Method and device for distributing active power and reactive power of energy storage power station and medium

Abstract

The invention discloses an energy storage power station active power and reactive power distribution method and device and a medium, and the method comprises the steps: determining a multi-target power distribution model of a target energy storage power station, the multi-target power distribution model comprising a plurality of target functions and constraint conditions; according to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem, judging an energy storage subsystem group participating in the active power distribution; solving the multi-target power distribution model by adopting a hybrid algorithm of a preset optimization algorithm in the energy storage subsystem group to obtain active power distribution of the energy storage power station; and finally, based on the converter power constraint and the active power distribution, solving the reactive power distribution of the energy storage power station by adopting a preset optimization algorithm. The energy consumption loss cost, the service life loss cost, the energy storage charge state consistency index and the operation number of the energy storage subsystems under the small-amplitude power instruction can be remarkably reduced.

Description

Technical Field

[0001] The present application relates to the field of energy storage optimization, and specifically to a method, device and medium for allocating active and reactive power of an energy storage power station. Background Art

[0002] In recent years, the installed capacity of new energy sources, mainly wind power and photovoltaic power, has been increasing year by year. Compared with conventional energy, the randomness and volatility of new energy generation bring new challenges to the safe and stable operation of the power system and the balance of power and electricity. Large-scale battery energy storage technology has fast response speed and high regulation accuracy, which can effectively smooth out wind power fluctuations and make wind power fluctuations meet the grid connection requirements. At the same time, it has active support functions such as frequency regulation and voltage regulation to maintain the safety and stability of the power system. The optimization of power distribution between energy storage subsystems can reduce the operating costs of power stations and improve the economy of energy storage. The internal power distribution of energy storage power stations is divided into active power distribution and reactive power distribution.

[0003] The active power distribution strategy of existing energy storage power stations focuses on single goals such as balancing the energy storage charge state and energy loss, without considering the battery health status indicators at the same time, which will affect the performance and reliability of the battery module.

[0004] Most of the existing research focuses on the problem that multiple inverters are connected in parallel, which leads to the inability to evenly distribute reactive power, resulting in reactive circulating current directly in the inverter. Virtual impedance is used to suppress reactive circulating current, or a consistency algorithm is used to distribute reactive power to distributed energy storage power stations. However, there is no distribution of reactive power in energy storage power stations based on economic efficiency.

[0005] The existing technology does not comprehensively consider multiple objectives such as the health status of energy storage batteries, energy loss of energy storage power stations, and consistency of energy storage charge state to optimize active power, and lacks a distribution model for optimizing the reactive power of energy storage power stations based on economic costs. Summary of the invention

[0006] In order to solve the above problems, the present application proposes a method, device and medium for allocating active and reactive power of an energy storage power station, wherein the method includes:

[0007] Determine a multi-objective power distribution model of a target energy storage power station, wherein the multi-objective power distribution model includes multiple objective functions and constraints; determine the energy storage subsystem group participating in this active power distribution according to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem; solve the multi-objective power distribution model within the energy storage subsystem group using a hybrid algorithm of a preset optimization algorithm to obtain the active power distribution of the energy storage power station; the preset optimization algorithm is a hybrid algorithm based on the Sky Eagle optimization algorithm and the African Vulture optimization algorithm; based on the converter power constraint and the active power distribution, use the preset optimization algorithm to solve the reactive power distribution of the energy storage power station.

[0008] In one example, the multiple objective functions specifically include: a first objective function for minimizing the expected energy storage loss cost, a second objective function for minimizing the expected energy storage subsystem health state loss cost, a third objective function for the best expected charge state consistency, and a fourth objective function for minimizing the expected energy storage converter reactive power regulation cost; the constraints include power current constraints, converter power constraints, power balance constraints, and battery module energy storage charge state constraints; before the hybrid algorithm of the preset optimization algorithm is used in the energy storage subsystem group to solve the multi-objective power allocation model, the method also includes: normalizing the multiple objective functions based on the maximum and minimum values ​​respectively corresponding to the multiple objective functions; and converting the normalized multiple objective functions into active objective functions and reactive objective functions using a linear weighted method.

[0009] In one example, the multi-objective power allocation model for determining the target energy storage power station specifically includes: determining the battery internal resistance of the battery module based on the ohmic internal resistance and the planned resistance of the battery module; determining the battery energy loss cost of the battery module based on the battery internal resistance and the current flowing through the battery module; determining the inverter static loss of the energy storage converter based on the voltage, temperature and charge and discharge current of the battery module; determining the inverter dynamic loss of the energy storage converter based on the charge and discharge current and temperature of the battery module; and determining the first objective function with the lowest expected energy storage loss cost based on the inverter dynamic loss, inverter static loss and the battery energy loss cost.

[0010] In one example, determining a multi-objective power allocation model for a target energy storage power station specifically includes:

[0011] The loss capacity of the battery module is determined based on the energy loss of the battery module, the temperature of the battery module, the SOC change of the battery module and the rated power of the battery module; the remaining capacity of the battery module is determined based on the rated power of the battery module and the loss capacity; and the second objective function of minimizing the loss cost of the health state of the expected energy storage subsystem is determined based on the remaining capacity of the battery module and the unit battery capacity investment cost.

[0012] In one example, determining the multi-objective power allocation model of the target energy storage power station specifically includes: determining a third objective function with the best consistency of the expected state of charge according to the SOC variance of each energy storage subsystem.

[0013] In one example, the multi-objective power allocation model of the target energy storage power station is determined, specifically including: determining a fourth objective function with the lowest reactive power regulation cost of the expected energy storage converter according to the reactive power of each energy storage subsystem.

[0014] In one example, the energy storage subsystem group participating in the current active power distribution is determined based on the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem, specifically including: if the total power demand of the current energy storage power station is a positive number, the lower the real-time charge state of the energy storage subsystem in the energy storage power station, the higher the active output priority of the energy storage subsystem; if the total power demand of the current energy storage power station is a negative number, the higher the real-time charge state of the energy storage subsystem in the energy storage power station, the lower the active output priority of the energy storage subsystem; the energy storage subsystem with a higher active output priority is preferentially added to the energy storage subsystem group until the sum of the maximum power of the energy storage subsystem group is greater than the total power demand of the energy storage power station, thereby obtaining the energy storage subsystem group participating in the current active power distribution.

[0015] In one example, the hybrid algorithm using a preset optimization algorithm solves the multi-objective power allocation model, specifically including: after generating an initial random population of an African vulture optimization algorithm, selecting the best release from the initial random population and placing it into a first best vulture group, and placing the second best release into a second best vulture group; calculating the hunger rates corresponding to the first best vulture group and the second best vulture group respectively; when the hunger rate is higher than a first preset threshold, replacing the position update formula of the exploration phase of the African vulture optimization algorithm with the position update formula of the Sky Eagle optimization algorithm in the exploration phase; when the hunger rate is lower than the first preset threshold, replacing the position update formula with the first development formula; when the absolute value of the hunger rate is lower than the second preset threshold, replacing the position update formula of the first best vulture group with the second best vulture group with the second development formula; repeating the iterative process until the maximum number of iterations is reached to obtain the active power distribution of the energy storage power station.

[0016] The present application also provides a device for distributing active and reactive power of an energy storage power station, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of the above examples.

[0017] The present application also provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the method described in any one of the above examples.

[0018] The method proposed in this application can bring the following beneficial effects:

[0019] (1) A multi-objective power allocation optimization model based on a hybrid algorithm of the Sky Eagle optimization algorithm and the African Vulture optimization algorithm was established, and the objective functions of energy storage energy loss cost, life loss cost, energy storage charge state consistency and reactive power regulation cost were established to allocate active and reactive power.

[0020] (2) Compared with the traditional active power allocation according to the proportion of energy storage charge state, the multi-objective power allocation strategy in this paper can significantly reduce the energy loss cost, life loss cost, energy storage charge state consistency index and the number of energy storage subsystems operating under small-amplitude power instructions; compared with the dual-objective allocation strategy that only considers the balance of energy consumption and energy storage charge state, the total cost is reduced.

[0021] (3) Compared with the existing strategies, the multi-objective power allocation strategy in this paper reduces the reactive power regulation cost and is beneficial to slowing down the life loss of the inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A schematic diagram of a flow chart of a method for allocating active and reactive power of an energy storage power station in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of an energy storage power station architecture in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of a power allocation strategy for an energy storage power station in an embodiment of the present application;

[0026] Figure 4 This is a schematic diagram of an equivalent circuit model of a battery module in an embodiment of the present application;

[0027] Figure 5 This is a schematic diagram of an optimization solution process in an embodiment of the present application;

[0028] Figure 6 This is a schematic diagram of an active power instruction of an energy storage power station in an embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of reactive power instructions for an energy storage power station in an embodiment of the present application;

[0030] Figure 8 A schematic diagram of active power of energy storage subsystem No. 1 under different strategies in a calculation example in an embodiment of the present application;

[0031] Fig. 9 A schematic diagram of active power of the second energy storage subsystem under different strategies in a calculation example in an embodiment of the present application;

[0032] Fig.10 This is a schematic diagram of active power of the No. 3 energy storage subsystem under different strategies in a calculation example in an embodiment of the present application;

[0033] Fig.11 This is a schematic diagram of the active power of the fourth energy storage subsystem under different strategies in a calculation example in the embodiment of the present application;

[0034] Fig.12 This is a schematic diagram of the SOC curve of the energy storage subsystem No. 1 under different strategies in a calculation example in the embodiment of the present application;

[0035] Fig.13 This is a schematic diagram of the SOC curve of the second energy storage subsystem under different strategies in a calculation example in the embodiment of the present application;

[0036] Fig.14 This is a schematic diagram of the SOC curve of the No. 3 energy storage subsystem under different strategies in a calculation example in the embodiment of the present application;

[0037] Fig.15 This is a schematic diagram of the SOC curve of the fourth energy storage subsystem under different strategies in a calculation example in the embodiment of the present application;

[0038] Fig.16 A reactive power diagram of a No. 1 energy storage subsystem under different strategies in a calculation example in an embodiment of the present application;

[0039] Fig.17 A reactive power diagram of the second energy storage subsystem under different strategies in a calculation example in an embodiment of the present application;

[0040] Fig.18 A reactive power diagram of the No. 3 energy storage subsystem under different strategies in a calculation example in an embodiment of the present application;

[0041] Fig.19 Schematic diagram of reactive power of the fourth energy storage subsystem under different strategies in a calculation example in the embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0043] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0044] Figure 1 A flow chart of a method for distributing active and reactive power of an energy storage power station provided in one or more embodiments of this specification. The method can be applied to Figure 2 The battery energy storage power station shown, Figure 2 The internal battery energy storage power station is composed of multiple energy storage subsystems in parallel. The energy storage subsystem consists of a battery system and a power storage converter (Power Control System, PCS). The battery system includes a battery pack and a battery management system. The battery pack is composed of single cells in series and parallel. The energy storage converter mainly includes energy storage converters, filters and other devices. bess , Q bess is the total power of the energy storage station, P1 bess , Q1 bess , P2 bess , Q2 bess , …, The active and reactive powers allocated to the 1st, 2nd, ..., Nth energy storage subsystems respectively. The process can be executed by a computing device in the corresponding field, and some input parameters or intermediate results in the process allow manual intervention and adjustment to help improve accuracy. The implementation of the analysis method involved in the embodiment of the present application can be a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail using the server as an example.

[0045] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and this application does not make any specific limitations on this.

[0046] like Figure 1 As shown, the embodiment of the present application provides a method for allocating active and reactive power of an energy storage power station, including:

[0047] S101: Determine a multi-objective power allocation model for a target energy storage power station, wherein the multi-objective power allocation model includes multiple objective functions and constraint conditions;

[0048] The power distribution block diagram of the energy storage power station is as follows: Figure 3 As shown in Figure 1, the purpose is to optimize the power command allocation of each energy storage subsystem. According to the total active power demand value P of the current battery energy storage system bess , total reactive power demand Q bess The real-time state of charge and health state (SOH) of each energy storage subsystem are taken into consideration to minimize the energy loss cost, health state loss cost, balance the real-time state of charge of each energy storage subsystem and reactive power regulation cost of the energy storage converter during operation of the energy storage power station. The preset optimization algorithm is used to calculate the power given value P1 of each energy storage subsystem. bess , Q1 bess , P2 bess , Q2 bess …P i bess , Q i bess … Where i=1,2,…,N.

[0049] Specifically, the objective functions include a first objective function for minimizing the expected energy storage loss cost, a second objective function for minimizing the expected energy storage subsystem health state loss cost, a third objective function for achieving the best charge state consistency, and a fourth objective function for minimizing the expected energy storage converter reactive power regulation cost.

[0050] For the first objective function, energy loss will occur during the energy storage charging and discharging process due to the existence of battery internal resistance and line resistance. The energy storage loss cost is composed of battery energy loss cost and energy storage converter loss cost. Battery energy loss will reduce the operating efficiency of energy storage. Contact resistance, line resistance and battery module internal resistance are the three major sources of energy storage energy loss. The battery module equivalent circuit model is as follows: Figure 4 As shown, R i in is the internal resistance of the battery, R i s is the contact resistance, R i L is the line resistance. i in Calculated from the ohmic internal resistance and polarization internal resistance of the Thevenin model.

[0051] The equivalent circuit expression of the battery module is as follows:

[0052]

[0053] Where: U i bess is the terminal voltage of the ith battery module, in V; I i DC is the charge and discharge current of the ith battery module, in A; U i OCV is the open circuit voltage of the battery module, in V; R i eq is the equivalent internal resistance of the ith battery module, in Ω; R0 is the ohmic internal resistance, in Ω; R1 is the polarization internal resistance, in Ω.

[0054] Therefore, the energy consumption of the battery module is affected by the equivalent internal resistance and current. The energy consumption of the i-th battery module is P i batt The expression is:

[0055]

[0056] The energy loss of the energy storage converter includes the static loss and dynamic loss of the inverter. The static loss of the inverter is divided into on-state and off-state losses. The on-state loss is affected by factors such as voltage, temperature and current. The specific calculation formula is formula (4). Among the inverter losses, the off-state loss accounts for a small proportion and can be ignored. The dynamic loss is affected by temperature and current. The specific calculation formula is formula (5):

[0057]

[0058] Where: P i Inv is the conduction loss of the ith inverter, in MW; P i fil is the dynamic loss of the ith inverter, in MW; A Inv , B Inv , C Inv , D Inv 、b Inv , A fil , α fil is the fitting quantity.

[0059] After adding the off-state loss and the dynamic loss and combining similar items, the energy storage inverter loss is obtained as follows:

[0060]

[0061] Where: A fit , B fit , C fit is the fitting coefficient of the inverter loss.

[0062] In summary, the minimum energy consumption cost of the energy storage unit can be expressed as:

[0063]

[0064] Where: △t is the sampling interval; n is the nth time interval; is the energy storage charging and discharging electricity price, which is taken as 520 yuan / MWh here.

[0065] For the second objective function, SOH is a representation of the battery health state, which is defined as the ratio of the remaining battery capacity to the initial capacity. The calculation formula is formula (8). As the battery is continuously charged and discharged, the battery continues to age. The smaller the SOH value, the greater the battery capacity loss and the worse the battery health state.

[0066]

[0067] Where: SOH i is the health status of the ith battery module; C i bess is the remaining capacity of the ith battery module, in MW; S i bessN is the rated capacity of the ith battery module, in MW.

[0068] The remaining capacity C of the i-th battery module i bess definition:

[0069]

[0070] The capacity loss of energy storage is defined as

[14] :

[0071]

[0072] Where: L i bess is the capacity loss of the ith battery module; A is the fitting quantity, which is 27128; H is a constant, which is 8.314 J / (mol·K); T e is the temperature, take 298K; △SOC i bess is the SOC change of the i-th battery module; P i bessN is the rated power of the ith battery module, in MW.

[0073] The specific calculation formula of SOC and △SOC is shown in formula (11):

[0074]

[0075] Therefore, the battery module health loss cost C i SOH for:

[0076]

[0077] Where: C E The unit battery capacity investment cost is 1.8 million yuan / MWh.

[15] .

[0078] The minimum loss of energy storage system health status is defined as:

[0079]

[0080] For the third objective function, SOC inconsistency will affect the parallel operation of energy storage subsystems, causing some energy storage subsystems to exit operation early. Higher SOC consistency is conducive to energy storage regulation, so the charge state balance index needs to be considered when allocating active power to each energy storage subsystem.

[0081] The SOC consistency objective function is represented by the SOC variance of each energy storage subsystem:

[0082]

[0083] For the fourth objective function, when the inverter is used to provide reactive power, the power cost increases due to the shortened inverter life. The objective function of the inverter life loss cost caused by reactive regulation is:

[0084]

[0085] Where: η0, η1, η2 are fitting coefficients; Q i bess is the reactive power of the i-th energy storage subsystem, in MVar.

[0086] Since the values ​​and dimensions of the above objective functions are different, in order to eliminate the influence of the dimensions, standardization is required before optimization. Since the maximum and minimum values ​​of each sub-function are fixed values, the newly added data will not affect the maximum and minimum values ​​of the function. Therefore, this paper uses the minimum-maximum method to standardize each function, subtracting the data from the minimum value of the column and then dividing it by the range. The calculation formula is as follows:

[0087]

[0088] Where: f v max is the maximum value of the vth objective function; f v min is the minimum value of the vth objective function; f v * is the function value obtained after normalization of the vth objective function.

[0089] In order to simplify the solution of the multi-objective problem, the linear weighting method is used to convert the multi-objective problem into two single-objective problems. The standardized objective functions 1 to 3 are weighted to obtain the active objective function f p , solve for f p Then solve the reactive power objective function f according to the constraints in Section 2.3 Q , as shown in formula (17):

[0090]

[0091] Where: w1, w2, w3 are weight coefficients; f1 * 、f2 * 、f3 * 、f4 * is the normalized function value of objective functions 1 to 4.

[0092] The constraints include: power-current constraint, converter capacity constraint, power balance constraint, and power balance constraint.

[0093] For the power-current constraint, according to the equivalent circuit model of the battery module, the relationship between the battery module power and current is:

[0094]

[0095] Where: P i bess is the active power of the ith energy storage subsystem, in MW.

[0096] For the converter capacity constraint, in order to ensure that the active and reactive power allocated to each energy storage converter does not exceed the inverter capacity, the following constraints are imposed:

[0097]

[0098] Where: Q i bess is the reactive power of the i-th energy storage subsystem, in MVar.

[0099] For the power balance constraint, in order to meet the requirements of the total power instruction, the output power of each energy storage subsystem must be equal to the total power instruction:

[0100]

[0101] For power balance constraints, the SOC operating range of the energy storage subsystem should be consistent with that of the energy storage power station, and the SOC of each energy storage subsystem should be limited to SOC min To SOC max between:

[0102] SOCmin <SOC i (nΔt) <SOC max (twenty one)

[0103] Where: SOC max and SOC min are the upper and lower limits of SOC.

[0104] S102: According to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem, determine the energy storage subsystem group participating in the current active power distribution.

[0105] To ensure the SOC balance of the energy storage subsystem, the energy storage active output priority is first determined by SOC, and then the energy storage total active power instruction requirements are used to determine whether each energy storage subsystem participates in this active power distribution. bess >0, the SOC of each energy storage subsystem is arranged in ascending order, and the one with low SOC is suitable for charging, that is, the one with low SOC has high priority. bess <0, the SOC of each energy storage subsystem is arranged in descending order, and the one with high SOC is suitable for discharge, that is, the one with high SOC has high priority. The selection starts from the highest priority until the sum of the maximum power of the selected subsystem is greater than the energy storage active power instruction, and the energy storage subsystem combination of the energy storage power station participating in the active power distribution is obtained.

[0106] Assume T is the total time length of the power command curve (in seconds), the algorithm flow is as follows: Figure 5 shown.

[0107] S103: using a hybrid algorithm of a preset optimization algorithm in the energy storage subsystem group to solve the multi-objective power distribution model to obtain the active power distribution of the energy storage power station; the preset optimization algorithm is a hybrid algorithm based on the Sky Eagle optimization algorithm and the African Vulture optimization algorithm.

[0108] S104: Based on the converter power constraint and the active power distribution, the preset optimization algorithm is used to solve the reactive power distribution of the energy storage power station.

[0109] The preset optimization algorithm is used to solve f in the selected energy storage subsystem. p Active power distribution is performed to promote SOC balance and reduce the frequent operation of subsystems. Finally, based on the power constraint of the energy storage subsystem, a preset optimization algorithm is used to solve f Q Reactive power is distributed among all energy storage subsystems.

[0110] The Aquila Optimizer (AO) algorithm has strong robustness and global detection capabilities, but its development stage is insufficient, and it is prone to premature convergence and falling into local minima.

[17] The African vultures optimization algorithm (AVOA) can effectively utilize the solution information in the search space and approach the global optimum, but the population diversity is poor in the early exploration stage, which makes the algorithm exploration ability and convergence speed poor.

[18] Based on the characteristics of the two algorithms, this paper adopts an improved hybrid algorithm of AO and AVOA. Here, the preset optimization algorithm is called IHAOAVOA algorithm for solution. IHAOAVOA algorithm combines the exploration phase of AO and the development phase of AVOA, extracting and inheriting the robust exploration and development capabilities of the two basic algorithms. IHAOAVOA is based on AVOA. A brief description of each step of IHAOAVOA is as follows:

[0111] (1) Phase 1: Selecting the best Vulture X B After generating the initial random population of the AVOA algorithm, the optimal solution is selected as the first group of best vultures, and the vultures corresponding to the suboptimal solution are placed in the second group:

[0112]

[0113] Where: Bv1, Bv2 are the best vultures of the first and second groups; L1, L2 are two parameters between 0 and 1; fit v is the fitness of the vulture; m is the total number of vultures in the first and second groups.

[0114] (2) The second stage: Calculate the vulture's hunger rate F. Model the vulture's hunger level as:

[0115]

[0116] Where: rand is a random number between 0 and 1; z is a random number between [-1, 1]; t ite 、T max is the current number of iterations and the maximum number of iterations; h is a random number in the interval [-2,2]; w is a constant.

[0117] (3) The third stage: exploration. For the AVOA algorithm, the transition between exploration and exploitation depends on the vulture's hunger rate. In the early exploration stage, the algorithm converges slowly due to the poor population diversity. In the exploration stage of the AO algorithm, the predatory behavior of the eagle in detecting potential fast-moving prey in a wide flight area is modeled, giving the algorithm a robust global search capability and a faster convergence speed. Since this paper needs to allocate the active power and reactive power of the energy storage power station, it is necessary to ensure a faster convergence speed and better global search capability when selecting the power allocation algorithm. Therefore, with the AVOA algorithm as the core framework, its original position update rule in the exploration stage is replaced by the exploration mechanism in AO, so as to fully utilize the advantages of the two basic algorithms.

[0118] When |F|≥1, the vulture searches for new food in different areas and enters the exploration phase. The original position update rule in the exploration phase is replaced by the position formula of the AO exploration phase:

[0119]

[0120] Where: X v (t ite +1) is the position of the vth vulture in the next iteration; X v (t ite ) is the current position of the vth vulture; C is a random number in [0,2]; ub and lb are the upper and lower bounds of the search range; X m (t ite ) is the average position of all eagles; Levy(·) is the Levy flight function, which is expressed as follows:

[0121]

[0122] Where u and V are random numbers in [0,1]; Γ(· is the gamma function; β is a constant; x and y are the spiral contours in the search process. The calculation formula is as follows:

[0123]

[0124] Where: r search is the number of search cycles between 1 and 20; U con is a constant; D1 is an integer vector from 1 to D dimensions; ω con Is a constant value.

[0125] (4) The fourth stage: development. When |F| < 1, it enters the development stage. When 0 ≤ |F| ≤ 1, the first part of the development is carried out, and the weaker vultures gather together to get food from the stronger vultures:

[0126] X v (tite +1)=

[0127] (F+rand)D v (t ite )-(X B (t ite )-X v (t ite ))(27)

[0128] When |F|<0.5, the algorithm enters the second part of the development. The two best vultures will feel hungry after a long period of exercise. Since a lot of food has been consumed at this time, it may cause the vultures to compete with each other for a food resource:

[0129]

[0130] The effect of power distribution of battery energy storage system is verified through two examples. Using the given active and reactive power curves, the power distribution effect of the traditional power distribution strategy based on SOC proportional distribution, the power distribution strategy that only considers SOC balance and energy consumption, and the power distribution effect under the IHAOVOA algorithm are compared and analyzed.

[0131] This paper selects the lithium-ion energy storage power station as the simulation object, sets the rated power of the energy storage power station to 10MVA, and the configuration time to two hours. The energy storage power station consists of four energy storage subsystems connected in parallel. The example parameters are shown in Table 1. The initial population is selected as 20, the number of iterations is 1500, L1 is 0.8, L2 is 0.2, m is 4, w is 2.5, β is 1.5, and U is con is 0.00565, ω con =0.005. Since the energy consumption cost is greatly affected by the working conditions, the SOH loss cost is sensitive to the market price, and the SOC consistency can be self-adjusted under long-term working conditions, this paper sets a larger weight coefficient for the lowest energy storage loss cost of f1 and the lowest health loss cost of the energy storage subsystem f2, and a smaller weight coefficient for the best charge state consistency of f3. The weight coefficients w1, w2, and w3 are 0.4, 0.4, and 0.2, respectively.

[0132] Table 1: Example parameters

[0133]

[0134]

[0135] Given a 250s power curve as the total power of the energy storage power station in Example 2, the total active power curve of the energy storage power station is as follows: Figure 6 , the total reactive power curve of the energy storage power station is as follows Figure 7 .

[0136] Comparison of the optimization control effect of energy storage power station under two different control strategies. Each strategy is briefly described as follows:

[0137] Strategy 1: The multi-objective-based power allocation strategy established in this paper.

[0138] Strategy 2: SOC ratio allocation strategy and the traditional power allocation strategy in which reactive power is allocated according to reactive capacity. This strategy determines the charging / discharging active power instructions allocated to each energy storage subsystem based on SOC and the total active power instruction of the energy storage power station. bess When it is a charging instruction, that is, P bess >0, the active power instructions allocated to each energy storage subsystem are:

[0139]

[0140] If the energy storage active power instruction P bess When it is a discharge instruction, that is, P bess <0, the active power instructions allocated to each energy storage subsystem are:

[0141]

[0142] In order to prevent the allocated reactive power from being superimposed on the reactive power and exceeding the apparent power of the subsystem, the reactive power is allocated according to the proportion of the remaining maximum reactive capacity of each subsystem. The reactive power allocated to each subsystem of the energy storage is:

[0143]

[0144] Strategy 3: Consider only two objective functions: SOC balance and minimum energy consumption of the energy storage subsystem, and follow Figure 5 The power allocation is performed according to the algorithm flow. The weight of the objective function for optimal energy consumption is 2 / 3, and the weight of the objective function for SOC balance is 1 / 3.

[0145] The active power comparison of each energy storage subsystem in Strategy 1, Strategy 2 and Strategy 3 is as follows: Figure 8 , Fig. 9 , Fig.10 , Fig.11As shown in the figure, the IHAOAVOA strategy proposed in this paper can meet the power demand of the energy storage power station and has good allocation accuracy. When the active power command is low, only some energy storage subsystems need to act, and some energy storage subsystems do not need to operate, such as subsystem 1 of 160s to 250s, subsystem 2 of 110s to 140s and 170s to 250s, subsystem 3 of 70s to 150s, and subsystem 4 of 0s to 30s and 60 to 90s. However, the traditional SOC ratio allocation requires all energy storage subsystems to operate frequently. Under the condition of meeting the total power demand of the energy storage power station, the number of energy storage subsystems operating in the strategy proposed in this paper is reduced. Since the initial value of the algorithm in this paper is randomly generated in the search space, different initial values ​​have a greater impact on the results, resulting in more random solutions, and it is easy to fall into the local optimal solution near the initial value, so there will be fluctuations in the power allocation of each subsystem.

[0146] The SOC curves of each energy storage subsystem under the three strategies are as follows: Fig.12 , Fig.13 , Fig.14 , Fig.15 , the initial average SOC of the energy storage subsystem is 0.45. After 250s of power instruction, the SOC of strategy 1 of subsystem 1 is 0.308 and that of strategy 2 is 0.302, the SOC of strategy 1 of subsystem 2 is 0.404 and that of strategy 2 is 0.399, the SOC of strategy 1 of subsystem 3 is 0.494 and that of strategy 2 is 0.497, the SOC of strategy 1 of subsystem 4 is 0.587 and that of strategy 2 is 0.595. By analyzing the SOC of each energy storage subsystem of strategy 1 and strategy 2, the SOC of the energy storage subsystem tends to 0.45 faster than the traditional SOC ratio allocation strategy in the strategy proposed in this paper, indicating that the strategy proposed in this paper is better than the traditional SOC ratio allocation strategy in terms of SOC balance. The SOC difference between strategy 1 and strategy 3 is not obvious, because the ratio of the weight coefficient between the energy consumption objective function and the SOC balance objective function considered by strategy 3 is the same as that of the weight coefficient of strategy 1, both of which are 2 to 1.

[0147] Comparison of energy storage reactive power allocation strategy 1, strategy 2 and strategy 3 Fig.16 , Fig.17 , Fig.18 , Fig.19 As shown, each energy storage system did not exceed the maximum apparent power of the subsystem.

[0148] The comparison of the four objective function values ​​of strategy 1, strategy 2 and strategy 3 is shown in Table 2. Given that after 250 seconds of power curve operation, the energy loss cost of strategy 1 is 35.41 yuan, the energy loss cost of strategy 2 is 45.28 yuan, and the energy loss cost of strategy 3 is 35.19 yuan. The life loss cost of strategy 1 is 3.32 yuan, strategy 2 is 4.06 yuan, and strategy 3 is 3.61 yuan. The SOC consistency of strategy 1 is 4.99%, strategy 2 is 5%, and strategy 3 is 4.99%. The reactive power regulation cost of strategy 1 is 18.40 yuan, strategy 2 is 20.71 yuan, and strategy 3 is 19.12 yuan. The comparative analysis shows that compared with the traditional SOC ratio allocation strategy, the energy loss cost, life loss cost, SOC balance and reactive power regulation cost of the IHAOAVOA strategy energy storage system in this paper are all reduced. When the active power command amplitude is small, the traditional SOC ratio allocation strategy requires each energy storage subsystem to output power. The power output of some energy storage subsystems is small, which causes the entire power station to operate in a low efficiency range, increasing a lot of energy consumption costs. The strategy in this paper reduces the number and frequency of energy storage subsystems under small-amplitude power commands, reduces the energy loss cost and life loss cost of the energy storage power station. And the SOC balance is improved by setting the charging and discharging priority according to SOC. Compared with the strategy in this paper, Strategy 3 has a lower energy consumption cost, but the total cost obtained by adding objective functions 1, 2 and 4 is higher than that of the strategy in this paper. Therefore, this paper obtains the minimum total cost by comprehensively considering the four objective functions.

[0149] Table 2: Comparison of objective function values ​​between strategy 1 and strategy 2

[0150]

[0151] In summary, this paper allocates the active and reactive power of each energy storage subsystem within the energy storage power station based on a multi-objective optimization model. The main conclusions are as follows:

[0152] This paper establishes a multi-objective power allocation optimization model based on the IHAOAVOA algorithm, and establishes the objective functions of energy storage energy loss cost, life loss cost, SOC consistency and reactive power regulation cost to allocate active and reactive power.

[0153] Compared with the traditional active power allocation according to SOC ratio, the multi-objective power allocation strategy in this paper reduces the energy loss cost by 9.87 yuan, the life loss cost by 0.74 yuan, the SOC consistency index by 0.01%, and the number of operating energy storage subsystems under small-amplitude power instructions under a given 250s active power curve; compared with the dual-objective allocation strategy that only considers energy consumption and SOC balance, the total cost is reduced.

[0154] Compared with the reactive power allocation strategy based on reactive capacity, the multi-objective power allocation strategy in this paper reduces the reactive power regulation cost by 2.31 yuan under a given 250s reactive power curve. This strategy is beneficial to slowing down the life loss of the inverter.

[0155] An embodiment of the present application also provides a device for distributing active and reactive power of an energy storage power station, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of the above embodiments.

[0156] An embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the method described in any one of the above embodiments.

[0157] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0158] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0159] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0164] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0165] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0166] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0167] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for distributing active and reactive power of an energy storage power station, characterized in that: The method comprises: Determine a multi-objective power allocation model for a target energy storage power station, wherein the multi-objective power allocation model includes multiple objective functions and constraints; According to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem, determine the energy storage subsystem group participating in this active power distribution; A hybrid algorithm of a preset optimization algorithm is used in the energy storage subsystem group to solve the multi-objective power distribution model to obtain the active power distribution of the energy storage power station; the preset optimization algorithm is a hybrid algorithm based on the Sky Eagle optimization algorithm and the African Vulture optimization algorithm; Based on the converter power constraint and the active power distribution, the preset optimization algorithm is used to solve the reactive power distribution of the energy storage power station.

2. The method according to claim 1, characterized in that: The multiple objective functions specifically include: a first objective function for minimizing the expected energy storage loss cost, a second objective function for minimizing the expected energy storage subsystem health state loss cost, a third objective function for achieving the best expected state of charge consistency, and a fourth objective function for minimizing the expected energy storage converter reactive power regulation cost; The constraints include power current constraints, converter power constraints, power balance constraints, and battery module energy storage charge state constraints; Before solving the multi-objective power allocation model using a hybrid algorithm of a preset optimization algorithm in the energy storage subsystem group, the method further includes: Normalizing the multiple objective functions based on the maximum values ​​and minimum values ​​respectively corresponding to the multiple objective functions; The normalized multiple objective functions are converted into active objective functions and reactive objective functions using a linear weighted method.

3. The method according to claim 2, characterized in that The multi-objective power allocation model for determining the target energy storage power station specifically includes: Determining a battery internal resistance of the battery module according to the ohmic internal resistance and the planned resistance of the battery module; Determining a battery energy loss cost of the battery module based on the battery internal resistance and the current flowing through the battery module; Determining the inverter static loss of the energy storage converter based on the voltage, temperature and charge and discharge current of the battery module; Determining the inverter dynamic loss of the energy storage converter based on the charge and discharge current and temperature of the battery module; Based on the inverter dynamic loss, the inverter static loss and the battery energy loss cost, a first objective function with the lowest expected energy storage loss cost is determined.

4. The method according to claim 2, characterized in that: The multi-objective power allocation model for determining the target energy storage power station specifically includes: Determine the loss capacity of the battery module based on the energy loss of the battery module, the temperature of the battery module, the SOC change of the battery module and the rated power of the battery module. determining a remaining capacity of the battery module based on the rated capacity of the battery module and the loss capacity; Based on the remaining capacity of the battery module and the unit battery capacity investment cost, a second objective function for minimizing the health state loss cost of the expected energy storage subsystem is determined.

5. The method according to claim 2, characterized in that: The multi-objective power allocation model for determining the target energy storage power station specifically includes: According to the SOC variance of each energy storage subsystem, a third objective function with the best expected state of charge consistency is determined.

6. The method according to claim 2, characterized in that The multi-objective power allocation model for determining the target energy storage power station specifically includes: According to the reactive power of each energy storage subsystem, a fourth objective function is determined to minimize the reactive power regulation cost of the expected energy storage converter.

7. The method according to claim 1, characterized in that The step of determining the energy storage subsystem group participating in the current active power distribution according to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem specifically includes: If the total power demand of the current energy storage power station is a positive number, the lower the real-time charge state of the energy storage subsystem in the energy storage power station, the higher the active output priority of the energy storage subsystem; If the total power demand of the current energy storage power station is a negative number, the higher the real-time charge state of the energy storage subsystem in the energy storage power station, the lower the active output priority of the energy storage subsystem; The energy storage subsystem with a higher active output priority is preferentially added to the energy storage subsystem group until the sum of the maximum power of the energy storage subsystem group is greater than the total power demand of the energy storage power station, thereby obtaining the energy storage subsystem group participating in this active power distribution.

8. The method according to claim 1, characterized in that The hybrid algorithm using the preset optimization algorithm to solve the multi-objective power allocation model specifically includes: After generating an initial random population of African vulture optimization algorithm, selecting the best release from the initial random population to enter the first best vulture group, and the second best release to enter the second best vulture group; Calculating hunger rates corresponding to the first best vulture group and the second best vulture group respectively; When the hunger rate is higher than a first preset threshold, replacing the position update formula of the exploration phase of the African vulture optimization algorithm with the position update formula of the Sky Eagle optimization algorithm in the exploration phase; When the hunger rate is lower than a first preset threshold, the position update formula is replaced by a first development formula; When the absolute value of the hunger rate is lower than a second preset threshold, replacing the position update formula of the first best vulture group with the second best vulture group with the second development formula; The iterative process is repeated until a maximum number of iterations is reached to obtain the active power distribution of the energy storage power station.

9. An active and reactive power distribution device for an energy storage power station, characterized in that: include: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to execute the method of any one of claims 1-8.

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