Multi-scene fusion operation control method, system and equipment of energy storage power station and medium

Abstract

The invention discloses a multi-scene fusion operation control method, system, equipment and medium for an energy storage power station, and belongs to the field of electrochemical energy storage, and the method comprises the steps: constructing a shared energy storage power station operation cost optimization model under a plurality of multi-scene fusion working conditions, solving the shared energy storage power station operation cost optimization model under each multi-scene fusion working condition, and obtaining a shared energy storage power station operation cost optimization model; obtaining the operation cost of the shared energy storage power station under each multi-scene fusion working condition; determining a corresponding multi-scene fusion working condition when the operation cost of the shared energy storage power station is lowest; and performing multi-scene fusion operation control on the shared energy storage power station according to the determined multi-scene fusion working condition, and the method, the system, the equipment and the medium can realize operation cost optimization of the shared energy storage power station under multi-scene 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. This 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 the application of a single scenario, which causes energy storage to be idle in certain time periods and capacities, and cannot fully realize its potential value. Considering the operating time periods, operating scenarios and aging costs of energy storage power stations, adopting a sharing model for the operation of energy storage power stations based on market returns is the economic driving force and technical support for promoting energy storage power stations to actively participate in the balance and regulation of the power system. The diversity of application scenarios of energy storage power stations makes the operating conditions they carry different. The characteristic parameters of the operating conditions include charge and discharge depth, rate, number of cycles, etc., which 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 fusion operation control method, system, equipment and medium for an energy storage power station. The method, system, equipment and medium can achieve the optimization of the operating cost of a shared energy storage power station under multi-scenario fusion.

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

[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 optimization model of the shared energy storage power station operation cost under various scenarios fusion conditions, and obtain the shared energy storage power station operation cost under various scenarios fusion conditions;

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

[0010] According to 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 fusion operation control method of the energy storage power station of 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 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 determined multi-scenario fusion working condition and the process of performing multi-scenario fusion operation control on the shared energy storage power station are as follows:

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

[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 is the daily operation and maintenance cost of the shared 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 constraints and the shared energy storage power station capacity balance constraints.

[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, is the status of whether the shared energy storage power station is chargeable at time t, P 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 fusion 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 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 conditions corresponding to the lowest operating cost of the shared energy storage power station;

[0030] A 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 operating 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 a number of 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 condition is constructed by weighted summation of a number of 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, including 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 the multi-scenario fusion operation control method of the energy storage power station are implemented.

[0035] According to 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 shared energy storage power station operation cost optimization model under several multi-scenario fusion conditions, and use this to calculate the operation cost of the shared energy storage power station under each multi-scenario fusion condition. In order to achieve the lowest cost, the present invention selects the multi-scenario fusion condition with the lowest operation cost of the shared energy storage power station to control the shared energy storage power station, so as to achieve the optimization of the operation cost of the shared energy storage power station under multi-scenario fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper 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 working condition of photovoltaic and energy storage combination;

[0042] Figure 4 This is a schematic diagram of a typical working condition of 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 diagram;

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

[0046] Figure 8 It is a test curve diagram of combined working condition;

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

[0048] Fig.10 A schematic diagram of the aging life distribution space of a shared energy storage power station;

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

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

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

[0052] Fig.14 It 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 scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments, and is not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concepts disclosed in the present invention. Based on the embodiments in 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 show schematic diagrams of structures according to embodiments disclosed in the present invention. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0055] refer to Figure 1 The multi-scenario fusion operation control method of the energy storage power station described in the present invention comprises 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 working conditions of the shared energy storage power station, select the characteristic parameters of M typical working conditions, M≥1, conduct cyclic aging experiments of T (T≥2) typical working conditions, and obtain the typical working condition aging life distribution of the shared energy storage power station. The characteristic parameters of the M typical working 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 typical power working condition P is 0.2~0.6P0, DOD is 20~60%, F is 0.0005~0.001Hz, and the fusion working condition P 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 a plurality of multi-scenario fusion operating conditions, wherein the multi-scenario fusion operating conditions are constructed by weighted summation of a plurality of 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 operating condition.

[0062] 13) Construct a shared energy storage power station operation cost optimization model under various multi-scenario fusion conditions;

[0063] In step 13), with the goal of minimizing the operating cost of the shared energy storage power station and with 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 the shared energy storage power station power balance constraints and the shared energy storage power station capacity balance constraints.

[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 power 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 scene 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. P is the status bit of whether the energy storage station is chargeable at time t. When it is 1, it is chargeable and when it is 0, it is not chargeable. 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, etc., 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 various scenarios and obtain the cost C of the shared energy storage power station. n , the optimized Y′ n , and the corresponding Y′ n The aging life S′ L ;

[0081] 15) Based on the optimized aging life S′ L , calculate the operating cost optimization model of the shared energy storage power station under the multi-scenario fusion condition, and obtain the optimal cost C′ of the shared energy storage power station 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 aging life of the shared energy storage power station is ΔS L , through interpolation, we get Y n Corresponding SOH, 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 corresponding to the lowest operating cost of the 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] Embodiment 2

[0087] This embodiment collects E=4 typical operating conditions of actual operation of lithium-ion battery energy storage power stations above MW level for analysis, including 73 sets of typical operating condition data for tracking planned output, 27 sets of typical operating condition data for photovoltaic and energy storage combination, 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, totaling 222 sets of operating condition data.

[0088] Figure 2 It indicates the typical working condition of tracking planned output, the average charge and discharge power (P)|P|<0.6P0, P0 is the rated power, the charge and discharge power conversion frequency (F is 0.0005Hz, and the capacity charge and discharge depth DOD is 50%. Figure 3 It represents the typical working condition of the combined photovoltaic and energy storage. In order to suppress the fluctuation of photovoltaic power generation, the charging and discharging power of the energy storage power station fluctuates greatly, |P|<0.8P0, F is 0.0004Hz, charging and discharging at nearly 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 It represents the typical working condition of peak shaving and valley filling. The energy storage power station completes 1 to 2 full-capacity charging and discharging according to the daily peak and valley electricity price, and provides auxiliary services. |P|<1P0, F is 0.0001Hz, and DOD is 80%.

[0089] according to Figures 2 to 5 The 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 operating condition characteristic distribution basically covers the main application scenarios of current electrochemical energy storage and meets power and capacity applications. Therefore, when determining the aging test plan for typical working conditions, based on the power and capacity application conditions, no less than 3 typical working condition aging test plans are designed, as shown in Table 1. The characteristic parameter selection range of each experimental plan is:

[0090] a) Capacity type working condition P is 0.6~1.0P0, DOD is 60~80%, 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 working condition: P is 0.5~0.7P0; DOD is 40~70%; F is 0.0004~0.0006Hz.

[0093] Table 1 Typical working condition experimental scheme characteristic parameter range table

[0094]

[0095] Table 2 Typical operating conditions experimental plan

[0096]

[0097] This embodiment constructs a 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 cut-off 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 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 capacity, power and fusion conditions are obtained as shown below. Fig. 9 shown.

[0098] The weight factor a of the average power under capacity condition n, aging life and health state SOH as parameters, and the cubic interpolation method is used to construct the aging life distribution of the shared energy storage power station, such as Fig.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 the 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: Fig.11 The working condition A, P 1-n >30%P0, SOC working range 30~60%, DOD=30%, F is 0.0008.

[0103] Using 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 power 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. The typical capacity application power, SOC and time-of-use peak-valley electricity price curves of a certain place are as follows: Fig.11 The working condition B shown is P 2-n <80%P0, SOC working 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 = {P 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 weight factors a for each typical working condition in the fusion working condition d-n (4≥n≥1), satisfying a 1-n +a 2-n =1, and get the multi-scene fusion condition Y n =a 1-n ·P 1-n+a 2-n ·P 2-n , as shown in Table 4, based on Fig.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 and capacity conditions are set. For power condition, the output power is according to the planned output instruction, and the 20% deviation of the planned output power is adopted for assessment, that is, the power constraint of the fusion condition is that the power exceeding 20% ​​of the planned output power deviation is the minimum power; for capacity condition, charging is carried out during valley electricity price period (0:00-7:00, 23:00-24:00), and discharging is carried out during peak electricity price period (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: Fig.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 get 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 = 97%; the rated capacity is corrected to: E ess_pr *SOH=50*97%=48.5kW, repeat the above steps until SOH≤70%.

[0111] At this point, the n=1th group of weight factors a is completed.d-1 The full life cycle operation cost calculation of the constructed multi-scenario fusion working conditions.

[0112] The multi-scenario fusion condition cost calculation 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 N = 4 groups of multi-scenario fusion condition cost C is obtained. n and the corresponding weight factor a for a single typical operating condition d-n The cost of multi-scenario fusion operation of a shared energy storage power station over its entire life cycle is as follows: Fig.13 As shown in the figure, as the weight factor of the capacity type 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 to 0.6, when a 2-n-min =0.51, the operating cost of the energy storage power station when it is not attenuated reaches the minimum value C n-min = 0.33 yuan, and increases slightly with ageing; when a 2-n =0.426, when the energy storage power station runs 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 3500 times, it reached 0.39 yuan.

[0113] Embodiment 3

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

[0115] A second construction module is used 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 a plurality of single typical operating conditions of the shared energy storage power station;

[0116] The first building module is used to construct a shared energy storage power station operation cost optimization model under several multi-scenario fusion 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 conditions corresponding to the lowest operating cost of the shared energy storage power station;

[0119] A 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 operating conditions.

[0120] As an implementation mode of the present invention, the process of determining the multi-scenario fusion working condition and performing multi-scenario fusion operation control on the shared energy storage power station 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, multi-scenario fusion operation control is performed on the shared energy storage power station.

[0122] As an implementation mode 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 is the daily operation and maintenance cost of the shared energy storage power station, C ess_ElP The daily electricity purchase cost of the shared energy storage power station.

[0125] As an implementation mode of the present invention, the constraint conditions 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 conditions and the shared energy storage power station capacity balance constraint conditions.

[0126] As an implementation mode 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, is the status of whether the shared energy storage power station is chargeable at time t, P ess_pr is the rated power of the shared energy storage power station.

[0129] As an implementation mode 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 schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0133] Embodiment 4

[0134] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multi-scenario fusion operation control method of the energy storage power station when executing the computer program, 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 summing the plurality of single typical operating conditions of the shared energy storage power station; constructing a shared energy storage power station operation cost optimization model under a plurality of multi-scenario fusion operating conditions; solving the shared energy storage power station operation cost optimization model under a plurality of 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 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 operating condition. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, network interface, and memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may 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 a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0135] Embodiment 5

[0136] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of implementing the multi-scenario fusion operation control method of the energy storage power station, for example, include: constructing several 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 through 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 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 operating condition. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include a random access memory (RAM) and / or a 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 may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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 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. Any modification or equivalent replacement that does 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 multi-scenario fusion 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 optimization model of the shared energy storage power station operation cost under various scenarios fusion conditions, and obtain the shared energy storage power station operation cost under various scenarios fusion conditions; Determine the multi-scenario fusion operating conditions corresponding to the lowest operating cost of the shared energy storage power station; According to the determined multi-scenario fusion operating conditions, multi-scenario fusion operation control is performed on the shared energy storage power station.

2. The multi-scenario fusion operation control method of the energy storage power station according to claim 1 is characterized in that: Before constructing a shared energy storage power station operation cost optimization model under multiple scenario fusion conditions, the following steps are also included: 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.

3. The multi-scenario fusion operation control method of the energy storage power station according to claim 2 is characterized in that: The process of determining the obtained multi-scenario fusion working condition and performing multi-scenario fusion operation control on the shared energy storage power station is as follows: 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, multi-scenario fusion operation control is performed on the shared energy storage power station.

4. The multi-scenario fusion 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 is the daily operation and maintenance cost of the shared energy storage power station, C ess_ElP The daily electricity purchase cost of the shared energy storage power station.

5. The multi-scenario fusion operation control method of the energy storage power station according to claim 1 is characterized in that: 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 constraints and the shared energy storage power station capacity balance constraints.

6. The multi-scenario fusion operation control method of the energy storage power station according to claim 5 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, is the status of whether the shared energy storage power station is chargeable at time t, P ess_pr is the rated power of the shared energy storage power station.

7. The multi-scenario fusion operation control method of the energy storage power station according to claim 5 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.

8. 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 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 conditions corresponding to the lowest operating cost of the shared energy storage power station; A 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 operating conditions.

9. The multi-scenario fusion operation control system of the energy storage power station according to claim 8 is characterized in that: Also includes: The second construction module is used to construct a number of 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 condition is constructed by weighted summation of a number of single typical operating conditions of the shared energy storage power station.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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 1-7 are implemented.

11. 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 7 are implemented.

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