CN117526429A - Distributed photovoltaic optimal scheduling method and device based on convex optimization and storage medium - Google Patents
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Abstract
The invention relates to a distributed photovoltaic optimal scheduling method, a device and a storage medium based on convex optimization, which are applied to the technical field of distributed photovoltaic operation scheduling and comprise the following steps: the method is based on operation optimization of the power distribution network, and an objective function for improving the power quality is established; under an objective function, corresponding constraint conditions are established, convex optimization processing of a model is conducted aiming at non-convex nonlinear constraint existing in the constraint conditions, an original non-convex nonlinear model is converted into a convex quadratic constraint quadratic programming model, the convex nonlinear model can be rapidly solved by an original dual interior point method, model convex errors are reduced through alternate iteration of tide calculation and optimization calculation, the solution difficulty is reduced, meanwhile, the solution precision is guaranteed, scheduling optimization is conducted on a power distribution network according to the solution result, and the problems of power distribution network voltage out-of-limit, three-phase imbalance, harmonic waves and the like are solved under the condition that various power quality compensation devices are not installed.
Description
Technical Field
The invention relates to the technical field of distributed photovoltaic operation scheduling, in particular to a distributed photovoltaic optimal scheduling method and device based on convex optimization and a storage medium.
Background
Tunable distributed photovoltaic (SDPV) power generation has been popularized and applied in medium and low voltage distribution networks as an important form of renewable energy utilization. However, with the increase of the capacity of the distributed photovoltaic and the access of a large number of single-phase distributed photovoltaic, the power distribution network also faces the electric energy quality problems of voltage out-of-limit, three-phase imbalance and the like. On the other hand, the ever-increasing nonlinear load and the single-phase load which are widely distributed also cause the problems of harmonic waves and unbalance of the power distribution network to become serious, and the normal operation of the power distribution network is affected.
In order to solve the problems of power distribution network voltage out-of-limit, three-phase imbalance, harmonic wave and the like, various power quality compensation devices are usually required to be installed, but the investment and operation cost of the power distribution network is increased.
Disclosure of Invention
In view of the above, the present invention aims to provide a distributed photovoltaic optimization scheduling method, device and storage medium based on convex optimization, so as to solve the problems of power distribution network voltage out-of-limit, three-phase imbalance, harmonic wave and the like in the prior art, and generally, various power quality compensation devices need to be installed, but thus the investment operation cost of the power distribution network is increased.
According to a first aspect of an embodiment of the present invention, there is provided a distributed photovoltaic optimization scheduling method based on convex optimization, the method comprising:
establishing a loss objective function by taking the active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function by taking the minimization of the harmonic voltage level of the whole power distribution network as a target, and establishing a fundamental objective function by taking the minimization of the three-phase fundamental voltage of each node of the power distribution network as a target;
combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
establishing constraint conditions of the final objective function, wherein the constraint conditions comprise: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
the nonlinear equation constraint in the fundamental wave power flow constraint and the harmonic wave power flow constraint is subjected to convex optimization transformation to obtain the linearization power flow constraint under a rectangular coordinate system;
performing convex optimization transformation on the operation constraint of the SDPV grid-connected inverter to obtain a new operation linear inequality constraint of the SDPV grid-connected inverter;
and solving a final objective function by taking the linearization power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as constraints of the final objective function, and scheduling the power distribution network according to the solving result.
Preferably, the method comprises the steps of,
the fundamental wave power flow constraint comprises:
and acquiring phase, active power, reactive power, load and current data among different nodes in the power distribution network, and establishing fundamental wave power flow constraint according to the phase, active power, reactive power, load and current data among the different nodes.
Preferably, the method comprises the steps of,
the harmonic power flow constraint comprises:
and acquiring the phase, active power, reactive power, current, amplitude of current and phase angle spectrum values among different nodes in the power distribution network, and establishing harmonic power flow constraint according to the phase, active power, reactive power, current, amplitude of current and phase angle spectrum values among different nodes.
Preferably, the method comprises the steps of,
the node voltage constraint includes:
node voltage amplitudes among different nodes in the power distribution network are obtained, and node voltage constraint is established by using the node voltage amplitudes of the different nodes to meet a preset voltage range.
Preferably, the method comprises the steps of,
the power quality constraint includes:
and respectively acquiring harmonic voltages and unbalanced voltages of different nodes in the power distribution network, and establishing power quality constraint by using the harmonic voltages and the unbalanced voltages of the different nodes to meet a preset harmonic voltage range and an unbalanced voltage range.
Preferably, the method comprises the steps of,
the SDPV grid-tie inverter operational constraints include:
and obtaining rated capacity and rated current of the inverter in the SDPV, and establishing operation constraint of the SDPV grid-connected inverter according to the rated capacity and the rated current of the inverter within a preset capacity range and a preset current range.
Preferably, the method comprises the steps of,
the step of obtaining the linearization power flow constraint under the rectangular coordinate system by convex optimization transformation of the nonlinear equation constraint in the fundamental wave power flow constraint and the harmonic power flow constraint comprises the following steps:
acquiring operation data of a low-voltage transformer area and an SDPV in the current power distribution network through fundamental wave power flow constraint and harmonic wave power flow constraint;
according to the operation data of the medium-low voltage transformer areas and the SDPV, carrying out primary fundamental wave power flow and harmonic power flow calculation through nonlinear constraint in fundamental wave power flow constraint and harmonic power flow constraint to obtain fundamental wave current initial values and harmonic current initial values of the low-voltage transformer areas;
substituting the fundamental current initial value and the harmonic current initial value into the linear constraint of the fundamental current constraint and the linear constraint of the harmonic current constraint respectively to obtain the linearization current constraint under a rectangular coordinate system.
Preferably, the method comprises the steps of,
the step of obtaining the new SDPV grid-connected inverter operation linear inequality constraint by convex optimization transformation of the SDPV grid-connected inverter operation constraint comprises the following steps:
acquiring operation data of an SDPV inverter in a current power distribution network;
substituting the operation data of the SDPV inverter into operation constraint of the SDPV grid-connected inverter to perform primary power and current calculation to obtain an initial power value and an initial current value of the SDPV inverter;
substituting the initial power value and the initial current value of the SDPV inverter into the operation constraint of the SDPV grid-connected inverter to obtain the new operation linear inequality constraint of the SDPV grid-connected inverter.
According to a second aspect of embodiments of the present invention, there is provided a distributed photovoltaic optimization scheduling device based on convex optimization, the device comprising:
an objective function establishment module: the method comprises the steps of establishing a loss objective function with active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function with the whole harmonic voltage level minimization of a power distribution network as a target, and establishing a fundamental objective function with the three-phase fundamental voltage minimization of each node of the power distribution network as a target;
and a weighted combination module: the method comprises the steps of combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
constraint establishment module: constraints for establishing the final objective function, the constraints comprising: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
and the tide convex optimization module is used for: the method comprises the steps of obtaining linearization power flow constraint under a rectangular coordinate system through convex optimization transformation of nonlinear equation constraint in fundamental wave power flow constraint and harmonic power flow constraint;
inverter convex optimization module: the method comprises the steps of obtaining a new SDPV grid-connected inverter operation linear inequality constraint by performing convex optimization transformation on the SDPV grid-connected inverter operation constraint;
and a scheduling module: and the method is used for solving the final objective function by taking the linearization power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as the constraint of the final objective function, and scheduling the power distribution network according to the solving result.
According to a third aspect of embodiments of the present invention, there is provided a storage medium storing a computer program which, when executed by a master, implements the steps of the above method.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
the method is based on operation optimization of the power distribution network, and an objective function for improving the power quality is established; under an objective function, corresponding constraint conditions are established, convex optimization processing of a model is conducted aiming at non-convex nonlinear constraint existing in the constraint conditions, an original non-convex nonlinear model is converted into a convex quadratic constraint quadratic programming model, the convex nonlinear model can be rapidly solved by an original dual interior point method, model convex errors are reduced through alternate iteration of tide calculation and optimization calculation, the solution difficulty is reduced, meanwhile, the solution precision is guaranteed, scheduling optimization is conducted on a power distribution network according to the solution result, and the problems of power distribution network voltage out-of-limit, three-phase imbalance, harmonic waves and the like are solved under the condition that various power quality compensation devices are not installed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a distributed photovoltaic optimization scheduling method based on convex optimization, according to an example embodiment;
FIG. 2 is a system diagram of a distributed photovoltaic optimization scheduler based on convex optimization, shown according to another exemplary embodiment;
in the accompanying drawings: the system comprises a 1-objective function building module, a 2-weighted combination module, a 3-constraint building module, a 4-tide convex optimization module, a 5-inverter convex optimization module and a 6-scheduling module.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention.
Example 1
FIG. 1 is a flow diagram illustrating a distributed photovoltaic optimization scheduling method based on convex optimization, according to an exemplary embodiment, as shown in FIG. 1, the method comprising:
s1, establishing a loss objective function by taking active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function by taking the minimization of the harmonic voltage level of the whole power distribution network as a target, and establishing a fundamental objective function by taking the minimization of the three-phase fundamental voltage of each node of the power distribution network as a target;
s2, combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
s3, establishing constraint conditions of the final objective function, wherein the constraint conditions comprise: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
s4, carrying out convex optimization transformation on nonlinear equation constraints in the fundamental wave power flow constraint and the harmonic wave power flow constraint to obtain linearization power flow constraint under a rectangular coordinate system;
s5, performing convex optimization transformation on the operation constraint of the SDPV grid-connected inverter to obtain a new operation linear inequality constraint of the SDPV grid-connected inverter;
s6, taking the linear power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as constraints of a final objective function, solving the final objective function, and scheduling the power distribution network according to the solving result;
it will be appreciated that establishing an objective function that improves power quality includes:
(1) from the viewpoint of power distribution network operation optimization, active power loss of a line is requiredMinimizing, the project label function expression is:
（1）
in the method, in the process of the invention,the number of lines of the power distribution network;His the maximum harmonic order considered; />Is a circuitkFirst, theφPhase 1hThe active loss under the subharmonic frequency can be calculated according to the voltage difference at two ends of the line;
(2) when nonlinear load exists in the power distribution network, from the perspective of improving the harmonic problem, the harmonic voltage level of the whole network is minimized, and the project label function expression is as follows:
（2）
in the method, in the process of the invention,an objective function for improving the harmonic problem of the power distribution network; />The number of nodes of the power distribution network; />Is a nodeiA kind of electronic deviceφPhase 1hSubharmonic voltage;
(3) when three-phase power in the power distribution network is unbalanced or network parameters are asymmetric, from the perspective of improving the unbalance problem, the negative sequence voltage of each node of the network is restrained, and the project standard function expression is:
（3）
in the method, in the process of the invention,to improve the objective function of the three-phase imbalance problem of the distribution network, < >>、/>、/>Respectively nodesiA, b, c phase fundamental voltages;
because the direct solving of the multi-objective optimization problem is difficult, the invention combines the objective functions to obtain the single objective function by a linear weighting methodf：
（4）
In the method, in the process of the invention,、/>、/>normalized coefficients for different objective functions; />、/>、/>In practical application, each weight coefficient can be set subjectively according to the attention degree or the severity degree of the electric energy quality problem, and more objective weight coefficients can be obtained by weight decision methods such as a hierarchical analysis method and the like;
establishing an optimal power flow model constraint with universality:
(1) the fundamental wave tide constraint is shown as formulas (5) - (7), wherein formulas (5) - (6) are nonlinear equation constraints, and the fundamental wave tide constraint is shown as follows:
（5）
（6）
（7）
in the method, in the process of the invention,、/>respectively connected to the nodesiIs->Phase and->Active and reactive power of the inter-phase SDPV; />、/>Respectively nodesiIs->Phase and->Phase 1hSubharmonic voltage; />、/>Respectively connected to the nodesiIs->Phase and->Net active and reactive power of the load and power supply of the low-voltage transformer area between phases; />、/>SDPV and low voltage transformer area fundamental current respectively; />Is a nodeiIs->The phase total injected fundamental current; />Is a node of a power distribution networkiIs->Phase and nodejIs->Admittance at fundamental frequency between phases;
for the three-phase SDPV, the output active power of the three-phase inverter is equal to the output active power of the direct current side, and the expression is as follows:
（8）
in the method, in the process of the invention,outputting total active power for the SDPV direct current side;
(2) the harmonic current constraint is shown in formulas (9) and (10), the harmonic current generated by the load of the low-voltage transformer area is mainly considered, and a harmonic constant current source model is adopted as a harmonic model of the load of the low-voltage transformer area, and the expression is as follows:
（9）
（10）
in the method, in the process of the invention,、/>respectively connected to the nodesiA kind of electronic deviceφPhase sumφ'Active and reactive power of the load of the low-voltage transformer area between phases; />、/>Fundamental current sum for low voltage transformer area loadhSubharmonic current; />、Respectively ishAmplitude and phase angle spectrum values of subharmonic current; />、/>Respectively connected to the nodesiA kind of electronic deviceφPhase sumφ'Low voltage zone load between phaseshSubharmonic current and SDPV active outputhSubharmonic current; />Is a nodeiA kind of electronic deviceφGeneral injection of phaseshSubharmonic current; />Is a node of a power distribution networkiIs->Phase and nodejIs->Phase-to-phase (B)hHypo-baseAdmittance at wave frequencies;
(3) node voltage constraint, in order to ensure safe operation of the power distribution network, generally requires that the node voltage amplitude cannot exceed a certain range, namely, the node voltage amplitude meets the following conditions:
（11）
in the method, in the process of the invention,and->The lower limit and the upper limit of the fundamental voltage amplitude are respectively set;
(4) line current constraints, in general, need to ensure that the total current flowing on the line cannot be excessive, and therefore need to be satisfied:
（12）
in the method, in the process of the invention,is a circuitkA kind of electronic deviceφFlowing over the phasehSubharmonic current; />An upper limit for the current allowed to flow on the line; />Is a circuitkA kind of electronic deviceφPhase sumφ'Phase-to-phase (B)hAdmittance at subharmonic frequencies;
(5) the power quality constraint, in order to ensure good power quality, requires that node harmonic voltage and unbalanced voltage cannot be too large, namely, the requirements are satisfied:
（13）
（14）
in the method, in the process of the invention,、/>the upper limit of the node harmonic voltage and unbalanced voltage respectively,>is a natural constant;
(6) the operation constraint of the SDPV grid-connected inverter is that the inverter is ensured to be not excessively excessive in order to not influence the normal operation of the SDPV, so that the output power and the current of the SDPV grid-connected inverter are required to be limited:
（15）
（16）
in the method, in the process of the invention,representing the rated capacity of the SDPV inverter; />Representing the rated current of the SDPV inverter;
the power distribution network OPF model for optimizing and scheduling the SDPV is obtained by combining the objective function and the constraint condition and aims to reduce the power distribution network loss, treat the power quality problems such as harmonic waves and unbalance, and comprises the following steps:
（17）
it should be noted that in the OPF model (17), there is a conflict between the inequality constraint formulas (11) - (14) and formulas (15) - (16), because when the residual capacity of the photovoltaic inverter is smaller after the active power is output, the reactive power and the harmonic current compensation amount of the photovoltaic inverter are correspondingly reduced, and in this case, it may not be guaranteed that the node voltage or the line current of the power distribution network is not out of standard, i.e. the model is not solved, and at this time, part of the power quality constraint can be properly relaxed or cancelled, so as to try to improve the power quality of the power distribution network on the premise of not affecting the active output of the photovoltaic inverter;
solving the formula (17) to obtain an optimal scheduling scheme of each SDPV in the power distribution network, however, the formula (17) is a typical non-convex nonlinear constraint optimization model, and the direct solution of the model has the problems of low calculation efficiency, difficult convergence and the like, so the invention provides a convex approximation method, which converts the formula (17) into a convex optimization model easier to solve, as follows:
in order to eliminate nonlinear equation constraint (5), (6) and (9), acquiring operation data of a low-voltage transformer area and an SDPV in the current power distribution network through fundamental wave power flow constraint and harmonic wave power flow constraint; according to the operation data of the medium-low voltage transformer areas and the SDPV, carrying out primary fundamental wave power flow and harmonic power flow calculation through nonlinear constraint in fundamental wave power flow constraint and harmonic power flow constraint to obtain fundamental wave current initial values and harmonic current initial values of the low-voltage transformer areas; and substituting the initial value of the current into formulas (7) and (10) to obtain a linearization flow equation under a rectangular coordinate system, wherein the linearization flow equation is shown as a formula (18):
（18）
in the method, in the process of the invention,、/>respectively low-voltage areashReal and imaginary parts of the initial value of the subharmonic current; />、For SDPV outputhReal and imaginary parts of the subharmonic current; />、/>Respectively->Real and imaginary parts of (a); />、/>Respectively->Real and imaginary parts of (a);
after applying equation (18) to replace the power flow constraint, the control variables in the OPF model equation (17) need to be replaced with the real and imaginary parts of the SDPV output current, i.eAnd->Meanwhile, to satisfy the constraint on the SDPV output active power, the following linear equation constraint is newly added, that is, the original formula (8) is replaced by the following formula (19):
（19）
in the method, in the process of the invention,to be connected at the nodeiA kind of electronic deviceφPhase sumφ'Active power of the inter-phase SDPV;
for SDPV inverter capacity constraint (15) and (16), firstly, performing primary power and current calculation according to operation data of an SDPV inverter in a current power distribution network to obtain SDPV inverter power and current initial values, and then substituting the current initial values into the equations (15) and (16) to obtain new inequality constraint (20) and (21), so as to perform salifying treatment:
（20）
（21）
through the above-mentioned protruding process, the protruding approximate model of OPF model type (17) can be obtained as follows:
（22）
since the objective function of equation (22) is a convex quadratic function, the equality constraints are both linear constraints, and the inequality constraints consist of linear constraints and convex quadratic constraints, so that it is a convex quadratic constraint quadratic programming (qqp) model, and the optimal solution of the convex qqp model can be reliably obtained by a nonlinear optimization algorithm, such as a primary-dual interior point method.
Example two
FIG. 2 is a system diagram of a convex optimization-based distributed photovoltaic optimization scheduler, according to another example embodiment, including:
objective function establishment module 1: the method comprises the steps of establishing a loss objective function with active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function with the whole harmonic voltage level minimization of a power distribution network as a target, and establishing a fundamental objective function with the three-phase fundamental voltage minimization of each node of the power distribution network as a target;
the weighted combination module 2: the method comprises the steps of combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
constraint establishment module 3: constraints for establishing the final objective function, the constraints comprising: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
and a tide convex optimization module 4: the method comprises the steps of obtaining linearization power flow constraint under a rectangular coordinate system through convex optimization transformation of nonlinear equation constraint in fundamental wave power flow constraint and harmonic power flow constraint;
inverter convex optimization module 5: the method comprises the steps of obtaining a new SDPV grid-connected inverter operation linear inequality constraint by performing convex optimization transformation on the SDPV grid-connected inverter operation constraint;
scheduling module 6: the method comprises the steps of solving a final objective function by using linearization power flow constraint, node voltage constraint, line circuit constraint, electric energy quality constraint and new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as constraints of the final objective function, and scheduling a power distribution network according to a solving result;
it can be understood that the objective function establishing module 1 is used for establishing a loss objective function with the minimum of the active power loss of the running line of the power distribution network as a target, establishing a harmonic objective function with the minimum of the harmonic voltage level of the whole power distribution network as a target, and establishing a fundamental objective function with the minimum of the three-phase fundamental voltages of all nodes of the power distribution network as a target; the weighting combination module 2 is used for combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function; the constraint establishment module 3 is used for establishing constraint conditions of the final objective function, wherein the constraint conditions comprise: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint; the power flow convex optimization module 4 is used for transforming nonlinear equation constraints in the fundamental wave power flow constraint and the harmonic power flow constraint through convex optimization to obtain linearization power flow constraint under a rectangular coordinate system; the inverter convex optimization module 5 is used for converting the operation constraint of the SDPV grid-connected inverter through convex optimization to obtain a new operation linear inequality constraint of the SDPV grid-connected inverter; the scheduling module 6 is used for solving a final objective function by taking the linearization power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as the constraint of the final objective function, and scheduling the power distribution network according to the solving result; the method is based on operation optimization of the power distribution network, and an objective function for improving the power quality is established; under an objective function, corresponding constraint conditions are established, convex optimization processing of a model is conducted aiming at non-convex nonlinear constraint existing in the constraint conditions, an original non-convex nonlinear model is converted into a convex quadratic constraint quadratic programming model, the convex nonlinear model can be rapidly solved by an original dual interior point method, model convex errors are reduced through alternate iteration of tide calculation and optimization calculation, the solution difficulty is reduced, meanwhile, the solution precision is guaranteed, scheduling optimization is conducted on a power distribution network according to the solution result, and the problems of power distribution network voltage out-of-limit, three-phase imbalance, harmonic waves and the like are solved under the condition that various power quality compensation devices are not installed.
Embodiment III:
the present embodiment provides a storage medium storing a computer program which, when executed by a master controller, implements each step in the above method;
it is to be understood that the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The distributed photovoltaic optimal scheduling method based on convex optimization is characterized by comprising the following steps of:
establishing a loss objective function by taking the active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function by taking the minimization of the harmonic voltage level of the whole power distribution network as a target, and establishing a fundamental objective function by taking the minimization of the three-phase fundamental voltage of each node of the power distribution network as a target;
combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
establishing constraint conditions of the final objective function, wherein the constraint conditions comprise: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
the nonlinear equation constraint in the fundamental wave power flow constraint and the harmonic wave power flow constraint is subjected to convex optimization transformation to obtain the linearization power flow constraint under a rectangular coordinate system;
performing convex optimization transformation on the operation constraint of the SDPV grid-connected inverter to obtain a new operation linear inequality constraint of the SDPV grid-connected inverter;
and solving a final objective function by taking the linearization power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as constraints of the final objective function, and scheduling the power distribution network according to the solving result.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the fundamental wave power flow constraint comprises:
and acquiring phase, active power, reactive power, load and current data among different nodes in the power distribution network, and establishing fundamental wave power flow constraint according to the phase, active power, reactive power, load and current data among the different nodes.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
the harmonic power flow constraint comprises:
and acquiring the phase, active power, reactive power, current, amplitude of current and phase angle spectrum values among different nodes in the power distribution network, and establishing harmonic power flow constraint according to the phase, active power, reactive power, current, amplitude of current and phase angle spectrum values among different nodes.
4. The method of claim 3, wherein the step of,
the node voltage constraint includes:
node voltage amplitudes among different nodes in the power distribution network are obtained, and node voltage constraint is established by using the node voltage amplitudes of the different nodes to meet a preset voltage range.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
the power quality constraint includes:
and respectively acquiring harmonic voltages and unbalanced voltages of different nodes in the power distribution network, and establishing power quality constraint by using the harmonic voltages and the unbalanced voltages of the different nodes to meet a preset harmonic voltage range and an unbalanced voltage range.
6. The method of claim 5, wherein the step of determining the position of the probe is performed,
the SDPV grid-tie inverter operational constraints include:
and obtaining rated capacity and rated current of the inverter in the SDPV, and establishing operation constraint of the SDPV grid-connected inverter according to the rated capacity and the rated current of the inverter within a preset capacity range and a preset current range.
7. The method of claim 6, wherein the step of providing the first layer comprises,
the step of obtaining the linearization power flow constraint under the rectangular coordinate system by convex optimization transformation of the nonlinear equation constraint in the fundamental wave power flow constraint and the harmonic power flow constraint comprises the following steps:
acquiring operation data of a low-voltage transformer area and an SDPV in the current power distribution network through fundamental wave power flow constraint and harmonic wave power flow constraint;
according to the operation data of the medium-low voltage transformer areas and the SDPV, carrying out primary fundamental wave power flow and harmonic power flow calculation through nonlinear constraint in fundamental wave power flow constraint and harmonic power flow constraint to obtain fundamental wave current initial values and harmonic current initial values of the low-voltage transformer areas;
substituting the fundamental current initial value and the harmonic current initial value into the linear constraint of the fundamental current constraint and the linear constraint of the harmonic current constraint respectively to obtain the linearization current constraint under a rectangular coordinate system.
8. The method of claim 7, wherein the step of determining the position of the probe is performed,
the step of obtaining the new SDPV grid-connected inverter operation linear inequality constraint by convex optimization transformation of the SDPV grid-connected inverter operation constraint comprises the following steps:
acquiring operation data of an SDPV inverter in a current power distribution network;
substituting the operation data of the SDPV inverter into operation constraint of the SDPV grid-connected inverter to perform primary power and current calculation to obtain an initial power value and an initial current value of the SDPV inverter;
substituting the initial power value and the initial current value of the SDPV inverter into the operation constraint of the SDPV grid-connected inverter to obtain the new operation linear inequality constraint of the SDPV grid-connected inverter.
9. Distributed photovoltaic optimization scheduling device based on convex optimization, which is characterized by comprising:
an objective function establishment module: the method comprises the steps of establishing a loss objective function with active power loss minimization of a power distribution network operation line as a target, establishing a harmonic objective function with the whole harmonic voltage level minimization of a power distribution network as a target, and establishing a fundamental objective function with the three-phase fundamental voltage minimization of each node of the power distribution network as a target;
and a weighted combination module: the method comprises the steps of combining the loss objective function, the harmonic objective function and the fundamental objective function in a linear weighting mode to obtain a final objective function;
constraint establishment module: constraints for establishing the final objective function, the constraints comprising: fundamental wave power flow constraint, harmonic wave power flow constraint, node voltage constraint, line current constraint, electric energy quality constraint and SDPV grid-connected inverter operation constraint;
and the tide convex optimization module is used for: the method comprises the steps of obtaining linearization power flow constraint under a rectangular coordinate system through convex optimization transformation of nonlinear equation constraint in fundamental wave power flow constraint and harmonic power flow constraint;
inverter convex optimization module: the method comprises the steps of obtaining a new SDPV grid-connected inverter operation linear inequality constraint by performing convex optimization transformation on the SDPV grid-connected inverter operation constraint;
and a scheduling module: and the method is used for solving the final objective function by taking the linearization power flow constraint, the node voltage constraint, the line circuit constraint, the electric energy quality constraint and the new SDPV grid-connected inverter operation linear inequality constraint under the rectangular coordinate system as the constraint of the final objective function, and scheduling the power distribution network according to the solving result.
10. A storage medium storing a computer program which, when executed by a master, implements the steps of the convex optimization-based distributed photovoltaic optimization scheduling method of any one of claims 1-8.
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