A shared energy storage system bi-level programming method, system, device and medium considering energy storage life loss

Abstract

The application discloses a shared energy storage system double-layer planning method, system, equipment and medium considering energy storage life loss, and relates to the technical field of energy storage system operation control and capacity planning. The method comprises the following steps: acquiring basic parameters of a shared energy storage system; the basic parameters comprise equipment cost, equipment parameters, load data, and wind, light and power grid data; inputting the basic parameters into a shared energy storage system double-layer optimization model, performing optimization operation through the shared energy storage system double-layer optimization model, outputting optimal energy storage power station capacity and other power station power, determining daily operation schemes of each equipment, and sharing energy storage power station cost based on a Shapley value method. The application can optimize daily operation modes and configuration capacity of the shared energy storage system by considering energy storage loss, and reduce system operation and maintenance and investment costs.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system operation control and capacity planning, and in particular to a double-layer planning method, system, equipment and medium for a shared energy storage system taking into account energy storage life loss. Background Art

[0002] Current renewable energy technologies are rapidly developing. However, with the large-scale integration of renewable energy into the power grid, their inherent intermittency and volatility pose unprecedented challenges to the safe and stable operation of the grid. To address the challenge of unstable renewable energy output, the integration of energy storage is being considered to increase its utilization rate. Energy storage can also play an important role in peak load shifting and valley filling, improving power quality. Batteries, as core components in energy storage systems, age more rapidly than other components in the system. This rapid aging not only poses a significant safety hazard but also further increases safety risks due to the use of aged batteries, inevitably leading to shorter battery replacement cycles. This frequent replacement requirement undoubtedly directly impacts the overall operation and maintenance of the energy storage system, as well as the investment cost. Summary of the Invention

[0003] The purpose of the present invention is to provide a two-tier planning method, system, equipment and medium for a shared energy storage system that takes into account the energy storage life loss, thereby optimizing the shared energy storage system and reducing system operation and maintenance and investment costs by considering the energy storage loss.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A two-level planning method for a shared energy storage system taking into account energy storage life loss comprises:

[0006] Obtaining basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, solar, and grid data;

[0007] The basic parameters are input into a two-layer optimization model of a shared energy storage system, and the two-layer optimization model of the shared energy storage system performs optimization calculations to output the optimal energy storage power station capacity and other power station powers, determine the typical daily operation plan of each device, and share the cost of the energy storage power station based on the Shapley value method; the two-layer optimization model of the shared energy storage system includes a battery single cycle life loss model based on the Arrhenius equation, an outer capacity optimization model, and an inner operation optimization model; the outer capacity optimization model includes outer constraints and a first objective function; the inner operation optimization model includes inner constraints and a second objective function for typical days in four seasons; wherein, the first objective function is a function with the goal of minimizing annual operating costs, and the second objective function is a function with the goal of minimizing daily operating costs.

[0008] Optionally, the battery single cycle life loss model based on the Arrhenius equation is:

[0009]

[0010] Among them, Q loss is the battery life loss, R is the molar gas constant, T is the ambient temperature, H is the depth of discharge, E c is the rated capacity of the energy storage battery.

[0011] Optionally, the outer constraint conditions include device capacity constraint and device power constraint;

[0012] The equipment capacity constraint is:

[0013] X k,min ≤X k ≤X k,max

[0014] Among them, X k,min is the minimum capacity of the kth type of equipment, X k,max is the maximum capacity of the kth type of equipment;

[0015] The device power constraint is:

[0016] P k,min ≤P k ≤P k,max

[0017] Among them, P k,min is the minimum power of the kth type of equipment, P k,max is the maximum power of the kth category equipment.

[0018] Optionally, the first objective function includes:

[0019]

[0020] Among them, C is the annual cost; C i C is the investment and construction cost; o is the annual operation and maintenance cost; C ef is the annual electricity purchase and sales cost and the annual gas turbine fuel cost; C loss is the annual energy storage loss cost; K is the set of investment equipment types; c k,i is the unit capacity cost of the kth type of equipment; X k is the capacity of the kth type of equipment; J is the set of summer, spring, autumn and winter; D is the total number of days in the jth season; c k,o P is the operation and maintenance cost coefficient of the kth type of equipment; k The operating power of the kth category equipment; c p P is the electricity price; buy P is the purchased power; sellP fuel is the fuel cost; P g is the gas turbine power.

[0021] Optionally, the four seasonal typical day intra-day constraints include an uncertain power constraint, a device daily power constraint, a power grid constraint, a shared energy storage system power balance constraint, an energy storage power constraint, an energy storage state of charge constraint, and an energy storage loss constraint.

[0022] The uncertain power constraint is:

[0023] (1-w k )·P n,k,max ≤P n,k ≤(1+w k )·P n,k,max

[0024] where w k is the uncertainty; P n,k,max is the maximum power of the nth period of the kth type of device; P n,k is the power of the nth period of the kth type of device.

[0025] The device daily power constraint is:

[0026] P n,k,min ≤P n,k ≤P n,k,max

[0027] where P n,k,min is the minimum power of the nth period of the kth type of device.

[0028] The power grid constraint is:

[0029]

[0030] where P n,buy is the power purchase of the nth period; P m,max is the power grid exchange power threshold; P n,sell is the power sale of the nth period; λ n,m is the power purchase and sale identifier, a Boolean variable; M takes 1×10 7 .

[0031] The shared energy storage system power balance constraint is:

[0032] p n,buy -p n,sell +p n,g -p n,ch +p n,dis +p n,wt +p n,pv =p n,load

[0033] Pn is the nth period gas turbine power; P n,g Pn is the nth period energy storage battery charging power; P n,ch Pn is the nth period energy storage battery discharging power; P n,dis Pn is the nth period energy storage battery discharging power; P n,wt Pn is the nth period wind turbine power; P n,pv Pn is the nth period photovoltaic power; P n,load Pn is the nth period load power;

[0034] The energy storage power constraint is:

[0035]

[0036] Pmax is the energy storage battery maximum power; λ bat,max is the charging and discharging identifier, a Boolean variable; P n,bat

[0037] The energy storage state of charge constraint is:

[0038]

[0039] Sn is the nth period energy storage battery SOC; Sn-1 is the (n-1)th period energy storage battery SOC; η is the energy storage battery charging and discharging efficiency; S n,bat n-1,bat bat,min bat,max d,bat,start d,bat,end

[0040] The energy storage wear constraint is:

[0041]

[0042] Bf is the energy storage wear segment identifier, a Boolean variable; Qf is the amount of energy storage battery life wear on the fth segment; F is the number of energy storage battery life wear function segments. f f,loss

[0043] Optionally, the second objective function includes:

[0044]

[0045] Cday is the daily cost; C d d,o d,ef d,loss d,k ​​​​​​​​​​​​​P is the operating power of the kth type equipment d on a typical day; d,buy is the purchased power on a typical day; P d,sell P is the electricity sales power on a typical day; d,g d is the gas turbine power on a typical day; c bat is the unit capacity cost of the battery; Q d,loss is the battery capacity loss in a typical day; L h E is the end of battery life limit; bat is the capacity of the energy storage power station; c bat,x is the energy storage loss conversion coefficient; N is the number of hours in a typical day; Q n,loss is the battery loss rate in the n-th hour period.

[0046] The present invention also provides a two-tier planning system for a shared energy storage system taking into account energy storage life loss, comprising:

[0047] A parameter acquisition module is used to obtain basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, solar, and grid data;

[0048] A two-layer planning module is used to input the basic parameters into a two-layer optimization model of a shared energy storage system, perform optimization calculations through the two-layer optimization model of the shared energy storage system, output the optimal energy storage power station capacity and other power station powers, determine the typical daily operation plan of each device, and share the cost of the energy storage power station based on the Shapley value method; the two-layer optimization model of the shared energy storage system includes a battery single cycle life loss model based on the Arrhenius equation, an outer capacity optimization model and an inner operation optimization model; the outer capacity optimization model includes outer constraints and a first objective function; the inner operation optimization model includes inner constraints and a second objective function for typical days in four seasons; wherein, the first objective function is a function with the goal of minimizing annual operating costs, and the second objective function is a function with the goal of minimizing daily operating costs.

[0049] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned two-tier planning method for a shared energy storage system taking into account the energy storage life loss.

[0050] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned two-tier planning method for a shared energy storage system taking into account energy storage life loss.

[0051] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0052] The application discloses a shared energy storage system double-layer planning method, system, equipment and medium considering energy storage life loss, the method comprises the following steps: acquiring basic parameters of a shared energy storage system; the basic parameters comprise equipment cost, equipment parameters, load data, and wind, light and power grid data; inputting the basic parameters into a shared energy storage system double-layer optimization model, performing optimization operation through the shared energy storage system double-layer optimization model, outputting optimal energy storage power station capacity and other power station power, determining daily operation schemes of each equipment, and sharing energy storage power station cost based on a Shapley value method. The application can optimize daily operation modes and configuration capacity of the shared energy storage system by considering energy storage loss, and reduce system operation and maintenance and investment costs. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0054] Figure 1 A step diagram of the shared energy storage system double-layer planning method considering energy storage life loss of the present application;

[0055] Figure 2 A schematic diagram of the results of fitting the Arrhenius equation for each temperature in the present embodiment;

[0056] Figure 3 A schematic diagram of the verification results in the present embodiment;

[0057] Figure 4 A schematic diagram of the linearization process of the battery loss curve in the present embodiment;

[0058] Figure 5 A shared energy storage system double-layer optimization structure diagram in the present embodiment;

[0059] Figure 6 A shared energy storage system double-layer optimization solution flowchart in the present embodiment;

[0060] Figure 7 A comparison diagram of whether to consider energy storage loss for four typical days in the present embodiment; wherein, (a) is a typical day 1 SOC comparison diagram; (b) is a typical day 2 SOC comparison diagram; (c) is a typical day 3 SOC comparison diagram; and (d) is a typical day 4 SOC comparison diagram. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0062] The purpose of the present invention is to provide a two-tier planning method, system, equipment and medium for a shared energy storage system that takes into account the loss of energy storage life. By considering the energy storage loss, the daily operation mode and configuration capacity of the shared energy storage system can be optimized, thereby reducing the system operation and maintenance and investment costs.

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] like Figure 1 As shown, the present invention provides a two-level planning method for a shared energy storage system taking into account energy storage life loss, comprising:

[0065] Step 100: Obtain basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, solar, and grid data;

[0066] Step 200: Input the basic parameters into the shared energy storage system two-layer optimization model, perform optimization calculations through the shared energy storage system two-layer optimization model, output the optimal energy storage power station capacity and other power station powers, determine the typical daily operation plan of each device, and share the energy storage power station cost based on the Shapley value method; the shared energy storage system two-layer optimization model includes a battery single cycle life loss model based on the Arrhenius equation, an outer capacity optimization model and an inner operation optimization model; the outer capacity optimization model includes outer constraints and a first objective function; the inner operation optimization model includes inner constraints and a second objective function for typical days in four seasons; wherein, the first objective function is a function with the goal of minimizing annual operating costs, and the second objective function is a function with the goal of minimizing daily operating costs.

[0067] Based on the above technical solution, the following embodiments are provided.

[0068] For the battery single cycle life loss model based on the Arrhenius equation, the life of the energy storage device is mainly reflected in the remaining capacity of the battery (State of Health, SOH). When the SOH is lower than 80%, it enters the scrap state. The reduction of the battery's SOH is mainly related to the formation of the solid electrolyte interphase (SEI) layer and lithium deposition. The SOH calculation method based on the Arrhenius equation combines the rigor of physical principles with the practicality of empirical laws. By introducing adjustable parameters, the model can calculate the battery's SOH even when the data is incomplete. The SOH calculation method that integrates the Arrhenius equation is also more suitable for long-term operation systems. Therefore, it is considered to use the SOH calculation method based on the Arrhenius equation to calculate the battery life loss in each period of daily operation of the shared energy storage system.

[0069] The SOH of a battery is mainly related to the depth of discharge, number of cycles, ambient temperature and charge and discharge rate.

[0070] Q=f(H,α,T e ,P rate ) (1)

[0071] Where, Q is the battery life loss percentage, %; H is the depth of discharge, %; α is the cycle time, s; P rate is the charge and discharge rate, %.

[0072] When the charge and discharge rate remains unchanged, the battery life loss and cycle number satisfy the Arrhenius equation.

[0073]

[0074] Where A is the Arrhenius constant; E a is the apparent activation energy, J / mol; R is the molar gas constant, J / (mol·K); T is the thermodynamic temperature, K; and z is the power law factor.

[0075] Introducing the total cycle power of the battery throughout its life cycle A h Instead of the cycle time α, the charge and discharge rate P rate When a certain time, A h Positively correlated with α.

[0076]

[0077] Where, t is the charge and discharge time, s; E c is the rated capacity of the battery, Ah; n is the number of cycles, times; A r For A h The Arrhenius constant after replacing α.

[0078] To calculate the A related to the material itself and the ambient temperature r and z, taking the logarithm of the function in equation (3).

[0079]

[0080] The slope Y at this time is:

[0081]

[0082] Based on the battery loss data from the NASA dataset, lithium iron phosphate (LiFePO4) batteries were cycled at 4°C, 24°C, and 43°C to determine the Arrhenius equation coefficients. The fitting results are shown in Figure 2. Figure 2 Except for 4℃, R 2 The fitting effect is poor, which may be related to the accelerated battery loss caused by lithium deposition in lithium batteries. The linear fitting slopes are basically parallel at 24℃ and 43℃, indicating that the power law factor z is independent of the ambient temperature.

[0083] Molar gas constant R and apparent activation energy E a As a constant, we can calculate A by substituting the ambient temperature T and slope Y into r Finally, all parameters A r 、E a Substituting , R and z into formula (3) can obtain the battery life loss Q.

[0084]

[0085] Under a single cycle, the battery life loss function Q loss for:

[0086]

[0087] The obtained battery loss function is verified using the battery aging data of Beijing Institute of Technology at 20℃. The verification results are as follows: Figure 3 shown.

[0088] according to Figure 3 As shown, before the end of the battery life (SOH=80%), the obtained battery loss function is basically consistent with the actual loss curve trend.

[0089] Because the battery life loss function Q loss Since it is a nonlinear function, Taylor expansion is used for linearization. When the linearization line is expanded to 50 segments, the trend of the linearized line and the curve are basically the same. The linearization process is as follows: Figure 4 shown.

[0090] The Taylor expansion linearization model of the battery loss curve is:

[0091] Q loss (H)=Q loss (H0)+Q' loss (H0)(H-H0)+o((H-H0) 2 ) (8)

[0092] where H0 is the midpoint of the discharge depth of the segment, %.

[0093] For the shared energy storage system double-layer optimization model, the operation control and capacity configuration of the shared energy storage system affect each other. By constructing a double-layer optimization model, the configuration and control problems can be considered simultaneously. The outer layer takes the minimum annual investment cost as the objective, and the optimal capacity of the equipment is solved; the inner layer takes the minimum daily operation and maintenance cost as the objective, and the optimal strategy of daily operation control is obtained. The double-layer optimization configuration structure of the shared energy storage system is shown in FIG. 1. Figure 5

[0094] The capacity optimization layer (the outer layer) takes the minimum annual cost C as the objective function, including the investment and construction cost C i , the annual operation and maintenance cost C o , the annual purchase and sale of electricity and the annual gas turbine fuel cost C ef , and the annual energy storage loss cost C loss , wherein the annual energy storage loss cost C loss is obtained by the operation optimization layer (the inner layer). The annual cost calculation formula is as follows:

[0095]

[0096] wherein K is the investment equipment type set; c k,i is the unit capacity cost of the kth equipment, yuan; X k is the capacity of the kth equipment; J is the set of summer, spring, autumn and winter; D is the total number of days in the jth season; c k,o is the operation and maintenance cost coefficient of the kth equipment; P k is the operation power of the kth equipment, kW; c p is the electricity selling price, yuan; P buy is the purchase power, kW; P sell is the sale power, kW; c fuel is the fuel cost, yuan; P g is the gas turbine power, kW.

[0097] wherein the battery operation and maintenance cost coefficient c bat,o is mainly caused by the life loss, and is in a discount rate relationship with the battery degree of electricity operation and maintenance cost c kW,bat,o .

[0098]

[0099] wherein r is the investment recovery coefficient, taken as 7%; r​bat For the service life of the energy storage power station, years.

[0100] Capacity optimization layer constraint condition

[0101] 1) Capacity constraint:

[0102] X k,min ≤X k ≤X k,max (11)

[0103] In the formula, X k,min is the minimum capacity of the kth type of equipment, kWh; X k,max is the maximum capacity of the kth type of equipment, kWh.

[0104] 2) Power constraint:

[0105] P k,min ≤P k ≤P k,max (12)

[0106] In the formula, P k,min is the minimum power of the kth type of equipment, kW; P k,max is the maximum power of the kth type of equipment, kW.

[0107] Operation optimization layer objective function

[0108] The operation optimization layer (inner layer) minimizes the daily cost C d , including the daily operation and maintenance cost C d,o , the daily electricity purchase and sale, and the daily gas turbine fuel cost C d,ef , and the daily energy storage loss cost C d,loss . The daily cost calculation formula is:

[0109]

[0110] In the formula, P d,k is the operation power of the kth type of equipment in the d typical day, kW; P d,buy is the electricity purchase power in the d typical day, kW; P d,sell is the electricity sale power in the d typical day, kW; P d,g is the gas turbine power in the d typical day, kW; c bat is the unit capacity cost of the battery, yuan; Q d,loss is the battery capacity loss in the d typical day, %; L h is the battery life termination limit, %; E bat is the energy storage power station capacity, kWh; c bat,x is the energy storage loss conversion coefficient; N is the number of hour segments in the d typical day, taking 24 segments; Q n,loss is the battery loss rate in the nth hour segment, %.

[0111] Run optimization layer constraints

[0112] 1) Uncertain power constraints

[0113] (1-w k )·P n,k,max ≤P n,k ≤(1+w k )·P n,k,max (14)

[0114] Where w k is the uncertainty, %; P n,k,max is the maximum power of the kth type of equipment in the nth period, kW; P n,k is the power of the kth type of equipment in the nth period, kW.

[0115] 2) Equipment power constraints

[0116] P n,k,min ≤P n,k ≤P n,k,max (15)

[0117] Where, P n,k,min is the minimum power of the kth type of equipment in the nth period, kW.

[0118] 3) Distribution network constraints

[0119] Because the distribution network constraint is a nonlinear constraint, the Big-M method is used to linearize the nonlinear condition, where the M value does not need to be infinite, as long as the complementary condition is satisfied. 7 .

[0120]

[0121] Where, P n,buy P is the power purchased in the nth period, kW; m,max is the power exchange threshold of the distribution network, kW; P n,sell is the electricity sales power in the nth period, kW; λ n,m It is the identifier of electricity purchase and sale, a Boolean variable.

[0122] 4) Power balance constraints of shared energy storage systems

[0123] p n,buy -p n,sell +p n,g -p n,ch +p n,dis +p n,wt +p n,pv =p n,load (17)

[0124] Where, P n,gis the gas turbine power in the nth period, kW; P n,ch P is the energy storage battery charging power in the nth period, kW; n,dis P is the discharge power of the energy storage battery in the nth period, kW; n,wt P is the fan power in the nth period, kW; n,pv is the photovoltaic power in the nth period, kW; P n,load is the load power in the nth period, kW.

[0125] 5) Energy storage power constraints

[0126] The M variable linearization constraint is also introduced.

[0127]

[0128] Where, P bat,max is the maximum power of the energy storage battery, kW; λ n,bat It is the charge and discharge identifier, a Boolean variable.

[0129] 6) Energy storage state of charge (SOC) constraints

[0130]

[0131] Where S n,bat is the SOC of the energy storage battery in the nth period, %; S n-1,bat is the SOC of the energy storage battery in the n-1 period, %; η is the charge and discharge efficiency of the energy storage battery, %; S bat,min is the minimum SOC of the energy storage battery, %; S bat,max is the maximum SOC of the energy storage battery, %; S d,bat,start is the starting SOC of the energy storage battery for one day, %; S d,bat,end Ending SOC of the energy storage battery for one day, %;

[0132] 7) Energy storage loss constraints

[0133]

[0134] Where B f Q is the energy storage loss segment identifier, a Boolean variable; f,loss is the life loss of the energy storage battery on the fth segment, %; F is the number of segments of the energy storage battery life loss function, which is 50.

[0135] Cost sharing method for shared energy storage system based on Shapley value method

[0136] The core of the Shapley value method is to apportion total costs based on each participant's average marginal contribution to the system. In other words, the cost each participant should bear is the average of their marginal contributions to each collaborative project they participate in. For multi-party collaborative projects like shared energy storage systems, using the Shapley value method to allocate costs based on the needs of each participant is more beneficial to all parties than an equal distribution.

[0137] Assume that there are θ parties participating in the construction of a shared energy storage system. There are (|β|-1)! permutations of one party participating in the construction of the shared energy storage system, where |β| is the number of parties that have reached a cooperation agreement to build a shared energy storage power station. The remaining (θ-|β|) parties that have not reached a cooperation agreement have (θ-|β|)! permutations and combinations. The allocation weight is calculated as follows:

[0138]

[0139] Where, ω v is the weight of the participation of the vth party.

[0140] The cost of the third party participating in cost sharing is C v for:

[0141]

[0142] Where C v (β) is the cost of participating in co-construction, RMB; C v (β / v) is the cost when not participating in co-construction, RMB.

[0143] Solution steps

[0144] For the constructed two-layer optimization model, the battery life loss curve derived from the Arrhenius equation is first added as a constraint to the inner layer. At the same time, the Cplex solver is used to obtain the optimal operation control plan with the minimum daily operation cost as the objective function. The operation control plan is then passed to the outer layer, and the equipment configuration capacity is obtained with the minimum annual operation cost as the objective function. Finally, the cost of the shared energy storage system is allocated based on the Shapley value method. The specific solution process is as follows:

[0145] 1) Initialize the system, enter the basic system parameters, and set the maximum number of iterations I.

[0146] 2) Determine the coefficients of the Arrhenius equation based on the cycle test data at different temperatures, and use Taylor expansion to obtain the linearized battery life loss function, which is passed to the inner layer as an inner layer constraint.

[0147] 3) Taking the minimum daily operating cost as the objective function, the Cplex solver is called to solve the inner layer operation control scheme, and the operation control scheme is passed to the outer layer as a constraint condition.

[0148] 4) According to the input system basic parameters and internal, capacity and power constraints, the optimal configuration capacity and power of the shared energy storage system equipment are solved with the minimum annual operating cost as the objective function.

[0149] 5) Determine whether the maximum number of iterations I has been reached. If not, repeat steps 3)-4); if reached, output the optimal value.

[0150] 6) Based on the Shapley value method, the cost of the shared energy storage system is allocated to obtain the economic cost that each party needs to bear.

[0151] The flowchart of the two-layer optimization solution of the shared energy storage system is as follows: Figure 6 shown.

[0152] Therefore, from the above process calculation, it can be concluded that in order to solve the problem of battery loss calculation of shared energy storage system, the battery single cycle life loss model based on Arrhenius equation is adopted; in terms of daily operation control, whether the comparison before and after energy storage loss is considered on four typical days Figure 7 As shown in the figure, it can be seen that when the battery is not charged and discharged, the depth of charge and discharge is large, and the battery loss degree increases. If the loss problem is not considered, the equipment replacement cost will increase significantly. In terms of configuration capacity, the energy storage capacity configuration is reduced by 9.45% after considering the energy storage loss. By using shared energy storage instead of distributed independent energy storage power stations, and after using the Shapley value method to share the cost calculation, the way of jointly building and sharing energy storage power stations is adopted. New energy can reduce the total investment cost by about 4%, and traditional power stations can reduce the total investment cost by about 2%.

[0153] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0154] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A two-level planning method for a shared energy storage system taking into account energy storage life loss, characterized in that: include: Obtaining basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, solar, and grid data; The basic parameters are input into a two-layer optimization model of a shared energy storage system, and the two-layer optimization model of the shared energy storage system performs optimization calculations to output the optimal energy storage power station capacity and other power station powers, determine the typical daily operation plan of each device, and allocate the cost of the shared energy storage power station based on the Shapley value method; the two-layer optimization model of the shared energy storage system includes a battery single cycle life loss model based on the Arrhenius equation, an outer capacity optimization model, and an inner operation optimization model; the outer capacity optimization model includes outer constraints and a first objective function; the inner operation optimization model includes inner constraints and a second objective function for typical days in four seasons; wherein, the first objective function is a function with the goal of minimizing annual operating costs, and the second objective function is a function with the goal of minimizing daily operating costs; The battery single cycle life loss model based on the Arrhenius equation is: Among them, Q loss is the battery life loss, R is the molar gas constant, T is the ambient temperature, H is the depth of discharge, E c is the rated capacity of the energy storage battery; The outer constraints include equipment capacity constraints and equipment power constraints; The equipment capacity constraint is: X k,min ≤X k ≤X k,max Among them, X k,min is the minimum capacity of the kth type of equipment, X k,max is the maximum capacity of the kth type of equipment; The device power constraint is: P k,min ≤P k ≤P k,max Among them, P k,min is the minimum power of the kth type of equipment, P k,max is the maximum power of the kth category equipment; The inner constraints of typical days in the four seasons include uncertain power constraints, equipment daily power constraints, distribution network constraints, shared energy storage system power balance constraints, energy storage power constraints, energy storage state of charge constraints, and energy storage loss constraints; The uncertain power constraint is: (1-w k )·P n,k,max ≤P n,k ≤(1+w k )·P n,k,max Among them, w k is the uncertainty; P n,k,max is the maximum power of the kth type of equipment in the nth period; P n,k The power of the kth type of equipment in the nth period; The equipment daily power constraint is: P n,k,min ≤P n,k ≤P n,k,max Among them, P n,k,min The minimum power of the kth category equipment in the nth period; The distribution network constraints are: Among them, P n,buy P is the electricity purchased in the nth period; m,max is the power exchange threshold of the distribution network; P n,sell is the electricity sold in the nth period; n,m is the electricity purchase and sale identifier, a Boolean variable; M is 1×10 7 ; The power balance constraint of the shared energy storage system is: p n,buy -p n,sell +p n,g -p n,ch +p n,dis +p n,wt +p n,pv =p n,load Among them, P n,g is the gas turbine power in the nth period; P n,ch P is the charging power of the energy storage battery in the nth period; n,dis P is the discharge power of the energy storage battery in the nth period; n,wt is the fan power in the nth period; P n,pv is the photovoltaic power in the nth period; P n,load is the load power in the nth period; The energy storage power constraint is: Among them, P bat,max is the maximum power of the energy storage battery; n,bat is the charge and discharge identifier, a Boolean variable; The energy storage state of charge constraint is: Among them, S n,bat is the SOC of the energy storage battery in the nth period; S n-1,bat is the SOC of the energy storage battery in the n-1 period; η is the charge and discharge efficiency of the energy storage battery; S bat,min is the minimum SOC of the energy storage battery; S bat,max is the maximum SOC of the energy storage battery; S d,bat,start The starting SOC of the energy storage battery for one day; S d,bat,end Termination of SOC for one-day energy storage battery; The energy storage loss constraint is: Among them, B f Q is the energy storage loss segment identifier, a Boolean variable; f,loss is the life loss of the energy storage battery on the fth segment; F is the number of segments of the energy storage battery life loss function.

2. The two-level planning method for a shared energy storage system taking into account energy storage life loss according to claim 1 is characterized in that: The first objective function includes: Among them, C is the annual cost; C i C is the investment and construction cost; o is the annual operation and maintenance cost; C ef is the annual electricity purchase and sales cost and the annual gas turbine fuel cost; C loss is the annual energy storage loss cost; K is the set of investment equipment types; c k,i is the unit capacity cost of the kth type of equipment; X k is the capacity of the kth type of equipment; J is the set of summer, spring, autumn and winter; D is the total number of days in the jth season; c k,o P is the operation and maintenance cost coefficient of the kth type of equipment; k The operating power of the kth category equipment; c p P is the electricity price; buy P is the purchased power; sell is the electricity sold; c fuel is the fuel cost; P g is the gas turbine power.

3. The two-level planning method for a shared energy storage system taking into account energy storage life loss according to claim 1 is characterized in that: The second objective function includes: Among them, C d is the daily cost; C d,o is the daily operation and maintenance cost; C d,ef The daily electricity purchase and sales cost and the daily gas turbine fuel cost; C d,loss is the daily energy storage loss cost; P d,k P is the operating power of the kth type equipment d on a typical day; d,buy is the purchased power on a typical day; P d,sell P is the electricity sales power on a typical day; d,g is the gas turbine power on a typical day; c bat is the unit capacity cost of the battery; Q d,loss is the battery capacity loss in a typical day; L h E is the end of battery life limit; bat is the capacity of the energy storage power station; c bat,x is the energy storage loss conversion coefficient; N is the number of hours in a typical day; Q n,loss is the battery loss rate in the n-th hour period.

4. A two-tier planning system for a shared energy storage system taking into account energy storage life loss, applying the method according to any one of claims 1 to 3, characterized in that: include: A parameter acquisition module is used to obtain basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, solar, and grid data; A two-layer planning module is used to input the basic parameters into a two-layer optimization model of a shared energy storage system, perform optimization calculations through the two-layer optimization model of the shared energy storage system, output the optimal energy storage power station capacity and other power station powers, determine the typical daily operation plan of each device, and share the cost of the energy storage power station based on the Shapley value method; the two-layer optimization model of the shared energy storage system includes a battery single cycle life loss model based on the Arrhenius equation, an outer capacity optimization model and an inner operation optimization model; the outer capacity optimization model includes outer constraints and a first objective function; the inner operation optimization model includes inner constraints and a second objective function for typical days in four seasons; wherein, the first objective function is a function with the goal of minimizing annual operating costs, and the second objective function is a function with the goal of minimizing daily operating costs.

5. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the two-layer planning method of the shared energy storage system taking into account the energy storage life loss according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements a two-layer planning method for a shared energy storage system taking into account energy storage life loss as described in any one of claims 1 to 3.

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