Optimal configuration method of hybrid energy storage system on user side considering demand response and carbon cost

Abstract

The application discloses a user-side hybrid energy storage optimal configuration method considering demand response and carbon cost, and belongs to the field of energy storage system optimal configuration. The optimal configuration method comprises the following steps: obtaining typical original load data of a hybrid energy storage user to be configured; obtaining load reduction of the user load after the load participates in demand response based on an electricity quantity and electricity price elasticity matrix, correcting the typical original load data to obtain load data after demand response; introducing a VSQF frequency domain decomposition algorithm to decouple the load data after demand response into high-frequency components and low-frequency components; substituting the high-frequency components into an upper-layer multi-objective optimal configuration model to output high-frequency fluctuation suppression results and a super capacitor configuration scheme; substituting the low-frequency components combined with the high-frequency fluctuation suppression results into a lower-layer multi-objective optimal configuration model to output a storage battery configuration scheme, and obtaining a final hybrid energy storage configuration scheme.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system optimization configuration, specifically involving a user-side hybrid energy storage optimization configuration method that considers demand response and carbon costs. Background Technology

[0002] The rational allocation of energy storage resources is crucial for improving the stable operating environment of demand-side resources. Compared with single energy storage devices, hybrid energy storage can typically combine the advantages of both capacity-based and power-based energy storage. However, hybrid energy storage configurations still inevitably face challenges such as high initial investment and long payback periods. Therefore, how to rationally allocate the proportion of the two types of energy storage to maximize returns remains a key and challenging research area.

[0003] In a power market environment, agreements can be signed with users to incentivize them to reduce their electricity demand during peak hours through economic compensation. Currently, most research on user-side energy storage configurations neglects to explore the potential for user participation in the market. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a user-side hybrid energy storage optimization configuration method that considers demand response and carbon costs.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A user-side hybrid energy storage optimization configuration method considering demand response and carbon costs includes the following steps:

[0007] Obtain typical raw load data of the hybrid energy storage user to be configured;

[0008] Based on the electricity price elasticity matrix, the load reduction after user load participates in demand response is derived, and the typical original load data is corrected to obtain the load data after demand response.

[0009] The VSQF frequency domain decomposition algorithm is introduced to decouple the load data after demand response into high-frequency components and low-frequency components;

[0010] Substitute the high-frequency components into the upper-level multi-objective optimization configuration model to output the high-frequency fluctuation smoothing results and the supercapacitor configuration scheme.

[0011] The low-frequency components are combined with the high-frequency fluctuation smoothing results and substituted into the lower-level multi-objective optimization configuration model to output the battery configuration scheme and obtain the final hybrid energy storage configuration scheme.

[0012] Furthermore, the steps for obtaining load data after demand response include:

[0013] S21, the original user's typical load data is divided into peak load P in the time domain according to the time-of-use pricing rules. Load,p Normal load P Load,f Valley load P Load,v And using the electricity price elasticity matrix K, the load reduction in each period after demand response is obtained;

[0014]

[0015]

[0016]

[0017] In the K matrix, each element represents the price elasticity of demand; e p e f e v These represent the original time-of-use electricity price; Δe p Δe f Δe v These represent electricity price fluctuations following the implementation of demand response; ΔP represents the actual electricity price after demand response; Load,p ΔP Load,f ΔP Load,v These represent the load reductions at different time periods following the demand response;

[0018] S22, Calculate the load data after demand response:

[0019]

[0020] Wherein, ΔP Load This represents the reduction amount based on the original typical load data after demand response, with its values ​​corresponding to ΔP during peak, normal, and valley hours. Load,p ΔP Load,f ΔP Load,v ;P Load Typical raw load data; This is the load data after the demand has been met.

[0021] Furthermore, in the demand response phase, it is necessary to control the demand response price to ensure user satisfaction with electricity usage (index I). satisfaction Electricity comfort index I comfort and responsiveness index I response All are within the constraints; that is:

[0022]

[0023] Where, δ satisfaction δ comfort δ responseLet represent the limits of each indicator, and t represent the time index variable within a day, t = 1, 2, ..., n D ;n D This indicates the number of sampling points per day.

[0024] Furthermore, the objective function of the upper-level multi-objective optimization configuration model includes:

[0025] f1:minC HF,net =C HF,inv +C HF,ope -C HF,inc

[0026]

[0027]

[0028]

[0029]

[0030] f2:

[0031]

[0032] f3:

[0033] Among them, C HF,net C represents the net investment cost of the supercapacitor; HF,inv Investment cost for supercapacitor construction; C HF,ope The total life-cycle operation and maintenance cost of a supercapacitor; C HF,inc The supercapacitor arbitrage profit is based on a typical load; Flu represents the smoothing of fluctuations in high-frequency load components; P HF,net (t) represents the net value after the high-frequency components are superimposed with the output of the supercapacitor; Carbon cost of supercapacitor configuration Indicates the price of carbon dioxide; ε C,inv ε C,ope ε C,rec These represent the carbon emission factors during the entire life cycle of a supercapacitor: installation, operation and maintenance, and disposal. This represents the cost coefficient per unit capacity of a supercapacitor. This represents the cost coefficient per unit power of a supercapacitor. Indicates the rated capacity of the supercapacitor; This indicates the rated operating power of the supercapacitor; This indicates the fixed cost of assembling a supercapacitor; Indicates the operating and maintenance factor of a supercapacitor; C HF,y,dThis represents the arbitrage profit from supercapacitor prices on day d of year y. and i represents the supercapacitor's discharge and charging power at time t, respectively; r Indicates the discount rate; i d The variable represents the inflation rate; d represents the day index variable within a year, d = 1, 2, ..., D; D represents the number of days available in a year; y represents the year index variable, y = 1, 2, ..., Y. C ;Y C Δt represents the operational lifespan of the supercapacitor; Δt represents the sampling time interval.

[0034] Furthermore, the constraints of the objective function of the upper-level multi-objective optimization configuration model include:

[0035]

[0036] In the formula, SOC C (t) represents the state of charge of the supercapacitor at time t; This indicates the discharge efficiency of the supercapacitor; This indicates the charging efficiency of the supercapacitor; This represents the maximum state of charge of the supercapacitor. This represents the minimum state of charge of a supercapacitor. As an auxiliary Boolean variable, 1 indicates that the supercapacitor is discharging at time t; As an auxiliary Boolean variable, 1 indicates that the supercapacitor is charging at time t.

[0037] Furthermore, the multi-objective optimization problem at the higher level is transformed into a single-objective optimization problem:

[0038] minf up =λ1f1+λ2f2+λ3f3

[0039] Wherein, λ1, λ2, and λ3 are the weight coefficients of each sub-objective.

[0040] Furthermore, the objective function of the lower-level multi-objective optimization configuration model is:

[0041] f4:minC LF,net =C LF,inv +C LF,ope -C LF,inc1 -C LF,inc2

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] Among them, C LF,net C represents the net investment cost of the battery. LF,inv C represents the investment cost for battery construction. LF,ope The total life-cycle operation and maintenance cost of the battery; C LF,inc1 C represents the arbitrage profit from battery prices based on typical load. LF,inc2 Monthly peak shaving revenue for batteries based on typical load; This represents the cost coefficient per unit capacity of the battery; This represents the cost coefficient per unit power of the battery; Indicates the rated capacity of the battery; Indicates the rated operating power of the battery; Y represents the fixed cost of battery assembly; B Indicates the battery's service life; The coefficient of performance for the battery is represented by q; q represents the percentage increase in battery maintenance costs over ten years; C represents the percentage increase in battery maintenance costs over ten years. LF.y.d This represents the arbitrage profit from battery prices on day d of year y. This represents the peak shaving revenue of the battery in month m of year y; and γ and δ represent the supercapacitor's discharge and charging power at time t, respectively; γ represents the capacity electricity price; m Indicates the monthly peak reduction rate; P peak,m This represents the monthly peak load; m represents the monthly index variable, m = 1, 2, ..., M; M represents the number of months that can be run in a year; Carbon costs associated with battery configuration; Indicates the price of carbon dioxide; ε B,inv ε B,ope ε B,rec These represent the carbon emission factors during the entire life cycle of a battery, including installation, operation and maintenance, and disposal.

[0050] Furthermore, the constraints of the objective function of the lower-level multi-objective optimization configuration model are as follows:

[0051]

[0052]

[0053] In the formula, SOC B(t) represents the state of charge of the battery at time t; This indicates the discharge efficiency of the battery; This indicates the charging efficiency of the battery; This represents the battery's maximum state of charge. This represents the minimum state of charge of the battery. As an auxiliary Boolean variable, 1 indicates that the battery is discharging at time t; As an auxiliary Boolean variable, 1 indicates that the battery is charging at time t; To optimize the target curve for the lower layer.

[0054] Furthermore, the lower-level multi-objective optimization problem is transformed into a single-objective optimization problem:

[0055] minf down =f4+f5.

[0056] User-side hybrid energy storage optimization configuration systems that consider demand response and carbon costs include:

[0057] Data acquisition module: Acquires typical raw load data of the hybrid energy storage user to be configured;

[0058] Data correction module: Based on the electricity price elasticity matrix, it derives the load reduction after user load participates in demand response, corrects typical original load data, and obtains load data after demand response;

[0059] Data decomposition module: Introduces the VSQF frequency domain decomposition algorithm to decouple the load data after demand response into high-frequency components and low-frequency components;

[0060] Supercapacitor configuration module: Substitutes the high-frequency components into the upper-level multi-objective optimization configuration model, and outputs the high-frequency fluctuation smoothing results and the supercapacitor configuration scheme;

[0061] In addition, the battery configuration module combines the low-frequency components with the high-frequency fluctuation smoothing results, substitutes them into the lower-level multi-objective optimization configuration model, outputs the battery configuration scheme, and derives the final hybrid energy storage configuration scheme.

[0062] The beneficial effects of this invention are:

[0063] 1. Before using the VSQF algorithm to decompose the load curve, the demand response stage is first carried out; under the premise of meeting the user electricity satisfaction index, user electricity comfort index and responsiveness index, the potential for load self-reduction is explored, thereby reducing the overall cost of hybrid energy storage configuration.

[0064] 2. The configuration of the user-side hybrid energy storage system is beneficial to ensuring the stability of user power supply from the user's perspective, and helps to smooth load fluctuations, reduce peak power demand, and increase the system's reserve capacity from the power supply side.

[0065] 3. A two-layer multi-objective optimization model considering the carbon cost of energy storage was constructed: the upper layer considers the net cost of supercapacitor configuration, the carbon cost of charging and discharging, and the smoothing of high-frequency fluctuations, and uses the deviation sorting method to transform it into a single objective solution; the lower layer, based on the optimization results of the upper layer, considers the net cost of battery configuration, the carbon cost of charging and discharging, and other objectives, and uses commercial optimization software to solve the linear summation. This model is easy to solve and easy to implement. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of the optimized configuration method of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, the user-side hybrid energy storage optimization configuration method considering demand response and carbon costs includes the following steps:

[0070] S1, based on the periodic characteristics of the user's historical electricity consumption curve, uses curve aggregation technology to obtain typical raw load data of the user to be configured with hybrid energy storage, denoted as P. Load ;

[0071] S2, based on the electricity price elasticity matrix, derives the load reduction after user load participates in demand response, and corrects the typical original load data obtained from S1 to obtain the load data after demand response, denoted as...

[0072] The specific steps include:

[0073] S21, Typical load data of original users P Load According to the time-of-use pricing rules, the load is divided into peak load P in the time domain. Load,p Normal load P Load,f Valley load P Load,v And using the electricity price elasticity matrix K, the load reduction in each period after demand response is obtained;

[0074]

[0075]

[0076]

[0077] In the K matrix, each element represents the price elasticity of demand, indicating the coupling relationship between electricity consumption and electricity price at different time periods; e p e f e v These represent the original time-of-use electricity price; Δe p Δe f Δe v These represent electricity price fluctuations following the implementation of demand response; ΔP represents the actual electricity price after demand response; Load,p ΔP Load,f ΔP Load,v These represent the load reductions at different time periods following the demand response.

[0078] S22, Correcting typical raw load data P Load To obtain load data after demand response

[0079] The expression for calculating load data after demand response is:

[0080]

[0081] In the formula, ΔP Load This represents the reduction amount based on the original typical load data after demand response, with its values ​​corresponding to ΔP during peak, normal, and valley hours. Load,p ΔP Load,f ΔP Load,v .

[0082] In the demand response phase, it is also necessary to control the demand response price to ensure user satisfaction with electricity usage (index I). satisfaction Electricity comfort index I comfort and responsiveness index I response All are within the constraints; that is:

[0083]

[0084] Where, δ satisfaction δ comfort δ response Let represent the limits of each indicator, and t represent the time index variable within a day, t = 1, 2, ..., n D ;n D This indicates the number of sampling points per day.

[0085] S3, by introducing the VSQF frequency domain decomposition algorithm [1], the load data after demand response is... Decoupling to high-frequency component P HF With low-frequency component P LF Two parts;

[0086] The specific steps include:

[0087] S31, after decoupling the original load data by using variational mode decomposition algorithm, the S-transform is used to convert each time domain curve into a time-frequency spectrum, and the frequency domain marginal spectrum is further obtained by time domain integration;

[0088] S32, by using quantum particle swarm optimization fuzzy clustering to perform binary classification of the frequency domain marginal spectrum, curve labels of high-frequency clusters and low-frequency clusters can be obtained;

[0089] S33, based on the binary classification labels, the corresponding time-domain curves are re-superimposed to obtain the high-frequency component curve P. HF With low-frequency component curve P LF .

[0090] S4, decompose the high-frequency component P. HF Substitute the upper-level multi-objective optimization configuration model to output the high-frequency fluctuation smoothing results and the supercapacitor configuration scheme;

[0091] The objective functions of the upper-level multi-objective optimization configuration model include:

[0092] (1) Upper-level optimization objective f1: Minimize the net investment cost C of the supercapacitor. HF,net :

[0093] f1:minC HF,net =C HF,inv +C HF,ope -C HF,inc

[0094] Among them, C HF,net C represents the net investment cost of the supercapacitor; HF,inv Investment cost for supercapacitor construction; C HF,ope The total life-cycle operation and maintenance cost of a supercapacitor; C HF,inc For supercapacitor arbitrage profits based on typical loads;

[0095]

[0096]

[0097]

[0098]

[0099] in, This represents the cost coefficient per unit capacity of a supercapacitor. This represents the cost coefficient per unit power of a supercapacitor. Indicates the rated capacity of the supercapacitor; This indicates the rated operating power of the supercapacitor; This indicates the fixed cost of assembling a supercapacitor; Indicates the operating and maintenance factor of a supercapacitor; C HF,y,d This represents the arbitrage profit from supercapacitor prices on day d of year y. and i represents the supercapacitor's discharge and charging power at time t, respectively; r Indicates the discount rate; i d The variable represents the inflation rate; d represents the day index variable within a year, d = 1, 2, ..., D; D represents the number of days available in a year; y represents the year index variable, y = 1, 2, ..., Y. C ;Y C Δt represents the operational lifespan of the supercapacitor; Δt represents the sampling time interval.

[0100] (2) Upper-level optimization objective f2: Minimize the fluctuation of high-frequency load components and smooth out Flu;

[0101] f2:

[0102]

[0103] Where Flu represents the smoothing of high-frequency load component fluctuations; P HF,net (t) represents the net value after the high-frequency components are superimposed on the output of the supercapacitor.

[0104] (3) Upper-level optimization objective f3: Minimize the carbon cost of supercapacitor configuration

[0105] f3:

[0106] in, Carbon cost of supercapacitor configuration Indicates the price of carbon dioxide; ε C,inv ε C,ope ε C,rec These represent the carbon emission factors during the entire life cycle of a supercapacitor: installation, operation and maintenance, and disposal.

[0107] The constraints of the objective function of the upper-level multi-objective optimization configuration model include:

[0108] Considering the state-of-charge constraints and charge / discharge constraints of the supercapacitor:

[0109]

[0110] In the formula, SOC C (t) represents the state of charge of the supercapacitor at time t; This indicates the discharge efficiency of the supercapacitor; This indicates the charging efficiency of the supercapacitor; This represents the maximum state of charge of the supercapacitor. This represents the minimum state of charge of a supercapacitor. As an auxiliary Boolean variable, 1 indicates that the supercapacitor is discharging at time t; As an auxiliary Boolean variable, 1 indicates that the supercapacitor is charging at time t.

[0111] The multi-objective optimization problem at the upper level has inconsistent dimensions among its sub-objectives. Therefore, we consider using the deviation sorting method [2] to convert the multi-objective optimization problem into a single-objective optimization problem, and then use commercial solution software to solve it.

[0112] minf up =λ1f1+λ2f2+λ3f3

[0113] Wherein, λ1, λ2, and λ3 are the weight coefficients of each sub-objective.

[0114] Since the upper-level optimization for supercapacitors may not completely eliminate high-frequency component fluctuations, the remaining components are substituted into the lower-level optimization model:

[0115]

[0116] In the formula, The target curve for lower-level optimization is, in physical terms, the curve resulting from the superposition of the low-frequency load component with the high-frequency net value.

[0117] S5 will reduce the low-frequency component P LF Combining the high-frequency fluctuation smoothing results output in S4, the results are substituted into the lower-level multi-objective optimization configuration model to output the battery configuration scheme and derive the final hybrid energy storage configuration scheme.

[0118] The objective functions of the lower-level multi-objective optimization configuration model include:

[0119] (1) Lower-level optimization objective f4: Minimize the net investment cost C of the battery. LF,net

[0120] f4:minC LF,net =C LF,inv +C LF,ope -C LF,inc1 -C LF,inc2

[0121] Among them, C LF,netC represents the net investment cost of the battery. LF,inv C represents the investment cost for battery construction. LF,ope The total life-cycle operation and maintenance cost of the battery; C LF,inc1 C represents the arbitrage profit from battery prices based on typical load. LF,inc2 Monthly peak shaving revenue for batteries based on typical load;

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] in, This represents the cost coefficient per unit capacity of the battery; This represents the cost coefficient per unit power of the battery; Indicates the rated capacity of the battery; Indicates the rated operating power of the battery; Y represents the fixed cost of battery assembly; B Indicates the battery's service life; The coefficient of performance for the battery is represented by q; q represents the percentage increase in battery maintenance costs over ten years; C represents the percentage increase in battery maintenance costs over ten years. LF.y.d This represents the arbitrage profit from battery prices on day d of year y. This represents the peak shaving revenue of the battery in month m of year y; and γ and δ represent the supercapacitor's discharge and charging power at time t, respectively; γ represents the capacity electricity price; m Indicates the monthly peak reduction rate; P peak,m This represents the monthly peak load; m represents the monthly index variable, m = 1, 2, ..., M; M represents the number of months that can be run in a year.

[0129] (2) Lower-level optimization objective f5: Minimize the carbon cost of battery configuration:

[0130]

[0131] in, Carbon costs associated with battery configuration; Indicates the price of carbon dioxide; ε B,inv ε B,ope ε B,recThese represent the carbon emission factors during the entire life cycle of a battery, including installation, operation and maintenance, and disposal.

[0132] The constraints of the objective function of the lower-level multi-objective optimization configuration model are:

[0133] Considering battery state of charge constraints, charge / discharge constraints, and peak clipping constraints

[0134]

[0135] Among them, SOC B (t) represents the state of charge of the battery at time t; This indicates the discharge efficiency of the battery; This indicates the charging efficiency of the battery; This represents the battery's maximum state of charge. This represents the minimum state of charge of the battery. As an auxiliary Boolean variable, 1 indicates that the battery is discharging at time t; As an auxiliary Boolean variable, 1 indicates that the battery is charging at time t.

[0136] Since the two optimization objectives of the lower-level optimization model have the same dimensions, they can be directly added together to transform it into a single-objective optimization problem, which can then be solved directly using commercial solution software.

[0137] minf down =f4+f5

[0138] Note: [1] Rongchuan Tang, Qingshan Xu, Jicheng Fang, et cl. Optimal configuration strategy of hybrid energy storage system on industrial load sidebased on frequency division algorithm. Journal of Energy Storage. Vol: 50, 2022.

[0139] [2] Tan Xingguo, Wang Hui, Zhang Li, Zou Liang. Multi-objective optimization configuration method and evaluation index of microgrid composite energy storage[J]. Automation of Electric Power Systems, 2014, 38(08):7-14.

[0140] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0141] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A user-side hybrid energy storage optimization configuration method considering demand response and carbon costs, characterized in that, Includes the following steps: Obtain typical raw load data of the hybrid energy storage user to be configured; Based on the electricity price elasticity matrix, the load reduction after user load participates in demand response is derived, and the typical original load data is corrected to obtain the load data after demand response. The VSQF frequency domain decomposition algorithm is introduced to decouple the load data after demand response into high-frequency components and low-frequency components; Substitute the high-frequency components into the upper-level multi-objective optimization configuration model to output the high-frequency fluctuation smoothing results and the supercapacitor configuration scheme. The low-frequency components are combined with the high-frequency fluctuation smoothing results and substituted into the lower-level multi-objective optimization configuration model to output the battery configuration scheme and obtain the final hybrid energy storage configuration scheme. The objective function of the upper-level multi-objective optimization configuration model includes: in, CHF,net This represents the net investment cost of the supercapacitor. CHF,inv Investment costs for supercapacitor construction; CHF,ope The total life-cycle operation and maintenance cost of a supercapacitor; CHF,inc For supercapacitor arbitrage profits based on typical loads; Flu To smooth out fluctuations in high-frequency load components; This represents the net value after the high-frequency components are superimposed with the output of the supercapacitor; Carbon cost of supercapacitor configuration Indicates the price of carbon dioxide; , , These represent the carbon emission factors during the entire life cycle of a supercapacitor: installation, operation and maintenance, and disposal. This represents the cost coefficient per unit capacity of a supercapacitor. This represents the cost coefficient per unit power of a supercapacitor. Indicates the rated capacity of the supercapacitor; This indicates the rated operating power of the supercapacitor; This indicates the fixed cost of assembling a supercapacitor; This indicates the operation and maintenance coefficient of a supercapacitor; CHF,y,d This represents the arbitrage profit from supercapacitor prices on day d of year y. and They represent in t Real-time supercapacitor discharge and charging power; ir Indicates the discount rate; id Indicates the inflation rate; d This represents the day index variable within a year. d =1,2,…, D ; D Indicates the number of days a year the system can operate; y Represents the year index variable. y =1,2,…, YC ; YC Indicates the operational lifespan of a supercapacitor; Δ t Indicates the sampling time interval; This represents the actual electricity price after demand response.

2. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost as described in claim 1, characterized in that, The steps for obtaining load data after demand response include: S21, the original user's typical load data is divided into peak load in the time domain according to the time-of-use pricing rules. P Load,p Normal load P Load,f and valley load P Load,v And using the electricity price elasticity matrix K, the load reduction in each period after demand response is obtained; in, K Each element in the matrix represents the price elasticity of demand coefficient; e p , e f , e v These represent the original time-of-use electricity price; Δ e p Δ e f Δ e v These represent electricity price fluctuations following the implementation of demand response; Indicates the actual electricity price after demand response; Δ P Load,p Δ P Load,f Δ P Load,v These represent the load reductions at different time periods following the demand response; S22, Calculate the load data after demand response: in, This represents the reduction amount based on the original typical load data after demand response, with its values ​​corresponding to Δ during peak, normal, and valley hours, respectively. P Load,p Δ P Load,f Δ P Load,v ; P Load Typical raw load data; This is the load data after the demand response.

3. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 2, characterized in that, In the demand response phase, it is necessary to control the demand response price to ensure user satisfaction with electricity usage. I satisfaction Electricity comfort index I comfort and responsiveness metrics I response All are within the constraints; that is: in, δ satisfaction δ comfort δ response These represent the limits for each indicator. t This represents a time index variable within a day. t =1,2,…, n D ; n D This indicates the number of sampling points per day.

4. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 1, characterized in that, The constraints of the objective function of the upper-level multi-objective optimization configuration model include: In the formula, Indicates that supercapacitors are in t The state of charge at any given moment; This indicates the discharge efficiency of the supercapacitor; This indicates the charging efficiency of the supercapacitor; This represents the maximum state of charge of the supercapacitor. This represents the minimum state of charge of a supercapacitor. As an auxiliary Boolean variable, 1 represents a supercapacitor. t Discharges continuously; As an auxiliary Boolean variable, 1 indicates that the supercapacitor is charging at time t.

5. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 1, characterized in that, The multi-objective optimization problem at the higher level is transformed into a single-objective optimization problem: Wherein, λ1, λ2, and λ3 are the weight coefficients of each sub-objective.

6. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 1, characterized in that, The objective function of the lower-level multi-objective optimization configuration model is: in, CLF,net This represents the net investment cost of the battery. CLF,inv Investment costs for battery construction; CLF,ope The total life-cycle operation and maintenance cost of the battery; CLF,inc1 This refers to arbitrage profits based on battery prices under typical loads. CLF, inc2 Monthly peak shaving revenue for batteries based on typical load; This represents the cost coefficient per unit capacity of the battery; This represents the cost coefficient per unit power of the battery; Indicates the rated capacity of the battery; Indicates the rated operating power of the battery; This indicates the fixed cost of battery assembly; YB Indicates the battery's service life; This indicates the operating and maintenance coefficient of the battery; q This represents the percentage increase in battery maintenance costs over a decade. CLF.yd This represents the arbitrage profit from battery prices on day d of year y. This represents the peak shaving revenue of the battery in month m of year y; and They represent in t Real-time supercapacitor discharge and charging power; γ This indicates the electricity price based on capacity. δm Indicates the monthly peak reduction rate; Ppeak,m Indicates monthly peak load; m Represents the monthly index variable. m =1,2,…, M ; M Indicates the number of months the system can operate in a year; Carbon costs associated with battery configuration; Indicates the price of carbon dioxide; , , These represent the carbon emission factors during the entire life cycle of a battery, including installation, operation and maintenance, and disposal.

7. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 6, characterized in that, The constraints of the objective function of the lower-level multi-objective optimization configuration model are: In the formula, Indicates that the battery is in t The state of charge at any given moment; This indicates the discharge efficiency of the battery; This indicates the charging efficiency of the battery; This represents the battery's maximum state of charge. This represents the minimum state of charge of the battery. As an auxiliary Boolean variable, 1 represents the battery. t Discharges continuously; As an auxiliary Boolean variable, 1 indicates that the battery is charging at time t; To optimize the target curve for the lower layer.

8. The user-side hybrid energy storage optimization configuration method considering demand response and carbon cost according to claim 7, characterized in that, Transform the lower-level multi-objective optimization problem into a single-objective optimization problem: 。 9. A user-side hybrid energy storage optimization configuration system considering demand response and carbon costs, comprising the method described in any one of claims 1-8, characterized in that, include: Data acquisition module: Acquires typical raw load data of the hybrid energy storage user to be configured; Data correction module: Based on the electricity price elasticity matrix, it derives the load reduction after user load participates in demand response, corrects typical original load data, and obtains load data after demand response; Data decomposition module: Introduces the VSQF frequency domain decomposition algorithm to decouple the load data after demand response into high-frequency components and low-frequency components; Supercapacitor configuration module: Substitutes the high-frequency components into the upper-level multi-objective optimization configuration model, and outputs the high-frequency fluctuation smoothing results and the supercapacitor configuration scheme; In addition, the battery configuration module combines the low-frequency components with the high-frequency fluctuation smoothing results, substitutes them into the lower-level multi-objective optimization configuration model, outputs the battery configuration scheme, and derives the final hybrid energy storage configuration scheme.

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