Reactive power compensation optimization method and apparatus for transformer area energy storage system, device, and product

Abstract

A reactive power compensation optimization method and apparatus for a transformer area energy storage system, a device, and a product, relating to the technical field of energy storage systems. The method comprises: constructing a multi-objective reactive power compensation optimization model for a transformer area energy storage system, wherein a total objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the transformer area energy storage system, and constraint conditions for the multi-objective reactive power compensation optimization model include: a power constraint condition, a capacity constraint condition and a remaining power constraint condition for an energy storage apparatus; acquiring operating status information of the transformer area energy storage system; and using a Pareto optimization solution method to solve the multi-objective reactive power compensation optimization model on the basis of the operating status information, so as to obtain an optimal reactive power compensation strategy for the transformer area energy storage system.

Description

Reactive power compensation optimization method, device, equipment and product of transformer area energy storage system

[0001] This application claims priority to the Chinese patent application No. 202410829498.6 filed on June 25, 2024 with the China Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of energy storage systems, for example to a reactive power compensation optimization method, device, equipment and product of transformer area energy storage system. BACKGROUND

[0003] An energy storage system (ESS) is a system that integrates various energy storage components in multiple dimensions to complete the storage and supply of electrical energy, and is an important part of an active distribution network (ADN). It can effectively alleviate the power shortage during peak periods and delay the expansion of transmission capacity.

[0004] Transformer area energy storage refers to a technology that installs energy storage devices (such as batteries, super capacitors, etc.) in the distribution network to store and release electrical energy, balance the load of the power grid, and improve the reliability of power supply. Such energy storage devices are usually installed in power distribution stations or transformer areas where distribution transformers are located, and are used to cope with transient load fluctuations and peak load demands in the distribution network, improve the stability and response speed of the power grid. The transformer area energy storage system plays an important role in energy storage and balancing, frequency and phase adjustment, load smoothing and scheduling, emergency backup power, and intelligent monitoring and management in smart grids, providing key support for the efficient and stable operation of smart grids.

[0005] The optimization methods for transformer area energy storage systems in related technologies mainly include the following: (1) a multi-objective optimal allocation model is used to alleviate power imbalance; (2) an ESS optimal allocation model is established to adapt to time-of-use electricity prices to solve power fluctuations and load shortages in ADN; (3) a robust optimal allocation method for energy storage systems is used to alleviate voltage rise. However, these studies only focus on solving the problems in active distribution networks through optimization of energy storage systems, ignoring the consideration of the cost of energy storage systems themselves. SUMMARY

[0006] The present application provides a reactive power compensation optimization method, device, equipment and product of transformer area energy storage system to solve the problem that the optimization method of energy storage system ignores the consideration of the cost of energy storage systems themselves.

[0007] According to an aspect of the present application, a reactive power compensation optimization method of transformer area energy storage system is provided, applied to a transformer area energy storage system including a distribution network, a reactive power compensation device and an energy storage device, the method comprising:

[0008] establish a multi-objective reactive power compensation optimization model of the transformer area energy storage system; a total objective function of the multi-objective reactive power compensation optimization model is a minimum operation cost of the transformer area energy storage system, and constraint conditions of the multi-objective reactive power compensation optimization model include power constraint conditions, capacity constraint conditions and residual power constraint conditions of the energy storage device;

[0009] obtain operation state information of the transformer area energy storage system;

[0010] adopt a Pareto optimization solving method, solve the multi-objective reactive power compensation optimization model according to the operation state information, and obtain an optimal reactive power compensation strategy of the transformer area energy storage system.

[0011] According to another aspect of the present application, a reactive power compensation optimization device of a transformer area energy storage system is provided, and the device includes:

[0012] a model establishing module configured to establish a multi-objective reactive power compensation optimization model of the transformer area energy storage system; a total objective function of the multi-objective reactive power compensation optimization model is a minimum operation cost of the transformer area energy storage system, and constraint conditions of the multi-objective reactive power compensation optimization model include power constraint conditions, capacity constraint conditions and residual power constraint conditions of the energy storage device;

[0013] a state information obtaining module configured to obtain operation state information of the transformer area energy storage system;

[0014] an optimization solving module configured to adopt a Pareto optimization solving method, solve the multi-objective reactive power compensation optimization model according to the operation state information, and obtain an optimal reactive power compensation strategy of the transformer area energy storage system.

[0015] According to another aspect of the present application, an electronic device is provided, and the electronic device includes:

[0016] at least one processor; and

[0017] a memory connected with the at least one processor in communication; wherein

[0018] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the reactive power compensation optimization method of the transformer area energy storage system according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions for enabling a processor to implement the reactive power compensation optimization method of the transformer area energy storage system according to any one of the embodiments of the present application when the processor executes the computer instructions.

[0020] According to another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the reactive power compensation optimization method of the transformer area energy storage system according to any of the embodiments of the present application.

[0021] The technical solution of the embodiments of the present application establishes a multi-objective reactive power compensation optimization model of the transformer area energy storage system. The total objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the transformer area energy storage system. The constraint conditions of the multi-objective reactive power compensation optimization model include the power constraint condition, the capacity constraint condition and the residual power constraint condition of the energy storage device. The operating state information of the transformer area energy storage system is obtained. The multi-objective reactive power compensation optimization model is solved according to the operating state information by using the Pareto optimization solving method, and the optimal reactive power compensation strategy of the transformer area energy storage system is obtained. The cost of various distribution networks can be fully considered when the transformer area energy storage system is compensated for reactive power, and the safety and economy of the power system are taken into account. The problem that the optimization method of the energy storage system ignores the consideration of the cost of the energy storage system itself is solved, and the beneficial effect of taking into account the safety and economy of the power system is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0022] FIG. 1 is a flowchart of a reactive power compensation optimization method of a transformer area energy storage system according to an embodiment of the present application;

[0023] FIG. 2 is a flowchart of a reactive power compensation optimization method of a transformer area energy storage system according to an embodiment of the present application;

[0024] FIG. 3 is a structural schematic diagram of a reactive power compensation optimization device of a transformer area energy storage system according to an embodiment of the present application;

[0025] FIG. 4 is a structural schematic diagram of an electronic device for implementing the reactive power compensation optimization method of the transformer area energy storage system according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions of the embodiments of the present application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0027] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and the above-described accompanying drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or apparatuses.

[0028] Embodiment one

[0029] FIG. 1 is a flowchart of a reactive power compensation optimization method of a transformer area energy storage system according to an embodiment of the present application. The embodiment can be applied to the optimization of a reactive power compensation scheme of a transformer area energy storage system. The method can be executed by a reactive power compensation optimization device of the transformer area energy storage system. The reactive power compensation optimization device can be realized in the form of hardware and / or software, and can be configured in an electronic device. In the embodiment, the transformer area energy storage system includes a power distribution network, a reactive power compensation device, and an energy storage device.

[0030] As shown in FIG. 1, the method includes:

[0031] S110, a multi-objective reactive power compensation optimization model of the transformer area energy storage system is established. The total objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the transformer area energy storage system. The constraint conditions of the multi-objective reactive power compensation optimization model include the power constraint condition, the capacity constraint condition, and the remaining power constraint condition of the energy storage device.

[0032] The multi-objective reactive power compensation optimization model can be understood as a model including multiple objectives for optimizing the reactive power compensation strategy. The total objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the transformer area energy storage system. The operating cost can be understood as the cost paid by the transformer area energy storage system in the operating state, and can be represented by the operating cost. For example, it includes the electricity cost, the equipment cost, and the maintenance cost, etc. The constraint conditions of the multi-objective reactive power compensation optimization model include the power constraint condition, the capacity constraint condition, and the remaining power constraint condition of the energy storage device. The power constraint condition is used to constrain the value range and the power relationship of the power. The capacity constraint condition is used to constrain the range of the planned capacity of the energy storage battery. The remaining power constraint is used to constrain the value range of the remaining power.

[0033] Specifically, a total objective function is constructed with the minimum operation cost of the transformer area energy storage system as a total target, constraint conditions are constructed with power constraints, capacity constraints and residual power constraints of the energy storage device, and a multi-objective reactive power compensation optimization model is constructed according to the total objective function and the constraint conditions.

[0034] S120, operation state information of the transformer area energy storage system is acquired.

[0035] The operation state information can be understood as relevant system information of the transformer area energy storage system in the operation state. The operation state information can include historical operation state information and current state information. For example, it can include charging and discharging power supply, discharging depth, planned capacity supplement and residual power.

[0036] Exemplarily, the operation state information of the power distribution network, the reactive power compensation device and the energy storage device is acquired through a sensor arranged in the transformer area energy storage system, or the operation state information is acquired through a log system of the transformer area energy storage system.

[0037] The multi-objective reactive power compensation optimization model provided by the embodiment of the application takes the minimum operation cost of the transformer area energy storage system as a target, takes the power constraints, capacity constraints and residual power constraints of the energy storage device as constraint conditions, considers various power distribution network costs, and effectively improves the reactive power reserve and network loss level of the active power distribution network by reducing the network loss of the reactive power compensation device, thereby greatly improving the power supply reliability.

[0038] S130, a Pareto optimization solving method is adopted to solve the multi-objective reactive power compensation optimization model according to the operation state information, and an optimal reactive power compensation strategy of the transformer area energy storage system is obtained.

[0039] The optimal reactive power compensation strategy can be understood as a reactive power compensation strategy determined based on the optimal solution of the multi-objective reactive power compensation optimization model. The Pareto optimization solving algorithm is a multi-objective optimization algorithm, and the purpose is to find the optimal solution among multiple targets. The core idea is to find the optimal solution by balancing the relationship between different targets.

[0040] Specifically, the operation state information is input into the multi-objective reactive power compensation optimization model, the multi-objective reactive power compensation optimization model is optimized and solved by using the Pareto optimization solving method, and the optimal reactive power compensation strategy of the transformer area energy storage system is obtained.

[0041] The technical scheme of the embodiment of the application establishes a multi-objective reactive power compensation optimization model of the transformer area energy storage system; the total objective function of the multi-objective reactive power compensation optimization model is the minimum operation cost of the transformer area energy storage system, the constraint conditions of the multi-objective reactive power compensation optimization model include: the power constraint condition, the capacity constraint condition and the residual power constraint condition of the energy storage device; the operation state information of the transformer area energy storage system is acquired; the multi-objective reactive power compensation optimization model is solved according to the operation state information by using a Pareto optimization solving method, and the optimal reactive power compensation strategy of the transformer area energy storage system is obtained, so that the cost of various distribution networks can be fully considered when the transformer area energy storage system is compensated for reactive power, and the safety and economy of the power system are taken into account.

[0042] Embodiment two

[0043] Fig. 2 is a flowchart of a reactive power compensation optimization method of a transformer area energy storage system provided by the embodiment two of the application, and the embodiment further limits the objective function of the multi-objective reactive power compensation optimization model to include: a first single-objective function with the power purchase cost of the distribution network as the target, a second single-objective function with the network loss cost of the distribution network as the target, a third single-objective function with the operation and maintenance cost of the energy storage device as the target, and a fourth single-objective function with the operation and maintenance cost of the reactive power compensation device as the target.

[0044] The operation state information includes: historical operation state information and current operation state information; the step of solving the multi-objective reactive power compensation optimization model according to the operation state information to obtain the optimal reactive power compensation strategy of the transformer area energy storage system includes: solving each single-objective function according to the historical operation state information and the current operation state information respectively to obtain the increments between each target endpoint of the Pareto frontier and the corresponding initial endpoint; the single-objective function includes: the first single-objective function, the second single-objective function, the third single-objective function or the fourth single-objective function; each increment is taken as a weight factor of the corresponding single-objective function in the multi-objective reactive power compensation optimization model, the multi-objective reactive power compensation optimization model is updated to obtain a final multi-objective reactive power compensation optimization model; and the final multi-objective reactive power compensation optimization model is solved according to the current operation state information to obtain the optimal reactive power compensation strategy.

[0045] As shown in Fig. 2, the method includes:

[0046] S210, a multi-objective reactive power compensation optimization model of the transformer area energy storage system is established; the total objective function of the multi-objective reactive power compensation optimization model is the minimum operation cost of the transformer area energy storage system, and the constraint conditions of the multi-objective reactive power compensation optimization model include: the power constraint condition, the capacity constraint condition and the residual power constraint condition of the energy storage device.

[0047] In an optional embodiment, the total objective function of the multi-objective reactive power compensation optimization model comprises: a first single-objective function aiming at the power purchase cost of the power distribution network, a second single-objective function aiming at the network loss cost of the power distribution network, a third single-objective function aiming at the operation and maintenance cost of the energy storage device, and a fourth single-objective function aiming at the operation and maintenance cost of the reactive power compensation device.

[0048] Specifically, the total objective function of the multi-objective reactive power compensation optimization model is: P min(F Loss +F B +F C );

[0049] Wherein, F P is the power purchase cost of the power distribution network, F Loss is the network loss cost of the power distribution network, F B is the operation and maintenance cost of the energy storage device, F C is the operation and maintenance cost of the reactive power compensation device, and min() is the minimum operator.

[0050] For example, the calculation formula of the power purchase cost F P of the power distribution network is:

[0051] Wherein, P i,t is the power supply at node i (1≤i≤N) at time t (0≤t≤T) within the planning time range, N is the total number of nodes, and T is the maximum number of time nodes; α t is the electricity price at time t.

[0052] The calculation formula of the network loss cost F Loss of the power distribution network is:

[0053] Wherein, r ij is the branch resistance between node i and node j; I ij,t is the current value flowing from node i to node j at time t; the values of i and t are the same as above. The network loss cost of the power distribution network is obtained by multiplying the total active network loss of the power distribution network by the electricity price at the corresponding time. The total active network loss of the power distribution network can be obtained by accumulating the loss of each branch.

[0054] Chemical energy storage devices have the advantages of fast response speed and strong environmental adaptability, and are particularly suitable for application in power distribution network scenarios. The energy storage device is usually composed of an energy storage battery and a converter, and the calculation formula of the operation and maintenance cost F B of the energy storage device is: C=r(1+r) n / [(1+r) n -1];

[0055] where r is the discount rate; n is the operating life of the energy storage device; is the present value of the one-time investment cost of the energy storage device, which can be estimated comprehensively according to the power of the converter and the capacity of the energy storage battery; is the maintenance cost of the energy storage device, which can be approximately estimated according to the power of the converter; T B is the rated power of the energy storage device; is the unit power investment cost; E B is the rated capacity of the energy storage device; is the unit capacity investment cost; l B is the land occupation cost of the energy storage device; is the maintenance cost coefficient of the energy storage device; C is the coefficient.

[0056] The operation and maintenance cost F of the reactive power compensation device C The calculation formula is:

[0057] wherein, is the present value of the one-time investment cost of the reactive power compensation device; is the maintenance cost; Q C is the reactive power compensation capacity; is the unit reactive power capacity investment cost; l C is the land occupation cost of the reactive power compensation device; is the maintenance cost coefficient.

[0058] In another optional embodiment, the power constraint condition of the energy storage device includes: an apparent power constraint condition, a charging power constraint condition and a discharging power constraint condition; the apparent power constraint condition is: wherein, is the decision variable of whether to install the energy storage device at node i, is the planned apparent power of the energy storage device at node i, is the minimum planned apparent power of the energy storage device at node i, is the maximum planned apparent power of the energy storage device at node i;

[0059] The active and reactive power characteristics of the energy storage device can be approximately represented by a circular curve. Therefore, the charging power constraint condition is: wherein, is the active charging power at node i at time t and σ is the battery charging and discharging efficiency and 0≤σ≤1, is the reactive power at node i at time t; the discharging power constraint condition is: wherein, is the active discharging power at node i at time t and

[0060] The capacity constraint is: wherein, is the planned capacity of the energy storage device at node i, is the minimum planned capacity of the energy storage device at node i, is the maximum planned capacity of the energy storage device at node i;

[0061] The energy storage device generally needs to repeatedly perform charging and discharging operations in daily operation, but in order to improve the service life of the battery, the remaining power thereof should meet the maximum discharge depth constraint, and therefore the remaining power constraint condition is: wherein, D is the maximum discharge depth of the energy storage battery in the energy storage device, is the remaining power of the energy storage battery in the energy storage device at node i at time t.

[0062] Further, the constraint condition further includes a relationship constraint condition between the charging and discharging power of the energy storage battery and the remaining power, and specifically is:

[0063] wherein, is the charging decision variable of the energy storage device at node i at time t; is the discharging decision variable of the energy storage device at node i at time t; and Δt is a discrete time step. It is indicated that the daily charging and discharging capacity of the energy storage battery is equal.

[0064] Further, the constraint condition further includes an operation state constraint condition and an operation safety constraint condition of the reactive power compensation device, and wherein:

[0065] The reactive power compensation includes capacitive reactive power compensation and inductive reactive power compensation, and the operation state constraint condition is taken as the reference direction of the inflow node. wherein, is the decision variable of whether the reactive power compensation device is installed at node i, is the injected reactive power of the reactive power compensation device at node i at time t, is the planned minimum reactive power compensation capacity at node i, is the planned maximum reactive power compensation capacity at node i;

[0066] A large number of distributed photovoltaic power sources are generally connected inside the active distribution network, and the output characteristics thereof are represented as:

[0067] wherein, is the actual output of the photovoltaic power source at node i at time t; is the maximum output of the photovoltaic power source at node i at time t.

[0068] Further, the power distribution network usually only has one (or a few) power supply nodes, so the distflow model is used to constrain the power flow of the power distribution network, and KCL equations are listed for each node as follows:

[0069] Where, r ij is the resistance of the branch between node i and node j, x ij is the reactance of the branch between node i and node j, P ij,t is the active power from node i to node j at time t, Q ij,t is the reactive power from node i to node j at time t, is the active load power at node j at time t, is the reactive load power at node j at time t, I ij,t is the current value from node i to node j at time t; u(j) is the set of branch starting nodes with node j as the terminal, and v(j) is the set of branch terminal nodes with node j as the starting node.

[0070] The current and voltage (per unit) relationship constraints of each branch are

[0071] The operation safety constraint condition is: Where, U i,t is the voltage value at node i at time t, U i,t+1 is the voltage value at node i at time t+1, U min is the minimum value of node voltage, U max is the maximum value of node voltage, U N is the rated voltage value; I i,t is the current value at node i at time t+1, I max is the maximum value of node current. |U i,t+1 -U i,t |≤2%U N indicates that the voltage fluctuation within a unit time does not exceed 2% of the rated voltage.

[0072] S220, obtaining the operation state information of the transformer area energy storage system; the operation state information includes: historical operation state information and current operation state information.

[0073] S230, respectively solving each single objective function according to the historical operation state information and the current operation state information to obtain the increment between each target endpoint of the Pareto frontier and the corresponding initial endpoint; the single objective function includes: the first single objective function, the second single objective function, the third single objective function or the fourth single objective function.

[0074] In an optional embodiment, the increments between the target endpoints and the initial endpoints of the Pareto frontier are obtained by solving each single-objective function according to the historical operating state information and the current operating state information, comprising: solving each single-objective function according to the historical operating state information to obtain a plurality of initial endpoints of the Pareto frontier; marking the coordinates of each initial endpoint in the normalized objective function space; solving each single-objective function according to the current operating state information to obtain a target endpoint of the Pareto frontier; and determining the increments of the target endpoint in the direction of the initial endpoints according to the coordinates of the initial endpoints.

[0075] Specifically, the endpoints of the Pareto frontier are obtained by solving each single-objective optimization problem according to the historical operating information, specifically comprising solving the power purchase cost F p as the first single-objective function to obtain the first endpoint (F pmin1 , F pmax1 ) of the Pareto frontier. loss as the second single-objective function to obtain the second endpoint (F lossmin1 , F lossmax1 ) of the Pareto frontier. B as the third single-objective function to obtain the third endpoint (F Bmin1 , F Bmax1 ) of the Pareto frontier. C as the single-objective function to obtain the fourth endpoint (F Cmin1 , F Cmax1 ) of the Pareto frontier. The coordinates of the above four endpoints in the normalized objective function space are marked as (0, 1), (1, 0), (0, -1) and (-1, 0) respectively. The endpoint offset trend of the Pareto frontier is obtained by solving each single-objective optimization problem according to the real-time operating state information of the substation energy storage system again, specifically comprising solving the power purchase cost F p as the first single-objective function to obtain the first endpoint (F pmin2 , F pmax2 ) of the Pareto frontier, and obtaining the increment i1 in the (0, 1) direction. loss as the second single-objective function to obtain the second endpoint (F lossmin2 , F lossmax2 ) of the Pareto frontier, and obtaining the increment i2 in the (1, 0) direction. B as the third single-objective function to obtain the endpoint (F Bmin2 , F Bmax2), obtain the increment i3 of F in the (0, -1) direction, solve the operation and maintenance cost F of the reactive power compensation device C The fourth single-objective function is obtained, and the end point (F Cmin2 , F Cmax2 ) of the Pareto front is obtained.

[0076] S240, update the multi-objective reactive power compensation optimization model by taking each increment as a weight factor of the corresponding single-objective function in the multi-objective reactive power compensation optimization model, and obtain a final multi-objective reactive power compensation optimization model.

[0077] Specifically, each increment obtained is taken as a weight factor of each single-objective optimization problem, and the reactive power compensation optimization model min(F P +F Loss +F B +F C ) is updated to min(i1xF P +i2xF Loss +i3xF B +i4xF C ) for solving, and a final multi-objective reactive power compensation optimization model is obtained.

[0078] The embodiments of the present application aim at the phenomenon that the spatial distance of each machine table distributed by the distributed control of the multi-module is not equal and the line impedance of the multi-machine parallel is inconsistent when the energy storage system of the transformer area is actually operated, which further causes the power to be not evenly divided, thereby causing problems such as voltage fluctuation and power supply reliability decline. The above-mentioned Pareto optimization solving method is used to solve the reactive power compensation optimization model, and the influence of the actual unstable state such as voltage fluctuation and power supply reliability decline on the model is reduced in the solving process.

[0079] S250, solve the final multi-objective reactive power compensation optimization model according to the current operation state information, and obtain an optimal reactive power compensation strategy.

[0080] Specifically, the embodiments of the present application do not limit the solving method of the optimal solution of the final multi-objective reactive power compensation optimization model.

[0081] The technical scheme of the embodiment of the application establishes a multi-objective reactive power compensation optimization model of a transformer area energy storage system; a total objective function of the multi-objective reactive power compensation optimization model is minimum operation cost of the transformer area energy storage system; constraint conditions of the multi-objective reactive power compensation optimization model include power constraint conditions, capacity constraint conditions and residual power constraint conditions of the energy storage device; operation state information of the transformer area energy storage system is acquired; the operation state information includes historical operation state information and current operation state information; each single objective function is solved according to the historical operation state information and the current operation state information respectively, and increments between each target endpoint of a Pareto frontier and a corresponding initial endpoint are obtained; the single objective function includes a first single objective function, a second single objective function, a third single objective function or a fourth single objective function; each increment is used as a weight factor of a corresponding single objective function in the multi-objective reactive power compensation optimization model, the multi-objective reactive power compensation optimization model is updated, and a final multi-objective reactive power compensation optimization model is obtained; the final multi-objective reactive power compensation optimization model is solved according to the current operation state information, and an optimal reactive power compensation strategy is obtained. When the transformer area energy storage system is compensated for reactive power, the cost of various distribution networks is fully considered, and the safety and economy of the power system are taken into account. Moreover, a Pareto optimization solving method is used to solve the reactive power compensation optimization model, and the influence of actual operation instable states such as voltage fluctuation and power supply reliability reduction on the model is reduced in the solving process.

[0082] Embodiment three

[0083] FIG. 3 is a structural schematic diagram of a reactive power compensation optimization device of a transformer area energy storage system provided by the embodiment three of the application. As shown in FIG. 3, the device includes a model establishing module 310, a state information acquiring module 320 and an optimization solving module 330; wherein,

[0084] The model establishing module 310 is configured to establish a multi-objective reactive power compensation optimization model of a transformer area energy storage system; a total objective function of the multi-objective reactive power compensation optimization model is minimum operation cost of the transformer area energy storage system; constraint conditions of the multi-objective reactive power compensation optimization model include power constraint conditions, capacity constraint conditions and residual power constraint conditions of the energy storage device;

[0085] The state information acquiring module 320 is configured to acquire operation state information of the transformer area energy storage system;

[0086] The optimization solving module 330 is configured to use a Pareto optimization solving method to solve the multi-objective reactive power compensation optimization model according to the operation state information, and obtain an optimal reactive power compensation strategy of the transformer area energy storage system.

[0087] The technical scheme of the embodiment of the application establishes a multi-objective reactive power compensation optimization model of a transformer area energy storage system; a total objective function of the multi-objective reactive power compensation optimization model is minimum operation cost of the transformer area energy storage system; constraint conditions of the multi-objective reactive power compensation optimization model include power constraint conditions, capacity constraint conditions and residual power constraint conditions of the energy storage device; operation state information of the transformer area energy storage system is acquired; the multi-objective reactive power compensation optimization model is solved according to the operation state information by using a Pareto optimization solving method, and an optimal reactive power compensation strategy of the transformer area energy storage system is obtained, so that the cost of various distribution networks can be fully considered and the safety and economy of a power system can be taken into account when the transformer area energy storage system is compensated for reactive power.

[0088] Optionally, the operation state information includes historical operation state information and current operation state information; the optimization solving module 330 includes:

[0089] An increment solving unit is configured to solve each single objective function according to the historical operation state information and the current operation state information respectively, and obtain increments between each target endpoint of a Pareto frontier and a corresponding initial endpoint; the single objective function includes a first single objective function, a second single objective function, a third single objective function or a fourth single objective function;

[0090] A model generating unit is configured to update the multi-objective reactive power compensation optimization model by taking each increment as a weight factor of a corresponding single objective function in the multi-objective reactive power compensation optimization model, and obtain a final multi-objective reactive power compensation optimization model;

[0091] A model solving unit is configured to solve the final multi-objective reactive power compensation optimization model according to current operation state information, and obtain an optimal reactive power compensation strategy.

[0092] Optionally, the increment solving unit is specifically configured to:

[0093] Solve each single objective function according to the historical operation state information, and obtain a plurality of initial endpoints of a Pareto frontier;

[0094] Mark coordinates of each initial endpoint in a normalized objective function space;

[0095] Solve each single objective function according to the current operation state information, and obtain a target endpoint of a Pareto frontier;

[0096] Determine an increment of the target endpoint in a direction of the initial endpoint according to the coordinates of the initial endpoint.

[0097] Optionally, the power constraint conditions of the energy storage device include apparent power constraint conditions, charging power constraint conditions and discharging power constraint conditions; the apparent power constraint condition is: wherein, is a decision variable for whether to install an energy storage device at node i, is a planned apparent power of the energy storage device at node i, is a minimum planned apparent power of the energy storage device at node i, is a maximum planned apparent power of the energy storage device at node i;

[0098] The charging power constraint condition is: wherein, is an active charging power at node i at time t, and σ is a battery charging and discharging efficiency and 0≤σ≤1, is a reactive power at node i at time t;

[0099] The discharging power constraint condition is: wherein, is an active discharging power at node i at time t, and

[0100] The capacity constraint condition is: wherein, is a planned capacity of the energy storage device at node i, is a minimum planned capacity of the energy storage device at node i, is a maximum planned capacity of the energy storage device at node i;

[0101] The residual power constraint condition is: wherein, D is a maximum discharging depth of an energy storage battery in the energy storage device, is a residual power of the energy storage battery in the energy storage device at node i at time t.

[0102] Optionally, the constraint conditions further include an operation state constraint condition and an operation safety constraint condition of the reactive power compensation device, including:

[0103] The operation state constraint condition is: wherein, is a decision variable for whether to install a reactive power compensation device at node i, is an injected reactive power of the reactive power compensation device at node i at time t, is a planned minimum reactive power compensation capacity at node i, is a planned maximum reactive power compensation capacity at node i;

[0104] The operation safety constraint condition is: wherein, U i,t is a voltage value at node i at time t, Ui,t+1 Uti is the voltage value at node i at time t+1 min Umin is the minimum value of the node voltage, U max Umax is the maximum value of the node voltage, U N Uref is the rated voltage value; I i,t Ii is the current value at node i at time t+1 max Imax is the maximum value of the node current.

[0105] The reactive power compensation optimization device of the transformer area energy storage system provided in the embodiments of the present application can execute the reactive power compensation optimization method of the transformer area energy storage system provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0106] Embodiment four

[0107] FIG. 4 shows a structural schematic diagram of an electronic device 10 that can be used to implement the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections, and their functions, as well as the software implemented by the electronic device, are merely examples and are not intended to limit the implementations of the present application described and / or claimed herein.

[0108] As shown in FIG. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0109] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0110] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the reactive power compensation optimization method for a transformer energy storage system.

[0111] In some embodiments, the reactive power compensation optimization method for a transformer energy storage system can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the reactive power compensation optimization method for a transformer energy storage system described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the reactive power compensation optimization method for a transformer energy storage system by any other appropriate means, such as by means of firmware.

[0112] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0113] In some embodiments, the reactive power compensation optimization method of the transformer area energy storage system can be implemented as a computer program, which is tangibly embodied in a computer program product, the computer program, when executed by a processor, implements the reactive power compensation optimization method of the transformer area energy storage system of the present application, and the computer program product can be understood as a software product which mainly realizes the solution thereof through the computer program. The computer program for implementing the method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, so that the computer programs, when executed by the processor, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, partially on a machine as a separate software package, and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above. The storage medium can be a non-transitory storage medium.

[0115] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0116] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, and solves the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0118] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0119] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A reactive power compensation optimization method for a distribution network energy storage system, applied to the distribution network energy storage system, wherein the distribution network energy storage system includes a distribution network, a reactive power compensation device, and an energy storage device, wherein, The method includes: A multi-objective reactive power compensation optimization model for the distribution area energy storage system is established. The overall objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the distribution area energy storage system. The constraints of the multi-objective reactive power compensation optimization model include: power constraints, capacity constraints, and remaining power constraints of the energy storage device. Obtain operational status information of the energy storage system in the transformer substation area; The Pareto optimization method is used to solve the multi-objective reactive power compensation optimization model based on the operating status information, thereby obtaining the optimal reactive power compensation strategy for the transformer area energy storage system.

2. The method according to claim 1, wherein, The overall objective function of the multi-objective reactive power compensation optimization model includes: a first single objective function with the power purchase cost of the distribution network as the objective, a second single objective function with the network loss cost of the distribution network as the objective, a third single objective function with the operation and maintenance cost of the energy storage device as the objective, and a fourth single objective function with the operation and maintenance cost of the reactive power compensation device as the objective.

3. The method according to claim 2, wherein, The operational status information includes historical operational status information and current operational status information. The steps of solving the multi-objective reactive power compensation optimization model using the Pareto optimization method based on the operational status information to obtain the optimal reactive power compensation strategy for the distribution area energy storage system include: Based on the historical operating status information and the current operating status information, each single objective function is solved to obtain the increment between each objective endpoint and the corresponding initial endpoint of the Pareto front; Each of the increments is used as a weight factor for the corresponding single objective function in the multi-objective reactive power compensation optimization model to update the multi-objective reactive power compensation optimization model and obtain the final multi-objective reactive power compensation optimization model. The final multi-objective reactive power compensation optimization model is solved based on the current operating status information to obtain the optimal reactive power compensation strategy.

4. The method according to claim 3, wherein, Based on the historical operating state information and the current operating state information, each single objective function is solved to obtain the increment between the target endpoint and the initial endpoint of the Pareto front, including: Based on the historical operating state information, each single objective function is solved to obtain multiple initial endpoints of the Pareto front; Mark the coordinates of each of the initial endpoints in the normalized objective function space; Based on the current operating status information, each of the single objective functions is solved to obtain the target endpoint of the Pareto front. The increment of the target endpoint in the direction of the initial endpoint is determined based on the coordinates of the initial endpoint.

5. The method according to claim 1, wherein, The power constraints of the energy storage device include: apparent power constraints, charging power constraints, and discharging power constraints; the apparent power constraint is as follows: in, Let i be the decision variable for whether to install an energy storage device at node i. Let the planned apparent power of the energy storage device at node i be . Let i be the minimum planned apparent power of the energy storage device at node i. Let be the maximum planned apparent power of the energy storage device at node i; The charging power constraint is as follows: in, Let be the active charging power at node i at time t and σ is the battery charge / discharge efficiency and 0 ≤ σ ≤ 1. Reactive power at time node i; The discharge power constraint condition is as follows: in, Let be the active discharge power at node i at time t and The capacity constraint is as follows: in, Let i be the planned capacity of the energy storage device at node i. Let i be the minimum planned capacity of the energy storage device at node i. Let i be the maximum planned capacity of the energy storage device at node i. The remaining power constraint is as follows: Where D represents the maximum depth of discharge of the energy storage battery in the energy storage device. Let be the remaining charge of the energy storage battery at node i in the energy storage device at time t.

6. The method according to claim 5, wherein, The constraints also include: constraints on the relationship between the charging and discharging power of the energy storage battery and the remaining capacity, constraints on the operating status of the reactive power compensation device, and constraints on operational safety; among which... The constraint condition for the relationship between the charging and discharging power and the remaining capacity of the energy storage battery is as follows: in, Let i be the charging decision variable for the energy storage device at node i at time t. Let be the discharge decision variable of the energy storage device at node i at time t; Δt is the discrete time step. The operating state constraints are as follows: in, Let i be the decision variable for whether to install a reactive power compensation device at node i. The reactive power injected into the reactive power compensation device at node i at time t. Let i be the minimum reactive power compensation capacity planned at node i. The maximum reactive power compensation capacity planned at node i; The operational safety constraints are as follows: Among them, U i,t Let U be the voltage value at node i at time t. i,t+1 Let U be the voltage value at node i at time t+1. min U is the minimum value of the node voltage. max U is the maximum value of the node voltage. N This is the rated voltage value; I i,t Let I be the current value at node i at time t+1. max This represents the maximum value of the node current.

7. A reactive power compensation optimization device for a transformer substation energy storage system, applied to the transformer substation energy storage system, wherein the transformer substation energy storage system includes a distribution network, a reactive power compensation device, and an energy storage device, wherein, The reactive power compensation optimization device includes: The model building module is set to establish a multi-objective reactive power compensation optimization model for the distribution area energy storage system; the overall objective function of the multi-objective reactive power compensation optimization model is the minimum operating cost of the distribution area energy storage system, and the constraints of the multi-objective reactive power compensation optimization model include: the power constraint, capacity constraint, and remaining power constraint of the energy storage device; The status information acquisition module is configured to acquire the operating status information of the energy storage system in the distribution area. The optimization solution module is configured to use the Pareto optimization method to solve the multi-objective reactive power compensation optimization model based on the operating status information, thereby obtaining the optimal reactive power compensation strategy for the transformer area energy storage system.

8. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the reactive power compensation optimization method for the distribution area energy storage system according to any one of claims 1-6.

9. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the reactive power compensation optimization method for the distribution area energy storage system as described in any one of claims 1-6.

10. A computer program product, wherein, The computer program product includes a computer program that, when executed by a processor, implements the reactive power compensation optimization method for the distribution area energy storage system according to any one of claims 1-6.

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