A three-level planning method for capacity configuration of shared hybrid energy storage systems in integrated energy microgrids

Abstract

The present invention provides a three-layer planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid, the steps of which include: an upper-layer shared hybrid energy storage system determines the energy storage type rental capacity, uses a PSO algorithm to generate rental capacity information and sends it to a middle layer; the middle-layer integrated energy microgrid selects energy storage usage according to the rental capacity information and grid price, solves the MILP problem through Gurobi, calculates the energy storage operating power and equipment output, and sends the results to the bottom layer; the bottom-layer power decomposition and distribution layer adjusts the rental capacity and power distribution according to the optimized power result, uses an improved ISSA-VMD algorithm to reconstruct the power signal and distribute it to the energy storage unit, and feeds the results back to the upper and middle layers; the middle layer calculates the energy storage operating power and equipment output again, and transmits the information to the upper layer; the upper layer calculates the objective function value according to the new rental capacity and energy storage operating power, iteratively optimizes through the PSO algorithm until convergence, and finally determines the optimal energy storage rental solution.

Description

Technical Field

[0001] The present invention relates to capacity configuration optimization of an energy storage system, and in particular to a three-layer planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid. Background Art

[0002] Energy storage, as an effective means of balancing supply and demand within power systems and smoothing fluctuations in renewable energy output, has been widely used in scenarios such as demand-side response, peak frequency management, and improving power supply reliability. This demonstrates the indispensability of energy storage systems in modern power systems, especially with the increasing integration of renewable energy sources, where their flexible adjustment capabilities are particularly crucial. Single-user energy storage systems have high initial investment costs and long payback periods, which pose a significant burden for users with limited funds. The capacity of energy storage systems often struggles to precisely match fluctuating load profiles, resulting in low utilization rates. When energy storage equipment is idle, its capacity cannot be sold, further reducing the economic benefits of the equipment. A single type of energy storage system struggles to meet the technical and economic requirements for loads that fluctuate on multiple timescales. Different types of energy storage systems have varying characteristics, such as response speed, energy density, and cycle life. A single type of energy storage system often cannot simultaneously meet the demands of multiple loads.

[0003] Shared hybrid energy storage systems offer an effective solution to these challenges, offering a new paradigm that offers multiple response speeds, reduces system size, improves resource utilization efficiency, increases device storage capacity, extends system lifespan, and reduces initial and recurring costs. By sharing energy storage resources, optimal configuration and efficient utilization of energy storage equipment can be achieved, reducing user storage costs and improving the overall economic benefits of the system.

[0004] Existing research has largely focused on the planning and optimization of single-type traditional energy storage resources, lacking comprehensive consideration of hybrid energy storage systems. Microgrids, as one of the participants, mostly only address electrical loads, with few studies considering their thermal load requirements. Thermal load requirements involve both electricity and gas consumption, and to some extent, also affect the operational state of the energy storage system. Existing research is largely limited to the combination of hybrid energy storage power stations and renewable energy sites, where the fluctuating net load at each moment is a fixed value. However, in an integrated energy microgrid, the fluctuating net load is an uncertain value, affected by the price of electricity purchased from the grid and the price of gas required for gas turbine power generation at different times. Therefore, the decomposition steps used in existing research are relatively simple and cannot adapt to the complex and changing real-world conditions. Summary of the Invention

[0005] In view of the shortcomings of existing research and the problems faced by single-user energy storage and single-type energy storage systems, a three-level planning method for capacity configuration of shared hybrid energy storage systems in integrated energy microgrids is proposed, aiming to achieve optimal configuration and efficient utilization of energy storage resources.

[0006] To achieve the above objectives, the technical solutions adopted by the present invention include the following:

[0007] A three-tier planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid includes the following steps:

[0008] Construct an upper-layer shared hybrid energy storage system optimization configuration layer, set the objective function of minimizing the annual comprehensive cost and the corresponding constraints, solve the objective function of minimizing the annual comprehensive cost and the corresponding constraints through the particle swarm algorithm, obtain the rental capacity information of various energy storage types, and send the rental capacity information of various energy storage types to the intermediate integrated energy microgrid operation optimization layer;

[0009] Construct an intermediate integrated energy microgrid operation optimization layer, set the objective function of minimizing the annual operating cost and the corresponding constraints, and solve the objective function of minimizing the annual operating cost and the corresponding constraints by solving the mixed integer linear programming method using the Gurobi solver based on the rental capacity information of various energy storage types transmitted by the upper shared hybrid energy storage system optimization configuration layer. Obtain the energy storage operating power leased by the integrated energy microgrid, and send the energy storage operating power information leased by the integrated energy microgrid to the bottom power decomposition and distribution layer;

[0010] Constructing a bottom power decomposition and allocation layer, setting an objective function and corresponding constraints for minimizing the sum of the estimated bandwidths of each modal, solving the objective function and corresponding constraints for minimizing the sum of the estimated bandwidths of each modal based on an improved sparrow search optimization variational modal decomposition algorithm and the energy storage operating power information leased by the integrated energy microgrid, obtaining newly generated leased capacity information of various energy storage types, and sending the newly generated leased capacity information of various energy storage types to the upper shared hybrid energy storage system optimization configuration layer and the intermediate integrated energy microgrid operation optimization layer;

[0011] The intermediate integrated energy microgrid operation optimization layer recalculates the leased energy storage operating power and the operating power information of the integrated energy microgrid system based on the newly generated leased capacity information of various energy storage types, and transmits the leased energy storage operating power and the operating power information of the integrated energy microgrid system to the upper shared hybrid energy storage system optimization configuration layer;

[0012] The upper-layer shared hybrid energy storage system optimization configuration layer calculates the objective function value based on the newly generated rental capacity information of various energy storage types, the rented energy storage operating power, and the operating power information of the integrated energy microgrid system. Through continuous iteration through the particle swarm algorithm, the objective function value of the upper-layer model in each iteration is compared until the convergence condition is reached, thereby obtaining the global optimal solution; the integrated energy microgrid operation planning is carried out by solving the optimal rental capacity plan for each type of energy storage.

[0013] The three-layer planning method for capacity configuration of the shared hybrid energy storage system for the integrated energy microgrid described above further includes the objective function for minimizing the annual comprehensive cost and the corresponding constraints, specifically including:

[0014] Set the objective function as the minimum annual comprehensive cost of the shared hybrid energy storage system and set the corresponding constraints.

[0015] The annual comprehensive cost includes the sum of the investment cost of the annual leased energy storage capacity and the annual operation and maintenance cost of the annual leased energy storage capacity, minus the rental income and service fee income of the annual leased energy storage capacity;

[0016] Constraints include capacity limits for various types of energy storage.

[0017] The three-tier planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid as described above further includes the objective function for minimizing annual operating costs and the corresponding constraints, specifically including:

[0018] Set the objective function as the minimum annual operating cost of the integrated energy microgrid and set the corresponding constraints.

[0019] Among them, the minimum annual operating cost includes the cost of purchasing electricity and gas from the grid for the integrated energy microgrid and the rental cost and service fee cost of the shared hybrid energy storage system for the integrated energy microgrid;

[0020] The constraints include power constraints of various types of energy storage, operating status constraints and capacity constraints of energy storage units, power constraints for interaction with the grid, power constraints and ramping constraints of gas turbines, power constraints and ramping constraints of gas turbines, power constraints of gas boilers, upper and lower operating power constraints of photovoltaic and wind turbines, electric power balance constraints within the integrated energy microgrid, thermal power balance constraints within the integrated energy microgrid, and thermal power balance constraints of gas turbines.

[0021] The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid as described above further has a convergence condition, specifically including: the difference between the objective function values ​​of the previous iteration and the current iteration does not exceed ±1% for more than 10 times.

[0022] The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid as described above, further comprising:

[0023] The objective function is set to minimize the sum of the estimated bandwidths of the K modes decomposed from the charge and discharge power signal. The constraints include the equality of the sum of all modes and the original decomposed signal, the upper and lower limits of the number of modes K, and the upper and lower limits of the bandwidth parameter α.

[0024] The K modes and bandwidth parameter α are optimized based on the improved sparrow search algorithm;

[0025] Substitute the K modes and bandwidth parameter α obtained through optimization into the variational mode decomposition algorithm;

[0026] The operating power information is decomposed according to the substituted variational mode decomposition algorithm, and the decomposed signal is reconstructed according to the contribution ratio of each type of energy storage component in the total energy storage in the integrated energy microgrid.

[0027] Compared with the existing technology, the present invention has the following beneficial effects: the present invention proposes a three-layer planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid. Its advantages are: innovatively applying the grid-side shared hybrid energy storage power station service to a combined heat and power multi-microgrid system, and establishing a three-layer planning model that considers two different time scale problems. By solving the planning model, power curves of different frequencies can be obtained, and the high, medium and low frequency dividing points can be determined and power reconstruction can be performed and then distributed to supercapacitors, batteries and pumped storage in sequence. This can effectively improve the utilization efficiency of energy storage resources, give full play to the characteristics of various energy storage equipment, and enhance the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A schematic diagram of a three-layer planning model provided by an embodiment of the present invention.

[0030] Figure 2 A system diagram provided for an embodiment of the present invention.

[0031] Figure 3 Flowchart of the ISSA-VMD algorithm in an embodiment of the present invention.

[0032] Figure 4This is the overall operating power diagram of the system in an embodiment of the present invention.

[0033] Figure 5 This is the operating power diagram of various types of energy storage after power decomposition and reconstruction in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying 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 embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] Example:

[0036] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] See also Figures 1 to 5 A three-layer planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid according to an embodiment of the present invention includes the following steps:

[0038] Step 1: Construct an upper-layer shared hybrid energy storage system optimization configuration layer, set the objective function of minimizing the annual comprehensive cost and the corresponding constraints, solve the objective function of minimizing the annual comprehensive cost and the corresponding constraints through the particle swarm algorithm, obtain the rental capacity information of various energy storage types, and send the rental capacity information of various energy storage types to the intermediate integrated energy microgrid operation optimization layer.

[0039] In this step, an upper-layer shared hybrid energy storage system optimization configuration layer is constructed. The shared hybrid energy storage system, as the main decision maker, determines the rental capacity of various energy storage types based on market demand and price, and provides energy storage services to other integrated energy microgrids to minimize its own comprehensive costs. Based on the objectives and constraints of the annual comprehensive cost model, the particle swarm optimization (PSO) algorithm is used to generate the rental capacity information of various energy storage types and send it to the intermediate integrated energy microgrid operation optimization layer.

[0040] In one embodiment, the objective function for minimizing the annual comprehensive cost and the corresponding constraints specifically include: setting the objective function to the minimum annual comprehensive cost of the shared hybrid energy storage system, and setting corresponding constraints, wherein the annual comprehensive cost includes the sum of the annual investment cost of the leased energy storage capacity and the annual operation and maintenance cost of the leased energy storage capacity, minus the annual rental income and service fee income of the leased energy storage capacity; and the constraints include capacity size restrictions for various types of energy storage.

[0041] Specifically, the objective function is the minimum annual comprehensive cost of the shared hybrid energy storage system, and the constraints are the capacity limits of each type of energy storage:

[0042] (1) The objective function is the minimum annual comprehensive cost of the shared hybrid energy storage system, as shown below:

[0043] minf1=C inv +C ope -R rent -R ser

[0044] C inv is the annual investment cost of the leased energy storage capacity, which is calculated as:

[0045]

[0046] Among them, μ ess,x is the investment cost per unit capacity of energy storage type x, E ess,x is the capacity of the rented energy storage type x, r is the discount rate, n ess,x is the operating age of the leased energy storage type x;

[0047] C ope is the annual operation and maintenance cost of the leased energy storage capacity, which is calculated as follows:

[0048]

[0049] Among them, ρ ess,x is the operation and maintenance cost per unit capacity of energy storage type x;

[0050] R rentis the annual rental income of the rented energy storage capacity, which is calculated as follows:

[0051]

[0052] Among them, J ess,rent,x is the rental cost per unit capacity of energy storage type x;

[0053] R ser It is the service fee income collected by the shared hybrid energy storage system for providing charging and discharging services to the integrated energy microgrid within the scope of the leased capacity. It is calculated as follows:

[0054]

[0055] Where N is the number of days in a year, P ess,cha,t and P ess,dis,t are the charging and discharging power between the integrated energy microgrid and the shared hybrid energy storage system at time t, J ess,ser,t is the unit price of the charging and discharging service at time t;

[0056] (2) The capacity limits of each type of energy storage are:

[0057]

[0058] in, and It is the upper and lower limits of the capacity of the rented energy storage type x.

[0059] Step 2: Construct an intermediate integrated energy microgrid operation optimization layer, set the objective function of minimizing the annual operating cost and the corresponding constraints, and solve the objective function of minimizing the annual operating cost and the corresponding constraints by solving the mixed integer linear programming using the Gurobi solver according to the rental capacity information of the various energy storage types transmitted by the upper shared hybrid energy storage system optimization configuration layer, and obtain the energy storage operating power leased by the integrated energy microgrid, and send the energy storage operating power information leased by the integrated energy microgrid to the bottom power decomposition and distribution layer.

[0060] In this step, an intermediate integrated energy microgrid operation optimization layer is constructed. The integrated energy microgrid serves as the main decision maker and calculates the operation status of other equipment based on the rental capacity information of various energy storage types transmitted by the upper shared hybrid energy storage system optimization configuration layer in step 1, so as to minimize its own operating costs. Based on the objectives and constraints of the annual operating cost model, the mixed integer linear programming (MILP) problem is solved by the Gurobi solver. When the output of distributed power sources and electric and thermal loads is determined, the operating power of the energy storage leased by the integrated energy microgrid, the power of the interaction between the integrated energy microgrid and the grid, and the output power of the microgrid operating equipment, such as gas turbines and gas boilers, are calculated. The operating power information of the energy storage leased by the integrated energy microgrid is sent to the bottom power decomposition and distribution layer.

[0061] In one embodiment, the objective function for minimizing the annual operating cost and the corresponding constraints specifically include: setting the objective function as the minimum annual operating cost of the integrated energy microgrid and setting corresponding constraints, wherein the minimum annual operating cost includes the cost of the integrated energy microgrid purchasing electricity and gas from the grid and the rental cost and service fee cost of the integrated energy microgrid to the shared hybrid energy storage system; the constraints include power constraints of various types of energy storage, operating status constraints and capacity constraints of energy storage units, power constraints for interaction with the grid, power constraints and ramping constraints of gas turbines, power constraints and ramping constraints of gas turbines, power constraints of gas boilers, upper and lower operating power constraints of photovoltaic and wind turbines, electric power balance constraints within the integrated energy microgrid, thermal power balance constraints within the integrated energy microgrid, and thermal power balance constraints of gas turbines.

[0062] Specifically, the objective function is the minimum annual operating cost of the integrated energy microgrid, and the constraints are the power constraints of various types of energy storage, the operating status constraints and capacity constraints of energy storage units, the power constraints for interaction with the grid, the operating power constraints of each device, and the power balance constraints within the integrated energy microgrid:

[0063] (1) The objective function is the minimum annual operating cost of the integrated energy microgrid, as shown below:

[0064] min f2=C grid +C gas +C ser +C rent

[0065] C ser =R ser

[0066] C rent =R rent

[0067] C gridis the cost of the integrated energy microgrid purchasing electricity from the grid, which is calculated as follows:

[0068]

[0069] Among them, P grid,t is the power that the integrated energy microgrid purchases from the grid at time t, J grid,sell,t is the unit price of electricity sold by the power grid at time t;

[0070] C gas is the cost of purchasing gas for the integrated energy microgrid, which is calculated as follows:

[0071]

[0072] Among them, c gas is the unit price of gas, L gas is the calorific value of natural gas, G gt,t is the gas consumption of the gas turbine, G gb,t It is the gas consumption of gas boiler.

[0073] (2) Power constraints of various types of energy storage, operating status constraints of energy storage units, and capacity constraints:

[0074]

[0075]

[0076] U cha,x,t +U dis,x,t ≤1

[0077]

[0078] E x,t+1 =E x,t +ΔT(P cha,x,t η cha,x -P dis,x,t / η dis,x )

[0079] Among them, P cha,x,t and P dis,x,t is the charge and discharge power of different types of energy storage x at time t; and They are the upper and lower limits of charge and discharge power respectively; U cha,x,t and U dis,x,t It is the charge and discharge start and stop status flag of different types of energy storage x. Its value is 0 for shutdown and its value is 1 for startup. x,t is the capacity of the shared hybrid energy storage system at time t, and are the upper and lower limits of its capacity, η cha,x and η dis,xis the charge and discharge efficiency of different types of energy storage x.

[0080] (3) Interaction power constraints with the grid:

[0081]

[0082] in, and It is the upper and lower limits of the power that the integrated energy microgrid can purchase from the grid.

[0083] (4) Gas turbine power constraints and ramp constraints:

[0084]

[0085] Where Pgt,t is the operating power of the gas turbine at time t, and are the upper and lower limits of the gas turbine electrical power, and are the upper and lower limits of the gas turbine ramp rate.

[0086] (5) Power constraints of gas boilers:

[0087] H gb，t =G gb，t η gb

[0088]

[0089] Among them, H gb,t is the heating power output by the gas boiler at time t, G gb,t is the natural gas consumption of the gas boiler at time t, η gb is the efficiency coefficient of the gas boiler, and They are the upper and lower limits of the heating power of the gas boiler.

[0090] (6) The upper and lower limits of the operating power of photovoltaic and wind turbines are:

[0091]

[0092] Among them, P pv,t and P wt,t is the operating power of photovoltaic and wind turbine at time t, and are the upper and lower limits of photovoltaic operating power, and These are the upper and lower limits of the fan operating power.

[0093] (7) Electric power balance constraints within the integrated energy microgrid:

[0094] Pgt,t +P wt,t +P pv,t +P grid,t +P dis,x,t -P c a,x,t =P load,t

[0095] Among them, P load,t It is the electrical load within the integrated energy microgrid.

[0096] (8) Thermal power balance constraints within the integrated energy microgrid:

[0097] H gb,t +H he,t =H heat,t

[0098] Among them, H he,t is the heating power output of the heat exchanger at time t, H heat,t It is the heat load inside the integrated energy microgrid.

[0099] (9) Gas turbine thermal power balance constraints:

[0100] H he,t =P gt,t γ gt η wh η he

[0101] Among them, γ gt is the thermal power ratio of the gas turbine, η wh is the efficiency of the waste heat boiler, η he is the conversion efficiency of the heat exchanger.

[0102] Step 3: Construct a bottom power decomposition and allocation layer, set the objective function of minimizing the sum of the estimated bandwidths of each mode and the corresponding constraints, solve the objective function of minimizing the sum of the estimated bandwidths of each mode and the corresponding constraints based on the improved sparrow search optimization variational modal decomposition algorithm and the energy storage operating power information leased by the integrated energy microgrid, obtain the newly generated rental capacity information of various energy storage types, and send the newly generated rental capacity information of various energy storage types to the upper shared hybrid energy storage system optimization configuration layer and the middle integrated energy microgrid operation optimization layer.

[0103] In this step, a bottom power decomposition and distribution layer is constructed. The system controller, as the main decision maker, modifies the leasing capacity and power distribution mode of various energy storage types according to the energy storage operating power information leased by the integrated energy microgrid transmitted in step 2, so as to maintain the safety of the overall life of the shared hybrid energy storage system and the operating efficiency of the integrated energy microgrid; the charging and discharging power of the shared multi-type energy storage power station is decomposed by the improved sparrow search optimized variational mode decomposition (ISSA-VMD) algorithm, and the power signal is strategically reconstructed and then distributed to each type of energy storage unit, and the decomposed and reconstructed energy storage operating power information and the corresponding decomposed and reconstructed energy storage leasing capacity information are sent to the upper shared hybrid energy storage system optimization configuration layer and the middle integrated energy microgrid operation optimization layer.

[0104] In one embodiment, the improved sparrow search optimization variational modal decomposition algorithm specifically includes: setting the objective function as minimizing the sum of the estimated bandwidths of K modes decomposed from the charging and discharging power signals according to the variational modal decomposition algorithm, wherein the constraints include the equality of the sum of all modes with the original decomposed signal, the upper and lower limit constraints of the number of modes K, and the upper and lower limit constraints of the bandwidth parameter α; optimizing the K modes and the bandwidth parameter α according to the improved sparrow search algorithm; substituting the K modes and the bandwidth parameter α obtained from the optimization into the variational modal decomposition algorithm; decomposing the operating power information according to the substituted variational modal decomposition algorithm, and reconstructing the decomposed signal according to the contribution ratio of each type of energy storage component in the total energy storage in the integrated energy microgrid.

[0105] Specifically, the lower layer of the model mainly decomposes the charging and discharging power of a shared multi-type energy storage power station, strategically reconstructs the power signal, and then redistributes it to each type of energy storage unit. The objective function is the sum of the minimum estimated bandwidth of the K modes decomposed from the charging and discharging power signal. The constraints are that the sum of all modes is equal to the original decomposed signal, the upper and lower limits of the number of modes K, and the upper and lower limits of the bandwidth parameter α. Specifically expressed as:

[0106] (1) The objective function is to minimize the sum of the estimated bandwidths of each mode, as shown below:

[0107]

[0108] Among them, u k ={u1,u2,…,u k} are the modal functions, ω k ={ω1,ω2,…,ω k} is the center frequency of each mode, is the Hilbert transform, δ(t) is the impulse function, The purpose of the correction index is to modulate the spectrum of each modal function to the corresponding baseband. Finally, the charging and discharging power signals of the shared multi-type energy storage power station are demodulated by Gaussian smoothing (i.e., the square root of the L2 norm gradient) to obtain the bandwidth of each modal function.

[0109] To solve this variational problem, we can introduce the Lagrange multiplication operator and the quadratic penalty term to transform the constrained variational problem into an unconstrained variational problem, as shown in the following expression:

[0110]

[0111] Where α is the bandwidth constraint parameter and λ is the Lagrangian multiplier.

[0112] (2) The sum of all modes is equal to the original decomposed signal:

[0113]

[0114] P ess,t =P dis,x,t -P cha,x,t

[0115] Among them, is the sum of the energy storage charging and discharging power, charging is a negative value, and discharging is a positive value.

[0116] (3) Upper and lower limits of the modal number K:

[0117] K min ≤K≤K max

[0118] Among them, K max and K min are the upper and lower bounds of the number of modes K.

[0119] (4) Upper and lower limit constraints of bandwidth parameter α:

[0120] α min ≤α≤α max

[0121] Among them, α max and α min are the upper and lower limits of the bandwidth constraint parameter α.

[0122] The proposed ISSA-VMD algorithm aims to decompose the charging and discharging power of shared multi-type energy storage power stations. ISSA optimizes the number of modes K and the bandwidth constraint parameter α in the VMD algorithm. The algorithm process is divided into 12 steps:

[0123] (1) Initialize parameters, set the sparrow population N, parameter h, maximum number of generations M, safety threshold R, warning threshold ST, Cat random numbers a, b, c, etc.;

[0124] (2) Use the Cat chaos mapping strategy and reverse learning strategy to initialize the position of the sparrow population, calculate the fitness of the individuals and sort them from high to low, and determine the best and worst individuals respectively, which can be expressed as follows:

[0125] Cat mapping is a two-dimensional reversible chaotic mapping. The chaotic sequence generated in [0,1] is evenly distributed. The mathematical expression is:

[0126]

[0127] Among them, a, b, and c can be any real numbers; i represents the population size; j represents the chaos sequence number; in the process of optimizing the population, a=1, b=1, and c=1 are taken.

[0128] The reverse learning strategy takes advantage of the fact that elite individuals have more useful information than ordinary individuals. It constructs a reverse population from the elite individuals in the current population and adds it to the current population to increase the diversity of the population. It then selects the best specific individuals from the expanded new population to form a new generation of individuals and enters iterative updates.

[0129] The population initialization steps based on Cat mapping and reverse learning strategy are:

[0130] ① Generate N initial solutions X based on Cat chaotic map i .

[0131] ②Use the reverse learning strategy to generate the corresponding reverse solution for the initial solution:

[0132]

[0133] Among them: OP i For X i The reverse solution of , r is a random number uniformly distributed in the interval [0,1], are the minimum and maximum values ​​of the d-th dimension vector in all initial solutions respectively.

[0134] ③ Combine the initial solution with the reverse solution and sort them in increasing order of fitness value, and select the best solutions with the top N fitness values ​​as the initial population N i ;

[0135] (3) Dynamically adjust the number of discoverers and followers, specifically expressed as:

[0136] Add parameter h to dynamically adjust the number of discoverers N PD and the number of followers N SD , the expression of h is as follows:

[0137]

[0138] N PD = N × h

[0139] N SD = N × (1 - h)

[0140] Where, b takes the value of 0.7; m is the current iteration number; M is the total number of iterations; D is the perturbation factor with a value of 0.1;

[0141] (4) Update the position of the discoverer. The positions of the followers and the vigilant are updated according to the formula of the ordinary sparrow algorithm, specifically expressed as:

[0142] The position of the discoverer is only affected by the position of the discoverer in the previous iteration. The original formula is expressed as follows:

[0143]

[0144] Where, represents the position of the i-th sparrow in the d-th dimension at the m-th iteration; R is a random safety threshold that follows a uniform distribution in the interval (0, 1); ST is a set warning threshold in the interval (0, 1). When R < ST, the sparrows conduct global search for foraging; when R ≥ ST, the sparrows perform random walks with a normal distribution. Q is a random number that follows a normal distribution; L represents a 1×d matrix with all elements being 1.

[0145] In the above ordinary sparrow algorithm, the discoverer is not much different from the global optimal solution at the beginning of the iteration, resulting in too small population search range and falling into local optimum. Therefore, it is necessary to improve the formula for updating the position of the discoverer:

[0146]

[0147] In the formula, when R < ST, a new position update formula is adopted to have a larger value at the beginning of the iteration, which can effectively improve the search range. As the number of iterations increases, the formula value decreases, and the discoverer gradually switches to a depth mining mode near the optimal value, so as to better perform local search, improve the convergence speed, and avoid local optimum;

[0148] (5) Calculate the fitness of the entire population after this iteration is completed, and find the sparrow with the global optimum and introduce Cauchy mutation for perturbation to generate a new solution;

[0149] (6) Delete the duplicate solutions and randomly generate solutions for supplementation to enhance population diversity and avoid falling into local;

[0150] (7) Judge whether the algorithm has reached the maximum number of iterations. If so, jump to step 8; otherwise, continue the iteration and jump to step 3;

[0151] (8) The program ends and outputs the optimal solution of the number of modes K and bandwidth constraint parameter α in the VMD algorithm;

[0152] (9) Initialize the maximum number of iterations, modal function u, modal center frequency w, and Lagrangian multiplier in the VMD algorithm;

[0153] (10) Input the mode number K and bandwidth constraint parameter α solved by the ISSA algorithm, and iteratively solve u and w according to the lower-level objective function until the number of decompositions reaches the maximum number of iterations or the convergence condition is met and the loop stops.

[0154] The convergence conditions are as follows:

[0155]

[0156] Where χ is the convergence threshold;

[0157] (11) Extract each modal component from the original signal one by one.

[0158] (12) The proportion of each type of energy storage is set based on personal experience, and the decomposed signal is reconstructed according to the set proportion.

[0159] Step 4: The intermediate integrated energy microgrid operation optimization layer recalculates the leased energy storage operating power and the operating power information of the integrated energy microgrid system based on the newly generated rental capacity information of various energy storage types, and passes the leased energy storage operating power and the operating power information of the integrated energy microgrid system to the upper shared hybrid energy storage system optimization configuration layer.

[0160] In this step, the intermediate integrated energy microgrid operation optimization layer runs the global optimization solver Gurobi again to determine the leased energy storage operating power and the operating status of the integrated energy microgrid, and passes the above leased energy storage operating power and integrated energy microgrid operating power information to the upper layer.

[0161] Step 5: The upper-layer shared hybrid energy storage system optimization configuration layer calculates the objective function value based on the newly generated rental capacity information of various energy storage types, the rented energy storage operating power, and the operating power information of the integrated energy microgrid system. Through continuous iteration through the particle swarm algorithm, the objective function value of the upper-layer model in each iteration is compared until the convergence condition is reached, and then the global optimal solution is obtained; the integrated energy microgrid operation planning is carried out by solving the optimal rental capacity plan for each type of energy storage.

[0162] In this step, the upper model calculates the objective function value based on the decomposed and reconstructed energy storage operating power information and the corresponding decomposed and reconstructed energy storage rental capacity information transmitted in step 3 and the leased energy storage operating power and the operating power information of the integrated energy microgrid transmitted in step 4. Through continuous iteration through the particle swarm algorithm, the objective function value of the upper model in each iteration is compared to obtain the global optimal solution until the convergence condition is reached. The convergence condition is that the difference between the objective function of the previous iteration and the current iteration does not exceed ±1% for more than 10 times; the integrated energy microgrid operation planning is carried out by solving the optimal energy storage rental capacity plan of each type.

[0163] As an example, according to step 5, the optimal capacity plan for leasing various types of energy storage is solved, and the comprehensive annual cost of the shared hybrid energy storage system and the annual operating cost of the integrated energy microgrid in the plan are calculated. The results are shown in Table 1. The data shows that the integrated energy microgrid rents more energy storage resources. If it is necessary to invest in the construction of energy storage equipment of the same capacity, the economic cost of the integrated energy microgrid will be greatly increased. However, by adopting the sharing economy model of the shared hybrid energy storage system, the economic cost of the integrated energy microgrid has dropped by more than 50%. For the shared hybrid energy storage system, the benefits of adopting the sharing economy model are also considerable. It obtains income by charging rent for leasing energy storage capacity and providing charging and discharging service fees, and the income accounts for 25.79% of the cost. The intelligent algorithm is continuously updated and iterated to solve the optimal configuration plan and the various calculation results in the plan. The results of the integrated energy microgrid's leasing energy storage charging and discharging power, the operating power of various equipment within the microgrid, and the power of interaction with the grid are shown in the following table. Figure 4 As shown in the figure, the integrated energy microgrid mainly purchases electricity from the grid between 02:00 and 08:00, and exchanges electricity with the shared hybrid energy storage system during the rest of the time. This is because the grid price is higher outside of this period, and the energy storage service fee provided by the shared hybrid energy storage system is also cheaper. In addition, the operating power fluctuations of various types of energy storage are relatively reasonable, such as Figure 5 As shown, it is beneficial to the overall life of the energy storage system.

[0164] In summary, the present invention proposes a three-layer planning method for capacity configuration of shared hybrid energy storage systems in integrated energy microgrids. This method realizes the accurate calculation of the shared multi-type energy storage rental configuration capacity and comprehensive consideration of the economic cost by constructing an upper-layer shared hybrid energy storage system optimization configuration layer, an intermediate integrated energy microgrid operation optimization layer, and a bottom power decomposition and allocation layer. In the three-layer planning model, the upper-layer model uses a particle swarm algorithm to solve the long-time scale user-side energy storage total capacity leasing optimization configuration problem to minimize the annual comprehensive cost of the shared hybrid energy storage system; the middle-layer model uses a Gurobi solver to solve the short-time scale user-side combined heat and power microgrid system optimization operation problem to minimize the annual operating cost of the integrated energy microgrid; the lower-layer model uses an improved sparrow search optimized variational mode decomposition (ISSA-VMD) algorithm to decompose the charging and discharging power of the shared multi-type energy storage power station, and strategically reconstructs the power signal and then distributes it to each type of energy storage unit.

[0165] This method not only effectively calculates the optimal configuration capacity of shared multi-type energy storage, but also demonstrates the effectiveness of the shared economy model for shared hybrid energy storage systems in integrated energy microgrids. Examples demonstrate that this model can significantly reduce the operating and overall investment costs of integrated energy microgrids, while also enabling the shared hybrid energy storage system to achieve a good rate of return.

[0166] Table 1. Capacity configuration and economic cost results of shared multi-type energy storage rental

[0167]

[0168] The photovoltaic, wind turbine and electric heating load data for a typical day are shown in Table 2.

[0169] Table 2 Typical daily photovoltaic, wind turbine and electric heating load data

[0170]

[0171]

[0172] Table 3 below shows the specific price parameters of the shared hybrid energy storage system involved in the present invention. It should be noted here that the specific parameter settings of the system are not limited to the one provided in this embodiment and can be specially designed according to the needs of the researcher himself.

[0173] Table 3 Specific price parameters of shared hybrid energy storage system

[0174]

[0175] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0176] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A three-layer planning method for capacity configuration of a shared hybrid energy storage system in an integrated energy microgrid, characterized by: The steps include: Construct an upper-layer shared hybrid energy storage system optimization configuration layer, set the objective function of minimizing the annual comprehensive cost and the corresponding constraints, solve the objective function of minimizing the annual comprehensive cost and the corresponding constraints through the particle swarm algorithm, obtain the rental capacity information of various energy storage types, and send the rental capacity information of various energy storage types to the intermediate integrated energy microgrid operation optimization layer; Construct an intermediate integrated energy microgrid operation optimization layer, set the objective function of minimizing the annual operating cost and the corresponding constraints, and solve the objective function of minimizing the annual operating cost and the corresponding constraints by solving the mixed integer linear programming method using the Gurobi solver based on the rental capacity information of various energy storage types transmitted by the upper shared hybrid energy storage system optimization configuration layer. Obtain the energy storage operating power leased by the integrated energy microgrid, and send the energy storage operating power information leased by the integrated energy microgrid to the bottom power decomposition and distribution layer; Constructing a bottom power decomposition and allocation layer, setting an objective function and corresponding constraints for minimizing the sum of the estimated bandwidths of each modal, solving the objective function and corresponding constraints for minimizing the sum of the estimated bandwidths of each modal based on an improved sparrow search optimization variational modal decomposition algorithm and the energy storage operating power information leased by the integrated energy microgrid, obtaining newly generated leased capacity information of various energy storage types, and sending the newly generated leased capacity information of various energy storage types to the upper shared hybrid energy storage system optimization configuration layer and the intermediate integrated energy microgrid operation optimization layer; The intermediate integrated energy microgrid operation optimization layer recalculates the leased energy storage operating power and the operating power information of the integrated energy microgrid system based on the newly generated leased capacity information of various energy storage types, and transmits the leased energy storage operating power and the operating power information of the integrated energy microgrid system to the upper shared hybrid energy storage system optimization configuration layer; The upper-layer shared hybrid energy storage system optimization configuration layer calculates the objective function value based on the newly generated rental capacity information of various energy storage types, the rented energy storage operating power, and the operating power information of the integrated energy microgrid system. Through continuous iteration through the particle swarm algorithm, the objective function value of the upper-layer model in each iteration is compared until the convergence condition is reached, thereby obtaining the global optimal solution; the integrated energy microgrid operation planning is carried out by solving the optimal rental capacity plan for each type of energy storage.

2. The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid according to claim 1, characterized in that: The objective function and corresponding constraints for minimizing the annual comprehensive cost include: Set the objective function as the minimum annual comprehensive cost of the shared hybrid energy storage system and set the corresponding constraints. The annual comprehensive cost includes the sum of the investment cost of the annual leased energy storage capacity and the annual operation and maintenance cost of the annual leased energy storage capacity, minus the rental income and service fee income of the annual leased energy storage capacity; Constraints include capacity limits for various types of energy storage.

3. The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid according to claim 1, characterized in that: The objective function and corresponding constraints for minimizing annual operating costs include: Set the objective function as the minimum annual operating cost of the integrated energy microgrid and set the corresponding constraints. Among them, the minimum annual operating cost includes the cost of purchasing electricity and gas from the grid for the integrated energy microgrid and the rental cost and service fee cost of the shared hybrid energy storage system for the integrated energy microgrid; The constraints include power constraints of various types of energy storage, operating status constraints and capacity constraints of energy storage units, power constraints for interaction with the grid, power constraints and ramping constraints of gas turbines, power constraints and ramping constraints of gas turbines, power constraints of gas boilers, upper and lower operating power constraints of photovoltaic and wind turbines, electric power balance constraints within the integrated energy microgrid, thermal power balance constraints within the integrated energy microgrid, and thermal power balance constraints of gas turbines.

4. The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid according to claim 1, characterized in that: The convergence conditions specifically include: the difference between the objective function value of the previous iteration and the current iteration does not exceed ±1% for more than 10 times.

5. The three-layer planning method for capacity configuration of a shared hybrid energy storage system for an integrated energy microgrid according to claim 1, characterized in that: The improved sparrow search optimization variational mode decomposition algorithm specifically includes: The objective function is set to minimize the sum of the estimated bandwidths of the K modes decomposed from the charge and discharge power signal. The constraints include the equality of the sum of all modes and the original decomposed signal, the upper and lower limits of the number of modes K, and the upper and lower limits of the bandwidth parameter α. The K modes and bandwidth parameter α are optimized based on the improved sparrow search algorithm; Substitute the K modes and bandwidth parameter α obtained through optimization into the variational mode decomposition algorithm; The operating power information is decomposed according to the substituted variational mode decomposition algorithm, and the decomposed signal is reconstructed according to the contribution ratio of each type of energy storage component in the total energy storage in the integrated energy microgrid.

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