CN116896089A - Cooperative sagging voltage control method and device for power distribution network - Google Patents
Cooperative sagging voltage control method and device for power distribution network Download PDFInfo
- Publication number
- CN116896089A CN116896089A CN202310915746.4A CN202310915746A CN116896089A CN 116896089 A CN116896089 A CN 116896089A CN 202310915746 A CN202310915746 A CN 202310915746A CN 116896089 A CN116896089 A CN 116896089A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power distribution
- node
- information
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000007665 sagging Methods 0.000 title claims abstract description 52
- 230000000875 corresponding effect Effects 0.000 claims description 82
- 238000012549 training Methods 0.000 claims description 53
- 230000006870 function Effects 0.000 claims description 39
- 230000009471 action Effects 0.000 claims description 32
- 238000004590 computer program Methods 0.000 claims description 26
- 230000008569 process Effects 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 17
- 238000012546 transfer Methods 0.000 claims description 14
- 230000001276 controlling effect Effects 0.000 claims description 13
- 230000007704 transition Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000002787 reinforcement Effects 0.000 description 3
- 238000010248 power generation Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method and a device for controlling cooperative sagging voltage of a power distribution network, wherein the method comprises the following steps: global information and local information in the power distribution network are obtained, and a sagging equation and an inverter of each node are optimized according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, and the local information comprises all load information and photovoltaic output information of the node; updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter respectively; when the voltage of the power distribution network fluctuates, reactive power adjustment is respectively carried out on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating, so that the voltage control is realized. The invention solves the technical problems that the prior art lacks global cooperativity, has lower solving efficiency and cannot complete the real-time control of the voltage.
Description
Technical Field
The invention relates to the technical field of power distribution network control, in particular to a power distribution network cooperative droop voltage control method and device.
Background
As an important renewable energy source, the application of solar photovoltaic in modern distribution networks has increased significantly, and with the access of large-scale photovoltaic, the problem of voltage out-of-limit of the distribution network has become more and more serious, which seriously affects the operation capability of the distribution network. Conventional distribution networks typically employ adjusting transformer tap positions and adding reactive compensation devices to adjust voltages. However, the conventional control measures have a slow response speed, low adjustment accuracy, and are not able to act frequently.
At present, reactive compensation is provided by an inverter of a distributed power generation device to cope with voltage fluctuation caused by large-scale new energy access, and reactive-voltage control is generally performed on the inverter based on a traditional Q-V sagging method. The control method belongs to an on-site control method in voltage control of a power distribution network, a control object of on-site control is usually an inverter of a new energy power generation device, a Q-V sagging curve is usually fixed and cannot be adjusted according to real-time working conditions, the reactive capacity of the inverter can not be fully mobilized to cause poor voltage control effect, meanwhile, the on-site control method lacks global cooperativity, the obtained control effect can not obtain optimal control effect, and the method for solving the reactive-voltage optimization control problem by adopting a random planning and robust optimization method is low in solving efficiency and cannot finish real-time control of voltage.
Therefore, there is a need for a control method that can achieve cooperative control of the system while improving the rapidity of voltage control
Disclosure of Invention
The invention provides a method and a device for controlling collaborative droop voltage of a power distribution network, which are used for solving the technical problems that the prior art lacks global cooperativity, has lower solving efficiency and cannot complete real-time control of voltage.
In order to solve the technical problems, an embodiment of the present invention provides a method for controlling a coordinated sagging voltage of a power distribution network, including:
global information and local information in the power distribution network are obtained, and a sagging equation and an inverter of each node are optimized according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises all load information and photovoltaic output information of the nodes, and each node has a corresponding droop equation and an inverter;
updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter respectively;
when the voltage of the power distribution network fluctuates, reactive power adjustment is respectively carried out on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating, so that the voltage control is realized.
Preferably, before the optimization of the droop equation and the inverter of each node according to the global information, the method further includes:
constructing a Markov decision process model according to global information and local information in the power distribution network; wherein the Markov decision process model comprises a state set, an action set, a reward function and a state transfer function;
and solving the Markov decision model to obtain a strategy network model.
Preferably, the state set includes active and reactive load information of each node and photovoltaic output information of each node, denoted as s t ＝(p t ,q t ,p PV,t )；
The set of actions includes the reactive output of each node photovoltaic inverter and the sag intercept of each photovoltaic inverter, expressed as
The reward function is: r is (r) t ＝-∑[P i loss (t)+β i ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is i loss (t) represents line loss, β represents a voltage penalty factor, and i represents node i;
the state transfer function is used to describe the state from the current state s t Transition to the next state s t+1 Is a process of (2).
As a preferred scheme, the solution to the markov decision model is performed to obtain a policy network model, which specifically includes:
training a preset initial strategy network model according to a preset total training round number and a preset exploration phase round number, so that in each training round, according to an obtained state set at the current moment, selecting a corresponding action set to feed back to the power distribution network, after the power distribution network executes the corresponding action set, calculating a corresponding rewarding value according to the rewarding function, thus completing the solution of a Markov decision model, taking the state set, the action set, the rewarding function and the state transfer function of the current round as training sets, inputting the training sets into the initial strategy network model, and outputting the strategy network model for completing training until the current training round number reaches the preset total training round number.
As a preferred solution, the selecting a corresponding action set is fed back to the power distribution network, specifically:
if the current training round number is smaller than the exploration phase round number, randomly generating an action set, and selecting to feed back the randomly generated action set to the power distribution network;
if the current training round number is larger than the exploration phase round number, based on the initial strategy network model of the current training, a corresponding action set is obtained according to the state set of the current round, and the action set is fed back to the power distribution network.
As a preferred solution, the droop equation and the inverter of each node are optimized according to the global information, specifically:
and inputting global information in the power distribution network to the strategy network model, so as to output and obtain a droop equation and an inverter optimized by corresponding nodes.
As a preferred solution, the reactive power adjustment is performed on the inverter corresponding to each node according to the local information and the sagging equation corresponding to each node after updating, specifically:
and according to the local information, performing droop control on the inverters corresponding to the nodes respectively, and performing reactive power adjustment on the inverters corresponding to the nodes respectively through the updated droop equation corresponding to the nodes.
Correspondingly, the invention also provides a power distribution network cooperative sagging voltage control device, which comprises the following components: the device comprises an optimizing module, an updating module and a control module;
the optimization module is used for acquiring global information and local information in the power distribution network and optimizing a sagging equation and an inverter of each node according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises all load information and photovoltaic output information of the nodes, and each node has a corresponding droop equation and an inverter;
the updating module is used for updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter respectively;
and the control module is used for respectively carrying out reactive power adjustment on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating when the voltage of the power distribution network fluctuates, so as to realize the control of the voltage.
Correspondingly, the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and is characterized in that the processor realizes the power distribution network cooperative droop voltage control method according to any one of the above when executing the computer program.
Correspondingly, the invention further provides a computer program product, which when run on a terminal device, causes the terminal device to execute the power distribution network cooperative droop voltage control method according to any one of the above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the technical scheme, the sagging equation and the inverter of each node are optimized by acquiring global information and local information in the power distribution network, so that the global information and the local information of the power distribution network can be fully combined, the cooperativity of the global information is ensured, each node is updated through the optimized sagging equation and the inverter, when the voltage of the power distribution network fluctuates, the inverter corresponding to each node is subjected to reactive power adjustment through the local information and the sagging equation corresponding to each node after updating, real-time control of the voltage is realized, the solving efficiency of cooperative sagging voltage control of the power distribution network is improved, and cooperative optimization control of the voltage of the power distribution network is realized.
Drawings
Fig. 1: is a traditional reactive-voltage sag curve schematic diagram;
fig. 2: the method for controlling the coordinated droop voltage of the power distribution network comprises the following steps of a flow chart;
fig. 3: schematic diagrams of cooperative control structures provided by the embodiments of the present invention;
fig. 4: the embodiment of the invention provides a structural schematic diagram of a power distribution network cooperative sagging voltage control device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The conventional reactive-voltage sag can be represented by the following formula and fig. 1:
reactive-voltage droop equation:
wherein beta is i The slope representing the droop equation can be calculated from the voltage sensitivity of the node where it is located, and is typically a fixed value; v (V) i (t) represents the real-time voltage of the node; v (V) i * Representing the intercept of the droop equation, which can be adjusted, different intercepts represent different droop equations.
As can be seen from the droop control formula, when the voltage fluctuates, the real-time reactive outputs of different inverters are based on the real-time node voltage V i (t) and voltage intercept V i * Is calculated from the difference between (a) and (b). Thus, different voltage intercepts correspond to different droop equations, i.e., different reactive outputs. Under some extreme working conditions, the voltage fluctuation is large, but may be limited by droop appointed by the inverter, and enough reactive power output compensation cannot be obtained, so that the voltage adjustment effect is poor. Meanwhile, droop control belongs to local control, and voltage control is completed only according to local information, so that global cooperativity is lacked.
Example 1
Referring to fig. 2, a method for controlling coordinated droop voltage of a power distribution network according to an embodiment of the present invention includes the following steps S101 to S103:
step S101: global information and local information in the power distribution network are obtained, and a sagging equation and an inverter of each node are optimized according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises all load information and photovoltaic output information of the nodes, and each node has a corresponding droop equation and an inverter.
It should be noted that, the first embodiment may be used in a cooperative control structure, where a specific cooperative control structure is shown in fig. 3. Distribution network voltage control can be divided into two control levels: a central control layer and a local control layer. The central control layer can acquire global information, including load information of all nodes in the power distribution network system and real-time output information of all photovoltaics; the local control layer can only acquire local information including all loads and photovoltaic output information of the node, and particularly can only acquire real-time loads and real-time photovoltaic output information, historical loads and historical photovoltaic output information of the node.
Further, the conventional local control generally uses a fixed droop function in combination with the voltage control performed by the local information, and the cooperative control structure of the first embodiment can combine the central control hierarchy and the local control hierarchy, and is implemented by first completing the optimization of the droop equation of each node and the basic reactive output of the inverter by using global information through the central control hierarchy in step S101.
It should be noted that, the basic reactive power is an inverter basic reactive power output given based on global information, and is used for maintaining the voltage of the power distribution network under the conventional working condition, the basic reactive power is maintained in the whole control stage, and the droop control is to superimpose a real-time reactive power compensation according to the real-time voltage fluctuation to better cope with the randomness of the new energy output.
As a preferable solution of this embodiment, before the optimizing the droop equation and the inverter of each node according to the global information, the method further includes:
constructing a Markov decision process model according to global information and local information in the power distribution network; wherein the Markov decision process model comprises a state set, an action set, a reward function and a state transfer function; and solving the Markov decision model to obtain a strategy network model.
In the embodiment, the collaborative voltage optimization control is modeled into a Markov decision process (Markov Decision Process, MDP), and the solution is performed by using a deep reinforcement learning method, so that the rapidity and the real-time performance of the voltage control are ensured while the collaborative control is realized.
As a preferred solution of this embodiment, the state set includes active and reactive load information of each node and photovoltaic output information of each node, denoted as s t ＝(p t ,q t ,p PV,t ) The method comprises the steps of carrying out a first treatment on the surface of the The set of actions includes the reactive output of each node photovoltaic inverter and the sag intercept of each photovoltaic inverter, expressed asThe reward function is: r is (r) t ＝-∑[P i loss (t)+β i ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is i loss (t) represents line loss, β represents a voltage penalty factor, and i represents node i; the state transfer function is used to describe the state from the current state s t Transition to the next state s t+1 Is a process of (2).
Further, the Markov decision process typically consists of a quadruple of < S, A, R, P >, where S represents a set of states; a represents an action set; r represents a reward function; p represents a state transfer function. In this problem, the quaternion is modeled as:
s: the state input at each moment comprises active and reactive load information of each node, photovoltaic output information of each node and the like, and can be expressed as s t ＝(p t ,q t ,p PV,t )。
A: the action output at each moment includes the base reactive output of the photovoltaic inverter at each node and the sag intercept of each photovoltaic inverter, which can be expressed as
R: the objective of this optimization problem is to make it possible to keep the voltage stability as low as possibleSo the bonus function can be expressed as r t ＝-∑[P i loss (t)+β i ]Wherein P is i loss (t) represents line loss; beta represents a voltage penalty factor when the voltage is over-limited (i.e., v t ＞1.05,v t < 0.95) is 1, and 0 when the voltage is not more limited.
P: the state transfer function is used to describe the state of the agent from the current state s when the action is performed t Transition to the next state s t+1 Is a process of (2).
In this embodiment, the intercept V of the droop equation can be adaptively adjusted in real time according to different working conditions i * Different sagging equations are obtained so as to fully exert the reactive potential of the photovoltaic inverter.
As a preferred solution of this embodiment, the solving the markov decision model to obtain a policy network model specifically includes:
training a preset initial strategy network model according to a preset total training round number and a preset exploration phase round number, so that in each training round, according to an obtained state set at the current moment, selecting a corresponding action set to feed back to the power distribution network, after the power distribution network executes the corresponding action set, calculating a corresponding rewarding value according to the rewarding function, thus completing the solution of a Markov decision model, taking the state set, the action set, the rewarding function and the state transfer function of the current round as training sets, inputting the training sets into the initial strategy network model, and outputting the strategy network model for completing training until the current training round number reaches the preset total training round number.
In this embodiment, referring to fig. 3, the agent is configured to store the trained policy network model, so that after data is input, the corresponding reactive output of the ratio pickup foundation and the optimized droop equation can be directly output through the policy network model in the agent, thereby implementing control on the voltage of the power distribution network.
In this embodiment, after modeling the voltage optimization control problem as MDP, the invention is intended to takeThe agent was trained using the soft actor-critic (SAC) algorithm for solving the MDP. Specifically, firstly setting a total training round number n and an exploration phase round number m; at the beginning of each round, the agent obtains the state s at the current moment from the environment, i.e. the distribution network model t And selects the corresponding action a t Feedback to the environment, and after the environment executes the action, the corresponding rewarding value r is calculated according to the rewarding function t And transitions to the next state s t+1 Will be<s t ,a t ,r t ,s t+1 >And storing the training data into an experience pool (training set) for training a strategy network model in the intelligent agent, and sequentially reciprocating until the training round is finished.
It will be appreciated that as the number of training rounds increases, the strategic network model in the agent becomes more empirical, and will progressively select the optimal action to maximize the prize value and ultimately converge. The trained agent can be packaged, corresponding neural network parameters in the strategy network model are stored, no additional training is needed in the using stage, only real-time states are needed to be input, and corresponding outputs can be obtained, so that the voltage can be rapidly controlled.
As a preferred solution of this embodiment, the selecting a corresponding action set to be fed back to the power distribution network specifically includes:
if the current training round number is smaller than the exploration phase round number, randomly generating an action set, and selecting to feed back the randomly generated action set to the power distribution network; if the current training round number is larger than the exploration phase round number, based on the initial strategy network model of the current training, a corresponding action set is obtained according to the state set of the current round, and the action set is fed back to the power distribution network.
In the present embodiment, for action a t If the current round number is smaller than the round number of the exploration phase, randomly generating actions; if the current round number is greater than the round number of the exploration phase, actions are generated based on the strategy network of the intelligent agent of the current training degree, so that further correction training on the strategy network model of the intelligent agent is realized.
As a preferred solution of this embodiment, the optimization of the droop equation and the inverter of each node according to the global information is specifically:
and inputting global information in the power distribution network to the strategy network model, so as to output and obtain a droop equation and an inverter optimized by corresponding nodes.
Step S102: and updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter.
In this embodiment, the optimized droop equation and the inverter are distributed to each photovoltaic node of the power distribution network through the communication line, and each photovoltaic node updates the droop equation after receiving the information.
Step S103: when the voltage of the power distribution network fluctuates, reactive power adjustment is respectively carried out on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating, so that the voltage control is realized.
As a preferred solution of this embodiment, the reactive power adjustment is performed on the inverter corresponding to each node according to the local information and the sagging equation corresponding to each node after updating, which specifically includes:
and according to the local information, performing droop control on the inverters corresponding to the nodes respectively, and performing reactive power adjustment on the inverters corresponding to the nodes respectively through the updated droop equation corresponding to the nodes.
In this embodiment, this step is performed in the local control layer. And in the local control layer, when the voltage fluctuation of the power distribution network is caused by larger fluctuation of the photovoltaic output, each photovoltaic node only needs to complete reactive power adjustment of the inverter according to the local information and the optimized droop function, so that the voltage adjustment is realized. The cooperative control structure shown in fig. 3 can combine the central control layer with the local control layer well, so as to realize cooperative control of the voltage of the power distribution network.
It can be understood that, through the cooperative control structure, the embodiment can fully combine the global information and the local information of the power distribution network to realize cooperative optimization control on the system voltage, and further, the deep reinforcement learning method is used for solving the voltage optimization control problem, so that the advantage of deep reinforcement learning is fully exerted, and the real-time and rapid control on the voltage is realized on the basis of ensuring the voltage control cooperativity.
The implementation of the above embodiment has the following effects:
according to the technical scheme, the sagging equation and the inverter of each node are optimized by acquiring global information and local information in the power distribution network, so that the global information and the local information of the power distribution network can be fully combined, the cooperativity of the global information is ensured, each node is updated through the optimized sagging equation and the inverter, when the voltage of the power distribution network fluctuates, the inverter corresponding to each node is subjected to reactive power adjustment through the local information and the sagging equation corresponding to each node after updating, real-time control of the voltage is realized, the solving efficiency of cooperative sagging voltage control of the power distribution network is improved, and cooperative optimization control of the voltage of the power distribution network is realized.
Example two
Referring to fig. 4, the present invention further provides a power distribution network cooperative droop voltage control device, including: an optimization module 201, an update module 202 and a control module 203.
The optimizing module 201 is configured to obtain global information and local information in the power distribution network, and optimize a droop equation and an inverter of each node according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises all load information and photovoltaic output information of the nodes, and each node has a corresponding droop equation and an inverter.
The updating module 202 is configured to update the droop equation and the inverter corresponding to each node through the optimized droop equation and the inverter, respectively.
The control module 203 is configured to, when the voltage of the power distribution network fluctuates, perform reactive power adjustment on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to each node after updating, so as to control the voltage.
Preferably, before the optimization of the droop equation and the inverter of each node according to the global information, the method further includes:
constructing a Markov decision process model according to global information and local information in the power distribution network; wherein the Markov decision process model comprises a state set, an action set, a reward function and a state transfer function; and solving the Markov decision model to obtain a strategy network model.
Preferably, the state set includes active and reactive load information of each node and photovoltaic output information of each node, denoted as s t ＝(p t ,q t ,p PV,t ) The method comprises the steps of carrying out a first treatment on the surface of the The set of actions includes the reactive output of each node photovoltaic inverter and the sag intercept of each photovoltaic inverter, expressed asThe reward function is: r is (r) t ＝-∑[P i loss (t)+β i ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is i loss (t) represents line loss, β represents a voltage penalty factor, and i represents node i; the state transfer function is used to describe the state from the current state s t Transition to the next state s t+1 Is a process of (2).
As a preferred scheme, the solution to the markov decision model is performed to obtain a policy network model, which specifically includes:
training a preset initial strategy network model according to a preset total training round number and a preset exploration phase round number, so that in each training round, according to an obtained state set at the current moment, selecting a corresponding action set to feed back to the power distribution network, after the power distribution network executes the corresponding action set, calculating a corresponding rewarding value according to the rewarding function, thus completing the solution of a Markov decision model, taking the state set, the action set, the rewarding function and the state transfer function of the current round as training sets, inputting the training sets into the initial strategy network model, and outputting the strategy network model for completing training until the current training round number reaches the preset total training round number.
As a preferred solution, the selecting a corresponding action set is fed back to the power distribution network, specifically:
if the current training round number is smaller than the exploration phase round number, randomly generating an action set, and selecting to feed back the randomly generated action set to the power distribution network; if the current training round number is larger than the exploration phase round number, based on the initial strategy network model of the current training, a corresponding action set is obtained according to the state set of the current round, and the action set is fed back to the power distribution network.
As a preferred solution, the droop equation and the inverter of each node are optimized according to the global information, specifically:
and inputting global information in the power distribution network to the strategy network model, so as to output and obtain a droop equation and an inverter optimized by corresponding nodes.
As a preferred solution, the reactive power adjustment is performed on the inverter corresponding to each node according to the local information and the sagging equation corresponding to each node after updating, specifically:
and according to the local information, performing droop control on the inverters corresponding to the nodes respectively, and performing reactive power adjustment on the inverters corresponding to the nodes respectively through the updated droop equation corresponding to the nodes.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the above-described apparatus, which is not described herein again.
The implementation of the above embodiment has the following effects:
according to the technical scheme, the sagging equation and the inverter of each node are optimized by acquiring global information and local information in the power distribution network, so that the global information and the local information of the power distribution network can be fully combined, the cooperativity of the global information is ensured, each node is updated through the optimized sagging equation and the inverter, when the voltage of the power distribution network fluctuates, the inverter corresponding to each node is subjected to reactive power adjustment through the local information and the sagging equation corresponding to each node after updating, real-time control of the voltage is realized, the solving efficiency of cooperative sagging voltage control of the power distribution network is improved, and cooperative optimization control of the voltage of the power distribution network is realized.
Example III
Correspondingly, the invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method for controlling a coordinated sag voltage of a power distribution network according to any one of the embodiments above when executing the computer program.
The terminal device of this embodiment includes: a processor, a memory, a computer program stored in the memory and executable on the processor, and computer instructions. The processor, when executing the computer program, implements the steps of the first embodiment described above, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor, when executing the computer program, performs the functions of the modules/units in the above-described device embodiments, such as the control module 203.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device. For example, the control module is configured to, when the voltage of the power distribution network fluctuates, perform reactive power adjustment on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to each node after updating, so as to control the voltage.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine some components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Example IV
Correspondingly, the invention further provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the power distribution network cooperative droop voltage control method according to any one of the embodiments.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The utility model provides a power distribution network cooperation sagging voltage control method which is characterized in that the method comprises the following steps:
global information and local information in the power distribution network are obtained, and a sagging equation and an inverter of each node are optimized according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises all load information and photovoltaic output information of the node where the local information is located, and each node has a corresponding droop equation and an inverter;
updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter respectively;
when the voltage of the power distribution network fluctuates, reactive power adjustment is respectively carried out on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating, so that the voltage control is realized.
2. A method of controlling a coordinated sag voltage of a power distribution network according to claim 1, further comprising, prior to said optimizing sag equations and inverters for each node based on said global information:
constructing a Markov decision process model according to global information and local information in the power distribution network; wherein the Markov decision process model comprises a state set, an action set, a reward function and a state transfer function;
and solving the Markov decision model to obtain a strategy network model.
3. A method of controlling a coordinated sag voltage of a power distribution network according to claim 2, wherein said set of states comprises active and reactive load information of each node and photovoltaic output information of each node, denoted s t ＝(p t ,q t ,p PV,t )；
The set of actions includes the reactive output of each node photovoltaic inverter and the sag intercept of each photovoltaic inverter, expressed as
The reward function is: r is (r) t ＝-∑[P i loss (t)+β i ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is i loss (t) represents line loss, β represents a voltage penalty factor, and i represents node i;
the state transfer function is used to describe the state from the current state s t Transition to the next state s t+1 Is a process of (2).
4. A method for controlling a coordinated sag voltage of a power distribution network according to claim 3, wherein the solving the markov decision model is performed to obtain a policy network model, which specifically includes:
training a preset initial strategy network model according to a preset total training round number and a preset exploration phase round number, so that in each training round, according to an obtained state set at the current moment, selecting a corresponding action set to feed back to the power distribution network, after the power distribution network executes the corresponding action set, calculating a corresponding rewarding value according to the rewarding function, thus completing the solution of a Markov decision model, taking the state set, the action set, the rewarding function and the state transfer function of the current round as training sets, inputting the training sets into the initial strategy network model, and outputting the strategy network model for completing training until the current training round number reaches the preset total training round number.
5. The method for controlling a coordinated sag voltage of a power distribution network according to claim 4, wherein the selecting a corresponding action set is fed back to the power distribution network, specifically:
if the current training round number is smaller than the exploration phase round number, randomly generating an action set, and selecting to feed back the randomly generated action set to the power distribution network;
if the current training round number is larger than the exploration phase round number, based on the initial strategy network model of the current training, a corresponding action set is obtained according to the state set of the current round, and the action set is fed back to the power distribution network.
6. A method for controlling a coordinated sag voltage of a power distribution network according to any one of claims 2-5, wherein the sag equation and the inverter of each node are optimized according to the global information, specifically:
and inputting global information in the power distribution network to the strategy network model, so as to output and obtain a droop equation and an inverter optimized by corresponding nodes.
7. The method for controlling the coordinated sag voltage of a power distribution network according to claim 1, wherein reactive power adjustment is performed on inverters corresponding to each node according to the local information and the sag equation corresponding to each node after updating, specifically:
and according to the local information, performing droop control on the inverters corresponding to the nodes respectively, and performing reactive power adjustment on the inverters corresponding to the nodes respectively through the updated droop equation corresponding to the nodes.
8. The utility model provides a distribution network is sagging voltage control device in coordination which characterized in that includes: the device comprises an optimizing module, an updating module and a control module;
the optimization module is used for acquiring global information and local information in the power distribution network and optimizing a sagging equation and an inverter of each node according to the global information; the global information comprises real-time load information and real-time photovoltaic output information of all nodes in the power distribution network, the local information comprises historical load information and historical photovoltaic output information of the nodes, and each node has a corresponding droop equation and an inverter;
the updating module is used for updating the sagging equation and the inverter corresponding to each node through the optimized sagging equation and the inverter respectively;
and the control module is used for respectively carrying out reactive power adjustment on the inverters corresponding to the nodes according to the local information and the sagging equation corresponding to the nodes after updating when the voltage of the power distribution network fluctuates, so as to realize the control of the voltage.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of controlling a coordinated sag voltage of a power distribution network according to any one of claims 1 to 7.
10. A computer program product, characterized in that the computer program product, when run on a terminal device, causes the terminal device to perform the power distribution network co-droop voltage control method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310915746.4A CN116896089A (en) | 2023-07-25 | 2023-07-25 | Cooperative sagging voltage control method and device for power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310915746.4A CN116896089A (en) | 2023-07-25 | 2023-07-25 | Cooperative sagging voltage control method and device for power distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116896089A true CN116896089A (en) | 2023-10-17 |
Family
ID=88310609
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310915746.4A Pending CN116896089A (en) | 2023-07-25 | 2023-07-25 | Cooperative sagging voltage control method and device for power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116896089A (en) |
-
2023
- 2023-07-25 CN CN202310915746.4A patent/CN116896089A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pervez et al. | Most valuable player algorithm based maximum power point tracking for a partially shaded PV generation system | |
| Arya | Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller | |
| CN110120670B (en) | DPV-containing power distribution network reactive voltage optimization method, terminal equipment and storage medium | |
| CN114977172A (en) | Power grid carbon energy mixed flow optimization method and device, terminal and storage medium | |
| CN112803434A (en) | Reactive power optimization method, device, equipment and storage medium for active power distribution network | |
| CN113315166B (en) | Multi-virtual synchronous machine inertia configuration method and device and terminal equipment | |
| CN112636396A (en) | Photovoltaic power distribution network control method and terminal | |
| Hu et al. | Deep reinforcement learning based coordinated voltage control in smart distribution network | |
| CN116896089A (en) | Cooperative sagging voltage control method and device for power distribution network | |
| CN117424234A (en) | Harmonic suppression method, device, equipment and storage medium | |
| CN116976514A (en) | Distributed power supply location and volume-fixing method, device and storage medium | |
| CN115360768A (en) | Power scheduling method and device based on muzero and deep reinforcement learning and storage medium | |
| CN113517698B (en) | Active power distribution network optimal power flow salifying control method and device | |
| CN112542834B (en) | Control system and method for power grid topological structure | |
| CN110768294B (en) | Random scheduling method and device for distributed power supply | |
| Nguyen et al. | Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer | |
| CN113922384A (en) | Wind power plant distributed reactive voltage optimization coordination control method | |
| CN114329317B (en) | Output power scheduling method and device based on hybrid generator set and terminal | |
| CN110048431B (en) | Reactive power optimization method, device, equipment and storage medium for power distribution network | |
| CN116707036B (en) | Reactive power compensation method, device and equipment based on photovoltaic inverter | |
| US20240232472A1 (en) | Multi-asset placement and sizing for robust operation of distribution systems | |
| CN118095765A (en) | Two-stage planning method, device and equipment for power distribution network and storage medium | |
| CN117859132A (en) | Multi-asset configuration and sizing for robust operation of power distribution systems | |
| CN117638877A (en) | Power grid dynamic partitioning method and device based on deep reinforcement learning | |
| CN117578506A (en) | Energy storage primary frequency modulation self-adaptive control method and system for new energy station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |