CN114825383B - Three-phase imbalance two-stage optimization method for distribution type photovoltaic power distribution network - Google Patents
Three-phase imbalance two-stage optimization method for distribution type photovoltaic power distribution network Download PDFInfo
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Abstract
The invention discloses a three-phase unbalance two-stage optimization method for a distribution-type photovoltaic power distribution network, which comprises the following steps: step 1, calculating the unbalance degree of three-phase voltage of a power distribution network, the line loss rate of a line and the number of participating optimization households; step 2, constructing a three-phase imbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network, wherein the voltage out-of-limit risk is taken into consideration; step 3, calculating three-phase unbalance indexes, voltage out-of-limit risks and optimized action times of the power distribution network; step 4, constructing a three-phase imbalance day real-time optimization model; step 5, constructing a three-phase unbalanced prediction optimization-in-day real-time optimization two-stage optimization framework; and 6, completing the two-stage optimization solution of three-phase imbalance of the distribution network containing the distributed photovoltaic power, adjusting the operation parameters of the distribution network according to the obtained optimization strategy, and comparing and analyzing the effectiveness of the obtained optimization strategy. The method provided by the invention obviously optimizes the problem of unbalanced three-phase load and well inhibits the voltage out-of-limit risk of the system.
Description
Technical Field
The invention relates to the field of electric power data analysis, in particular to a three-phase imbalance two-stage optimization method for a distribution network containing distributed photovoltaic power.
Background
Three-phase four-wire system wiring is adopted in a low-voltage distribution network, low-voltage users are almost all single-phase grid-connected modes, and the current situations cause three-phase imbalance optimization to become a key link for optimizing load distribution, reducing network loss and improving operation economy of the distribution network. Especially in the distribution network containing distributed photovoltaic, the large-scale distributed photovoltaic infiltration low-voltage distribution network changes the traditional single-source-unidirectional tide flow characteristic, and the source-load interactive response in the multi-source distribution network becomes complicated. On one hand, distributed photovoltaic output and low-voltage user power consumption have strong randomness, and the three-phase imbalance condition can be aggravated to a certain extent by the time sequence fluctuation of source charge; on the other hand, the distributed photovoltaic output and the electric energy supply and demand between low-voltage users have strong time sequence difference, and voltage out-of-limit conditions caused by small load and large photovoltaic can occur, and even the safe operation of the system can be damaged under severe conditions.
Disclosure of Invention
In order to solve the problems, the invention provides a distributed photovoltaic power distribution network three-phase imbalance two-stage optimization method which utilizes distributed photovoltaic to carry out voltage control so as to inhibit the system node voltage out-of-limit risk and better adapt to the three-phase imbalance optimization requirement in a distributed photovoltaic power distribution network.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a three-phase unbalance two-stage optimization method for a distribution-type photovoltaic power distribution network comprises the following steps:
step 1, calculating the unbalance degree of three-phase voltage of the power distribution network, the line loss rate of a line and the number of participating optimization households in a prediction optimization stage in the day ahead according to the operation data of the power distribution network;
step 2, in a day-ahead prediction optimization stage, a three-phase unbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network, which takes the phase sequence of the user as a decision variable and the three-phase voltage unbalance degree, the line loss rate and the number of participating optimization households of the power distribution network calculated in the step 1 as a minimum optimization target, is constructed;
step 3, calculating three-phase unbalance indexes, voltage out-of-limit risks and optimized action times of the power distribution network according to the power distribution network operation data;
step 4, in an intra-day real-time optimization stage, a distributed photovoltaic inverter parameter is taken as a decision variable, and a distributed photovoltaic inverter parameter is taken as a minimum optimization target, so that a distributed photovoltaic power distribution network three-phase imbalance intra-day real-time optimization model considering voltage out-of-limit risks is constructed;
and 6, completing two-stage optimization solution including distributed photovoltaic power distribution network three-phase imbalance prediction optimization-in-day real-time optimization, further correcting operation parameters of users and distributed photovoltaics in the power distribution network according to the obtained optimization strategy, comparing the unoptimized power distribution network three-phase voltage imbalance degree with the line loss rate, and analyzing the effectiveness of the obtained optimization strategy.
The calculation process of the three-phase voltage unbalance of the power distribution network in the step 1 is as follows:
in the formula:U P is the node positive sequence voltage;ain order to convert the factor into a vector,a=∠120°；U A 、U B 、U C is the three-phase voltage phasor of the system;
is a nodenThe three-phase voltage unbalance degree;U N is the node negative sequence voltage;
the unbalance degree of the three-phase voltage of the whole system is obtained;
is a nodenStructural coefficient of (a);Nthe number of system nodes;
the line loss rate of the distribution network is expressed as:
when calculating the number of participating optimization households, firstly defining a decision variable X L Represents a low voltage user decision vector, and
(ii) a Wherein, adoptx ,nL Taking values to represent usersnWhen connecting the phase sequence ofx ,nL Indicates user when =1nAt phase A whenx ,nL Indicates user when =2nAt phase B, whenx ,nL Indicates user when =3nConnecting to phase C; x L0 Initial phase sequence of the load of the pre-distribution network is optimized;
the phase sequence of the last household resident is represented;
definition ofC (n,t)u Is as followsnA usertAn optimized identifier of the time of day, anC (n,t)u The calculation formula of (a) is as follows:
in the formula: x n,tL() Representing a usernIn thattPhase sequence of time;C (n,t)u =1 indicates that the user is involved in optimizing commutation,C (n,t)u =0 means that the user is not involved in commutation;
the number of participating optimization users is expressed as:
in the formula: o is user For the number of users the user participates in the optimization,Tin order to optimize the total number of hours of the process,N L and the optimized total number of users in the power distribution network is referred to.
In step 2, the expression of the constructed three-phase imbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network, which takes the voltage out-of-limit risk into account, is as follows:
in the formula:X L representing the user phasor.
In step 3, three-phase unbalance indexes of the power distribution networkD all Expressed as:
the voltage out-of-limit risk of the distribution network is expressed as:
in the formula:V n is a nodenVoltage per unit value of;Nthe number of system nodes;
the number of optimization actions is expressed as:
in the formula:C (n,t)PV is as followsnDistributed photovoltaic systemtNumber of times of operation at time, O PV For the number of optimization actions for which the distributed photovoltaic participates in the optimization,Tin order to optimize the total number of hours of the process,N PV the method is the total distributed photovoltaic number participating in optimization in the power distribution network.
In step 4, the expression of the constructed three-phase imbalance day-interior real-time optimization model of the distributed photovoltaic power distribution network, which takes the voltage out-of-limit risk into account, is as follows:
in the formula:X PV representing the time-sequence control vector, O, of a DPV photovoltaic inverter PV For the number of optimization actions for which the distributed photovoltaic participates in the optimization,VCI all indicating the risk of voltage violations of the distribution network,D all and the three-phase unbalance index of the power distribution network is represented.
The invention has the beneficial effects that: aiming at different operating characteristics caused by photovoltaic grid connection in a distribution-type photovoltaic power distribution network, the invention considers the problem of node voltage deviation while optimizing three-phase imbalance, and provides a two-stage optimization framework of three-phase imbalance of the distribution-type photovoltaic power distribution network, which takes voltage out-of-limit risk into consideration. By means of two-stage optimization of prediction optimization before the day and real-time optimization within the day, a treatment scheme for the three-phase imbalance problem of a distributed photovoltaic power distribution network is provided from the source load interaction perspective by taking low-voltage users and distributed photovoltaics as control objects. The optimization framework can effectively improve the problem of three-phase imbalance in the power distribution network, can also obviously inhibit the voltage out-of-limit risk of the system compared with the traditional optimization method, and has important academic significance and engineering practical value.
Drawings
Fig. 1 is a three-phase imbalance two-stage optimization framework including a distributed photovoltaic power distribution network according to the invention.
FIG. 2 is a flow chart of an embodiment of the present invention.
Fig. 3 is a diagram of a modified IEEE34 node topology.
FIG. 4a is a schematic diagram of a non-optimization and day-ahead prediction optimization strategy for node 806 three-phase voltage unbalance in the embodiment.
Fig. 4b is a schematic diagram of the optimization-free and day-ahead prediction optimization strategy of the node 854 three-phase voltage unbalance in the embodiment.
Fig. 4c is a schematic diagram of the optimization-free and the prediction optimization strategies in the future of the node 836 three-phase voltage unbalance in the embodiment.
FIG. 5 shows the voltage deviation values of the nodes under different optimization methods.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, so that those skilled in the art can implement the technical solutions in reference to the description text.
As shown in fig. 2, the invention relates to a three-phase imbalance two-stage optimization method for a distribution-type photovoltaic power distribution network, which comprises the following steps:
step 1, calculating the unbalance degree of three-phase voltage of a power distribution network, the line loss rate of a line and the number of participating optimization households in the optimization stage of the prediction in the future according to the operation data of the power distribution network;
the calculation process of the three-phase voltage unbalance of the power distribution network in the step 1 is as follows:
in the formula:U P is the node positive sequence voltage;ain order to convert the factor into a vector,a=∠120°；U A 、U B 、U C is the three-phase voltage phasor of the system;
is a nodenThe three-phase voltage unbalance degree;U N is the node negative sequence voltage;
the unbalance degree of the three-phase voltage of the whole system is obtained;
is a nodenStructural coefficient of (a);Nthe number of system nodes;
the line loss rate of the distribution network is expressed as:
in the formula:P Load is the active load of the system and is,P Loss is the active loss of the system;
when calculating the number of participating optimization households, firstly defining a decision variable X L Represents a low voltage user decision vector, and
(ii) a Wherein, adoptx ,nL Taking values to represent usersnWhen connecting the phase sequence ofx ,nL Indicates user when =1nAt phase A whenx ,nL Is user is represented when =2nAt phase B, whenx ,nL Indicates user when =3nConnecting to phase C; x L0 Initial phase sequence of the load of the pre-distribution network is optimized;
the phase sequence of the last resident is shown;
definition ofC (n,t)u Is as followsnIndividual usertAn optimized identifier of the time of day, anC (n,t)u The calculation formula of (a) is as follows:
in the formula: x n,tL() Representing a usernIn thattPhase sequence of time;C (n,t)u =1 indicates that the user is involved in optimizing commutation,C (n,t)u =0 means that the user is not involved in commutation;
the number of participating optimization users is expressed as:
in the formula: o is user For the number of users the user participates in the optimization,Tin order to optimize the total number of hours of the process,N L and the optimized total number of users in the power distribution network is referred to.
Step 2, in a day-ahead prediction optimization stage, a three-phase unbalance degree of the distribution network, a line loss rate and the number of participating optimization households, which are calculated in the step 1, are taken as a minimum optimization target, and a day-ahead prediction optimization model of the distribution type photovoltaic distribution network, which takes voltage out-of-limit risks into consideration, is constructed:
wherein:X L representing the user's phase-joining vector,
the overall three-phase voltage unbalance degree of the system is% Loss Is the line loss rate, O, of the distribution network user The number of users participating in optimization for the user;
step 3, calculating three-phase unbalance indexes, voltage out-of-limit risks and optimized action times of the power distribution network according to the power distribution network operation data;
in step 3, three-phase unbalance indexes of the power distribution networkD all Expressed as:
the voltage out-of-limit risk of the distribution network is expressed as:
in the formula:V n is a nodenPer unit voltage value of;Nthe number of system nodes;
the number of optimization actions is expressed as:
in the formula:C (n,t)PV is a firstnDistributed photovoltaic systemtNumber of times of operation at time, O PV For the number of optimization actions for which the distributed photovoltaic participates in the optimization,Tin order to optimize the total number of hours of the process,N PV the method is the total distributed photovoltaic number participating in optimization in the power distribution network.
And 4, in an intra-day real-time optimization stage, constructing a distributed photovoltaic power distribution network three-phase imbalance intra-day real-time optimization model considering voltage out-of-limit risks by taking the distributed photovoltaic inverter parameters as decision variables and taking the distributed photovoltaic inverter parameters as a minimum optimization target:
wherein:X PV representing a timing control vector of the DPV photovoltaic inverter; o is PV Indicating the number of participating optimization users corresponding to the scheme,VCI all indicating a voltage out-of-limit risk for the distribution network;D all and the three-phase unbalance index of the power distribution network is represented.
and aiming at the three-phase unbalance two-stage optimization model of the distributed photovoltaic power distribution network, which is established in the steps and takes the voltage out-of-limit risk into consideration, optimizing and solving by adopting an NSGA-II multi-objective optimization algorithm. And solving the power flow distribution and the node voltage state of the power distribution network through simulation software OpenDSS. The method mainly comprises four parts, namely initialization, prediction optimization before the day, real-time optimization in the day and a multi-objective optimization algorithm, and an integral two-stage optimization framework is shown in figure 1.
The method comprises the steps of firstly, initializing power distribution network structure parameters and user side grid-connected parameters in OpenDSS, wherein the power distribution network structure parameters and the user side grid-connected parameters comprise the number of users and DPVs of access nodes, the types and power consumption modes of the users, the capacity and position of the DPVs, the output characteristics of the DPVs and the like.
And secondly, a day-ahead prediction optimization part calls OpenDSS to read the network node access load and the operation state of the DPV, determines the three-phase imbalance degree of the power distribution network, constructs a three-phase imbalance day-ahead prediction optimization model, performs optimization solution based on an NSGA-II algorithm to obtain a low-voltage user phase sequence adjustment strategy in the power distribution network, and corrects the user phase sequence at the user side of the power distribution network.
And then, a daily real-time optimization part is used for constructing a daily three-phase imbalance optimization model of the distributed photovoltaic power distribution network, wherein the daily three-phase imbalance optimization model accounts for voltage out-of-limit risks, the OpenDSS load flow calculation is called to obtain the active/reactive power distribution, the node voltage state, the DPV output condition and other operation data in the power distribution network, and the operation parameters of the distributed photovoltaic at the user side are corrected according to decision variables.
And finally, a multi-objective optimization part is used for solving a two-stage three-phase imbalance optimization problem of the distribution-type photovoltaic power distribution network based on an NSGA-II algorithm. Take real-time daily optimization as an example: and carrying out iterative optimization on the objective function value in the distributed photovoltaic power distribution network three-phase imbalance day-in-time real-time optimization model according to the network operation data calculation formula and the voltage out-of-limit risk to obtain an optimization strategy, and transmitting the optimization strategy to OpenDSS to complete parameter adjustment of user side operation parameters so as to complete closed-loop control.
And 6, completing two-stage optimization solution including distributed photovoltaic power distribution network three-phase imbalance prediction optimization-in-day real-time optimization, further correcting operation parameters of users and distributed photovoltaics in the power distribution network according to the obtained optimization strategy, comparing the unoptimized power distribution network three-phase voltage imbalance degree with the line loss rate, and analyzing the effectiveness of the obtained optimization strategy.
In the embodiment, an improved IEEE34 standard node is taken as an example, the reference capacity of the system is 25MVA, and the reference voltage is 69 kV. The PYTHON is adopted to construct a three-phase unbalanced two-stage optimization model, the OpenDSS is combined to carry out real-time power flow operation, and the NSGA-II algorithm is adopted to carry out solving verification on the two-stage optimization model. In the embodiment, the access users comprise 32-user three-phase adjustable low-voltage single-phase users, 6-user single-phase fixed-phase users, 15-user inter-phase users, 6-user three-phase users and 4 distributed photovoltaics. The adjustable user access points are as shown by arrows in fig. 3, and the distributed photovoltaic access points all select end nodes of the system.
The specific parameters of the connected distributed photovoltaic are as follows: the rated active power of the PV1 connected to the node 840 is 10kW, the rated active power of the PV2 connected to the node 890 is 100kW, the rated active power of the PV3 connected to the node 848 is 15kW, and the rated active power of the PV4 connected to the node 848 is 5 kW. And, four distributed photovoltaics all maintain a power factor of 0.95 operation. The access conditions of the single-phase users and the distributed photovoltaic are as described above, and assuming that the phase sequence adjustment of the single-phase users in the optimized object is not limited (three-phase adjustable), the control range of the given node voltage is [0.93,1.07], and the adjustable range of the power factor of the distributed photovoltaic is [0.95,1.05 ].
And calculating a target function value in a three-phase imbalance two-stage optimization model considering the voltage out-of-limit risk by combining the selected 34-node power distribution network topological structure, performing three-phase imbalance optimization on the improved distributed photovoltaic IEEE34 node-containing system, and analyzing the obtained strategy.
First, under optimization of a predictive optimization model in the day ahead. And selecting nodes 806, 854 and 836 at the head end, the middle part and the tail end of the topology for analysis. The effectiveness of the improvement on the overall load distribution of the network is compared with different three-phase imbalance optimization strategies, as shown in fig. 4a, 4b and 4 c. The overall mean value of the unbalance degree at the head end node 806 is reduced from 0.076% to 0.023%, the overall mean value of the unbalance degree at the middle node 854 is reduced from 1.297% to 0.537%, the overall mean value of the unbalance degree at the tail end node 836 is reduced from 1.655% to 0.845%, and the voltage unbalance conditions of the three nodes are remarkably improved.
And secondly, further optimizing through a daily real-time optimization model. The operation parameters of the distributed photovoltaic inverter are subjected to sequential control by combining the node voltage state, the reactive power of the distributed photovoltaic inverter is adjusted to inhibit the voltage out-of-limit risk, and the node voltage distribution can be effectively adjusted compared with the traditional three-phase unbalance optimization strategy. Taking the deviation value between the voltage per unit value of each node in the C phase and the standard voltage as an example, the optimization effect of the conventional optimization method and the optimization method provided by the invention on the node voltage is compared, as shown in fig. 5.
Finally, the effectiveness of the method in optimizing the three-phase imbalance problem of the distribution-containing photovoltaic power distribution network is comprehensively analyzed. The overall optimization effects of the conventional optimization method and the strategy provided herein on the average voltage unbalance and the average network line loss rate of the power distribution network are compared in table 1. It should be noted that, in order to solve the problem of uneven phase sequence distribution of users in the low-voltage distribution network, more single-phase users are accessed in the simulation example, which results in the average network line loss rate of the network being 10.89%, which is higher than the actual network loss rate of the distribution network during operation; wherein table 1 specifically is:
table 1 is the comparison analysis table of the optimization effectiveness of the algorithm provided by the invention
In summary, the optimization framework has a good optimization effect on solving the problem of phase imbalance of the three distributed photovoltaic power distribution networks and the problem of voltage out-of-limit caused by DPV. Compared with the traditional optimization strategy only considering the problem of three-phase imbalance, the three-phase imbalance optimization is carried out by considering the voltage control of the distributed photovoltaic, the distribution state of the node voltage can be effectively improved on the basis of hardly losing the line loss rate optimization value of the network, and the current situation of large-scale DPV access in the current power distribution network can be more adapted.
Claims (5)
1. A three-phase unbalance two-stage optimization method for a distribution-type photovoltaic power distribution network is characterized by comprising the following steps: the method comprises the following steps:
step 1, calculating the unbalance degree of three-phase voltage of the power distribution network, the line loss rate of a line and the number of participating optimization households in a prediction optimization stage in the day ahead according to the operation data of the power distribution network;
step 2, in a day-ahead prediction optimization stage, a three-phase unbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network, which takes the phase sequence of the user as a decision variable and the three-phase voltage unbalance degree, the line loss rate and the number of participating optimization households of the power distribution network calculated in the step 1 as a minimum optimization target, is constructed;
step 3, calculating three-phase unbalance indexes, voltage out-of-limit risks and optimized action times of the power distribution network according to the power distribution network operation data;
step 4, in an intra-day real-time optimization stage, a distributed photovoltaic inverter parameter is taken as a decision variable, and a distributed photovoltaic inverter parameter is taken as a minimum optimization target, so that a distributed photovoltaic power distribution network three-phase imbalance intra-day real-time optimization model considering voltage out-of-limit risks is constructed;
step 5, constructing a two-stage optimization framework of the three-phase imbalance day-ahead prediction optimization-day-in real-time optimization of the distributed photovoltaic power distribution network, considering the voltage out-of-limit risk, based on the three-phase imbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network and the three-phase imbalance day-in real-time optimization model of the distributed photovoltaic power distribution network, considering the voltage out-of-limit risk;
and 6, completing two-stage optimization solution including distributed photovoltaic power distribution network three-phase imbalance prediction optimization-in-day real-time optimization, further correcting operation parameters of users and distributed photovoltaics in the power distribution network according to the obtained optimization strategy, comparing the unoptimized power distribution network three-phase voltage imbalance degree with the line loss rate, and analyzing the effectiveness of the obtained optimization strategy.
2. The three-phase imbalance two-stage optimization method for the distribution network containing the distributed photovoltaic power generation system according to claim 1, wherein the two-stage imbalance three-phase imbalance three-stage optimization method comprises the following steps: the calculation process of the three-phase voltage unbalance of the power distribution network in the step 1 is as follows:
in the formula:U P is the node positive sequence voltage;ain order to convert the factor into a vector,a=∠120°；U A 、U B 、U C is the three-phase voltage phasor of the system;
is a nodenThe three-phase voltage unbalance degree;U N is the node negative sequence voltage;
the unbalance degree of the three-phase voltage of the whole system is obtained;
is a nodenStructural coefficient of (a);Nthe number of system nodes;
the line loss rate of the distribution network is expressed as:
in the formula:P Load is the active load of the system，P Loss Is the active loss of the system;
when calculating the number of participating optimization households, firstly defining a decision variable X L Represents a low voltage user decision vector, and
(ii) a Wherein, adoptx ,nL Taking values to represent usersnWhen connecting the phase sequence ofx ,nL Indicates user when =1nAt phase A whenx ,nL Indicates user when =2nAt phase B, whenx ,nL Indicates user when =3nConnecting to phase C; x L0 Initial phase sequence of the load of the pre-distribution network is optimized;
the phase sequence of the last resident is shown;
definition ofC (n,t)u Is as followsnIndividual usertAn optimized identifier of the time of day, anC (n,t)u The calculation formula of (a) is as follows:
in the formula: x n,tL() Representing a usernIn thattPhase sequence of time;C (n,t)u =1 indicates that the user is involved in optimizing commutation,C (n,t)u =0 means that the user is not involved in commutation;
the number of participating optimization users is expressed as:
in the formula: o is user For the number of users the user participates in the optimization,Tin order to optimize the total number of hours of the process,N L and the optimized total number of users in the power distribution network is referred to.
3. The three-phase imbalance two-stage optimization method for the distribution network containing the distributed photovoltaic power generation system according to claim 2, wherein the two-stage imbalance three-phase imbalance three-stage optimization method comprises the following steps: in step 2, the expression of the constructed three-phase imbalance day-ahead prediction optimization model of the distributed photovoltaic power distribution network, which takes the voltage out-of-limit risk into account, is as follows:
in the formula:X L representing the user phasor.
4. The three-phase imbalance two-stage optimization method for the distribution network containing the distributed photovoltaic power generation system according to claim 3, wherein the two-stage imbalance three-phase imbalance three-stage optimization method comprises the following steps: in step 3, three-phase unbalance indexes of the power distribution networkD all Expressed as:
the voltage out-of-limit risk of the distribution network is expressed as:
in the formula:V n is a nodenPer unit voltage value of;Nthe number of system nodes;
the number of optimization actions is expressed as:
in the formula:C (n,t)PV is as followsnDistributed photovoltaic systemtNumber of times of operation at time, O PV For the number of optimization actions for which the distributed photovoltaic participates in optimization,Tin order to optimize the total number of hours of the process,N PV the method is the total distributed photovoltaic number participating in optimization in the power distribution network.
5. The three-phase imbalance two-stage optimization method for the distribution network containing the distributed photovoltaic power generation system according to claim 4, wherein the two-stage imbalance three-phase imbalance optimization method comprises the following steps: in step 4, the expression of the constructed three-phase imbalance day-interior real-time optimization model of the distributed photovoltaic power distribution network, which takes the voltage out-of-limit risk into account, is as follows:
in the formula:X PV representing the time-sequence control vector, O, of a DPV photovoltaic inverter PV For the number of optimization actions for which the distributed photovoltaic participates in the optimization,VCI all indicating the risk of voltage violations of the distribution network,D all and the three-phase unbalance index of the power distribution network is represented.
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