Shared energy storage system double-layer planning method, system, equipment and medium considering energy storage life loss

Abstract

The invention discloses a shared energy storage system bilevel planning method, system and device considering energy storage life loss and a medium, 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 the shared energy storage system; the basic parameters comprise equipment cost, equipment parameters, load data and wind, light and power grid data; and 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 the optimal energy storage power station capacity and other power station power, determining a typical daily operation scheme of each device, and sharing energy storage power station cost allocation based on a Shapley value method. According to the invention, by considering the energy storage loss, the daily operation mode and configuration capacity of the shared energy storage system are optimized, and the operation and maintenance and investment cost of the system is reduced.

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 new energy technologies are developing rapidly. However, with the large-scale access of new energy to the power grid, its inherent intermittency and volatility have brought unprecedented challenges to the safe and stable operation of the power grid. In order to cope with the challenge of unstable output of new energy, it is considered to add energy storage to improve the utilization rate of new energy. At the same time, energy storage can also play an important role in peak shaving and valley filling, improving power quality, etc. Compared with other components in the energy storage system, batteries, as core components, age faster. This rapid aging not only poses a major safety hazard, but also further increases safety risks because of the use of aging batteries, which inevitably leads to a shorter replacement cycle for batteries. This frequent replacement demand will undoubtedly directly affect the overall operation and maintenance and investment costs of the energy storage system. Summary of the invention

[0003] The purpose of the present invention is to provide a two-layer planning method, system, equipment and medium for a shared energy storage system taking into account the energy storage life loss, which can optimize the shared energy storage system and reduce the 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, comprising:

[0006] Obtaining basic parameters of the shared energy storage system; the basic parameters include equipment cost, equipment parameters, load data, and wind, light, and power 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 stations, 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] Where 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 and 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 k-th 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; sellis the electricity sold; c fuel is the fuel cost; P g is the gas turbine power.

[0021] Optionally, the inner layer 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 charge state constraints and energy storage loss constraints;

[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] Among them, w k is the uncertainty; P n,k,max P is the maximum power of the kth type of equipment in the nth period; n,k is the power of the kth type of equipment in the nth period;

[0025] The equipment daily power constraint is:

[0026] P n,k,min ≤P n,k ≤P n,k,max

[0027] Among them, P n,k,min The minimum power of the kth category equipment in the nth period;

[0028] The distribution network constraints are:

[0029]

[0030] Among them, P n,buy P is the power 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 ;

[0031] The power balance constraint of the shared energy storage system 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] 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;

[0034] The energy storage power constraint is:

[0035]

[0036] 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;

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

[0038]

[0039] 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-1th period; η is the charging and discharging 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 storage battery;

[0040] The energy storage loss constraint is:

[0041]

[0042] 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.

[0043] Optionally, the second objective function includes:

[0044]

[0045] Among them, C d is the daily cost; C d,o is the daily operation and maintenance cost; C d,ef is the daily electricity purchase and sales and daily gas turbine fuel cost; C d,loss is the daily energy storage loss cost; P d,kis the operating power of the kth type equipment d on a typical day; P d,buy is the power purchased on a typical day; P d,sell P is the power sold 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 consumption rate in the nth hour period.

[0046] The present invention also provides a two-layer 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, light, and power 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 stations, 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 minimum annual operating cost as the goal, and the second objective function is a function with the minimum daily operating cost as the goal.

[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-layer 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-layer planning method for a shared energy storage system taking into account the 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 present invention discloses a two-layer planning method, system, equipment and medium for a shared energy storage system taking into account the loss of energy storage life, the method comprising obtaining basic parameters of the shared energy storage system; the basic parameters comprising equipment cost, equipment parameters, load data and wind, light and power grid data; inputting the basic parameters into a two-layer optimization model of the shared energy storage system, performing optimization calculations through the two-layer optimization model of the shared energy storage system, outputting the optimal energy storage power station capacity and other power station powers, determining the typical daily operation plan of each device, and allocating the cost of the shared energy storage power station based on the Shapley value method. The present invention can optimize the daily operation mode and configuration capacity of the shared energy storage system by considering the loss of energy storage, thereby reducing the system operation and maintenance and investment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 A step diagram of a two-layer planning method for a shared energy storage system taking into account energy storage life loss according to the present invention;

[0055] Figure 2 This is a schematic diagram of the results of fitting the Arrhenius equation at each temperature in this embodiment;

[0056] Figure 3 This is a schematic diagram of the verification results in this embodiment;

[0057] Figure 4 This is a schematic diagram of the linearization process of the battery loss curve in this embodiment;

[0058] Figure 5 This is a double-layer optimized structure diagram of the shared energy storage system in this embodiment;

[0059] Figure 6 This is a flowchart of double-layer optimization solution of the shared energy storage system in this embodiment;

[0060] Figure 7 4 are comparison diagrams of whether energy storage loss is considered for four typical days in this embodiment; among them, part (a) is the SOC comparison diagram of typical day 1; part (b) is the SOC comparison diagram of typical day 2; part (c) is the SOC comparison diagram of typical day 3; and part (d) is the SOC comparison diagram of typical day 4. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0062] The purpose of the present invention is to provide a two-layer planning method, system, equipment and medium for a shared energy storage system taking into account the energy storage life loss, which can optimize the daily operation mode and configuration capacity of the shared energy storage system by considering the energy storage loss, 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-layer 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, light, and power grid data;

[0066] Step 200: Input the basic parameters into the shared energy storage system double-layer optimization model, perform optimization calculations through the shared energy storage system double-layer optimization model, output the optimal energy storage power station capacity and other power stations, 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 double-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 minimum annual operating cost as the goal, and the second objective function is a function with the minimum daily operating cost as the goal.

[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 equipment is mainly reflected in the remaining capacity of the battery (State of Health, SOH). When the SOH is less 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 also calculate the battery's SOH when the data is incomplete, and the SOH calculation method that integrates the Arrhenius equation is more suitable for long-term operation systems. Therefore, consider using 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 discharge depth, %; 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; 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, at the charge and discharge rate P rate When a certain time comes, 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 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] According to the battery loss data of NASA data set, lithium iron phosphate (LiFePO4) battery was cycled at 4℃, 24℃ and 43℃ to determine the Arrhenius equation coefficients. The fitting results are shown in Figure 2 Except for 4℃, R 2 The fitting effect is not good, 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 has nothing to do with 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. r Finally, all parameters A r 、E a Substituting , R and z into equation (3) can give the battery life loss Q.

[0084]

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

[0086]

[0087] The obtained battery loss function is verified using the 20℃ battery aging data from Beijing Institute of Technology. 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 It is a nonlinear function, so Taylor expansion is used for linearization. When the linearization line is expanded to 50 segments, the trend of the linearization line and the curve is 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 discharge depth at the midpoint of the segment, %.

[0093] For the two-layer optimization model of shared energy storage system, the operation control and capacity configuration of shared energy storage system affect each other. By constructing a two-layer optimization model, configuration and control issues can be considered at the same time. The outer layer takes the minimum annual investment cost as the goal to solve the optimal capacity of the equipment; the inner layer takes the minimum daily operation and maintenance cost as the goal to obtain the optimal daily operation control strategy. The two-layer optimization configuration structure of the shared energy storage system is as follows: Figure 5 shown.

[0094] The capacity optimization layer (outer layer) takes the minimum annual cost C as the objective function, including the investment and construction cost C i , Annual operation and maintenance cost C o , annual electricity purchase and sales and annual gas turbine fuel cost C ef and annual energy storage loss cost C loss , where the annual energy storage loss cost C loss Obtained from the operation optimization layer (inner layer). The annual cost calculation formula is:

[0095]

[0096] Where K is the set of investment equipment types; c k,i is the unit capacity cost of the k-th type of equipment, yuan; 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 is the operating power of the kth type of equipment, kW; c p is the electricity price, yuan; P buy P is the power purchased, kW; sell is the electricity sales power, kW; c fuel is the fuel cost, yuan; P g is the gas turbine power, kW.

[0097] Among them, the battery operation and maintenance cost coefficient c bat,o Mainly caused by life loss, and battery operation and maintenance costs c kW,bat,o Discount rate relationship.

[0098]

[0099] In the formula, r is the investment recovery coefficient, which is 7%; rbat The service life of the energy storage power station is years.

[0100] Capacity Optimization Layer Constraints

[0101] 1) Capacity constraints:

[0102] X k,min ≤X k ≤X k,max (11)

[0103] Where, 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 Constraints:

[0105] P k,min ≤P k ≤P k,max (12)

[0106] Where 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] Run the optimization layer objective function

[0108] Run the optimization layer (inner layer) at a daily cost of C d Minimum, including daily operation and maintenance cost C d,o , daily electricity purchase and sales and daily gas turbine fuel cost C d,ef and daily energy storage loss cost C d,loss The daily cost calculation formula is:

[0109]

[0110] Where P d,k is the typical daily operating power of equipment of type k, kW; P d,buy is the power purchased on a typical day, kW; P d,sell is the power sold on a typical day, kW; P d,g is the gas turbine power on a typical day, kW; c bat is the unit capacity cost of the battery, RMB; Q d,loss is the battery capacity loss in a typical day, %; L h is the battery life end limit, %, take 20%; E bat is the capacity of the energy storage power station, kWh; c bat,x is the energy storage loss conversion coefficient; N is the number of hours in a typical day, which is 24; Q n,loss is the battery loss rate in the n-hour period, %.

[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] In the formula, 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 constraints are nonlinear constraints, the Big-M method is used to linearize the nonlinear conditions, where the M value does not need to be infinite, as long as the complementary conditions are met. 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; 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 charging power of the energy storage battery in the nth period, kW; n,dis is the discharge power of the energy storage battery in the nth period, kW; P n,wt is the fan power in the nth period, kW; P 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; n,bat It is the charge and discharge identifier, a Boolean variable.

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

[0130]

[0131] In the formula, 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 The end SOC of the energy storage battery for one day, %;

[0132] 7) Energy storage loss constraints

[0133]

[0134] In the formula, 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 the total cost according to the average marginal contribution of each participant to the system, that is, the cost that each participant should bear is the average of its marginal contribution to each cooperative project it participates in. For multi-party cooperative projects such as shared energy storage systems, using the Shapley value method to apportion the cost according to the needs of each participant is more in the interests of all parties than average distribution.

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

[0138]

[0139] In the formula, ω v is the weight of the vth party’s participation.

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

[0141]

[0142] In the formula, 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 to the inner layer as a constraint condition, and the Cplex solver is used to obtain the optimal operation control plan with the minimum daily operation cost as the objective function; secondly, the operation control plan is 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, a 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 in four typical days Figure 7 As shown, it can be seen that when the battery is not considered for charging and discharging, the depth of charging and discharging is large, and the degree of battery loss increases. If the loss problem is not considered, the cost of equipment replacement will increase significantly; in terms of configuration capacity, the energy storage capacity configuration is reduced by 9.45% after considering the energy storage loss; shared energy storage is used instead of distributed independent energy storage power stations, and the cost is calculated by the Shapley value method after allocating the cost. 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 article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood 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, light, and power 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 stations, 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.

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 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.

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 outer constraint conditions include equipment capacity constraint and equipment power constraint; 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.

4. 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: Where 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 and 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 k-th 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.

5. 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 inner layer 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 charge state 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 P is the maximum power of the kth type of equipment in the nth period; n,k is 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 power 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 It 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-1th period; η is the charging and discharging 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 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.

6. The two-level planning method for a shared energy storage system taking into account energy storage life loss according to claim 1, 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 is the daily electricity purchase and sales and daily gas turbine fuel cost; C d,loss is the daily energy storage loss cost; P d,k is the operating power of the kth type equipment d on a typical day; P d,buy is the power purchased on a typical day; P d,sell P is the power sold 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 consumption rate in the nth hour period.

7. A two-layer planning system for a shared energy storage system taking into account energy storage life loss, characterized in that: include: Parameter acquisition module, used to obtain basic parameters of the shared energy storage system; The basic parameters include equipment cost, equipment parameters, load data, and wind, light, and power 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 stations, 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 minimum annual operating cost as the goal, and the second objective function is a function with the minimum daily operating cost as the goal.

8. 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 a shared energy storage system taking into account the energy storage life loss according to any one of claims 1-6.

9. 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 1-6.

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