Methods for determining the optimal tiered utilization of retired power batteries

Abstract

This invention relates to a method for determining the optimal tiered utilization of retired power batteries. It is applicable to the fields of new energy and energy-saving technologies. The technical solution adopted in this invention is as follows: The SOH value range a~d corresponding to the retired power battery is divided into three SOH value intervals, including a first interval, a second interval, and a third interval. The first interval is c~d, the second interval is b~c, and the third interval is a~b, where 0%≤a≤b≤c≤d≤100%. Three application scenarios—power source side, grid side, and load side—are randomly sorted, and each sorted sequence forms a tiered utilization sorting scheme. In each tiered utilization sorting scheme, the three application scenarios are sequentially associated with the first, second, and third intervals according to their sorting order. Based on the SOH value intervals corresponding to each application scenario and the net benefit model corresponding to each application scenario, the total tiered utilization benefit of the retired power battery for each tiered utilization sorting scheme is calculated. The tiered utilization sorting scheme with the largest total tiered utilization benefit is selected as the optimal tiered utilization sorting scheme.

Description

Technical Field

[0001] This invention relates to a method for determining the optimal cascade utilization of retired power batteries. It is applicable to the fields of new energy and energy-saving technologies. Background Technology

[0002] Lithium-ion batteries retired from electric vehicles (referred to as retired power batteries) still possess high usable capacity and practical value. Their secondary utilization can reduce the total life-cycle cost of power batteries, enhance their usability, conserve resources, and protect the environment. Secondary utilization of retired batteries refers to the process where the remaining capacity of an electric vehicle's power battery drops to 70%-80% of its initial capacity, rendering it unsuitable for onboard use. After testing, screening, and reconfiguration, retired power batteries can still be used in other applications with relatively good operating conditions and lower battery performance requirements. However, battery aging during secondary utilization alters the battery's internal state and output performance, introducing uncertainty into effective battery management and thus affecting the smooth implementation of secondary utilization.

[0003] Retired power batteries are gradually being put into practical use in fields such as backup power for communication base stations, grid energy storage, and low-speed vehicle battery storage. In communication base station backup power, retired power batteries are used as backup power sources for communication base stations to supply power when the grid fails to provide power. Grid energy storage uses retired power batteries to achieve grid load regulation, transmission and distribution energy storage, peak shaving and valley filling, etc. Low-speed vehicle battery storage uses retired power batteries to replace lead-acid batteries in low-speed electric vehicles, reducing the weight of the battery and lowering battery procurement costs. The main applications include electric delivery vehicles, electric logistics vehicles, and electric sanitation vehicles.

[0004] The applications of retired power batteries in communication base station backup power, grid energy storage, and low-speed vehicle battery storage can be summarized into three application scenarios: source, grid, and load. Current research on the multi-scenario application of retired power batteries only considers multiple application scenarios in one of the three application scenarios, without fully exploring the potential for the cascade utilization of retired power batteries or maximizing the value of the remaining capacity of retired power batteries. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method for determining the optimal tiered utilization of retired power batteries, in view of the above-mentioned problems.

[0006] The technical solution adopted in this invention is: a method for determining the optimal tiered utilization of retired power batteries, characterized in that:

[0007] The SOH value range a~d corresponding to the retired power battery is divided into three SOH value intervals, including the first interval, the second interval and the third interval. The first interval is c~d, the second interval is b~c, and the third interval is a~b, with 0%≤a≤b≤c≤d≤100%.

[0008] The three application scenarios of power supply side, grid side and load side are randomly sorted, and each sorting sequence forms a tiered utilization sorting scheme. In each tiered utilization sorting scheme, the three application scenarios are sequentially associated with the first interval and the third interval according to the sorting order.

[0009] Based on the SOH value range and net income model corresponding to each application scenario, the total income of retired power battery cascade utilization for each of the cascade utilization ranking schemes is calculated.

[0010] The optimal tiered utilization ranking scheme is selected based on the tiered utilization ranking scheme that maximizes the total benefit of tiered utilization.

[0011] Let the first interval be y~d, the second interval be x~y, and the third interval be a~x. Combining the correspondence between each application scenario in the optimal tiered utilization sorting scheme and the first, second, and third intervals, we construct the total revenue function for the tiered utilization of retired power batteries, where a≤x≤y≤d.

[0012] With the goal of maximizing profits, the total revenue function of the secondary utilization of the retired power batteries is solved to obtain the SOH values ​​corresponding to x and y;

[0013] Based on the SOH value range determined by the SOH values ​​corresponding to x and y in the first, second, and third intervals, and combined with the correspondence between the first, second, and third intervals and each application scenario in the optimal tiered utilization sorting scheme, the optimal tiered utilization scheme for retired power batteries is obtained.

[0014] The total revenue from the cascade utilization of retired power batteries for each of the aforementioned cascade utilization ranking schemes is calculated based on the SOH value range corresponding to each application scenario and the net revenue model corresponding to each application scenario, including:

[0015] Optimize the SOC-t curve of retired power batteries in the k-th month for each application scenario with the goal of maximizing net profit;

[0016] Based on the SOC-t curve of the battery in month k, the SOH of the retired power battery at the end of month k can be obtained.

[0017] If the SOH of the battery at the end of month k is still within the SOH value range corresponding to the application scenario, then the retired power battery will be operated for month k+1 under this application scenario; if the SOH of the battery at the end of month k exceeds the SOH value range corresponding to the application scenario, then the operation of the retired power battery under this application scenario will be stopped.

[0018] The method of calculating the SOH of the retired power battery at the end of the k-th month based on the SOC-t curve of the battery includes:

[0019] Based on the SOC-t curve of the battery in month k, the number of cycles under different DODs is counted.

[0020] Divide the DOD range corresponding to the SOC-t curve of the battery in the kth month into M intervals. Let the DOD range corresponding to the sth interval be X(s) and the corresponding cycle number be y(s), where s = 1, 2, 3...M;

[0021] Analyze the battery SOH sequentially according to the increasing DOD. The SOH corresponding to the s-th interval is:

[0022]

[0023] in, Let SOH be the value corresponding to the s-th interval. f is the SOH corresponding to the (s-1)th interval; s This is a function defined by the relationship between battery SOH and the number of cycles under different DODs.

[0024] The relationship function between the battery SOH and the number of cycles under different DODs includes:

[0025] SOH DOD (l)=g(1+B DOD l+C DOD l 2 +D DOD l 3 )

[0026] Where l is the number of cycles corresponding to a specific DOD value; SOH DOD (l) represents the SOH of the battery after cycling for 1 cycle at this specific DOD value; B DOD C DOD and D DOD These represent the coefficients of the first, second, and third terms, respectively.

[0027] The net income model is determined based on the monthly depreciated cost of retired power batteries as energy storage systems and the monthly revenue generated by retired power batteries as energy storage systems in corresponding application scenarios.

[0028] The monthly depreciated cost of the retired power battery as an energy storage system includes:

[0029]

[0030] In the formula, The monthly depreciated cost of retired power batteries used as energy storage systems; N is the operating life of the cascaded battery energy storage system in years; dr is the discount rate for the energy storage project. The initial investment cost for retiring power batteries as energy storage systems; C om The annual operating and maintenance cost of retired power batteries used as energy storage systems;

[0031]

[0032] In the formula, The cost of recycling energy storage systems; The unit cost of processing and remanufacturing retired power batteries; The cost of the energy conversion portion of the energy storage system; Rated capacity of retired power battery energy storage systems; The rated power of the retired power battery energy storage system;

[0033]

[0034] In the formula, C om Annual operating and maintenance costs; Annual operating and maintenance cost per unit power.

[0035] The net revenue model includes a monthly net revenue model for the power source side, using retired power batteries as the energy storage system for wind farms. This monthly net revenue model for the power source side includes:

[0036]

[0037] In the formula, E r.i To reduce the wind power spinning reserve capacity of the energy storage system in time period i; E epa.i For energy storage systems to profit from low storage and high energy consumption in the i-th time period; n m t represents the number of days the energy storage system was in operation that month; t represents the number of calculation periods in a day. Monthly depreciated cost of retired power batteries used as energy storage systems;

[0038]

[0039] In the formula, e c.i χ represents the price of reserve capacity in the i-th time period; χ represents the reliability of wind power prediction technology; P w.i P represents the power output of the wind farm during the i-th time period. max This refers to the rated power of the energy storage system.

[0040] E epa.i =e w (P i + -P i- )Δt

[0041]

[0042] In the formula, P i + P i - These represent the discharge power and charging power of the energy storage device during the i-th time period, respectively; e w Δt represents the on-grid tariff for wind power; Δt represents the time length of the time period.

[0043] The net revenue model includes a grid-side monthly net revenue model corresponding to the grid side, which includes:

[0044]

[0045] In the formula, S represents the system's monthly net revenue; E def To delay the monthly returns on investment in power distribution network upgrades; E NL Monthly revenue from reducing line network losses for energy storage systems; E EPA For low-storage, high-release arbitrage profits; E RC Monthly revenue from replacing standby capacity expenditures with energy storage systems; E IEA Monthly benefits for improving power grid reliability; Monthly depreciated cost of retired power batteries used as energy storage systems;

[0046]

[0047]

[0048] In the formula, C inv The cost of one-time investment in grid upgrades; ir is the inflation rate; dr is the discount rate for energy storage projects; ΔN is the deferral period; τ is the annual load growth rate; α is the ratio of the rated power Pmax of the energy storage system to the peak load power P. imax The ratio;

[0049]

[0050] In the formula, ΔP i =P i + -P i - ; and P represents the reactive power provided or absorbed by the energy storage device during the i-th time period; i and Q i Let n be the active power and reactive power of the load in the i-th time period, respectively; mR is the number of days the energy storage device was put into operation in that month; R is the equivalent resistance from the upstream substation to the installation point of the energy storage device; V is the voltage at the connection point of the energy storage device.

[0051]

[0052] E RC =e r P RC / 12

[0053]

[0054] In the formula, e r Price for standby capacity (RMB 10,000 / MW / year); P RC P represents the expected value of the reserve capacity replaced by energy storage devices within one year; P represents the renewable energy generation absorbed by the grid; P a The limit on the amount of reserve capacity required for the grid to absorb renewable energy generation; P μ P represents the average power generation capacity of new energy sources. σ This refers to the fluctuation deviation in the power generation capacity of new energy sources;

[0055] E IEA =R IEA E ENS λ S [1-p{W i <E ENS}]

[0056] E ENS =T s (1-A s P0

[0057]

[0058] In the formula, R IEA The rate of power outage losses for electricity users; λ s For power supply failure rate; E ENS T represents the user's expected power shortage during each instance of insufficient power supply. s For the number of hours of electricity used by the user per month; A s The reliability of power supply to the power grid; P0 is the power required to guarantee the user's electricity demand; p{W i <E ENS The remaining energy capacity of the energy storage system, calculated by hourly rate on a typical monthly day, is less than E when the user experiences a power outage. ENS The probability, W i Let be the remaining electricity in the energy storage device at hour i.

[0059] The net revenue model includes a load-side monthly net revenue model corresponding to the load side, and the load-side monthly net revenue model includes:

[0060]

[0061] In the formula, E RDC The corresponding benefits generated by reducing the required power distribution capacity for users due to energy storage systems are equivalent to the present value of each month; E CY The monthly revenue generated by reducing users' basic electricity costs through energy storage systems; E ET The monthly revenue generated by reducing electricity costs for users through energy storage systems; E TL Monthly revenue generated by reducing user transformer losses in energy storage systems; E OC To reduce monthly revenue generated by users' losses due to power outages; Monthly depreciated cost of retired power batteries used as energy storage systems;

[0062]

[0063] P c =P imax -P m

[0064] In the formula, P max P is the rated power of the energy storage system. c To reduce peak load to the maximum power required for the daily average load, when P max >2P c At that time, the utilization rate of energy storage systems was not high; P imax P represents the maximum daily load. m C is the daily average power of the load; d The unit cost of the user's power distribution system; γ d Depreciation rate for user's power distribution equipment;

[0065]

[0066] In the formula, e cp The basic electricity fee paid by users based on their maximum demand;

[0067]

[0068]

[0069] In the formula, P k For the short-circuit loss of the distribution transformer; S N φ represents the transformer's capacity (MVA); φ represents the transformer's power factor on the load side.

[0070] E OC =R IEA E ENS λ S [1-P{W i <EENS}]+(λ s -λ′ s E λ

[0071] E ENS =T s (1-A s P0

[0072] λ′ s =λ s λ b (r s +r b )

[0073]

[0074] In the formula, λ′ s The outage rate of the system after the energy storage device is connected to the distribution bus; λ b The failure rate of the energy storage device; r s and r b These are the repair times for the load and the repair times for the energy storage device, respectively; E λ The expected economic loss to users due to product scrapping caused by each power outage.

[0075] a is 20%, b is 40%, c is 60%, and d is 80%.

[0076] The beneficial effects of this invention are as follows: This invention solves the optimal correspondence between the state of health (SOH) value of retired power batteries and application scenarios in two stages. In the first stage, the optimal tiered utilization ranking scheme for three application scenarios—power supply side, grid side, and load side—is calculated based on three SOH value intervals divided according to experience. In the second stage, based on the optimal tiered utilization ranking scheme, two partitioning nodes are calculated in the three SOH value intervals to correct the empirically divided SOH intervals. Through the solution of the above two stages, the optimal correspondence between the state of health (SOH) value of retired power batteries and application scenarios can finally be determined. Attached Figure Description

[0077] Figure 1 This is the battery capacity degradation calculation process in the embodiment.

[0078] Figure 2 This is a flowchart illustrating the sequential optimization of the tiered utilization scenario for retired power batteries in the embodiment.

[0079] Figure 3 The flowchart for optimizing the SOH (State of Health) partitioning of retired power batteries is shown in the example. Detailed Implementation

[0080] This embodiment describes a method for determining the optimal tiered utilization of retired power batteries, specifically including the following steps:

[0081] S1. In this embodiment, the SOH value range of the retired power battery for cascade utilization is 20% to 80%. Based on experience, the SOH value range is divided into three SOH value intervals, including the first interval, the second interval and the third interval, where the first interval is 60% to 80%, the second interval is 40% to 60%, and the third interval is 20% to 40%.

[0082] S2. Randomly sort the three application scenarios of power supply side, grid side and load side, and each sorting sequence forms a tiered utilization sorting scheme. In each tiered utilization sorting scheme, the three application scenarios are sequentially matched with the first interval and the third interval according to the sorting order.

[0083] In this embodiment, the tiered utilization sorting scheme includes six schemes: source-grid-load (application scenario 1 with the power source side as application scenario 2, application scenario 3 with the load side), source-load-grid (application scenario 1 with the power source side as application scenario 1, application scenario 2 with the load side as application scenario 2, application scenario 3 with the grid side), load-source-grid, load-grid-source, grid-source-load, and grid-load-source. Application scenario 1 corresponds to the first interval of SOH value, application scenario 2 corresponds to the second interval, and application scenario 3 corresponds to the third interval.

[0084] S3. Based on the SOH value range corresponding to each application scenario and the net income model corresponding to each application scenario, and combined with the battery capacity degradation model, calculate the total income of retired power battery cascade utilization for each of the above-mentioned cascade utilization ranking schemes. The total income of retired power battery cascade utilization is the sum of the income of each application scenario in its corresponding SOH value range.

[0085] The battery capacity degradation model in this embodiment includes:

[0086] The approximate relationship between the battery's SOH and the number of cycles l at different depths of discharge (DOD) is as follows:

[0087]

[0088] Where l represents the number of cycles corresponding to a specific DOD value; SOH DOD (l) represents the SOH of the battery after cycling for 1 cycle at this specific DOD value; B DOD C DOD and D DOD These represent the coefficients of the first, second, and third terms, respectively.

[0089] The lifespan of energy storage batteries is closely related to their operating mode; the greater the depth of discharge, the fewer the number of cycles. Simulating battery capacity degradation requires first analyzing the depth of discharge and the corresponding number of cycles during battery operation; then, based on the relationship between the depth of discharge and cycle life, the equivalent lifespan of the battery is derived, and finally, the remaining capacity of the battery is calculated as follows:

[0090] 1) Optimize the battery charging / discharging power curve of retired power batteries in the kth month in each application scenario with the goal of maximizing net profit, and calculate the curve of battery state of charge (SOC) changing over time (SOC-t curve).

[0091] 2) Based on the SOC-t curve of the battery in month k, the SOH of the retired power battery at the end of month k is obtained.

[0092] 21) Based on the SOC-t curve of the kth month, the number of cycles under different DODs was statistically analyzed using the rainflow counting method.

[0093] 22) Divide the DOD range into M intervals. Let X(s) be the DOD range corresponding to the s-th interval, and y(s) be the corresponding number of iterations, where s = 1, 2, 3...M. The relationship with the loop count is as follows:

[0094]

[0095] In the formula, f s It is a function defined by formula (1).

[0096] 23) Arrange different DOD intervals and their corresponding cycle time series in the order of DOD increase, and gradually analyze the changes in battery SOH. When the execution reaches the s-th interval, the SOH is:

[0097]

[0098] in, Let SOH be the value corresponding to the s-th interval. Let SOH be the SOH corresponding to the (s-1)th interval.

[0099] 24) Based on the SOH of the energy storage system, calculate the remaining capacity E of the energy storage system at the end of month k. k

[0100]

[0101] In the formula, E BESS It is the rated capacity of the energy storage system. This represents the SOH of the battery at the end of month k.

[0102] 3) If the SOH of the battery at the end of the kth month is still within the SOH value range corresponding to the application scenario, then the retired power battery will be operated in the (k+1)th month of the application scenario; if the SOH of the battery at the end of the kth month exceeds the SOH value range corresponding to the application scenario, then the operation of the retired power battery in the application scenario will be stopped.

[0103] This embodiment establishes monthly net revenue models for energy storage in three application scenarios: power supply side, grid side, and load side. The cost model for retired power batteries as energy storage systems is consistent across the three application scenarios, considering only the initial investment cost and operation and maintenance cost.

[0104] I. Establishment of an Energy Storage Cost Model

[0105] (1) Initial investment cost

[0106]

[0107] In the formula, The initial investment cost for retired battery energy storage systems; The recovery cost of the energy storage system is expressed in yuan / kWh. The unit cost of processing and remanufacturing retired batteries, expressed in yuan / kWh; The cost of the energy conversion portion of the energy storage system is expressed in yuan / kW. The rated capacity of the retired battery energy storage system is expressed in kWh. The rated power of the retired battery energy storage system is expressed in kW.

[0108] (2) Operation and maintenance costs

[0109]

[0110] In the formula, C om Annual operating and maintenance costs; Annual operating and maintenance cost per unit power, expressed in yuan / kW / year.

[0111] (3) Monthly cost conversion of retired battery energy storage

[0112] Based on the lifespan of the energy storage system and the discount rate, the operation and maintenance costs over the entire lifespan of the energy storage system are summed up and then added to the initial investment cost to obtain the total monthly cost of the energy storage system. for,

[0113]

[0114] In the formula, N is the operating life of the cascaded battery energy storage system, in years; dr is the discount rate of the energy storage project.

[0115] II. Establishment of the Monthly Net Income Model for the Power Supply Side

[0116] In this embodiment, retired power batteries are used as the application background of the wind farm energy storage system. It is assumed that the wind farm operates according to a typical daily curve each month, and the operation of the energy storage system is consistent every day of the month. The objective function is to maximize the monthly net income of the energy storage system, and the typical daily operation curve of the energy storage system is optimized each month, as shown in the following formula:

[0117]

[0118] In the formula, E r.i To reduce the wind power spinning reserve capacity of the energy storage system in time period i; E epa.i For energy storage systems to profit from low storage and high energy consumption in the i-th time period, n m t represents the number of days the energy storage system was in operation that month, and t represents the number of calculation periods in a day.

[0119] (1) Benefits of reducing wind power spinning reserve capacity

[0120]

[0121] In the formula, e c.i χ represents the price of reserve capacity in the i-th time period (ten thousand yuan / MW); χ represents the reliability of wind power prediction technology; P w.i P represents the power output of the wind farm during the i-th time period. max This is the rated power of the energy storage system.

[0122] (2) Reduce wind curtailment revenue

[0123] E epa.i =e w (P i + -P i - )Δt

[0124]

[0125] In the formula, P i + P i - These represent the discharge power and charging power of the energy storage device during the i-th time period, respectively (charging power is 0 during discharge; discharge power is 0 during charging); e w Δt represents the on-grid tariff for wind power; Δt represents the time length (h) of the time period.

[0126] III. Establishment of the Monthly Net Income Model for the Power Grid Side

[0127] This embodiment analyzes and models the value of grid-side battery energy storage from multiple aspects, including delaying grid expansion, reducing grid loss costs, generating profits from low-storage and high-generation capacity, reducing the reserve capacity required for distributed generation, and lowering grid reliability costs.

[0128] Assuming the distribution network operates according to a typical daily curve each month, and the energy storage system operates consistently every day of the month, optimize the typical daily operating curve of the energy storage system each month with the objective function of maximizing the monthly net benefit of the energy storage system:

[0129]

[0130] In the formula, S represents the system's monthly net revenue; E def To delay the monthly returns on investment in power distribution network upgrades; E NL Monthly revenue from reducing line network losses for energy storage systems; E EPA For low-storage, high-release arbitrage profits; E RC Monthly revenue from replacing standby capacity expenditures with energy storage systems; E IEA Monthly revenue to improve power grid reliability.

[0131] (1) Monthly benefits of delaying power grid upgrade investment

[0132]

[0133]

[0134] In the formula, C inv The cost of one-time investment in grid upgrades; ir is the inflation rate; dr is the discount rate for energy storage projects; ΔN is the deferral period; τ is the annual load growth rate; α is the rated power P of the energy storage system. max With peak load power P imax The ratio, where 1-α is defined here as the peak reduction factor of the energy storage device.

[0135] (2) Reduce network loss costs

[0136]

[0137] In the formula, ΔP i =P i + -P i - ; and P represents the reactive power provided or absorbed by the energy storage device during the i-th time period; i and Q i Let n be the active power and reactive power of the load in the i-th time period, respectively; mR is the number of days the energy storage device was put into operation in that month; R is the equivalent resistance from the upstream substation to the installation point of the energy storage device; V is the voltage at the connection point of the energy storage device.

[0138] (3) Low storage and high release arbitrage

[0139]

[0140] (4) Reduce the reserve capacity required for distributed generation

[0141] E RC =e r P RC / 12 (16)

[0142]

[0143] In the formula, e r Price for standby capacity (RMB 10,000 / MW / year); P RC P represents the expected value of the reserve capacity replaced by energy storage devices within one year; P represents the renewable energy generation absorbed by the grid; P a The limit on the amount of reserve capacity required for the grid to absorb renewable energy generation; P μ P represents the average power generation capacity of new energy sources. σ This refers to the fluctuation deviation in the power generation capacity of new energy sources;

[0144] (5) Benefits of improving grid reliability

[0145] E IEA =R IEA E ENS λ S [1-p{W i <E ENS}] (18)

[0146] E ENS =T s (1-A s P0 (19)

[0147]

[0148] In the formula, R IEA The rate of power outage losses for electricity users; λ s Power supply failure rate (times / month); E ENS T represents the user's expected power shortage during each instance of insufficient power supply. s For the number of hours of electricity used by the user per month; A s The reliability of power supply to the power grid; P0 is the power required to guarantee the user's electricity demand; p{W i <E ENSThe remaining energy capacity of the energy storage system, calculated by hourly rate on a typical monthly day, is less than E when the user experiences a power outage. ENS The probability W (that the power supply from the energy storage system is insufficient to support continued production by the user at this point) i Let be the remaining electricity in the energy storage device at hour i.

[0149] IV. Establishment of the Monthly Net Income Model on the Load Side

[0150] This embodiment studies the various benefits generated by the energy storage system on the load side, including the benefit of reducing the amount of distribution capacity required by users, reducing users' basic electricity costs, reducing users' electricity consumption costs, reducing users' transformer loss costs, and reducing users' power outage loss costs. Assuming the load operates according to a typical daily curve each month, and the energy storage system operates consistently every day of the month, the objective function is to maximize the monthly net benefit of the energy storage system. The typical daily operating curve of the energy storage system is optimized each month, as shown in the following expression:

[0151]

[0152] In the formula, E RDC The corresponding benefits generated by reducing the required power distribution capacity for users due to energy storage systems are equivalent to the present value of each month; E CY The monthly revenue generated by reducing users' basic electricity costs through energy storage systems; E ET The monthly revenue generated by reducing electricity costs for users through energy storage systems; E TL Monthly revenue generated by reducing user transformer losses in energy storage systems; E OC Monthly revenue generated to reduce user losses due to power outages.

[0153] (1) Reduce the power distribution capacity required by users.

[0154] The revenue generated by the energy storage system is equivalent to the present value E of each month. RDC It can be represented as:

[0155]

[0156] P c =P imax -P m (twenty three)

[0157] In the formula, P max P is the rated power of the energy storage system. c To reduce peak load to the maximum power required for the daily average load, when P max >2P c At that time, the utilization rate of the energy storage system is not high, so it is not considered in this method. imax P represents the maximum daily load (MW). m C is the daily average power of the load;d Unit cost of user power distribution system (ten thousand yuan / MW); γ d The depreciation rate for the user's power distribution equipment.

[0158] (2) Reduce users' basic electricity charges

[0159]

[0160] In the formula, e cp The basic electricity fee (ten thousand yuan / MW / month) paid by users based on their maximum demand.

[0161] (3) Reduce electricity bills for users

[0162]

[0163] (4) Reduce user transformer loss costs

[0164]

[0165] In the formula, P k For the short-circuit loss of the distribution transformer; S N φ represents the transformer capacity (MVA); φ represents the transformer load-side power factor.

[0166] (5) Reduce user losses due to power outages

[0167] E OC =R IEA E ENS λ S [1-P{W i <E ENS}]+(λ s -λ′ s E λ (27)

[0168] E ENS =T s (1-A s P0 (28)

[0169] λ′ s =λ s λ b (r s +r b (29)

[0170]

[0171] In the formula, λ′ s The outage rate of the system after the energy storage device is connected to the distribution bus; λ b The failure rate of the energy storage device; r s and rb These are the repair times for the load and the repair times for the energy storage device, respectively; E λ The expected economic loss to users due to product scrapping caused by each power outage.

[0172] S4. Select the optimal tiered utilization ...

[0173] If the total benefit of the source-grid-load sequencing scheme is maximized, then the source-grid-load sequencing scheme is taken as the optimal tiered utilization scheme. The power supply side is the application scenario 1, corresponding to the first range of SOH values ​​(60% to 80%); the grid side is the application scenario 2, corresponding to the second range of SOH values ​​(40% to 60%); and the load side is the application scenario 3, corresponding to the second range of SOH values ​​(20% to 40%).

[0174] S5. Since step S1 divides the SOH value intervals based on experience, the SOH value interval division in step S1 needs to be modified. Let x and y be the two segmentation points of the intervals, which are the variables to be optimized. The first interval is set as y~80%, the second interval is set as x~y, and the third interval is set as 20%~x, where 20%≤x≤y≤80%.

[0175] Based on the application scenarios 1, 2, and 3 determined in the optimal tiered utilization ranking scheme, a total revenue function for the tiered utilization of retired power batteries is constructed. This function is the sum of the revenue of the retired power battery throughout its entire life cycle under application scenarios 1, 2, and 3.

[0176] S6. With the goal of maximizing profits, solve the total profit function of the secondary utilization of the retired power battery to obtain the SOH values ​​corresponding to x and y.

[0177] S7. Based on the SOH values ​​corresponding to x and y in step S6, the SOH value range for the secondary utilization of retired power batteries is redefined to obtain new first, second, and third intervals. Combined with the application scenarios 1, 2, and 3 determined in step S4, the optimal secondary utilization scheme for retired power batteries is obtained.

[0178] In this embodiment, steps S1 to S4, in order to obtain a suitable tiered scenario, are actually a two-layer optimization problem:

[0179] The upper-level optimization determines the optimization scheme for each scenario. For each application scenario, the retired power battery's energy storage charge-discharge curve is optimized with the objective function of maximizing net monthly revenue. The number of energy storage cycles and state of charge are calculated, ultimately yielding the remaining energy storage capacity. From the model established above, which includes application scenarios for retired power batteries, the objective functions for source, grid, and load are all related to the State of Health (SOH) value of the retired power battery. Therefore, the revenue obtained by the retired power battery in different scenarios varies at different stages of SOH decline. Based on experience, SOH can be divided into three intervals: 80%-60%, 60%-40%, and 40%-20%. Using a traversal algorithm, each interval corresponds to one scenario, resulting in six schemes: source-grid-load, source-load-grid, grid-source-load, grid-load-source, load-grid-source, and load-source-grid. The total revenue from the tiered utilization of retired batteries for each of these six schemes is calculated, and the scheme with the highest total revenue is selected as the second-stage application scenario for the tiered utilization of retired power batteries.

[0180] The lower-level optimization determines the net benefit of the energy storage system over its lifespan in each scenario. Based on the lower-resolution energy storage system capacity degradation model proposed in this invention, it is assumed that the energy storage system operates in its optimal mode, and the remaining capacity of the energy storage is calculated at the end of the month for use in the next cycle. In the next cycle, the remaining capacity of the battery is iteratively calculated until the remaining capacity reaches a set value, and then the upper-level optimization is initiated.

Claims

1. A method for determining the optimal tiered utilization of retired power batteries, characterized in that: The SOH value range a~d corresponding to the retired power battery is divided into three SOH value intervals, including the first interval, the second interval and the third interval. The first interval is c~d, the second interval is b~c, and the third interval is a~b, with 0%≤a≤b≤c≤d≤100%. The three application scenarios of power supply side, grid side and load side are randomly sorted, and each sorting sequence forms a tiered utilization sorting scheme. In each tiered utilization sorting scheme, the three application scenarios are sequentially associated with the first interval and the third interval according to the sorting order. Based on the SOH value range and net income model corresponding to each application scenario, the total income of retired power battery cascade utilization for each of the cascade utilization ranking schemes is calculated. The optimal tiered utilization ranking scheme is selected based on the tiered utilization ranking scheme that maximizes the total benefit of tiered utilization. Let the first interval be y~d, the second interval be x~y, and the third interval be a~x. Combining the correspondence between each application scenario in the optimal tiered utilization sorting scheme and the first, second, and third intervals, construct the total revenue function for the tiered utilization of retired power batteries, where a≤x≤y≤d. With the goal of maximizing profits, the total revenue function of the secondary utilization of the retired power batteries is solved to obtain the SOH values ​​corresponding to x and y; Based on the range of SOH values ​​determined by x and y corresponding to the first, second, and third intervals, and combined with the correspondence between the first, second, and third intervals and each application scenario in the optimal tiered utilization sorting scheme, the optimal tiered utilization scheme for retired power batteries is obtained. The total revenue from the cascade utilization of retired power batteries for each of the aforementioned cascade utilization ranking schemes is calculated based on the SOH value range corresponding to each application scenario and the net revenue model corresponding to each application scenario, including: Optimize the SOC-t curve of retired power batteries in the k-th month for each application scenario with the goal of maximizing net profit; Based on the SOC-t curve of the battery in month k, the SOH of the retired power battery at the end of month k can be obtained. If the SOH of the battery at the end of month k is still within the SOH value range corresponding to the application scenario, then the retired power battery will be operated for month k+1 under this application scenario; if the SOH of the battery at the end of month k exceeds the SOH value range corresponding to the application scenario, then the operation of the retired power battery under this application scenario will be stopped. The method of calculating the SOH of the retired power battery at the end of the k-th month based on the SOC-t curve of the battery includes: Based on the SOC-t curve of the battery in month k, the number of cycles under different DODs is counted. Divide the DOD range corresponding to the SOC-t curve of the battery in month k into M intervals, and let the DOD range corresponding to the s-th interval be . The corresponding number of loops is s=1,2,3...M; Analyze the battery SOH sequentially according to the increasing DOD. The SOH corresponding to the s-th interval is: ； in, Let SOH be the value corresponding to the s-th interval. The SOH corresponding to the (s-1)th interval; This is a function defined by the relationship between battery SOH and the number of cycles under different DODs.

2. The method for determining the optimal tiered utilization of retired power batteries according to claim 1, characterized in that, The relationship function between the battery SOH and the number of cycles under different DODs includes: ； Where l is the number of iterations corresponding to a specific DOD value; The SOH of the battery after cycling at this specific DOD value; and These represent the coefficients of the first, second, and third terms, respectively.

3. The method for determining the optimal tiered utilization of retired power batteries according to claim 1, characterized in that, The net income model is determined based on the monthly depreciated cost of retired power batteries as energy storage systems and the monthly revenue generated by retired power batteries as energy storage systems in corresponding application scenarios.

4. The method for determining the optimal tiered utilization of retired power batteries according to claim 3, characterized in that, The monthly depreciated cost of the retired power battery as an energy storage system includes: ； In the formula, The monthly depreciated cost of retired power batteries used as energy storage systems; N is the operating life of the cascaded battery energy storage system in years; dr is the discount rate for the energy storage project. The initial investment cost for retiring power batteries as energy storage systems; The annual operating and maintenance cost of retired power batteries used as energy storage systems; ； In the formula, The cost of recycling energy storage systems; The unit cost of processing and remanufacturing retired power batteries; The cost of the energy conversion portion of the energy storage system; Rated capacity of retired power battery energy storage systems; The rated power of the retired power battery energy storage system; ； In the formula, Annual operating and maintenance costs; Annual operating and maintenance cost per unit power.

5. The method for determining the optimal tiered utilization of retired power batteries according to claim 3, characterized in that, The net revenue model includes a monthly net revenue model for the power source side, using retired power batteries as the energy storage system for wind farms. This monthly net revenue model for the power source side includes: ； In the formula, For energy storage systems in the first Benefits of reducing wind power spinning reserve capacity during certain periods; For energy storage systems to achieve high energy density through low storage capacity in the first stage Arbitrage during specific time periods; t represents the number of days the energy storage system was in operation that month; t represents the number of calculation periods in a day. Monthly depreciated cost of retired power batteries used as energy storage systems; ； In the formula, For the first Price of spare capacity during the specified time period; To assess the reliability of wind power prediction technology; For the wind farm in the first Efforts during the time period; This refers to the rated power of the energy storage system. ； In the formula, The first Discharge power and charging power of the energy storage device during the time period; For wind power grid connection price; The time length for dividing the period.

6. The method for determining the optimal tiered utilization of retired power batteries according to claim 3, characterized in that, The net revenue model includes a grid-side monthly net revenue model corresponding to the grid side, which includes: ； In the formula, This represents the system's monthly net profit. To delay the monthly returns on investment in power distribution network upgrades; Monthly revenue from reducing power line losses for energy storage systems; To generate arbitrage profits from low-storage and high-release; Monthly revenue from replacing standby capacity expenditures with energy storage systems; Monthly benefits for improving power grid reliability; Monthly depreciated cost of retired power batteries used as energy storage systems; ； ； In the formula, The cost of one-time investment in power grid upgrades; dr is the inflation rate; dr is the discount rate for energy storage projects. The number of years to be postponed; The annual growth rate of the load; Rated power of the energy storage system With peak load power The ratio; ； In the formula, ; , and The first The reactive power provided or absorbed by the energy storage device during the time period; and The first Active and reactive power of load during a given time period; This refers to the number of days the energy storage device was in operation that month. The equivalent resistance from the upstream substation to the energy storage device installation point; The voltage at the connection point of the energy storage device; ； ； ； In the formula, Price for standby capacity, in RMB 10,000 / MW / year; This represents the expected value of the standby capacity that the energy storage device can replace within one year. The amount of new energy power generated that is absorbed by the power grid; Limits on the amount of backup capacity required for the grid to absorb renewable energy generation; This represents the average power generation capacity of new energy sources. This refers to the fluctuation deviation in the power generation capacity of new energy sources; ； ； ； In the formula, Evaluation rate of power outage losses for electricity users; Power supply failure rate; This represents the user's expected power shortage during each instance of insufficient power supply. The number of hours of electricity used by the user per month; Reliability of power supply to the power grid; To ensure the power supply to meet users' electricity needs; The remaining power of the energy storage system, calculated by hourly rate on a typical monthly day, is less than [amount missing] when the user experiences a power outage. The probability, Let be the remaining electricity in the energy storage device at hour i.

7. The method for determining the optimal tiered utilization of retired power batteries according to claim 3, characterized in that, The net revenue model includes a load-side monthly net revenue model corresponding to the load side, and the load-side monthly net revenue model includes: ； In the formula, The corresponding benefits generated by reducing the required power distribution capacity for users to build energy storage systems are equivalent to the present value of each month; The monthly revenue generated by reducing users' basic electricity costs for energy storage systems; The monthly revenue generated by reducing electricity costs for users through energy storage systems; Monthly revenue generated by reducing user transformer losses for energy storage systems; To reduce monthly revenue generated by users' losses due to power outages; Monthly depreciated cost of retired power batteries used as energy storage systems; ； ； In the formula, P max Rated power of the energy storage system; To reduce peak load to the maximum power required for the daily average load, when P max >2P c At that time, the utilization rate of energy storage systems was not high; This represents the maximum daily load. The average daily power of the load; The unit cost of the user's power distribution system; Depreciation rate for user's power distribution equipment; ； In the formula, The basic electricity fee paid by users based on their maximum demand; ； ； In the formula, For the short-circuit loss of the distribution transformer; The capacity of the distribution transformer is expressed in MVA. The power factor on the load side of the transformer; ； ； ； ； In the formula, The failure rate of the system after the energy storage device is connected to the distribution bus; The failure rate of energy storage devices; and These refer to the repair time of the load and the repair time of the energy storage device, respectively. The expected economic loss to users due to product scrapping caused by each power outage.

8. The method for determining the optimal tiered utilization of retired power batteries according to claim 1, characterized in that: a is 20%, b is 40%, c is 60%, and d is 80%.

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