A method and system for economic configuration of hybrid energy storage

Abstract

The present invention proposes a method and system for economic configuration of hybrid energy storage. The method includes: obtaining the automatic generation control (AGC) instructions issued by the power dispatching and trading organization to be processed and the original power generation samples of the thermal power units following the AGC instructions; performing spectral analysis on the difference of the original samples through complete adaptive noise ensemble empirical mode decomposition to obtain the optimal decomposition order and perform hybrid energy storage power allocation; determining the fitness function and constraint conditions of the chaotic firefly algorithm; and applying an improved chaotic optimization firefly algorithm to optimize the capacity of the hybrid energy storage system through a preset economic optimal index to determine the optimal energy storage capacity. The present invention can reasonably configure the energy storage capacity of the hybrid energy storage system and improve the economy, stability and security of the system.

Description

Technical Field

[0001] The present invention relates to the field of flexible application of energy storage in power systems, and particularly to a method and system for economically configuring hybrid energy storage. Background Art

[0002] Due to the inherent properties of thermal power units, such as large inertia and slow response speed, they cannot track the automatic generation control command (AGC) well, resulting in fluctuations in grid voltage and frequency, which greatly affect the power quality and power supply stability.

[0003] Energy storage systems (ESS) have received increasing attention due to their fast power response and accurate power consumption characteristics. Therefore, assisting thermal power units to participate in AGC command frequency modulation control has high economic benefits.

[0004] Energy storage devices include power-type energy storage elements and energy-type energy storage elements. Flywheel energy storage, as a power-type energy storage element, has the characteristics of long cycle life, high energy conversion rate, fast response speed but low energy storage; lithium iron phosphate battery, as an energy-type energy storage element, has the characteristics of high energy density, relatively long cycle life and high energy storage; the two complement each other, and the hybrid energy storage system composed of them can extend the life of the energy storage system while meeting energy consumption, improve the performance and economic benefits of the energy storage system, and has higher use value.

[0005] The key to the application of hybrid energy storage systems is the reasonable distribution of power. Currently, the commonly used power distribution methods include: low-pass filtering, wavelet transform, Fourier-Huang transform, etc. However, these methods have certain problems and limitations in power distribution applications. Patent: Capacity Optimization Method and Device for Hybrid Energy Storage System Based on Low-Pass Filtering (Publication No.: CN114781266A) proposes a capacity configuration method for using hybrid energy storage to suppress the active power fluctuations of wind power / solar power. Low-pass filtering is performed on the active power of wind power / solar power using various filtering methods, and the capacity of the hybrid energy storage system is optimized through a genetic algorithm. However, this method does not specifically describe the power distribution method and the optimal capacity applied to different energy storage elements, so the economic configuration effect of hybrid energy storage needs to be improved. Summary of the Invention

[0006] The technical problem solved by the present invention is: to provide a method for economically configuring hybrid energy storage, so as to reasonably decompose and distribute the reference power of the hybrid energy storage system, enabling the hybrid energy storage system to well assist thermal power units in tracking AGC commands.

[0007] To achieve the above object, the present invention provides a method for economically configuring hybrid energy storage, including the following steps:

[0008] Step S1: Obtain the difference between the original automatic generation control instruction and the power generation power of the thermal power unit following instruction. By analyzing the power ratio situation, set the compensation power interval, and use it as the reference power of the hybrid energy storage system;

[0009] Step S2: Decompose the reference power of the hybrid energy storage system to obtain n modal components, and allocate them to the flywheel energy storage and lithium battery energy storage according to high frequency and low frequency respectively;

[0010] Step S3: Analyze the construction cost and service life affecting the flywheel energy storage and lithium battery energy storage respectively, and establish the optimization objective function and constraint conditions for the economic operation of the energy storage system;

[0011] Among them, the objective function is min{C LCC}=min{C BI +C BOM +C BS +C FI +C FOM};

[0012] In the formula: C LCC is the total cost of the hybrid energy storage system; C BI is the investment cost of the lithium battery, C BOM is the operation and maintenance cost of the lithium battery, C BS is the retirement cost of the lithium battery, C FI is the investment cost of the flywheel energy storage, C FOM is the operation and maintenance cost of the flywheel energy storage;

[0013] The constraint conditions include:

[0014] Considering the power balance constraint of the hybrid energy storage system, make the absorbed power of the hybrid energy storage system equal to the reference power of the hybrid energy storage system. The expression is as follows:

[0015]

[0016] In the formula: is the power that the hybrid energy storage system needs to satisfy at time t; is the power that the lithium battery needs to absorb at time t; is the power that the flywheel energy storage needs to absorb at time t;

[0017] Considering that the hybrid energy storage system operates within a safe working range, and the SOC of the lithium battery energy storage and the flywheel energy storage should be controlled within the upper and lower limits. The expression is as follows:

[0018]

[0019] SOC min ≤SOC t ≤SOC max

[0020] In the formula: represents the charging power at time t, represents the discharging power at time t, and at least one of them is 0 at time t; SOC min and SOC max respectively represent the lower limit and upper limit of the SOC set for the energy storage elements in the hybrid energy storage; SOC t and SOC t-1 respectively represent the SOC of the hybrid energy storage elements at time t and at time (t - 1); E rat represents the rated capacity of the hybrid energy storage system;

[0021] Considering the safety of the energy storage system and ensuring reasonable control of the heat generation, the charging and discharging powers of the lithium - battery energy storage and the flywheel energy storage are controlled within the upper and lower limits. The expression is as follows:

[0022]

[0023]

[0024] In the formula: respectively represent the maximum charging and discharging powers of the energy storage elements of the hybrid energy storage system; P rat represents the rated charging and discharging power of the energy storage elements of the hybrid energy storage system; Δt represents the sampling interval;

[0025] Considering the rated capacity constraint of the hybrid energy storage system, the expression is as follows:

[0026]

[0027] In the formula: SOC i is the SOC of the energy storage elements of the hybrid energy storage system at any time; is the power that can be absorbed by the energy storage elements of the hybrid energy storage system at time t; k represents the number of Δt, which is determined according to the sampling interval and the total duration;

[0028] Step S4, under the constraint conditions of step S3, apply the chaotic firefly algorithm to solve the objective function to obtain the economic capacity configuration.

[0029] Furthermore, the specific execution process of step S2 is as follows: Add the noise component ε0ω i [n] composed of the Gaussian white noise ω i-1 satisfying the standard normal distribution and its adaptive coefficient ε i to the reference power signal x[n], and perform the decomposition of x[n]+ε0ω i [n] through classical mode decomposition to obtain the intrinsic mode function IMF1[n];

[0030] The result after adaptive noise complete ensemble empirical mode decomposition is as follows:

[0031]

[0032] Among them, is the initial sample of the reference power of the hybrid energy storage system corresponding to the t-th moment, I represents the number of power signal samples decomposed using CEEMDAN, is the IMF component obtained after each decomposition by CEEMDAN, and R CEEMDAN,t (i) is the residual component.

[0033] Furthermore, in the step S3,

[0034]

[0035] Among them, C PB is the unit power cost of the lithium battery; P BO is the optimized power of the lithium battery; C CB is the unit capacity cost of the lithium battery; E BO is the optimized capacity of the lithium battery; r represents the ratio of converting the expected future limited-term income into present value; T LCC represents the time determined considering all links in the equipment life cycle; l is the number of replacements for equipment whose life cannot reach the full life cycle, and a total of l + 1 energy storages are invested, l = T LCC / T life , where T life is the equivalent cycle life of the energy storage; T life = Bcyc × 90%, and 90% is the ideal linear aging rate of the lithium battery;

[0036] B cyc = 56910 * DOD 2 - 10900 * DOD + 54530,

[0037] DOD is the depth of discharge of the lithium battery, and B cyc is the cycle life of the lithium battery;

[0038]

[0039] Among them, C BPOM is the unit power operation and maintenance cost; C EOM is the unit capacity operation and maintenance cost; W(t) is the annual charge and discharge power of the energy storage;

[0040]

[0041] Among them, C BPscr is the unit power retirement cost; C BEscr is the unit capacity retirement cost;

[0042] C FI = C PF P FO + C CF C FO

[0043] Among them, C PF is the unit power cost of the flywheel battery; P FO is the optimized power of the flywheel battery; C CF is the unit capacity cost of the flywheel battery; C FO is the optimized capacity of the flywheel battery;

[0044]

[0045] Among them, C FPOM is the operation and maintenance cost per unit power.

[0046] The present invention also provides a hybrid energy storage economic configuration system, including:

[0047] A hybrid energy storage system reference power calculation module, which is used to obtain the difference between the original automatic generation control instruction and the power generation power of the thermal power unit following instruction, set a compensation power interval by analyzing the power ratio situation, and use it as the hybrid energy storage system reference power;

[0048] A modal component calculation module, which decomposes the hybrid energy storage system reference power to obtain n modal components, and distributes them to the flywheel energy storage and lithium battery energy storage according to high frequency and low frequency respectively;

[0049] A target function and constraint condition setting module, which respectively analyzes the construction cost and service life affecting the flywheel energy storage and lithium battery energy storage, and establishes an optimization target function and constraint conditions for the economic operation of the energy storage system;

[0050] Among them, the target function is min{C LCC}} = min{C BI + C BOM + C BS + C FI + C FOM};

[0051] In the formula: C LCC is the total cost of the hybrid energy storage system; C BI is the investment cost of the lithium battery, C BOM is the operation and maintenance cost of the lithium battery, C BS is the retirement cost of the lithium battery, C FI is the investment cost of the flywheel energy storage, C FOM is the operation and maintenance cost of the flywheel energy storage;

[0052] The constraint conditions include:

[0053] Considering the power balance constraint of the hybrid energy storage system, the absorbed power of the hybrid energy storage system is equal to the reference power of the hybrid energy storage system. The expression is as follows:

[0054]

[0055] In the formula: is the power that the hybrid energy storage system needs to satisfy at time t; is the power that the lithium battery needs to absorb at time t; is the power that the flywheel energy storage needs to absorb at time t;

[0056] Considering that the hybrid energy storage system operates within a safe working range, and the SOC of the lithium battery energy storage and the flywheel energy storage should be controlled within the upper and lower limits. The expression is as follows:

[0057]

[0058] SOC min ≤SOC t ≤SOC max

[0059] In the formula: represents the charging power at time t, represents the discharging power at time t; at least one of them is 0 at time t; SOC min 、SOC max respectively represent the lower and upper limits of the SOC set for the energy storage elements in the hybrid energy storage; SOC t 、SOC t-1 respectively represent the SOC of the hybrid energy storage elements at time t and (t - 1); E rat represents the rated capacity of the hybrid energy storage system;

[0060] Considering the safety of the energy storage system and ensuring reasonable control of the heat generation, the charging and discharging powers of the lithium battery energy storage and the flywheel energy storage are controlled within the upper and lower limits. The expression is as follows:

[0061]

[0062] In the formula: respectively represent the maximum charging and discharging powers of the energy storage elements of the hybrid energy storage system; P rat represents the rated charging and discharging power of the energy storage elements of the hybrid energy storage system; Δt represents the sampling interval. Considering the rated capacity constraint of the hybrid energy storage system, the expression is as follows:

[0063]

[0064] The economic capacity configuration module solves the objective function by applying the chaotic firefly algorithm under the constraint conditions to obtain the economic capacity configuration.

[0065] Further, the specific execution process of the modal component calculation module is as follows: Add the noise component ε0ω i [n] composed of the Gaussian white noise ω i-1 that satisfies the standard normal distribution and its adaptive coefficient ε i to the reference power signal x[n], and perform decomposition on x[n]+ε0ω i [n] through classical modal decomposition to obtain the intrinsic mode function IMF1[n].

[0066] The result after complete ensemble empirical mode decomposition with adaptive noise is:

[0067]

[0068] Among them, is the initial sample of the reference power of the hybrid energy storage system corresponding to the t-th moment, I represents the number of power signal samples decomposed using CEEMDAN, is the IMF component obtained after each decomposition by CEEMDAN, and R CEEMDAN,t (i) is the residual component.

[0069] Further, in the objective function and constraint condition setting module,

[0070]

[0071] Among them, C PB is the unit power cost of the lithium battery; P BO is the optimized power of the lithium battery; C CB is the unit capacity cost of the lithium battery; E BO is the optimized capacity of the lithium battery; r represents the ratio of the future finite-term expected income discounted to the present value; T LCC represents the time determined considering all links in the equipment life cycle; l is the number of replacements of equipment whose life cannot reach the full life cycle, and a total of l + 1 energy storages are invested, l = T LCC / T life , where T life is the equivalent cycle life of the energy storage; T life = Bcyc×90%, and 90% is the ideal linear aging rate of the lithium battery;

[0072] B cyc = 56910*DOD 2 - 10900*DOD + 54530, DOD is the depth of discharge of the lithium battery, and B cyc is the cycle life of the lithium battery;

[0073]

[0074] Among them, C BPOM is the operation and maintenance cost per unit power; C EOM is the operation and maintenance cost per unit capacity; W(t) is the annual charge and discharge power of the energy storage;

[0075]

[0076] Among them, C BPscr is the retirement cost per unit power; C BEscr is the retirement cost per unit capacity;

[0077] C FI = C PF P FO + C CF C FO

[0078] Among them, C PF is the unit power cost of the flywheel battery; P FO is the optimized power of the flywheel battery; C CF is the unit capacity cost of the flywheel battery; C FO is the optimized capacity of the flywheel battery;

[0079]

[0080] Among them, C FPOM is the operation and maintenance cost per unit power.

[0081] The present invention also provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the above method.

[0082] The present invention also provides a processing device, including at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above method by calling the program instructions.

[0083] Advantages of the present invention: Considering the inherent characteristics of thermal power units, such as large inertia and slow response speed, the present invention determines a method for compensating the power difference that cannot be compensated by thermal power units using hybrid energy storage, and introduces the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the power of a representative power difference. The high-frequency power is allocated to the flywheel energy storage, and the low-frequency power is allocated to the lithium battery energy storage. This method substitutes the influencing factors affecting the service life of the lithium battery energy storage into the adaptive function, and at the same time uses an intelligent algorithm to optimize and solve the economy of the system operation. While assisting the thermal power unit to complete the AGC command following, it can also reasonably customize the capacity configuration method of the hybrid energy storage on the basis of power assistance, maximizing the economy and safety of the hybrid energy storage system operation. Description of the Drawings

[0084] Figure 1 It is a flowchart of the economic configuration method of the hybrid energy storage provided by the present invention;

[0085] Figure 2 It is a reference power curve diagram of the hybrid energy storage system of the present invention;

[0086] Figure 3 It is a reference power IMF spectrum result diagram of the present invention;

[0087] Figure 4 It is a power distribution result diagram of the hybrid energy storage of the present invention;

[0088] Figure 5 It is a flowchart of the energy storage capacity optimization method using the chaotic firefly algorithm of the present invention;

[0089] Figure 6 It is a curve of the fitness function of the algorithm of the present invention changing with the number of algorithm iterations. Detailed Embodiment

[0090] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0091] This embodiment includes the following steps:

[0092] S1: Obtain the difference between the original automatic generation control instruction and the power generation power of the thermal power unit following the instruction. By analyzing the power ratio situation, set the compensation power range, and use it as the reference power of the hybrid energy storage system. Specifically, due to the advantages of fast instruction response speed and high control accuracy of the energy storage system, it has good application prospects in making up for the insufficient power response of thermal power units. Compared with a single energy storage element, the flywheel energy storage power-type energy storage element and the lithium battery energy-type energy storage element can not only meet the rapidly fluctuating power changes but also make up for the energy requirements for long-term power output. Therefore, while the thermal power unit follows the AGC instruction, supplementing with hybrid energy storage to make up for the power deficiency can slow down the power fluctuation and thus improve the economic benefits of the thermal power plant. The power that needs to be compensated by energy storage is as Figure 2 shown. The reference power samples of the hybrid energy storage system total 6 hours, with a sampling interval of 1 second. Through analysis, the reference power samples with a power range of [-30MW, 30MW] exceed 95% of the total instruction power, and the remaining less than 5% of the power instructions are on the high side, doubling the total applied power of the hybrid energy storage and greatly increasing the power cost of the hybrid energy storage.

[0093] S2: Since the capacity cost of flywheel energy storage is high, and at the same time high-frequency power means very low capacity. If the lithium battery receives high-frequency power, it will seriously affect the battery service life and increase the investment cost of the lithium battery. Therefore, based on the characteristics of the flywheel itself being suitable for meeting high-frequency power and the lithium battery being suitable for low-frequency power, on this basis, find the optimal demarcation point through an algorithm to minimize the cost, and at the same time can also give play to the advantages of the hybrid energy storage compared with a single energy storage, and the two complement each other. For the above reasons, in this embodiment, the complete adaptive noise ensemble empirical mode decomposition method is used to decompose the reference power of the hybrid energy storage system, obtain n modal components, and distribute them to the flywheel energy storage and the lithium battery energy storage according to high frequency and low frequency respectively. Subsequent costs (i.e., various constraint conditions) are calculated on this basis.

[0094] (1) Add the Gaussian white noise ω i [n] that satisfies the standard normal distribution (mean of 0 and standard deviation of 1) and its adaptive coefficient ε i-1 to form the noise component ε0ω i [n] to the reference power sample x[n] of the hybrid energy storage system, and realize the decomposition of x[n] + ε0ω i [n] through EMD to obtain the Intrinsic Mode Functions (IMFs) IMF1[n].

[0095]

[0096] In the formula: K: Gaussian white noise sequence; The first mode component obtained by adding white noise for the k-th time. The reference power samples of the entire hybrid energy storage system are decomposed according to this formula, and IMF1[n] is the first mode component generated by EMD decomposition after adding Gaussian white noise k times.

[0097] (2) Calculate the first residue r i [n], which is the first residual component obtained after CEEMDAN decomposition.

[0098] r i [n] = x[n] - IMF1[n]

[0099] (3) After adding the adaptive white noise ε1E1(ω k [n]) to the first residue, perform EMD on r i [n] + ε1E1(ω k [n]) again to obtain the second component IMF2[n] by the CEEMDAN decomposition method.

[0100]

[0101] Where: E i (·): Represents the IMFi component obtained by decomposing a sequence through EMD.

[0102] (4) Repeat steps (2) to (3) until the residue cannot be decomposed by EMD again, and solve for the residue and mode components.

[0103] r i [n] = r i-1 [n] - IMF i [n]

[0104]

[0105] (5) If E i (·) cannot be performed, then terminate and obtain the residue R(n).

[0106]

[0107] Where: n: The number of IMF components.

[0108] The result after complete ensemble empirical mode decomposition with adaptive noise is:

[0109]

[0110] Among them, is the initial sample of the reference power of the hybrid energy storage system corresponding to 1 second at time t, is the IMF component obtained after each CEEMDAN decomposition, RCEEMDAN,t (i) is the residual component. The final decomposition result is as follows Figure 3 As shown, according to the division of high-frequency power and low-frequency power, the first 8 high-order frequencies are allocated to flywheel energy storage, and the remaining low-frequency power is allocated to lithium battery energy storage.

[0111] S3: Analyze the factors affecting the construction cost and service life of flywheel energy storage and lithium battery energy storage respectively, and establish the optimization objective function and constraints for the economic operation of the energy storage system.

[0112] Among them, the objective function is min{C LCC}=min{C BI +C BOM +C BS +C FI +C FOM};

[0113] Where: C LCC is the total cost of the hybrid energy storage system; C BI is the investment cost of lithium battery, C BOM is the operation and maintenance cost of lithium batteries, C BS is the lithium battery decommissioning cost, C FI is the investment cost of flywheel energy storage, C FOM Operation and maintenance costs for flywheel energy storage;

[0114] The expression of investment cost of lithium iron phosphate battery in the whole life cycle is:

[0115]

[0116] Among them, C PB is the unit power cost of lithium battery (yuan / kW); P BO is the optimized lithium battery power (kW); C CB is the unit capacity cost of lithium battery (yuan / kWh); E BO is the optimized lithium battery capacity (kWh); r = 6%, which indicates the ratio of the expected future limited-term benefits converted into present value; T LCC It represents the time determined by taking into account all aspects of the equipment life cycle, which is tentatively set at 20 years in this example; C CB is the unit capacity cost (yuan / kWh); l is the number of replacements for equipment whose service life cannot reach 20 years (a total of (l+1 times) of energy storage is invested), l = T LCC / T life , where T life The battery discharge depth is selected as 60%, and the available cycle number is determined by fitting; the battery cycle number during the power regulation process is measured based on the raindrop flow meter method to obtain the battery service life.

[0117] T life = Bcyc × 90%, where 90% is the ideal linear aging rate of the lithium battery; B cyc is the cycle life of the lithium battery;

[0118] It should be noted here that compared with the flywheel energy storage life in the whole life cycle, the flywheel energy storage life can be considered as infinite life and is replaced by a constant in the embodiment, that is, within the whole life cycle, the flywheel energy storage does not need to be replaced. The service life of the lithium battery energy storage is affected by the working temperature, discharge depth and cycle charge-discharge rate. In this embodiment, the working temperature of the lithium battery is constant and the maximum charge-discharge rate within the whole life cycle is 1C. Therefore, the only factor affecting the life of the lithium battery energy storage is the discharge depth.

[0119] In this embodiment, the corresponding relationship between the discharge depth and cycle life of a certain type of lithium iron phosphate battery is analyzed, and the N-order function method is used to fit the relationship curve, and the expression is as follows:

[0120] B cyc = 56910 * DOD 2 - 10900 * DOD + 54530

[0121] Where: DOD is the discharge depth of the lithium battery.

[0122] In this embodiment, the rain flow counting method is used to calculate the discharge depth of the lithium battery energy storage, and thus calculate the operation life of the lithium battery energy storage, and simplify the aging process of the lithium battery energy storage into a linear process, and take 90% of the operation life as the final life of the lithium battery energy storage.

[0123] B cyc = 56910 * DOD 2 - 10900 * DOD + 54530,

[0124] DOD is the discharge depth of the lithium battery.

[0125] The operation cost of the lithium battery energy storage includes operation and repair cost, decommissioning treatment cost and other related costs. Among them, the operation and repair cost is the cost to ensure the efficient and reliable operation of the lithium battery energy storage, and it increases with the increase of the operation years of each device in the energy storage system. Since the metal elements inside the lithium iron phosphate battery can be recycled and reused, and the harmful components need to be harmlessly treated, the funds spent on treating the lithium battery energy storage equipment are the decommissioning cost. The expression of the operation and repair cost of the lithium battery during the operation cycle of the energy storage system is as follows:

[0126]

[0127] Among them, C BPOM is the unit power operation and maintenance cost (yuan / kW); C EOMLet \(y\) be the operation and maintenance cost per unit capacity (yuan / kW); and \(W(t)\) be the annual charge and discharge power of the energy storage (yuan / kW·h).

[0128] The expression for the retirement treatment cost of lithium batteries over the entire life cycle is:

[0129]

[0130] Among them, \(C\) BPscr is the retirement cost per unit power (yuan / kW); \(C\) BEscr is the retirement cost per unit capacity (yuan / kWh).

[0131] The expression for the investment cost of flywheel batteries over the entire life cycle is:

[0132] \(C\) FI = \(C\) PF \(P\) FO + \(C\) CF \(C\) FO

[0133] In the formula: \(C\) PF is the unit power cost of flywheel batteries (yuan / kW); \(P\) FO is the optimized power of flywheel batteries (kW); \(C\) CF is the unit capacity cost of flywheel batteries (yuan / kWh); \(C\) FO is the optimized capacity of flywheel batteries (kWh).

[0134] Based on the above discussion, the life of flywheel batteries is infinitely long over the entire life cycle and does not need to be replaced, that is, there is no replacement cost. Since the main material of flywheel batteries is metal, there is no need for harmless treatment and no scrapping cost.

[0135] The expression for the operation and maintenance cost of flywheel batteries over the entire life cycle is:

[0136]

[0137] In the formula: \(C\) FPOM is the operation and maintenance cost per unit power (yuan / kW).

[0138] The constraints include:

[0139] Considering the power balance constraint of the hybrid energy storage system, the absorbed power of the hybrid energy storage system is equal to the reference power of the hybrid energy storage system. The expression is as follows:

[0140]

[0141] Considering that the hybrid energy storage system operates within a safe working range, and the SOC of lithium battery energy storage and flywheel energy storage should be controlled within the upper and lower limits. The expression is as follows:

[0142]

[0143] SOC min ≤SOC t ≤SOC max

[0144] Where: represents the charging power at time t, represents the discharging power at time t. At least one of them is 0 at time t; SOC min and SOC max represent the lower and upper limits of the SOC set for the energy storage element in the hybrid energy storage respectively; SOC t and SOC t-1 represent the SOC of the hybrid energy storage element at time t and (t - 1) respectively;

[0145] Considering the safety of the energy storage system and ensuring reasonable control of the heat generation, the charging and discharging powers of the lithium - battery energy storage and the flywheel energy storage are controlled within the upper and lower limits. The expression is as follows:

[0146]

[0147] Where: represent the maximum charging and discharging powers of the energy storage elements of the hybrid energy storage system respectively; P rat represents the rated charging and discharging power of the energy storage elements of the hybrid energy storage system; Δt represents the sampling interval;

[0148] Considering the rated capacity constraint of the hybrid energy storage system, the expression is as follows:

[0149]

[0150] Based on the above objective function, three energy storage schemes can be selected in this embodiment:

[0151] Scheme 1: Hybrid energy storage system

[0152] min{C LCC} = min{C BI + C BOM + C BS + C FI + C FOM}

[0153] At this time, C BI 、C BOM 、C BS 、C FI 、C FOM are all involved in the calculation.

[0154] Scheme 2: Lithium - battery energy storage

[0155] min{C LCC} = min{C BI + C BOM + C BS}

[0156] At this time, only the costs of each part of the lithium battery are involved in the calculation.

[0157] Solution 3: Flywheel energy storage.

[0158] min{C LCC} = min{C FI + C FOM}

[0159] At this time, only the costs of each part of the flywheel energy storage are involved in the calculation.

[0160] S4: Apply the chaotic glowworm swarm optimization algorithm to solve the objective function to achieve economic capacity configuration. In this embodiment, the chaotic glowworm swarm optimization algorithm is also used to optimize the hybrid energy storage capacity method. By initializing the glowworm positions with a chaotic sequence and corresponding the search process to the traversal process of the chaotic orbit, the search process can have the ability to avoid falling into local optimal solutions and finally obtain the global optimal solution or a relatively satisfactory solution. In the optimization process, the brightness and attractiveness of the glowworms determine the moving direction and distance of the glowworms. The principle is to transform this process into a process of solving and optimizing the objective function. The embodiment belongs to a strongly coupled nonlinear optimization problem and is prone to generating local optimal solutions. Therefore, an improved glowworm swarm optimization algorithm is used to solve the energy storage system capacity configuration problem. The specific steps are as Figure 5 shown.

[0161] Use the chaotic glowworm swarm optimization algorithm to solve the minimum value of the fitness function. Transform the mutual attraction and movement process between glowworm individuals into a process of solving and optimizing the objective function. The moving direction and distance of the glowworms are mainly determined by their own brightness and attractiveness. The optimization process of the glowworm swarm optimization algorithm is as follows:

[0162] (1) The relative fluorescence brightness of the glowworms is:

[0163]

[0164] In the formula: I0 represents the maximum fluorescence brightness of the glowworms, that is, the fluorescence brightness at its own position (r = 0), which is related to the initial value of the objective function; γ is the light intensity absorption factor, which represents the characteristic that the fluorescence brightness changes due to the influence of the propagation medium and can generally be set as a constant; r ij represents the spatial distance between glowworm i and glowworm j;

[0165] (2) The attractiveness of the glowworms is:

[0166]

[0167] In the formula, β0 is the maximum attraction degree, that is, the attraction degree at the position where the maximum fluorescence brightness is located.

[0168] (3) In each iteration of the attraction of firefly i to the position of firefly j, the following expression is followed:

[0169]

[0170] In the formula: X i represents the initial spatial position of the firefly. Here, α[rand - 0.5] is a random perturbation to avoid falling into local optimal values.

[0171] Furthermore, chaotic motion is adopted as the search process to avoid the firefly algorithm falling into local minima. In this embodiment, the Logistic mapping function is used to control and adjust the parameters of the firefly algorithm. The updated formula of the improved firefly is:

[0172]

[0173] γ(t) = u1γ(t - 1)[1 - γ(t - 1)]

[0174] α(t) = u2α(t - 1)[1 - α(t - 1)]

[0175] In this embodiment, the chaotic theory parameters are set as [u1, u2]. After each iteration, the Logistic mapping system will generate a set of random parameter values. The range of the three parameters in the whole iteration process is range[0, 1]. This is beneficial to jumping out of local extrema.

[0176] In the improved firefly algorithm of this embodiment, the Cartesian distance is used to calculate the distance between other fireflies and the position of the currently globally optimal firefly. The calculation formula is as follows:

[0177]

[0178] In the formula, X gbest represents the position of the global optimal value; r igbest represents the distance between the current firefly and the globally optimal firefly.

[0179] The following takes a certain power grid company as an example for detailed description:

[0180] Based on the AGC data of a certain power grid company at a certain time period and the following of the thermal power units of a certain thermal power plant to the AGC command, the power situation that the hybrid energy storage needs to meet is obtained through difference and power ratio analysis, as Figure 2 shown. Figure 3It is the power spectrum diagram of the reference power of the hybrid energy storage system after CEEMDAN decomposition, which contains a total of 16 IMF components. The first 8 high-frequency components are allocated to the flywheel energy storage, and the last 8 low-frequency components are allocated to the lithium battery energy storage. The allocation results are as Figure 4 shown.

[0181] The following three energy storage methods are selected in this implementation plan for comparison: Plan 1: Flywheel energy storage + lithium battery energy storage; Plan 2: Lithium battery energy storage; Plan 3: Flywheel energy storage; The final costs are shown in Table 1. The cost of Plan 1 is the lowest, which is 2.19e8 yuan. The configured capacity of the lithium battery energy storage is 18.54 MWh, and the capacity of the flywheel energy storage is 4.94 MWh.

[0182] Table 1 Results of optimized energy storage configuration

[0183]

[0184] The above embodiments are only used to illustrate the technical solutions of this embodiment, rather than to limit it; as is well known to those skilled in the art, without departing from the idea and scope of this embodiment, various changes or equivalent replacements can be made to these examples or features. Under the guidance of this embodiment, the examples or features can be modified to match specific scenario examples without departing from the spirit and scope of this embodiment.

Claims

1. A method for economic configuration of hybrid energy storage, characterized in that, It includes the following steps: Step S1: Obtain the difference between the original automatic generation control instruction and the power generation power of the thermal power unit following instruction. By analyzing the power ratio situation, set the compensation power interval and use it as the reference power of the hybrid energy storage system; Step S2: Decompose the reference power of the hybrid energy storage system to obtain n modal components, and distribute them to the flywheel energy storage and lithium battery energy storage according to high frequency and low frequency respectively; Step S3: Analyze the construction cost and service life affecting the flywheel energy storage and lithium battery energy storage respectively, and establish the optimization objective function and constraint conditions for the economic operation of the energy storage system; where the objective function is min{C LCC} = min{C BI + C BOM + C BS + C FI + C FOM}; Where: C LCC is the total cost of the hybrid energy storage system; C BI is the investment cost of the lithium battery, C BOM is the operation and maintenance cost of the lithium battery, C BS is the retirement cost of the lithium battery, C FI is the investment cost of the flywheel energy storage, C FOM is the operation and maintenance cost of the flywheel energy storage; The constraint conditions include: Considering the power balance constraint of the hybrid energy storage system, make the absorbed power of the hybrid energy storage system equal to the reference power of the hybrid energy storage system. The expression is as follows: Where: is the power that the hybrid energy storage system needs to meet at time t; is the power that the lithium battery needs to absorb at time t; is the power that the flywheel energy storage needs to absorb at time t; Considering that the hybrid energy storage system operates within a safe working range, and the SOC of the lithium battery energy storage and flywheel energy storage should be controlled within the upper and lower limits. The expression is as follows: SOC min ≤ SOC t ≤ SOC max In the formula: represents the charging power at time t, represents the discharging power at time t, at least one of which is 0 at time t; SOC min and SOC max respectively represent the lower limit and upper limit of the SOC set for the energy storage element in the hybrid energy storage; SOC t and SOC t-1 respectively represent the SOC of the hybrid energy storage element at time t and at time (t - 1); E rat represents the rated capacity of the hybrid energy storage system; Considering the safety of the energy storage system, ensuring reasonable control of the calorific value, and controlling the charge and discharge power of the lithium battery energy storage and flywheel energy storage within the upper and lower limits. The expression is as follows: Where: respectively represent the maximum charge and discharge power of the energy storage elements of the hybrid energy storage system; P rat represents the rated charge and discharge power of the energy storage elements of the hybrid energy storage system; Δt represents the sampling interval; Considering the rated capacity constraint of the hybrid energy storage system. The expression is as follows: Where: SOC i is the SOC of the energy storage element of the hybrid energy storage system at any time; is the power that can be absorbed by the energy storage element of the hybrid energy storage system at time t; k represents the number of Δt, which is determined according to the sampling interval and the total duration; Step S4: Under the constraint conditions of Step S3, apply the chaotic firefly algorithm to solve the objective function and obtain the economic capacity configuration.

2. The economic configuration method of a hybrid energy storage according to claim 1, wherein The specific execution process of the step S2 is as follows: The Gaussian white noise ω i [n] that satisfies the standard normal distribution and its adaptive coefficient ε i-1 The composed noise component ε0ω i [n] is added to the reference power signal x[n], and the decomposition of x[n] + ε0ω i [n] is achieved through classical mode decomposition to obtain the intrinsic mode function IMF1[n]; The result after complete ensemble empirical mode decomposition with adaptive noise is: Among them, is the initial sample of the reference power of the hybrid energy storage system corresponding to the t moment, and I represents the number of power signal samples decomposed using CEEMDAN. is the IMF component obtained after each decomposition by CEEMDAN, and R CEEMDAN,t (i) is the residual component.

3. The method for economically configuring a hybrid energy storage according to claim 1, characterized in that, In the said Step S3, Among them, C PB is the unit power cost of the lithium battery; P BO is the optimized power of the lithium battery; C CB is the unit capacity cost of the lithium battery; E BO is the optimized capacity of the lithium battery; r represents the ratio of converting the expected future income within a limited period into the present value; T LCC represents the time determined considering all links in the equipment life cycle; l is the replacement times of the equipment whose life cannot reach the full life cycle, and the energy storage is invested l + 1 times, l = T LCC / T life , where T life is the equivalent cycle life of the energy storage; T life = Bcyc × 90%, and 90% is the ideal linear aging rate of the lithium battery; B cyc = 56910 * DOD 2 - 10900 * DOD + 54530, DOD is the depth of discharge of the lithium battery, and B cyc is the cycle life of the lithium battery; Among them, C BPOM is the operation and maintenance cost per unit power; C EOM is the operation and maintenance cost per unit capacity; W(t) is the annual charge and discharge power of the energy storage; Among them, C BPscr is the retirement cost per unit power; C BEscr is the retirement cost per unit capacity; C FI = C PF P FO + C CF C FO Among them, C PF is the unit power cost of the flywheel battery; P FO is the optimized power of the flywheel battery; C CF is the unit capacity cost of the flywheel battery; C FO is the optimized capacity of the flywheel battery; C FOM = C FPOM P FO {[(1 + r) TLCC - 1]} Among them, C FPOM is the operation and maintenance cost per unit power.

4. A hybrid energy storage economic configuration system, characterized in that, It includes: A reference power calculation module for the hybrid energy storage system, which is used to obtain the difference between the original automatic generation control instruction and the power generation power of the thermal power unit following instruction. By analyzing the power ratio situation, set the compensation power interval and use it as the reference power of the hybrid energy storage system; A modal component calculation module, which decomposes the reference power of the hybrid energy storage system to obtain n modal components, and distributes them to the flywheel energy storage and lithium battery energy storage according to high frequency and low frequency respectively; An objective function and constraint condition setting module, which analyzes the construction cost and service life affecting the flywheel energy storage and lithium battery energy storage respectively, and establishes the optimization objective function and constraint conditions for the economic operation of the energy storage system; Among them, the objective function is min{C LCC} = min{C BI + C BOM + C BS + C FI + C FOM}; Where: C LCC is the total cost of the hybrid energy storage system; C BI is the investment cost of the lithium battery, C BOM is the operation and maintenance cost of the lithium battery, C BS is the retirement cost of the lithium battery, C FI is the investment cost of the flywheel energy storage, C FOM is the operation and maintenance cost of the flywheel energy storage; The constraint conditions include: Considering the power balance constraint of the hybrid energy storage system, make the absorbed power of the hybrid energy storage system equal to the reference power of the hybrid energy storage system. The expression is as follows: Where: is the power that the hybrid energy storage system needs to meet at time t; is the power that needs to be absorbed by the lithium battery at time t; is the power that needs to be absorbed by the flywheel energy storage at time t; Considering that the hybrid energy storage system operates within a safe working range, and the SOC of the lithium battery energy storage and flywheel energy storage should be controlled within the upper and lower limits. The expression is as follows: SOC min ≤ SOC t ≤ SOC max In the formula: represents the charging power at time t, represents the discharging power at time t, with at least one value being 0 at time t; SOC min and SOC max respectively represent the lower limit and upper limit of the SOC set for the energy storage elements in the hybrid energy storage; SOC t and SOC t-1 respectively represent the SOC of the hybrid energy storage elements at time t and at time (t - 1); E rat represents the rated capacity of the hybrid energy storage system; Considering the safety of the energy storage system, ensuring reasonable control of the calorific value, and controlling the charge and discharge power of the lithium battery energy storage and flywheel energy storage within the upper and lower limits. The expression is as follows: In the formula: respectively represent the maximum charge and discharge power of the energy storage elements of the hybrid energy storage system; P rat represents the rated charge and discharge power of the energy storage elements of the hybrid energy storage system; Δt represents the sampling interval; Considering the rated capacity constraint of the hybrid energy storage system. The expression is as follows: Where: SOC i is the SOC of the energy storage element of the hybrid energy storage system at any time; is the power that can be absorbed by the energy storage element of the hybrid energy storage system at time t; k represents the number of Δt, which is determined according to the sampling interval and the total duration; An economic capacity configuration module, which under the said constraint conditions, applies the chaotic firefly algorithm to solve the objective function and obtain the economic capacity configuration.

5. A hybrid energy storage economic configuration system according to claim 4, wherein The specific execution process of the modal component calculation module is as follows: Add the noise component ε0ω i [n] composed of the Gaussian white noise ω i-1 [n] that satisfies the standard normal distribution and its adaptive coefficient ε i to the reference power signal x[n], and perform decomposition on x[n] + ε0ω i [n] through classical modal decomposition to obtain the intrinsic mode function IMF1[n]; The result after complete ensemble empirical mode decomposition with adaptive noise is: Among them, is the initial sample of the reference power of the hybrid energy storage system corresponding to the t moment, I represents the number of power signal samples decomposed using CEEMDAN, is the IMF component obtained after each decomposition by CEEMDAN, R CEEMDAN,t (i) is the residual component.

6. The hybrid energy storage economic configuration system according to claim 4, wherein In the said objective function and constraint condition setting module, Among them, C PB is the unit power cost of the lithium battery; P BO is the optimized power of the lithium battery; C CB is the unit capacity cost of the lithium battery; E BO is the optimized capacity of the lithium battery; r represents the ratio of converting the expected future income within a limited period into the present value; T LCC represents the time determined considering all links in the life cycle of the equipment; l is the number of replacements of the equipment whose life cannot reach the full life cycle, and the energy storage is invested l + 1 times, l = T LCC / T life , where T life is the equivalent cycle life of the energy storage; T life = Bcyc × 90%, and 90% is the ideal linear aging rate of the lithium battery; B cyc = 56910 * DOD 2 - 10900 * DOD + 54530, where DOD is the depth of discharge of the lithium battery, and B cyc is the cycle life of the lithium battery; Among them, C BPOM is the operation and maintenance cost per unit power; C EOM is the operation and maintenance cost per unit capacity; W(t) is the annual charge and discharge power of the energy storage; Among them, C BPscr is the retirement cost per unit power; C BEscr is the retirement cost per unit capacity. C FI = C PF P FO + C CF C FO Among them, C PF is the unit power cost of the flywheel battery; P FO is the optimized power of the flywheel battery; C CF is the unit capacity cost of the flywheel battery; C FO is the optimized capacity of the flywheel battery; C FOM = C FPOM P FO {[(1 + r) TLCC - 1]} Among them, C FPOM is the operation and maintenance cost per unit power.

7. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to make the computer execute the method described in any one of claims 1 to 3.

8. A processing device, characterized in that, Comprising at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 3 by invoking the program instructions.

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