on a fast time scale is minimum, the control deviation of a control quantity and the control deviation of the upper control model are simultaneously ensured to be minimum, the unit and the energy storage equipment are modified according to the real-time load and the output of renewable energy, and the voltage safety is ensured;
the time resolution of the upper control model is 1h, and the target function is as follows:
wherein, C e As a unit cost function; c bess For the cost of energy storage devices, C Q For the cost of the reactive power compensation device, α is the voltage deviation factor, U j Is the voltage value of the central bus j, U ref Is a reference value of voltage; the upper layer constraint conditions comprise generator set output constraint, energy storage equipment output constraint, power flow constraint and safe operation constraint;
the lower-layer control model is optimized by rolling time windows in a day by adopting a model predictive control method, the optimized objective function is the minimum voltage deviation and the minimum active and reactive power regulating quantity of control equipment, the time resolution of the lower-layer control model is 5min, and the objective function of the lower-layer control model is as follows:
wherein, W p Is a weight coefficient of the voltage deviation, U n Is the voltage of each node; delta P gen,i ，ΔQ gen,i The output variation of the active power and the reactive power of the generator set compared with the output variation of a day-ahead scheduling layer is represented by PT which is the size of a control time domain time window; the lower layer constraint conditions comprise voltage range constraint, generator output variation constraint, energy storage equipment output variation constraint and power variation balance constraint, the rest constraints are the same as the upper layer control model, and the machine set only participates in voltage adjustment in the day without influencing the start and stop of the machine set; the reactive compensation equipment does not participate in the in-day model.
2. The active-reactive coordination optimization-based transmission network voltage double-layer control method according to claim 1, wherein the active-reactive coupling model in the step S101 comprises: constructing an active reactive coupling model of wind power photovoltaic; constructing an active reactive coupling model of the reactive compensation device; and constructing an active and reactive power coupling model of the energy storage equipment.
3. The active-reactive coordination optimization-based transmission network voltage double-layer control method according to claim 1, wherein the specific step of S102 building an AC power flow equation constraint comprises: 1) establishing a BFM model of the alternating current power flow; 2) converting the non-convex limit by a second-order cone relaxation method; 3) the uniqueness of the tidal current solution is guaranteed by the relaxed phase angle constraint.
4. The transmission network voltage double-layer control method based on active and reactive power coordination optimization according to claim 1, wherein the lower-layer control model comprises the following specific rolling optimization steps:
1) solving the lower-layer voltage control problem in the [ T, T + PT-1] time period at the moment T, so that the voltage is as close to the reference voltage as possible, and the variation of the active and reactive power output is as small as possible;
2) outputting the active and reactive distribution quantity of the predicted time domain moment T;
3) updating system data, including current machine set active and inactive output values, load fluctuation and renewable energy output fluctuation predicted values;
4) let T ═ T +1, repeat step 1) to step 3).
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