Multi-scenario integrated operation control method, system, equipment and medium for energy storage power station

Abstract

The present invention discloses a multi-scenario fusion operation control method, system, equipment and medium for an energy storage power station, which belongs to the field of electrochemical energy storage, including: constructing a number of shared energy storage power station operation cost optimization models under multi-scenario fusion working conditions, solving the shared energy storage power station operation cost optimization models under each multi-scenario fusion working condition, and obtaining the shared energy storage power station operation cost under each multi-scenario fusion working condition; determining the multi-scenario fusion working condition corresponding to the lowest operating cost of the shared energy storage power station; and performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion working condition. The method, system, equipment and medium can achieve the optimization of the operation cost of the shared energy storage power station under multi-scenario fusion.

Description

Technical Field

[0001] The present invention belongs to the field of electrochemical energy storage and relates to a multi-scenario fusion operation control method, system, equipment and medium for an energy storage power station. Background Art

[0002] Modern power systems, characterized by a high proportion of renewable energy and large-scale power electronic equipment, can effectively compensate for the instability of renewable energy through large-scale energy storage with unique flexibility and instant response advantages. It also helps to optimize the operation of the power system and improve its economic benefits and reliability.

[0003] A large number of energy storage power stations are concentrated in single-scenario applications, which results in energy storage being idle in certain time periods and capacities, preventing it from fully realizing its potential value. Taking into account the operating time periods, operating scenarios, and aging costs of energy storage power stations, adopting a shared model for energy storage power station operation based on market returns is the economic driving force and technical support for promoting the active participation of energy storage power stations in power system balancing and regulation. The diversity of energy storage power station application scenarios results in different operating conditions. The characteristic operating parameters, including charge and discharge depth, rate, and number of cycles, determine the service life of the energy storage battery. Due to the uncertainty of multi-scenario integration and the differences in the service life of energy storage batteries, it is difficult to optimize the operating costs of shared energy storage power stations. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a multi-scenario integrated operation control method, system, equipment and medium for an energy storage power station. This method, system, equipment and medium can optimize the operating cost of a shared energy storage power station under multi-scenario integration.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In one aspect, the present invention provides a multi-scenario integrated operation control method for an energy storage power station, comprising:

[0007] Construct a shared energy storage power station operation cost optimization model under several multi-scenario fusion conditions;

[0008] Solve the shared energy storage power station operation cost optimization model under the multi-scenario fusion conditions and obtain the shared energy storage power station operation cost under the multi-scenario fusion conditions;

[0009] Determine the multi-scenario fusion operating conditions corresponding to the lowest operating cost of the shared energy storage power station;

[0010] Based on the determined multi-scenario fusion operating conditions, multi-scenario fusion operation control is performed on the shared energy storage power station.

[0011] The multi-scenario integrated operation control method of the energy storage power station described in the present invention is further improved in that:

[0012] Furthermore, before solving the shared energy storage power station operation cost optimization model under the multiple scenario fusion working conditions, the method further includes:

[0013] Based on a single typical operating condition of a shared energy storage power station, several multi-scenario fusion operating conditions are constructed, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station.

[0014] Furthermore, the process of performing multi-scenario fusion operation control on the shared energy storage power station based on the determined multi-scenario fusion operating condition is as follows:

[0015] Based on each single typical operating condition in the determined multi-scenario fusion operating condition and its corresponding single operating condition weight factor, the shared energy storage power station is controlled for multi-scenario fusion operation.

[0016] Furthermore, the objective function of the shared energy storage power station operation cost optimization model under the multi-scenario fusion working condition is:

[0017] C ess =min(C ess_INV +C ess_OP +C ess_ElP )

[0018] Among them, C ess represents the operating cost of the shared energy storage power station, C ess_INV is the daily investment cost of the shared energy storage power station, C ess_OP To share the daily operation and maintenance costs of the energy storage power station, C ess_ElP The daily electricity purchase cost of the shared energy storage power station.

[0019] Furthermore, the constraints of the shared energy storage power station operation cost optimization model under the multi-scenario fusion working condition include the shared energy storage power station power balance constraint condition and the shared energy storage power station capacity balance constraint condition.

[0020] Furthermore, the power balance constraint condition of the shared energy storage power station is:

[0021]

[0022] Among them, P ess (t) is the output power of the shared energy storage power station at time t, is the charging power of scenario d at time t, is the charging power of scenario d at time t, P ess_gr (t) is the exchange power with the grid at time t, is the status bit of whether the shared energy storage power station can discharge at time t in scenario d, P is the status bit of whether the shared energy storage power station is chargeable at time t, ess_pr is the rated power of the shared energy storage power station.

[0023] Furthermore, the capacity balance constraint condition of the shared energy storage power station is:

[0024]

[0025] Where E(t) is the state of charge of the shared energy storage at time t, η ch and η dis Corresponding to BESS charging / discharging efficiency, E ess_pr Rated capacity of shared energy storage power station, is the charging power of scenario d at time t, is the charging power of scenario d at time t.

[0026] In a second aspect, the present invention provides a multi-scenario integrated operation control system for an energy storage power station, comprising:

[0027] The first building module is used to build a shared energy storage power station operation cost optimization model under multiple multi-scenario fusion conditions;

[0028] A solution module is used to solve the shared energy storage power station operation cost optimization model under several multi-scenario fusion conditions to obtain the shared energy storage power station operation cost under each multi-scenario fusion condition;

[0029] A determination module is used to determine the multi-scenario fusion operating condition corresponding to the lowest operating cost of the shared energy storage power station;

[0030] The control module is used to perform multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion working conditions.

[0031] The multi-scenario fusion operation control system of the energy storage power station described in the present invention is further improved in that:

[0032] Furthermore, it also includes:

[0033] The second construction module is used to construct several multi-scenario fusion operating conditions based on a single typical operating condition of the shared energy storage power station, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station.

[0034] In a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-scenario fusion operation control method of the energy storage power station are implemented.

[0035] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the multi-scenario fusion operation control method of the energy storage power station are implemented.

[0036] The present invention has the following beneficial effects:

[0037] During specific operation, the multi-scenario fusion operation control method, system, equipment, and medium of the energy storage power station described in the present invention construct a number of shared energy storage power station operation cost optimization models under multi-scenario fusion working conditions, and use this to calculate the operating cost of the shared energy storage power station under each multi-scenario fusion working condition. In order to achieve the lowest cost, the present invention selects the multi-scenario fusion working condition with the lowest operating cost of the shared energy storage power station to control the shared energy storage power station, so as to achieve the optimization of the operating cost of the shared energy storage power station under multi-scenario fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 is a flow chart of the method of the present invention;

[0040] Figure 2 A schematic diagram of typical working conditions for tracking planned output;

[0041] Figure 3 This is a schematic diagram of a typical operating condition of a solar-storage combination;

[0042] Figure 4 This is a schematic diagram of a typical working condition for wind-storage output smoothing;

[0043] Figure 5 This is a schematic diagram of a typical working condition of peak shaving and valley filling;

[0044] Figure 6 This is a capacity-type working condition test curve;

[0045] Figure 7 This is a power type working condition test curve diagram;

[0046] Figure 8 This is a test curve diagram for combined working conditions;

[0047] Figure 9 This is the result of aging life test under typical operating conditions;

[0048] Figure 10 Schematic diagram of the aging life distribution space of shared energy storage power stations;

[0049] Figure 11 It is a single working condition operation curve diagram;

[0050] Figure 12 It is the operation curve diagram of the fusion working condition;

[0051] Figure 13 Cost distribution diagram for multi-scenario fusion working conditions throughout the life cycle;

[0052] Figure 14 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments, and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts disclosed in the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0054] The accompanying drawings illustrate schematic diagrams of the structures of the disclosed embodiments of the present invention. These figures are not drawn to scale; for the purpose of clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0055] refer to Figure 1 The multi-scenario integrated operation control method of the energy storage power station of the present invention includes the following steps:

[0056] 1) Construct a shared energy storage power station operation cost optimization model under multi-scenario fusion conditions, solve the shared energy storage power station operation cost optimization model under each multi-scenario fusion condition, and obtain the shared energy storage power station operation cost under each multi-scenario fusion condition;

[0057] The process of step 1) is:

[0058] 11) Analyze the operating data of E typical operating conditions of the shared energy storage power station, select characteristic parameters of M typical operating conditions, M ≥ 1, and conduct cyclic aging experiments under T (T ≥ 2) typical operating conditions to obtain the typical operating condition aging life distribution of the shared energy storage power station, wherein the characteristic parameters of the M typical operating conditions include but are not limited to: average charge and discharge power (P), capacity charge and discharge depth (DOD), and charge and discharge power conversion frequency (F).

[0059] For example, when completing the aging experiment of T=3 typical working conditions, the specific experimental plan includes but is not limited to: the P of the typical capacity working condition is 0.6~1.0P0, P0 is the rated power, DOD is 60~80%, and F is 0.0001~0.0005Hz; the P of the typical power working condition is 0.2~0.6P0, DOD is 20~60%, and F is 0.0005~0.001Hz; the P of the fusion working condition is 0.5~0.7P0; DOD is 40~70%; F is 0.0004~0.0006Hz.

[0060] 12) Based on a single typical operating condition of a shared energy storage power station, construct several multi-scenario fusion operating conditions, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station;

[0061] Specifically, according to the range of the typical working condition characteristic parameters obtained in step 11), the working condition characteristic parameter group X of a single typical working condition is determined. d (D≥d≥1), where X d It includes m (M≥m≥1) working condition characteristic parameter values; for N groups of single typical working conditions, the weight factor of the anth single typical working condition is set to a d-n , N≥n≥1, satisfying a 1-n +a 2-n +...+a d-n =1, and obtain the characteristic parameter group Y of the multi-scenario fusion working condition of the shared energy storage power station n =a 1-n ·X1+a 2-n ·X2+...+a d-n ·X d ; The typical operating condition aging life distribution obtained in step 11) is interpolated to obtain the SOH of the multi-scenario fusion operating condition of the shared energy storage power station from 100% to SOH L Lifespan S L ; Then set the operating constraints for each single typical working condition.

[0062] 13) Constructing a shared energy storage power station operating cost optimization model under various scenarios;

[0063] In step 13), with the goal of minimizing the operating cost of the shared energy storage power station and the power balance and capacity balance of the shared energy storage power station as constraints, a shared energy storage power station operating cost optimization model is constructed;

[0064] The objective function of the shared energy storage power station cost optimization model is:

[0065] C ess =min(C ess_INV +C ess_OP +C ess_ElP )

[0066] Among them, C ess_INV 、C ess_OP and C ess_ElP They are the daily investment (depreciation) cost, operation and maintenance cost, and electricity purchase cost of the shared energy storage power station.

[0067] C ess_INV =C ess_I_inv *F A / P / D year

[0068]

[0069] Among them, N op Annual operation and maintenance fee rate of energy storage power station, C ess_I_inv The initial investment cost of the energy storage power station, F A / P Energy storage power station equal payment capital recovery (A / P) coefficient, D year The energy storage power station operates for a number of days each year, with one equivalent cycle running every day.

[0070] C ess_I_inv =C p *P ess_pr +C e *E ess_pr

[0071]

[0072] Among them, C p Unit power cost of energy storage power station, P ess_pr Rated power of energy storage station, C e Unit capacity cost of energy storage power station, E ess_pr Rated capacity of energy storage power station, i0 benchmark rate of return, S L-Y The service life of the energy storage power station, S L-Y =S L / D year .

[0073] The constraints of the shared energy storage power station cost optimization model include a shared energy storage power station power balance constraint and a shared energy storage power station capacity balance constraint.

[0074] The power balance constraints of the shared energy storage power station include power balance, discharge power range, charging power range, avoiding simultaneous charging and discharging, and purchasing power from the grid, namely:

[0075]

[0076] Among them, P ess (t) is the output power of shared energy storage at time t, is the charging power of scenario d at time t, is the charging power of scenario d at time t, P ess_gr (t) is the power exchanged with the grid at time t, The status bit of whether the shared energy storage can be discharged at time t in scenario d. When the status bit is 1, it can be discharged, and 0, it cannot be discharged. The status bit of whether the energy storage station is chargeable at time t, 1 means chargeable, 0 means not chargeable, P ess_pr Rated power of the shared energy storage power station.

[0077] The capacity balance constraint of the shared energy storage power station is that during the multi-scenario shared energy storage charging / discharging period, the energy storage capacity value at each moment should be kept within a certain range, including capacity state continuity, capacity range, and the same initial and ending capacity. That is:

[0078]

[0079] Where E(t) is the state of charge of the shared energy storage at time t, η ch and η dis Corresponding to BESS charging / discharging efficiency, E ess_pr Rated capacity of the shared energy storage power station.

[0080] 14) Calculate the operating cost optimization model of the shared energy storage power station under the multi-scenario fusion working conditions and obtain the cost C of the shared energy storage power station n , the optimized Y′ n , and the corresponding Y′ n Aging life S′ L ;

[0081] 15) Based on the optimized aging life S' L , calculate the shared energy storage power station operation cost optimization model under multi-scenario fusion conditions, and obtain the optimized shared energy storage power station cost C′ n , when |C′ n -C n |>5%C n Then go to step 14) until |C′ n -C n|≤5%C n , then go to step 16);

[0082] 16) Assume that the cumulative decay of the shared energy storage power station aging life ΔS L , through interpolation, obtain Y n The corresponding SOH is used to calculate the corrected rated capacity E. ess_pr *SOH, repeat steps 14) to 15) until SOH ≤ SOH L , the operating cost C of the shared substation under N groups of multi-scenario fusion conditions is obtained n And its corresponding weight factor a for a single typical working condition d-n ;

[0083] 2) Determine the multi-scenario fusion operating conditions that minimize the operating costs of a shared energy storage power station;

[0084] 3) Based on the determined multi-scenario fusion operating conditions, multi-scenario fusion operation control is performed on the shared energy storage power station.

[0085] The process of step 3) is: according to the weight factor a of each single typical working condition and its corresponding single typical working condition in the determined multi-scenario fusion working condition d-n-min , and perform multi-scenario integrated operation control on the shared energy storage power station.

[0086] Example 2

[0087] This example collects E=4 typical operating conditions of actual MW-class and above lithium-ion battery energy storage power stations for analysis. These include 73 sets of typical operating condition data for tracking planned output, 27 sets of typical operating condition data for combined PV and energy storage, 96 sets of typical operating condition data for wind and energy storage output smoothing, and 26 sets of typical operating condition data for peak shaving and valley filling, for a total of 222 sets of operating condition data.

[0088] Figure 2 Indicates the typical working condition of tracking planned output, average charge and discharge power (P)|P|<0.6P0, P0 is the rated power, charge and discharge power conversion frequency (F) is 0.0005Hz, and capacity charge and discharge depth DOD is 50%. Figure 3 This represents the typical operating condition of the combined photovoltaic and energy storage system. In order to suppress the fluctuation of photovoltaic power generation, the charging and discharging power of the energy storage station fluctuates greatly, |P|<0.8P0, F is 0.0004Hz, charging and discharging are close to full capacity, and DOD is 80%. Figure 4 It represents the typical working condition of wind-storage output smoothness, where |P|<0.2P0, F is 0.001Hz, and the randomness is significant, and DOD<20%. Figure 5 This represents a typical peak shaving and valley filling operating condition. The energy storage station completes 1 to 2 full-capacity charge and discharge cycles based on the daily peak and valley electricity prices and provides auxiliary services. |P| < 1P0, F is 0.0001 Hz, and DOD is 80%.

[0089] according to Figures 2 to 5 The aforementioned E = 4 typical operating conditions are characterized by M = 3 operating condition characteristic parameters: P = 0.1 ~ 1P0, DOD = 20 ~ 80%, F = 0.0001 ~ 0.001Hz. The distribution of the operating condition characteristics basically covers the main application scenarios of current electrochemical energy storage and meets both power-type and capacity-type applications. Therefore, when determining the aging test plan for typical operating conditions, based on the power-type and capacity-type application conditions, no less than 3 typical operating condition aging test plans are designed, as shown in Table 1. The characteristic parameter selection range of each experimental plan is:

[0090] a) Capacitive operating condition: P is 0.6~1.0P0, DOD is 60~80%, and F is 0.0001~0.0004Hz;

[0091] b) Power type working condition: P is 0.2~0.6P0, DOD is 20~60%, F is 0.0006~0.001Hz;

[0092] c) Fusion operating condition: P is 0.5~0.7P0; DOD is 40~70%; F is 0.0004~0.0006Hz.

[0093] Table 1 Characteristic parameter ranges of typical working condition experimental schemes

[0094]

[0095] Table 2 Typical operating conditions experimental plan

[0096]

[0097] This embodiment constructs the characteristic parameter table of the capacity type, power type and fusion type working condition aging test scheme with T=3, such as Figure 2 As shown, P is 0.8, 0.4 and 0.6P0 respectively; DOD is 80%, 40% and 60% respectively; F is 0.0001, 0.001 and 0.0005Hz respectively. The initial condition of the aging experiment is: SOC = 50%; the end condition of the aging experiment is: normalized capacity ≤ 70%. The capacity type working condition test curve, power type working condition test curve and fusion type working condition test curve of the above T = 3 working condition aging experiment scheme are shown as follows Figure 6 、 Figure 7 and Figure 8 As shown. Using lithium iron phosphate batteries, at room temperature of 25°C, according to the above working condition test curve, cyclic charge and discharge, the aging test results of three types of working conditions, capacity type, power type and fusion type, are obtained as shown below. Figure 9 shown.

[0098] The weight factor a of the average power under capacity condition n, aging life and health state SOH as parameters, the cubic interpolation method is used to construct the aging life distribution of the shared energy storage power station, such as Figure 10 As shown. Weight factor a n =1 capacity type working condition, P = 0.8 × 1 + 0.4 × 0.0 = 0.8P0; weight factor a n =0 power type working condition, P = 0.8 × 0.0 + 0.4 × 0.1 = 0.4P0; weight factor a n =0.5 fusion working condition, P=0.8×0.5+0.4×0.5=0.6P0.

[0099] The parameters involved in the cost optimization model of the shared energy storage power station operation strategy in this embodiment are shown in Table 3.

[0100] Table 3 Basic parameters of the cost optimization model for shared energy storage power station operation strategy

[0101]

[0102] This embodiment is to formulate a fusion operation strategy for power type and capacity type D=2 scenario conditions. The actual operation data of a shared energy storage power station based on the planned dispatch instruction is used. Its typical power type application power and SOC curve are as follows: Figure 11 The working condition A, P 1-n >30%P0, SOC operating range 30~60%, DOD=30%, F is 0.0008.

[0103] Exploiting peak-valley price arbitrage is one of the main profit-making methods for shared energy storage power stations. Shared energy storage power stations purchase electricity from the grid, charge during valley and normal periods, and discharge during peak and peak periods to achieve full capacity charging and discharging, thereby obtaining peak-valley price difference benefits. Its typical capacity application power, SOC and time-of-use peak-valley electricity price curve in a certain place are as follows: Figure 11 The working condition B shown is P 2-n <80% P0, SOC operating range 10~90%, DOD=80%, F is 0.0002.

[0104] In order to improve the utilization rate of shared energy storage power stations, a multi-scenario fusion operating condition is constructed based on single typical operating conditions A and B. The characteristic parameter group X1 of single typical operating condition A is 1-n =0.4}, the characteristic parameter group X2 of a single typical working condition B = {P 2-n =0.8}. Set N=4 groups of single typical working conditions and the weight factor a in the fusion working condition d-n (4≥n≥1), satisfying a 1-n +a 2-n =1, and get the multi-scene fusion working condition Y n =a 1-n ·P 1-n+a 2-n ·P 2-n , as shown in Table 4, based on Figure 10 The aging life distribution of the shared energy storage power station shown in the figure uses the cubic interpolation method to obtain the multi-scenario fusion condition Y n Aging life S L The initial value of

[0105] Table 4

[0106]

[0107] The operating constraints of power-type and capacity-type operating conditions are set. For power-type operating conditions, the output power is based on the planned output instruction, and the assessment is performed using a 20% deviation of the planned output power. That is, the power constraint of the fusion operating condition is that the power exceeding the 20% planned output power deviation is the minimum power; for capacity-type operating conditions, charging is performed during valley electricity price periods (0:00-7:00, 23:00-24:00), and discharging is performed during peak electricity price periods (10:00-15:00, 18:00-21:00).

[0108] First, calculate the cost optimization model of the multi-scenario fusion working condition of the shared energy storage power station when n=1 and the energy storage power station is not attenuated. The optimized multi-scenario fusion working condition Yˊ1 is as follows: Figure 12 As shown. The fusion condition power output of the shared energy storage power station deviates slightly from the output power of condition A, but the deviation is controlled within 20%, so the assessment penalty power is zero. During the valley electricity price period, the fusion condition is charged to SOC>80%, and during the peak electricity price period, it is discharged to SOC<40%. Within the 24-hour operation cycle, DOD>40%, and the capacity utilization rate of the energy storage power station increases. The cost of the shared energy storage power station C1=0.435, and the corresponding aging life S L =5126 times.

[0109] Based on the optimized S L =5126, calculate the cost optimization model of the shared energy storage power station multi-scenario fusion condition Y1, and obtain the new shared energy storage power station cost Cˊ1=0.44, |Cˊ n -C n |=0.005≤5%C n =0.02.

[0110] Assume that the aging life of the shared energy storage power station has accumulated a decay of ΔS L =300, and Y is obtained by interpolation n The corresponding SOH is 97%; the rated capacity is corrected to: E ess_pr *SOH=50*97%=48.5kW, repeat the above steps until SOH≤70%.

[0111] So far, the n=1th group of weight factors a is completed.d-1 Calculation of the full life cycle operating cost of the constructed multi-scenario fusion working conditions.

[0112] The cost calculation of the multi-scenario fusion condition of the shared energy storage power station over the entire life cycle consisting of N=4 groups of weight factors is completed in sequence, and the cost C of the multi-scenario fusion condition of N=4 groups is obtained. n and the corresponding weight factor a for a single typical operating condition d-n The cost of multi-scenario fusion operation during the entire life cycle of a shared energy storage power station is as follows: Figure 13 As shown in the figure, as the weight factor of the capacity-based operating condition increases by 0.2 to 0.8, the overall life cycle cost of the shared energy storage power station shows a high-low-high trend. 2-n In the range of 0.2 to 0.4, the life cycle cost of the shared energy storage power station increases with a 2-n The increase of 2-n In the range of 0.4~0.6, when a 2-n-min =0.51, the operating cost of the energy storage power station reaches the lowest value C when it is not attenuated n-min = 0.33 yuan, and increases slightly with ageing; when a 2-n =0.426, when the energy storage power station has been operated for 3500 times, the operating cost increases to 0.36 yuan. 2-n In the range of 0.6 to 0.8, the operating cost increases with the life aging and a 2-n Increase and increase, when a 2-n =0.8, and when the energy storage power station ran 3,500 times, it reached 0.39 yuan.

[0113] Example 3

[0114] refer to Figure 14 The present invention provides a multi-scenario integrated operation control system for an energy storage power station, comprising:

[0115] A second construction module is configured to construct a plurality of multi-scenario fusion operating conditions based on a single typical operating condition of a shared energy storage power station, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of the plurality of single typical operating conditions of the shared energy storage power station;

[0116] The first construction module is used to construct a shared energy storage power station operation cost optimization model under several multi-scenario fusion working conditions.

[0117] A solution module is used to solve the shared energy storage power station operation cost optimization model under several multi-scenario fusion conditions to obtain the shared energy storage power station operation cost under each multi-scenario fusion condition;

[0118] A determination module is used to determine the multi-scenario fusion operating condition corresponding to the lowest operating cost of the shared energy storage power station;

[0119] The control module is used to perform multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion working conditions.

[0120] As an embodiment of the present invention, the process of performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion operating condition is as follows:

[0121] According to the weight factors of each single typical operating condition and its corresponding single typical operating condition in the determined multi-scenario fusion operating condition, the shared energy storage power station is controlled for multi-scenario fusion operation.

[0122] As an embodiment of the present invention, the objective function of the shared energy storage power station operation cost optimization model under the multi-scenario fusion working condition is:

[0123] C ess =min(C ess_INV +C ess_OP +C ess_ElP )

[0124] Among them, C ess represents the operating cost of the shared energy storage power station, C ess_INV is the daily investment cost of the shared energy storage power station, C ess_OP To share the daily operation and maintenance costs of the energy storage power station, C ess_ElP The daily electricity purchase cost of the shared energy storage power station.

[0125] As an embodiment of the present invention, the constraints of the shared energy storage power station operation cost optimization model under the multi-scenario fusion working condition include the shared energy storage power station power balance constraint condition and the shared energy storage power station capacity balance constraint condition.

[0126] As an embodiment of the present invention, the power balance constraint condition of the shared energy storage power station is:

[0127]

[0128] Among them, P ess (t) is the output power of the shared energy storage power station at time t, is the charging power of scenario d at time t, is the charging power of scenario d at time t, P ess_gr (t) is the exchange power with the grid at time t, is the status bit of whether the shared energy storage power station can discharge at time t in scenario d, P is the status bit of whether the shared energy storage power station is chargeable at time t, ess_pr is the rated power of the shared energy storage power station.

[0129] As an embodiment of the present invention, the capacity balance constraint condition of the shared energy storage power station is:

[0130]

[0131] Where E(t) is the state of charge of the shared energy storage at time t, η ch and η dis Corresponding to BESS charging / discharging efficiency, E ess_pr Rated capacity of shared energy storage power station, is the charging power of scenario d at time t, is the charging power of scenario d at time t.

[0132] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0133] Example 4

[0134] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, steps of a multi-scenario fusion operation control method for the energy storage power station are implemented, for example, comprising: constructing a plurality of multi-scenario fusion operating conditions based on a single typical operating condition of a shared energy storage power station, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of the plurality of single typical operating conditions of the shared energy storage power station; constructing an operation cost optimization model for a shared energy storage power station under a plurality of multi-scenario fusion operating conditions; solving the operation cost optimization model for a shared energy storage power station under a plurality of multi-scenario fusion operating conditions to obtain the operation cost of the shared energy storage power station under each multi-scenario fusion operating condition; determining the multi-scenario fusion operating condition corresponding to the lowest operation cost of the shared energy storage power station; and performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion operating condition. The memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus. This internal bus may be an Industry Standard Architecture bus, a Peripheral Component Interconnect Standard bus, an Extended Industry Standard Architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0135] Example 5

[0136] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-scenario fusion operation control method of the energy storage power station, for example, including: constructing several multi-scenario fusion operating conditions based on a single typical operating condition of the shared energy storage power station, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station; constructing a shared energy storage power station operation cost optimization model under several multi-scenario fusion operating conditions; solving the shared energy storage power station operation cost optimization model under several multi-scenario fusion operating conditions to obtain the shared energy storage power station operation cost under each multi-scenario fusion operating condition; determining the multi-scenario fusion operating condition corresponding to the lowest shared energy storage power station operation cost; and performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion operating condition. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, and the like.

[0137] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A multi-scenario integrated operation control method for an energy storage power station, characterized in that: include: Construct a shared energy storage power station operation cost optimization model under several multi-scenario fusion conditions; Solve the shared energy storage power station operation cost optimization model under the multi-scenario fusion conditions and obtain the shared energy storage power station operation cost under the multi-scenario fusion conditions; Determine the multi-scenario fusion operating conditions corresponding to the lowest operating cost of the shared energy storage power station; Performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion operating conditions; The process of constructing a plurality of shared energy storage power station operation cost optimization models under multiple scenario fusion conditions, solving the shared energy storage power station operation cost optimization models under each multiple scenario fusion condition, and obtaining the shared energy storage power station operation cost under each multiple scenario fusion condition is as follows: 11) Analyze E typical operating conditions of the shared energy storage power station, select characteristic parameters of M typical conditions, M ≥ 1, conduct cyclic aging experiments under T typical conditions, and obtain the typical operating condition aging life distribution of the shared energy storage power station, wherein the characteristic parameters of the M typical conditions include but are not limited to: average charge and discharge power (P), capacity charge and discharge depth (DOD), and charge and discharge power conversion frequency (F); 12) Based on a single typical operating condition of a shared energy storage power station, construct several multi-scenario fusion operating conditions, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station; Specifically, according to the range of the typical working condition characteristic parameters obtained in step 11), the working condition characteristic parameter group X of a single typical working condition is determined. d (D≥d≥1), where X d It includes m (M≥m≥1) working condition characteristic parameter values; for N groups of single typical working conditions, the weight factor of the anth single typical working condition is set to a d-n , N≥n≥1, satisfying a 1-n +a 2-n +...+a d-n =1, and obtain the characteristic parameter group Y of the multi-scenario fusion working condition of the shared energy storage power station n =a 1-n ·X1+a 2-n ·X2+...+a d-n ·X d ; The typical operating condition aging life distribution obtained in step 11) is interpolated to obtain the SOH of the multi-scenario fusion operating condition of the shared energy storage power station from 100% to SOH L Lifespan S L ; Then set the operating constraints of each single typical working condition; 13) Constructing a shared energy storage power station operating cost optimization model under various scenarios; In step 13), with the goal of minimizing the operating cost of the shared energy storage power station and the power balance and capacity balance of the shared energy storage power station as constraints, a shared energy storage power station operating cost optimization model is constructed; 14) Calculate the operating cost optimization model of the shared energy storage power station under the multi-scenario fusion working conditions and obtain the cost C of the shared energy storage power station n , the optimized Y′ n , and the corresponding Y′ n Aging life S′ L ; 15) Based on the optimized aging life S' L , calculate the shared energy storage power station operation cost optimization model under multi-scenario fusion conditions, and obtain the optimized shared energy storage power station cost C′ n , when |C′ n -C n |>5%C n Then go to step 14) until |C′ n -C n |≤5%C n , then go to step 16); 16) Assume that the cumulative decay of the shared energy storage power station aging life ΔS L , through interpolation, obtain Y n The corresponding SOH is used to calculate the corrected rated capacity E. ess_pr *SOH, repeat steps 14) to 15) until SOH ≤ SOH L , the operating cost C of the shared substation under N groups of multi-scenario fusion conditions is obtained n And its corresponding weight factor a for a single typical working condition d-n ; The process of performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion working condition is as follows: according to the weight factor a of each single typical working condition and its corresponding single typical working condition in the determined multi-scenario fusion working condition, d-n-min , and perform multi-scenario integrated operation control on the shared energy storage power station.

2. The multi-scenario integrated operation control method of the energy storage power station according to claim 1 is characterized in that: The objective function of the shared energy storage power station operation cost optimization model under the multi-scenario fusion working condition is: C ess =min(C ess_INV +C ess_OP +C ess_ElP ) Among them, C ess represents the operating cost of the shared energy storage power station, C ess_INV is the daily investment cost of the shared energy storage power station, C ess_OP To share the daily operation and maintenance costs of the energy storage power station, C ess_ElP The daily electricity purchase cost of the shared energy storage power station.

3. The multi-scenario integrated operation control method of the energy storage power station according to claim 1 is characterized in that: The power balance constraint condition of the shared energy storage power station is: Among them, P ess (t) is the output power of the shared energy storage power station at time t, is the charging power of scenario d at time t, is the charging power of scenario d at time t, P ess_gr (t) is the exchange power with the grid at time t, is the status bit of whether the shared energy storage power station can discharge at time t in scenario d, P is the status bit of whether the shared energy storage power station is chargeable at time t, ess_pr is the rated power of the shared energy storage power station.

4. The multi-scenario integrated operation control method of the energy storage power station according to claim 1 is characterized in that: The capacity balance constraint condition of the shared energy storage power station is: Where E(t) is the state of charge of the shared energy storage at time t, η ch and η dis Corresponding to BESS charging / discharging efficiency, E ess_pr Rated capacity of shared energy storage power station, is the charging power of scenario d at time t, is the charging power of scenario d at time t.

5. A multi-scenario fusion operation control system for an energy storage power station, characterized in that: include: The first building module is used to build a shared energy storage power station operation cost optimization model under multiple multi-scenario fusion conditions; A solution module is used to solve the shared energy storage power station operation cost optimization model under various multi-scenario fusion conditions to obtain the shared energy storage power station operation cost under various multi-scenario fusion conditions; A determination module is used to determine the multi-scenario fusion operating condition corresponding to the lowest operating cost of the shared energy storage power station; A control module, configured to perform multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion operating conditions; The process of constructing a plurality of shared energy storage power station operation cost optimization models under multiple scenario fusion conditions, solving the shared energy storage power station operation cost optimization models under each multiple scenario fusion condition, and obtaining the shared energy storage power station operation cost under each multiple scenario fusion condition is as follows: 11) Analyze E typical operating conditions of the shared energy storage power station, select characteristic parameters of M typical conditions, M ≥ 1, conduct cyclic aging experiments under T typical conditions, and obtain the typical operating condition aging life distribution of the shared energy storage power station, wherein the characteristic parameters of the M typical conditions include but are not limited to: average charge and discharge power (P), capacity charge and discharge depth (DOD), and charge and discharge power conversion frequency (F); 12) Based on a single typical operating condition of a shared energy storage power station, construct several multi-scenario fusion operating conditions, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station; Specifically, according to the range of the typical working condition characteristic parameters obtained in step 11), the working condition characteristic parameter group X of a single typical working condition is determined. d (D≥d≥1), where X d It includes m (M≥m≥1) working condition characteristic parameter values; for N groups of single typical working conditions, the weight factor of the anth single typical working condition is set to a d-n , N≥n≥1, satisfying a 1-n +a 2-n +...+a d-n =1, and obtain the characteristic parameter group Y of the multi-scenario fusion working condition of the shared energy storage power station n =a 1-n ·X1+a 2-n ·X2+...+a d-n ·X d ; The typical operating condition aging life distribution obtained in step 11) is interpolated to obtain the SOH of the multi-scenario fusion operating condition of the shared energy storage power station from 100% to SOH L Lifespan S L ; Then set the operating constraints of each single typical working condition; 13) Constructing a shared energy storage power station operating cost optimization model under various scenarios; In step 13), with the goal of minimizing the operating cost of the shared energy storage power station and the power balance and capacity balance of the shared energy storage power station as constraints, a shared energy storage power station operating cost optimization model is constructed; 14) Calculate the operating cost optimization model of the shared energy storage power station under the multi-scenario fusion working conditions and obtain the cost C of the shared energy storage power station n , the optimized Y′ n , and the corresponding Y′ n Aging life S′ L ; 15) Based on the optimized aging life S' L , calculate the shared energy storage power station operation cost optimization model under multi-scenario fusion conditions, and obtain the optimized shared energy storage power station cost C′ n , when |C′ n -C n |>5%C n Then go to step 14) until |C′ n -C n |≤5%C n , then go to step 16); 16) Assume that the cumulative decay of the shared energy storage power station aging life ΔS L , through interpolation, obtain Y n The corresponding SOH is used to calculate the corrected rated capacity E. ess_pr *SOH, repeat steps 14) to 15) until SOH ≤ SOH L , the operating cost C of the shared substation under N groups of multi-scenario fusion conditions is obtained n And its corresponding weight factor a for a single typical working condition d-n ; The process of performing multi-scenario fusion operation control on the shared energy storage power station according to the determined multi-scenario fusion working condition is as follows: according to the weight factor a of each single typical working condition and its corresponding single typical working condition in the determined multi-scenario fusion working condition, d-n-min , and perform multi-scenario integrated operation control on the shared energy storage power station.

6. The multi-scenario integrated operation control system of the energy storage power station according to claim 5 is characterized in that: Also includes: The second construction module is used to construct several multi-scenario fusion operating conditions based on a single typical operating condition of the shared energy storage power station, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of several single typical operating conditions of the shared energy storage power station.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the multi-scenario fusion operation control method of the energy storage power station as described in any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-scenario integrated operation control method of the energy storage power station as described in any one of claims 1 to 4 are implemented.

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