CN116722608A - Reactive power compensation system based on photovoltaic inverter - Google Patents
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Abstract
The embodiment of the disclosure provides a reactive power compensation system based on a photovoltaic inverter, which is applied to the technical field of power systems. The system comprises: the data acquisition equipment, the intelligent control terminal and the reactive compensation equipment are sequentially connected; the data acquisition equipment is used for acquiring the current load and the historical load of each node in the power grid, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node; the intelligent control terminal is used for predicting the maximum reactive power of the load, the photovoltaic output and the photovoltaic inverter of each node in the next time period according to the current load and the historical load of each node, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node, so as to solve the reactive power optimization model, generate a reactive power demand curve of each node, and adjust the reactive power of the reactive power compensation equipment in each node in the next time period according to the reactive power demand curve of each node, thereby improving the reactive power compensation effect.
Description
Technical Field
The disclosure relates to the technical field of power systems, in particular to a reactive power compensation system based on a photovoltaic inverter.
Background
At present, the power of power sources such as power utilization of a transformer area, photovoltaic output and the like has rapid and slow fluctuation changes, so that the time-varying scales of the voltage changes of a power grid are different. Reactive power compensation is difficult to quickly respond according to short-time fluctuation changes of voltage by only using traditional reactive power compensation equipment, and the risk of voltage out-of-limit and the like is increased. Therefore, how to improve the reactive power compensation effect becomes a technical problem to be solved.
Disclosure of Invention
Embodiments of the present disclosure provide a reactive power compensation system based on a photovoltaic inverter.
In a first aspect, embodiments of the present disclosure provide a photovoltaic inverter-based reactive power compensation system, the system comprising:
the data acquisition equipment, the intelligent control terminal and the reactive compensation equipment are sequentially connected;
the data acquisition equipment is used for acquiring the current load and the historical load of each node in the power grid, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node;
the intelligent control terminal is used for predicting the maximum reactive power of the load, the photovoltaic output and the photovoltaic inverter of each node in the next time period according to the current load and the historical load of each node, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node, so as to solve the reactive power optimization model, generate a reactive power demand curve of each node, and adjust the reactive power of the reactive compensation equipment in each node in the next time period according to the reactive power demand curve of each node.
In some implementations of the first aspect, the intelligent control terminal is specifically configured to:
predicting the load of each node in a power grid in the next time period and the photovoltaic output of the photovoltaic array in each node in the next time period according to the current load and the historical load of each node;
and calculating the photovoltaic output of each node in the next time period and the maximum reactive power of the photovoltaic inverter participating in the network construction control according to the photovoltaic output of the photovoltaic array in each node in the next time period.
In some implementations of the first aspect, the intelligent control terminal is specifically configured to:
inputting the current load and the historical load of each node into a pre-trained load prediction model to obtain the load of each node at each moment in the next time period;
the load prediction model is obtained by training a preset neural network by using a training data set, wherein a sample in the training data set takes the current load and the historical load of a node at a certain moment as sample characteristic data, and takes the actual load of the node at each moment in the next time period at the certain moment as a label;
Inputting the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node into a photovoltaic output prediction model trained in advance to obtain the photovoltaic output of the photovoltaic array in each node at each moment in the next time period;
the photovoltaic output prediction model is obtained by training a preset neural network by using a training data set, wherein a sample in the training data set takes the current photovoltaic output and the historical photovoltaic output of the photovoltaic array at a certain moment as sample characteristic data, and takes the actual photovoltaic output of the photovoltaic array at each moment in the next time period of the certain moment as a label.
In some implementations of the first aspect, the intelligent control terminal is specifically configured to:
aiming at any node, calculating the probability distribution of the load behavior state of the next moment of the node according to the probability distribution of the current load behavior state of the node and the probability matrix of the load behavior state transition at the current moment;
calculating the load behavior state of the node at the next moment according to the load behavior state probability distribution and the load behavior state mapping function of the node at the next moment;
calculating the load of the node at the next moment according to the load behavior state of the node at the next moment and the corresponding load probability density function;
Iterative computation is continuously carried out until the load of the node at each moment in the next time period is calculated;
aiming at the photovoltaic array in any node, calculating the probability distribution of the photovoltaic output behavior state at the next moment of the photovoltaic array according to the probability distribution of the current photovoltaic output behavior state of the photovoltaic array and the probability matrix of the transition probability of the photovoltaic output behavior state at the current moment;
calculating the photovoltaic output behavior state of the photovoltaic array at the next moment according to the probability distribution of the photovoltaic output behavior state of the photovoltaic array at the next moment and the mapping function of the photovoltaic output behavior state;
calculating the photovoltaic output of the photovoltaic array at the next moment according to the photovoltaic output behavior state of the photovoltaic array at the next moment and the corresponding photovoltaic output probability density function;
and continuously iterating the calculation until the photovoltaic output of the photovoltaic array at each moment in the next time period is calculated.
In some implementations of the first aspect, before predicting the load of each node in the power grid for a next time period and the photovoltaic output of the photovoltaic array in each node for the next time period, the intelligent control terminal is further configured to:
aiming at any node, dividing the load behavior state of the node at each time in the day according to the historical load of the node, counting the transition probability of the load behavior state of the node at each time in the day, and generating a load behavior state transition probability matrix at each time;
Dividing the photovoltaic output behavior state of the photovoltaic array at each moment in the day according to the historical photovoltaic output of the photovoltaic array aiming at the photovoltaic array in any node, counting the transition probability of the photovoltaic output behavior state of the photovoltaic array at each moment in the day, and generating a photovoltaic output behavior state transition probability matrix at each moment.
In some implementations of the first aspect, the photovoltaic inverter and the photovoltaic array in each node form a distributed photovoltaic cluster variable dc topology for enabling the photovoltaic inverter to be connected in a changeable manner, wherein the photovoltaic inverter not connected to the photovoltaic array is in a free state, which is both a grid-built reactive power compensation device and a grid-connected reactive power compensation device;
the intelligent control terminal is specifically used for:
according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period, solving a distributed photovoltaic inverter free state quantity optimization model to obtain the maximum number of photovoltaic inverters in the free state at each moment in each node in the next time period;
calculating the maximum reactive power of the photovoltaic inverter of each node at each moment in the next time period participating in the network formation control according to the maximum number of the photovoltaic inverters of each node at each moment in the next time period in a free state and the apparent power of the photovoltaic inverter;
And calculating the photovoltaic output of each node at each moment in the next time period according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period.
In some implementations of the first aspect, the reactive power optimization model is constructed by:
and taking the minimum sum of the voltage deviations of all the nodes at any moment as a model solving target, taking node power balance constraint at any moment, node control variable constraint at any moment and node voltage constraint at any moment as model constraint, and constructing a reactive power optimization model.
In some implementations of the first aspect, the intelligent control terminal is specifically configured to:
and decomposing the reactive power demand curves of the nodes to obtain reactive power demand curves of reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes, and adjusting the reactive power of the corresponding reactive power compensation equipment in the next time period according to the reactive power demand curves of the reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes.
In some implementations of the first aspect, the intelligent control terminal is specifically configured to:
and decomposing the reactive power demand curves of the nodes by adopting a particle swarm algorithm to obtain reactive power demand curves of reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes.
In some implementations of the first aspect, the adjustable periodic response level of the reactive compensation device is determined by:
if the reactive compensation equipment is a parallel capacitor, determining that the response level of the adjustable period is a first level;
if the reactive power compensation equipment is the power electronic equipment controlled by the follow-up network, determining that the adjustable periodic response level is a second level;
if the reactive power compensation equipment is power electronic equipment with network formation control, determining that the adjustable periodic response level is a third level;
the response speed corresponding to the first level is lower than the response speed corresponding to the second level, and the response speed corresponding to the second level is lower than the response speed corresponding to the third level.
In the embodiment of the disclosure, based on the reactive power compensation system, the networking capability of the photovoltaic inverter can be developed, the network following and networking capability of the photovoltaic inverter can be fully exerted, the multi-equipment collaborative reactive power optimization is realized, the reactive power compensation effect is effectively improved, and the voltage operation level of a power grid is effectively improved while the investment of special reactive power compensation equipment is reduced.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
fig. 1 shows a block diagram of a reactive power compensation system based on a photovoltaic inverter provided by an embodiment of the present disclosure;
fig. 2 shows a schematic diagram of a distributed photovoltaic cluster variable dc topology provided by an embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of another distributed photovoltaic cluster variable DC topology provided by embodiments of the present disclosure;
fig. 4 shows a flowchart of a reactive power compensation method based on a photovoltaic inverter provided by an embodiment of the present disclosure.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the disclosure, are within the scope of the disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In view of the problems occurring in the background art, embodiments of the present disclosure provide a reactive power compensation system based on a photovoltaic inverter. Specifically, based on the reactive power compensation system, the load, the photovoltaic output and the maximum reactive power of the photovoltaic inverter of each node in the power grid in the next time period can be predicted, so that a reactive power optimization model is solved, a reactive power demand curve of each node is generated, and the reactive power of reactive power compensation equipment in each node is adjusted in the next time period according to the reactive power demand curve of each node.
Therefore, based on the reactive power compensation system, the networking capability of the photovoltaic inverter can be developed, the following and networking capabilities of the photovoltaic inverter are fully exerted, the multi-equipment collaborative reactive power optimization is realized, the reactive power compensation effect is effectively improved, the investment of special reactive power compensation equipment is reduced, and meanwhile, the voltage operation level of a power grid is effectively improved.
The reactive power compensation system based on the photovoltaic inverter provided by the embodiments of the present disclosure is described in detail below by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 illustrates a block diagram of a reactive power compensation system based on a photovoltaic inverter provided by an embodiment of the present disclosure, and as illustrated in fig. 1, a reactive power compensation system 100 may include: the data acquisition equipment, the intelligent control terminal and the reactive compensation equipment are sequentially connected, and the connection can be wired connection or wireless connection, and is not limited.
The data acquisition equipment is used for acquiring the current load and the historical load of each node in the power grid, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node;
the intelligent control terminal is used for predicting the maximum reactive power of the load, the photovoltaic output and the photovoltaic inverter of each node in the next time period according to the current load and the historical load of each node, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node, so as to solve the reactive power optimization model, generate a reactive power demand curve of each node, and adjust the reactive power of the reactive compensation equipment in each node in the next time period according to the reactive power demand curve of each node.
Specifically, the intelligent control terminal may be configured to predict a load of each node in a next period of time and a photovoltaic output of the photovoltaic array in each node in a next period of time according to a current load and a historical load of each node, calculate a maximum reactive power of the photovoltaic output and the photovoltaic inverter of each node in the next period of time according to the photovoltaic output of the photovoltaic array in each node in the next period of time, solve a reactive power optimization model according to the load of each node in the next period of time, the maximum reactive power of the photovoltaic output and the photovoltaic inverter of each node in the network control, generate a reactive power demand curve of each node, and adjust the reactive power of the reactive power compensation device in each node in the next period of time according to the reactive power demand curve of each node.
In some embodiments, the intelligent control terminal may be used to input the current load and the historical load of each node in the power grid into a pre-trained load prediction model, and calculate by the load prediction model, so as to quickly obtain the load of each node in the power grid at each moment in the next time period, thereby effectively improving the prediction effect.
The load prediction model is obtained by training a preset neural network (such as a convolutional neural network, a cyclic neural network, a long-short-term memory neural network and the like) by using a training data set, wherein samples in the training data set take the current load and the historical load of a node at a certain moment as sample characteristic data, and take the actual load of the node at each moment in the next time period at the certain moment as labels.
Meanwhile, the intelligent control terminal can be used for inputting the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node in the power grid into a photovoltaic output prediction model trained in advance, and the photovoltaic output prediction model is used for calculating so as to quickly obtain the photovoltaic output of the photovoltaic array in each node in the power grid at each moment in the next time period, and further the prediction effect is effectively improved.
The photovoltaic output prediction model is obtained by training a preset neural network (such as a convolutional neural network, a cyclic neural network, a long-short-term memory neural network and the like) by using a training data set, wherein a sample in the training data set takes the current photovoltaic output and the historical photovoltaic output of the photovoltaic array at a certain moment as sample characteristic data, and takes the actual photovoltaic output of the photovoltaic array at each moment in the next time period of the certain moment as a label.
In other embodiments, for any node, the intelligent control terminal may be configured to calculate, according to a current load behavior state probability distribution of the node and a load behavior state transition probability matrix at the current time, a load behavior state probability distribution of the node at a next time, calculate, according to a load behavior state probability distribution of the node at the next time and a load behavior state mapping function, a load behavior state of the node at the next time, calculate, according to a load behavior state of the node at the next time and a corresponding load probability density function, a load of the node at the next time, and perform iterative calculation until a load of the node at each time in the next time period is calculated.
It should be noted that before predicting the load of each node in the power grid in the next time period and the photovoltaic output of the photovoltaic array in each node in the next time period, for any node, the intelligent control terminal may be configured to divide the load behavior states of the node at each time in the day according to the historical load of the node, count the transition probabilities of the load behavior states of the node at each time in the day, and accurately and quickly generate a load behavior state transition probability matrix at each time.
Meanwhile, aiming at the photovoltaic array in any node, the intelligent control terminal can be used for calculating the photovoltaic output behavior state probability distribution of the next moment of the photovoltaic array according to the current photovoltaic output behavior state probability distribution of the photovoltaic array and the photovoltaic output behavior state transition probability matrix of the current moment, calculating the photovoltaic output behavior state of the photovoltaic array at the next moment according to the photovoltaic output behavior state probability distribution of the photovoltaic array at the next moment and the photovoltaic output behavior state mapping function, and calculating the photovoltaic output of the photovoltaic array at the next moment according to the photovoltaic output behavior state of the photovoltaic array at the next moment and the corresponding photovoltaic output probability density function, and continuously and iteratively calculating until the photovoltaic output of the photovoltaic array at each moment in the next time period is calculated.
It should be noted that before predicting the load of each node in the power grid in the next time period and the photovoltaic output of the photovoltaic array in each node in the next time period, the intelligent control terminal may be configured to divide the photovoltaic output behavior state of the photovoltaic array at each time in the day according to the historical photovoltaic output of the photovoltaic array for the photovoltaic array in any node, count the transition probabilities of the photovoltaic output behavior state of the photovoltaic array at each time in the day, and generate a photovoltaic output behavior state transition probability matrix at each time.
As an example, the photovoltaic inverter and the photovoltaic array in each node form a distributed photovoltaic cluster variable dc topology for enabling the photovoltaic inverter to be connected in a changeable manner to the photovoltaic array, wherein the photovoltaic inverter not connected to the photovoltaic array is in a free state, and is not only a grid-built reactive power compensation device but also a grid-connected reactive power compensation device.
The intelligent control terminal can be used for solving a distributed photovoltaic inverter free state quantity optimization model according to photovoltaic output of the photovoltaic array in each node at each moment in the next time period to obtain the maximum number of photovoltaic inverters in the free state at each moment in the next time period, and then quickly calculating the maximum reactive power of the photovoltaic inverters in each moment in the next time period of each node participating in network formation control according to the maximum number of the photovoltaic inverters in the free state at each moment in the next time period and apparent power of the photovoltaic inverters. Meanwhile, the photovoltaic output of each node at each moment in the next time period can be rapidly calculated according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period.
Correspondingly, the intelligent control terminal can be used for solving the reactive power optimization model according to the load, the photovoltaic output and the maximum reactive power of the photovoltaic inverter of each node at each moment in the next time period, so as to obtain the reactive power required by each node at each moment in the next time period, and further generate a reactive power demand curve of each node.
Wherein, the reactive power optimization model can be constructed by the following steps:
and the reactive power optimization model is constructed by taking the minimum sum of the voltage deviations of all the nodes at any moment as a model solving target and taking the node power balance constraint at any moment, the node control variable constraint at any moment and the node voltage constraint at any moment as model constraint, so that the reactive power required by all the nodes at all the moments in the next time period can be accurately calculated.
It can be understood that the reactive power demand curves of the nodes can be decomposed, for example, the reactive power demand curves of the nodes are rapidly decomposed by adopting a high-efficiency particle swarm algorithm, so as to obtain reactive power demand curves of reactive power compensation devices with different adjustable period response levels in the nodes, and the reactive power of the corresponding reactive power compensation device is adjusted in the next time period according to the reactive power demand curves of the reactive power compensation devices with different adjustable period response levels in the nodes.
Alternatively, the adjustable periodic response level of the reactive compensation device may be determined by:
if the reactive compensation equipment is a parallel capacitor, determining that the response level of the adjustable period is a first level, namely slow speed;
if the reactive power compensation equipment is power electronic equipment controlled by the follow-up network, determining that the response level of the adjustable period is a second level, namely a medium speed;
if the reactive power compensation equipment is power electronic equipment with network formation control, the adjustable periodic response level is determined to be a third level, namely the reactive power compensation equipment is rapid.
In the embodiment of the disclosure, based on the reactive power compensation system, the networking capability of the photovoltaic inverter can be developed, the network following and networking capability of the photovoltaic inverter can be fully exerted, the multi-equipment collaborative reactive power optimization is realized, the reactive power compensation effect is effectively improved, and the voltage operation level of a power grid is effectively improved while the investment of special reactive power compensation equipment is reduced.
The reactive power compensation system provided by the embodiment of the present disclosure is described in detail below with reference to a specific embodiment, and specifically as follows:
(1) The data acquisition equipment is used for acquiring line parameters, load operation data, photovoltaic operation data and reactive compensation equipment operation data of the power grid.
The load operation data comprise historical load power data and current load power data of each node; the photovoltaic operation data comprise historical irradiance data, current irradiance data, photovoltaic installation positions and capacities of all photovoltaic inverters; the reactive power compensation equipment operation data comprises a reactive power compensation equipment installation position, a single capacity and a total capacity. Optionally, the reactive compensation device includes a special reactive compensation device and a dual-purpose reactive compensation device, where the special reactive compensation device may be a parallel capacitor bank and an SVG, and the dual-purpose reactive compensation device may be a photovoltaic inverter.
(2) The intelligent control terminal is used for inputting the current load and the historical load of each node in the power grid into a pre-trained load prediction model, and calculating the current load and the historical load of each node in the power grid by the load prediction model to obtain the load of each node in the power grid at each moment in the next time period. And inputting the current photovoltaic output and the historical photovoltaic output of the photovoltaic arrays in all nodes in the power grid into a pre-trained photovoltaic output prediction model, and calculating by the photovoltaic output prediction model to obtain the photovoltaic output of the photovoltaic arrays in all nodes in the power grid at all times in the next time period.
(3) The intelligent control terminal is used for constructing photovoltaic inverter networking control conditions by utilizing the existing distributed photovoltaic cluster variable direct current topological structure. And (3) redistributing the input power of each photovoltaic inverter to ensure that part of the photovoltaic inverters in the cluster bear all power generation work, and defining the rest photovoltaic inverters in the cluster as photovoltaic inverters in a free state, so that the photovoltaic output of each node at each moment in the next time period and the maximum reactive power of the photovoltaic inverters participating in network construction control are calculated according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period.
Specifically, the photovoltaic inverter in a free state is not affected by power fluctuation of the photovoltaic panel, and reactive compensation can be performed by adopting network construction or network following control according to a centralized regulation and control instruction.
Defining a photovoltaic panel and a panel connected with each photovoltaic inverter under the conventional connection of the photovoltaic panel and the photovoltaic inverters as 1 photovoltaic array, and arranging the photovoltaic arrays according to the proportion of 1,2, … and X zc The photovoltaic inverter is numbered in the order of 1,2, …, Y inv Is numbered in the order of (2). The photovoltaic arrays are divided into m groups, wherein the number of photovoltaic arrays in each group may be different. Each group is connected with 1 photovoltaic inverter, and the photovoltaic inverters which are not connected with the photovoltaic array are in a free state, so that the network construction or the network following control operation can be selected according to the dispatching instruction.
Alternatively, the variable dc topology of the distributed photovoltaic cluster may be as shown in fig. 2, where the 1 st photovoltaic array and the 2 nd photovoltaic array have a switch connection; the 2 nd photovoltaic array is connected with a switch arranged on the 1 st photovoltaic array and the 3 rd photovoltaic array; the 3 rd photovoltaic array is connected with a switch on the 2 nd and 4 th photovoltaic arrays; up to the X zc -1 photovoltaic array and X-th zc -2 and X zc The photovoltaic array has a switch connection.
In addition, distributed photovoltaic cluster variable DC topology The puff structure can also be shown in FIG. 3, 1 st photovoltaic array and 2 nd to X th zc Each photovoltaic array is connected with a switch; 2 nd photovoltaic array and 3 rd to X rd zc Each photovoltaic array is connected with a switch; 3 rd photovoltaic array and 4 th to X th zc Each photovoltaic array is connected with a switch; up to the X zc -1 photovoltaic array and X-th zc The photovoltaic array has a switch connection.
In particular, the distributed light Fu Jiqun cannot be infinitely large due to limitations of the size of the photovoltaic field and the like, and the larger the number of photovoltaic inverters in one cluster, the more complicated the connection structure of the switch. Thus, one distributed photovoltaic cluster is not too large in size, and the photovoltaic in one node of the grid may be operated in multiple clusters.
At this time, the y-th predicted at the next time is defined inv Input power of photovoltaic inverterThe z-th photovoltaic array power (photovoltaic output) in the photovoltaic group j connected to the photovoltaic inverter is shown in formula (1).
Wherein k is j For the number of photovoltaic arrays in the j-th group of photovoltaic groups, j is E [1, m]。
According to the number of the photovoltaic arrays of each photovoltaic group, the total number X of the photovoltaic arrays zc The relation with the number of the photovoltaic arrays of each group is shown in a formula (2).
And calculating the power of the photovoltaic inverter connected with the photovoltaic group j after the direct-current side switch is to be reconstructed, wherein the power is shown in a formula (3).
The matching connection mechanism of each photovoltaic group and each photovoltaic inverter is that each photovoltaic group is sequentially connected according to the number of the photovoltaic inverter from small to large, so that the larger the number of the photovoltaic inverter is, the higher the priority is in a free state. Neglecting the difference of DC/AC conversion efficiency of each photovoltaic inverter at different moments, and defining eta inv Obtaining the y-th photovoltaic cluster for the conversion efficiency of the input and output of the photovoltaic inverter inv Input power of photovoltaic inverterAnd adjustable residual capacity->As shown in equation (4).
Wherein S is inv The apparent power of the photovoltaic inverter.
When the photovoltaic inverter inputs powerWhen the state of the photovoltaic inverter is a free state, the number of the photovoltaic inverters in the free state is defined as y zy，t The reactive power of the photovoltaic inverter in the photovoltaic cluster which can be controlled by adopting the grid structure is Q gw ＝y zy，t ·S inv 。
And (3) establishing a distributed photovoltaic inverter free state quantity optimization model, namely taking the maximum free state quantity of the photovoltaic inverters in the distributed photovoltaic cluster as a function of a target, namely a maximum reactive adjustable quantity target function of photovoltaic inverter network construction control in the distributed photovoltaic cluster, as shown in a formula (5).
max F sc ＝Q gw →max F sc ＝y zy，t，i (5)
Considering that the distributed photovoltaic in one node of the power distribution network can be divided into a plurality of clusters for operation, the photovoltaic clusters output active power and residual capacity as shown in a formula (6) assuming that the cluster sizes of the nodes are consistent.
The photovoltaic output of the node i at the moment of the power grid t is shown in a formula (7).
P PV，t，i ＝k gc，i ·P gct，t，i (7)
Wherein k is gc，i The number of photovoltaic clusters for node i; p (P) gct，t，i And (5) outputting power for each photovoltaic cluster in the node i at the moment t.
The callable residual capacity relationship of the grid-structured photovoltaic inverter is shown in a formula (8).
Q gwmax，t，i ＝y zy，t，i ·S inv (8)
Wherein y is zy，t，i And the number of photovoltaic inverters in a free state in the photovoltaic group control system is the node i at the moment t.
And (3) obtaining the maximum reactive adjustable quantity of the photovoltaic cluster networking control of the node i at the moment t (namely the maximum reactive power of the photovoltaic inverter participating in the networking control) according to the formula (8).
In this way, the photovoltaic output of each node at each moment in the next time period and the maximum reactive power of the photovoltaic inverter participating in the grid formation control are determined.
(4) The intelligent control terminal is used for establishing a reactive power optimization model by taking the minimum sum of voltage deviations of all nodes at any moment as a model solving target, taking node power balance constraint at any moment, node control variable constraint at any moment and node voltage constraint at any moment as model constraint.
The method is characterized in that the sum of voltage deviations of all nodes at any moment is taken as a model solving target, and the model solving target is specifically shown as a formula (9).
Wherein t is the time, t.epsilon.1, T m ]，T m The number of analysis moments divided for a day; n is the number of nodes; u (U) t，i The voltage of the node i at the moment t; u (U) 0 Is the nominal value of the node voltage.
The node power balance constraint at any moment is taken as a model constraint, and the model constraint is specifically shown as a formula (10).
Wherein P is t，i And Q t，i Injecting active power and reactive power of a node i at the moment t respectively, whereinP PV，t，i And Q PV，t，i Respectively outputting active power and reactive power by the photovoltaic inverter at the node i at the moment t; p (P) L，t，i And Q L，t，i Active power and reactive power consumed by the load of the node i at the moment t are respectively; q (Q) C，t，i The switching capacity of the parallel capacitor bank at the node i at the moment t; q (Q) SVG，t，i The reactive power compensated for SVG at node i at time t; u (U) t，i And U t，j The voltages of the node i and the node j at the moment t are respectively; g ij And B ij Line conductance and susceptance between node i and node j, respectively; θ t，ij The voltage phase angle difference between the node i and the node j at the time t.
The node control variable constraint at any moment is taken as a model constraint, and the model constraint is specifically shown as a formula (11).
Wherein Q is PVm ax，t，i The rest capacity of the photovoltaic inverter is the node i at the moment t; q (Q) SVGm ax，i Installing capacity for the SVG of node i; n (N) C，t，i For node i at time tThe number of switching groups of the parallel capacitors; n (N) Cmax The maximum switching group number of the parallel capacitor groups; q C Switching capacity for a single group of parallel capacitors; s is S ins，i Photovoltaic installation capacity for node i; q (Q) PVGFM，t，i And Q PVGFL，t，i The reactive power of the photovoltaic inverter at the node i at the moment t and the reactive power of the grid following control compensation are respectively formed; q (Q) SVGGFM，t，i And Q SVGGFL，t，i And respectively constructing a network for SVG at a node i at a moment t and controlling and compensating reactive power with the network.
The node voltage constraint at any moment is taken as a model constraint, and the model constraint is specifically shown as a formula (12).
U Nmin ≤U t，i ≤U Nmax (12)
Wherein U is Nmax And U Nmin The upper limit and the lower limit of the voltage of the node i at the moment t are respectively, U t，i The voltage at node i at time t.
(5) The intelligent control terminal is used for dividing the reactive compensation equipment into network-structured equipment and network-following equipment according to the attribute of the reactive compensation equipment, dividing the reactive compensation equipment into a plurality of adjustable periodic response levels according to the tracking response capability, and formulating cooperative control means under different adjustable periodic response levels.
Specifically, according to the analysis of the special and dual-purpose reactive compensation equipment, the reactive instruction control types and the adjustable periodic response grades of the parallel capacitor, the SVG and the photovoltaic inverter are divided from the time point of issuing the centralized control instruction as shown in table 1. The parallel capacitor is divided into following-net type control according to the control type because of the mechanical action characteristic of the parallel capacitor and the time scale of the regulation instruction period is an hour level, the response level of the adjustable period of the equipment is defined as slow speed, and the corresponding reactive power optimization outermost layer of the power grid, namely the cooperative operation sequence of the equipment is considered firstly; the SVG and the photovoltaic inverter are similar in characteristic, the grid-following control of the SVG and the photovoltaic inverter mainly follows the action of the centralized control instruction, and the control instruction period and the issuing time of the centralized control instruction are the same, so that the response level of the controllable period is defined as medium speed, and the reactive power optimization outermost layer and the reactive power optimization middle layer of the corresponding power grid are realized; the network-structured equipment can freely control reactive power output during the interval of the centralized control instruction, and the reactive power instruction is equivalent to real-time adjustment, so that the adjustable periodic response grade is defined as quick and corresponds to the innermost reactive power optimization layer of the power grid, and the capacity of the network-structured equipment can be used for optimizing the first two layers under the condition that the reactive power optimization of the first two layers of the power grid is insufficient. The multi-time-scale cooperative control relation of various reactive power compensation equipment at an hour level, a centralized control instruction time level and a real-time level provides a basis for the subsequent solving of a reactive power optimization model.
TABLE 1
When the control means of the network construction type equipment is formulated, the possible scenes of load and photovoltaic output in the next two moments need to be predicted, when more than 50% of predicted scenes meet the condition that the network construction type equipment is started to be beneficial to reducing voltage deviation, the network construction type equipment is started, and otherwise, the network construction type equipment is not started.
When the network-structured equipment installation node generates voltage self k in the next period F The forced regulation is performed when% float is changed, and whether output or reactive power absorption is performed is determined according to the formula (13). When the node voltage is greater than (1+k) in the next period F )U t The time-structured network type equipment consumes reactive power which is less than (1-k) F )U t The time-structured network type device outputs reactive power until the maximum compensation capacity of the device is reached.
(6) The intelligent control terminal is used for substituting the load, the photovoltaic output and the maximum reactive power of the photovoltaic inverter, which participate in the network construction control, of each node in the next time period into corresponding parameter items in the reactive power optimization model so as to solve the reactive power optimization model, obtain the reactive power required by each node in each time in the next time period and generate a reactive power demand curve of each node.
(7) The intelligent control terminal is used for rapidly decomposing the reactive power demand curves of the nodes by adopting a particle swarm algorithm to obtain reactive power demand curves of reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes.
The method comprises the steps that medium-speed reactive power compensation equipment and slow-speed reactive power compensation equipment are considered to participate in hour-level reactive power optimization, reactive power optimization is conducted on power grid operation in a centralized control instruction time period by the aid of the medium-speed reactive power compensation equipment, and finally voltage fluctuation adjustment in a shorter time by the aid of the fast-speed reactive power compensation equipment is considered.
Because the reactive power compensation resource carries out network construction or network following control according to the regulation and control instruction, and judges whether to carry out network construction control on the residual available reactive power compensation reactive resource on the premise of preferentially filling the heel network control, the reactive power compensation resource for network construction control such as SVG can fully use the capacity of the reactive power compensation resource for network following control according to the requirement of the dispatching instruction.
And calculating the optimal reactive power demand in the hour time period by adopting a particle swarm algorithm in consideration of the slow response capability of the equipment, obtaining the reactive power demand curve of the special and dual-purpose reactive power compensation equipment for the hour, and taking the reactive power demand curve as the reactive power demand curve of the outermost layer, namely the reactive power demand curve of the slow reactive power compensation equipment. If the photovoltaic installation node and the regional reactive power compensation equipment are the same in installation position, the reactive power demand of the special and dual-purpose reactive power compensation equipment is the total amount after superposition; if the two positions are different, the reactive power demand curves of the special reactive power compensation equipment and the reactive power compensation equipment are optimized respectively, and the principle of the reactive power demand curves of each layer is the same.
And (3) further performing reactive power optimization on the special and dual-purpose medium-speed reactive power compensation equipment on the basis of the outermost reactive power demand by considering the medium-speed response capability of the equipment, obtaining a special and dual-purpose medium-speed reactive power compensation equipment reactive power demand curve, and taking the special and dual-purpose medium-speed reactive power compensation equipment reactive power demand curve as an intermediate-speed reactive power demand curve, namely a medium-speed reactive power compensation equipment reactive power demand curve.
And (3) taking the quick response capability of the equipment into consideration, selecting the network-structured reactive power compensation equipment to perform reactive power optimization on the operation of the power grid on the basis of the reactive power demand of the middle layer, obtaining a reactive power demand curve of the special network-structured reactive power compensation equipment, and taking the reactive power demand curve as an innermost reactive power demand curve, namely the reactive power demand curve of the quick reactive power compensation equipment.
(8) The intelligent control terminal is used for adjusting the reactive power of the corresponding reactive compensation equipment in the next time period according to reactive power demand curves of the reactive compensation equipment with different adjustable and controllable periodic response levels in each node.
Fig. 4 shows a flowchart of a reactive power compensation method based on a photovoltaic inverter according to an embodiment of the present disclosure, and as shown in fig. 4, a reactive power compensation method 400 may be applied to the reactive power compensation system 100 shown in fig. 1, including the following steps:
S410, the data acquisition equipment acquires the current load and the historical load of each node in the power grid, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node.
S420, the intelligent control terminal predicts the maximum reactive power of the load, the photovoltaic output and the photovoltaic inverter of each node in the next time period according to the current load and the historical load of each node and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node, so as to solve the reactive power optimization model and generate a reactive power demand curve of each node.
S430, the intelligent control terminal adjusts the reactive power of the reactive power compensation equipment in each node in the next time period according to the reactive power demand curve of each node.
It can be appreciated that the reactive power compensation method 400 shown in fig. 4 is applied to the reactive power compensation system 100 shown in fig. 1, so that corresponding technical effects can be achieved, and for brevity, a detailed description is omitted herein.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A photovoltaic inverter-based reactive power compensation system, comprising: the data acquisition equipment, the intelligent control terminal and the reactive compensation equipment are sequentially connected;
the data acquisition equipment is used for acquiring the current load and the historical load of each node in the power grid, and the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node;
the intelligent control terminal is used for predicting the load of each node in the next time period, the photovoltaic output and the maximum reactive power of the photovoltaic inverter participating in the network construction control according to the current load and the historical load of each node, the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node, solving the reactive power optimization model, generating a reactive power demand curve of each node, and adjusting the reactive power of the reactive compensation equipment in each node in the next time period according to the reactive power demand curve of each node.
2. The system of claim 1, wherein the intelligent control terminal is specifically configured to:
predicting the load of each node in the next time period and the photovoltaic output of the photovoltaic array in each node in the next time period according to the current load and the historical load of each node;
and calculating the photovoltaic output of each node in the next time period and the maximum reactive power of the photovoltaic inverter participating in the network construction control according to the photovoltaic output of the photovoltaic array in each node in the next time period.
3. The system of claim 2, wherein the intelligent control terminal is specifically configured to:
inputting the current load and the historical load of each node into a pre-trained load prediction model to obtain the load of each node at each moment in the next time period;
the load prediction model is obtained by training a preset neural network by using a training data set, wherein a sample in the training data set takes the current load and the historical load of a node at a certain moment as sample characteristic data, and takes the actual load of the node at each moment in the next time period at the certain moment as a label;
Inputting the current photovoltaic output and the historical photovoltaic output of the photovoltaic array in each node into a photovoltaic output prediction model trained in advance to obtain the photovoltaic output of the photovoltaic array in each node at each moment in the next time period;
the photovoltaic output prediction model is obtained by training a preset neural network by using a training data set, wherein a sample in the training data set takes current photovoltaic output and historical photovoltaic output of a photovoltaic array at a certain moment as sample characteristic data, and takes actual photovoltaic output of the photovoltaic array at each moment in a next time period at the certain moment as a label.
4. The system of claim 2, wherein the intelligent control terminal is specifically configured to:
aiming at any node, calculating the probability distribution of the load behavior state of the next moment of the node according to the probability distribution of the current load behavior state of the node and the probability matrix of the load behavior state transition at the current moment;
calculating the load behavior state of the node at the next moment according to the load behavior state probability distribution and the load behavior state mapping function of the node at the next moment;
calculating the load of the node at the next moment according to the load behavior state of the node at the next moment and the corresponding load probability density function;
Iterative computation is continuously carried out until the load of the node at each moment in the next time period is calculated;
aiming at the photovoltaic array in any node, calculating the probability distribution of the photovoltaic output behavior state at the next moment of the photovoltaic array according to the probability distribution of the current photovoltaic output behavior state of the photovoltaic array and the probability matrix of the transition probability of the photovoltaic output behavior state at the current moment;
calculating the photovoltaic output behavior state of the photovoltaic array at the next moment according to the probability distribution of the photovoltaic output behavior state of the photovoltaic array at the next moment and the mapping function of the photovoltaic output behavior state;
calculating the photovoltaic output of the photovoltaic array at the next moment according to the photovoltaic output behavior state of the photovoltaic array at the next moment and the corresponding photovoltaic output probability density function;
and continuously iterating the calculation until the photovoltaic output of the photovoltaic array at each moment in the next time period is calculated.
5. The system of claim 4, wherein prior to predicting the load of each node in the power grid for a next time period and the photovoltaic output of the photovoltaic array in each node for the next time period, the intelligent control terminal is further configured to:
aiming at any node, dividing the load behavior state of the node at each time in the day according to the historical load of the node, counting the transition probability of the load behavior state of the node at each time in the day, and generating a load behavior state transition probability matrix at each time;
Dividing the photovoltaic output behavior state of the photovoltaic array at each moment in the day according to the historical photovoltaic output of the photovoltaic array aiming at the photovoltaic array in any node, counting the transition probability of the photovoltaic output behavior state of the photovoltaic array at each moment in the day, and generating a photovoltaic output behavior state transition probability matrix at each moment.
6. The system according to claim 3 or 4, wherein the photovoltaic inverter in each node and the photovoltaic array form a distributed photovoltaic cluster variable dc topology for enabling the photovoltaic inverter to be connected in a changeable manner, wherein the photovoltaic inverter not connected to the photovoltaic array is in a free state, which is both a grid-built reactive power compensation device and a grid-following reactive power compensation device;
the intelligent control terminal is specifically used for:
according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period, solving a distributed photovoltaic inverter free state quantity optimization model to obtain the maximum number of photovoltaic inverters in the free state at each moment in each node in the next time period;
calculating the maximum reactive power of the photovoltaic inverter of each node at each moment in the next time period participating in the network formation control according to the maximum number of the photovoltaic inverters of each node at each moment in the next time period in a free state and the apparent power of the photovoltaic inverter;
And calculating the photovoltaic output of each node at each moment in the next time period according to the photovoltaic output of the photovoltaic array in each node at each moment in the next time period.
7. The system of claim 1, wherein the reactive optimization model is constructed by:
and taking the minimum sum of the voltage deviations of all the nodes at any moment as a model solving target, taking node power balance constraint at any moment, node control variable constraint at any moment and node voltage constraint at any moment as model constraint, and constructing a reactive power optimization model.
8. The system of claim 1, wherein the intelligent control terminal is specifically configured to:
and decomposing the reactive power demand curves of the nodes to obtain reactive power demand curves of reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes, and adjusting the reactive power of the corresponding reactive power compensation equipment in the next time period according to the reactive power demand curves of the reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes.
9. The system of claim 8, wherein the intelligent control terminal is specifically configured to:
And decomposing the reactive power demand curves of the nodes by adopting a particle swarm algorithm to obtain reactive power demand curves of reactive power compensation equipment with different adjustable and controllable periodic response levels in the nodes.
10. The system of claim 9, wherein the adjustable periodic response level of the reactive compensation device is determined by:
if the reactive compensation equipment is a parallel capacitor, determining that the response level of the adjustable period is a first level;
if the reactive power compensation equipment is the power electronic equipment controlled by the follow-up network, determining that the adjustable periodic response level is a second level;
if the reactive power compensation equipment is power electronic equipment with network formation control, determining that the adjustable periodic response level is a third level;
the response speed corresponding to the first level is lower than the response speed corresponding to the second level, and the response speed corresponding to the second level is lower than the response speed corresponding to the third level.
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