Capacity optimization method of micro-grid hybrid energy storage system and electronic equipment

Abstract

The invention relates to a capacity optimization method of a micro-grid hybrid energy storage system and electronic equipment. The method comprises the following steps: constructing a full life cycle cost static model of the micro-grid hybrid energy storage system; a capacity optimization model of the micro-grid hybrid energy storage system is constructed, an energy storage device of the model adopts a storage battery and a super capacitor, and the model takes the minimum full life cycle cost as an objective function and takes the power supply reliability index load power shortage rate and the energy storage capacity of the energy storage system as constraint conditions; an Archimedes algorithm is improved by adopting a Sin chaos reverse learning strategy and an adaptive t distribution strategy; and solving the configuration of the energy storage device in the capacity optimization model by using an improved Archimedes algorithm. Compared with the prior art, the method has the advantages that the optimal configuration of the energy storage device can be quickly solved, the full-life-cycle cost of the micro-grid hybrid energy storage system is minimized, and iteration is prevented from falling into a local extreme value.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity optimization configuration, and in particular to a capacity optimization method and electronic equipment for a microgrid hybrid energy storage system. Background Art

[0002] As a key sector of the national economy, the power industry is rapidly transforming into a new energy industry and moving towards a new power system. By 2024, our new energy installed capacity will be 1.27 billion kW, accounting for more than 40% of the total installed power generation capacity, specifically 40.7%. In August 2024, my country's non-fossil energy power generation accounted for 40% of the total social electricity consumption that month, an increase of 53.4 billion kWh year-on-year.

[0003] Microgrids play a vital role in new power systems. They are not only a key technical means to achieve sustainable energy development, but also an important support for ensuring the stable operation of large power grids. Considering the randomness and instability of wind and solar power generation equipment within microgrids, energy storage systems are usually considered to smooth the fluctuations of new energy and improve the efficiency of new energy utilization. In engineering practice applications, commonly used energy storage devices include batteries and supercapacitors. The advantage of batteries is that they have a high energy ratio and can store electricity for a long time, but they have disadvantages such as low power density, short cycle life and pollution; supercapacitors have the advantages of high power density, high charging and discharging efficiency and long cycle life, but their cost is relatively expensive. In order to optimize the charging and discharging efficiency of the energy storage system and extend its service life, batteries and supercapacitors can be used as a hybrid energy storage system.

[0004] At present, many scholars at home and abroad have conducted a lot of research on the capacity planning of hybrid energy storage systems. However, most of the research only considers the initial purchase cost, and does not consider other costs, such as installation cost, maintenance cost and the cost of energy storage waste utilization, which is the overall life cycle cost. And most of them use more traditional PSO algorithms, which are prone to fall into local extreme values ​​and cannot guarantee the search for the global optimal solution.

[0005] How to more quickly achieve the optimal configuration of the number of batteries and supercapacitors in the hybrid energy storage system and reduce the full life cycle cost of the microgrid hybrid energy storage system has become a technical problem that needs to be solved. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a capacity optimization method and electronic equipment for a microgrid hybrid energy storage system. By using an improved Archimedean algorithm to solve, a faster convergence speed and accuracy can be achieved in the capacity configuration of batteries and supercapacitors, thereby reducing the full life cycle cost of the microgrid hybrid energy storage system.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] According to one aspect of the present invention, a capacity optimization method for a microgrid hybrid energy storage system is provided, the method comprising the following steps:

[0009] Step 1, construct a static model of the full life cycle cost of the microgrid hybrid energy storage system;

[0010] Step 2: construct a capacity optimization model for the microgrid hybrid energy storage system. The energy storage devices of the model use batteries and supercapacitors. The model takes the minimum cost of the entire life cycle as the objective function and the power supply reliability index load power shortage rate and the storage capacity of the energy storage system as the constraints.

[0011] Step 3, using Sin chaos reverse learning strategy and adaptive t distribution strategy to improve the Archimedes algorithm;

[0012] Step 4: Use the improved Archimedean algorithm to solve the configuration of the energy storage device in the capacity optimization model.

[0013] Preferably, the static model of the full life cycle cost of the microgrid hybrid energy storage system is as follows:

[0014] LCC=C 1 +C o +C .M +C D

[0015] Where, LCC is the life cycle cost; C 1 is the equipment purchase cost; C o is the operating cost of the equipment; C M is the operation and maintenance cost of the equipment; C D It is the residual value and scrapping cost of the equipment at the end of its life cycle.

[0016] Preferably, the objective function is to minimize the life cycle cost C, specifically:

[0017] minC=C 1 +C o +C M +C D =(1+f ob +f mb +f db )N b P b +(1+f oc +f dc )N c P c

[0018] In the formula, C 1 is the equipment purchase cost; Co is the operating cost of the equipment; C M is the operation and maintenance cost of the equipment; C D The residual value and scrapping cost of the equipment at the end of its life cycle; N b is the number of batteries; N c is the number of supercapacitors; P b is the battery unit price; P c is the unit price of supercapacitor; f ob 、f mb and f db are the operation factor, maintenance factor and treatment factor of the battery respectively; f oc and f dc They are the operation coefficient and treatment coefficient of the supercapacitor respectively.

[0019] Preferably, the constraint condition of the load power failure rate is: the load power failure rate is less than or equal to the maximum value of the load power failure rate.

[0020] Preferably, the energy storage constraint is:

[0021] E bmin <E b (k)<E bn

[0022] E cmin <E c (k)<E cmax

[0023] E b (k)≤μΔE

[0024] ΔE=(E w (k)+E s (k))η c -E L (k)

[0025] In the formula, E b (k) is the energy storage of the battery at time k; E c (k) is the energy storage of the supercapacitor bank at time k; E bmin and E bn are the minimum and maximum storage energy of the battery respectively; E cmin and E cmax are the minimum and maximum energy storage of the supercapacitor group respectively; μ is the proportionality coefficient; E w (k) and E s (k) are the wind power and solar power generation at time k; E L (k) is the power consumption of the load at time k; η c is the conversion efficiency of the inverter.

[0026] Preferably, the calculation process of the load power shortage rate includes:

[0027] Step 201, determine whether the sum of wind and solar power generation multiplied by the conversion efficiency of the inverter is greater than the load demand, that is, ΔE=(E w (k)+E s (k))η c -E L (k) whether it is greater than 0, if yes, go to step S202, otherwise go to step S203;

[0028] Step 202, power shortage E lps =0, the corresponding load power shortage rate is 0, and the hybrid energy storage device is in a charging state;

[0029] Step 203, the hybrid energy storage system is in a discharging state to make up for the shortfall in load. At this time, let ΔE = -ΔE, that is, the shortfall in load is E lps =E L (k)-(E W (k)+E s (k))η c , power failure rate f LPSP It is the ratio of the total power shortage of the load to the total load demand, that is:

[0030]

[0031] Among them, E lps (k) is the power shortage of the load at time k, E L (k) is the power consumption of the load at time k.

[0032] More preferably, in step 202, the hybrid energy storage device is in a charging state.

[0033] ΔE≥(E bn +E cmax )·η c , the battery is charged at the rated value and the supercapacitor is charged at the maximum value; otherwise, when ΔE ≥ E bn ·η c When ΔE<E bn ·η c When the power is on, only the supercapacitor is charged, and the battery is not charged.

[0034] More preferably, in step 203, the hybrid energy storage system is in a discharging state, if ΔE≥(E bn +E cmax )·η c , the battery is discharged at the rated value, and the supercapacitor is discharged at the maximum value; otherwise, when ΔE ≥ E bn ·η cWhen ΔE<E bn ·η c When the supercapacitor is charged, only the supercapacitor is discharged, and the battery is not discharged.

[0035] Preferably, the step 3 comprises:

[0036] Parameter initialization;

[0037] Use the Sin chaos reverse learning strategy to initialize the population and select N individuals with the best fitness as the initial population;

[0038] Calculate the fitness value of each individual and update the density and volume of each individual;

[0039] Calculate the migration operator and update the individual position according to the migration operator;

[0040] Calculate the fitness value of the target individual and update the target position;

[0041] An adaptive t-distribution strategy is used to perturb the position of the individual and iteratively output the optimal solution.

[0042] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1) The present invention constructs a static model of the full life cycle cost and a capacity optimization model for a microgrid hybrid energy storage system, improves the traditional Archimedean algorithm based on the Sin hybrid reverse learning strategy and the adaptive t distribution strategy, and uses the improved Archimedean algorithm to solve the configuration of the energy storage device in the capacity optimization model, so that the individual can jump out of the local extreme value, and has better global search performance and local search performance, and can quickly solve the optimal configuration of batteries and supercapacitors in the energy storage system, so as to minimize the full life cycle cost of the microgrid hybrid energy storage system.

[0045] 2) The full life cycle cost of the present invention includes not only the equipment purchase cost, but also the equipment operation cost, operation and maintenance cost, and the residual value cost and scrapping cost of the equipment at the end of its life cycle. Comprehensive consideration is taken into account, so that the configuration of the energy storage device in the energy storage system is more in line with the actual situation.

[0046] 3) In the calculation of the load power shortage rate of the present invention, the battery and the supercapacitor respectively perform different charging and discharging action combinations for various situations of wind and solar power generation and load demand. Reasonable settings enable efficient use of electrical energy, fundamentally reducing the cost of the entire life cycle.

[0047] 4) The present invention has been verified through simulation. Compared with the traditional Archimedes algorithm and the PSO algorithm, the convergence curve of the improved Archimedes algorithm is always at the bottom of the curve, indicating that the convergence accuracy is the highest; the convergence curve of the improved Archimedes algorithm is basically close to the optimal solution at the first time, indicating that the algorithm converges the fastest, avoids falling into local extreme values, optimizes the configuration results and minimizes the cost of the entire life cycle, proving that this method is optimal in terms of speed and performance of capacity optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the calculation process of the load power failure rate in the present invention;

[0049] Figure 2 A schematic diagram of a process for solving the configuration of an energy storage device using an improved Archimedean algorithm in the present invention;

[0050] Figure 3 It is a schematic diagram of wind power generation in the present invention;

[0051] Figure 4 It is a schematic diagram of photovoltaic power generation in the present invention;

[0052] Figure 5 It is a schematic diagram of load power consumption in the present invention;

[0053] Figure 6 It is a schematic diagram of the convergence process of the algorithm in the present invention;

[0054] Figure 7 It is a schematic flow chart of the capacity optimization method of the microgrid hybrid energy storage system in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0056] The present embodiment relates to a capacity optimization method for a microgrid hybrid energy storage system. By adopting a basic Archimedean algorithm improved by a Sin chaos reverse learning initialization strategy and an adaptive t distribution mutation strategy, the number of batteries and supercapacitors in the hybrid energy storage system is reasonably configured to achieve the optimal configuration capacity and avoid the defects of falling into local extreme values ​​and premature convergence.

[0057] like Figure 7 , the method comprises the following steps:

[0058] Step 1: Construct a static model of the full life cycle cost of the microgrid hybrid energy storage system;

[0059] Step 2: Determine the objective function, power supply reliability index and constraints, and build a capacity optimization model for the microgrid hybrid energy storage system;

[0060] Step 3, the traditional Archimedean algorithm is improved by using the Sin chaos reverse learning strategy and the adaptive t distribution strategy;

[0061] Step 4: Use the improved Archimedean algorithm to solve the number of batteries and supercapacitors in the hybrid energy storage system.

[0062] In step 1, the static model of the life cycle cost of the microgrid hybrid energy storage system is as follows:

[0063] LCC=C 1 +C o +C .M +C D

[0064] Where, LCC is the life cycle cost; C 1 is the equipment purchase cost; C o is the operating cost of the equipment; C M is the operation and maintenance cost of the equipment; C D It is the residual value and scrapping cost of the equipment at the end of its life cycle.

[0065] In step 2, the energy storage devices of the microgrid hybrid energy storage system use batteries and supercapacitors. The objective function is to minimize the cost of the entire life cycle, and the power supply reliability index load power shortage rate and the storage capacity of the energy storage system are used as constraints to build a capacity optimization model of the microgrid hybrid energy storage system.

[0066] Taking the minimum cost of the entire life cycle as the objective function, the objective function is:

[0067] minC=C 1 +C o +C M +C D =(1+f ob +f mb +f db )N b P b +(1+f oc +f dc )N c P c

[0068] Where N b is the number of batteries; N c is the number of supercapacitors; P b is the battery unit price; P c is the unit price of supercapacitor; fob 、f mb and f db are the operation factor, maintenance factor and treatment factor of the battery respectively; f oc and f dc They are the operation coefficient and treatment coefficient of the supercapacitor, and the maintenance coefficient f of the supercapacitor mc Usually 0.

[0069] The power supply reliability index load power failure rate and the storage capacity of the energy storage system are used as constraints:

[0070] The load power shortage rate is an important indicator of power supply reliability, but it must be within an acceptable range, that is:

[0071] f LPSP ≤f LPSPmax

[0072] In the formula, f LPSP is the load power failure rate; f LPSPmax is the maximum value of the load power failure rate;

[0073] Taking the energy storage capacity of the energy storage system as the constraint, the energy storage range of batteries and supercapacitors is as follows:

[0074] E bmin <E b (k)<E bn

[0075] E cmin <E c (k)<E cmax

[0076] In the formula, E b (k) is the energy storage of the battery at time k; E c (k) is the energy storage of the supercapacitor bank at time k; E bmin and E bn are the minimum and maximum storage energy of the battery respectively; E cmin and E cmax are the minimum and maximum energy storage of the supercapacitor bank, respectively.

[0077] Figure 1 The rated storage energy of the battery is E bn , in MWh, the minimum remaining energy storage is E bmin , where the minimum storage energy of the battery is E bmin and maximum energy storage E bn They are:

[0078] E bmin =N b ·C b ·U b(1-DOD) / 10 6

[0079] E bn =N b C b U b / 10 6

[0080] In the formula, C b is the rated capacity (in Ah), N b is the number of batteries, U b (V) is the rated voltage of the battery; C b (Ah) is the rated capacity; DOD is the maximum depth of discharge.

[0081] In actual situations, the working voltage of the supercapacitor is cmin ~U cmax The maximum energy storage of the supercapacitor bank is:

[0082]

[0083] The minimum energy storage capacity of the supercapacitor bank is:

[0084]

[0085] Where U c max and U c min are the maximum and minimum terminal voltages of the supercapacitor respectively; C c is the capacitance value; Nc is the number of supercapacitors.

[0086] The load power shortage rate is understood as the total power shortage of the load and the total load demand E L That is:

[0087]

[0088] Denoted as: ΔE=(E w (k)+E s (k))η c -E L (k) where E w (k) and E s (k) are the wind power and solar power generation at time k; E L (k) is the power consumption of the load; η c is the conversion efficiency of the inverter.

[0089] ΔE mainly consists of two parts, the basic part and the fluctuation part. The battery mainly bears the basic part, which must meet the following requirements:

[0090] E b (k)≤μ·ΔE

[0091] Where μ is the proportionality coefficient.

[0092] Figure 1 The load power shortage rate calculation flow chart is as follows: When the sum of wind and solar power generation multiplied by the inverter conversion efficiency is greater than the load demand, that is, ΔE>0, the power shortage E lps =0, the hybrid energy storage device is in a charging state, specifically:

[0093] If ΔE≥(E bn +E cmax )·η c , the battery is charged at the rated value and the supercapacitor is charged at the maximum value; otherwise, when ΔE ≥ E bn ·η c When ΔE<E bn ·η c When the power is on, only the supercapacitor is charged, and the battery is not charged.

[0094] When the sum of wind and solar power generation multiplied by the conversion efficiency of the inverter is less than the load demand, the hybrid energy storage system is in a discharge state to make up for the load shortfall. At this time, let ΔE = -ΔE, that is:

[0095] E lps =E L (k)-(E W (k)+E s (k))η c

[0096] The hybrid energy storage system is in a discharging state to make up for the load shortfall:

[0097] ΔE≥(E bn +E cmax )·η c , the battery is discharged at the rated value, and the supercapacitor is discharged at the maximum value; otherwise, when ΔE ≥ E bn ·η c When ΔE<E bn ·η c When the supercapacitor is charged, only the supercapacitor is discharged, and the battery is not discharged.

[0098] from Figure 1 It can be seen that the battery is charged and discharged at the rated value, which effectively reduces the number of charge and discharge times and the depth of discharge of the battery, and extends the service life of the battery.

[0099] In step 2, the following constraints are also set:

[0100] Eb (k)≤μΔE

[0101] ΔE=(E w (k)+E s (k))η c -E L (k)

[0102] Among them, E b (k) is the energy storage of the battery at time k; μ is the proportionality coefficient; E w (k) and E s (k) are the power generation of wind energy and solar energy respectively; E L (k) is the power consumption of the load; η c is the conversion efficiency of the inverter.

[0103] In step 3, the Archimedean algorithm is improved based on Sin chaos reverse learning and adaptive t distribution, which reduces the life cycle cost of the microgrid hybrid energy storage system and accelerates the convergence speed of the system to the optimal value.

[0104] Introduction to the standard Archimedean algorithm: The Archimedean optimization algorithm (AOA) is an optimization algorithm inspired by Archimedean theorem. Its core principle is that when an object is fully or partially immersed in a fluid, the buoyancy exerted by the fluid on the object is proportional to the mass (volume) of the discharged liquid: if the buoyancy of the object is equal to the mass of the discharged liquid, the object is in equilibrium. The specific principle is expressed as follows. Suppose many objects are immersed in the same fluid. Each object tries to reach equilibrium. The immersed objects have completely different densities p and volumes v, so there are different accelerations a.

[0105]

[0106] Among them: b is the fluid; o represents the individual object; p b 、v b and a b are the density, volume and acceleration of the fluid respectively; p o 、v o and a o are the density, volume and acceleration of the object respectively. b W is the buoyancy exerted by the fluid on the object; o The mass of the discharged liquid.

[0107] According to the above formula, the acceleration a of the object can be calculated 0 , as shown below:

[0108]

[0109] If object o collides with another object r at a closer distance, causing its own equilibrium state to be affected by r, then the equilibrium state of object o is:

[0110]

[0111] Among them, W b , W o , and W r are the mass of the discharged liquid, the mass of object o and the mass of object r respectively; p o 、v o and a o are the density, volume and acceleration of object o respectively; p r 、v r and a r are the density, volume and acceleration of object r respectively; p b 、v b and a b are the density, volume and acceleration of the fluid respectively.

[0112] Similar to other heuristic algorithms, AOA randomly initializes the density, volume, and acceleration of the object at the beginning. After calculating the fitness value of each individual in the initial population, AOA starts to iterate and update the position until the number of iterations is met and the position update stops. In each iteration, the position update method of AOA needs to be selected based on whether the individual collides with other individuals and updates its own attributes. The updated attributes determine the new position of the next generation of individuals. The detailed process of AOA is as follows Figure 2 shown.

[0113] During the initialization phase, AOA will randomly initialize the volume (vol), density (den), and acceleration (acc) of each object. During this process, AOA will evaluate the initial population and select the current optimal individual (x best ), the density of the optimal individuals (den best )、Volume best ), acceleration (acc best ) is used to update the density, volume and acceleration of other individuals. The formula for updating the density and volume of individuals is as follows:

[0114]

[0115] Where: den i t and den i t+1 is the density of the ith individual in generation t and generation t+1; vol i t and vol i t+1is the volume of the ith individual in the tth and t+1th generations; rand is a random number between (0,1).

[0116] Depending on whether the individuals collide, AOA will perform different search phases, namely global search phase and local search phase. If no collision occurs, AOA performs global search; otherwise, it performs local search. The judgment of which search phase to enter depends on the value of the transfer operator (TF). The calculation formula of TF is as follows:

[0117] TF=exp((tt max ) / t max )

[0118] Where: t is the current iteration number; t max is the maximum number of iterations.

[0119] When TF≤0.5, AOA enters the global search mode, and the update formula of individual acceleration is shown in formula (17):

[0120]

[0121] Where: acc i t+1 is the acceleration of the ith individual in the t+1 iteration; den mr and vol mr is the density and volume of an individual randomly selected in the current iteration.

[0122] When TF>0.5, AOA enters local search mode. The individual acceleration calculation formula is as follows:

[0123]

[0124] In order to make the individual position update step more standardized, AOA needs to normalize the individual acceleration. The specific normalization formula is shown below:

[0125]

[0126] Where: is the normalization operation of the acceleration of the ith individual in the t+1th iteration; u and l are the ranges used to adjust the normalization value.

[0127] In the global search phase, the individual position update formula is as follows:

[0128]

[0129] Where: and is the position of the i-th individual in the t+1 generation and the t generation; x rand is the individual position randomly selected in the current iteration; rand is a random number between (0,1); C 1 is a constant, d is the density factor, and the update formula of d is shown in formula (21):

[0130] d t+1 =exp[((t max -t) / t max )-(t / t max )]

[0131] In the local search phase, the AOA individual position is updated according to the following formula:

[0132]

[0133] Where: C 2 is a constant; F is a parameter that determines the direction of iterative position update. The specific expression of F is:

[0134]

[0135] Where: P = 2rand-C 4 , C 4 is a constant; T = C 3 ×TF, and T∈[0.3C 3 ,1],C 3 is a constant, and TF is the value of the migration operator.

[0136] The specific Sin chaos reverse learning strategy is: the diversity of population initialization prevents some particles from gathering in a certain local range, which expands the contraction range of the algorithm to a certain extent. The sequence generated by the Sin chaos map can traverse each value within the interval range and is easy to implement. The reverse learning strategy is the most common improvement strategy in the field of swarm intelligence. The idea is to generate a reverse solution based on the current solution, compare the fitness values ​​of the current solution and the reverse solution, and select the best one to enter the next generation. The two strategies are integrated into the Archimedean algorithm at the same time. First, the Sin chaos map is used to form an initial solution with better population diversity; secondly, the reverse learning strategy is used for the population of the initial solution to form a new reverse solution population; finally, the fitness value of the initial solution population and the fitness value of the reverse solution are calculated, and the best one is retained as the new population. The expression of the Sin chaos one-dimensional mapping is as follows:

[0137]

[0138] Map the Sin chaotic sequence to the solution space and obtain the population X = {X i ,i=1,2,…,N},X j ={X j,j=1,2,…,dim}, the individuals in the population are represented as follows:

[0139] X i+1,j = sin(2 / X i,j )

[0140] Where, X i+1,j is the position of the i+1th individual in the jth dimension.

[0141] Calculate the reverse population of the initial population according to the reverse learning strategy Reverse population individuals It is expressed as follows:

[0142]

[0143] In the formula, [X minj , X maxj ] are the upper and lower bounds of the search space. The population X generated by the Sin chaotic mapping and the reverse population X* form a new population {X∪X *}, sort the fitness values ​​of individuals in the new population, and select the top N individuals with better fitness to form a new population.

[0144] The above adaptive t distribution strategy is as follows:

[0145] The t distribution is the abbreviation of the Student distribution. The shape of the curve is determined by the value of the parameter freedom n. The probability density function is as follows:

[0146]

[0147] In the formula, is the Euler integral of the second kind.

[0148] When the degree of freedom n is smaller, the shape of the curve is flatter and the middle of the curve is lower; when the degree of freedom n = 1, the t distribution is the Cauchy distribution, that is, t(n = 1) → C(0,1). When the degree of freedom n is larger, the shape of the curve is taller and the curve is similar to the standard normal distribution curve; when the degree of freedom n is infinite, the t distribution is the Gaussian distribution, that is, t(n = ∞) → N(0,1).

[0149] The position of the AOA individual is updated using the adaptive t distribution as shown below:

[0150]

[0151] in, is the position of the i-th individual after updating using the adaptive t distribution; t(k) is the adaptive t distribution function; x i is the position of the ith individual.

[0152] Adding t-distribution to the AOA algorithm is essentially to add a disturbance after the AOA position is updated, so that individuals can jump out of local extreme values. At the same time, based on the characteristics of t-distribution similar to Cauchy distribution and Gaussian distribution, the algorithm can have better global search performance and local search performance.

[0153] This embodiment also involves a simulation verification of a capacity optimization method for a microgrid hybrid energy storage system. The present invention first considers the problem of poor population quality in the early stage of iteration of the traditional Archimedean algorithm, and proposes to use an improved Sin chaotic directional learning strategy to improve the quality of the initial solution; secondly, the traditional Archimedean algorithm is prone to falling into local extreme values ​​during iteration, and an adaptive t distribution strategy is used to perturb the position of individuals to improve the global and local search capabilities of the algorithm, which can further reduce the full life cycle cost of the microgrid hybrid energy storage system. Through simulation, the feasibility and effectiveness of the proposed algorithm in the capacity optimization of the microgrid hybrid energy storage system are verified.

[0154] After simulation verification, the following conclusions can be drawn:

[0155] 1) The proposed Sin hybrid reverse learning strategy and adaptive t distribution strategy can be well applied to the capacity optimization configuration of microgrid hybrid energy storage system.

[0156] 2) Compared with other optimization algorithms, the proposed improved Archimedes algorithm can further reduce the full life cycle cost of the hybrid energy storage system and improve the convergence speed of the system.

[0157] According to the content of the present invention, Matlab is run to perform example analysis. The wind power generation, photovoltaic power generation and load power consumption data in the example are as follows: Figure 3 , Figure 4 and Figure 5 The parameters of the battery and supercapacitor are shown in Table 1.

[0158] Table 1

[0159] Battery Capacitors Rated voltage / V 12 2.7 Rated capacity / Ah 100 Charging efficiency 0.75 0.98 Discharge efficiency 0.85 0.98 Discharge Depth 0.4 Operation coefficient 0.1 0.01 Maintenance factor 0.02 Cycle life / times 1500 500000 Unit price / yuan 400 350 Treatment coefficient 0.08 0.04

[0160] The simulation parameters are set as follows:

[0161] The conversion efficiency of the inverter is 90%, and the power failure rate of the system is 0.05. AOA parameter settings: C 1 =2, C 2 =6, C 3 =2, C 4 =0.5, u=0.9, l=0.1, the population size is 30, and the maximum number of iterations is 1000. At the same time, in order to highlight the advantages of AOA, the PSO algorithm is added as a comparison algorithm. The parameter settings of PSO are: learning factor c 1 =c2 =1.5, the population size is 30, and the maximum number of iterations is 1000.

[0162] The optimization results of the three algorithms are shown in Table 2, and the algorithm curve convergence diagram is shown in Figure 6 shown.

[0163] Table 2

[0164] Optimization parameters PSO AOA IAOA Battery / pcs 1.432E+04 1.432E+04 6.602E+03 Supercapacitor / unit 6.933E+06 6.272E+06 6.173E+06 Minimum fee / yuan 1.8230E+05 1.6933E+05 1.6055E+05

[0165] From the optimization configuration results and the minimum cost in Table 2, it can be seen that the minimum cost obtained by the PSO algorithm is 1.8230E+05, the minimum cost obtained by the AOA algorithm is 1.6933E+05, and the minimum cost obtained by the IAOA algorithm is 1.6055E+05. The cost obtained by IAOA is reduced by 13% compared with PSO and about 5% compared with AOA. It can be seen that PSO has disadvantages in dealing with hybrid energy storage capacity configuration. The AOA algorithm improved by the Sin chaos reverse learning initialization strategy and the adaptive t distribution strategy has a good optimization effect in dealing with the hybrid energy storage optimization problem to a certain extent, which shows the effectiveness of the improved strategy.

[0166] Depend on Figure 6 From the convergence curves of each curve, it can be seen that the convergence curve of the improved Archimedes algorithm is always at the bottom of the curve, indicating that the convergence accuracy is the highest; the convergence curve of the improved Archimedes algorithm is basically close to the optimal solution at the first time, indicating that the algorithm converges the fastest.

[0167] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0168] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0169] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method in any other appropriate manner (e.g., by means of firmware).

[0170] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0171] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0172] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A capacity optimization method for a microgrid hybrid energy storage system, characterized in that: The method comprises the following steps: Step 1, construct a static model of the full life cycle cost of the microgrid hybrid energy storage system; Step 2: construct a capacity optimization model for the microgrid hybrid energy storage system. The energy storage devices of the model use batteries and supercapacitors. The model takes the minimum cost of the entire life cycle as the objective function and the power supply reliability index load power shortage rate and the storage capacity of the energy storage system as the constraints. Step 3, using Sin chaos reverse learning strategy and adaptive t distribution strategy to improve the Archimedes algorithm; Step 4: Use the improved Archimedean algorithm to solve the configuration of the energy storage device in the capacity optimization model.

2. A capacity optimization method for a microgrid hybrid energy storage system according to claim 1, characterized in that: The static model of the full life cycle cost of the microgrid hybrid energy storage system is as follows: LCC=C1+C o +C .M +C D In the formula, LCC is the life cycle cost; C1 is the equipment purchase cost; C o is the operating cost of the equipment; C M is the operation and maintenance cost of the equipment; C D It is the residual value and scrapping cost of the equipment at the end of its life cycle.

3. The capacity optimization method of a microgrid hybrid energy storage system according to claim 1, characterized in that: The objective function is to minimize the life cycle cost C, specifically: minC=C1+C o +C M +C D =(1+f ob +f mb +f db )N b P b +(1+f oc +f dc )N c P c In the formula, C1 is the equipment purchase cost; C o is the operating cost of the equipment; C M is the operation and maintenance cost of the equipment; C D The residual value and scrapping cost of the equipment at the end of its life cycle; N b is the number of batteries; N c is the number of supercapacitors; P b is the battery unit price; P c is the unit price of supercapacitor; f ob 、f mb and f db are the operation factor, maintenance factor and treatment factor of the battery respectively; f oc and f dc They are the operation coefficient and treatment coefficient of the supercapacitor respectively.

4. The capacity optimization method of a microgrid hybrid energy storage system according to claim 1, characterized in that: The constraint condition of the load power failure rate is: the load power failure rate is less than or equal to the maximum value of the load power failure rate.

5. The capacity optimization method of a microgrid hybrid energy storage system according to claim 1, characterized in that: The energy storage constraints are: AND bmin <And b (k)<And bn AND cmin <And c (k)<And cmax E b (k)≤μΔE ΔE=(E w (k)+E s (k))η c -E L (k) In the formula, E b (k) is the energy storage of the battery at time k; E c (k) is the energy storage of the supercapacitor bank at time k; E bmin and E bn are the minimum and maximum storage energy of the battery respectively; E cmin and E cmax are the minimum and maximum energy storage of the supercapacitor group respectively; μ is the proportionality coefficient; E w (k) and E s (k) are the wind power and solar power generation at time k; E L (k) is the power consumption of the load at time k; η c is the conversion efficiency of the inverter.

6. A capacity optimization method for a microgrid hybrid energy storage system according to claim 1, characterized in that: The calculation process of the load power failure rate includes: Step 201, determine whether the sum of wind and solar power generation multiplied by the conversion efficiency of the inverter is greater than the load demand, that is, ΔE=(E w (k)+E s (k))η c -E L (k) whether it is greater than 0, if yes, go to step S202, otherwise go to step S203; Step 202, power shortage E lps =0, the corresponding load power shortage rate is 0, and the hybrid energy storage device is in a charging state; Step 203, the hybrid energy storage system is in a discharging state to make up for the shortfall in load. At this time, let ΔE = -ΔE, that is, the shortfall in load is E lps =E L (k)-(E W (k)+E s (k))η c , power failure rate f LPSP It is the ratio of the total power shortage of the load to the total load demand, that is: Among them, E lps (k) is the power shortage of the load at time k, E w (k) and E s (k) are the wind power and solar power generation at time k; E L (k) is the power consumption of the load at time k; η c is the conversion efficiency of the inverter.

7. A capacity optimization method for a microgrid hybrid energy storage system according to claim 6, characterized in that: In step 202, the hybrid energy storage device is in a charging state. If ΔE≥(E bn +E cmax )·η c , the battery is charged at the rated value and the supercapacitor is charged at the maximum value; otherwise, when ΔE ≥ E bn ·η c When ΔE<E bn ·η c When the power is on, only the supercapacitor is charged, and the battery is not charged.

8. A capacity optimization method for a microgrid hybrid energy storage system according to claim 6, characterized in that: In step 203, the hybrid energy storage system is in a discharging state. If ΔE≥(E bn +E cmax )·η c , the battery is discharged at the rated value, and the supercapacitor is discharged at the maximum value; otherwise, when ΔE ≥ E bn ·η c When ΔE<E bn ·η c When the supercapacitor is charged, only the supercapacitor is discharged, and the battery is not discharged.

9. The capacity optimization method of a microgrid hybrid energy storage system according to claim 1, characterized in that: The step 3 comprises: Parameter initialization; Use the Sin chaos reverse learning strategy to initialize the population and select N individuals with the best fitness as the initial population; Calculate the fitness value of each individual and update the density and volume of each individual; Calculate the migration operator and update the individual position according to the migration operator; Calculate the fitness value of the target individual and update the target position; An adaptive t-distribution strategy is used to perturb the position of the individual and iteratively output the optimal solution.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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