CN109873447B - Multi-time-level active-reactive power regulation and control method for multi-source cooperative active power distribution network - Google Patents
Multi-time-level active-reactive power regulation and control method for multi-source cooperative active power distribution network Download PDFInfo
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Abstract
The invention discloses a multi-source cooperative active-reactive power regulation and control method for multiple time levels of an active power distribution network, which comprises the following steps: (1) Predicting wind power, photovoltaic and load power sequences; (2) Carrying out uncertain sampling of source-load prediction errors to obtain a scene set covering the whole sampling space; (3) Scene reduction is carried out by utilizing a synchronous back-substitution reduction technology to obtain a typical scene set; (4) In a long-time scale scheduling stage, obtaining an adjusting scheme of adjusting equipment with adjusting frequency limitation and an energy storage charging and discharging state; (5) In a short time scale scheduling stage, a scheduling instruction of the rapid continuous adjustable equipment is obtained through solving; (6) And in the real-time control stage, each adjustable device is sequentially adjusted based on the sensitivity analysis of the node voltage, so that the safe and stable operation of the power grid is guaranteed. The method of the invention can realize the coordinated optimization operation of each adjustable device in the active power distribution network, and the full consumption and the efficient utilization of renewable energy sources.
Description
Technical Field
The invention relates to a multi-source cooperative active power distribution network multi-time level active-reactive power regulation and control method, and belongs to the field of active power distribution network optimized operation.
Background
At present, in the face of the double pressure of energy exhaustion and environmental deterioration, distributed Generation (DG) having renewable green clean characteristics is rapidly moving to the stage of the world electric power industry. Meanwhile, the energy storage technology changes the problem of simultaneity of transmission and distribution of the traditional power system due to the time sequence energy regulation function of the energy storage technology, and has wide development prospect in the power system. The permeability of DG, ESS, SVC equipment in the distribution network improves day by day, and traditional distribution network is gradually evolving into the initiative distribution network that has numerous adjustable controllable resources, its core characterized in that is active and initiative: the active power distribution network contains distributed energy such as a fan (WT), a Photovoltaic (PV) and an ESS; the initiative refers to that the active power distribution network has active control capability and can perform coordination control and active management on controllable resources in the network.
The uncertainty of wind power and photovoltaic power brings great challenges to the safe operation and optimal scheduling of the power system. The DG grid connection with high permeability can cause voltage fluctuation or overvoltage to cause grid disconnection, the capacity of an active power distribution network for absorbing renewable energy sources for power generation is seriously restricted, and power grid resources and renewable energy sources are wasted. Core technologies capable of better solving the problems of overvoltage, wind abandonment, light abandonment, power blockage, line loss and the like of the active power distribution network are urgently needed. A multi-source cooperative active-reactive power regulation and control method for multiple time levels of an active power distribution network is an effective tool for solving the problems.
Disclosure of Invention
The invention provides a multi-time-level active-reactive power regulation and control method for a multi-source collaborative active power distribution network. The method adopts the principles of time sequence progressive and progressive refinement, takes the operation characteristics of adjustable resources into consideration, arranges the scheduling strategy of each device from a prospective view, finely manages and controls the problems occurring in real-time operation, can solve the key problems of overvoltage, wind abandonment, light abandonment, power blockage, line loss and the like of the active power distribution network, and ensures the safe, stable, efficient and green operation of the power grid.
The invention provides a multi-time-level active-reactive power regulation and control method for a multi-source collaborative active power distribution network, which comprises the following steps:
s1: predicting wind power, photovoltaic and load power based on a time sequence algorithm of a wavelet-BP neural network, decomposing a wind power output sequence, a photovoltaic output sequence and a load power sequence on different scales by utilizing wavelet transformation, predicting components of different frequencies by using a plurality of BP neural networks, and reconstructing each prediction result to obtain a complete prediction result; the method comprises the following specific steps:
s1.1: 3-scale wavelet decomposition is carried out on the original power sequence x to obtain a low-frequency part l 3 And a high-frequency part h 1 、h 2 And h 3 ；
S1.2: using BP neural network to measure low frequency part l 3 And a high-frequency part h 1 、h 2 、h 3 Performing prediction to obtain a low-frequency part L of the prediction component 3 And a high frequency part H 1 、H 2 And H 3 ；
S1.3: reconstructing the low-frequency prediction result and the high-frequency prediction result to obtain a complete prediction power sequence x * ＝L 3 +H 1 +H 2 +H 3 。
S2: based on a wind power, photovoltaic and load prediction error probability model, adopting Latin hypercube sampling to sample source-load uncertainty, and obtaining a scene set capable of covering all sampling spaces;
in consideration of the multi-dimensional randomness of wind power, photovoltaic and load which need to be described simultaneously in the generation of the source-load probability scene, the method utilizes a Latin hypercube sampling method to carry out multi-dimensional layered sampling by means of a multi-dimensional sampling theory and based on a wind power, photovoltaic and load prediction error probability model, and effectively avoids the phenomenon that the number of scenes is still too much after reduction caused by independent sampling of the wind power, the photovoltaic and the load.
The probability error model of the wind-solar load is as follows:
s2.1: the prediction error probability of the wind power output power meets Beta distribution, and the probability density function is as follows:
in the formula, alpha and beta are respectively a shape parameter and a scale parameter of a wind power output power prediction error probability density function; Δ P is the power prediction error.
S2.2: the load and photovoltaic contribution prediction errors are generally considered to satisfy a normal distribution, i.e., Δ P-N (μ, σ) 2 ) The probability density function is:
where μ and σ are the expected and standard deviation of the prediction error, respectively.
S3: scene reduction is carried out by utilizing a synchronous back-substitution reduction technology, a typical scene which can better reflect an original scene set is obtained, and corresponding probability of the typical scene is determined;
s4: in a long-time scale scheduling stage, a long-time scale active-reactive scheduling model of the active power distribution network facing the full consumption of renewable energy sources is built; obtaining a regulation scheme of regulation equipment with regulation frequency limit, such as OLTC, CB and the like, and the charge-discharge state of the ESS;
in multi-time scale scheduling, a long-time scale optimization scheduling model facing renewable energy sources to be fully consumed for the active power distribution network is built in a long-time scale scheduling stage, the action times of discrete reactive power adjusting equipment are strictly limited in a scheduling period, and in the long-time scale scheduling stage, adjusting schemes of adjusting equipment with adjusting time limit, such as OLTC (on-line communication technology), CB (circuit board) and the like are determined; considering the influence of frequent charge-discharge conversion on the ESS, determining the charge-discharge state of the ESS at the same time, and keeping the charge-discharge state unchanged in short-time scale scheduling, wherein an objective function of an active power distribution network long-time scale optimization scheduling model for full consumption of renewable energy sources is as follows:
in the formula (13), C T And C CB The calculation method of (c) is shown in equation (14).
In formula (13), P g,t ,P ess,t ,B ess,t ,Q g,t ,Q SVC,t ,Q CB,t ,Q dis,t ,k T,t Actual active power of renewable energy, charging and discharging power of ESS, charging and discharging states of ESS, reactive output power of renewable energy, reactive compensation power of SVC, discrete compensation power of CB, reactive output power of ESS and switching gear of OLTC at the moment t respectively; t is a regulation and control period; delta T is a long time scale regulation time interval; e is a system branch set; i all right angle ij,t The square of the current amplitude flowing through the branch ij at the moment t; r is a radical of hydrogen ij Resistance for branch ij; c loss 、C T And C CB The unit cost of the system operation grid loss, the gear adjusting cost in the OLTC dispatching cycle and the gear adjusting cost in the dispatching cycle of the capacitor group CB are respectively. The last term of the formula (13) is a power reduction penalty term of the renewable energy source, and lambda is an active power reduction penalty coefficient of the renewable energy source; phi is a re A node set accessed by renewable energy sources in the power distribution network;
and predicting the active power output of the renewable energy source at the moment t.
In the formula (14), p T And ρ C Unit adjustment costs of OLTC and CB, respectively; Δ K T And Δ K CB The adjustment times of the scheduling periods of the OLTC and the CB are respectively;
s5: on the basis of S4, in a short-time scale scheduling stage, a scheduling scheme of OLTC and CB obtained in a long-time scale scheduling stage is fixed, meanwhile, the influence of frequent charge-discharge conversion on the service life of the ESS is considered, in the short-time scale scheduling, the charge-discharge state of the ESS is consistent with the long-time scale, and on the basis of the prediction information of short-time scale wind, light and load, the minimum correction of a control variable is taken as an optimization target; solving to obtain a scheduling instruction of adjustable equipment such as wind power, photovoltaic, ESS and SVC;
the objective function of the short-time scale scheduling model for the efficient utilization of renewable energy sources is as follows:
wherein the content of the first and second substances,
S(k+i|k)＝[P g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k+i|k)] T (16)
in the formula (15), S (k + i | k) represents a scheduling plan vector of the short-time-scale-adjustable device for predicting the future k + i time at the time k; p g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k + i | k) predicting the active output of the renewable energy source, the charging and discharging power of the ESS, the reactive output of the renewable energy source and the reactive output power scheduling sequence of the ESS at the future time k + i for the time k respectively;
scheduling plan vectors of all adjustable devices obtained by long-time scale scheduling are used as reference values of short-time scale scheduling;
the active output power of the renewable energy source, the charging and discharging power of the ESS, the reactive output power of the renewable energy source and the reactive output power of the ESS at the k + i moment of the long-time scale scheduling stage are scheduled in a plan sequence; and N is a short time scale scheduling step length.
S6: and in the real-time control stage, monitoring the voltage of each node, and when node voltage is out of limit, sequentially adjusting each adjustable device based on the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node, so as to ensure the safe and stable operation of the power grid. The method for calculating the sensitivity of each adjustable device to the voltage out-of-limit node comprises the following steps:
s6.1: based on the alternating current power flow equation, linearizing the nonlinear power flow equation at the steady state solution, and obtaining the following matrix expression:
in the formula, Δ P and Δ Q are respectively an active and reactive variable matrix of each node injection system; delta theta and delta V are respectively variable matrixes of voltage phase angle and amplitude of each node; j is Jacobian matrix.
S6.2: inverting equation (18) in S6.1 yields:
s6.3: the relation between the voltage amplitude of each node in the power distribution network and the active power-reactive power can be expressed as follows:
ΔV＝S 21 ΔP+S 22 ΔQ (20)
wherein, in the formula (20), S 21 、S 22 The voltage-active sensitivity factor and the voltage-reactive sensitivity factor are respectively, and the sensitivity of the node where the adjustable equipment is located to the voltage out-of-limit node can be measured by S 21 And S 22 And (4) calculating.
The invention has the beneficial effects that:
(1) Considering that the line resistance and the reactance value in the power distribution network are close to each other, so that the active-reactive power coupling is stronger, the method calculates the operating characteristics of each adjustable device from the perspective of active-reactive coordination optimization regulation and control, and performs comprehensive coordination optimization regulation and control on the adjustable devices in the active power distribution network;
(2) Compared with the prior art, the multi-time-level active-reactive power regulation and control method for the multi-source collaborative active power distribution network has the advantages that scheduling problems and control problems in optimized operation of the power distribution network are considered cooperatively, so that full consumption and efficient operation of renewable energy sources in the power distribution network are facilitated, and the safe, stable and efficient operation of the system is guaranteed;
(3) Compared with the prior art, the invention fully utilizes the PCS to provide reactive support for the active Power distribution network, improves the utilization rate of the ESS and exerts the value of the ESS to a greater extent;
(4) According to the multi-time-level active-reactive power regulation and control method, in a long-time scale scheduling stage, the regulation scheme of regulation equipment with regulation frequency limit, such as OLTC and CB, and the charging and discharging state of the ESS are determined, and the influence of frequent regulation of the OLTC and CB and frequent charging and discharging conversion of the ESS on the service life of the ESS is solved; meanwhile, on the basis of the prediction information of wind, light and load on the short time scale, the scheduling instruction of equipment which can be rapidly and frequently adjusted such as wind power, photovoltaic, ESS and SVC is corrected, the influence of inaccurate prediction of wind, light and load on the charging and discharging state scheduling instruction of OLTC, CB and ESS on the long time scale is made up, and the reliability of system operation is enhanced;
(5) In the real-time control stage, when the node voltage is out of limit, each adjustable device is adjusted in sequence based on the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node, so that the impact of simultaneous action of multiple devices on the safety and stability of the system is reduced to a greater extent, and the safe and stable operation of a power grid can be effectively ensured.
Drawings
Fig. 1 is an overall flow chart of a multi-time-level active-reactive power regulation and control method of a multi-source collaborative active power distribution network of the invention;
FIG. 2 is a flow chart of prediction of wind power, photovoltaic and load power based on a wavelet-BP neural network time series algorithm.
Fig. 3 is a schematic diagram of a 3-scale wavelet decomposition.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 is a general method flowchart of the present invention, and the present invention provides a multi-time-level active-reactive power regulation method for a multi-source cooperative active power distribution network, which includes the following steps:
s1: predicting wind power, photovoltaic and load power based on a time series algorithm of a wavelet-BP neural network, decomposing a wind power output sequence, a photovoltaic output sequence and a load power sequence on different scales by utilizing wavelet transformation, predicting components of different frequencies by using a plurality of BP neural networks, and reconstructing each prediction result to obtain a complete prediction result; as shown in the attached figure 2, the method comprises the following specific steps:
s1.1: 3-scale wavelet decomposition is carried out on the original power sequence x to obtain a low-frequency part l 3 And a high-frequency part h 1 、h 2 And h 3 As shown in fig. 3;
s1.2: using BP neural network to measure low frequency part l 3 And a high-frequency part h 1 、h 2 、h 3 Performing prediction to obtain a low-frequency part L of the prediction component 3 And a high frequency part H 1 、H 2 And H 3 ；
S1.3: reconstructing the low-frequency prediction result and the high-frequency prediction result to obtain a complete prediction power sequence x * ＝L 3 +H 1 +H 2 +H 3 。
S2: based on a wind power, photovoltaic and load prediction error probability model, adopting Latin hypercube sampling to sample source-load uncertainty, and obtaining a scene set capable of covering all sampling spaces;
in consideration of the multi-dimensional randomness of wind power, photovoltaic and load which need to be described simultaneously in the generation of the source-load probability scene, the method utilizes a Latin hypercube sampling method to carry out multi-dimensional layered sampling by means of a multi-dimensional sampling theory and based on a wind power, photovoltaic and load prediction error probability model, and effectively avoids the phenomenon that the number of scenes is still too much after reduction caused by independent sampling of the wind power, the photovoltaic and the load.
The probability error model of the wind-solar load is as follows:
s2.1: the prediction error probability of the wind power output power meets Beta distribution, and the probability density function is as follows:
in the formula, alpha and beta are respectively a shape parameter and a scale parameter of a wind power output power prediction error probability density function; Δ P is the power prediction error.
S2.2: the load and photovoltaic contribution prediction errors are generally considered to satisfy a normal distribution, i.e., Δ P-N (μ, σ) 2 ) The probability density function is:
where μ and σ are the expected and standard deviation of the prediction error, respectively.
S3: scene reduction is carried out by utilizing a synchronous back-substitution reduction technology, a typical scene which can better reflect an original scene set is obtained, and corresponding probability of the typical scene is determined;
s4: in a long-time scale scheduling stage, a long-time scale active-reactive scheduling model of the active power distribution network facing the full consumption of renewable energy sources is built; obtaining an adjustment scheme of adjustment equipment with adjustment frequency limit, such as an on-load tap changing transformer, a capacitor bank and the like, and a charge-discharge state of an energy storage system;
in multi-time scale scheduling, a long-time scale optimization scheduling model facing renewable energy sources to be fully consumed for the active power distribution network is built in a long-time scale scheduling stage, the action times of discrete reactive power adjusting equipment are strictly limited in a scheduling period, and in the long-time scale scheduling stage, adjusting schemes of adjusting equipment with adjusting time limit, such as OLTC (on-line communication technology), CB (circuit board) and the like are determined; considering the influence of frequent charge-discharge conversion on the ESS, determining the charge-discharge state of the ESS, and keeping the charge-discharge state unchanged in short-time scale scheduling, wherein an objective function of a long-time scale optimization scheduling model of the active power distribution network for fully consuming renewable energy sources is as follows:
in the formula (23), C T And C CB The calculation method of (2) is shown in formula (24).
In formula (23), P g,t ,P ess,t ,B ess,t ,Q g,t ,Q SVC,t ,Q CB,t ,Q dis,t ,k T,t Actual active power of renewable energy sources, charging and discharging power of an ESS, charging and discharging states of the ESS, reactive output power of the renewable energy sources, reactive compensation power of an SVC, discrete compensation power of a CB, reactive output power of the ESS and switching gears of an OLTC at the moment t respectively; t is a regulation and control period; delta T is a long time scale regulation time interval; e is a system branch set; i all right angle ij , t The square of the current amplitude flowing through the branch ij at the moment t; r is ij Resistance for branch ij; c loss 、C T And C CB The unit cost of the system operation network loss, the gear adjusting cost in the OLTC dispatching cycle and the gear adjusting cost in the dispatching cycle of the capacitor group CB are respectively. The last item of the formula (23) is a power reduction penalty item of the renewable energy source, and lambda is an active power reduction penalty coefficient of the renewable energy source; phi is a unit of re A node set accessed by renewable energy sources in the power distribution network;
the predicted active power output of the renewable energy source at time t.
In the formula (24), p T And ρ C Unit adjustment costs of OLTC and CB, respectively; Δ K T And Δ K CB The adjustment times of the scheduling periods of the OLTC and the CB are respectively;
s5: on the basis of S4, in short time scale scheduling, on the basis of more accurate short-term prediction information of wind power, photovoltaic and load obtained from a time sequence algorithm of a wavelet-BP neural network, the switching capacity Q of the capacitor group CB obtained in a long time scale is kept CB,t Switching gear k of OLTC T,t And charging and discharging state B of ESS ess,t The minimum correction quantity of the other controlled variables is optimized without changingThe method comprises the following steps of aiming at building a short time scale scheduling model for efficient utilization of renewable energy, and solving to obtain scheduling instructions of adjustable equipment such as wind power, photovoltaic, ESS and SVC; the objective function of the short-time scale scheduling model for efficient utilization of renewable energy sources is as follows:
wherein the content of the first and second substances,
S(k+i|k)＝[P g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k+i|k)] T (26)
in the formula (25), S (k + i | k) represents a scheduling plan vector of the short-time-scale-adjustable device for predicting the k + i time in the future at the time k; p g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k + i | k) predicting the active output of the renewable energy source, the charging and discharging power of the ESS, the reactive output of the renewable energy source and the reactive output power scheduling sequence of the ESS at the future time k + i for the time k respectively;
scheduling plan vectors of all adjustable devices obtained by long-time scale scheduling are used as reference values of short-time scale scheduling;
the active output power of the renewable energy source, the charging and discharging power of the ESS, the reactive output power of the renewable energy source and the reactive output power of the ESS at the k + i moment of the long-time scale scheduling stage are scheduled in a plan sequence; and N is a short time scale scheduling step length.
S6: and in the real-time control stage, monitoring the voltage of each node, and when node voltage is out of limit, sequentially adjusting each adjustable device based on the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node, so as to ensure the safe and stable operation of the power grid. The method for calculating the sensitivity of each adjustable device to the voltage out-of-limit node comprises the following steps:
s6.1: based on the alternating current power flow equation, the nonlinear power flow equation is linearized at the steady state solution, and the following matrix expression can be obtained:
in the formula, Δ P and Δ Q are respectively an active and reactive variable matrix of each node injection system; delta theta and delta V are respectively variable matrixes of voltage phase angle and amplitude of each node; j is the Jacobian matrix.
S6.2: inversion of equation (28) in S6.1 yields:
s6.3: the relation between the voltage amplitude of each node in the power distribution network and the active power-reactive power can be expressed as follows:
ΔV＝S 21 ΔP+S 22 ΔQ (30)
wherein, in the formula (30), S 21 、S 22 The voltage-active sensitivity factor and the voltage-reactive sensitivity factor are respectively, and the sensitivity of the node where the adjustable equipment is located to the voltage out-of-limit node can be measured by S 21 And S 22 And (4) calculating.
The foregoing describes specific embodiments of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended as illustrations of the strategy of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and the invention is to be construed as falling within the scope of the invention as claimed. The scope of the invention is defined by the appended claims.
Claims (5)
1. A multi-time-level active-reactive power regulation and control method for a multi-source collaborative active power distribution network comprises the following steps:
step S1: predicting wind power, photovoltaic and load power sequences based on a time sequence algorithm of a wavelet-BP neural network;
step S2: based on a wind power, photovoltaic and load prediction error probability model, performing source-load uncertainty sampling by adopting Latin hypercube sampling to obtain a scene set capable of covering all sampling spaces;
and step S3: scene reduction is carried out by utilizing a synchronous back-substitution reduction technology, a typical scene capable of reflecting an original scene set is obtained, and the corresponding probability of the typical scene is determined;
and step S4: in a long-time scale scheduling stage, building a day-ahead active-reactive power scheduling model of the active power distribution network facing the full consumption of renewable energy; obtaining a regulation scheme of discrete regulation equipment with regulation frequency limit of an on-load tap changer (OLTC) and a Capacitor Bank (CB) and a charging and discharging state of an Energy Storage System (ESS);
step S5: in a short-time scale scheduling stage, on the basis of the predicted power of short-time scale wind power, photovoltaic and load, the minimum correction quantity of a control variable is taken as an optimization target; solving to obtain a scheduling instruction of the wind power, photovoltaic and energy storage system and SVC adjustable equipment;
step S6: in the real-time control stage, monitoring the voltage of each node, and when node voltage is out of limit, sequentially adjusting each adjustable device based on the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node to ensure the safe and stable operation of the power grid;
in the step S4, in the long time scale scheduling stage, the adjusting scheme of the adjusting equipment with the adjusting frequency limit and the charging and discharging state of the stored energy are determined and are kept unchanged in the short time scale scheduling; the objective function of the active power distribution network long-time scale optimization scheduling model for fully absorbing renewable energy sources is as follows:
in the formula (3), C T And C CB The calculation method of (2) is shown in formula (4):
in the formula (3), P g,t ,P ess,t ,B ess,t ,Q g,t ,Q SVC,t ,Q CB,t ,Q dis,t ,k T,t Actual active power of renewable energy sources, charging and discharging power of the ESS, charging and discharging states of the ESS, reactive output power of the renewable energy sources, reactive compensation power of a Static Var Compensator (SVC), discrete compensation power of a CB, reactive output power of the ESS and switching gears of an OLTC at the moment t respectively; t is a regulation and control period; delta T is a long time scale regulation time interval; e is a system branch set; i.e. i ij,t The square of the current amplitude flowing through the branch ij at the moment t; r is ij Resistance for branch ij; c loss 、C T And C CB Unit cost of system operation network loss, gear adjusting cost in an OLTC dispatching cycle and gear adjusting cost in a dispatching cycle of a capacitor group CB are respectively set; the last item of the formula (3) is a power reduction penalty item of the renewable energy source, and lambda is an active power reduction penalty coefficient of the renewable energy source; phi is a unit of re A node set accessed by renewable energy sources in the power distribution network;
predicting the active power output of the renewable energy source at the time t;
in the formula (4) (. Rho) T And ρ C Unit adjustment costs of OLTC and CB, respectively; Δ K T And Δ K CB The adjustment times of the scheduling periods of the OLTC and CB, respectively.
2. The multi-source cooperative active power distribution network multi-time level active-reactive power regulation and control method of claim 1, wherein: in step S1, the specific steps are as follows:
s1.1: 3-scale wavelet decomposition is carried out on the original power sequence x to obtain a low-frequency part l 3 And a high-frequency part h 1 、h 2 And h 3 ；
S1.2: using BP neural network to measure low frequency part l 3 And a high-frequency part h 1 、h 2 、h 3 Performing prediction to obtain a low-frequency part L of the prediction component 3 And a high frequency part H 1 、H 2 And H 3 ；
S1.3: reconstructing the low-frequency prediction result and the high-frequency prediction result to obtain a complete prediction power sequence x * ＝L 3 +H 1 +H 2 +H 3 。
3. The multi-source cooperative active power distribution network multi-time-level active-reactive power regulation and control method of claim 1, wherein: in the step S2, the process is carried out,
the probability error model of the wind-solar load is as follows:
s2.1: the prediction error probability of the wind power meets Beta distribution, and the probability density function is as follows:
in the formula, alpha and beta are respectively a shape parameter and a scale parameter of a wind power output power prediction error probability density function; Δ P is the power prediction error;
s2.2: the photovoltaic and load prediction errors satisfy a normal distribution, and the DeltaP follows the normal distribution, i.e., deltaP-N (mu, sigma) 2 ) The probability density function is:
where μ and σ are the expected and standard deviation of the prediction error, respectively.
4. The multi-source cooperative active power distribution network multi-time level active-reactive power regulation and control method of claim 1, wherein: in the step S5, on the basis of the predicted power of the short-time-scale wind power, the photovoltaic and the load, the minimum correction quantity of the control variable is taken as an optimization target; solving to obtain a scheduling instruction of the wind power, photovoltaic, ESS and SVC adjustable equipment; the objective function of the short-time scale scheduling model for the efficient utilization of renewable energy sources is as follows:
wherein the content of the first and second substances,
S(k+i|k)＝[P g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k+i|k)] T (6)
in the formula (5), S (k + i | k) represents a scheduling plan vector of the short-time-scale adjustable device for predicting the future k + i time at the time k; p g,t (k+i|k),P ess,t (k+i|k),Q g,t (k+i|k),Q SVC,t (k+i|k),Q dis,t (k + i | k) predicting a renewable energy active output, ESS charge and discharge power, renewable energy reactive output and ESS reactive output power scheduling sequence of the ESS at the future time k respectively;
scheduling plan vectors of all adjustable devices obtained by long-time scale scheduling are used as reference values of short-time scale scheduling;
are respectively longA renewable energy active power output, ESS charge and discharge power, renewable energy reactive output power and ESS reactive output power scheduling plan sequence at the time of k + i in the time scale scheduling stage; and N is a short time scale scheduling step size.
5. The multi-source cooperative active power distribution network multi-time level active-reactive power regulation and control method of claim 1, wherein:
in step S6, the method for calculating the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node is as follows:
s6.1: based on the alternating current power flow equation, linearizing the nonlinear power flow equation at the steady state solution to obtain the following matrix expression:
in the formula, Δ P and Δ Q are respectively an active and reactive variable matrix of each node injection system; delta theta and delta V are respectively variable matrixes of voltage phase angle and amplitude of each node; j is Jacobian matrix;
s6.2: inversion of equation (8) in S6.1 yields:
s6.3: the relation between the voltage amplitude of each node in the power distribution network and the active power-reactive power can be expressed as follows:
ΔV＝S 21 ΔP+S 22 ΔQ (10)
wherein, in formula (10), S 21 、S 22 The voltage-active and voltage-reactive sensitivity factors are respectively, and the sensitivity of the node where each adjustable device is located to the voltage out-of-limit node can be measured by S 21 And S 22 And (4) calculating.
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