WO2022193531A1 - 一种源储荷分布式协同电压控制方法及其系统 - Google Patents

一种源储荷分布式协同电压控制方法及其系统 Download PDF

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WO2022193531A1
WO2022193531A1 PCT/CN2021/109975 CN2021109975W WO2022193531A1 WO 2022193531 A1 WO2022193531 A1 WO 2022193531A1 CN 2021109975 W CN2021109975 W CN 2021109975W WO 2022193531 A1 WO2022193531 A1 WO 2022193531A1
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node
voltage
voltage control
nodes
distribution network
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PCT/CN2021/109975
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English (en)
French (fr)
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岳东
窦春霞
张智俊
丁孝华
罗剑波
李延满
黄堃
韩韬
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南京邮电大学
国网电力科学研究院有限公司
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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
    • 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]

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  • the invention relates to a source-storage-load distributed coordinated voltage control method and a system thereof, belonging to the technical field of distribution network voltage control.
  • the present invention proposes a source-storage-load distributed coordinated voltage control method and system.
  • the system platform can solve the over/under voltage problem of the distribution network due to other small disturbances such as new energy fluctuations through distributed coordinated control of flexible resources such as source storage and load, which can be achieved without the coordination of the distribution network management center. Full autonomy of local over/under voltage issues.
  • the present invention adopts the following technical means:
  • the present invention proposes a distributed coordinated voltage control method for source-storage loads, including the following steps:
  • the method for obtaining the priority control node is:
  • ⁇ V i n (k) represents the voltage deviation of the ith node at time k
  • V i (k) represents the voltage of the ith node at time k
  • the voltage deviation of each node in the distribution network is transmitted to other nodes through N-1 iterations, the voltage deviation of all nodes is compared, and the node with the largest voltage deviation is selected as the priority control node.
  • the iterative equation is as follows:
  • ⁇ i [l] represents the maximum voltage deviation in the ith node and its adjacent nodes in the l-th iteration
  • ⁇ j [l-1] represents the j-th node and its adjacent nodes in the l-1th iteration
  • Ni is the adjacent node of the ith node in the distribution network
  • the number of , l 1,2,...,N.
  • a distributed voltage prediction model is established based on the sensitivity of node voltage to output active and reactive power:
  • the voltage control objective function is constructed, and the expression is as follows:
  • N p is the length of the prediction domain
  • k) represents the voltage value predicted by the i-th node at time k+n at time k
  • k) represents the voltage control command of the ith node at time k+n-1 predicted at time k, that is, the ith node k+n
  • the output of active power and reactive power at time -1, n ⁇ 1, ri and ⁇ i are the weight coefficient matrix of the ith node
  • P i RES represents the lower limit of the new energy output of the ith node
  • P i RES (k ) represents the new energy output of the ith node at time k
  • E i (k) represents the SOC state of the i-th node’s energy storage at time k
  • ⁇ i (k) is the charge and discharge of
  • the weight coefficient matrices ri and ⁇ i satisfy the condition: ⁇ i ⁇ r i .
  • the method for iteratively updating and obtaining the voltage control strategy includes the following steps:
  • the preset convergence conditions are:
  • Represents the voltage control sequence of the g-th iteration of the i-th node at time k, g>1, ⁇ is the preset error threshold, i 1, 2,...,N, N is the total number of distribution network nodes.
  • the calculation method of the voltage control sequence of all nodes is:
  • the voltage control sequence based on the priority control node uses the voltage control objective function to process the voltage information of each node, predicts the active power and reactive power output of each node in the time period [k,k+N c -1], and generates each node.
  • the voltage control sequence of the node uses the voltage control objective function to process the voltage information of each node, predicts the active power and reactive power output of each node in the time period [k,k+N c -1], and generates each node.
  • the present invention proposes a source-storage-load distributed coordinated voltage control system, comprising a plurality of layered multi-agents, each layered multi-agent is configured on a node of a distribution network; the said The hierarchical multi-agent is used to collect the voltage information of the node, calculate the voltage control strategy of the node, and control the voltage output of the node according to the voltage control strategy.
  • the layered multi-agent includes an upper-level agent and a lower-level agent
  • the upper-level agent includes an upper-level perception module, an upper-level decision module and an upper-level execution module
  • the lower-level agent includes a lower-level perception module, a lower-level perception module, and an upper-level execution module.
  • the upper-layer perception layer is used to collect the voltage information of the node, and share the voltage information of the node with the hierarchical multi-agent of other nodes;
  • the upper-layer decision module is used to control the target by using the voltage The function processes node voltage information, and calculates the voltage control sequence of the node;
  • the upper-layer execution module is used to send the voltage control sequence of the node to the lower-layer decision-making module;
  • the lower-layer sensing module is used for the sensing module to collect the operation of the source load storage resources status information;
  • the lower decision module is used to receive the voltage control sequence of the node, and control the lower execution module to execute the voltage control sequence;
  • the lower execution module is used to adjust the voltage output of the node according to the voltage control sequence.
  • the upper-level decision-making module includes a knowledge base module
  • the knowledge base module includes the distribution network topology, the voltage control objective function, and the value range of the weight coefficient matrix of the voltage control objective function.
  • the invention proposes a source-storage-load distributed coordinated voltage control method and system.
  • the method of the invention can sense global voltage information without the coordination of a central node, analyzes the spatiotemporal distribution characteristics of the voltage of the distribution network nodes based on the power flow sensitivity, and constructs The voltage prediction and control model for the coordinated output of active power and reactive power including flexible resources such as source storage and load, determines the node with the most serious voltage problem according to the voltage information of all nodes in the distribution network, that is, the priority control node.
  • the online decision-making and distribution of the output of the source and storage loads are made by dynamically adjusting the model weights, and then all flexible resources are coordinated to reasonably regulate the voltage, which ensures the accuracy of voltage regulation and effectively ensures the voltage control of the distribution network. speed, economy and flexibility.
  • the system of the invention realizes the distributed autonomy of the voltage problem through the coordination and interaction of the two-layer agents, and the two-layer agents respectively perform operations such as data collection, sharing, voltage control decision making, decision execution, etc.
  • the plug-and-play of resources can quickly solve the overvoltage and undervoltage problems of the distribution network and ensure the accuracy of the voltage regulation of the distribution network.
  • Fig. 1 is a flow chart of the steps of a source-storage-load distributed coordinated voltage control method according to the present invention
  • FIG. 2 is a schematic structural diagram of a source-storage-load distributed coordinated voltage control system
  • Fig. 3 is the schematic diagram of the experimental platform in the embodiment of the present invention.
  • FIG. 4 is a voltage change curve diagram of the experimental platform inverter 1 in the embodiment of the present invention.
  • Fig. 5 is the voltage change curve diagram of the experimental platform inverter 2 in the embodiment of the present invention.
  • FIG. 6 is a voltage change curve diagram of the experimental platform inverter 3 in the embodiment of the present invention.
  • FIG. 7 is a power change curve diagram of the experimental platform inverter 1 in the embodiment of the present invention.
  • FIG. 8 is a power variation curve diagram of the experimental platform inverter 2 in the embodiment of the present invention.
  • FIG. 9 is a power change curve diagram of the experimental platform inverter 3 in the embodiment of the present invention.
  • 1 is the upper-level agent
  • 2 is the lower-level agent
  • 101 is the upper-level perception module
  • 102 is the upper-level decision-making module
  • 103 is the upper-level execution module
  • 201 is the lower-level perception module
  • 202 is the lower-level decision-making module
  • 203 is the lower-level execution module .
  • the present invention proposes a source-storage-load distributed coordinated voltage control method, as shown in FIG. 1 , which specifically includes the following steps:
  • Step A Calculate the voltage deviation of each node according to the voltage information of all nodes in the distribution network, and obtain the priority control node of the distribution network according to the voltage deviation;
  • Step B obtaining a voltage control objective function constructed based on the sensitivity of the node voltage to the output active and reactive power
  • Step C when the priority control node is over-voltage or under-voltage, use the voltage control objective function to sequentially calculate the voltage control sequence of the priority control node and other nodes;
  • Step D Iteratively update the voltage control sequences of all nodes according to a preset convergence condition to obtain a voltage control strategy.
  • each layered multi-agent includes upper-layer agent 1 and lower-layer agent 2, both of which are BDI agents; the upper-layer agent is triggered by voltage safety events and is responsible for coordinating the output of flexible resources such as source storage and load , the lower-level agent is responsible for the local dynamic behavior control of flexible resources such as source and load, and realizes the distributed autonomy of the voltage problem through the coordination and interaction of the two-layer agent.
  • the upper layer agent includes an upper layer perception module 101 , an upper layer decision module 102 and an upper layer execution module 103
  • the lower layer agent includes a lower layer perception module 201 , a lower layer decision module 202 and a lower layer execution module 203
  • the upper sensing layer is mainly used to collect the voltage information of the node, and share the voltage information of the node with the hierarchical multi-agent of other nodes
  • the upper decision module is used to process the voltage information of the node by using the voltage control objective function, and calculate the voltage of the node.
  • the upper-layer execution module is used to send the voltage control sequence of the node to the lower-layer decision-making module;
  • the lower-layer sensing module is mainly used for the sensing module to collect the operating status information of the source and load storage resources, such as the voltage and current of the node inverter output. , phase angle information, etc.;
  • the lower decision module is used to receive the voltage control sequence of the node, and control the lower execution module to execute the voltage control sequence;
  • the lower execution module is used to adjust the voltage output of the node according to the voltage control sequence.
  • the upper-level agent can calculate the voltage control sequence through the upper-level decision-making module, and use the lower-level agent to execute the voltage control sequence to achieve the effect of rapid voltage control.
  • step A of the method of the present invention proposes a fully distributed voltage safety event trigger mechanism based on the hierarchical multi-agent architecture, that is, the global voltage information can be sensed without the coordination of the central node, and according to the voltage The node with the most serious voltage problem (priority control node) is obtained from the information.
  • the specific operations are as follows:
  • Step A01 Calculate the voltage deviation of the ith node according to the voltage information of the ith node in the distribution network at time k:
  • ⁇ V i n (k) represents the voltage deviation of the ith node at time k
  • V i (k) represents the voltage of the ith node at time k
  • V i n represents the rated voltage of the ith node
  • Step A02 Share the voltage deviation of each node through hierarchical multi-agents, transmit the voltage deviation of each node in the distribution network to other nodes through N-1 iterations, and compare the voltage deviations of all nodes, And select the node with the largest voltage deviation as the priority control node, where the iterative equation is as follows:
  • ⁇ i [l] represents the maximum voltage deviation in the ith node and its adjacent nodes in the l-th iteration
  • ⁇ j [l-1] represents the j-th node and its adjacent nodes in the l-1th iteration
  • Ni is the adjacent node of the ith node in the distribution network
  • the number of , l 1,2,...,N.
  • step B of the method of the present invention analyzes the temporal and spatial distribution characteristics of the node voltage of the distribution network based on the power flow sensitivity, and constructs a mathematical model of distributed voltage control, and proposes active power and reactive power including flexible resources such as source storage and load.
  • the voltage control method of cooperative output is as follows:
  • Step B01 except the first reference node is known, other nodes are regarded as PQ nodes (active power P and reactive power Q are given, node voltage and phase (V, ⁇ ) are to be determined), establish Equations of injected current and voltage at each node of the distribution network:
  • V i represents the voltage of the ith node
  • ⁇ 21 ,..., ⁇ i1 ,..., ⁇ N1 ⁇ is a series of constant gains
  • R im represents the resistance between the ith node and the mth node
  • X im represents the line reactance between the ith node and the mth node
  • R im +jX im represents the line impedance between the ith node and the mth node
  • j is a unit imaginary number
  • V n represents the bus reference voltage
  • Si represents the injected power of the ith node
  • Pi represents the injected active power of the ith node
  • Qi represents the injected reactive power of the ith node.
  • the superscript re represents the real part of the variable
  • the superscript im represents the imaginary part of the variable
  • P m represents the active power injected by the m-th node
  • V ⁇ i V n ⁇ ⁇ i1 .
  • Q m represents the reactive power injected by the mth node.
  • Step B02 establishing a distributed voltage prediction model based on the sensitivity of the node voltage to the output active and reactive power, the expression is as follows:
  • x i (k) represents the amplitude of the voltage of the ith node in the distribution network at time k
  • x i (k) [V i (k)]
  • V i (k) represents the ith node in the distribution network at time k
  • the voltage of the i node, B ii represents the sensitivity of the i node voltage to the active and reactive output of the i node
  • u i (k) represents the change value of the active and reactive power output of the i-th node at time k
  • ⁇ P i RES (k) [ ⁇ P i PV (k) ⁇ P i WT (k)]
  • ⁇ P i PV (k) represents the active power change value of the photovoltaic power generation unit at the ith node at time k
  • ⁇ P i WT (k ) represents the change in active power of the wind power generation unit at the ith node at time k
  • ⁇ P i S (k) represents the change in active power of the energy storage unit at the ith node at time k
  • Step B03 constructing a voltage control objective function according to the distributed voltage prediction model, the expression is as follows:
  • N p is the artificially set prediction domain length
  • k) represents the voltage value predicted by the i-th node at time k+n at time k, represents the voltage rating of the ith node, is the reference value, usually greater than the rated voltage of the node
  • k) represents the voltage control command at the time k+n-1 predicted by the ith node at time k, that is, the ith node k+
  • the output of active power and reactive power at time n-1, n ⁇ 1, ri and ⁇ i are the weight coefficient matrix of the ith node
  • P i RES represents the lower limit of the new energy output of the ith node
  • P i RES ( k) represents the new energy output of the ith node at time k
  • E i (k) represents the SOC state of the i-th node’s
  • the weight coefficient matrices ri and ⁇ i of the voltage control objective function satisfy the condition: ⁇ i ⁇ r i .
  • the weight coefficient matrix ⁇ i can be expressed as:
  • ⁇ i,3 ⁇ i,4 ⁇ i,1 ⁇ i,2 , ⁇ i,1 is the control quantity
  • the weight coefficient of ⁇ P i RES (k) in , ⁇ i,2 is the weight coefficient of ⁇ P i S (k), ⁇ i,3 is The weight coefficient of , ⁇ i,4 is weight factor.
  • step C of the present invention dynamically adjusts the weights through the upper-level decision-making module, and reasonably performs online decision-making and distribution on the output of the source and storage loads, ensuring the accuracy of voltage regulation and taking into account the economy of regulation costs.
  • Step C01 when the priority control node has an overvoltage problem, determine whether the new energy output of the optimal control node is greater than the sum of the load demand and the energy storage capacity:
  • P i RES (k) represents the new energy output of the ith node at time k
  • P i L (k) represents the load demand power of the ith node at time k
  • the new energy output of the optimal control node satisfies the formula (19)
  • the new energy output needs to be reduced until the new energy output is not greater than the sum of the load demand and the energy storage capacity.
  • the load demand of the optimal control node satisfies the formula (20)
  • the load needs to be cut until the load demand is not greater than the sum of the output of the new energy and the capacity of the energy storage.
  • Step C02 When the priority control node is over-voltage or under-voltage, input the voltage information of the priority control node at time k into the voltage control objective function, and dynamically allocate the weight coefficient matrix of the voltage control objective function according to the preset value range. At the same time, the new energy of the node and the reactive power of the energy storage are preferentially used for voltage compensation. When the reactive power compensation still cannot meet the voltage safety requirements, the voltage is controlled by adjusting the active power of the new energy. The predicted time period [k ,k+N c -1], the output of active power and reactive power of the node is preferentially controlled, and the voltage control sequence of the preferential control node is generated.
  • Step C03 Send the voltage control sequence of the priority control node to the adjacent nodes of the priority control node, and use the voltage control objective function to process the voltage information of the adjacent nodes based on the voltage control sequence of the priority control node, and predict the time period [k, k The output of active power and reactive power of adjacent nodes within +N c -1] generates a voltage control sequence of adjacent nodes.
  • the voltage control sequence of the adjacent node is sent to the adjacent nodes of the adjacent node, and so on, the voltage control sequence of each node is calculated.
  • k) represents the active power of the i-th node at time k+o predicted by the i-th node at time k and no Output of work power, o 1,2,...,N c -1, N c is a preset time value.
  • step D of the method of the present invention the method for iteratively updating and obtaining the voltage control strategy includes the following steps:
  • Step D01 initialize the weight coefficient matrix of the voltage control objective function, and obtain the initial voltage control sequence of all nodes, namely
  • Step D02 in each iteration process, update the weight coefficient matrix of the voltage control objective function according to the preset value range, and use the updated voltage control objective function to calculate the voltage control sequence of all nodes in the current iteration, the kth
  • the voltage control sequence of the gth iteration of i nodes is:
  • Step D03 after each iteration, perform error judgment on the voltage control sequences of all nodes in the current iteration based on the preset convergence conditions: if the convergence conditions are not met, repeat step D02, and continue to iteratively calculate the voltage control sequences of all nodes; When the convergence condition is reached, the voltage control strategy is generated using the voltage control sequence of all nodes in the current iteration.
  • the preset convergence condition in the present invention is:
  • ⁇ is the preset error threshold
  • is the 2-norm operator
  • the upper-layer execution module of the upper-layer agent of the i-th node node uses the upper-layer execution module of the upper-layer agent of the i-th node node to deliver the first control variable (ie, u i (k
  • the upper decision-making module in the system of the present invention mainly includes a node selection module, an overvoltage and undervoltage judgment module, a decision calculation module and a knowledge base module.
  • the node selection module is used to calculate the voltage deviation of the node according to the node voltage information, and on the other hand, it is used to share the information with the node selection modules of other hierarchical multi-agents, and then select the power distribution according to the voltage deviation of all nodes.
  • the priority control node of the network The priority control node of the network; the overvoltage and undervoltage judgment module is used to judge whether the priority control node is overvoltage or undervoltage according to the voltage information of the priority control node; the decision calculation module is used to iterate using the voltage control objective function according to the data in the knowledge base module Calculate the voltage control sequence of the node and generate the voltage control strategy; the knowledge base module is used to store the experience data and professional knowledge in the distribution network, and then assist the decision-making calculation module to make decisions.
  • the knowledge base module includes the distribution network topology, distribution The connection information of each node in the network, the voltage control objective function and the value range of the weight coefficient matrix of the voltage control objective function, etc.
  • the embodiment of the present invention provides the following experiments:
  • Fig. 3 is an architecture diagram of an experimental platform built based on the present invention.
  • the experimental platform consists of three photovoltaic inverters with a rated power of 3kW, a SIMATIC S7-1500 PLC and a load box.
  • the three photovoltaic inverters are placed in the experimental platform.
  • the photovoltaic panel on the roof of the room provides power support, the communication link is provided by SIMATIC S7-1500 PLC, and the load box is used to simulate the node load.
  • the rated voltage is set to 220V
  • the voltage safety range is set to 220V to 226V
  • the control period is set to 10s.
  • the method and system of the present invention are used to control the voltage of the experimental platform.
  • the embodiment of the present invention introduces external disturbances at the 151s and 547s, thereby simulating the overvoltage and undervoltage problems in the system.
  • the experimental platform The voltage changes and power changes of the inverters are shown in Figures 4 to 9. From Figures 4, 5 and 6, it can be seen that the voltages of the three inverters in the experimental platform can converge to a safe range at a relatively fast speed. There is only a little fluctuation at the boundary of the safe range, which is caused by the normal fluctuation of the photovoltaic inverter itself. As can be seen from Figure 7, Figure 8 and Figure 9, the power of the three inverters can quickly restore to a stable state, so the present invention can effectively solve the problems of overvoltage and undervoltage caused by disturbance, and has a good engineering application prospect.
  • the invention constructs a control system for voltage regulation of distribution network based on double-layer BDI multi-agents, so as to realize plug-and-play of flexible resources such as source storage and load. It also establishes a voltage control model of distributed coordination of source storage and load, and uses distributed model predictive control to realize the voltage regulation of the distribution network, which effectively ensures the rapidity, economy and flexibility of the voltage control of the distribution network. Solve the overvoltage and undervoltage problems of the distribution network and ensure the accuracy of the voltage regulation of the distribution network.

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Abstract

本发明公开了一种源储荷分布式协同电压控制方法及其系统，旨在解决现有技术中配电网电压控制对柔性资源的协同控制不足的技术问题。其包括：根据配电网中所有节点的电压信息获取配电网的优先控制节点；当优先控制节点过压或欠压时，利用预先构建的电压控制目标函数依次计算优先控制节点和其他节点的电压控制序列；根据预设的收敛条件对所有节点的电压控制序列进行迭代更新，获得电压控制策略。本发明基于源网荷储网络化云决策控制系统平台，能够解决由于新能源波动等其他小扰动情况下配电网的过/欠电压问题，有效地保证了配电网电压控制的快速性、经济性和灵活性。

Description

一种源储荷分布式协同电压控制方法及其系统 技术领域
本发明涉及一种源储荷分布式协同电压控制方法及其系统，属于配电网电压控制技术领域。
背景技术
随着智能电网技术的发展，新能源因其绿色、环保、成本低等优点而被广泛应用，其在为智能电网提供重要能源支撑的同时也会给智能电网带来安全、稳定与经济方面的难题，例如，在新能源高渗透率的配电网中，新能源的不确定性和波动性会给配电网馈线末端带来电压安全问题，这点在中低压电网中尤其明显。当前的电压调控方式尚以无功功率的补偿为主，这会导致系统的功率因数降低，从而降低电网的输电效率。而目前的智能电网中存在诸多的柔性资源，如分布式储能、柔性负荷等，如何有效的协同利用这些柔性资源，从而为智能电网的电压调节提供功率支持是当前的研究热点问题。
发明内容
为了解决现有技术中配电网电压控制对柔性资源的协同控制不足的问题，本发明提出了一种源储荷分布式协同电压控制方法及其系统，基于源网荷储网络化云决策控制系统平台，通过对源储荷等柔性资源的分布式协同控制，解决由于新能源波动等其他小扰动情况下配电网的过/欠电压问题，不需要通过配网管理中心的协调即可实现局部过/欠电压问题的完全自治。
为解决上述技术问题，本发明采用了如下技术手段：
第一方面，本发明提出了一种源储荷分布式协同电压控制方法，包括如下步骤：
根据配电网中所有节点的电压信息计算每个节点的电压偏差量，并根据电压偏差量获取配电网的优先控制节点；
获取基于节点电压对输出有功和无功的灵敏度构建的电压控制目标函数；
当优先控制节点过压或欠压时，利用电压控制目标函数依次计算优先控制节点和其他节点的电压控制序列；
根据预设的收敛条件对所有节点的电压控制序列进行迭代更新，获得电压控制策略。
结合第一方面，进一步的，所述优先控制节点的获取方法为：
根据配电网中第i个节点在k时刻的电压信息计算第i个节点的电压偏差量：
Figure PCTCN2021109975-appb-000001
其中，ΔV i n(k)表示k时刻第i个节点的电压偏差量，V i(k)表示k时刻第i个节点的电压，V i n表示第i个节点的额定电压，i＝1,2,…,N，N为配电网节点总数；
通过N-1次迭代将配电网中每个节点的电压偏差量传输给其他节点，比较所有节点的电压偏差量，并选择电压偏差量最大的节点作为优先控制节点，其中，迭代方程如下：
Figure PCTCN2021109975-appb-000002
其中，α i[l]表示第l次迭代中第i个节点及其相邻节点中的最大电压偏差量，α j[l-1]表示第l-1次迭代中第j个节点及其相邻节点中的最大电压偏差量，第j个节点为第i个节点的相邻节点，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，l＝1,2,…,N。
结合第一方面，进一步的，电压控制目标函数的构建过程为：
基于节点电压对输出有功和无功的灵敏度建立分布式电压预测模型：
Figure PCTCN2021109975-appb-000003
其中，x i(k)＝[V i(k)]，V i(k)表示k时刻配电网中第i个节点的电压，B ii表示第i个节点电压对第i个节点输出有功和无功的灵敏度，u i(k)表示k时刻第i个节点输出有功和无功的变化值，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，B ij表示第i个节点电压对第j个节点输出有功和无功的灵敏度，i＝1,2,…,N，N为配电网节点总数；
根据分布式电压预测模型构建电压控制目标函数，表达式如下：
Figure PCTCN2021109975-appb-000004
其中，N p为预测域长度，x i(k+n|k)表示第i个节点在k时刻预测的k+n时刻的电压值，
Figure PCTCN2021109975-appb-000005
表示第i个节点的电压额定值，u i(k+n-1|k)表示第i个节点在k时刻预测的k+n-1时刻的电压控制指令，即第i个节点k+n-1时刻有功功率和无功功率的出力，n≥1，r i和ω i为第i个节点的权重系数矩阵， P i RES表示第i个节点的新能源出力下限，P i RES(k)表示k时刻第i个节点的新能源出力，
Figure PCTCN2021109975-appb-000006
表示第i个节点的新能源出力上限，E i(k)表示第i个节点的储能在k时刻的SOC状态，δ i(k)为第i个节点的储能在k时刻的充放电指示函数，P i S(k)表示第i个节点的储能在k时刻的输出有功功率，
Figure PCTCN2021109975-appb-000007
为第i个节点的储能的放电效率，
Figure PCTCN2021109975-appb-000008
为第i个节点的储能的充电效率， E i表示第i个节点的储能的SOC状态下限，
Figure PCTCN2021109975-appb-000009
表示第i个节点的储能的SOC状态上限， P i S表示第i个节点的储能的输出有功功率下限，
Figure PCTCN2021109975-appb-000010
表示第i个节点的储能的输出有功功率上限。
结合第一方面，进一步的，权重系数矩阵r i和ω i满足条件：ω i＜＜r i。
结合第一方面，进一步的，迭代更新并获得电压控制策略的方法包括如下步骤：
初始化电压控制目标函数的权重系数矩阵，获得所有节点的初始电压控制序列；
在每次迭代过程中，根据预设的取值范围更新电压控制目标函数的权重系数矩阵，并利用更新后的电压控制目标函数计算当前迭代中所有节点的电压控制序列；
在每次迭代后，基于预设的收敛条件对当前迭代中所有节点的电压控制序列进行误差判断：不满足收敛条件时，继续迭代计算所有节点的电压控制序列，满足收敛条件时，利用当前迭代中所有节点的电压控制序列生成电压控制策略；
所述预设的收敛条件为：
Figure PCTCN2021109975-appb-000011
其中，
Figure PCTCN2021109975-appb-000012
表示k时刻第i个节点第g次迭代的电压控制序列，g>1，ε为预设的误差阈值，i＝1,2,…,N，N为配电网节点总数。
结合第一方面，进一步的，所有节点的电压控制序列的计算方法为：
将k时刻优先控制节点的电压信息输入电压控制目标函数，根据预设的取值范围动态分配电压控制目标函数的权重系数矩阵，预测时间段[k,k+N c-1]内优先控制节点有功功率和无功功率的出力，生成优先控制节点的电压控制序列；
将优先控制节点的电压控制序列发送至优先控制节点的相邻节点；
基于优先控制节点的电压控制序列利用电压控制目标函数处理每个节点的电压信息，预测时间段[k,k+N c-1]内每个节点有功功率和无功功率的出力，生成每个节点的电压控制序列。
第二方面，本发明提出了一种源储荷分布式协同电压控制系统，包括多个分层式多智能体，每个分层式多智能体配置在配电网的一个节点上；所述分层式多智能体用于采集节点的电压信息，计算节点的电压控制策略，并根据电压控制策略控制节点的电压输出。
结合第二方面，进一步的，所述分层式多智能体包括上层智能体和下层智能体，上层智能体包括上层感知模块、上层决策模块和上层执行模块，下层智能体包括下层感知模块、下层决策模块和下层执行模块；所述上层感知层用于采集节点的电压信息，并将该节点的电压信息与其他节点的分层式多智能体共享；所述上层决策模块用于利用电压控制目标函数处理节点电压信息，计算节点的电压控制序列；所述上层执行模块用于将节点的电压控制序列发送到下层决策模块；所述下层感知模块用于感知模块用于采集源储荷资源的运行状态信息；所述下层决策模块用于接收节点的电压控制序列，并控制下层执行模块执行电压控制序列；所述下层执行模块用于根据电压控制序列调整节点的电压输出。
结合第二方面，进一步的，所述上层决策模块包括知识库模块，所述知识库模块包括配电网拓扑结构、电压控制目标函数和电压控制目标函数的权重系数矩阵的取值范围。
采用以上技术手段后可以获得以下优势：
本发明提出了一种源储荷分布式协同电压控制方法及其系统，本发明方法不需要中心节点的协调便可以感知全局电压信息，基于潮流灵敏度分析配电网节点电压的时空分布特性，构建包含源储荷等柔性资源的有功功率和无功功率协同出力的电压预测和控制模型，根据配电网中所有节点的电压信息确定电压问题最严重的节点，即优先控制节点，并在优先控制节点的基础上通过动态调整模型权重对对源储荷的出力情况进行在线决策与分配，进而协调所有柔性资源合理地进行电压调控，保证电压调控准确性的同时有效地保证了配电网电压控制的快速性、经济性和灵活性。本发明系统通过双层智能体的协调互动实现电压问题的分布式自治，双层智能体分别进行配电网数据采集、共享、电压控制决策制定、决策执行等操作，实现了源储荷等柔性资源的即插即用，能够快速解决配电网的过电压和欠电压问题，保证了配电网电压调控的准确性。
附图说明
图1为本发明一种源储荷分布式协同电压控制方法的步骤流程图；
图2为一种源储荷分布式协同电压控制系统的结构示意图；
图3为本发明实施例中实验平台的示意图；
图4为本发明实施例中实验平台逆变器1的电压变化曲线图；
图5为本发明实施例中实验平台逆变器2的电压变化曲线图；
图6为本发明实施例中实验平台逆变器3的电压变化曲线图；
图7为本发明实施例中实验平台逆变器1的功率变化曲线图；
图8为本发明实施例中实验平台逆变器2的功率变化曲线图；
图9为本发明实施例中实验平台逆变器3的功率变化曲线图；
图中，1是上层智能体，2是下层智能体，101是上层感知模块，102是上层决策模块，103是上层执行模块，201是下层感知模块，202是下层决策模块，203是下层执行模块。
具体实施方式
下面结合附图对本发明的技术方案作进一步说明：
本发明提出了一种源储荷分布式协同电压控制方法，如图1所示，具体包括如下步骤：
步骤A、根据配电网中所有节点的电压信息计算每个节点的电压偏差量，并根据电 压偏差量获取配电网的优先控制节点；
步骤B、获取基于节点电压对输出有功和无功的灵敏度构建的电压控制目标函数；
步骤C、当优先控制节点过压或欠压时，利用电压控制目标函数依次计算优先控制节点和其他节点的电压控制序列；
步骤D、根据预设的收敛条件对所有节点的电压控制序列进行迭代更新，获得电压控制策略。
本发明还提出了一种源储荷分布式协同电压控制系统，包括多个分层式多智能体，每个分层式多智能体配置在配电网的一个节点上，不同分层式多智能体之间可以共享信息。如图2所示，每个分层式多智能体包括上层智能体1和下层智能体2，均为BDI智能体；上层智能体由电压安全事件触发，负责协调源储荷等柔性资源的出力，下层智能体负责源储荷等柔性资源的本地动态行为控制，通过双层智能体的协调互动实现电压问题的分布式自治。
上层智能体包括上层感知模块101、上层决策模块102和上层执行模块103，下层智能体包括下层感知模块201、下层决策模块202和下层执行模块203。上层感知层主要用于采集节点的电压信息，并将该节点的电压信息与其他节点的分层式多智能体共享；上层决策模块用于利用电压控制目标函数处理节点电压信息，计算节点的电压控制序列；上层执行模块用于将节点的电压控制序列发送到下层决策模块；下层感知模块主要用于感知模块用于采集源储荷资源的运行状态信息，比如节点逆变器输出端的电压、电流、相角信息等；下层决策模块用于接收节点的电压控制序列，并控制下层执行模块执行电压控制序列；下层执行模块用于根据电压控制序列调整节点的电压输出。当节点电压超出安全范围时，上层智能体可以通过上层决策模块计算出电压控制序列，并利用下层智能体执行电压控制序列，实现电压快速控制效果。
在本发明方法的步骤A中，本发明在分层式多智能体架构的基础上提出完全分布式的电压安全事件触发机制，即不需要中心节点的协调便可以感知全局电压信息，并根据电压信息获得电压问题最严重的节点(优先控制节点)，具体操作如下：
步骤A01、根据配电网中第i个节点在k时刻的电压信息计算第i个节点的电压偏差量：
Figure PCTCN2021109975-appb-000013
其中，ΔV i n(k)表示k时刻第i个节点的电压偏差量，V i(k)表示k时刻第i个节点的电压，V i n表示第i个节点的额定电压，i＝1,2,…,N，N为配电网节点总数。
步骤A02、通过分层式多智能体共享每个节点的电压偏差量，通过N-1次迭代将配电网中每个节点的电压偏差量传输给其他节点，比较所有节点的电压偏差量，并选择电压偏差量最大的节点作为优先控制节点，其中，迭代方程如下：
Figure PCTCN2021109975-appb-000014
其中，α i[l]表示第l次迭代中第i个节点及其相邻节点中的最大电压偏差量，α j[l-1]表示第l-1次迭代中第j个节点及其相邻节点中的最大电压偏差量，第j个节点为第i个节点的相邻节点，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，l＝1,2,…,N。
在本发明方法的步骤B中，本发明基于潮流灵敏度分析配电网节点电压的时空分布特性，并构建分布式电压控制的数学模型，提出包含源储荷等柔性资源的有功功率和无功功率协同出力的电压控制方法，具体操作如下：
步骤B01、除第一个参考节点已知外，其它节点均视为PQ节点(有功功率P和无功功率Q是给定的，节点电压和相位(V，δ)是待求量)，建立配电网各节点注入电流与电压的方程：
Figure PCTCN2021109975-appb-000015
其中，V i表示第i个节点的电压，{η 21,…,η i1,…,η N1}为一系列常数增益，R im表示第i个节点与第m个节点之间的电阻，X im表示第i个节点与第m个节点之间的线路电抗， R im+jX im表示第i个节点与第m个节点之间的线路阻抗，j为单位虚数，V n表示母线参考电压，I i表示第i个节点的注入电流，i＝1,2,…,N，N为配电网节点总数。
I i＝(S i/V i) *＝((P i+jQ i)/V i) *     (9)
其中，S i表示第i个节点的注入功率，P i表示第i个节点的注入有功功率，Q i表示第i个节点的注入无功功率。
根据采集到的节点电压信息，计算每个节点电压对输出有功的灵敏度：
Figure PCTCN2021109975-appb-000016
Figure PCTCN2021109975-appb-000017
Figure PCTCN2021109975-appb-000018
其中，
Figure PCTCN2021109975-appb-000019
表示第i个节点对第m个节点输出有功的灵敏度，上标 re代表该变量实部，上标 im代表该变量虚部，P m表示第m个节点注入有功功率，V ηi＝V n·η i1。
根据采集到的节点电压信息，计算每个节点电压对输出无功的灵敏度：
Figure PCTCN2021109975-appb-000020
Figure PCTCN2021109975-appb-000021
Figure PCTCN2021109975-appb-000022
其中，Q m表示第m个节点注入无功功率。
步骤B02、基于节点电压对输出有功和无功的灵敏度建立分布式电压预测模型，表达式如下：
Figure PCTCN2021109975-appb-000023
其中，x i(k)表示k时刻配电网中第i个节点电压的幅值，x i(k)＝[V i(k)]，V i(k)表示k时刻配电网中第i个节点的电压，B ii表示第i个节点电压对第i个节点输出有功和无功的灵敏度，
Figure PCTCN2021109975-appb-000024
u i(k)表示k时刻第i个节点输出有功和无功的变化值，
Figure PCTCN2021109975-appb-000025
ΔP i RES(k)＝[ΔP i PV(k) ΔP i WT(k)]，ΔP i PV(k)表示k时刻第i个节点的光伏发电单元的有功功率变化值，ΔP i WT(k)表示k时刻第i个节点的风力发电单元的有功功率变化值，ΔP i S(k)表示k时刻第i个节点的储能单元的有功功率变化值，
Figure PCTCN2021109975-appb-000026
表示k时刻第i个节点的光伏发电单元的无功功率变化值，
Figure PCTCN2021109975-appb-000027
表示k时刻第i个节点的风力发电单元的无功功率变化值，
Figure PCTCN2021109975-appb-000028
表示k时刻第i个节点的储能单元的无功功率变化值，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，B ij表示第i个节点电压对第j个节点输出有功和无功的灵敏度，
Figure PCTCN2021109975-appb-000029
步骤B03、根据分布式电压预测模型构建电压控制目标函数，表达式如下：
Figure PCTCN2021109975-appb-000030
其中，N p为人为设置的预测域长度，x i(k+n|k)表示第i个节点在k时刻预测的k+n时刻的电压值，
Figure PCTCN2021109975-appb-000031
表示第i个节点的电压额定值，
Figure PCTCN2021109975-appb-000032
为参考值，通常大于节点的额定电压，u i(k+n-1|k)表示第i个节点在k时刻预测的k+n-1时刻的电压控制指令，即第i个节点k+n-1时刻有功功率和无功功率的出力，n≥1，r i和ω i为第i个节点的权重系数矩阵， P i RES表示第i个节点的新能源出力下限，P i RES(k)表示k时刻第i个节点的新能源出力，
Figure PCTCN2021109975-appb-000033
表示第i个节点的新能源出力上限，E i(k)表示第i个节点的储能在k时刻的SOC状态，δ i(k)为第i个节点的储能在k时刻的充放电指示函数，δ i(k)＝1表示储能处于放电状态，δ i(k)＝0表示储能处于充电状态，P i S(k)表示第i个节点的储能在k时刻的输出有功功率，
Figure PCTCN2021109975-appb-000034
为第i个节点的储能的放电效率，
Figure PCTCN2021109975-appb-000035
为第i个节点的储能的充电效率， E i表示第i个节点的储能的SOC状态下限，
Figure PCTCN2021109975-appb-000036
表示第i个节点的储能的SOC状态上限， P i S表示第i个节点的储能的输出有功功率下限，
Figure PCTCN2021109975-appb-000037
表示第i个节点的储能的输出有功功率上限。
电压控制目标函数的权重系数矩阵r i和ω i满足条件：ω i＜＜r i。
权重系数矩阵ω i可以表示为：
Figure PCTCN2021109975-appb-000038
其中，ω i,3<ω i,4＜＜ω i,1<ω i,2，ω i,1为控制量
Figure PCTCN2021109975-appb-000039
中ΔP i RES(k)的权重系数，ω i,2为ΔP i S(k)的权重系数，ω i,3为
Figure PCTCN2021109975-appb-000040
的权重系数，ω i,4为
Figure PCTCN2021109975-appb-000041
的权重系数。
在本发明的步骤C中，本发明通过上层决策模块动态调整权重，合理地对源储荷的出力情况进行在线决策与分配，保证电压调控准确性的同时兼顾了调控成本的经济性，具体操作如下：
步骤C01、当优先控制节点出现过电压问题时，判断最优控制节点的新能源出力是否大于负荷需求与储能的容量之和：
Figure PCTCN2021109975-appb-000042
其中，P i RES(k)表示k时刻第i个节点的新能源出力，P i L(k)表示k时刻第i个节点的负荷需求功率，
Figure PCTCN2021109975-appb-000043
表示第i个节点的储能容量上限。
当最优控制节点的新能源出力满足公式(19)时，需要降低新能源出力直至新能源的出力不大于负荷需求与储能的容量之和。
当优先控制节点出现欠电压问题时，判断最优控制节点的负荷需求是否大于新能源出力与储能的容量之和：
Figure PCTCN2021109975-appb-000044
当最优控制节点的负荷需求满足公式(20)时，需要切负荷直至负荷需求不大于新能源的出力与储能的容量之和。
步骤C02、当优先控制节点过压或欠压时，将k时刻优先控制节点的电压信息输入电压控制目标函数，根据预设的取值范围动态分配电压控制目标函数的权重系数矩阵，在满足电压安全的同时优先使用节点的新能源和储能的无功功率进行电压补偿，当无功功率补偿仍无法满足电压安全要求时，再通过调节新能源的有功功率进行电压控制，预测时间段[k,k+N c-1]内优先控制节点有功功率和无功功率的出力，生成优先控制节点的电压控制序列。
步骤C03、将优先控制节点的电压控制序列发送至优先控制节点的相邻节点，基于优先控制节点的电压控制序列利用电压控制目标函数处理其相邻节点的电压信息，预测 时间段[k,k+N c-1]内相邻节点有功功率和无功功率的出力生成相邻节点的电压控制序列。将相邻节点的电压控制序列发送到相邻节点的相邻节点，以此类推，计算出每个节点的电压控制序列。
k时刻第i个节点的电压控制序列u i(k)可以表示为：u i(k)＝[u i(k|k)u i(k+1|k)…u i(k+m|k)…u i(k+N c-1|k)]，其中，u i(k+o|k)表示第i个节点在k时刻预测的k+o时刻第i个节点有功功率和无功功率的出力，o＝1,2,…,N c-1，N c为预设的时间值。
在本发明方法的步骤D中，迭代更新并获得电压控制策略的方法包括如下步骤：
步骤D01、初始化电压控制目标函数的权重系数矩阵，获得所有节点的初始电压控制序列，即
Figure PCTCN2021109975-appb-000045
步骤D02、在每次迭代过程中，根据预设的取值范围更新电压控制目标函数的权重系数矩阵，并利用更新后的电压控制目标函数计算当前迭代中所有节点的电压控制序列，k时刻第i个节点第g次迭代的电压控制序列为
Figure PCTCN2021109975-appb-000046
步骤D03、在每次迭代后，基于预设的收敛条件对当前迭代中所有节点的电压控制序列进行误差判断：不满足收敛条件时，重复步骤D02，继续迭代计算所有节点的电压控制序列；满足收敛条件时，利用当前迭代中所有节点的电压控制序列生成电压控制策略。
本发明中的预设的收敛条件为：
Figure PCTCN2021109975-appb-000047
其中，ε为预设的误差阈值，|||为2范数运算符。
获得电压控制策略后，利用第i个节点节点上层智能体的上层执行模块下发u i(k)中的第一个控制量(即u i(k|k))至节点下层智能体，利用下层执行模块执行电压控制策略。
本发明系统中的上层决策模块主要包括节点选择模块、过欠压判断模块、决策计算模块和知识库模块。节点选择模块一方面用于根据节点电压信息计算节点的电压偏差量， 另一方面用于与其他分层式多智能体的节点选择模块共享信息，进而根据所有节点的电压偏差量选出配电网的优先控制节点；过欠压判断模块用于根据优先控制节点的电压信息判断优先控制节点是否过压或欠压；决策计算模块用于根据知识库模块中的数据，利用电压控制目标函数迭代计算节点的电压控制序列，生成电压控制策略；知识库模块用于存储配电网中的经验数据和专业知识，进而协助决策计算模块进行决策，知识库模块中包括配电网拓扑结构、配电网中各个节点的连接信息、电压控制目标函数和电压控制目标函数的权重系数矩阵的取值范围等数据。
为了验证本发明的效果，本发明实施例给出如下实验：
图3为基于本发明搭建的实验平台架构图，该实验平台由3台额定功率为3kW的光伏逆变器、SIMATIC S7-1500型PLC以及负载箱构成，3台光伏逆变器由置于实验室楼顶的光伏面板提供电力支撑，通信链路由SIMATIC S7-1500型PLC提供，负载箱用于模拟节点负荷。额定电压设置为220V，电压安全范围设置为220V～226V，控制周期设置为10s。
利用本发明方法和系统对该实验平台的电压进行控制，为了模拟电压波动情况，本发明实施例在第151s和第547s时引入外部扰动，从而模拟系统中的过电压和欠电压问题，实验平台逆变器的电压变化和功率变化如图4～9所示，由图4、图5和图6可知，实验平台中的3台逆变器的电压能够以较快的速度收敛到安全范围之内，仅仅在安全范围的边界处有少许波动，该波动是由光伏逆变器本身正常的波动引起的。由图7、图8和图9可知，3台逆变器的功率能够快速恢复稳定状态，因此本发明能够有效地解决因扰动产生的过电压和欠电压问题，具有很好的工程应用前景。
本发明基于双层BDI多智能体构建配电网电压调节的控制系统，以实现源储荷等柔性资源的即插即用，在此基础上，基于潮流灵敏度分析确定配电网电压的时空分布特性，并建立源储荷分布式协同的电压控制模型，利用分布式模型预测控制实现配电网的电压调控，有效地保证了配电网电压控制的快速性、经济性和灵活性，能够快速解决配电网的过电压和欠电压问题，保证了配电网电压调控的准确性。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和变形，这些改进和变形也应视为本发明的保护范围。

Claims (9)

  1. 一种源储荷分布式协同电压控制方法，其特征在于，包括如下步骤：
    根据配电网中所有节点的电压信息计算每个节点的电压偏差量，并根据电压偏差量获取配电网的优先控制节点；
    获取基于节点电压对输出有功和无功的灵敏度构建的电压控制目标函数；
    当优先控制节点过压或欠压时，利用电压控制目标函数依次计算优先控制节点和其他节点的电压控制序列；
    根据预设的收敛条件对所有节点的电压控制序列进行迭代更新，获得电压控制策略。
  2. 根据权利要求1所述的一种源储荷分布式协同电压控制方法，其特征在于，所述优先控制节点的获取方法为：
    根据配电网中第i个节点在k时刻的电压信息计算第i个节点的电压偏差量：
    Figure PCTCN2021109975-appb-100001
    其中，
    Figure PCTCN2021109975-appb-100002
    表示k时刻第i个节点的电压偏差量，V i(k)表示k时刻第i个节点的电压，
    Figure PCTCN2021109975-appb-100003
    表示第i个节点的额定电压，i＝1,2,…,N，N为配电网节点总数；
    通过N-1次迭代将配电网中每个节点的电压偏差量传输给其他节点，比较所有节点的电压偏差量，并选择电压偏差量最大的节点作为优先控制节点，其中，迭代方程如下：
    Figure PCTCN2021109975-appb-100004
    其中，α i[l]表示第l次迭代中第i个节点及其相邻节点中的最大电压偏差量，α j[l-1]表示第l-1次迭代中第j个节点及其相邻节点中的最大电压偏差量，第j个节点为第i个节点的相邻节点，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，l＝1,2,…,N。
  3. 根据权利要求1所述的一种源储荷分布式协同电压控制方法，其特征在于，电压控制目标函数的构建过程为：
    基于节点电压对输出有功和无功的灵敏度建立分布式电压预测模型：
    Figure PCTCN2021109975-appb-100005
    其中，x i(k)＝[V i(k)]，V i(k)表示k时刻配电网中第i个节点的电压，B ii表示第i个节点电压对第i个节点输出有功和无功的灵敏度，u i(k)表示k时刻第i个节点输出有功和无功的变化值，j＝1,2,…,N i，N i为配电网中第i个节点的相邻节点的数量，B ij表示第i个节点电压对第j个节点输出有功和无功的灵敏度，i＝1,2,…,N，N为配电网节点总数；
    根据分布式电压预测模型构建电压控制目标函数，表达式如下：
    Figure PCTCN2021109975-appb-100006
    Figure PCTCN2021109975-appb-100007
    Figure PCTCN2021109975-appb-100008
    Figure PCTCN2021109975-appb-100009
    Figure PCTCN2021109975-appb-100010
    Figure PCTCN2021109975-appb-100011
    其中，N p为预测域长度，x i(k+n|k)表示第i个节点在k时刻预测的k+n时刻的电压值，
    Figure PCTCN2021109975-appb-100012
    表示第i个节点的电压额定值，u i(k+n-1|k)表示第i个节点在k时刻预测的k+n-1时刻的电压控制指令，即第i个节点k+n-1时刻有功功率和无功功率的出力，n≥1，r i和ω i为第i个节点的权重系数矩阵， P i RES表示第i个节点的新能源出力下限，P i RES(k)表示k时刻第i个节点的新能源出力，
    Figure PCTCN2021109975-appb-100013
    表示第i个节点的新能源出力上限，E i(k)表示第i个节点的储能在k时刻的SOC状态，δ i(k)为第i个节点的储能在k时刻的充放电指示函数，P i S(k)表示第i个节点的储能在k时刻的输出有功功率，
    Figure PCTCN2021109975-appb-100014
    为第i个节点的储能的放电效率，
    Figure PCTCN2021109975-appb-100015
    为第i个节点的储能的充电效率， E i表示第i个节点的储能的SOC状态下限，
    Figure PCTCN2021109975-appb-100016
    表示第i个节点的储能的SOC状态上限， P i S表示第i个节点的 储能的输出有功功率下限，
    Figure PCTCN2021109975-appb-100017
    表示第i个节点的储能的输出有功功率上限。
  4. 根据权利要求3所述的一种源储荷分布式协同电压控制方法，其特征在于，权重系数矩阵r i和ω i满足条件：ω i＜＜r i。
  5. 根据权利要求1所述的一种源储荷分布式协同电压控制方法，其特征在于，迭代更新并获得电压控制策略的方法包括如下步骤：
    初始化电压控制目标函数的权重系数矩阵，获得所有节点的初始电压控制序列；
    在每次迭代过程中，根据预设的取值范围更新电压控制目标函数的权重系数矩阵，并利用更新后的电压控制目标函数计算当前迭代中所有节点的电压控制序列；
    在每次迭代后，基于预设的收敛条件对当前迭代中所有节点的电压控制序列进行误差判断：不满足收敛条件时，继续迭代计算所有节点的电压控制序列，满足收敛条件时，利用当前迭代中所有节点的电压控制序列生成电压控制策略；
    所述预设的收敛条件为：
    Figure PCTCN2021109975-appb-100018
    其中，
    Figure PCTCN2021109975-appb-100019
    表示k时刻第i个节点第g次迭代的电压控制序列，g>1，ε为预设的误差阈值，i＝1,2,…,N，N为配电网节点总数。
  6. 根据权利要求1或5所述的一种源储荷分布式协同电压控制方法，其特征在于，所有节点的电压控制序列的计算方法为：
    将k时刻优先控制节点的电压信息输入电压控制目标函数，根据预设的取值范围动态分配电压控制目标函数的权重系数矩阵，预测时间段[k,k+N c-1]内优先控制节点有功功率和无功功率的出力，生成优先控制节点的电压控制序列；
    将优先控制节点的电压控制序列发送至优先控制节点的相邻节点；
    基于优先控制节点的电压控制序列利用电压控制目标函数处理每个节点的电压信息，预测时间段[k,k+N c-1]内每个节点有功功率和无功功率的出力，生成每个节点的电压控制序列。
  7. 一种源储荷分布式协同电压控制系统，其特征在于，包括多个分层式多智能体，每个分层式多智能体配置在配电网的一个节点上；所述分层式多智能体用于采集节点的 电压信息，计算节点的电压控制序列，并根据电压控制序列控制节点的电压输出。
  8. 根据权利要求7所述的一种源储荷分布式协同电压控制系统，其特征在于，所述分层式多智能体包括上层智能体和下层智能体，上层智能体包括上层感知模块、上层决策模块和上层执行模块，下层智能体包括下层感知模块、下层决策模块和下层执行模块；所述上层感知层用于采集节点的电压信息，并将该节点的电压信息与其他节点的分层式多智能体共享；所述上层决策模块用于利用电压控制目标函数处理节点电压信息，计算节点的电压控制序列；所述上层执行模块用于将节点的电压控制序列发送到下层决策模块；所述下层感知模块用于感知模块用于采集源储荷资源的运行状态信息；所述下层决策模块用于接收节点的电压控制序列，并控制下层执行模块执行电压控制序列；所述下层执行模块用于根据电压控制序列调整节点的电压输出。
  9. 根据权利要求8所述的一种源储荷分布式协同电压控制系统，其特征在于，所述上层决策模块包括知识库模块，所述知识库模块包括配电网拓扑结构、电压控制目标函数和电压控制目标函数的权重系数矩阵的取值范围。
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