User side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling

Abstract

The invention discloses a user side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling, and the method comprises the steps: measuring and calculating the annual growth rate of the power demands of a user based on an industrial user historical load curve data set, and constructing a typical weekly load curve of the future power demands of the user; a typical weekly load curve is decomposed into a high-frequency component and a low-frequency component by adopting an FMD-SET frequency domain decoupling algorithm, a parallel optimization model of hybrid energy storage is solved based on a decomposition result, and the maximum annual average net benefit of the hybrid energy storage system in the commissioning stage is taken as an optimization target. The physical operation constraint and the residual value evaluation of the hybrid energy storage system are comprehensively considered to obtain a dynamic configuration strategy, and the hybrid energy storage configuration scale suggestion and the optimal operation year setting are provided for the distribution and storage user in combination with the operation age limit of the hybrid energy storage. According to the invention, the hybrid energy storage system is configured for the industrial user under the background of two-part electricity price, and the demand cost saving of the distribution and storage user is realized by controlling the charging and discharging of the hybrid energy storage system.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology and relates to an energy storage optimization configuration technology for an electric power system, which is a user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling. Background Art

[0002] With the continuous development of energy internet, the demand for distributed energy storage is growing. The planning of energy storage systems is mostly based on cost-benefit models. While the energy storage cost model needs to be refined, the key and difficulty of related research lies in how to further broaden the source of energy storage benefits. Compared with energy storage on the power generation side and the grid side, the application of energy storage on the user side and the comprehensive use of energy-power hybrid energy storage to improve the electricity flexibility of industrial and commercial users have received widespread attention in recent years.

[0003] The high- and low-frequency decomposition of the load curve is a prerequisite for the parallel solution of the user-side hybrid energy storage optimization configuration model. Most existing studies use some relatively simple decomposition algorithms, and the decomposition process is affected by human factors. In addition, for the configuration of user-side energy storage, most existing studies are based on fixed load curves. However, in actual practice, for energy storage systems with an operating life of more than ten years, the impact of the dynamic growth of user electricity demand cannot be ignored, so it is necessary to consider the capacity expansion and replacement of energy storage systems.

[0004] Chinese patent application CN115130775A "Method and system for optimizing configuration of hybrid energy storage for renewable energy based on frequency division algorithm" discloses a method and system for optimizing configuration of hybrid energy storage for renewable energy based on frequency division algorithm. By constructing a VMD-Hilbert-FCM frequency division algorithm, the polymer load data is divided into two parts: high-frequency component and low-frequency component. Supercapacitors and lithium batteries are used to smooth the fluctuation of polymer load for the high-frequency component and low-frequency component, respectively, to achieve the optimal configuration of hybrid energy storage. The variational mode decomposition VMD used in this method realizes mode separation through bandwidth constraints, but the Hilbert transform has certain limitations on frequency resolution in instantaneous frequency calculation, especially in the case of more modal components, interference frequency and aliasing may appear in high-frequency and multi-modal signals, and the mode separation effect of signals containing multiple frequency components is not ideal. In addition, this method is based on historical data analysis, that is, based on fixed load curve analysis, and the load fluctuation of the object of hybrid energy storage optimization configuration is characterized by randomness and uncertainty. Therefore, the optimization configuration scheme obtained by this method is difficult to flexibly cope with the challenges brought by dynamic load growth in actual operation. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, the optimization configuration schemes for user-side hybrid energy storage are mostly analyzed based on the fixed load curve of historical data. The resulting schemes are applicable to historical load conditions but not necessarily to future load changes. The high- and low-frequency decomposition method of the load curve adopted by the existing user-side hybrid energy storage optimization configuration model has an unsatisfactory decomposition effect.

[0006] The technical solution of the present invention is: a user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling, comprising the following steps:

[0007] 1) Based on the historical load curve dataset of industrial users, the load baseline of users in the 12 months of the year is constructed, and the annual growth rate of users' electricity demand is calculated based on the load baseline, and the typical weekly load curve of users' future electricity demand is constructed;

[0008] 2) Construct the FMD-SET load curve frequency domain decoupling algorithm, decompose the typical weekly load curve into two parts: high-frequency component and low-frequency component. The decomposed low-frequency curve component is allocated to the capacity-type energy storage battery, and the high-frequency component is allocated to the power-type energy storage supercapacitor, which is used to achieve the parallel optimization solution of the high and low frequency components of the optimal configuration model of the user-side hybrid energy storage;

[0009] The load frequency domain decoupling algorithm includes:

[0010] The typical weekly load curve is decomposed into k sub-modal components using eigenmode decomposition (FMD);

[0011] The synchronous extraction transform SET is used to transform each submodal component from the time domain to the time-frequency domain to obtain the time-frequency domain component;

[0012] After integrating the time-domain components and superimposing k frequency-domain marginal spectral lines, the lowest point in the frequency domain is set as the high- and low-frequency division boundary;

[0013] Finally, the high- and low-frequency frequency domain curves are decomposed according to the frequency domain spectrum line division boundary, and the high- and low-frequency time domain components are superimposed accordingly to obtain the high-frequency component and the low-frequency component;

[0014] 3) Build a model for the user-side hybrid energy storage system, take the maximum annual average net benefit of the hybrid energy storage system during the commissioning phase as the optimization goal, and comprehensively consider the physical operation constraints and residual value evaluation of the hybrid energy storage system to obtain the optimal configuration strategy for the user's future electricity demand;

[0015] 4) According to the optimization configuration strategy, combined with the current operating life and capacity of the hybrid energy storage system, provide storage users with hybrid energy storage configuration scale recommendations and optimal operating life settings.

[0016] Furthermore, after obtaining the optimized configuration strategy, the configuration scale recommendation refers to: judging whether to increase the capacity of energy storage and replace it according to the user's future electricity demand and the current configuration of the hybrid energy storage system. If energy storage capacity is increased or replaced, the optimized configuration strategy is re-solved based on the hybrid energy storage system after replacement.

[0017] Furthermore, after a hybrid energy storage system operation cycle is completed, the optimal configuration strategy of the hybrid energy storage system for the next stage is re-solved in combination with the user's latest actual load growth rate to obtain the configuration scale recommendation for the next stage and the optimal operating life setting.

[0018] Furthermore, the dynamic optimization configuration method is implemented by a computer program, which is at least one instruction or at least one program stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, an optimization configuration strategy is solved to output a configuration scale recommendation and an optimal operating life setting for the user-side hybrid energy storage.

[0019] The beneficial effects of the present invention are that it has three advantages compared with the prior art:

[0020] (1) The present invention constructs a FMD-SET load frequency domain decoupling algorithm, which adaptively decomposes the typical weekly load curve into high- and low-frequency components and low-frequency components. Based on the decomposition of FMD, SET synchronous extraction effectively eliminates non-correlated frequency components, eliminates noise and interference frequencies, makes the frequency boundaries of each mode clearer, and has a higher frequency resolution. FMD-SET is suitable for signals with complex load fluctuation frequencies and obvious multi-band characteristics. Compared with traditional decomposition algorithms, the method of the present invention can effectively avoid modal aliasing and interference from human factors.

[0021] (2) The present invention measures the annual growth rate of electricity demand based on the load baseline, and gradually constructs a typical annual load curve for industrial users by quantifying the annual growth rate, which can reflect the load change trend in the next few years, so that the configured energy storage system can flexibly respond to the challenges brought by load growth in actual operation. The user-side hybrid energy storage optimization configuration method of the present invention fully considers the dynamic growth of electricity demand caused by production expansion of industrial users during the commissioning stage of the energy storage system, so that the optimal configuration of the hybrid energy storage system can be considered under a longer dynamic change time scale.

[0022] (3) The user-side hybrid energy storage optimization configuration strategy of the method of the present invention not only provides the optimal configuration scale of the hybrid energy storage, but also provides the optimal operating life of the hybrid energy storage system. After reaching the operating life, the scale of hybrid energy storage to be replaced in the next operation phase can be replanned based on user needs and the latest load curve forecast. The present invention takes the operating life of energy storage as one of the optimization variables, and takes into account the utilization rate of energy storage equipment and profitability indicators, and adds corresponding constraints to these two indicators, so as to more accurately obtain the capacity configuration results of the hybrid energy storage and the optimal operating life, so as to maximize the benefits and resource utilization of energy storage throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0025] The present invention proposes a user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling, which calculates the annual growth rate of the user's electricity demand based on the historical load curve data set of industrial users, and constructs a typical weekly load curve of the user's future electricity demand; the FMD-SET frequency domain decoupling algorithm is used to decompose the typical weekly load curve into two parts: high-frequency component and low-frequency component, and the parallel optimization model of the hybrid energy storage is solved based on the decomposition result. The annual average net benefit of the hybrid energy storage system in the commissioning stage is maximized as the optimization goal, and the physical operation constraints and residual value evaluation of the hybrid energy storage system are comprehensively considered to obtain a dynamic configuration strategy, and combined with the operating years of the hybrid energy storage, the configuration scale recommendation of the hybrid energy storage and the optimal operating year setting are provided for the storage users. The present invention configures a hybrid energy storage system of a certain scale for industrial users under the background of a two-part electricity price, and realizes demand savings for storage users by controlling the charging and discharging of the hybrid energy storage system.

[0026] The implementation of the method of the present invention is specifically described below. The present invention includes the following steps.

[0027] 1) Based on the historical load curve dataset of industrial users, the load baseline of users in the 12 months of the year is constructed, and the annual growth rate of users' electricity demand is calculated based on the load baseline. The typical weekly load curve of users' future electricity demand is constructed.

[0028] 2) Construct the FMD-SET load curve frequency domain decoupling algorithm, decompose the typical weekly load curve into two parts: high-frequency component and low-frequency component. The decomposed low-frequency curve component is allocated to the capacity-type energy storage battery, and the high-frequency component is allocated to the power-type energy storage supercapacitor, which is used to achieve the parallel optimization solution of the high and low frequency components of the optimal configuration model of the user-side hybrid energy storage.

[0029] The present invention constructs a new FMD-SET load curve frequency domain decoupling algorithm, which includes four calculation links: first, Feature Mode Decomposition (FMD) decomposes the typical weekly load curve into k sub-modal components; second, Synchroextracting Transform (SET) is used to transform each sub-mode from the time domain to the time-frequency domain to obtain the time-frequency domain components; then, after the time-frequency domain components are integrated in the time domain and k frequency domain marginal spectral lines are superimposed, the lowest point in the frequency domain is set as the high-low frequency dividing boundary; finally, the high- and low-frequency frequency domain curves are decomposed according to the frequency domain spectral line dividing boundary, and the high- and low-frequency time domain components are superimposed accordingly to obtain the high-frequency component and the low-frequency component.

[0030] 3) Build a model for the user-side hybrid energy storage system, take the maximum annual average net benefit of the hybrid energy storage system during the operation phase as the optimization goal, and comprehensively consider the physical operation constraints and residual value evaluation of the hybrid energy storage system to obtain the optimal configuration strategy for the user's future electricity demand.

[0031] 4) According to the optimization configuration strategy, combined with the current operating life and capacity of the hybrid energy storage system, provide storage users with hybrid energy storage configuration scale recommendations and optimal operating life settings.

[0032] Furthermore, after obtaining the optimized configuration strategy, the configuration scale recommendation refers to: judging whether to increase the capacity of energy storage and replace it according to the user's future electricity demand and the current configuration of the hybrid energy storage system. If energy storage capacity is increased or replaced, the optimized configuration strategy is re-solved based on the hybrid energy storage system after replacement.

[0033] The solution of the optimization configuration strategy in step 3) of the present invention is described in detail below. The objective function and constraint conditions for solving the dynamic optimization configuration of the user-side hybrid energy storage are as follows.

[0034] (I) The objective function is the average annual net benefit V during the energy storage operation phase ES maximize:

[0035] maxV ES =C ES,inv +C ES,ope +C ES,rep +C ES,deal -R ES,M -RES,arb -R ES,rec

[0036] The subscript ES refers to energy storage battery (Battery Storage, BS) or power storage (Supercapacitor, SC); ES,inv represents the average annual initial investment cost of the energy storage system; C ES,ope represents the average annual operation and maintenance cost of the energy storage system; C ES,rep represents the one-time replacement and expansion cost of the energy storage system; C ES,deal Represents the scrapping cost of the energy storage system; R ES,M represents the peak shaving benefit of the energy storage system; R ES,arb represents the arbitrage profit of the energy storage system; R ES,rec Represents the recoverable benefit of the energy storage system.

[0037] (1) Average annual initial investment cost

[0038]

[0039] in, They represent the capacity cost coefficient, power cost coefficient, and supporting equipment cost coefficient of energy storage configuration respectively; They represent the rated capacity and rated power of the configured energy storage respectively; Y ES represents the optimal operating life of the hybrid energy storage system; i r Represents the inflation rate.

[0040] (2) Average annual operation and maintenance costs

[0041]

[0042] in, Represents the operation and maintenance cost coefficient of energy storage configuration; d r represents the discount rate; y is the year index variable.

[0043] (3) One-time replacement and expansion cost

[0044]

[0045] in, represents the replacement cost coefficient of the energy storage system; L ES Indicates the factory-set operable life of the energy storage system; b r Represents the base discount rate.

[0046] (4) Scrapping costs

[0047]

[0048] in, Represents the scrapping cost coefficient of replacement energy storage.

[0049] (5) Peak shaving benefits

[0050]

[0051] Among them, p M It represents the monthly demand electricity price of industrial users; Indicates the peak shaving ratio requirement of the user load curve; M ES,base (y,m) represents the user’s maximum monthly demand in the mth month of the yth year before storage allocation; m is the month index variable.

[0052] (6) Arbitrage Profits

[0053]

[0054] Among them, p t (t) represents the time-of-use electricity price during period t; It represents the energy storage discharge power in m months, d days and t periods;

[0055] represents the energy storage charging power in the period of m months and d days and t periods; Δt represents the optimization decision interval; t represents the intraday period index variable; n D represents the total number of decision periods in a day; d represents the day index variable within a month; D(m) represents the number of days in month m.

[0056] (7) Recyclable benefits:

[0057]

[0058] in, It represents the recoverable coefficient of replacement energy storage; ε represents the residual value attenuation coefficient.

[0059] (II) Constraints:

[0060] (1) Charge and discharge power constraints:

[0061]

[0062] in, and are Boolean state variables, State 1 means that the energy storage is in the discharge state for m months, d days and t periods. State 1 means that the energy storage is in charging state for m months, d days and t periods of time.

[0063] (2) Charge and discharge state constraints

[0064]

[0065] (3) State of charge constraints

[0066]

[0067] Among them, SOC ES (m, d, t) represents the real-time charge state of the energy storage at m month d day t period; and and represent the maximum and minimum state of charge of energy storage, respectively; and They represent the charging efficiency and discharging efficiency of energy storage respectively.

[0068] (4) Energy storage equipment utilization index:

[0069] I ES,URD,min ≤I ES,URD (y)≤I ES,URD，max

[0070]

[0071] Among them, I ES,URD (y) represents the equipment utilization rate of energy storage in year y; and I ES,URD,max and I ES,URD,min Respectively represent the upper and lower limits of equipment utilization rate; T ES,URD (y) represents the number of hours of equipment use in the energy storage in year y.

[0072] (5) Profitability indicators:

[0073] I P (y)≥I P，min

[0074]

[0075] Among them, I P (y) represents the profitability index of energy storage in year y; I P,min Represents the lower limit of energy storage profitability index; V ES (y) represents the net operating benefit of energy storage in year y; ρ represents the annual bank interest rate.

[0076] Furthermore, in the method of the present invention, after an operation cycle of the hybrid energy storage system is completed, the optimal configuration strategy of the hybrid energy storage system in the next stage is re-solved in combination with the user's latest actual load growth rate to obtain the configuration scale recommendation for the next stage and the optimal operating life setting.

[0077] The present invention also implements the above-mentioned user-side hybrid energy storage dynamic optimization configuration method through a computer program, wherein the computer program is at least one instruction or at least one program, which is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, the optimization configuration strategy is solved and the configuration scale recommendation and the optimal operating life setting of the user-side hybrid energy storage are output. The computer program code for implementing the method of the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling, characterized by: The following steps are involved: 1) Based on the historical load curve dataset of industrial users, the load baseline of users in the 12 months of the year is constructed, and the annual growth rate of users' electricity demand is calculated based on the load baseline, and the typical weekly load curve of users' future electricity demand is constructed; 2) Construct the FMD-SET load curve frequency domain decoupling algorithm, decompose the typical weekly load curve into two parts: high-frequency component and low-frequency component. The decomposed low-frequency curve component is allocated to the capacity-type energy storage battery, and the high-frequency component is allocated to the power-type energy storage supercapacitor, which is used to achieve the parallel optimization solution of the high and low frequency components of the optimal configuration model of the user-side hybrid energy storage; The load frequency domain decoupling algorithm includes: The typical weekly load curve is decomposed into k sub-modal components using eigenmode decomposition (FMD); The synchronous extraction transform SET is used to transform each submodal component from the time domain to the time-frequency domain to obtain the time-frequency domain component; After integrating the time-domain components and superimposing k frequency-domain marginal spectral lines, the lowest point in the frequency domain is set as the high- and low-frequency division boundary; Finally, the high- and low-frequency frequency domain curves are decomposed according to the frequency domain spectrum line division boundary, and the high- and low-frequency time domain components are superimposed accordingly to obtain the high-frequency component and the low-frequency component; 3) Build a model for the user-side hybrid energy storage system, take the maximum annual average net benefit of the hybrid energy storage system during the commissioning phase as the optimization goal, and comprehensively consider the physical operation constraints and residual value evaluation of the hybrid energy storage system to obtain the optimal configuration strategy for the user's future electricity demand; 4) According to the optimization configuration strategy, combined with the current operating life and capacity of the hybrid energy storage system, provide storage users with hybrid energy storage configuration scale recommendations and optimal operating life settings.

2. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 1 is characterized by: After obtaining the optimized configuration strategy, the configuration scale recommendation means: judging whether to increase the capacity of energy storage and replace it according to the user's future electricity demand and the current configuration of the hybrid energy storage system. If energy storage capacity is increased or replaced, the optimized configuration strategy is obtained by re-solving the hybrid energy storage system after replacement.

3. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 1 is characterized by: Solve the optimal configuration strategy of the user-side hybrid energy storage system, and construct the objective function as the average annual net benefit V during the energy storage operation phase. ES maximize: maxV ES =C ES,inv +C ES,ope +C ES,rep +C ES,deal -R ES,M -R ES,arb -R ES,rec Among them, C ES,inv represents the average annual initial investment cost of the energy storage system; C ES,ope represents the average annual operation and maintenance cost of the energy storage system; C ES,rep represents the one-time replacement and expansion cost of the energy storage system; C ES,deal Represents the scrapping cost of the energy storage system; R ES,M represents the peak shaving benefit of the energy storage system; R ES,arb represents the arbitrage profit of the energy storage system; R ES,rec Represents the recoverable benefit of the energy storage system.

4. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 3 is characterized by: The specific calculation of the objective function is: (1) Average annual initial investment cost: in, They represent the capacity cost coefficient, power cost coefficient, and supporting equipment cost coefficient of energy storage configuration respectively; They represent the rated capacity and rated power of the configured energy storage respectively; Y ES represents the optimal operating life of the hybrid energy storage system; i r represents the inflation rate; (2) Average annual operation and maintenance cost: in, Represents the operation and maintenance cost coefficient of energy storage configuration; d r represents the discount rate; y is the year index variable; (3) One-time replacement and expansion cost: in, represents the replacement cost coefficient of the energy storage system; L ES Indicates the factory-set operable life of the energy storage system; b r represents the base discount rate; (4) Scrap disposal costs: in, represents the scrapping cost coefficient of the replacement energy storage; (5) Peak shaving benefits: Among them, p M It represents the monthly demand electricity price of industrial users; Indicates the peak shaving ratio requirement of the user load curve; M ES,base (y,m) represents the maximum monthly demand of the user in the mth month of the yth year before storage allocation; m is the month index variable; (6) Arbitrage income: Among them, p t (t) represents the time-of-use electricity price during period t; It represents the energy storage discharge power in m months, d days and t periods; represents the energy storage charging power in the period of m months and d days and t periods; Δt represents the optimization decision interval; t represents the intraday period index variable; n D represents the total number of decision periods in a day; d represents the index variable of the day within the month; D(m) represents the number of days in month m; (7) Recyclable benefits: in, It represents the recoverable coefficient of replacement energy storage; ε represents the residual value attenuation coefficient.

5. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 4 is characterized by: The constraints of the objective function are: (1) Charge and discharge power constraints: in, and are Boolean state variables, State 1 means that the energy storage is in the discharge state for m months, d days and t periods. State 1 means that the energy storage is in charging state for m months, d days and t periods of time; (2) Charge and discharge state constraints: (3) State of charge constraints: Among them, SOC ES (m, d, t) represents the real-time charge state of the energy storage at m month d day t period; and and represent the maximum and minimum state of charge of energy storage, respectively; and They represent the charging efficiency and discharging efficiency of energy storage respectively; (4) Energy storage equipment utilization index: I ES,URD,min ≤I ES,URD (y)≤I ES,URD，max Among them, I ES,URD (y) represents the equipment utilization rate of energy storage in year y; and I ES,URD,max and I ES,URD,min Respectively represent the upper and lower limits of equipment utilization rate; T ES,URD (y) represents the number of hours of equipment use in the energy storage in year y; (5) Profitability indicators: I P (y)≥I P，min Among them, I P (y) represents the profitability index of energy storage in year y; I P,min Represents the lower limit of energy storage profitability index; V ES (y) represents the net operating benefit of energy storage in year y; ρ represents the annual bank interest rate.

6. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 1 is characterized by: After a hybrid energy storage system operation cycle is completed, the optimal configuration strategy of the hybrid energy storage system for the next stage is re-solved based on the user's latest actual load growth rate to obtain the configuration scale recommendation for the next stage and the optimal operating life setting.

7. The user-side hybrid energy storage dynamic optimization configuration method based on load frequency domain decoupling according to claim 1 is characterized by: The dynamic optimization configuration method is implemented by a computer program. The computer program is at least one instruction or at least one program, which is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, an optimization configuration strategy is solved to output a configuration scale recommendation and an optimal operating life setting for the user-side hybrid energy storage.

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