CN109861202B - Dynamic optimization scheduling method and system for flexible interconnected power distribution network - Google Patents
Dynamic optimization scheduling method and system for flexible interconnected power distribution network Download PDFInfo
- Publication number
- CN109861202B CN109861202B CN201811356652.3A CN201811356652A CN109861202B CN 109861202 B CN109861202 B CN 109861202B CN 201811356652 A CN201811356652 A CN 201811356652A CN 109861202 B CN109861202 B CN 109861202B
- Authority
- CN
- China
- Prior art keywords
- node
- voltage
- distribution network
- power
- flexible
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000005457 optimization Methods 0.000 title claims description 73
- 230000035945 sensitivity Effects 0.000 claims abstract description 9
- 230000006870 function Effects 0.000 claims description 29
- 238000005096 rolling process Methods 0.000 claims description 26
- 239000013598 vector Substances 0.000 claims description 17
- 230000005611 electricity Effects 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000013178 mathematical model Methods 0.000 claims description 13
- 230000003044 adaptive effect Effects 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- IAZDPXIOMUYVGZ-UHFFFAOYSA-N Dimethylsulphoxide Chemical compound CS(C)=O IAZDPXIOMUYVGZ-UHFFFAOYSA-N 0.000 claims description 2
- 230000017105 transposition Effects 0.000 claims description 2
- 238000012937 correction Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a dynamic optimal scheduling method and system for a flexible interconnected power distribution network. Firstly, based on renewable energy and load ultra-short-term power prediction information, a voltage prediction model is established by calculating the voltage sensitivity of each node, then an optimized operation model of the flexible power distribution network is established, the aim of minimizing the deviation between the predicted voltage and the rated voltage and the comprehensive power supply cost is fulfilled, and the scheduling instruction value of the MBVH is optimally solved and issued by combining a self-adaptive dynamic weight method and feedback correction.
Description
Technical Field
The invention belongs to the technical field of optimized operation of active power distribution networks, and particularly relates to a dynamic optimization scheduling method and system of a flexible interconnected power distribution network based on model predictive control.
Background
The 'closed-loop design and open-loop operation' mode which is carried out for a long time by the distribution network in China not only influences the further improvement of the power supply reliability, but also is difficult to meet the DG (distributed generation) friendly access. Multi-terminal back-to-back flexible direct voltage converter (MBVH) is a newly developed power grid flexible control technology, which not only can realize long-term safe loop-closing operation of any feeder line, but also can accurately regulate and control the power flow distribution among feeder lines. The flexible interconnection operation of the power distribution network is an important way for improving the power supply reliability and ensuring the full-scale consumption of the high-permeability DG.
Renewable energy output and load demand prediction errors in the flexible interconnected power distribution network increase along with the increase of the prediction time scale, so that the day-ahead optimized scheduling result is directly applied to the day-to-day operation of the flexible interconnected power distribution network, and the voltage out-of-limit risk caused by the uncertainty of wind, light and load cannot be fully considered. Model Predictive Control (MPC) can solve the problem of system optimization control containing various uncertain factors based on the ideas of rolling optimization and feedback correction. The coordination between the operation economy and the safety of the active power distribution network belongs to a multi-objective optimization problem, and the coordination problem among the objectives is necessary to be considered for determining the overall optimal scheme of the operation of the distribution network. The multi-objective optimization problem is solved mainly by a multi-objective optimization algorithm and a weighted summation method. Solving by adopting a multi-objective optimization algorithm, and when a plurality of local optimal solutions are contained, convergence to a global optimal solution cannot be guaranteed; and the method adopts a weighted summation method to solve, and is difficult to adapt to the difficulty brought by the change of the network running state to weight selection. In addition, unlike the regulation and control objects such as an on-load tap changer, a compensation capacitor bank and an interruptible load, the MBVH in the flexible interconnected power distribution network has rapid and flexible power regulation capability, so that the model prediction control can generate better application effect in the flexible interconnected power distribution network if the characteristics of the regulation and control object MBVH can be combined.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a model prediction control-based dynamic optimization scheduling method and system for a flexible interconnected power distribution network, which can balance the economy and the safety of the operation of the flexible interconnected power distribution network.
The technical scheme is as follows: the invention relates to a dynamic optimization scheduling method of a flexible interconnected power distribution network based on model predictive control, which comprises the following steps:
(1) active power and reactive power of each converter of the multi-end back-to-back flexible-to-direct MBVH are used as control variables, and voltage of each node of the system in a k +1 period is predicted by using a pre-established voltage prediction model;
(2) the method comprises the steps that predicted voltages of all nodes of a k +1 time period system are used as input variables, a pre-established mathematical model of the flexible interconnected power distribution network with the multiple ends connected in a back-to-back flexible and direct mode is optimized, active power and reactive power of all converters with the multiple ends connected in the back-to-back flexible and direct mode in the k +1 time period are solved and used as scheduling instruction values of the multiple ends connected in the back-to-back flexible and direct mode in the k +1 time period;
(3) issuing a multi-end back-to-back flexible and straight scheduling instruction value in the k +1 time period to a scheduling center;
(4) measuring actual voltages of all nodes in the system after a multi-end back-to-back flexible and straight scheduling instruction value in a k +1 time period is issued, taking the actual voltages as initial values of a voltage prediction model in the next rolling optimization time period, and turning to the step (1) when k is equal to k +1 to enter a new round of optimization; and ending the optimization until the whole scheduling period is traversed.
The pre-established constraint equation for the operation of the multi-end back-to-back flexible and direct connection flexible interconnected power distribution network mathematical model is as follows:
active power balance constraint:
and (3) converter capacity constraint:
wherein N isVSCTotal number of converters for MBVH; pk(t)、Qk(t) the active power and the reactive power of the kth converter at the moment t are respectively, and the flowing-in feeder line is in a positive direction; a. thekThe loss coefficient of the kth converter; skThe rated capacity of the kth converter.
Active power and reactive power of each converter of the multi-end back-to-back flexible-to-direct MBVH are used as control variables, and voltage of each node of a k +1 time period system is predicted by using a pre-established voltage prediction model, and the method comprises the following steps:
respectively solving partial derivatives of the voltage amplitude and the phase of each node of the tidal current equation to obtain a Jacobian matrix J, and solving the inverse of the Jacobian matrix J to obtain a sensitivity matrix of each node voltage relative to the active power and the reactive power injected into each node:
[Δ1,ΔU1,…,Δn,ΔUn]T＝J-1[ΔP1,ΔQ1,…,ΔPn,ΔQn]T
wherein: delta1,ΔU1Voltage phase and amplitude variation of the distribution network node 1 are respectively; deltan,ΔUnRespectively representing the voltage phase and amplitude variation of the distribution network node n; delta P1,ΔQ1Active power and reactive power variable quantity are respectively injected into the distribution network node 1; delta Pn,ΔQnRespectively injecting active power and reactive power variable quantity into a distribution network node n;
predicting the voltage of each node according to the sensitivity matrix:
U(k+Δt)＝U(k)+ΔUG(k)+ΔUD(k)
wherein: u (k) represents the vector formed by the voltages of the nodes of the distribution network at the moment k, delta UG(k) Indicating the change of output power of each converter at the moment k by MBVHVector of voltage changes at each node, Δ UD(k) A vector which represents voltage changes of each node caused by renewable energy sources and load power fluctuation at the k moment; u (k + delta t) represents a vector formed by voltages of nodes of the distribution network at the moment of k + delta t;
repeatedly iterating the voltage prediction equation until the step P is predicted forwards to obtain a vector U formed by the prediction output values of the voltages of all nodes in the prediction time domain P delta tfU for vectors formed by rated values of voltages at nodesRRepresents:
UR＝[U1 r(k+Δt)…Un r(k+Δt),…,U1 r(k+PΔt)…Un r(k+PΔt)]T
wherein, UfA vector formed by the estimated output values of the voltages of all the nodes in the predicted time length P delta t;
the predicted voltages of the node 1 and the node n at the moment k + delta t are respectively;
the predicted voltages of the node 1 and the node n at the moment k + P delta t are respectively;
rated voltages of the node 1 and the node n at the moment k + delta t are respectively set;
rated voltages of the node 1 and the node n at the moment k + P delta t are respectively set; t denotes a symbol of matrix transposition.
The pre-established mathematical model of the flexible interconnected power distribution network with the multiple ends connected flexibly and directly back to back takes the minimum deviation between the comprehensive power supply cost and the predicted value and the rated value of the voltage of each node in the system as the optimization target.
The pre-established objective function of the flexible interconnected power distribution network mathematical model with the multiple ends connected back-to-back flexibly and directly is as follows:
wherein, t0Is the current time; f is a total objective function; f1A target function for the lowest comprehensive power supply cost; f2Is a node voltage deviation minimum objective function; f. of1(t) and f2(t) the electricity purchasing cost and the network loss cost at the moment t are respectively; ci(t)、PSTi(t)、PDGi(t)、PDi(t) respectively representing the electricity price of a bus node at a node i at the moment t, the outlet power of a transformer substation, the active output of a distributed power supply and the active power of a load; cw(t) the electricity price for purchasing electricity at the moment t; Δ t represents the time interval of a single period in one scheduling cycle; n represents the number of nodes in the power distribution network;
the pre-established constraint conditions of the multi-end back-to-back flexible and direct connection flexible interconnected power distribution network mathematical model are as follows:
wherein, Ui(t)、Uj(t) is the voltage amplitude of the node i and the node j at the moment t; gij、BijRespectively are mutual conductance and mutual susceptance between a node i and a node j;ij(t) is the phase difference between node i and node j at time t; pVSCi(t)、QVSCi(t) respectively outputting active power and reactive power by the MBVH converter at a node i at the time t; qDi(t) is the reactive power of the load at the node j at the time t; sij(t) is the line power between node i and node j at time t; the superscripts "-" and subscripts "_" of the variables denoteAn upper limit and a lower limit.
The flexible interconnected distribution network mathematical model to the many ends back-to-back gentle straight connections that establish in advance optimizes, includes:
self-adaptive adjustment F according to distribution network operation condition1And F2And (3) weight distribution between the two objective functions, and establishing a total objective function F:
where α is the adaptive weight, and the optimization objective function F2The correlation is linear and the correlation is linear,1and2are coefficients of corresponding linear relations, and1、2≥0，F2maxand F1maxThe maximum values of the voltage deviation and the comprehensive power supply cost are respectively;
the integrated power supply cost target F1And voltage deviation target F2Is determined based on the adaptive weights.
The initial values of the voltage prediction model are:
U(k+1)＝Ureal(k+1)
wherein, Ureal(k +1) after the MBVH scheduling command value at the k +1 time period is issued, measuring the actual node voltage value at the k +1 time period through an actual measuring system; u (k +1) is an initial value of the voltage prediction model.
The scheduling period is one day.
A dynamic optimization scheduling system of a flexible interconnected power distribution network comprises a prediction module, a rolling optimization module and a feedback correction module;
the prediction module is used for predicting the voltage of each node of the system in the k +1 time period by using the active power and the reactive power of each converter of the multi-end back-to-back flexible-direct MBVH as control variables and utilizing a pre-established voltage prediction model, and transmitting the prediction result to the rolling optimization module;
the rolling optimization module is used for optimizing a pre-established mathematical model of the flexible interconnected power distribution network with the multi-end back-to-back flexible and direct connection by taking the predicted voltage of each node of the system at the k +1 time period as an input variable, solving active power and reactive power of each converter at the multi-end back-to-back flexible and direct connection at the k +1 time period, taking the active power and the reactive power as scheduling instruction values of the multi-end back-to-back flexible and direct connection at the k +1 time period, issuing the scheduling instruction values to a scheduling center, and repeating rolling optimization;
the feedback correction module is used for measuring actual voltage of each node in the system after a multi-end back-to-back flexible and straight scheduling instruction value in the k +1 time period is issued, and transmitting the actual voltage to the prediction module as an initial value of a voltage prediction model in the next rolling optimization time period.
Compared with the prior art, the invention has the beneficial effects that:
the problems that the voltage generated by the random fluctuation of the distributed power output and the load demand exceeds the limit and the like are effectively solved, and the economical efficiency and the safety of the operation of the flexible interconnected power distribution network are effectively balanced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an optimized scheduling architecture based on model predictive control;
FIG. 3 is a flexible interconnect power distribution network;
FIG. 4 is a 33 node power distribution system;
fig. 5 is a comparison graph of the actual output of the feeder line a DG before, during and during the day;
fig. 6 is a comparison graph of the actual output of the B feeder DG before, in the day and in the daytime;
fig. 7 is a comparison graph of the actual output of the C feeder DG before, during and during the day;
fig. 8 is a comparison graph of the actual output of the D feeder DG before, during and after the day;
FIG. 9 is a diagram of the result of MBVH scheduling in example A;
FIG. 10 is a diagram of the result of MBVH scheduling in example B;
FIG. 11 is a graph comparing system node voltages;
FIG. 12 is a comparison of the total power supply costs;
FIG. 13 is a graph of the system voltage average offset index;
fig. 14 is a diagram of adaptive weight change.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
The model prediction control-based flexible interconnected power distribution network optimal scheduling method mainly comprises three links of prediction model, rolling optimization and feedback correction. The prediction model is mainly based on ultra-short-term prediction data of wind power, photovoltaic and load, combined with a voltage sensitivity method, predicts the voltage of each node of the distribution network in a prediction time domain, and transmits the prediction result to a rolling optimization link. In the rolling optimization link, the deviation of the predicted value and the rated value of the voltage of each node of the distribution network in a control time domain and the minimum comprehensive power supply cost are taken as targets, a scheduling instruction of multi-end back-to-back flexible direct (MBVH) in the control time domain is obtained, the scheduling instruction of a first time interval is issued, a time window is sequentially shifted backwards by one time interval, and rolling optimization is repeated. It should be noted that, in the present invention, a single time interval is used as a control time domain in combination with the characteristic of fast and flexible power adjustment of the MBVH as the control object, and the calculation time length can be reduced without affecting the optimization effect. The feedback correction link mainly measures the actual voltage value of each node of the distribution network and transmits the actual measurement result as feedback information to the rolling optimization link. The three links are closely connected to form a dynamic optimization scheduling framework of the flexible interconnected power distribution network based on model predictive control, so that the running safety and economy of the flexible interconnected power distribution network are improved, and the optimization scheduling framework based on the model predictive control is shown in fig. 2.
As shown in fig. 1, a dynamic optimization scheduling method for a flexible interconnected power distribution network includes the following steps:
1. active power and reactive power of each converter of the multi-end back-to-back flexible-direct MBVH are used as control variables, and voltage of each node of the system in the k +1 time period is predicted by using a pre-established voltage prediction model.
Each feeder in the flexible interconnected power distribution network is connected with each other through multi-end back-to-back flexible and straight lines, the feeders comprise distributed power supplies such as wind power and photovoltaic power and user loads, and the structure of the feeder is shown in figure 3. The direct current sides of a plurality of groups of AC/DC bidirectional converters in the multi-end back-to-back flexible-to-straight mode are connected in parallel to the same direct current bus, and the alternating current sides are connected with all the feeder lines respectively. The power can be flexibly exchanged among the feeders and mutual support can be formed, so that uniform and flexible interconnection is realized. And the multi-end back-to-back flexible and straight control variables are the active power and the reactive power of each group of converters. Each MVBH converter can generate certain power loss when active power flow is transferred in a large range, and certain loss coefficients are considered in the modeling process. The isolation of the direct current link enables the output reactive power of the converters not to be influenced mutually, so that the capacity constraint of each converter is only required to be met. The constraint equation for MBVH operation is as follows:
1) active power balance constraint
2) Converter capacity constraint
In the formula: n is a radical ofVSCTotal number of converters for MBVH; pk(t)、Qk(t) the active power and the reactive power of the kth converter at the moment t are respectively, and the flowing-in feeder line is in a positive direction; a. thekThe loss coefficient of the kth converter; skThe rated capacity of the kth converter.
And predicting the voltage of each node in the flexible interconnected power distribution network based on the prediction data of the renewable energy sources and the load and a voltage sensitivity method.
Firstly, respectively solving partial derivatives of voltage amplitude and phase of each node of a power flow equation to obtain a Jacobian matrix J, further solving the inverse of the Jacobian matrix J to obtain a sensitivity matrix of each node voltage relative to active power and reactive power injected into each node, wherein the formula (3) is as follows:
and secondly, based on the ultra-short-term power prediction information of DG output and load demand in the power distribution network, and in combination with the sensitivity matrix shown in the formula (3), the voltage of each node can be approximately predicted, and the voltage is shown in the formula (4).
U(k+Δt)＝U(k)+ΔUG(k)+ΔUD(k) (4)
In the formula: u (k) represents a vector formed by voltages of nodes of the distribution network at the moment k; delta UG(k) A vector which represents voltage variation of each node caused by output power variation of each converter of MBVH at the moment k; delta UD(k) And a vector which represents voltage changes of each node caused by renewable energy sources and load power fluctuation at the k moment.
Then, according to the voltage prediction equation, combining DG and the ultra-short-term power prediction data of the load, repeatedly iterating the voltage prediction equation until the step P is predicted forwards, and obtaining a vector U formed by the prediction output values of the voltages of all nodes in the prediction time domain P delta tfU for vectors formed by rated values of voltages at nodesRAnd (4) showing.
2. And (3) with the predicted voltage of each node of the system in the k +1 time period as an input variable, optimizing a pre-established mathematical model of the flexible interconnected power distribution network with the multiple ends connected back-to-back flexibly and directly, and solving active power and reactive power of each converter with the multiple ends connected back-to-back flexibly and directly in the k +1 time period as scheduling instruction values of the multiple ends connected back-to-back flexibly and directly in the k +1 time period.
And (3) taking the minimum deviation between the comprehensive power supply cost and the predicted value and the rated value of the voltage of each node in the system as an optimization target, wherein the equations (10) and (11) are respectively an objective function and a constraint condition in a rolling optimization stage.
An objective function:
in the formula: t is t0Is the current time; f is a total objective function; f1A target function for the lowest comprehensive power supply cost; f2Is a node voltage deviation minimum objective function; f. of1(t) and f2(t) the electricity purchasing cost and the network loss cost at the moment t are respectively; ci(t)、PSTi(t)、PDGi(t)、PDi(t) respectively representing the electricity price of a bus node at a node i at the moment t, the outlet power of a transformer substation, the active output of a distributed power supply and the active power of a load; cwAnd (t) is the electricity purchase price at the time t.
Constraint conditions are as follows:
in the formula: u shapei(t)、Uj(t) is the voltage amplitude of the node i and the node j at the moment t; gij、BijRespectively are mutual conductance and mutual susceptance between a node i and a node j;ij(t) is the phase difference between node i and node j at time t; pVSCi(t)、QVSCi(t) respectively outputting active power and reactive power by the MBVH converter at a node i at the time t; qDiAnd (t) is the reactive power of the load at the node j at the time t. Sij(t) is the line power between node i and node j at time t; the superscript "-" and subscript "_" of a variable denote the upper and lower limits of the variable.
In order to solve the multi-objective optimization problem in the power distribution network, the invention uses the sub-objective function F in the formula (10)1And F2By passingThe weighted summation method is aggregated into a single function, and a self-adaptive dynamic weight optimization method is adopted to self-adaptively adjust the weight distribution between two targets according to the operation condition of the distribution network. Firstly, performing per unit processing on each target function to make the dimensions of the target functions identical, and then establishing a total target function F:
where α is the adaptive weight, and the optimization objective function F2The correlation is linear and the correlation is linear,1and2are coefficients of corresponding linear relations, and1、2≥0，F2maxand F1maxRespectively, the maximum value of the voltage deviation and the comprehensive power supply cost. The relevant constraint is shown in equation (11).
Final integrated power supply cost target F1And voltage deviation target F2The adaptive weight is determined by equation (12). If the voltage deviation F2The smaller, as can be seen from equation (12), the weight α will be correspondingly reduced, and the weight 1- α of the total power supply cost will be increased; on the contrary, if the voltage deviation F2If the weight is larger, the weight alpha is increased, and the weight 1-alpha corresponding to the comprehensive power supply cost is reduced.
3. And issuing the multi-end back-to-back flexible and straight dispatching instruction value in the k +1 time period to a dispatching center.
And obtaining an optimized control sequence formed by MBVH scheduling instruction values in the control time domain after solving the optimized model, only issuing the scheduling instruction value of the first time interval in the control time domain, and repeating the rolling optimization process when waiting for the arrival of the next control time domain.
4. Measuring actual voltages of all nodes in the system after a multi-end back-to-back flexible and straight scheduling instruction value in a k +1 time period is issued, taking the actual voltages as initial values of a voltage prediction model in the next rolling optimization time period, and turning to the step (1) when k is equal to k +1 to enter a new round of optimization; and ending the optimization until the whole scheduling period is traversed.
After the MBVH dispatching instruction value in the k +1 time period is issued, the actual measurement value of each node voltage of the system is used as the initial value of a voltage forecasting model in the next round of rolling optimization process, so that the error of the node voltage forecasting value is reduced, and the whole dispatching process is more practical.
U(k+1)＝Ureal(k+1) (13)
In the formula: u shapereal(k +1) after the MBVH scheduling command value at the k +1 time period is issued, measuring the actual node voltage value at the k +1 time period through an actual measuring system; u (k +1) is an initial value of the voltage prediction model.
As the Model Predictive Control (MPC) takes the actual value of the system voltage as the initial value in the voltage predictive model when the rolling optimization is executed each time, and updates the renewable energy ultra-short term predictive power value, the stability and the robustness of the rolling optimization strategy are ensured.
In order to verify the feasibility and effectiveness of the optimized scheduling method provided by the invention, the invention takes a 33-node arithmetic system as shown in fig. 4 as an example for analysis, and the system is formed by connecting 4 feeders from 4 different substations through MBVH.
The rated voltage of the system is 10kV, and the YJV 22-3X 400 type cable used in the urban distribution network mainstream in China is selected as the line. In the example, 5 groups of photovoltaic systems and 4 wind power units are accessed, and the configuration parameters are shown in table 1. The peak time electricity prices (07: 00-19: 00) and the valley time electricity prices (19: 00-07: 00) of the electricity purchase are shown in Table 2. The rated capacity of the MBVH converter is 3MVA, and the loss coefficient is 0.02. The power interval of the substation outlet is 0 MW-8 MW (power is not allowed to be sent backwards), the line capacity is 8MVA, and the value range of each node voltage is [0.93,1.07] (per unit value).
MPC parameter setting takes the prediction time and the control time as 5min, the execution cycle of rolling optimization control is 5 min/time, the total execution is 288 times in one day, and the adaptive weight coefficient1And2the coefficient is respectively 0.6 and 0.4, the coefficient is selected only as demonstration setting, and the coefficient can be adjusted according to the distribution network state and the requirement during actual operation. An example simulation was programmed to solve in the MATLAB R2014a environment.
TABLE 1 DG configuration parameters
TABLE 2 Electricity price parameters
Analyzing the optimization result of MPC:
in order to compare and verify the effectiveness of the MPC-based optimal scheduling method, the invention respectively sets two calculation examples: carrying out day-ahead scheduling on MBVH in the flexible interconnected power distribution network, and applying a scheduling result to actual operation of the flexible interconnected power distribution network to set the scheduling result as an example A; and (3) scheduling the MBVH in the flexible interconnected power distribution network by adopting an MPC (MPC) optimization scheduling method, and applying a scheduling result to actual operation of the flexible interconnected power distribution network to set the scheduling result as an example B.
The comparison curves of the power output of each feed line DG before the day, in the day and the actual power output are shown in FIGS. 5-8. The MBVH scheduling results in example A, B are shown in fig. 9 and 10, respectively. FIG. 11 shows the maximum and minimum voltages at each node of the system of example A, B. Fig. 12 is a comparison of the integrated power supply cost of the system in example A, B.
As can be seen from fig. 5 to 8, the output of each feeder DG has strong randomness and volatility, the prediction accuracy of the output of each feeder DG before the day is low, the deviation from the actual output is large, and the node voltage out-of-limit condition occurs in a part of time period in the example a, as shown in the example a in fig. 11, so that the current scheduling instruction of the MBVH cannot meet the actual operation requirement of the flexible interconnected power distribution network. In view of the above, the present invention uses the prediction information of DG and load in a period of time window in the future as input variables, aims to minimize the system node voltage deviation and the comprehensive power supply cost, combines a self-adaptive dynamic weighting method, uses the actual value of the system voltage in each period as feedback information, and adopts MPC rolling optimization to solve the scheduling command value of each converter of MBVH, as shown in fig. 10. The fast and flexible power regulation capability of the control object MBVH in the flexible interconnection power distribution network can be seen from the graph.
The node voltage curve obtained by applying the MBVH scheduling instruction obtained by the MPC optimization scheduling method to the actual system is shown as an example B in fig. 11, and it can be seen from the graph that the node voltage out-of-limit problem in part of time period caused by the DG output prediction error is effectively solved, and the safety of the system operation is improved. Further, as can be seen from fig. 12, the overall power supply cost in example B is significantly reduced as a whole as compared to example a. The analysis shows that although the calculation example A solves the MBVH scheduling instruction by taking the lowest comprehensive power supply cost as an optimization target and issues the MBVH scheduling instruction, the deviation between the DG day-ahead output prediction data and the actual value reduces the economical efficiency of the actual operation of the system. And the calculation example B adopts an MPC optimization scheduling method, prediction information of DG and load in a future period is used as input variables, rolling optimization solution is carried out, prediction errors are reduced compared with the calculation example A, and the economy of the MBVH scheduling instruction value obtained after optimization solution applied to the actual operation of the system is improved compared with the calculation example A.
In conclusion, after the MPC optimization scheduling method is adopted, the system voltage deviation is reduced, the comprehensive power supply cost is reduced, and the economical efficiency and the safety of system operation are improved on the whole.
In addition, the invention combines the fast and flexible power regulation capability of the MBVH of the control object, and a single time interval is selected as a rolling optimization period in the MPC optimization process. To verify the superiority of the method, the present invention sets examples B1 and B2 for comparative analysis. The MPC optimization method adopted in the example B1 is the same as that of the example B, and all the MPC optimization methods take a single time interval as a rolling optimization period; example B2 shows a rolling optimization period in three time intervals. Through calculation, the comprehensive power supply cost in the example B1 is slightly different from that in the example B2, namely 47463.57 yuan and 47548.26 yuan, but the calculation time length of the single rolling in the example B1 is reduced by 1.819s compared with the example B2, and the advantage of the example B1 in the calculation time length is more obvious along with the increase of the scale of the flexible interconnection power distribution network.
Analyzing a self-adaptive dynamic weight multi-objective optimization result:
the invention respectively adopts the following three schemes to compare and verify the effectiveness of the self-adaptive dynamic weight multi-objective optimization method: in the scheme 1, fixed weights of 0.7 and 0.3 are respectively taken for voltage deviation and comprehensive power supply cost; scheme 2 is voltage deviation and comprehensive power supplyRespectively taking fixed weights of 0.9 and 0.1; scheme 3 is the adaptive dynamic weight multi-objective optimization method adopted by the invention. For the convenience of analysis, the invention defines the average offset index (AVO) of the system voltage after per unit processing in each time interval ii) Comprises the following steps:
the node voltage evaluation index (VI) after per unit processing in the whole operation period of the system is as follows:
in the formula: i represents a time period number; j represents a node number; m represents the total time period number; n represents the total number of system nodes; | Δ UijI represents the absolute value of the voltage amplitude offset of the j node in the ith period, | Delta Uij.maxAnd | represents the maximum value of the absolute value of the voltage amplitude offset of the j node in the ith period.
FIG. 13 illustrates an example of a period of 08:00 to 18:00, comparing the average offset index AVO of the system voltage under three schemesiAs can be seen from the figure, in the scheme 2, the weight corresponding to the voltage deviation is increased on the basis of the scheme 1, and as the weight of the voltage deviation is increased, the average offset index AVO of the system voltage is increasediThe optimization is remarkable, and the optimization is obviously reduced in the whole time period. However, a lower voltage average offset indicator also results in an increase in the overall power supply cost, as shown in table 3. The scheme adopts a fixed weight method, and the weight setting between the node voltage deviation and the comprehensive power supply cost is difficult to balance and reasonably reject in the actual operation process of the distribution network. Meanwhile, with the gradual increase of the permeability of the renewable distributed power supply in the flexible interconnected power distribution network, the running state of the power distribution network becomes more variable. In this regard, the present invention adopts the adaptive dynamic weighting method (scheme 3), and the simulation results are shown in fig. 13 and 14.
Table 3 comparison of the results of three protocol runs
As can be seen from fig. 14, the weight α of the voltage deviation is adaptively adjusted according to the change of the distribution network operation state to effectively balance the node voltage deviation and the comprehensive power supply cost. Wherein, taking the period of 08:00 to 18:00 as an example, the dynamic change process of the weight is analyzed, as shown in fig. 14. In combination with fig. 13, it can be seen that, in the periods of 08:00 to 11:00 and 15:15 to 18:00, the voltage deviation of the system node is relatively low, the corresponding weight is small, the weight of the comprehensive power supply cost is large, and the economical efficiency of the system operation is improved. In the period of 11: 15-15: 00, the voltage deviation of the system node is large, and the corresponding weight is increased, so that the safety of system operation is ensured. Therefore, the weight of the voltage deviation and the comprehensive power supply cost is adaptively adjusted according to the actual operation condition of the system, the economical efficiency and the safety of the operation of the system are effectively balanced, and the adaptive optimization coordination control of the overall operation of the flexible interconnected power distribution network is realized.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (5)
1. A dynamic optimization scheduling method for a flexible interconnected power distribution network is characterized by comprising the following steps:
(1) active power and reactive power of each converter of the multi-end back-to-back flexible-to-direct MBVH are used as control variables, and voltage of each node of the system in a k +1 period is predicted by using a pre-established voltage prediction model;
(2) the method comprises the steps that predicted voltages of all nodes of a k +1 time period system are used as input variables, a pre-established mathematical model of the flexible interconnected power distribution network with the multiple ends connected in a back-to-back flexible and direct mode is optimized, active power and reactive power of all converters with the multiple ends connected in the back-to-back flexible and direct mode in the k +1 time period are solved and used as scheduling instruction values of the multiple ends connected in the back-to-back flexible and direct mode in the k +1 time period;
(3) issuing a multi-end back-to-back flexible and straight scheduling instruction value in the k +1 time period to a scheduling center;
(4) measuring actual voltages of all nodes in the system after a multi-end back-to-back flexible and straight scheduling instruction value in a k +1 time period is issued, taking the actual voltages as initial values of a voltage prediction model in the next rolling optimization time period, and turning to the step (1) when k is equal to k +1 to enter a new round of optimization; ending the optimization until traversing the whole scheduling period;
the voltage of each node of the system in the k +1 time period is predicted by using a pre-established voltage prediction model in the step (1), and the implementation process is as follows:
respectively solving partial derivatives of the voltage amplitude and the phase of each node of the tidal current equation to obtain a Jacobian matrix J, and solving the inverse of the Jacobian matrix J to obtain a sensitivity matrix of each node voltage relative to the active power and the reactive power injected into each node:
[Δ1,ΔU1,…,Δn,ΔUn]T＝J-1[ΔP1,ΔQ1,…,ΔPn,ΔQn]T
wherein: delta1,ΔU1Voltage phase and amplitude variation of the distribution network node 1 are respectively; deltan,ΔUnRespectively representing the voltage phase and amplitude variation of the distribution network node n; delta P1,ΔQ1Active power and reactive power variable quantity are respectively injected into the distribution network node 1; delta Pn,ΔQnRespectively injecting active power and reactive power variable quantity into a distribution network node n;
predicting the voltage of each node according to the sensitivity matrix:
U(k+Δt)＝U(k)+ΔUG(k)+ΔUD(k)
wherein: u (k) represents the vector formed by the voltages of the nodes of the distribution network at the moment k, delta UG(k) A vector, Δ U, representing voltage changes at each node caused by output power changes of MBVH converters at time kD(k) A vector which represents voltage changes of each node caused by renewable energy sources and load power fluctuation at the k moment; u (k + delta t) represents a vector formed by voltages of nodes of the distribution network at the moment of k + delta t;
the voltage of each node is obtained by repeatedly iterating the voltage prediction equation until the P step is predicted forwardsVector U formed by prediction output values in prediction time domain P Δ tfU for vectors formed by rated values of voltages at nodesRRepresents:
UR＝[U1 r(k+Δt)…Un r(k+Δt),…,U1 r(k+PΔt)…Un r(k+PΔt)]T
wherein, UfA vector formed by the estimated output values of the voltages of all the nodes in the predicted time length P delta t;
the predicted voltages of the node 1 and the node n at the moment k + delta t are respectively;
the predicted voltages of the node 1 and the node n at the moment k + P delta t are respectively;
rated voltages of the node 1 and the node n at the moment k + delta t are respectively set;
rated voltages of the node 1 and the node n at the moment k + P delta t are respectively set; t represents a symbol of matrix transposition;
the pre-established objective function of the multi-end back-to-back flexible and direct connection flexible interconnected power distribution network mathematical model in the step (2) is as follows:
wherein, t0Is the current time; f is a total objective function; f1A target function for the lowest comprehensive power supply cost; f2Is a section ofA point voltage deviation minimum objective function; f. of1(t) and f2(t) the electricity purchasing cost and the network loss cost at the moment t are respectively; ci(t)、PSTi(t)、PDGi(t)、PDi(t) respectively representing the electricity price of a bus node at a node i at the moment t, the outlet power of a transformer substation, the active output of a distributed power supply and the active power of a load; cw(t) the electricity price for purchasing electricity at the moment t; Δ t represents the time interval of a single period in one scheduling cycle; n represents the number of nodes in the power distribution network;
step (2) the flexible interconnected power distribution network mathematical model of the multi-end back-to-back flexible direct connection that is established in advance is optimized, including: self-adaptive adjustment F according to distribution network operation condition1And F2And (3) weight distribution between the two objective functions, and establishing a total objective function F:
where α is the adaptive weight, and the optimization objective function F2The correlation is linear and the correlation is linear,1and2are coefficients of corresponding linear relations, and1、2≥0，F2maxand F1maxThe maximum values of the voltage deviation and the comprehensive power supply cost are respectively; integrated power supply cost target F1And voltage deviation target F2Is determined based on the adaptive weights.
2. The dynamic optimization scheduling method for the flexible interconnected power distribution network according to claim 1, wherein the pre-established constraint equation for the operation of the mathematical model of the flexible interconnected power distribution network with the multiple ends connected back-to-back flexibly and directly is as follows:
active power balance constraint:
and (3) converter capacity constraint:
wherein N isVSCTotal number of converters for MBVH; pk(t)、Qk(t) the active power and the reactive power of the kth converter at the moment t are respectively, and the flowing-in feeder line is in a positive direction; a. thekThe loss coefficient of the kth converter; skThe rated capacity of the kth converter.
3. The dynamic optimization scheduling method for the flexible interconnected power distribution network according to claim 1, wherein the pre-established mathematical model of the flexible interconnected power distribution network with the multiple ends connected back-to-back flexibly and directly takes the minimum deviation between the predicted value and the rated value of the voltage of each node in the system and the comprehensive power supply cost as the optimization target.
4. The method according to claim 1, wherein the initial value of the voltage prediction model is as follows:
U(k+1)＝Ureal(k+1)
wherein, Ureal(k +1) after the MBVH scheduling command value at the k +1 time period is issued, measuring the actual node voltage value at the k +1 time period through an actual measuring system; u (k +1) is an initial value of the voltage prediction model.
5. The method according to claim 1, wherein the scheduling period is one day.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811356652.3A CN109861202B (en) | 2018-11-15 | 2018-11-15 | Dynamic optimization scheduling method and system for flexible interconnected power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811356652.3A CN109861202B (en) | 2018-11-15 | 2018-11-15 | Dynamic optimization scheduling method and system for flexible interconnected power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109861202A CN109861202A (en) | 2019-06-07 |
| CN109861202B true CN109861202B (en) | 2020-12-01 |
Family
ID=66889988
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811356652.3A Active CN109861202B (en) | 2018-11-15 | 2018-11-15 | Dynamic optimization scheduling method and system for flexible interconnected power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109861202B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111092429B (en) * | 2019-12-23 | 2022-02-08 | 国网北京市电力公司 | Optimized scheduling method of flexible interconnected power distribution network, storage medium and processor |
| CN111200299A (en) * | 2020-01-16 | 2020-05-26 | 国网浙江省电力有限公司电力科学研究院 | Self-control method for micro-grid group containing high-permeability distributed power supply |
| CN111682594B (en) * | 2020-06-15 | 2023-02-21 | 天津大学 | Data-driven model-free adaptive voltage control method for flexible substation of power distribution network |
| CN111900719B (en) * | 2020-06-22 | 2022-01-28 | 国电南瑞科技股份有限公司 | Power grid adequacy evaluation method, device and system considering flexible controllable load |
| CN112039057B (en) * | 2020-08-17 | 2023-10-03 | 云南电网有限责任公司丽江供电局 | Low-voltage treatment method based on two-stage scheduling |
| CN112510715B (en) * | 2020-10-31 | 2023-05-05 | 上海交通大学 | Multi-port alternating current power grid flexible interconnection device and control method and system thereof |
| CN112636362B (en) * | 2020-12-23 | 2023-05-09 | 国网河北省电力有限公司电力科学研究院 | Voltage control method and related device applied to power distribution network |
| CN113937783B (en) * | 2021-06-30 | 2022-08-30 | 国家电投集团电站运营技术（北京）有限公司 | Wind power plant reactive voltage coordination control method and device based on model prediction control |
| CN113394805B (en) * | 2021-07-26 | 2024-03-22 | 国网江苏省电力有限公司 | Method for optimizing intra-day rolling of flexible interconnection power distribution network based on self-adaptive dynamic update |
| CN114006415A (en) * | 2021-11-24 | 2022-02-01 | 广东电网有限责任公司 | Method, system and medium for evaluating improvement of new energy accepting capacity of distribution network |
| CN115133538B (en) * | 2022-06-22 | 2023-11-10 | 贵州电网有限责任公司 | Prediction control method for important load voltage model of power distribution network |
| CN117767306B (en) * | 2023-12-29 | 2024-06-14 | 杭州久益电力科技有限公司 | Method, system and storage medium for short-time correction of network load deviation by using flexible resources |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820673B (en) * | 2012-08-10 | 2014-10-08 | 沈阳工业大学 | Multiterminal flexible direct current power transmission system included power grid operation control system and method |
| CN104269861A (en) * | 2014-10-13 | 2015-01-07 | 国家电网公司 | Electromagnetic looped network reactive power ring current optimal control method based on flexible looped network controller |
| CN105740980A (en) * | 2016-01-29 | 2016-07-06 | 国网福建省电力有限公司 | Optimization planning method for alternating current and direct current hybrid grid with flexible direct current power transmission system |
| CN107749635A (en) * | 2017-11-15 | 2018-03-02 | 江苏方天电力技术有限公司 | A kind of closed loop network running optimizatin method |
-
2018
- 2018-11-15 CN CN201811356652.3A patent/CN109861202B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109861202A (en) | 2019-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109861202B (en) | Dynamic optimization scheduling method and system for flexible interconnected power distribution network | |
| CN110690732B (en) | Photovoltaic reactive power partition pricing power distribution network reactive power optimization method | |
| CN114362196B (en) | Multi-time-scale active power distribution network voltage control method | |
| CN109361242B (en) | Automatic voltage control method for photovoltaic power generation | |
| CN111092429B (en) | Optimized scheduling method of flexible interconnected power distribution network, storage medium and processor | |
| CN103441506A (en) | Method for multi-target coordination reactive power optimization control of distributed wind farm in different time scales | |
| CN111756050A (en) | Distributed voltage coordination control method and system for wind power plant group | |
| Meirinhos et al. | Multi-temporal optimal power flow for voltage control in MV networks using distributed energy resources | |
| CN113516278A (en) | Active power distribution network multi-time scale active and reactive power coordinated optimization scheduling method and system | |
| CN115640963A (en) | Offshore wind power access system robust planning method considering investment operation mode | |
| CN116365506A (en) | Energy-saving and loss-reducing optimization method and system for active power distribution network containing distributed photovoltaic | |
| Wang et al. | Multi-objective robust optimization of hybrid AC/DC distribution networks considering flexible interconnection devices | |
| CN115481856A (en) | Comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response | |
| CN111193295A (en) | Distribution network flexibility improvement robust optimization scheduling method considering dynamic reconfiguration | |
| CN113690930B (en) | NSGA-III algorithm-based medium and long term locating and sizing method for distributed photovoltaic power supply | |
| CN114996908A (en) | Active power distribution network extension planning method and system considering intelligent soft switch access | |
| CN114548597A (en) | Optimization method for alternating current-direct current hybrid optical storage and distribution power grid | |
| Chen et al. | Real-time volt/var optimization for distribution systems with photovoltaic integration | |
| Šemić et al. | Distributed generation allocation in low voltage distribution network using artificial neural network | |
| Vakil-Baghmisheh et al. | Dynamic voltage stability assessment of power transmission systems using neural networks | |
| Han et al. | Regression model-based adaptive receding horizon control of soft open points for loss minimization in distribution networks | |
| CN115133540A (en) | Power distribution network model-free real-time voltage control method | |
| CN115528670A (en) | Distributed power supply and matching power grid investment decision modeling method based on multi-main game | |
| Esmaeili et al. | A new multiobjective optimal allocation of multitype FACTS devices for total transfer capability enhancement and improving line congestion using the harmony search algorithm | |
| Benoit et al. | Deterministic optimal power flow for smart grid short-term predictive energy management |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |