Power distribution network photovoltaic energy storage cluster cooperative voltage control method and device

Abstract

The invention provides a power distribution network photovoltaic energy storage cluster cooperative voltage control method and device. The method comprises the following steps: establishing a first objective function with the minimum photovoltaic light abandoning cost, the minimum line network loss and the minimum energy storage charging and discharging cost; processing the branch power flow equation by using a second-order cone relaxation method to generate an optimal power flow model; solving to obtain an optimal solution, and recording a cluster boundary value; exchanging boundary data of upstream and downstream clusters, and constructing a second objective function with boundary variable constraints; and an ADMM algorithm is adopted for solving until the boundary data deviation is smaller than a preset threshold value, and a globally optimal solution is obtained. And through energy storage participated regulation and control and cluster-based distributed optimization control, the photovoltaic light abandoning amount is reduced, the photovoltaic power generation income is improved, and economic optimization is realized.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic energy storage cluster division, and in particular, relates to a method and device for coordinated voltage control of photovoltaic energy storage clusters in a distribution network. Background Art

[0002] The background technology of this application mainly involves the problems caused by the increasing access ratio of distributed photovoltaic power generation in the distribution network in my country. Due to the intermittent and random characteristics of distributed photovoltaic power generation, the increase in its access ratio has brought huge hidden dangers to the safe and stable operation of the distribution network, leading to the emergence of a series of problems, such as power flow backflow, voltage over-limit, and distributed photovoltaic output is difficult to be consumed locally. Especially in Northwest China, due to the rich new energy resources, these problems are more prominent. In addition, the characteristics of the distribution network in Northwest China, such as long lines and obvious load peaks and valleys, can easily cause the voltage to exceed the upper limit during the day and the voltage to exceed the lower limit at night. Therefore, voltage control is particularly important and difficult to control.

[0003] In terms of voltage control of active distribution networks, it is mainly divided into local optimization control, centralized optimization control and cluster-based distributed optimization control. However, the current control means are mainly achieved by controlling the output of photovoltaic inverters and reactive equipment. Photovoltaic inverters can only reduce power control when photovoltaic output suddenly increases, and cannot work when the distribution network voltage exceeds the lower limit. At the same time, there are limited reactive regulation devices in the distribution network. These situations limit the voltage regulation capacity of the distribution network, and will also increase the amount of abandoned light in the photovoltaic regulation process, leading to rising costs.

[0004] As a flexible regulation resource, the energy storage system has the advantages of strong instantaneous power throughput, rapid response, and precise regulation. It can regulate line power flow very well and also plays an important role in the consumption of new energy and peak and frequency regulation of the power grid. By regulating energy storage and photovoltaics together, the regulation effect of the distribution network on voltage is significantly enhanced. However, the coordinated regulation of energy storage and photovoltaics also increases the difficulty of distribution network control. Although general optimization control can eliminate the problem of voltage over-limit to a certain extent, it does not take into account the power exchange regulation between clusters, which will cause excessive regulation of photovoltaics and energy storage within the cluster, thereby increasing the global regulation cost. Summary of the invention

[0005] The purpose of this application is to overcome the above-mentioned prior art and provide a method and device for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network.

[0006] The present application provides a method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network, comprising:

[0007] Establishing the first objective function within the photovoltaic energy storage cluster of the distribution network with the goal of minimizing the photovoltaic abandonment cost, line network loss and energy storage charging and discharging cost;

[0008] For the first objective function, a second-order cone relaxation method is used to convexify the branch power flow equation to generate an optimal power flow model of the photovoltaic energy storage cluster of the distribution network;

[0009] Solving the optimal power flow model to obtain the optimal solution of the photovoltaic energy storage cluster of the distribution network;

[0010] According to the optimal solution of the distribution network photovoltaic energy storage cluster, record the boundary value of each distribution network photovoltaic energy storage cluster;

[0011] According to the boundary value, the boundary data of the upstream cluster and the downstream cluster of each of the distribution network photovoltaic energy storage clusters are exchanged to construct a second objective function with boundary variable constraints;

[0012] The second objective function is cyclically solved by using the ADMM algorithm, so that the deviation of the boundary data is less than a preset threshold value, and a global optimal solution is obtained;

[0013] The coordinated voltage control of the photovoltaic energy storage cluster in the distribution network is performed according to the global optimal solution.

[0014] Optionally, the first objective function is established as follows:

[0015]

[0016] Among them, P dPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij Represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cj are the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage; E jmax is the rated capacity of energy storage; Gk is the set of all nodes in cluster k.

[0017] Optionally, the optimal power flow model is expressed as follows:

[0018]

[0019] Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

[0020] Optionally, the boundary value includes: the voltage of the virtual balancing node and the virtual load power.

[0021] Optionally, a second objective function with bound variable constraints is constructed, expressed as follows:

[0022]

[0023] Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

[0024] Optionally, the second objective function is cyclically solved by using the ADMM algorithm, including:

[0025] In the iterative process of solving the distribution network photovoltaic energy storage clusters, the Lagrange multiplier values ​​of the boundary data and the first and last segment boundary data are updated and calculated, where the initial Lagrange multiplier value is 0.

[0026] The present application also provides a distribution network photovoltaic energy storage cluster coordinated voltage control device, comprising:

[0027] The first function module establishes the first objective function in the photovoltaic energy storage cluster of the distribution network with the goal of minimizing the photovoltaic abandoned light cost, line network loss and energy storage charging and discharging cost;

[0028] A processing module, for the first objective function, uses a second-order cone relaxation method to convexify the branch power flow equation to generate an optimal power flow model of the photovoltaic energy storage cluster of the distribution network;

[0029] A first solution module solves the optimal power flow model to obtain an optimal solution within the photovoltaic energy storage cluster of the distribution network;

[0030] A boundary module records the boundary values ​​of each photovoltaic energy storage cluster of the distribution network according to the optimal solution within the photovoltaic energy storage cluster of the distribution network;

[0031] The second function module exchanges the boundary data of the upstream cluster and the downstream cluster of each of the distribution network photovoltaic energy storage clusters according to the boundary value, and constructs a second objective function with boundary variable constraints;

[0032] A second solving module uses an ADMM algorithm to cyclically solve the second objective function so that the deviation of the boundary data is less than a preset threshold value, thereby obtaining a global optimal solution;

[0033] A control module performs coordinated voltage control of the photovoltaic energy storage cluster of the distribution network according to the global optimal solution.

[0034] Optionally, the first objective function is established as follows:

[0035]

[0036] Among them, P dPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij Represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cjare the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage; E jmax is the rated capacity of energy storage; G k is the set of all nodes in cluster k.

[0037] Optionally, the optimal power flow model is expressed as follows:

[0038]

[0039] Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

[0040] Optionally, a second objective function with bound variable constraints is constructed, expressed as follows:

[0041]

[0042] Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

[0043] The beneficial effects of this application are:

[0044] The present application provides a method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network, including: establishing a first objective function in the photovoltaic energy storage cluster of the distribution network with the goal of minimizing photovoltaic abandonment cost, line network loss and energy storage charging and discharging cost; for the first objective function, using the second-order cone relaxation method to convexify the branch power flow equation to generate the optimal power flow model of the photovoltaic energy storage cluster of the distribution network; solving the optimal power flow model to obtain the optimal solution of the photovoltaic energy storage cluster of the distribution network; according to the optimal solution of the photovoltaic energy storage cluster of the distribution network, recording the boundary values ​​of each photovoltaic energy storage cluster of the distribution network; according to the boundary value, exchanging the boundary data of the upstream cluster and the downstream cluster of each photovoltaic energy storage cluster of the distribution network, constructing a second objective function with boundary variable constraints; using the ADMM algorithm to cyclically solve the second objective function so that the deviation of the boundary data is less than a preset threshold and a global optimal solution is obtained; according to the global optimal solution, the coordinated voltage control of the photovoltaic energy storage cluster of the distribution network is performed. The present application introduces energy storage to participate in regulation and control, and adopts cluster-based distributed optimization control means to reduce the amount of abandoned light in photovoltaic regulation, improve photovoltaic power generation benefits, and achieve economic optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the coordinated voltage control process of the photovoltaic energy storage cluster in the distribution network in this application;

[0046] Figure 2 It is a schematic diagram of the topological structure after the IEEE33 system cluster is divided in this application;

[0047] Figure 3 is a schematic diagram of node voltage after optimized control in the cluster in this application;

[0048] Figure 4 This is a schematic diagram of the amount of photovoltaic abandoned light after optimization in the cluster in this application;

[0049] Figure 5 is a schematic diagram of node voltage after intra-cluster and inter-cluster control in this application;

[0050] Figure 6 It is a schematic diagram of the amount of photovoltaic abandoned light after intra-cluster and inter-cluster control in this application. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementing the present disclosure should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] Please refer to Figure 1As shown, the present application provides a method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network, the steps of which include:

[0053] S101. Establishing a first objective function within the photovoltaic energy storage cluster of the distribution network with the goal of minimizing photovoltaic curtailment costs, line network losses, and energy storage charging and discharging costs.

[0054] Please refer to Figure 2 As shown in the figure, it is assumed that there are 11 controllable distributed photovoltaics on the distribution network photovoltaic energy storage cluster line, with a total installed capacity of approximately 5.4MVA, of which the photovoltaic installed capacity of nodes 5, 7, 8, 15, 18, and 20 is 0.4MVA, and the photovoltaic installed capacity of nodes 10, 23, 25, 29, and 31 is 0.6MWA. There are two controllable energy storage systems, connected to nodes 13 and 26 respectively, with an installed capacity of 0.8MW*1h.

[0055] This embodiment selects data at time 12 to carry out simulation. At this time, the photovoltaic output power is close to the maximum output power, the total load scale is 1.23MW, and a serious voltage limit problem will occur on the line.

[0056] As the controllable objects in the cluster are photovoltaic and energy storage, the first objective function is established with the minimum photovoltaic abandonment cost, line network loss and energy storage charging and discharging cost as the minimum:

[0057]

[0058] Among them, P dPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij Represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cjare the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage, which is about 3000 times; E jmax is the rated capacity of energy storage; G k is the set of all nodes in cluster k. PV and C loss 0.5 yuan / kWh and 0.4 yuan / kWh respectively, C inv 1000 yuan / kWh, C pre 200 yuan / kWh, C ec It is 0.5 yuan / kWh, and n is 3000.

[0059] S102: For the first objective function, use a second-order cone relaxation method to convexify the branch power flow equation to generate an optimal power flow model for the photovoltaic energy storage cluster of the distribution network.

[0060] According to the first objective function in the cluster, the branch power flow equation is further convexed by using the second-order cone relaxation. After transformation, the branch power flow equation is:

[0061]

[0062] Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

[0063] To ensure the stable operation of the power system, node voltage and current constraints need to be imposed. The node voltage and current constraints are as follows:

[0064]

[0065] Among them, V min 、V max are the lower and upper limits of node voltage safety operation, which are set to 0.95pu and 1.05pu respectively. min ,I max It indicates the minimum and maximum transmission current values ​​of the line between the measured node i and the node j.

[0066] To ensure the stable operation of the photovoltaic system, its output power needs to be constrained. The photovoltaic power constraints are as follows:

[0067]

[0068] Among them, S PVj is the rated apparent power of the PV inverter at node j.

[0069] In order to ensure the stable operation of the energy storage system and give full play to its regulatory role, it is necessary to constrain its charging and discharging power and state of charge. The energy storage charging and discharging power constraints and state of charge constraints are as follows:

[0070]

[0071] Among them, P essmax is the maximum active power of the energy storage system; u is the charge and discharge sign, 1 represents discharge, -1 represents charge; η d , η c They are discharge efficiency and charge efficiency respectively; SOC max , SOC min is the maximum and minimum value of the state of charge of the energy storage system; S essj is the apparent power of the energy storage.

[0072] S103: Solve the optimal power flow model to obtain the optimal solution within the photovoltaic energy storage cluster of the distribution network.

[0073] By solving the constructed optimal power flow model, the optimal configuration and operation strategy within the photovoltaic storage cluster are found, thereby obtaining the most economical objective function value.

[0074] The optimal solution can not only effectively reduce the operating cost of the power system, but also eliminate the problem of voltage exceeding the limit to a certain extent, and improve the stability and security of the power grid.

[0075] like Figure 3 As shown in the figure, the node voltage distribution under different regulation strategies is compared. When photovoltaic energy storage is not involved in regulation (shown by the red line), due to changes in factors such as light and load, the voltage of multiple nodes exceeds the safe range, and the voltage deviation range reaches 0.067pu. When the cluster relies only on photovoltaics for regulation (shown by the black line), the voltage over-limit problem is effectively suppressed by adjusting the output power of photovoltaics, and the voltage deviation range is reduced to 0.05pu. However, when photovoltaics and energy storage in the cluster participate in regulation together (shown by the blue line), the voltage no longer exceeds the limit, and the voltage deviation range remains at 0.05pu, but the voltage deviation of each node as a whole has been further reduced compared to when only photovoltaics are regulated, showing the superiority of coordinated regulation of photovoltaics and energy storage.

[0076] like Figure 4 As shown in the figure, the amount of abandoned light and the total regulation cost of distributed photovoltaics before and after the participation of energy storage in regulation are compared. When energy storage regulation is added to the cluster, the amount of abandoned light in photovoltaics is significantly lower than the amount of abandoned light when relying solely on photovoltaic regulation, because energy storage can store excess electricity when the sun is sufficient and release electricity to supplement the grid demand when the sun is insufficient. The amount of abandoned light is reduced by about 0.4751MW. This not only improves the utilization rate of photovoltaics, but also reduces energy waste. At the same time, from an economic perspective, the total regulation cost when only photovoltaics are regulated is 705.1 yuan, while when both photovoltaics and energy storage are involved in regulation, the total regulation cost is reduced to 504.3 yuan, indicating that the participation of energy storage in regulation can significantly improve the economy of the system.

[0077] The optimal solution for the photovoltaic storage cluster obtained by solving the optimal power flow model can not only effectively solve the voltage over-limit problem and improve the stability of the power grid, but also reduce the amount of photovoltaic abandonment, reduce the system operation cost, and achieve a win-win situation of economic and environmental benefits.

[0078] S104. Record the boundary values ​​of each photovoltaic energy storage cluster in the distribution network according to the optimal solution in the photovoltaic energy storage cluster in the distribution network.

[0079] According to the optimal solution of the photovoltaic energy storage cluster in the distribution network obtained in step S103, the boundary values ​​of each cluster are recorded. These boundary values ​​are crucial for subsequent system operation and monitoring, and they can ensure that each cluster can remain coordinated with the entire power grid when operating independently.

[0080] First, the cluster coordination optimization method is used to optimize each cluster independently. The purpose is to ensure the reasonable allocation and efficient use of resources at the cluster level, while meeting the power balance and voltage stability requirements within the cluster. Through this method, the optimal solution within each cluster is obtained, which includes key parameters such as voltage, current, and power distribution of each node in the cluster.

[0081] Next, record the boundary values ​​of each cluster. These boundary values ​​mainly include two parts:

[0082] Voltage of virtual balancing nodes: During cluster optimization, one or more nodes are usually selected as virtual balancing nodes, which are responsible for maintaining voltage stability within the cluster. Record the voltage values ​​of these nodes to ensure that the voltage level remains within a safe range when the cluster runs independently, avoiding device damage or system instability caused by voltage exceeding the limit.

[0083] Virtual load power: Virtual load power refers to the power value exchanged between the cluster and the external power grid, which reflects the demand or contribution of the cluster as a whole to the external power grid. Recording these power values ​​helps to understand the operating status of the cluster and regulate the interaction between the cluster and the external power grid when necessary.

[0084] By recording these boundary values, we can ensure that in the subsequent system operation, each cluster can operate independently in the predetermined manner while maintaining coordination with the entire power grid. This is of great significance for improving the flexibility, reliability and economy of the power grid. In addition, these boundary values ​​also serve as an important basis for system monitoring and troubleshooting, helping to discover and solve problems in a timely manner and ensure the safe and stable operation of the power grid.

[0085] S105. According to the boundary value, the boundary data of the upstream cluster and the downstream cluster in each of the photovoltaic energy storage clusters in the distribution network are exchanged to construct a second objective function with boundary variable constraints.

[0086] In the context of power systems and energy management, upstream clusters and downstream clusters are usually defined based on the direction of energy flow or information flow. The following is an explanation of these two concepts:

[0087] Upstream clusters usually refer to clusters at the front or starting stage in the energy supply chain or power transmission chain. These clusters are mainly responsible for energy collection, conversion or preliminary processing. In the context of distribution network photovoltaic energy storage clusters, upstream clusters include:

[0088] Photovoltaic clusters: These clusters mainly consist of a large number of photovoltaic power generation equipment, which are responsible for converting solar energy into electricity. They are the starting point of energy supply and are therefore considered upstream clusters.

[0089] Energy storage device cluster: In some cases, energy storage devices (such as battery storage systems) are also considered part of the upstream cluster, especially when they are used to store excess electricity generated by photovoltaic power generation.

[0090] Downstream clusters refer to clusters at the back end or receiving stage in the energy supply chain or power transmission chain. These clusters are mainly responsible for the distribution, utilization or consumption of energy. In the context of distribution network photovoltaic energy storage clusters, downstream clusters include:

[0091] Power distribution network clusters: These clusters are responsible for distributing the power generated by upstream clusters to various power consumption points. They include transformers, switchgear, transmission lines, etc.

[0092] End-user clusters: These clusters consist of a large number of electricity consumers such as households, businesses, public facilities, etc. They are the final users of electrical energy and are therefore considered downstream clusters.

[0093] Then the boundary data of the upstream cluster and the downstream cluster are exchanged and updated, using U up ,P down ,Q down Update separately And construct the objective function with boundary variable constraints as follows:

[0094]

[0095] Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

[0096] S106, using the ADMM algorithm to cyclically solve the second objective function, so that the deviation of the boundary data is less than a preset threshold, and a global optimal solution is obtained;

[0097] Based on the intra-cluster coordination optimization model, the inter-cluster coordination optimization model adds equality constraints on boundary nodes and inter-cluster line power, so that each cluster can ensure the convergence of inter-cluster distributed optimization on the basis of independent optimization within the cluster. The Lagrange multiplier update calculation of the boundary data and the first and last segment boundary data in the iterative process of inter-cluster solution is as follows, where the initial Lagrange multiplier value is 0.

[0098]

[0099] According to equations (2) to (7), the ADMM algorithm is used to repeatedly solve the inter-cluster model until the deviation of the cluster boundary data is less than a certain threshold, and the global optimal solution is obtained.

[0100] Please refer to Figure 5 As shown, after inter-cluster coordinated control, the node voltage is lower than that of intra-cluster coordinated control, and the voltage deviation range is 0.048pu.

[0101] Please refer to Figure 6 As shown in the figure, compared with the intra-cluster optimization, the amount of photovoltaic abandoned power after inter-cluster optimization was reduced by 0.4MW under the auxiliary coordination between clusters, and the total regulation cost was reduced to 456.19 yuan.

[0102] S107. Perform coordinated voltage control of the photovoltaic energy storage cluster in the distribution network according to the global optimal solution.

[0103] According to the voltage set point or voltage range provided in the global optimal solution, the voltage in each photovoltaic energy storage cluster is accurately set, including adjusting the output voltage of the photovoltaic inverter, the charging and discharging voltage of the energy storage system, etc., to ensure that the voltage level of each node meets the preset safety and economic standards.

[0104] In the coordinated voltage control process, the power output of each cluster is reasonably allocated and adjusted, including dynamically adjusting the power output of photovoltaic and energy storage systems according to factors such as lighting conditions, energy storage status, and load demand to achieve power balance and voltage stability.

[0105] The energy storage system plays an important role in coordinated voltage control. It stores excess energy when there is sufficient sunlight, and releases energy when there is insufficient sunlight or peak load to smooth out voltage fluctuations and provide voltage support. The voltage control effect can be further optimized by precisely controlling the charging and discharging process of the energy storage system.

[0106] In the process of coordinated voltage control, key parameters such as voltage level and power flow of each node are monitored in real time. Once an abnormal situation or deviation from the preset target is found, feedback adjustment should be made immediately to ensure the stable operation of the system.

[0107] The present application also provides a distribution network photovoltaic energy storage cluster coordinated voltage control device, comprising:

[0108] The first function module establishes the first objective function in the photovoltaic energy storage cluster of the distribution network with the goal of minimizing the photovoltaic abandoned light cost, line network loss and energy storage charging and discharging cost;

[0109] A processing module, for the first objective function, uses a second-order cone relaxation method to convexify the branch power flow equation to generate an optimal power flow model within the photovoltaic energy storage cluster of the distribution network;

[0110] A first solution module solves the optimal power flow model to obtain an optimal solution within the photovoltaic energy storage cluster of the distribution network;

[0111] A boundary module records the boundary values ​​of each cluster according to the optimal solution within the photovoltaic energy storage cluster of the distribution network;

[0112] The second function module exchanges the boundary data of the upstream cluster and the downstream cluster in each of the distribution network photovoltaic energy storage clusters according to the boundary value, and constructs a second objective function with boundary variable constraints;

[0113] A second solving module uses an ADMM algorithm to cyclically solve the second objective function so that the deviation of the boundary data is less than a preset threshold value, thereby obtaining a global optimal solution;

[0114] A control module performs coordinated voltage control of the photovoltaic energy storage cluster of the distribution network according to the global optimal solution.

[0115] Furthermore, the first objective function is established as follows:

[0116]

[0117] Among them, PdPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij Represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cj are the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage; E jmax is the rated capacity of energy storage; G k is the set of all nodes in cluster k.

[0118] Furthermore, the optimal power flow model is expressed as follows:

[0119]

[0120] Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

[0121] Furthermore, the second objective function with boundary variable constraints is constructed, and the expression is as follows:

[0122]

[0123] Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

Claims

1. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network, characterized in that: include: Establishing the first objective function within the photovoltaic energy storage cluster of the distribution network with the goal of minimizing the photovoltaic abandonment cost, line network loss and energy storage charging and discharging cost; For the first objective function, a second-order cone relaxation method is used to convexify the branch power flow equation to generate an optimal power flow model of the photovoltaic energy storage cluster of the distribution network; Solving the optimal power flow model to obtain the optimal solution of the photovoltaic energy storage cluster of the distribution network; According to the optimal solution of the distribution network photovoltaic energy storage cluster, record the boundary value of each distribution network photovoltaic energy storage cluster; According to the boundary value, the boundary data of the upstream cluster and the downstream cluster of each of the distribution network photovoltaic energy storage clusters are exchanged to construct a second objective function with boundary variable constraints; The second objective function is cyclically solved by using the ADMM algorithm, so that the deviation of the boundary data is less than a preset threshold value, and a global optimal solution is obtained; The coordinated voltage control of the photovoltaic energy storage cluster in the distribution network is performed according to the global optimal solution.

2. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network according to claim 1, characterized in that: The first objective function is established as follows: Among them, P dPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cj are the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage; E jmax is the rated capacity of energy storage; G k is the set of all nodes in cluster k.

3. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network according to claim 1, characterized in that: The optimal power flow model is expressed as follows: Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

4. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network according to claim 1, characterized in that: The boundary values ​​include: the voltage of the virtual balancing node and the virtual load power.

5. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network according to claim 1, characterized in that: Construct the second objective function with boundary variable constraints, the expression is as follows: Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

6. A method for coordinated voltage control of a photovoltaic energy storage cluster in a distribution network according to claim 1, characterized in that: The second objective function is cyclically solved by using the ADMM algorithm, including: In the iterative process of solving the distribution network photovoltaic energy storage clusters, the Lagrange multiplier values ​​of the boundary data and the first and last segment boundary data are updated and calculated, where the initial Lagrange multiplier value is 0.

7. A distribution network photovoltaic energy storage cluster coordinated voltage control device, characterized in that: include: The first function module establishes the first objective function in the photovoltaic energy storage cluster of the distribution network with the goal of minimizing the photovoltaic abandoned light cost, line network loss and energy storage charging and discharging cost; A processing module, for the first objective function, uses a second-order cone relaxation method to convexify the branch power flow equation to generate an optimal power flow model of the photovoltaic energy storage cluster of the distribution network; A first solution module solves the optimal power flow model to obtain an optimal solution within the photovoltaic energy storage cluster of the distribution network; A boundary module records the boundary values ​​of each photovoltaic energy storage cluster of the distribution network according to the optimal solution within the photovoltaic energy storage cluster of the distribution network; The second function module exchanges the boundary data of the upstream cluster and the downstream cluster of each of the distribution network photovoltaic energy storage clusters according to the boundary value, and constructs a second objective function with boundary variable constraints; A second solving module uses an ADMM algorithm to cyclically solve the second objective function so that the deviation of the boundary data is less than a preset threshold value, thereby obtaining a global optimal solution; A control module performs coordinated voltage control of the photovoltaic energy storage cluster of the distribution network according to the global optimal solution.

8. A distribution network photovoltaic energy storage cluster coordinated voltage control device according to claim 1, characterized in that: The first objective function is established as follows: Among them, P dPVj is the abandoned active power of the photovoltaic power plant at node j; P essj Q is the charging and discharging power of energy storage, which is positive when charging and negative when discharging; PVj and Q essj are the reactive power of the photovoltaic inverter and the reactive power of the energy storage at node j respectively; C PV is the photovoltaic power generation income per unit; C loss is the unit power loss cost; V i is the voltage amplitude of node i; I ij is the branch current between node i and node j; R ij represents the resistance value of the line between node i and node j; C ess is the unit charging and discharging cost of energy storage; C d and C c are the unit discharge cost and unit charging cost of energy storage respectively; C inv , C pre , C ec are energy storage investment cost, operation and maintenance cost and unit electricity cost respectively; P dcj , P cj are the discharge and charge powers of the energy storage, respectively; n is the number of complete cycles during the life of the energy storage; E jmax is the rated capacity of energy storage; G k is the set of all nodes in cluster k.

9. A distribution network photovoltaic energy storage cluster coordinated voltage control device according to claim 1, characterized in that: The optimal power flow model is expressed as follows: Among them, P j and Q j is the active and reactive power of the net load of node j; P ij , Q ij represents the active and reactive power flowing from node i to node j; P out and Q out They represent the active and reactive power transmitted between the upstream and downstream clusters, respectively, as the virtual load power of the terminal node; X ij represents the reactance value of the line between node i and node j; P loadj and Q loadj is the active and reactive power of the node j load; P PVj is the maximum active power that the photovoltaic node j can currently output.

10. A distribution network photovoltaic energy storage cluster coordinated voltage control device according to claim 1, characterized in that: Construct the second objective function with boundary variable constraints, the expression is as follows: Where ρ is the penalty coefficient, which is used to ensure the convergence of boundary data, where ρ = 10 5 ;x i Represented as the boundary data set between the upstream and downstream clusters; y i is the set of global variables of upstream and downstream cluster boundary data; i is the set of Lagrange multipliers of the boundary variables.

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