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

Abstract

The application provides a power distribution network photovoltaic energy storage cluster collaborative voltage control method and device. The method comprises the following steps: establishing a first target function of minimum photovoltaic light abandonment cost, line network loss and energy storage charging and discharging cost; processing branch power flow equation by using a second-order cone relaxation method to generate an optimal power flow model; obtaining an optimal solution and recording cluster boundary values; exchanging boundary data of upstream and downstream clusters to construct a second target function with boundary variable constraints; solving by using an ADMM algorithm until the boundary data deviation is less than a preset threshold value, and obtaining a global optimal solution. Through energy storage participation in regulation and control and cluster-based distributed optimization control, the photovoltaic light abandonment amount is reduced, the photovoltaic power generation income is improved, and economic optimization is realized.

Description

Technical Field

[0001] This application belongs to the field of photovoltaic-storage cluster classification, and in particular relates to a method and device for coordinated voltage control of photovoltaic energy storage clusters in power distribution networks. Background Technology

[0002] The background technology of this application mainly relates to the problems arising from the increasing proportion of distributed photovoltaic (PV) power generation in my country's distribution networks year by year. Due to the intermittent and random characteristics of distributed PV, the increased proportion of its integration poses a significant threat to the safe and stable operation of the distribution network, leading to a series of problems such as power flow reversal, voltage exceeding limits, and difficulty in local consumption of distributed PV output. These problems are particularly pronounced in Northwest China, where renewable energy resources are abundant. Furthermore, the long distribution lines and significant peak-valley loads in Northwest China easily cause voltage to exceed upper limits during the day and lower limits at night, making voltage control especially important and challenging.

[0003] In terms of voltage control in active power distribution networks, it is mainly divided into local optimization control, centralized optimization control, and cluster-based distributed optimization control. However, current control methods primarily achieve this by controlling the output of photovoltaic inverters and reactive power equipment. Photovoltaic inverters can only reduce power output when photovoltaic output suddenly increases, and cannot function when the distribution network voltage exceeds its lower limit. Simultaneously, the reactive power regulation equipment in the distribution network is limited. These factors restrict the voltage regulation capability of the distribution network and also increase the amount of curtailed photovoltaic power during regulation, leading to increased costs.

[0004] Energy storage systems, as flexible regulatory resources, possess advantages such as strong instantaneous power throughput, rapid response, and precise regulation. They effectively regulate power flow across lines and play a crucial role in the absorption of renewable energy and grid peak and frequency regulation. Coordinating energy storage and photovoltaic (PV) power significantly enhances the distribution network's voltage regulation capabilities. However, this synergistic regulation also increases the complexity of distribution network control. While general optimization control can mitigate voltage exceedance issues to some extent, it fails to consider power exchange regulation between clusters, leading to over-regulation of PV and energy storage within the cluster and consequently increasing overall regulation costs. 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 photovoltaic energy storage clusters in power distribution networks.

[0006] This application provides a method for coordinated voltage control of photovoltaic energy storage clusters in a distribution network, including:

[0007] Within the photovoltaic energy storage cluster of the distribution network, a first objective function is established with the goal of minimizing the cost of photovoltaic curtailment, line network loss, and energy storage charging and discharging cost.

[0008] For the first objective function, the branch power flow equations are made convex using the second-order cone relaxation method to generate the optimal power flow model of the photovoltaic energy storage cluster in the distribution network.

[0009] Solving the optimal power flow model yields the optimal solution for the photovoltaic energy storage cluster in the distribution network;

[0010] Based on the optimal solution of the photovoltaic energy storage cluster in the distribution network, record the boundary values ​​of each photovoltaic energy storage cluster in the distribution network.

[0011] Based on the boundary values, the boundary data of the upstream and downstream clusters of each photovoltaic energy storage cluster in the power distribution network are exchanged to construct a second objective function with boundary variable constraints.

[0012] The second objective function is solved iteratively using the ADMM algorithm to ensure that the deviation of the boundary data is less than a preset threshold, thereby obtaining the global optimal solution.

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

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

[0015]

[0016] Among them, P dPVj P represents the active power discarded by the photovoltaic system at node j. essj The charging and discharging power of energy storage is positive during charging and negative during discharging; Q PVj and Q essj These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively; C PV C represents the revenue generated per unit of photovoltaic power generation; loss Cost per unit of electrical energy loss; V i I represents the voltage magnitude at node i. ij R is the branch current between node i and node j; ij This represents the resistance value of the line between node i and node j; C ess The unit charge / discharge cost of energy storage; C d and C c These represent the unit discharge cost and unit charging cost of energy storage, respectively; C inv C pre C ec These are the energy storage investment cost, operation and maintenance cost, and unit electricity cost, respectively; P dcj P cj These represent the discharge and charging power of the energy storage, respectively; n is the number of complete cycles during the energy storage's lifetime; E jmax G represents the rated capacity of the energy storage.k Let k be 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 P represents the active and reactive power of the net load at node j; ij Q ij P represents the active and reactive power flowing from node i to node j; out and Q out These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes; X ij P represents the reactance value of the line between node i and node j; loadj and Q loadj P represents the active and reactive power of the load at node j; PVj Let j be the maximum active power that the photovoltaic power at node j can currently output.

[0020] Optionally, the boundary values ​​include: the voltage of the virtual balancing node and the virtual load power.

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

[0022]

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

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

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

[0026] This application also provides a photovoltaic energy storage cluster coordinated voltage control device for power distribution networks, including:

[0027] The first function module establishes a first objective function within the photovoltaic energy storage cluster of the distribution network, with the goal of minimizing the cost of photovoltaic curtailment, line network loss, and energy storage charging and discharging cost.

[0028] The processing module, for the first objective function, uses the second-order cone relaxation method to convexize the branch power flow equations to generate the optimal power flow model of the photovoltaic energy storage cluster in the distribution network.

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

[0030] The boundary module records the boundary values ​​of each photovoltaic energy storage cluster in the distribution network based on the optimal solution within the photovoltaic energy storage cluster in the distribution network.

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

[0032] The second solution module uses the ADMM algorithm to iteratively solve the second objective function, making the deviation of the boundary data less than a preset threshold, and thus obtaining the global optimal solution;

[0033] The control module performs coordinated voltage control of the photovoltaic energy storage cluster in the distribution network based on the global optimal solution.

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

[0035]

[0036] Among them, P dPVj P represents the active power discarded by the photovoltaic system at node j. essj The charging and discharging power of energy storage is positive during charging and negative during discharging; Q PVj and Q essj These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively; C PV C represents the revenue generated per unit of photovoltaic power generation; loss Cost per unit of electrical energy loss; V i I represents the voltage magnitude at node i. ij R is the branch current between node i and node j; ij This represents the resistance value of the line between node i and node j; C ess The unit charge / discharge cost of energy storage; C d and C c These represent the unit discharge cost and unit charging cost of energy storage, respectively; C inv C pre C ec These are the energy storage investment cost, operation and maintenance cost, and unit electricity cost, respectively; P dcj P cjThese represent the discharge and charging power of the energy storage, respectively; n is the number of complete cycles during the energy storage's lifetime; E jmax G represents the rated capacity of the energy storage. k Let k be 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 P represents the active and reactive power of the net load at node j; ij Q ij P represents the active and reactive power flowing from node i to node j; out and Q out These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes; X ij P represents the reactance value of the line between node i and node j; loadj and Q loadj P represents the active and reactive power of the load at node j; PVj Let j be the maximum active power that the photovoltaic power at node j can currently output.

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

[0041]

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

[0043] The beneficial effects of this application are:

[0044] This application provides a method for coordinated voltage control of photovoltaic (PV) energy storage clusters in a distribution network, comprising: establishing a first objective function within the PV energy storage cluster, with the objectives of minimizing PV curtailment costs, line losses, and energy storage charging / discharging costs; for the first objective function, using a second-order cone relaxation method to convexify the branch power flow equations to generate an optimal power flow model for the PV energy storage cluster; solving the optimal power flow model to obtain the optimal solution for the PV energy storage cluster; recording the boundary values ​​of each PV energy storage cluster based on the optimal solution; exchanging the boundary data of the upstream and downstream clusters of each PV energy storage cluster based on the boundary values ​​to construct a second objective function with boundary variable constraints; using the ADMM algorithm to iteratively solve the second objective function, ensuring that the deviation of the boundary data is less than a preset threshold to obtain a globally optimal solution; and performing coordinated voltage control of the PV energy storage cluster based on the globally optimal solution. This application, by introducing energy storage into regulation and employing cluster-based distributed optimization control, reduces PV curtailment, increases PV power generation revenue, and achieves economic optimization. Attached Figure Description

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

[0046] Figure 2 This is a schematic diagram of the topology of the IEEE 33 system cluster after partitioning in this application;

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

[0048] Figure 4 This is a schematic diagram of the optimized photovoltaic curtailment within the cluster in this application;

[0049] Figure 5 This is a schematic diagram of the node voltage after control within and between clusters in this application;

[0050] Figure 6 This is a schematic diagram of the amount of photovoltaic curtailment controlled within and between clusters in this application. Detailed Implementation

[0051] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that various forms of implementation of the present disclosure are intended 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 disclosure to those skilled in the art.

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

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

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

[0055] In this embodiment, data at time 12 is selected for simulation. At this time, the photovoltaic output power is close to the maximum output power, the total load is 1.23MW, and a serious voltage over-limit problem will occur on the line.

[0056] Based on the controllable objects within the cluster being photovoltaics and energy storage, a primary objective function is established that minimizes the costs of photovoltaic curtailment, grid losses, and energy storage charging and discharging.

[0057]

[0058] Among them, P dPVj P represents the active power discarded by the photovoltaic system at node j. essj The charging and discharging power of energy storage is positive during charging and negative during discharging; Q PVj and Q essj These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively; C PV C represents the revenue generated per unit of photovoltaic power generation; loss Cost per unit of electrical energy loss; V i I represents the voltage magnitude at node i. ij R is the branch current between node i and node j; ij This represents the resistance value of the line between node i and node j; C ess The unit charge / discharge cost of energy storage; C d and C c These represent the unit discharge cost and unit charging cost of energy storage, respectively; C inv C pre C ec These are the energy storage investment cost, operation and maintenance cost, and unit electricity cost, respectively; P dcj P cjThese represent the discharge and charging power of the energy storage, respectively; n is the number of complete cycles during the energy storage's lifespan, approximately 3000 times; E jmax G represents the rated capacity of the energy storage. k Let C be the set of all nodes in cluster k. PV and C loss The prices are 0.5 yuan / kWh and 0.4 yuan / kWh respectively, C inv C is 1000 yuan / kWh pre At 200 yuan / kWh, C ec The value is 0.5 yuan / kWh, and n is 3000.

[0059] S102. For the first objective function, the branch power flow equation is made convex using the second-order cone relaxation method to generate the optimal power flow model of the photovoltaic energy storage cluster in the distribution network.

[0060] Based on the first objective function within the cluster, the branch power flow equations are further convexized using second-order cone relaxation. The transformed branch power flow equations are as follows:

[0061]

[0062] Among them, P j and Q j P represents the active and reactive power of the net load at node j; ij Q ij P represents the active and reactive power flowing from node i to node j; out and Q out These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes; X ij P represents the reactance value of the line between node i and node j; loadj and Q loadj P represents the active and reactive power of the load at node j; PVj Let j be the maximum active power that the photovoltaic power at node j can currently output.

[0063] To ensure the stable operation of the power system, it is necessary to constrain node voltages and currents. The node voltage and current constraints are as follows:

[0064]

[0065] Among them, V min V max These are the lower and upper limits for safe operation of the node voltage, respectively, set to 0.95 pu and 1.05 pu. min I max This represents the minimum and maximum transmission current values ​​of the line between measurement node i and node j.

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

[0067]

[0068] Among them, S PVj Let J be the rated apparent power of the photovoltaic inverter at node j.

[0069] To ensure the stable operation of energy storage systems and fully utilize their regulatory role, it is necessary to constrain their charging and discharging power and state of charge. The constraints on energy storage charging and discharging power and state of charge are as follows:

[0070]

[0071] Among them, P essmax The maximum active power of the energy storage system; u is the charge / discharge indicator, where 1 represents discharging and -1 represents charging; η d η c These are discharge efficiency and charge efficiency, respectively; SOC max SOC min S represents the maximum and minimum values ​​of the state of charge of the energy storage system. essj The apparent power of 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, thereby improving the stability and security of the power grid.

[0075] like Figure 3 As shown, the node voltage distribution under different regulation strategies is compared. When photovoltaic (PV) and energy storage do not participate in regulation (red line), due to changes in factors such as illumination and load, the voltage of multiple nodes exceeds the safe range, with a voltage deviation of 0.067 pu. However, when the cluster relies solely on PV for regulation (black line), adjusting the PV output power effectively suppresses the voltage exceedance problem, reducing the voltage deviation to 0.05 pu. Furthermore, when PV and energy storage participate in regulation together (blue line), the voltage no longer exceeds the limit, and the voltage deviation remains at 0.05 pu. Overall, the voltage deviation of each node is further reduced compared to when only PV is regulated, demonstrating the superiority of synergistic regulation by PV and energy storage.

[0076] like Figure 4 As shown, the curtailment of distributed photovoltaic (PV) power and the total regulation cost were compared before and after energy storage was involved in regulation. When energy storage was added to the cluster for regulation, the curtailment of PV power was significantly lower than that when regulation relied solely on PV power, as energy storage can store excess electricity during periods of sufficient sunlight and release it to supplement grid demand during periods of insufficient sunlight. The curtailment was reduced by approximately 0.4751 MW. This not only improved the utilization rate of PV power but also reduced energy waste. Furthermore, from an economic perspective, the total regulation cost was 705.1 yuan when only PV power was regulated, while it decreased to 504.3 yuan when both PV power and energy storage were involved in regulation, indicating that the participation of energy storage in regulation significantly improved the system's economic efficiency.

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

[0078] S104. Based on the optimal solution within the photovoltaic energy storage cluster of the distribution network, record the boundary values ​​of each photovoltaic energy storage cluster of the distribution network.

[0079] Based on the optimal solution obtained in step S103 for the photovoltaic energy storage clusters in the distribution network, the boundary values ​​of each cluster are recorded. These boundary values ​​are crucial for subsequent system operation and monitoring, ensuring that each cluster maintains coordination with the entire power grid even when operating independently.

[0080] First, an intra-cluster coordinated optimization method is employed to perform independent optimization within each cluster. The aim is to ensure rational allocation and efficient utilization of resources at the cluster level, while simultaneously meeting the power balance and voltage stability requirements within the cluster. This method yields the optimal solution for each cluster, including key parameters such as voltage, current, and power distribution of each node within the cluster.

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

[0082] Voltage of virtual balancer nodes: During cluster optimization, one or more nodes are typically selected as virtual balancer nodes. These nodes are responsible for maintaining voltage stability within the cluster. Recording the voltage values ​​of these nodes ensures that the voltage level remains within a safe range when the cluster is running independently, preventing equipment damage or system instability caused by voltage exceeding limits.

[0083] Virtual load power: Virtual load power refers to the power exchanged between the power cluster and the external power grid. It reflects the cluster's overall demand for or contribution to the external power grid. Recording these power values ​​helps to understand the cluster's operating status and, when necessary, regulate the interaction between the cluster and the external power grid.

[0084] By recording these boundary values, it is ensured that each cluster can operate independently in a predetermined manner during subsequent system operation, while maintaining coordination with the entire power grid. This is of great significance for improving the flexibility, reliability, and economy of the power grid. Furthermore, these boundary values ​​serve as important bases for system monitoring and fault diagnosis, helping to identify and resolve problems promptly, ensuring the safe and stable operation of the power grid.

[0085] S105. Based on the boundary values, exchange the boundary data of the upstream and downstream clusters in each of the photovoltaic energy storage clusters in the power distribution network, and construct a second objective function with boundary variable constraints.

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

[0087] Upstream clusters typically refer to clusters located at the front end or initial stage of the energy supply chain or power transmission chain. These clusters are primarily responsible for energy harvesting, conversion, or preliminary processing. In the context of photovoltaic energy storage clusters in distribution networks, upstream clusters include:

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

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

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

[0091] Power distribution network clusters: These clusters are responsible for distributing the electricity generated by upstream clusters to various points of consumption. 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, and public utilities. They are the final users of electricity and are therefore considered downstream clusters.

[0093] Next, the boundary data between the upstream and downstream clusters is exchanged and updated using U. up ,P down Q down Update separately The objective function with boundary variable constraints is constructed as follows:

[0094]

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

[0096] S106. The second objective function is solved iteratively using the ADMM algorithm to make the deviation of the boundary data less than a preset threshold, thereby obtaining the global optimal solution.

[0097] Based on the intra-cluster coordinated optimization model, the inter-cluster coordinated optimization model adds equality constraints on boundary nodes and inter-cluster line power to ensure the convergence of inter-cluster distributed optimization while each cluster optimizes independently within the cluster. The Lagrange multiplier update calculation for boundary data and first and last segment boundary data in the inter-cluster solution iteration process is as follows, where the initial Lagrange multiplier value is 0.

[0098]

[0099] Based on equations (2) to (7), the ADMM algorithm is used to iteratively solve the inter-cluster model until the deviation of the cluster boundary data is less than a certain threshold, thus obtaining the global optimal solution.

[0100] Please refer to Figure 5 As shown, after inter-cluster coordination control, the voltage of the node decreases compared to intra-cluster coordination control, with a voltage deviation range of 0.048 pu.

[0101] Please refer to Figure 6 As shown, after optimization between clusters, the total amount of photovoltaic curtailment was reduced by 0.4MW compared to that within the cluster, with the assistance and 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 based on the global optimal solution.

[0103] Based on the voltage setpoint or range provided in the global optimal solution, the voltage within each photovoltaic energy storage cluster is precisely set. This includes adjusting the output voltage of the photovoltaic inverter and the charging and discharging voltage of the energy storage system to ensure that the voltage level of each node meets the preset safety and economic standards.

[0104] In the process of coordinated voltage control, the power output of each cluster is rationally allocated and adjusted. This includes dynamically adjusting the power output of photovoltaic and energy storage systems based on factors such as irradiance conditions, energy storage status, and load demand, in order to achieve power balance and voltage stability.

[0105] Energy storage systems play a crucial role in coordinated voltage control. They store excess electrical energy during periods of ample sunlight and release it during periods of insufficient sunlight or peak load, thus smoothing voltage fluctuations and providing voltage support. By precisely controlling the charging and discharging process of the energy storage system, voltage control performance can be further optimized.

[0106] During coordinated voltage control, key parameters such as voltage levels and power flow at each node are monitored in real time. If any abnormalities or deviations from preset targets are detected, immediate feedback adjustments should be made to ensure stable system operation.

[0107] This application also provides a photovoltaic energy storage cluster coordinated voltage control device for power distribution networks, including:

[0108] The first function module establishes a first objective function within the photovoltaic energy storage cluster of the distribution network, with the goal of minimizing the cost of photovoltaic curtailment, line network loss, and energy storage charging and discharging cost.

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

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

[0111] The boundary module records the boundary values ​​of each cluster based on the optimal solution within the photovoltaic energy storage cluster of the power distribution network.

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

[0113] The second solution module uses the ADMM algorithm to iteratively solve the second objective function, making the deviation of the boundary data less than a preset threshold, and thus obtaining the global optimal solution;

[0114] The control module performs coordinated voltage control of the photovoltaic energy storage cluster in the distribution network based on the global optimal solution.

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

[0116]

[0117] Among them, PdPVj P represents the active power discarded by the photovoltaic system at node j. essj The charging and discharging power of energy storage is positive during charging and negative during discharging; Q PVj and Q essj These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively; C PV C represents the revenue generated per unit of photovoltaic power generation; loss Cost per unit of electrical energy loss; V i I represents the voltage magnitude at node i. ij R is the branch current between node i and node j; ij This represents the resistance value of the line between node i and node j; C ess The unit charge / discharge cost of energy storage; C d and C c These represent the unit discharge cost and unit charging cost of energy storage, respectively; C inv C pre C ec These are the energy storage investment cost, operation and maintenance cost, and unit electricity cost, respectively; P dcj P cj These represent the discharge and charging power of the energy storage, respectively; n is the number of complete cycles during the energy storage's lifetime; E jmax G represents the rated capacity of the energy storage. k Let k be 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 P represents the active and reactive power of the net load at node j; ij Q ij P represents the active and reactive power flowing from node i to node j; out and Q out These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes; X ij P represents the reactance value of the line between node i and node j; loadj and Q loadj P represents the active and reactive power of the load at node j; PVj Let j be the maximum active power that the photovoltaic power at node j can currently output.

[0121] Furthermore, a second objective function with boundary variable constraints is constructed, expressed as follows:

[0122]

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

Claims

1. A method for coordinated voltage control of photovoltaic energy storage clusters in a power distribution network, characterized in that, include: Within the photovoltaic energy storage cluster of the distribution network, a first objective function is established with the goal of minimizing the cost of photovoltaic curtailment, line losses, and energy storage charging and discharging costs. The first objective function is established as follows: ； in, Let k be the first objective function for the photovoltaic energy storage clusters. The active power discarded by the photovoltaic system at node j. This refers to the charging and discharging power of energy storage; it is positive during charging and negative during discharging. and These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively. For the revenue generated by photovoltaic units, Cost per unit of electrical energy loss Let be the branch current between node i and node j. This represents the resistance value of the line between node i and node j. The unit charge / discharge cost of energy storage, and These are the unit discharge cost and unit charging cost of energy storage, respectively. These are the costs of energy storage investment, operation and maintenance, and unit electricity consumption. The number of complete cycles during the energy storage's lifespan. This refers to the rated capacity of the energy storage. Let k be the set of all nodes in cluster k. For the first objective function, the branch power flow equations are made convex using the second-order cone relaxation method to generate the optimal power flow model of the photovoltaic energy storage cluster in the distribution network. The expression of the optimal power flow model is as follows: ； in, and The active and reactive power of the net load at node j. , This represents the active and reactive power flowing from node i to node j. and These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes. This represents the reactance value of the line between node i and node j. Let be the branch current between node i and node j. This represents the resistance value of the line between node i and node j; Solving the optimal power flow model yields the optimal solution for the photovoltaic energy storage cluster in the distribution network; Based on the optimal solution of the photovoltaic energy storage cluster in the distribution network, record the boundary values ​​of each photovoltaic energy storage cluster in the distribution network. Based on the boundary values, the boundary data of the upstream and downstream clusters of each photovoltaic energy storage cluster in the power distribution network are exchanged to construct a second objective function with boundary variable constraints. The second objective function is solved iteratively using the ADMM algorithm to ensure that the deviation of the boundary data is less than a preset threshold, thereby obtaining the global optimal solution. The coordinated voltage control of the photovoltaic energy storage cluster in the distribution network is performed based on the global optimal solution.

2. The method for coordinated voltage control of photovoltaic energy storage clusters 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.

3. The method for coordinated voltage control of photovoltaic energy storage clusters in a distribution network according to claim 1, characterized in that, Construct a second objective function with boundary variable constraints, as shown below: ； in, Let k be the second objective function for the cluster in the ADMM algorithm. The penalty coefficient is used to ensure the convergence of boundary data, where ; Represented as the boundary data set between upstream and downstream clusters; This is a collection of global variables for the boundary data of upstream and downstream clusters. Let Lagrange multipliers be the set of boundary variables.

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

5. A photovoltaic energy storage cluster coordinated voltage control device for power distribution networks, characterized in that, include: The first function module establishes a first objective function within the photovoltaic energy storage cluster of the distribution network, aiming to minimize the cost of photovoltaic curtailment, line losses, and energy storage charging and discharging costs. The first objective function is established as follows: ； in, Let k be the first objective function for the photovoltaic energy storage clusters. The active power discarded by the photovoltaic system at node j. This refers to the charging and discharging power of energy storage; it is positive during charging and negative during discharging. and These represent the reactive power of the photovoltaic inverter at node j and the reactive power of the energy storage, respectively. For the revenue generated by photovoltaic units, Cost per unit of electrical energy loss Let be the branch current between node i and node j. This represents the resistance value of the line between node i and node j. The unit charge / discharge cost of energy storage, and These are the unit discharge cost and unit charging cost of energy storage, respectively. These are the costs of energy storage investment, operation and maintenance, and unit electricity consumption. The number of complete cycles during the energy storage's lifespan. This refers to the rated capacity of the energy storage. Let k be the set of all nodes in cluster k. The processing module, for the first objective function, uses a second-order cone relaxation method to convexify the branch power flow equations, generating the optimal power flow model of the photovoltaic energy storage cluster in the distribution network. The expression of the optimal power flow model is as follows: ； in, and The active and reactive power of the net load at node j. , This represents the active and reactive power flowing from node i to node j. and These represent the active and reactive power transmitted between upstream and downstream cluster lines, respectively, serving as the virtual load power of the end nodes. This represents the reactance value of the line between node i and node j. Let be the branch current between node i and node j. This represents the resistance value of the line between node i and node j; The first solution module solves the optimal power flow model to obtain the optimal solution within the photovoltaic energy storage cluster of the distribution network. The boundary module records the boundary values ​​of each photovoltaic energy storage cluster in the distribution network based on the optimal solution within the photovoltaic energy storage cluster in the distribution network. The second function module exchanges the boundary data of the upstream and downstream clusters of each photovoltaic energy storage cluster in the distribution network according to the boundary values, and constructs a second objective function with boundary variable constraints. The second solution module uses the ADMM algorithm to iteratively solve the second objective function, making the deviation of the boundary data less than a preset threshold, and thus obtaining the global optimal solution; The control module performs coordinated voltage control of the photovoltaic energy storage cluster in the distribution network based on the global optimal solution.

6. The photovoltaic energy storage cluster coordinated voltage control device for a power distribution network according to claim 5, characterized in that, Construct a second objective function with boundary variable constraints, as shown below: ； in, For use in ADMM algorithm, cluster The second objective function, The penalty coefficient is used to ensure the convergence of boundary data, where ; Represented as the boundary data set between upstream and downstream clusters; This is a collection of global variables for the boundary data of upstream and downstream clusters. Let Lagrange multipliers be the set of boundary variables.

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