Energy storage control method and device based on battery cycle attenuation, terminal equipment and storage medium

Abstract

The invention discloses an energy storage control method and device based on battery cycle attenuation, terminal equipment and a storage medium, and belongs to the technical field of energy storage control. According to the method, an energy storage system capacity recession model used for representing dynamic correlation between battery capacity recession and charging and discharging operation is constructed, meanwhile, multiple constraint conditions such as capacity recession constraint are fully considered when an energy storage system income model is constructed, and therefore the capacity recession of the battery can be calculated under the energy storage system capacity recession model and the related constraint conditions. The energy storage system income model is accurately solved, and a target control strategy when the income of the energy storage system is maximum is obtained.According to the method, the target control strategy with the battery attenuation considered can be obtained by considering the battery attenuation, the situation that the battery is in an unhealthy operation state for a long time is effectively prevented, and the service life of the battery is prolonged. The problem that the battery is in an unhealthy operation state for a long time due to the fact that the attenuation of the battery is not considered in the prior art can be effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage control, and in particular to an energy storage control method and device based on battery cycle attenuation, a terminal device and a storage medium. BACKGROUND

[0002] Battery cycle attenuation refers to the phenomenon that the available capacity of a battery gradually decreases due to factors such as electrode material loss, electrolyte decomposition, and separator aging during repeated charging and discharging cycles. This attenuation is a key factor affecting the service life of the battery and is directly related to the operating efficiency, cost, and reliability of the energy storage system. By reasonably controlling the charging and discharging strategy to slow down the battery cycle attenuation, the service life of the battery can be extended, the replacement cost can be reduced, and the long-term stable energy output capability of the energy storage system can be guaranteed.

[0003] Traditional energy storage control technology often takes maximizing short-term benefits as the core target when formulating strategies. It mainly relies on basic parameters such as market electricity price, charging and discharging power limit, etc. for scheduling, and does not adequately consider the dynamic characteristics of battery cycle attenuation. Therefore, since the traditional technology does not establish a quantitative correlation model between battery capacity degradation and charging and discharging operation, the control strategy without considering attenuation may cause the battery to be in a non-healthy operating state for a long time, leading to safety risks such as thermal runaway caused by excessive attenuation of the energy storage. SUMMARY

[0004] The embodiments of the present application provide an energy storage control method and device based on battery cycle attenuation, a terminal device and a storage medium. By constructing an energy storage system capacity degradation model for representing the dynamic correlation between battery capacity degradation and charging and discharging operation, and by constructing capacity degradation constraints and energy capacity constraints, the battery attenuation state is constrained, so that a target control strategy considering battery attenuation can be obtained, preventing the battery from being in a non-healthy operating state for a long time, reducing safety risks such as thermal runaway caused by excessive attenuation, and effectively solving the problem that, in the prior art, since a quantitative correlation model between battery capacity degradation and charging and discharging operation is not established, the control strategy without considering attenuation may cause the battery to be in a non-healthy operating state for a long time, leading to safety risks such as thermal runaway caused by excessive attenuation of the energy storage.

[0005] An embodiment of the present application provides an energy storage control method based on battery cycle attenuation, comprising:

[0006] Obtaining capacity characteristic data of the battery, storage energy data of the battery under charging and discharging operation, charging and discharging power data of the energy storage, market electricity price data, capacity market reward data, unit penalty coefficient, and capacity update cost data;

[0007] construct a capacity degradation model of the energy storage system according to the capacity characteristic data of the battery and the storage energy data; wherein the capacity degradation of the energy storage system is used to represent a dynamic correlation between the capacity degradation of the battery and the charging and discharging operation;

[0008] construct a benefit model of the energy storage system according to the charging and discharging power data of the energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient, the storage energy data of the battery under the charging and discharging operation and the capacity updating cost data; wherein the constraint conditions of the benefit model of the energy storage system include: the capacity degradation constraint, the storage energy constraint, the energy capacity constraint, the charging and discharging power constraint and the capacity updating constraint;

[0009] solve the benefit model of the energy storage system under the capacity degradation model of the energy storage system and the constraint conditions, and generate a target control strategy when the benefit of the energy storage system is maximum; wherein the target control strategy includes: the charging power, the discharging power, the storage energy and the capacity updating amount of the energy storage at different time points; and the energy storage is controlled according to the target control strategy.

[0010] Preferably, the capacity characteristic data of the battery includes: an initial energy capacity and an initial charging and discharging cycle number; and the storage energy data includes: an upper limit value of the storage energy and the storage energy data corresponding to the battery at different time points.

[0011] The capacity degradation model of the energy storage system is constructed according to the capacity characteristic data of the battery and the storage energy data, including:

[0012] generate a capacity degradation coefficient for quantifying the capacity degradation degree of the battery in the running process according to the initial energy capacity and the initial charging and discharging cycle number;

[0013] construct the capacity degradation model of the energy storage system according to the capacity degradation coefficient, the storage energy data corresponding to the battery at different time points and the upper limit value of the storage energy.

[0014] Preferably, the capacity market reward data is a capacity reward obtained when the energy storage performs the discharging capacity specified in a preset contract; and the capacity updating cost data includes: a unit updating capacity cost at different time points and a capacity updating amount at different time points.

[0015] The benefit model of the energy storage system is constructed according to the charging and discharging power data of the energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient, the storage energy data of the battery under the charging and discharging operation and the capacity updating cost data, including:

[0016] According to the charge and discharge power data of the energy storage and market electricity price data, an energy market benefit model is constructed, wherein the energy market benefit model is used to represent economic benefits obtained by the energy storage in the energy market through adjusting the charge and discharge behavior;

[0017] According to the charge and discharge power data of the energy storage, a unit penalty coefficient and a preset minimum discharge energy, a penalty cost is generated, wherein the penalty cost is used to quantify a cost value generated when the energy storage fails to fulfill a preset contract specified discharge capacity;

[0018] According to the capacity reward and the penalty cost, a capacity market benefit model is constructed;

[0019] According to the storage energy data of the battery under the charge and discharge operation, a unit update capacity cost at different time and a capacity update amount at different time, a capacity update cost model is constructed;

[0020] According to the energy market benefit model, the capacity market benefit model and the capacity update cost model, an energy storage system benefit model is constructed.

[0021] Preferably, the capacity degradation constraint comprises battery storage energy at different time and battery storage energy square term corresponding to each battery storage energy;

[0022] Before solving the energy storage system benefit model, it further comprises:

[0023] For the battery storage energy at each time in the capacity degradation constraint, the battery storage energy is discretized through a binary expansion formula to generate a first energy reconstruction formula containing a plurality of binary variables;

[0024] According to the first energy reconstruction formula, the battery storage energy square term is expanded to generate a second energy reconstruction formula, wherein the second energy reconstruction formula contains a plurality of binary variable square terms and a plurality of binary variable product terms;

[0025] A preset auxiliary variable is obtained;

[0026] According to the auxiliary variable, the binary variable and the binary variable product term, a linear inequality group is constructed, wherein the linear inequality group is used to constrain the equivalent relationship between the auxiliary variable, the binary variable and the binary variable product term;

[0027] According to the linear inequality group and the auxiliary variable, the second energy reconstruction formula is converted into a third energy reconstruction formula with linear relationship;

[0028] The first energy reconstruction formula and the third energy reconstruction formula are respectively substituted into a battery storage energy and a battery storage energy square term in a capacity degradation constraint to construct an updated capacity degradation constraint.

[0029] The updated capacity degradation constraint is used as a capacity degradation constraint for solving the energy storage system benefit model.

[0030] Preferably, the energy storage system benefit model comprises:

[0031]

[0032] R=R CM,t -P penalty,t ;

[0033] P penalty,t =δ t ×C penalty ×max(0,E contract,t -E t );

[0034] wherein, denotes an energy storage system benefit model, denotes an energy market benefit model, R denotes a capacity market benefit model, ρ(R t ) denotes a capacity update cost model, R t denotes a capacity update amount at time t, Δt denotes a time step, denotes a preset simulation time period, λ t denotes a market electricity price at time t, denotes a discharge power at time t, denotes a charge power at time t, R CM,t denotes a capacity reward at time t, P penalty,t denotes a penalty cost at time t, δ t denotes whether the energy storage can fulfill a preset contract specified discharge capacity at time t, δ t =1 denotes that the energy storage can fulfill the preset contract specified discharge capacity at time t, δ t =0 denotes that the energy storage cannot fulfill the preset contract specified discharge capacity at time t, C penalty denotes a unit penalty coefficient, E contract,t denotes a preset contract specified minimum discharge energy at time t, E t denotes an actually available energy capacity of the energy storage at time t.

[0035] Preferably, the capacity degradation constraint comprises:

[0036]

[0037] wherein, γt represents the degradation amount of the battery energy capacity at time t, and a is a capacity degradation coefficient, S t is the battery stored energy at time t, S t+1 is the stored energy at time t+1, is the upper limit value of the stored energy.

[0038] Preferably, the first energy reconstruction formula comprises:

[0039]

[0040] I = log2(M+1);

[0041]

[0042] wherein, S t is the battery stored energy at time t, S is the lower limit value of the stored energy, and β is the energy increment corresponding to each binary variable, is the i-th binary variable at time t, I is the minimum number of binary variables required for discretization, and M is the number of intervals of discretization;

[0043] The second energy reconstruction formula comprises:

[0044]

[0045] wherein, is the battery stored energy squared term corresponding to the battery stored energy at time t, is the z-th binary variable at time t, represents the product term between two binary variables;

[0046] The linear inequality set comprises:

[0047]

[0048] wherein, represents the auxiliary variable corresponding to the multiplication of and at time t;

[0049] The third energy reconstruction formula comprises:

[0050]

[0051] wherein, is the auxiliary variable corresponding to the multiplication of and at time t.

[0052] On the basis of the method embodiments described above, the application correspondingly provides device embodiments.

[0053] An embodiment of the application provides a battery cycle attenuation-based energy storage control device, comprising a data acquisition module, a first model construction module, a second model construction module, a model solving module and an energy storage scheduling control module.

[0054] The data acquisition module is configured to acquire capacity characteristic data of a battery, storage energy data of the battery under charging and discharging operation, charging and discharging power data of energy storage, market electricity price data, capacity market reward data, a unit penalty coefficient and capacity update cost data.

[0055] The first model construction module is configured to construct an energy storage system capacity degradation model according to the capacity characteristic data of the battery and the storage energy data, wherein the energy storage system capacity degradation is used to represent the dynamic correlation between battery capacity degradation and charging and discharging operation.

[0056] The second model construction module is configured to construct an energy storage system benefit model according to the charging and discharging power data of energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient, the storage energy data of the battery under charging and discharging operation and the capacity update cost data, wherein constraint conditions of the energy storage system benefit model include capacity degradation constraint, storage energy constraint, energy capacity constraint, charging and discharging power constraint and capacity update constraint.

[0057] The model solving module is configured to solve the energy storage system benefit model under the energy storage system capacity degradation model and the constraint conditions, and generate a target control strategy when the energy storage system benefit is maximum, wherein the target control strategy includes charging power, discharging power, storage energy and capacity update amount of energy storage at different time points.

[0058] The energy storage scheduling control module is configured to schedule and control energy storage according to the target control strategy.

[0059] On the basis of the method embodiments described above, the application correspondingly provides terminal device embodiments.

[0060] Another embodiment of the application provides a terminal device, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the battery cycle attenuation-based energy storage control method described in the above-mentioned application embodiments when executing the computer program.

[0061] On the basis of the method embodiments described above, the application correspondingly provides storage medium embodiments.

[0062] Another embodiment of the present application provides a storage medium, the computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the energy storage control method based on battery cycle attenuation described in the above embodiment of the present application when the computer program runs.

[0063] By implementing the present application, the following beneficial effects are achieved:

[0064] The embodiment of the present application provides an energy storage control method and device based on battery cycle attenuation, a terminal device and a storage medium. The present application can construct an energy storage system capacity degradation model according to capacity characteristic data of a battery and storage energy data under charging and discharging operation, clearly represents the dynamic correlation between battery capacity degradation and charging and discharging operation, and can quantify the influence of different charging and discharging strategies on battery degradation. The energy storage system benefit model not only includes direct benefits such as market electricity price and capacity return, but also quantifies the implicit cost caused by battery degradation through capacity update cost data, and ensures that the strategy will not sacrifice long-term life due to short-term excessive charging and discharging through capacity degradation constraints. Finally, the target control strategy of maximizing benefits is solved under the energy storage system capacity degradation model and constraint conditions, and the balance between short-term benefits and long-term costs is achieved. Compared with the prior art, the present application can obtain a quantitative correlation model between battery capacity degradation and charging and discharging operation based on the construction of the energy storage system capacity degradation model, and can realize the constraint of the battery degradation state through the construction of the capacity degradation constraint and the energy capacity constraint, so as to obtain the target control strategy considering the battery degradation. The scheduling and control of the energy storage through the target control strategy can prevent the battery from being in an unhealthy operating state for a long time, and reduce the safety risks such as thermal runaway caused by excessive degradation. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is a flow diagram of an energy storage control method based on battery cycle attenuation provided by an embodiment of the present application.

[0066] Figure 2 is a structural diagram of an energy storage control device based on battery cycle attenuation provided by an embodiment of the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0068] As Figure 1As shown, in order to solve the problem in the prior art that the control strategy without considering the attenuation may cause the battery to be in a non-healthy operating state for a long time, leading to safety risk problems such as thermal runaway of energy storage due to excessive attenuation caused by the failure to establish a quantitative correlation model between battery capacity degradation and charging and discharging operation, an embodiment of the present application provides a kind of energy storage control method based on battery cycle attenuation, comprising:

[0069] Step S1: obtaining the capacity characteristic data of the battery, the storage energy data of the battery under charging and discharging operation, the charging and discharging power data of the energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient and the capacity update cost data;

[0070] Illustratively, the present application can collect multi-dimensional data related to the operation of the energy storage system, such as the capacity characteristic data and the operating state data of the battery, and the real-time storage energy of the battery during charging and discharging (such as the SOC value at time t), the charging and discharging power (the power size and duration during charging / discharging);

[0071] Market and cost data can also be obtained, such as time-of-use electricity price (determining the income difference of charging and discharging), capacity market reward (income obtained by providing standby capacity), unit penalty coefficient (penalty for not meeting dispatching demand) and capacity update cost (cost of replacing battery).

[0072] Step S2: constructing a capacity degradation model of the energy storage system according to the capacity characteristic data of the battery and the storage energy data; wherein the energy degradation of the energy storage system is used to represent the dynamic correlation between battery capacity degradation and charging and discharging operation;

[0073] Illustratively, a mathematical model can be established based on the battery capacity characteristic data and the storage energy data obtained in step S1 to quantitatively describe the dynamic relationship between the degree of battery capacity degradation and the charging and discharging operation, thereby solving the problem of no quantitative correlation between degradation and operation in traditional technology, and explicitly showing the influence of battery degradation on energy storage income, and avoiding accelerating degradation due to blind operation.

[0074] Step S3: constructing an energy storage system revenue model according to the charging and discharging power data of the energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient, the storage energy data of the battery under charging and discharging operation and the capacity update cost data; wherein the constraint conditions of the energy storage system revenue model include: capacity degradation constraint, storage energy constraint, energy capacity constraint, charging and discharging power constraint and capacity update constraint;

[0075] Illustratively, the energy storage system revenue model constructed by the present application can include the whole cycle revenue calculation of the degradation constraint, which can construct a revenue model with the goal of maximizing the whole life cycle revenue, and set multiple constraint conditions:

[0076] Capacity degradation constraint: based on the model in step S2, the rate of degradation caused by charging and discharging operations can be limited, such as the single-cycle degradation amount not exceeding a threshold value;

[0077] Operation constraint: storage energy constraint (such as SOC not exceeding 0-100%), charging and discharging power constraint (not exceeding the maximum charging and discharging power of the battery), energy capacity constraint (current capacity meeting the minimum energy storage requirement);

[0078] Capacity update constraint: the capacity update amount needs to be within the economically feasible range, such as the update cost not exceeding the expected loss of income.

[0079] Therefore, the present application incorporates the implicit cost of battery degradation and the safety operation constraint into the model, ensuring that the income calculation meets the long-term safety and economic goals.

[0080] Step S4: under the capacity degradation model of the energy storage system and the constraint conditions, the energy storage system income model is solved, and the target control strategy is generated when the energy storage system income is maximum; wherein the target control strategy includes: the charging power, discharging power, storage energy and capacity update amount of the energy storage at different times;

[0081] Illustratively, under the double constraints of the capacity degradation model (providing degradation prediction) in step S2 and the constraint conditions in step S3, the energy storage system income model is solved by an optimization algorithm to find the strategy combination that maximizes the whole life cycle income: the charging power at different times (when to charge with low-price electricity), the discharging power (when to discharge with high-price electricity), the storage energy (maintaining how much SOC can balance degradation and income), and the capacity update amount (when to replace the battery more economically).

[0082] Therefore, the present application can maximize the income without accelerating the degradation and ensuring safety. For example: when the battery degradation is serious, the strategy will automatically reduce the charging and discharging power to slow down the degradation; when the price difference is not enough to cover the degradation cost, the charging and discharging times are reduced to reduce the storage energy; when the capacity is close to the critical value, the update is planned in advance to avoid sudden failure.

[0083] Step S5: according to the target control strategy, the energy storage is scheduled and controlled.

[0084] Illustratively, the target control strategy generated in step S4 is converted into specific operation instructions to control the charging and discharging behavior of the energy storage system, such as charging at 20kW at t1, discharging at 30kW at t2, and performing battery update as planned, etc.

[0085] By following the strategy based on the degradation state optimization, the battery can always be in a healthy operating interval, ensuring long-term safety (reducing the risk of thermal runaway) and maximizing economic income.

[0086] For step S2, in one preferred embodiment, the capacity characteristic data of the battery includes: initial energy capacity and initial charge-discharge cycle number; the storage energy data includes: upper limit value of storage energy and corresponding storage energy data of the battery at different time points;

[0087] The capacity degradation model of the energy storage system is constructed according to the capacity characteristic data of the battery and the storage energy data, including:

[0088] According to the initial energy capacity and the initial charge-discharge cycle number, a capacity degradation coefficient for quantifying the capacity degradation degree of the battery during operation is generated;

[0089] According to the capacity degradation coefficient, the corresponding storage energy data of the battery at different time points and the upper limit value of the storage energy, the capacity degradation model of the energy storage system is constructed.

[0090] Specifically, the capacity degradation model of the energy storage system can be:

[0091]

[0092] Wherein, γ t represents the degradation amount of the energy capacity of the battery at time t, a is the capacity degradation coefficient, S t is the storage energy of the battery at time t, S t+1 is the storage energy of the battery at time t+1, is the upper limit value of the storage energy;

[0093] is the initial energy capacity, and N is the initial charge-discharge cycle number. It can be understood that the initial charge-discharge cycle number refers to the maximum complete cycle number, which represents the maximum number of complete cycles that the battery can perform from full charge to complete discharge and then full charge under normal use, reflecting the durability of the battery and being one of the important indicators for measuring the service life of the battery. The less the maximum complete cycle number, the more likely the battery is to have capacity degradation. In the model, a is determined together with the initial energy capacity, thereby affecting the quantitative calculation of capacity degradation.

[0094] Based on the capacity degradation model of the energy storage system, the performance degradation degree of the battery can be directly associated with the stored energy through the capacity degradation coefficient. When the battery is charged and discharged, the change of the battery storage energy will affect the energy capacity degradation amount through the depth of discharge. For example, the fluctuation of the stored energy will change the depth of discharge, and the depth of discharge is associated with the maximum complete cycle number through the rain flow counting algorithm, thereby affecting the capacity attenuation amount of a single cycle.

[0095] The application can quantify the dynamic correlation between battery capacity degradation and charging / discharging operation by constructing an energy storage system capacity degradation model, so that the abstract battery degradation is converted into a calculable numerical value, and the degree of damage of each charging / discharging operation to the long-term capacity of the battery is determined.

[0096] Specifically, the construction process of the energy storage system capacity degradation model of the application is as follows:

[0097] First, a mathematical model of a battery energy storage system (BESS) is established; by collecting the charging / discharging data of the battery and establishing the operation model of the battery energy storage system, the dynamic evolution of the BESS operation can be described by the following formula:

[0098]

[0099] Wherein, S t represents the stored energy (SE) at time step Δt at the t time. and respectively represent the charging and discharging power at t time, η quantifies the charging and discharging efficiency of the BESS, and T is the preset time period for iterative solution of the model. The following are the constraint conditions for SE and BESS power:

[0100]

[0101] Wherein, S is the minimum value of SE, and are the upper limits of SE and power, respectively.

[0102] Electrochemical batteries exhibit performance degradation over their lifetime due to chemical processes occurring during the charging / discharging cycle, which can have a significant impact on the operation of the battery energy storage system (BESS) and thus its profitability; therefore, when evaluating the optimal operation of the BESS, it must be properly modeled and considered. The degradation of the battery can be quantified by the reduction of the energy capacity, assuming that this reduction depends on the charging / discharging operation of the battery energy storage system (BESS), while the effects of operating temperature and other non-operating factors (such as ambient temperature and humidity, etc.) on the energy capacity degradation are not considered. Under this assumption, the following formula represents the change of the BESS energy capacity E t over time:

[0103]

[0104] Wherein, γ t represents the degradation of the energy capacity, i.e. the amount of reduction of the energy capacity of the battery due to charging / discharging operation at t time.

[0105] According to the rainflow counting algorithm, each charging or discharging operation consists of one half-cycle, and its associated energy capacity fade γ t The capacity fade γ

[0106]

[0107] where the function can return the maximum number of full cycles that can be performed before the initial capacity decreases to a specified lower limit (usually ).

[0108] It is worth noting that the capacity fade of a single full cycle is approximated as a linear function of , given a fixed x. If the battery can perform a total of cycles at a discharge depth of x, then the capacity fade associated with a single cycle is equal to Furthermore, it is emphasized that the discharge depth (which can be denoted by d t ) is a dimensionless measure of the current state S relative to the maximum value t , as follows:

[0109]

[0110] The maximum number of full cycles can be derived from the following formula: which can be expressed as a function of the discharge depth x and the fixed parameter N, which is the maximum number of full cycles that can be performed when x = 1, so that:

[0111]

[0112] The constant parameter σ can vary depending on the type of BESS considered, ranging from 0.85 to 2.1, while the constant parameter σ is usually provided by the manufacturer and can be obtained through fitting techniques of experimental data. Since a larger σ value imposes less N on a certain specific , a conservative assumption is made that σ = 2.

[0113] Further, by relating the capacity fade γ t to the energy capacity and the number of charge-discharge cycles, the present application can link the capacity fade γ t to the decision variables of the BESS operation (i.e., the corresponding stored energy of the battery at different times), thereby constructing a capacity fade model for the energy storage system.

[0114] It can be understood that the energy storage system capacity degradation model of the present application can bind short-term operation decisions (dynamic changes of stored energy SE) with long-term capacity degradation. For example, when the stored energy SE is greatly reduced at a certain moment due to discharging at a high electricity price, the increased depth of discharge will cause the capacity degradation to accelerate; on the contrary, if the stored energy SE is maintained within a reasonable range (such as charging the storage energy at a low electricity price), the growth of the capacity degradation can be slowed down.

[0115] The energy storage system capacity degradation model can simultaneously consider the cost caused by long-term capacity degradation when optimizing short-term arbitrage income, thereby avoiding the battery life loss and contract breach risk caused by excessive charging and discharging in the short term.

[0116] For step S3, in a preferred embodiment, the capacity market reward data is the capacity reward obtained by the energy storage when performing the discharging capacity specified in the preset contract; and the capacity updating cost data includes unit updating capacity cost at different times and capacity updating amount at different times.

[0117] Then, the energy storage system income model is constructed according to the charging and discharging power data of the energy storage, the market electricity price data, the capacity market reward data, the unit penalty coefficient, the stored energy data of the battery under charging and discharging operation, and the capacity updating cost data, which includes:

[0118] The energy market income model is constructed according to the charging and discharging power data of the energy storage and the market electricity price data; wherein the energy market income model is used to represent the economic income obtained by the energy storage in the energy market by adjusting the charging and discharging behavior.

[0119] The penalty cost is generated according to the charging and discharging power data of the energy storage, the unit penalty coefficient, and the preset minimum discharging energy; wherein the penalty cost is used to quantify the cost value generated when the energy storage fails to perform the discharging capacity specified in the preset contract.

[0120] The capacity market income model is constructed according to the capacity reward and the penalty cost.

[0121] The capacity updating cost model is constructed according to the stored energy data of the battery under charging and discharging operation, the unit updating capacity cost at different times, and the capacity updating amount at different times.

[0122] The energy storage system income model is constructed according to the energy market income model, the capacity market income model, and the capacity updating cost model.

[0123] Specifically, the energy storage system income model includes:

[0124]

[0125] R = R CM,t -Ppenalty,t ;

[0126] P penalty,t =δ t ×C penalty ×max(0,E contract,t -E t );

[0127] wherein, denotes the energy market revenue model, denotes the capacity market revenue model, and t denotes the capacity update cost model, wherein t denotes the capacity update amount at time t, and denotes a preset simulation time period, and t denotes the market electricity price at time t, denotes the discharging power at time t, denotes the charging power at time t, CM,t denotes the capacity reward at time t, penalty,t denotes the penalty cost at time t, t denotes whether the energy storage can fulfill the preset contract specified discharging capacity at time t, t denotes that the energy storage can fulfill the preset contract specified discharging capacity at time t, t denotes that the energy storage cannot fulfill the preset contract specified discharging capacity at time t, penalty denotes a unit penalty coefficient, contract,t denotes the minimum discharging energy specified by the preset contract at time t, t denotes the actual available energy capacity of the energy storage at time t.

[0128] It can be understood that the energy storage system revenue model can integrate the energy market revenue model, the capacity market revenue model and the capacity update cost model, so as to maximize the whole life cycle revenue, while taking into account various constraint conditions (such as capacity degradation constraint, storage energy constraint, etc.), to balance the short-term arbitrage revenue and the long-term safe and economic goal.

[0129] Specifically, the energy market revenue model can be constructed according to the charging and discharging power data (charging power discharging power ) of the energy storage and the market electricity price data (electricity price t at different times). By calculating the revenue difference of charging (buying electricity at low price) and discharging (selling electricity at high price) at different times, the economic revenue obtained by adjusting the charging and discharging behavior in the energy market is accumulated, so as to quantify the low-buy-high-sell arbitrage revenue.

[0130] The penalty cost can be generated by the charge-discharge power data of the energy storage, the unit penalty coefficient (i.e. the fine amount per unit when the scheduling demand is not met) and the preset minimum discharge energy (the discharge energy required to be guaranteed by the contract). When the energy storage fails to perform the preset contract specified discharge capacity, i.e. the actual discharge energy does not meet the requirement, the cost value generated thereby is calculated to quantify the loss caused by the breach of contract, etc.

[0131] The capacity market revenue model can be constructed based on the capacity remuneration (the revenue obtained by the energy storage when performing the preset contract specified discharge capacity) and the penalty cost. For example, the net revenue obtained in the capacity market participation is obtained by subtracting the penalty cost (if there is a penalty) from the capacity remuneration, which reflects the revenue of the energy storage in the capacity market due to the provision of standby capacity and other services.

[0132] The capacity update cost model is constructed according to the storage energy data of the battery under charge-discharge operation (reflecting the current state of the battery, affecting whether the update is needed and the timing of the update), the unit update capacity cost at different times (the cost required for each unit capacity update) and the capacity update amount at different times (the scale of the battery capacity update at time t), so that the cost of battery capacity update can be quantified and taken into account in the revenue model for comprehensive consideration.

[0133] The storage energy S of the battery under charge-discharge operation t , which can affect the energy capacity degradation amount, thereby changing the long-term available capacity of the battery and the revenue potential of participating in the capacity market, and determines the charge-discharge strategy of the battery, such as charging at a lower time and discharging at a higher time. This strategy selection will actually affect the values of P t d and P t c , which will continuously affect the performance of the BESS in the capacity market and the modification strategy of the BESS in the long-term operation, thereby indirectly affecting the total revenue.

[0134] The capacity remuneration, which can quantify the monthly or annual fixed remuneration obtained by the battery energy storage system (BESS) by participating in the capacity market (CM), signing a contract with the market and committing to provide the contract specified discharge capacity during the system stress period (such as the peak demand period) to ensure the safety of power supply during the contract period, and will also face high cost penalties when the required power is not provided.

[0135] According to the capacity reward and the penalty cost, a capacity market revenue model is constructed, which can compensate for the long-term reserve capacity of the BESS, emphasizing the value of the BESS providing capacity adequacy guarantee for the power system. As a long-term stable income, the capacity market revenue can reduce the income fluctuation and uncertainty caused by the degradation of the battery capacity, and is an important source of income for balancing the long-term investment cost and operation risk of the BESS, and together with the arbitrage revenue of the short-term energy market, constitutes the main income of the BESS.

[0136] If the BESS cannot provide the CM capacity specified in the contract after receiving the notification, it will face high-cost penalties, which will greatly reduce the total income. In addition, the fulfillment of the contract requires the BESS to guarantee the minimum discharge energy If the upper limit of the BESS capacity is also considered Then the actual available energy capacity constraint of the energy storage at time t must be established:

[0137]

[0138] To ensure that the above energy capacity constraint is established and the degradation of energy is considered, additional energy capacity can also be purchased and equipped, such as by expanding the size of the BESS or regularly performing capacity modification, measures including replacing degraded batteries and increasing energy capacity.

[0139] Further, the R t denotes the capacity update amount at time t, then:

[0140]

[0141] It can be understood that the energy capacity E t must be maintained at least at the design value The above and below, it can be reasonably assumed that the energy capacity modification R t can be performed at a lower frequency. In the present application, the evaluation time range of the BESS operation can be Y=10 years, and the time step is adopted every hour T is the preset time period for model iteration solution, and is also a set of time steps.

[0142] wherein T=8760*Y=87600, assuming that the capacity modification is performed every six months, then corresponds to wherein K=19 and K represents the total number of modification operations, and the present application assumes 19 times, represents the time interval between each retrofit operation. Then, the BESS can only be retrofitted for energy capacity at every 6-month retrofit window (e.g. at hour 1, hour 4380, hour 8760,...) and cannot be retrofitted at other time steps.

[0143] Further, the capacity update constraint is:

[0144]

[0145] From the above formula, it can be seen that R t ≥ 0 is a non-negative quantity, which prevents the optimal strategy from intentionally reducing the energy capacity, and the BESS can only be retrofitted for capacity (i.e. capacity update) when and .

[0146] The capacity update cost model is:

[0147]

[0148] where θ t is the unit update capacity cost at time t, R t is the capacity update amount at time t, θ T+1 is the capacity update amount corresponding to the first time step after the end of the contract period, E T+1 is the actual energy capacity corresponding to the first time step after the end of the contract period, is the minimum discharge energy, represents the possibility of considering the possibility of the BESS earning income by selling energy capacity exceeding E at the end of the contract period, is the set of time steps that allow energy capacity retrofit.

[0149] Through the above formula, the present application can automatically balance the cost of the current retrofit and the benefit of the remaining capacity in the future through the capacity update cost model, avoiding the extreme cases of excessive retrofit leading to high cost or insufficient retrofit leading to low benefit of the remaining capacity.

[0150] For step S4, in one preferred embodiment, the capacity degradation constraint includes:

[0151]

[0152] where γ t represents the degradation amount of the battery energy capacity at time t, and α is the capacity degradation coefficient, S t is the battery storage energy at time t, S t+1 is the storage energy of the battery at time t+1, an upper limit value of the stored energy.

[0153] Then, for the capacity degradation constraint, it contains the battery storage energy at different time points and the battery storage energy square term corresponding to each battery storage energy;

[0154] It can be understood that the amount of battery capacity degradation γ t is directly related to the dynamic change of the stored energy S t (eg indirectly related through the depth of discharge), and this correlation has a nonlinear characteristic in the physical mechanism, for example, the capacity degradation rate accelerates with the increase of the change amplitude of the stored energy, showing a quadratic relationship.

[0155] If the square term is not introduced, it is difficult to accurately describe this nonlinear correlation, which may lead to distortion of the quantitative results of capacity degradation (such as underestimating the damage of deep charge and discharge to the battery), therefore, in the constraints of the energy storage system revenue model, the nonlinear constraint about S t in the capacity degradation constraint needs to be transformed into a linear constraint through technical means, that is, before solving the energy storage system revenue model, it also includes:

[0156] For the battery storage energy at each time point in the capacity degradation constraint, the battery storage energy is discretized through a binary expansion formula to generate a first energy reconstruction formula containing a plurality of binary variables;

[0157] According to the first energy reconstruction formula, the battery storage energy square term is expanded to generate a second energy reconstruction formula; wherein the second energy reconstruction formula contains a plurality of binary variable square terms and a plurality of binary variable product terms;

[0158] Obtain a preset auxiliary variable;

[0159] According to the auxiliary variable, the binary variable and the binary variable product term, a linear inequality set is constructed; wherein the linear inequality set is used to constrain the equivalent relationship between the auxiliary variable, the binary variable and the binary variable product term;

[0160] According to the linear inequality set and the auxiliary variable, the second energy reconstruction formula is converted into a third energy reconstruction formula with a linear relationship;

[0161] The first energy reconstruction formula and the third energy reconstruction formula are respectively replaced by the battery storage energy and the battery storage energy square term in the capacity degradation constraint to construct an updated capacity degradation constraint;

[0162] The updated capacity degradation constraint is used as the capacity degradation constraint when solving the energy storage system revenue model.

[0163] The first energy reconstruction formula includes:

[0164]

[0165] I = log2(M + 1);

[0166]

[0167] wherein S t is the battery storage energy at time t, S is the lower limit value of the storage energy, and β is the energy increment corresponding to each binary variable, is the i-th binary variable at time t, I is the minimum number of binary variables required for discretization, and M is the interval number of discretization;

[0168] The second energy reconstruction formula includes:

[0169]

[0170] wherein, is the battery storage energy square term corresponding to the battery storage energy at time t, is the z-th binary variable at time t, represents the product term between two binary variables;

[0171] The linear inequality set includes:

[0172]

[0173] wherein, represents the auxiliary variable corresponding to the multiplication of and at time t;

[0174] The third energy reconstruction formula includes:

[0175]

[0176] wherein, is the auxiliary variable corresponding to the multiplication of and at time t.

[0177] It can be understood that, for the above linear inequality set, and can be understood as: if or is 0, then must be 0 (because the product is 0);

[0178] can be understood as: if and then Combining the first two inequalities, If one of them is 0, such as then Combining

[0179] Then the three linear inequalities in the linear inequality system can achieve equivalent to the logic, so as to convert the binary linear constraint (i.e. the product of two binary variables) into a linear constraint.

[0180] Illustratively, since there are product terms of binary variables and continuous variables in the expressions of the first energy reconstruction formula and the second energy reconstruction formula after reconstruction, the present application can introduce auxiliary variables to convert the bilinear constraint into a linear constraint by using Fortet exact inequality technique. Specifically, a new auxiliary variable is defined for each product term, and by constructing a linear inequality system about the binary variables, the continuous variables and the auxiliary variables, it is ensured that the auxiliary variables and the original product terms are logically equivalent, so as to eliminate the nonlinear relationship.

[0181] The present application discretizes the continuous variable S t by binary expansion, and eliminates the bilinear term by Fortet exact inequality. All nonlinear constraints in the energy storage system benefit model can be converted into linear constraints, so that the model finally becomes a mixed integer linear programming (MILP) problem, ensuring that the model can be efficiently solved by a standard solver (such as CPLEX), so as to output the optimal solution (charging power, discharging power, stored energy, capacity update amount, etc.) that meets all constraints, providing a quantifiable basis for the landing of the energy storage control strategy.

[0182] Specifically, the linear constraints obtained after the above processing are integrated with the original capacity degradation constraints, stored energy constraints (such as upper and lower limits of stored energy), charging and discharging power constraints (such as charging and discharging power range), energy capacity constraints (such as meeting the minimum discharging energy requirement of the capacity market), and capacity update constraints (such as non-negative capacity update amount) in the energy storage system benefit model, so that the energy storage system benefit model can form a complete mixed integer linear programming (MILP) model. The integrated MILP model is solved by using a standard linear programming solver, and the energy storage system benefit is maximized under the premise of meeting all constraint conditions. The solution result outputs the charging power, discharging power, stored energy and capacity update amount at different times, which constitutes the target control strategy.

[0183] ​In step S5, this invention employs a target control strategy for energy storage scheduling, enabling BESS to accurately profit in the energy market (capitalizing on electricity price differences), stably fulfill its contractual obligations in the capacity market (meeting contractual constraints), and simultaneously ensure battery health (controlling degradation). Ultimately, this achieves a virtuous cycle of short-term price arbitrage, long-term minimal degradation, and high returns throughout the entire lifecycle.

[0184] In a preferred embodiment, a battery energy storage system (BESS) participates in the energy and capacity market. Initial energy capacity and other parameters are given, and time-of-use (TOU) electricity prices range from high to low. The target control strategy, derived from the energy storage system revenue model of this invention, is as follows: charging at 10MW from 00:00 to 08:00, storing energy from 50MWh to 100MWh; discharging at 10MW from 08:00 to 24:00, storing energy from 100MWh to 50MWh, with no capacity update required on the same day. During scheduling, charging stops at the upper limit of energy capacity during low-price periods, while discharging is constrained by a safety lower limit (simplified to 50MWh) during high-price periods. The net daily revenue is €10,000. Storing energy within the safe range, with shallow charging and discharging, helps slow battery degradation, achieving a balance between economic benefits and battery health.

[0185] like Figure 2 As shown, based on the above embodiments of various energy storage control methods based on battery cycle degradation, the present invention provides corresponding device embodiments;

[0186] An embodiment of the present invention provides an energy storage control device based on battery cycle degradation, comprising: a data acquisition module, a first model construction module, a second model construction module, a model solving module, and an energy storage scheduling control module;

[0187] The data acquisition module is used to acquire battery capacity characteristic data, battery energy storage data under charging and discharging operations, energy storage charging and discharging power data, market electricity price data, capacity market reward data, unit penalty coefficient, and capacity renewal cost data.

[0188] The first model building module is used to build an energy storage system capacity degradation model based on the battery capacity characteristic data and the stored energy data; wherein, the energy storage system energy degradation is used to characterize the dynamic correlation between battery capacity degradation and charge / discharge operations.

[0189] The second model construction module is used to construct an energy storage system revenue model based on energy storage charging and discharging power data, market electricity price data, capacity market return data, unit penalty coefficient, battery stored energy data under charging and discharging operations, and capacity update cost data; wherein, the constraints of the energy storage system revenue model include: capacity degradation constraint, stored energy constraint, energy capacity constraint, charging and discharging power constraint, and capacity update constraint.

[0190] The model solving module is configured to solve the energy storage system benefit model under the energy storage system capacity degradation model and the constraint condition, and generate a target control strategy when the energy storage system benefit is maximum; wherein the target control strategy comprises charging power, discharging power, stored energy and capacity update amount of the energy storage at different time points.

[0191] The energy storage scheduling control module is configured to schedule and control the energy storage according to the target control strategy.

[0192] It should be noted that the above-described device embodiments are only illustrative, and the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, and can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purpose of the present embodiment according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of the above-described specific working process of the device, the corresponding process in the foregoing method embodiments can be referred to, and will not be described here.

[0194] On the basis of the above-mentioned various embodiments of the energy storage control method based on battery cycle attenuation, the present application correspondingly provides terminal device embodiments.

[0195] An embodiment of the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements an energy storage control method based on battery cycle attenuation according to any one of the method embodiments of the present application when executing the computer program.

[0196] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing terminal devices. The terminal device can include, but is not limited to, a processor and a memory.

[0197] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0198] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function, etc. The data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device or other volatile solid-state memory device.

[0199] On the basis of the above-mentioned various embodiments of the energy storage control method based on battery cycle attenuation, the application correspondingly provides a storage medium embodiment.

[0200] An embodiment of the application provides a storage medium, which comprises a stored computer program, wherein when the computer program runs, a device where the computer readable storage medium is located performs an energy storage control method based on battery cycle attenuation, which is described in any method embodiment of the application.

[0201] The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium. When the computer program is executed by a processor, steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0202] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for controlling energy storage based on battery cycle degradation, characterized in that, include: Acquire battery capacity characteristic data, battery energy storage data under charge and discharge operations, energy storage charge and discharge power data, market electricity price data, capacity market return data, unit penalty coefficient, and capacity renewal cost data; Based on the battery capacity characteristic data and the stored energy data, a capacity degradation model for the energy storage system is constructed; wherein, the energy degradation of the energy storage system is used to characterize the dynamic correlation between battery capacity degradation and charge / discharge operations. Based on the energy storage charging and discharging power data, market electricity price data, capacity market return data, unit penalty coefficient, battery stored energy data under charging and discharging operations, and capacity renewal cost data, an energy storage system revenue model is constructed; wherein, the constraints of the energy storage system revenue model include: capacity degradation constraint, stored energy constraint, energy capacity constraint, charging and discharging power constraint, and capacity renewal constraint. Under the given energy storage system capacity decay model and the given constraints, the energy storage system revenue model is solved, and a target control strategy is generated when the energy storage system revenue is maximized. The target control strategy includes: the charging power, discharging power, stored energy, and capacity update amount of the energy storage at different times. The energy storage is then scheduled and controlled according to the target control strategy.

2. The energy storage control method based on battery cycle degradation as described in claim 1, characterized in that, The battery capacity characteristic data includes: initial energy capacity and initial charge-discharge cycle count; the stored energy data includes: upper limit of stored energy and stored energy data of the battery at different times; The step of constructing a capacity degradation model for the energy storage system based on the battery capacity characteristic data and the stored energy data includes: Based on the initial energy capacity and the initial number of charge-discharge cycles, a capacity degradation coefficient is generated to quantify the degree of capacity degradation of the battery during operation. Based on the capacity degradation coefficient, the battery's stored energy data at different times, and the upper limit of stored energy, a capacity degradation model for the energy storage system is constructed.

3. The energy storage control method based on battery cycle degradation as described in claim 2, characterized in that, The capacity market reward data refers to the capacity reward obtained by energy storage when fulfilling the discharge capacity stipulated in the pre-set contract. The capacity update cost data includes: the unit update capacity cost at different times and the capacity update amount at different times; The energy storage system revenue model is constructed based on energy storage charge / discharge power data, market electricity price data, capacity market return data, unit penalty coefficient, battery stored energy data under charge / discharge operations, and capacity replacement cost data, including: Based on the charging and discharging power data of energy storage and market electricity price data, an energy market revenue model is constructed; wherein, the energy market revenue model is used to characterize the economic benefits obtained by energy storage in the energy market by adjusting charging and discharging behavior; Based on the energy storage's charging and discharging power data, the unit penalty coefficient, and the preset minimum discharge energy, a penalty cost is generated; wherein, the penalty cost is used to quantify the cost value incurred when the energy storage fails to fulfill the discharge capacity stipulated in the preset contract. Based on the capacity rewards and the penalty costs, a capacity market revenue model is constructed. Based on the energy storage data of the battery during charging and discharging operations, the unit update cost of capacity at different times, and the amount of capacity update at different times, a capacity update cost model is constructed. Based on the energy market revenue model, capacity market revenue model, and capacity renewal cost model, a revenue model for energy storage systems is constructed.

4. The energy storage control method based on battery cycle degradation as described in claim 3, characterized in that, The capacity decay constraint includes: the battery storage energy at different times and the squared term of the battery storage energy corresponding to each battery storage energy. Before solving the revenue model of the energy storage system, the following steps are also included: For the battery stored energy at each moment under the capacity decay constraint, the battery stored energy is discretized by binary expansion to generate the first energy reconstruction formula containing several binary variables. Expanding the square term of the battery storage energy according to the first energy reconstruction formula generates a second energy reconstruction formula; wherein, the second energy reconstruction formula contains a number of square terms of binary variables and a number of product terms of binary variables; Get the preset auxiliary variables; Based on the auxiliary variable, the binary variable, and the product terms of the binary variable, a system of linear inequalities is constructed; wherein, the system of linear inequalities is used to constrain the equivalence relationships between the auxiliary variable, the binary variable, and the product terms of the binary variable. Based on the system of linear inequalities and the auxiliary variables, the second energy reconstruction equation is transformed into a third energy reconstruction equation with a linear relationship. The first energy reconstruction formula and the third energy reconstruction formula are used to replace the battery storage energy and the square term of battery storage energy in the capacity decay constraint, respectively, to construct the updated capacity decay constraint. The updated capacity decay constraint will be used as the capacity decay constraint when solving the revenue model of the energy storage system.

5. The energy storage control method based on battery cycle degradation as described in claim 4, characterized in that, The energy storage system revenue model includes: max{φ(P t c ,P t d ,l t )+R-ρ(R t )}; R=R CM,t -P penalty,t ; P penalty,t =δ t ×C penalty ×max(0,E contract,t -AND t ); Where, max{φ(P t c ,P t d ,λ t )+R-ρ(R t )} represents the revenue model of the energy storage system, φ(P) t c ,P t d ,λ t ) represents the energy market revenue model, R represents the capacity market revenue model, and ρ(R) represents the capacity market revenue model. t R represents the capacity update cost model. t Δt represents the capacity update at time t, and Δt represents the time step. λ represents the preset simulation time period. t P represents the market electricity price at time t. t d P represents the discharge power at time t. t c R represents the charging power at time t. CM,t P represents the capacity reward at time t. penalty,t δ represents the penalty cost at time t. t δ indicates whether the energy storage can fulfill the discharge capacity stipulated in the pre-set contract at time t. t =1 indicates that the energy storage can fulfill the discharge capacity specified in the pre-set contract at time t, δ t =0 indicates that the energy storage cannot fulfill the discharge capacity specified in the pre-set contract at time t, C penalty E represents the unit penalty coefficient. contract,t E represents the minimum discharge energy at time t as specified in the pre-defined contract. t This represents the actual usable energy capacity of the energy storage at time t.

6. The energy storage control method based on battery cycle degradation as described in claim 5, characterized in that, The capacity decay constraint includes: Where, γ t S represents the amount of energy capacity degradation at time t, where α is the capacity degradation coefficient. t S represents the energy stored in the battery at time t. t+1 The energy stored in the battery at time t+1. This represents the upper limit for stored energy.

7. The energy storage control method based on battery cycle degradation as described in claim 6, characterized in that, The first energy reconfiguration formula includes: I = log2(M+1); in, S t Let S be the energy stored in the battery at time t, S be the lower limit of the stored energy, and β be the energy increment corresponding to each binary variable. Let I be the binary variable of the i-th bit at time t, where I is the minimum number of binary variables required for discretization, and M is the number of discretization intervals. The second energy reconstruction formula includes: in, Let be the square term of the battery stored energy at time t. Let z be the binary variable at time t. This represents the product term between two binary variables; The system of linear inequalities includes: in, This indicates that at time t, and The auxiliary variables corresponding to the multiplication; The third energy reconstruction formula includes: in, To at time t and The auxiliary variable used when multiplying.

8. An energy storage control device based on battery cycle degradation, characterized in that, include: The system includes a data acquisition module, a first model construction module, a second model construction module, a model solving module, and an energy storage scheduling and control module. The data acquisition module is used to acquire battery capacity characteristic data, battery energy storage data under charging and discharging operations, energy storage charging and discharging power data, market electricity price data, capacity market reward data, unit penalty coefficient, and capacity renewal cost data. The first model building module is used to build an energy storage system capacity degradation model based on the battery capacity characteristic data and the stored energy data; wherein, the energy storage system energy degradation is used to characterize the dynamic correlation between battery capacity degradation and charge / discharge operations. The second model construction module is used to construct an energy storage system revenue model based on energy storage charging and discharging power data, market electricity price data, capacity market return data, unit penalty coefficient, battery stored energy data under charging and discharging operations, and capacity update cost data; wherein, the constraints of the energy storage system revenue model include: capacity degradation constraint, stored energy constraint, energy capacity constraint, charging and discharging power constraint, and capacity update constraint. The model solving module is used to solve the energy storage system revenue model under the energy storage system capacity decay model and the constraints, and generate a target control strategy when the energy storage system revenue is maximized; wherein, the target control strategy includes: the charging power, discharging power, stored energy and capacity update amount of the energy storage at different times. The energy storage scheduling and control module is used to schedule and control energy storage according to the target control strategy.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements an energy storage control method based on battery cycle degradation as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform an energy storage control method based on battery cycle decay as described in any one of claims 1 to 7.

Images