CN114447942B - Active power distribution network load side multi-element voltage regulation method, device and storage medium - Google Patents
Active power distribution network load side multi-element voltage regulation method, device and storage medium Download PDFInfo
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- 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
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- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- 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
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- 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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- 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
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- 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
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- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
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Abstract
The invention discloses a method, equipment and a storage medium for multi-element voltage regulation of a load side of an active power distribution network, wherein the method comprises the following steps: (1) Data preparation, including prediction data of renewable energy sources, basic characteristics of flexible resources and basic parameter related information of a power distribution network system; (2) Analyzing an active power distribution network structure accessed with a plurality of flexible resources, and researching the mode of the internal resources participating in the operation of the power distribution network; (3) Taking the power flow constraint of the power distribution network system and the operation constraint of each node in the power distribution network into consideration, and establishing a power distribution network voltage regulation mathematical model by taking the minimum system loss and voltage offset as targets; (4) Based on the idea of reinforcement learning, converting the voltage regulating task into a Markov decision process, and establishing a Markov model for regulating the voltage of the active power distribution network. The invention comprehensively considers the characteristics of the flexible resources of the distribution network such as the distributed power supply, the flexible load, the SVC and the like, and avoids the complex physical modeling while coordinating a plurality of resources in the distribution network to participate in the operation of the distribution network.
Description
Technical Field
The invention relates to the power technology, in particular to a method, a device, equipment and a storage medium for multi-element voltage regulation of a load side of an active power distribution network in a power internet of things environment.
Background
In recent years, the rapid development of distributed power supplies (DistributedGeneration, DG) has led to increased initiative and more flexible operation modes of the distribution network. Meanwhile, the DG is connected to influence single power flow distribution in the power distribution network, and the intermittent and random DG output easily causes problems of node voltage out-of-limit, network loss increase and the like of the power distribution network, so that challenges are brought to reactive power optimization regulation and control of the power distribution network.
In order to realize the coordinated control of the power distribution network and the internal controllable equipment thereof, expert students propose the concept of an Active power distribution network (Active DistributionNetwork, AND). The ADN is usually connected with flexible resources such as a distributed power supply, energy storage, flexible load and the like, which increases the difficulty of reactive power coordination optimization of the power distribution network. Therefore, how to design a new optimization algorithm to perform load-side multi-element voltage regulation on the ADN becomes an important subject for improving the stability of the power distribution network and promoting the intelligence of the power distribution network.
The multi-element reactive voltage regulation optimization of the ADN is to optimally regulate and coordinate control a power distribution network and internal controllable equipment thereof on the premise of ensuring safe operation constraints such as power flow limit of a power transmission line and meeting load requirements, so that the voltage deviation of an operation node of the power distribution network is minimized, and the economic and stable operation of the power distribution network is ensured. The method for processing dynamic reactive power optimization mainly comprises a traditional mathematical optimization method, an intelligent optimization method, a model simplification method and the like. However, these methods generally have the problems of large calculation amount, easy sinking into local optimum, severe dependence on prediction data, difficult realization of online control and the like.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide an active power distribution network load side multi-component voltage regulation control strategy based on a depth deterministic strategy gradient algorithm, which converts the reactive power optimization problem of the power distribution network into a Markov decision process, avoids complex physical modeling, and simultaneously coordinates a plurality of resources in the power distribution network to participate in the operation of the power distribution network, thereby realizing the multi-component reactive power optimization of the system. The method can fully utilize flexible response characteristics of flexible loads, ensure the operation economy of the active power distribution network and effectively stabilize the voltage fluctuation of the power grid. The work aims to provide a reference for a multi-element voltage regulation strategy on the load side of an active power distribution network.
The technical scheme is as follows: the invention discloses a multi-element voltage regulation method for a load side of an active power distribution network, which comprises the following steps:
s1, preparing data, wherein the data comprises basic characteristics of flexible resources in a power distribution network, prediction data of renewable energy sources and basic parameter related information of a power distribution network system;
S2, analyzing an active power distribution network structure accessed to a plurality of flexible resources, and researching a mode of participating in operation of internal resources of the power distribution network;
s3, considering power flow constraint of a power distribution network system and operation constraint of each node in the power distribution network, and establishing a power distribution network voltage regulation mathematical model considering flexible resource response by taking minimum system loss and voltage offset as targets;
And S4, converting the voltage regulating task in the step S3 into a Markov decision process based on the idea of reinforcement learning to obtain a Markov model for regulating the voltage of the active power distribution network, and solving based on DDPG algorithm.
Further, the specific analysis of the flexible resources accessed internally in S2 is as follows:
(1) Distributed power supply
The DG with high permeability is put into the power distribution network, so that the traditional passive power distribution network is gradually transformed into a novel ADN capable of flexibly allocating electric energy and actively absorbing renewable energy, and meanwhile, the power output of the DG can change the power flow distribution of the power distribution network, so that the reactive power flow in the network is changed;
(2) Flexible load
The flexible load with peak clipping and valley filling functions is connected to the power distribution network, and is used as an important part of the response of a demand side, so that the original load distribution can be changed in time and space, the load power curve of the power distribution network is effectively improved, and the virtual energy storage function is realized;
(3) Reactive power compensation device
The low-voltage side of the power distribution network is positioned at the tail end of the power system and is used as the last ring facing the user end, when the user load in the electricity utilization peak period is increased suddenly, the voltage of the low-voltage side of the power distribution network is reduced, so that the static reactive power compensator is connected in parallel in the power distribution network system, and the functions of regulating the system voltage, improving the system conveying capacity and stability and the like are realized by regulating reactive power;
(4) Energy storage device
When the power is excessive in the power generation peak period of the renewable energy sources in the power distribution network, an energy storage device is connected into the system to store the excessive power, and the energy storage device can be matched with the voltage regulation measures of the traditional power distribution network, so that the system trend is improved, and the voltage fluctuation and the network loss are reduced.
Further, the voltage regulation mathematical model of the active power distribution network in the step S3 is as follows:
(1) Objective function
The traditional reactive power optimization model generally takes network loss of a power distribution network system as an optimization target, but considers that distributed power and flexible load accessed by ADN can influence the stable operation of the system, and from the perspective of stable and economic operation of the power distribution network, the two aspects of network loss and node voltage offset of the system are comprehensively considered to construct an objective function:
Wherein i=1, 2, …, N, i is the node sequence number of the distribution network; n is the number of nodes of the power distribution network; u i is the voltage at node i; p loss,i is the network loss of the node i; lambda 1,λ2 respectively represents the weights corresponding to the network loss and the voltage offset;
(2) Constraint conditions
① Load flow equation constraint of distribution network system
Wherein, P G,i and Q G,i are respectively the active power and the reactive power of the power injected by the node i; p L,i and Q L,i are the active power and reactive power of the load at node i, respectively; p DG,i and Q DG,i are active power and reactive power output by the distributed power supply at node i; g ij、Bij is the branch conductance and susceptance;
② Distribution network internal node operation constraint
Uimin≤Ui≤Uimax (4)
Timin≤Ti≤Timax (7)
Wherein, U imin and U imax are safe ranges of voltage operation of the ith node; And/> The reactive output upper limit and the reactive output lower limit of the jth generator are set; /(I)And/>SVC reactive compensation capacity upper and lower limits of the ith node respectively; t imin and T imax are node i transformer tap node ranges.
Further, the voltage-regulating markov model of the active power distribution network in S4 is as follows:
(1) State space
The actual distribution network system is a nonlinear system, so that a proper state space needs to be selected to represent the actual running condition of the system, so that an intelligent agent can timely sense the change of the environment. The defined state space is as follows:
St＝{Ed,t,Pi,t,t} (8)
Wherein E d,t is each flexible load energy value; p i,t is the load value of each node; t is the current time value;
(2) Action space
The action space is the relevant decision quantity of the agent in the power distribution network voltage-regulating Markov model, and is defined as follows:
at＝{PDR,t,Qsvc,t,Tt} (9)
Wherein P DR,t is the response quantity of the flexible load in the power distribution network system at the current moment; q svc,t is the reactive output of SVC; tt is the tap position of the transformer.
(3) Reward function
The selection of the reward function is closely related to the regulation and control target, voltage control and network loss optimization are comprehensively considered, and the reward function of the number of the power distribution network nodes at the moment t is defined as follows:
rt＝rV,t+λrL,t (10)
wherein, r V,t and r L,t are respectively a penalty term of voltage out-of-limit and a penalty term of network loss; λ is the weight coefficient.
① Voltage out-of-limit penalty
If the node voltage of the power distribution network is out of limit, giving a certain punishment to prompt an intelligent agent to make proper actions so as to enable the node voltage to move into a normal range; in order to accurately quantify the voltage deviation degree of the whole power distribution system, the voltage out-of-limit penalty of the system is designed as follows:
Wherein U i is the voltage of node i; u imax is the maximum value of the node i voltage, U imin is the minimum value of the node i voltage, and k rv is the weight factor.
② Network loss penalty
The total loss during the day and the loss at each time are modeled as follows:
rL,t＝rLa,t+λ1·rLb,t (12)
Wherein r La,t is the total network loss of the power distribution network system in one day; r Lb,t is the network loss of the power distribution network system at each moment; lambda 1 is a weight coefficient;
r Lb,t is a reward shaping function of the net loss at time t, and is expressed by the following formula:
Where k Lb is a coefficient, ΔPloss, t is the reduction of the net loss at the current time relative to the nominal net loss, The average contribution degree of the flexible load at each moment before the current moment t to the reduction of the network loss is shown as follows: when the flexible load at the current moment has response potential and the contribution degree of the flexible load at the current moment to the reduction of the network loss is larger than the average contribution degree of the flexible load at each previous moment to the reduction of the network loss, the flexible load is not fully invoked at the current moment, and therefore a penalty is given.
The utility model provides an initiative distribution network load side multiple voltage regulation equipment which characterized in that includes:
One or more processors;
A memory for storing one or more programs;
the one or more programs are executed by the one or more processors to cause the one or more processors to implement the methods described above.
A storage medium containing computer-executable instructions, wherein the storage medium has stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by a processor to implement the above-described method.
The beneficial effects are that: compared with the prior art, the active power distribution network load side multielement voltage regulation method provided by the invention has the following advantages: the characteristics of flexible resources of the distribution network such as the distributed power supply, the flexible load and the SVC are comprehensively considered, a reactive power optimization model of the distribution network is built based on the idea of reinforcement learning, and the complex physical modeling is avoided while a plurality of resources in the distribution network are cooperated to participate in the operation of the distribution network. And a reference is provided for a multi-element voltage regulation strategy of the load side of the active power distribution network.
Drawings
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
FIG. 2 is a schematic diagram of a modified IEEE 33 node power distribution system architecture;
FIG. 3 is a flow chart of an active distribution network model training process based on DDPG algorithm;
FIG. 4 is a graph of load values at a typical day distribution network 24;
FIG. 5 is a graph of active distribution network flexible load optimization results;
FIG. 6 is an active distribution network optimization voltage regulation result;
fig. 7 is a schematic diagram of an active power distribution network flexible resource scheduling control device according to a second embodiment of the present invention;
fig. 8 is a schematic structural view of an apparatus according to a third embodiment of the present invention;
fig. 9 is a block diagram of a typical active power distribution network scheduling system.
Detailed Description
The inventive process is further illustrated below in conjunction with examples.
The invention discloses a multi-element voltage regulation method for a load side of an active power distribution network, which comprises the following steps:
(1) Data preparation, including prediction data of renewable energy sources, basic characteristics of flexible resources, basic parameter related information of a power distribution network system and the like;
(2) And analyzing the structure of the active power distribution network accessed with a plurality of flexible resources, and researching the mode of the internal resources participating in the operation of the power distribution network.
(3) And (3) taking the power flow constraint of the power distribution network system and the operation constraint of each node in the power distribution network into consideration, and establishing a power distribution network voltage regulation mathematical model by taking the minimum system loss and voltage offset as targets.
(4) Based on the idea of reinforcement learning, converting the voltage regulating task into a Markov decision process to obtain a Markov model for regulating the voltage of the active power distribution network, and solving based on DDPG algorithm.
Further, the step (2) specifically includes:
the topology of an active distribution network varies greatly with the access of high permeability distributed power sources and a multitude of flexible resources. The flexible resources of the internal access are specifically analyzed as follows:
(1) Distributed power supply
Traditional distribution network can not carry out initiative management to its internal energy, but the DG of high permeability is thrown into the distribution network and is made traditional passive distribution network gradually transform into can allocate the electric energy in a flexible way, the novel ADN of renewable energy is taken care of in initiative, and the running mode of distribution network is also more nimble changeable to traditional distribution network reactive power optimization regulating speed is slow and can not continuous regulation's problem has been improved. Meanwhile, the power output of DG can change the power flow distribution of the distribution network, so that reactive power flow in the network is changed, and the randomness and intermittence of distributed power sources such as photovoltaic power, wind power and the like can cause the phenomenon of insufficient reactive power or excessive reactive power of power distribution network system nodes at certain moments, so that voltage balance of a power system is impacted.
(2) Flexible load
In order to improve the influence of large-scale DG access on the stable operation of the power distribution network, the power distribution network can be accessed with flexible loads with peak clipping and valley filling functions. The flexible load is used as an important part of the response of the demand side, so that the original load distribution can be changed in time and space, the load power curve of the distribution network can be effectively improved, and the virtual energy storage function is realized. For example, a user can transfer a flexible peak load (such as an air conditioner load) to a photovoltaic power generation peak period under the excitation of a policy or price, so that the time sequence complementary effect of the flexible load and the DG output is fully exerted, and more renewable energy sources are consumed; or directly to partially cut down the flexible load. However, the temperature-controlled flexible load, such as shifting and reducing the air-conditioning load, necessarily affects the comfort of the user. Therefore, intensive study of the aggregate response characteristics of the flexible loads is required, and the loads to be transferred and cut down are distributed to the flexible loads below the distribution network, so that the influence on the user comfort is reduced to the greatest extent on the basis of ensuring the stable and economical operation of the distribution network
(3) Reactive power compensation device
The low-voltage side of the distribution network is positioned at the tail end of the power system and is used as the last ring facing the user end, when the user load in the electricity utilization peak period is increased, the voltage of the low-voltage side of the distribution network is reduced, so that a static var compensator (STATIC VAR compensator, SVC) can be connected in parallel in the distribution network system, and the functions of adjusting the system voltage, improving the system conveying capacity and stability and the like are realized by adjusting reactive power.
(4) Energy storage device
When the power is excessive in the power distribution network in the renewable energy power generation peak period, an energy storage device can be connected into the system to store the excessive power. And the energy storage device can be matched with the voltage regulation measures of the traditional power distribution network, so that the system tide is improved, and the voltage fluctuation and the network loss are reduced. The energy storage device is flexible in installation place, simple and convenient to maintain, and the power size and the flow direction can be flexibly adjusted, so that the energy storage device is an efficient flexible resource.
A typical active power distribution network scheduling system architecture is shown in fig. 9.
In the step (3), the active power distribution network voltage regulation mathematical model is as follows:
(1) Objective function
The traditional reactive power optimization model generally takes network loss of a power distribution network system as an optimization target, but considers that distributed power and flexible load accessed by ADN can influence the stable operation of the system, so that from the perspective of stable and economic operation of the power distribution network, an objective function is constructed by comprehensively considering two aspects of the network loss of the system operation and the node voltage offset:
Wherein i=1, 2, …, N, i is the node sequence number of the distribution network; n is the number of nodes of the power distribution network; u i is the voltage at node i; p loss,i is the network loss of the node i; lambda 1,λ2 represents the weights corresponding to the net losses and the voltage offsets, respectively.
(2) Constraint conditions
① Load flow equation constraint of distribution network system
Wherein, P G,i and Q G,i are respectively the active power and the reactive power of the power injected by the node i; p L,i and Q L,i are the active power and reactive power of the load at node i, respectively; p DG,i and Q DG,i are active power and reactive power output by the distributed power supply at node i; g ij、Bij is the branch conductance and susceptance.
② Distribution network internal node operation constraint
Uimin≤Ui≤Uimax (4)
Timin≤Ti≤Timax (7)
Wherein, U imin and U imax are safe ranges of voltage operation of the ith node; And/> The reactive output upper limit and the reactive output lower limit of the jth generator are set; /(I)And/>SVC reactive compensation capacity upper and lower limits of the ith node respectively; t imin and T imax are the node i transformer tap node ranges (%).
The active power distribution network voltage regulation Markov model in the step (4) is as follows:
(1) State space
The actual distribution network system is a nonlinear system, so that a proper state space needs to be selected to represent the actual running condition of the system, so that an intelligent agent can timely sense the change of the environment. The defined state space is as follows:
St＝{Ed,t,Pi,t,t} (8)
Wherein E d,t is each flexible load energy value; p i,t is the load value of each node; t is the current time value.
(2) Action space
The action space is the relevant decision quantity of the agent in the power distribution network voltage-regulating Markov model, and is defined as follows:
at＝{PDR,t,Qsvc,t,Tt} (9)
(3) Reward function
The selection of the reward function is closely related to the regulation and control target, voltage control and network loss optimization are comprehensively considered, and the reward function of the number of the power distribution network nodes at the moment t is defined as follows:
rt＝rV,t+λrL,t (10)
wherein, r V,t and r L,t are respectively a penalty term of voltage out-of-limit and a penalty term of network loss; λ is the weight coefficient.
① Voltage out-of-limit penalty
If the node voltage of the power distribution network is out of limit, certain punishment is given, so that the intelligent agent is prompted to make proper actions to enable the node voltage to move to be within a normal range. In order to accurately quantify the voltage deviation degree of the whole power distribution system, the voltage out-of-limit penalty of the system is designed as follows:
② Network loss penalty
Typically, the net loss in the reward function is the total net loss of 24 hours a day, but this means that the agent can calculate the net loss in the reward function at the end of the day during the training process. The overall network loss during the day and the network loss at each time are comprehensively considered, and the reward function is modeled as follows:
rL,t＝rLa,t+λ1·rLb,t (12)
Wherein r La,t is the total network loss of the power distribution network system in one day; r Lb,t is the network loss of the power distribution network system at each moment; lambda 1 is the weight coefficient.
R Lb,t is a reward shaping function of the net loss at time t, and can be expressed by the following formula:
Wherein k Lb is a coefficient, ΔPloss, t is the reduction of the current time loss relative to the rated loss, note Is the average contribution degree of the flexible load at each moment before the current moment t to the reduction of the network loss, and the expression can be understood as follows: when the flexible load at the current moment has response potential and the contribution degree of the flexible load at the current moment to the reduction of the network loss is larger than the average contribution degree of the flexible load at each previous moment to the reduction of the network loss, the flexible load is not fully invoked at the current moment, and therefore a penalty is given.
The embodiment of the invention also provides equipment of the active power distribution network load side multi-element voltage regulation strategy, which comprises the following components:
One or more processors;
A memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods described above.
Furthermore, an embodiment of the present invention provides a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are configured to perform the above-described method.
Example 1
Example the procedure according to the invention is carried out with particular reference to figure 1. The following describes the implementation process of the active power distribution network load side multi-element voltage regulation control strategy and the obtained beneficial effects by using a specific example. Fig. 2 illustrates an improved IEEE33 node power distribution system and access flexibility resources in accordance with an embodiment of the present invention. The distributed power supplies are connected to the node 7,10,16,18,21,31, and the capacities of the distributed power supplies are respectively 0.3MW,0.5MW and 0.6MW; in addition, flexible load is connected to the 7,8,14,24,25,30,32 node to participate in regulation, and the maximum potential of the regulation can be 30% of the load capacity before the day. An on-load voltage regulating transformer with adjustable transformation ratio is arranged on a branch between the [1 and 2] nodes.
When the network of the active power distribution network load side multi-element voltage regulation is trained based on DDPG algorithm, the super-parameters of the algorithm are set as follows: the state and action space dimensions are 40 and 9 respectively, the number of the hidden neurons of the Actor network is set to be 200, the number of the hidden neurons of the Critic network is set to be 300, the learning rates of the Actor and Critic networks are set to be 0.002, and the capacity of the experience playback pool is set to be 240. The training process is set to 24 hours as a period, and the net loss and the jackpot of the distribution network system are calculated at the end of each period. A training process flow diagram of the DDPG network is shown in fig. 3.
The optimal solution results of the active power output of the flexible load obtained by optimizing the load of each period of the power distribution network in a typical day through an algorithm are shown in fig. 4 and 5 respectively. As can be seen from fig. 7, the response curve of the flexible load is consistent with the trend of the load curve of the active distribution network. At 00:00 to 10: at the moment 00, the power distribution network is in a low-load period, and reactive voltage regulation is low in optimal pressure, so that excessive flexible load is not needed to participate in response; at 10:00 to 20: the load curve gradually rises at the moment 00, and the power supply in the active power distribution network cannot completely meet the load demand, so that the flexible load in the system participates in the increase of the response quantity, and the purposes of stabilizing the voltage fluctuation and maintaining the power grid to operate in an optimal state are achieved.
The voltage distribution of each node of the power distribution network after DDPG algorithm optimization is shown in fig. 6. The graph shows that node voltage out-of-range of the power distribution network does not occur in the power distribution network after optimization, the node voltages are all within the safe operation range of the power distribution network, the voltage distribution is stable, and the fluctuation is small. The algorithm has strong voltage stabilizing capability on impact and load fluctuation of the active power distribution network DG, voltage deviation can be effectively reduced, and operation safety of the power distribution network is improved.
To further illustrate the effectiveness of the algorithm, the following four scenarios were set up to explore the impact of flexible load and DG permeability on reactive power optimization of the distribution network. The results are shown in Table 1.
Scene 1: the DGs are connected to the distribution network, and flexible loads are not considered;
Scene 2: the DG is connected to the distribution network, and meanwhile, the flexible load participates in response;
scene 3: decreasing the flexible load response maximum capacity in scenario 2 by 50%;
scene 4: the renewable energy permeability in scenario 3 was reduced by 50%.
Table 1 reactive power optimization results of distribution network in different scenes
Example two
Fig. 7 is a schematic diagram of an active power distribution network flexible resource scheduling control device according to a second embodiment of the present invention. The embodiment is applicable to the situation of performing daily scheduling simulation on the target resource, the device can be realized in a software and/or hardware mode, and the device can be configured in terminal equipment. The determining device includes: a measured flexible resource parameter acquisition module 410 and a flexible resource metric output module 420.
The measured flexible resource parameter obtaining module 410 is configured to obtain a measured state parameter and a measured resource parameter of the target resource.
And the tested flexible resource scheduling amount output module 420 is used for inputting tested parameters of the target flexible resource into the target decision model to obtain the scheduling amount output of the tested flexible resource.
The determining device of the active power distribution network load side multi-element voltage regulation strategy provided by the embodiment of the invention can be used for executing the determining method of the active power distribution network load side multi-element voltage regulation strategy provided by the embodiment of the invention, and has the corresponding functions and beneficial effects of the executing method.
It should be noted that, in the embodiment of the determining apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 8 is a schematic structural diagram of an apparatus provided in embodiment 3 of the present invention, which provides services for implementing a method for determining a load side multi-element voltage regulation policy according to the foregoing embodiment of the present invention, and the determining device for a load side multi-element voltage regulation policy of an active power distribution network in the foregoing embodiment may be configured. Fig. 8 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 8 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 8, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects the various system components including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 8, commonly referred to as a "hard disk drive"). Although not shown in fig. 8, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown in fig. 8, network adapter 20 communicates with other modules of device 12 over bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes programs stored in the system memory 28 to perform various functional applications and data processing, for example, to implement the method for determining the active power distribution network load side multi-element voltage regulation strategy according to the embodiment of the present invention.
By the equipment, under the condition of fully considering the self characteristics of various flexible resources, a flexible resource scheduling strategy is provided for the active power distribution network, the voltage safety of the power distribution network is ensured, and meanwhile, the running economy of the power distribution network is improved.
Example IV
The fourth embodiment of the present invention also provides a storage medium containing computer executable instructions, which when executed by a computer processor, are used to perform a method for determining a load side multi-element voltage regulation strategy of an active power distribution network, the method comprising:
Obtaining measured parameters of a target resource;
and inputting the measured parameters into a preset target active power distribution network reactive power optimization model to obtain the adjustment quantity of different flexible resources.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium containing the computer executable instructions provided by the embodiment of the present invention is not limited to the above method operations, and may also perform the related operations in the method for determining the active power distribution network load side multi-element voltage regulation policy provided by any embodiment of the present invention.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (5)
1. The active power distribution network load side multielement voltage regulation method is characterized by comprising the following steps of:
s1, preparing data, wherein the data comprises basic characteristics of flexible resources in a power distribution network, prediction data of renewable energy sources and basic parameter related information of a power distribution network system;
S2, analyzing an active power distribution network structure accessed to a plurality of flexible resources, and researching a mode of participating in operation of internal resources of the power distribution network;
s3, considering power flow constraint of a power distribution network system and operation constraint of each node in the power distribution network, and establishing a power distribution network voltage regulation mathematical model considering flexible resource response by taking minimum system loss and voltage offset as targets;
S4, converting the voltage regulation model in the S3 into a Markov decision process based on the idea of reinforcement learning to obtain a Markov model for regulating the voltage of the active power distribution network, and solving based on DDPG algorithm;
and S3, the voltage regulation mathematical model of the active power distribution network is as follows:
(1) Objective function
The traditional reactive power optimization model generally takes network loss of a power distribution network system as an optimization target, but considers that distributed power and flexible load accessed by ADN can influence the stable operation of the system, and from the perspective of stable and economic operation of the power distribution network, the two aspects of network loss and node voltage offset of the system are comprehensively considered to construct an objective function:
Wherein i=1, 2, …, N, i is the node sequence number of the distribution network; n is the number of nodes of the power distribution network; u i is the voltage at node i; p loss,i is the network loss of the node i; lambda 1,λ2 respectively represents the weights corresponding to the network loss and the voltage offset;
(2) Constraint conditions
① Load flow equation constraint of distribution network system
Wherein, P G,i and Q G,i are respectively the active power and the reactive power of the power injected by the node i; p L,i and Q L,i are the active power and reactive power of the load at node i, respectively; p DG,i and Q DG,i are active power and reactive power output by the distributed power supply at node i; g ij、Bij is the branch conductance and susceptance;
② Distribution network internal node operation constraint
Uimin≤Ui≤Uimax (4)
Timin≤Ti≤Timax (7)
Wherein, U imin and U imax are safe ranges of voltage operation of the ith node; And/> The reactive output upper limit and the reactive output lower limit of the jth generator are set; /(I)And/>SVC reactive compensation capacity upper and lower limits of the ith node respectively; t imin and T imax are node i transformer tap node ranges.
2. The method for load side multi-element voltage regulation of an active power distribution network according to claim 1, wherein the flexible resources accessed internally in S2 are specifically analyzed as follows:
(1) Distributed power supply
The DG with high permeability is put into the power distribution network, so that the traditional passive power distribution network is gradually transformed into a novel ADN capable of flexibly allocating electric energy and actively absorbing renewable energy, and meanwhile, the power output of the DG can change the power flow distribution of the power distribution network, so that the reactive power flow in the network is changed;
(2) Flexible load
The flexible load with peak clipping and valley filling functions is connected to the power distribution network, and is used as an important part of the response of a demand side, so that the original load distribution can be changed in time and space, the load power curve of the power distribution network is effectively improved, and the virtual energy storage function is realized;
(3) Reactive power compensation device
The low-voltage side of the power distribution network is positioned at the tail end of the power system and is used as the last ring facing the user end, when the user load in the electricity utilization peak period is increased suddenly, the voltage of the low-voltage side of the power distribution network is reduced, so that the static reactive power compensator is connected in parallel in the power distribution network system, and the functions of regulating the system voltage, improving the system conveying capacity and stability and the like are realized by regulating reactive power;
(4) Energy storage device
When the power is excessive in the power generation peak period of the renewable energy sources in the power distribution network, an energy storage device is connected into the system to store the excessive power, and the energy storage device can be matched with the voltage regulation measures of the traditional power distribution network, so that the system trend is improved, and the voltage fluctuation and the network loss are reduced.
3. The method for load side multi-element voltage regulation of an active power distribution network according to claim 1, wherein the voltage regulation markov model of the active power distribution network in S4 is as follows:
(1) State space
The actual power distribution network system is a nonlinear system, so that a proper state space needs to be selected to represent the actual running condition of the system, so that an intelligent body can timely sense the change of the environment, and the defined state space is as follows:
St＝{Ed,t,Pi,t,t} (8)
Wherein E d,t is each flexible load energy value; p i,t is the load value of each node; t is the current time value;
(2) Action space
The action space is the relevant decision quantity of the agent in the power distribution network voltage-regulating Markov model, and is defined as follows:
at＝{PDR,t,Qsvc,t,Tt} (9)
Wherein P DR,t is the response quantity of the flexible load in the power distribution network system at the current moment; qsvc, t is the reactive output of SVC; tt is the tap position of the transformer;
(3) Reward function
The selection of the reward function is closely related to the regulation and control target, voltage control and network loss optimization are comprehensively considered, and the reward function of the number of the power distribution network nodes at the moment t is defined as follows:
rt＝rV,t+λrL,t (10)
Wherein, r V,t and r L,t are respectively a penalty term of voltage out-of-limit and a penalty term of network loss; lambda is a weight coefficient;
① Voltage out-of-limit penalty
If the node voltage of the power distribution network is out of limit, giving a certain punishment to prompt an intelligent agent to make proper actions so as to enable the node voltage to move into a normal range; in order to accurately quantify the voltage deviation degree of the whole power distribution system, the voltage out-of-limit penalty of the system is designed as follows:
Wherein U i is the voltage of node i; u imax is the maximum value of the voltage of the node i, U imin is the minimum value of the voltage of the node i, and k rv is a weight factor;
② Network loss penalty
The total loss during the day and the loss at each time are modeled as follows:
rL,t＝rLa,t+λ1·rLb,t (12)
Wherein r La,t is the total network loss of the power distribution network system in one day; r Lb,t is the network loss of the power distribution network system at each moment; lambda 1 is a weight coefficient;
r Lb,t is a reward shaping function of the net loss at time t, and is expressed by the following formula:
Where k Lb is a coefficient, ΔPloss, t is the reduction of the net loss at the current time relative to the nominal net loss, The average contribution degree of the flexible load at each moment before the current moment t to the reduction of the network loss is shown as follows: when the flexible load at the current moment has response potential and the contribution degree of the flexible load at the current moment to the reduction of the network loss is larger than the average contribution degree of the flexible load at each previous moment to the reduction of the network loss, the flexible load is not fully invoked at the current moment, and therefore a penalty is given.
4. The utility model provides an initiative distribution network load side multiple voltage regulation equipment which characterized in that includes:
One or more processors;
A memory for storing one or more programs;
the one or more programs being executable by the one or more processors to cause the one or more processors to implement the active power distribution network load side multi-component voltage regulation method of any of claims 1-3.
5. A storage medium containing computer executable instructions, wherein at least one instruction, at least one program, code set, or instruction set is stored in the storage medium, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement the active power distribution network load side multi-component voltage regulation method of any one of claims 1-3.
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| CN113363998A (en) * | 2021-06-21 | 2021-09-07 | 东南大学 | Power distribution network voltage control method based on multi-agent deep reinforcement learning |
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