Multi-load working condition power grid side energy storage multi-temporal-spatial-scale coordinated optimization control method

Abstract

The invention relates to a multi-load working condition power grid side energy storage multi-temporal-spatial-scale coordinated optimization control method. The method comprises the following steps: step 1, establishing a multi-temporal-scale and multi-load power grid output model; 2, power grid energy storage coordinated optimization control based on a GTO algorithm is adopted, and multi-load mode power grid energy storage multi-temporal-spatial-scale coordinated optimization control is achieved; 3, performing instance analysis; the method has the advantages that the GTO algorithm is adopted, multiple spatial and temporal scales are combined, and refined and intelligent control is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system control, and specifically relates to a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions. Background Art

[0002] In modern power systems, power grids are facing an increasingly complex operating environment. In order to ensure the stability of the power grid, energy storage systems have received widespread attention as an effective solution. Therefore, in order to give full play to the role of power grid energy storage under different load conditions and achieve efficient energy management and optimization, it is extremely necessary to study the multi-time and space scale coordinated optimization control method of power grid energy storage under multiple load conditions. The prior art proposes a coordinated design of a fuzzy hybrid energy storage unified power flow controller. In the power grid, due to the continuous growth of load demand, operation planning has become very necessary, but the study lacks an optimization method for the response strategy of the dual PWM converter of a variable speed constant frequency wind turbine at different time scales. For example, the prior art proposes a PWM coordination and optimization method in a battery energy storage system, introduces the basic principles and details of PWM coordination and the corresponding optimization method, and provides a practical demonstration of the proposed method in a battery energy storage system, but it does not consider how to achieve global optimization of the PWM rectifier control response under the changing characteristics of wind power output. Therefore, it is very necessary to provide a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions that adopts a GBO algorithm, combines multiple time and space scales, and realizes refined and intelligent control. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions using a GBO algorithm and combining multiple time and space scales to achieve refined and intelligent control.

[0004] The object of the present invention is achieved by: a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions, the method comprising the following steps:

[0005] Step 1: Multi-timescale and multi-load grid output model;

[0006] Step 2: Adopt the coordinated optimization control of power grid energy storage based on the GBO algorithm to achieve multi-load mode power grid energy storage multi-temporal and multi-spatial scale coordinated optimization control;

[0007] Step 3: Example analysis.

[0008] The multi-time scale and multi-load power grid output model in step 1 comprises the following steps:

[0009] Step 1.1: Multi-time-scale grid output modeling;

[0010] Step 1.2: Constraints.

[0011] The multi-time-scale grid output modeling in step 1.1 is specifically as follows: using the DFT analysis method to perform time-frequency transformation and inverse transformation on the original wind power output, and decomposing the wind power into power components of different time scales; according to the DFT characteristics, the spectral characteristics of the wind power output are consistent with the Nyquist frequency f N Symmetric, so we only need to select f N The amplitude-frequency characteristics are studied as the spectrum characteristics of wind power output. According to the sampling theorem and the symmetry of DFT data, f N is the highest resolution frequency of the spectrum analysis result, expressed as follows: In the formula, f s is the data sampling frequency; T s is the sampling period; according to the above formula, for a certain sampling frequency, f N is fixed and corresponds to the upper frequency limit of the wind energy output spectrum characteristics.

[0012] The step 1.1 is based on a multi-time scale wind power output model, taking into account the grid connection requirements and peak load demand of the wind farm, and decomposing the wind power output into two changing components, short-term and long-term, according to the frequency of change. According to the technical requirements of the volatility of wind power grid connection, the separation frequency f is selected. D , f D The smaller the value, the larger the high-frequency fluctuation component that the energy storage needs to compensate for, and the more obvious the improvement effect on wind power output fluctuation.

[0013] The constraints in step 1.2 include: system power balance constraint, system reserve capacity limitation and battery energy storage capacity balance constraint; wherein the system power balance constraint is: Where N G N is the number of installed fans; H is the number of installed energy storage units; are the energy storage and power generation of the energy storage power station unit j at time t; P i,t Generate electricity for wind farm; W_B The combined output of wind power and battery energy storage; P L,t is the load value of the system at time t; ρ and σ are the power consumption rate of the power station and the system line loss rate respectively.

[0014] The system reserve capacity is limited to: Where P i,max is the maximum technical output power of unit i; x i,t is the operating status of unit i at time t; R H,T is the reserve capacity provided by the energy storage unit at time t; R t is the reserve capacity value required by the system at time t.

[0015] The battery energy storage capacity balance constraint is: E B,min ≤E B,n ≤E B (5), where E B,n is the amount of electricity stored in the battery during the n sampling cycles; η c , η d E is the charging and discharging efficiency of battery energy storage; B,min is the lower limit of battery energy storage capacity; P B,n is the actual output power of the battery energy storage within n sampling cycles; E B The rated capacity of the battery energy storage; It is the charging symbol; It is the discharge sign.

[0016] The grid energy storage coordinated optimization control based on the GBO algorithm in step 2 includes the following steps:

[0017] Step 2.1: Initialization: In the GBO algorithm, the population of individuals is called a "vector" and can be represented as a D-dimensional search space. And randomly generate an initial vector X in the D-dimensional search space n , D represents the number of search space dimensions; N represents the number of individuals in the population; X n,d Indicates the position of individual n in the dth dimension: X n =X min +rand(0,1)×(X max -X min )(6), where X max , X min Represents the upper and lower limits of the search space; rand is a random number in the range of [0,1];

[0018] Step 2.2: Gradient search rule GSR: The GSR rule can help the GBO algorithm solve the random behavior in the optimization process. The motion direction DM is used to create an appropriate local search trend and use the global search capability of the GBO algorithm to find the optimal or approximately optimal solution that meets the solution space conditions.

[0019] Beneficial effects of the present invention: The present invention is a multi-time and space scale coordinated optimization control method for grid-side energy storage under multi-load conditions. In use, in view of the problem that the stability of grid operation is difficult to effectively ensure under different load conditions, the present invention proposes a multi-time and space scale coordinated optimization control method for grid energy storage under multi-load conditions based on the GBO algorithm; first, a multi-time and space scale model of grid load and energy storage system is constructed; then, the objective function of multi-time and space scale multi-load grid energy storage coordinated optimization control is constructed, and the constraint conditions are determined; finally, the GBO algorithm is used to optimize the capacity configuration, charging and discharging strategy and scheduling of the energy storage system under different time and space scales, so as to realize the coordinated control of the energy storage system under multiple time and space scales; through simulation verification and case analysis, it is proved that the proposed method is effective in improving the flexibility, reliability and economy of grid operation, and the method provides a new method and solution for the multi-time and space scale coordinated optimization control of grid energy storage system; the present invention has the advantages of adopting the GBO algorithm, combining multiple time and space scales, and realizing refined and intelligent control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of the multi-time and space scale collaborative optimization control method of multi-load mode power grid energy storage based on the GBO algorithm of the present invention.

[0021] Figure 2 It is a schematic diagram of the wind energy spectrum characteristics of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023] Example 1

[0024] like Figure 1-2 As shown, a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions is provided, wherein the method comprises the following steps:

[0025] Step 1: Multi-timescale and multi-load grid output model;

[0026] In the present invention, ① multi-time-scale grid output modeling: when establishing a multi-time-scale, multi-load grid output model, the dynamic characteristics of grid resources and load changes must be considered. Taking wind power generation as an example, in order to stabilize its output changes across time periods, a variety of energy storage methods are used to decompose the wind power generation signal, and the wind power generation signal is represented by the superposition of multi-time-scale components; Discrete Fourier Transform (DFT) is a signal analysis method that transforms discrete signals from the time domain to the frequency domain. The present invention uses the DFT analysis method to perform time-frequency transformation and inverse transformation on the original wind power output, and decomposes the wind power into power components of different time scales;

[0027] For the coordinated optimization of composite energy storage, it is necessary to study the changing characteristics of wind power output at different time scales. According to the DFT characteristics, the spectral characteristics of wind power output and the Nyquist frequency f N Symmetric, so we only need to select f N The amplitude-frequency characteristics are studied as the spectrum characteristics of wind power output. According to the sampling theorem and the symmetry of DFT data, f N is the highest resolution frequency of the spectrum analysis result, expressed as follows: In the formula, f s is the data sampling frequency; T s is the sampling period; according to the above formula, for a certain sampling frequency, f N is fixed and corresponds to the upper frequency limit of the wind energy output spectrum characteristics.

[0028] Based on the multi-time scale wind power output model, the present invention considers the grid connection requirements and peak load demand of wind farms, decomposes the wind power output into short-term and long-term change components according to the change frequency, and selects the separation frequency f according to the volatility technical requirements of wind power grid connection. D , f D The smaller the value, the larger the high-frequency fluctuation component that the energy storage needs to compensate for, and the more obvious the improvement effect on wind power output fluctuation.

[0029] Based on the spectrum characteristics of wind energy output, the present invention can adopt a trial and error method to determine the minimum smoothing frequency range that meets the grid connection requirements. D A certain value is determined to determine whether the grid-connected wind power wave power after compensating the corresponding high-frequency component meets the grid-connected requirements, and then determine the high-frequency compensation frequency band that can meet the volatility requirements and ensure the minimum energy storage capacity.

[0030] ② Constraints: The optimal operation of a power system using battery energy storage and large-capacity wind farms must not only meet the constraints of conventional unit operation, but also meet the following constraints:

[0031] 1) System power balance constraints: Where N G N is the number of installed fans; H is the number of installed energy storage units; are the energy storage and power generation of the energy storage power station unit j at time t; P i,t Generate electricity for wind farm; W_B The combined output of wind power and battery energy storage; P L,t is the load value of the system at time t; ρ and σ are the power consumption rate of the power station and the system line loss rate respectively.

[0032] 2) System reserve capacity limitation: Where P i,max is the maximum technical output power of unit i; xi,t is the operating status of unit i at time t, 1 represents operation and 0 represents shutdown; R H,T is the reserve capacity provided by the energy storage unit at time t; R t is the reserve capacity value required by the system at time t.

[0033] 3)Battery energy storage capacity balance constraints: E B,min ≤E B,n ≤E B (5), where E B,n is the amount of electricity stored in the battery during the n sampling cycles; η c , η d E is the charging and discharging efficiency of battery energy storage; B,min is the lower limit of battery energy storage capacity; P B,n is the actual output power of the battery energy storage within n sampling cycles; E B The rated capacity of the battery energy storage; All are 0-1 variables. It is the charging flag, and the value 1 indicates charging; It is the discharge flag, and the value 1 indicates discharge.

[0034] Step 2: Adopt the coordinated optimization control of power grid energy storage based on the GBO algorithm to achieve multi-load mode power grid energy storage multi-temporal and multi-spatial scale coordinated optimization control;

[0035] In the present invention, the gradient optimization algorithm (GBO) is a novel meta-heuristic optimization algorithm, which is inspired by Newton's gradient descent method and uses the gradient search rule (GSR) and the local escape operator (LEO) to explore the search space. Compared with the traditional meta-heuristic optimization algorithm, the GBO algorithm is not only simple to implement and has fewer parameter settings (only the population size and the maximum number of iterations are required), but also has good exploration, development, and convergence capabilities, and can effectively avoid local optimality. The flow chart of the multi-load mode power grid energy storage multi-time and space scale collaborative optimization control method based on the GBO algorithm is as follows: Figure 1 shown.

[0036] ① Initialization: In the GBO algorithm, the individual population is called a "vector" and can be represented as a D-dimensional search space. And randomly generate an initial vector X in the D-dimensional search space n , D represents the number of search space dimensions; N represents the number of individuals in the population; X n,d Indicates the position of individual n in the dth dimension: X n =X min +rand(0,1)×(X max -X min )(6), where Xmax , X min Represents the upper and lower limits of the search space; rand is a random number in the range [0,1].

[0037] ② Gradient search rule (GSR): The GSR rule can help the GBO algorithm solve the random behavior in the optimization process, thereby promoting exploration and avoiding local optimality; the motion direction (DM) is used to create appropriate local search trends to improve the convergence speed of the GBO algorithm; the global search capability of the GBO algorithm is used to find the optimal or approximately optimal solution that meets the solution space conditions.

[0038] Step 3: Example analysis.

[0039] In summary, the present invention utilizes the GBO algorithm to optimize the capacity configuration, charging and discharging strategy and scheduling of energy storage systems at different spatiotemporal scales, thereby realizing coordinated control of energy storage systems at multiple spatiotemporal scales; the coordinated optimization control method of power grid energy storage under multiple load conditions based on the GBO algorithm has good convergence characteristics under the CEC standard function. This mechanism enables the algorithm to quickly locate favorable areas in the search space, thereby improving the efficiency of the optimization process. This method provides a new method and solution for the coordinated optimization control of power grid energy storage systems at multiple spatiotemporal scales. The method of the present invention has certain advantages in improving the flexibility, reliability and economy of power grid operation.

[0040] The present invention is a multi-time and space scale coordinated optimization control method for grid-side energy storage under multi-load conditions. In use, in view of the problem that the stability of grid operation is difficult to effectively ensure under different load conditions, the present invention proposes a multi-time and space scale coordinated optimization control method for grid energy storage under multi-load conditions based on the GBO algorithm; first, a multi-time and space scale model of grid load and energy storage system is constructed; then, the objective function of multi-time and space scale multi-load grid energy storage coordinated optimization control is constructed, and the constraint conditions are determined; finally, the GBO algorithm is used to optimize the capacity configuration, charging and discharging strategy and scheduling of the energy storage system under different time and space scales, so as to realize the coordinated control of the energy storage system under multiple time and space scales; through simulation verification and case analysis, it is proved that the proposed method is effective in improving the flexibility, reliability and economy of grid operation, and the method provides a new method and solution for the multi-time and space scale coordinated optimization control of grid energy storage system; the present invention has the advantages of adopting the GBO algorithm, combining multiple time and space scales, and realizing refined and intelligent control.

[0041] Example 2

[0042] like Figure 1-2 As shown, a multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions is provided, wherein the method comprises the following steps:

[0043] Step 1: Multi-timescale and multi-load grid output model;

[0044] Step 2: Adopt the coordinated optimization control of power grid energy storage based on the GBO algorithm to achieve multi-load mode power grid energy storage multi-temporal and multi-spatial scale coordinated optimization control;

[0045] Step 3: Example analysis.

[0046] In the present invention, ① Experimental analysis: In order to verify the effectiveness of the designed multi-load grid energy storage optimization method based on the GBO algorithm, a simulation experiment was carried out using the IEEERTS-96 system and the adjusted wind farm data; Scheme 1 uses 100MW / 80MWh battery energy storage (16MWh per minute) with an efficiency of 90%, and is paired with a 300MW / 1500MWh energy storage device (150MWh per minute, with an efficiency of 81%).

[0047] ②Analysis of experimental results: 1) Analysis of wind energy time-frequency conversion: Based on the historical output data of wind power generation, the output of wind power generation is restored to obtain the output curves of four typical days throughout the year. Taking typical day 1 as an example, the amplitude-frequency characteristics of wind power output are as follows: Figure 2 shown.

[0048] like Figure 2 As shown in the figure, the energy of wind energy is concentrated in the low-frequency variation part, and the output amplitude of the high-frequency variation part is relatively small. By limiting the grid-connected fluctuation rate, the separation frequency f can be obtained on the wind power spectrum characteristic curve. D Value, according to the inverse DFT transform and f D , the high-frequency and low-frequency components of wind power output can be obtained.

[0049] 2) Global convergence ability and efficient optimization characteristics: Based on the above experimental design, the global convergence ability and efficient optimization characteristics of the GBO algorithm are verified. In order to comprehensively evaluate the performance of the GBO algorithm, it is compared with the methods proposed in the prior art 1 (a coordinated design of a fuzzy hybrid energy storage unified power flow controller) and the prior art 2 (PWM coordination and optimization method in battery energy storage system). First, the global convergence verification is carried out. The experimental results are shown in Table 1.

[0050] Table 1 Comparison of global convergence of different methods

[0051]

[0052] The experimental results show that the GBO algorithm can stably converge to the global optimal solution in all test scenarios, while the methods in prior art 1 and prior art 2 show local optimal solutions in some scenarios, proving the superiority of the GBO algorithm in global convergence; the coordinated optimization control efficiency of the three methods is verified, and the comparison results are shown in Table 2;

[0053] Table 2 Coordinated optimization control efficiency of different methods

[0054]

[0055] Table 2 shows the time consumption of different methods for coordinated optimization control of multi-load power grid energy storage at multiple spatiotemporal scales. The analysis shows that the time consumption of the method of the present invention is significantly shorter than that of the two comparative methods. It can be seen that the multi-spatiotemporal scale power grid energy storage coordinated optimization control method proposed in the present invention is more efficient. The GBO algorithm adopts a gradient search rule to enhance the search trend and accelerate the convergence speed. This mechanism enables the algorithm to quickly locate favorable areas in the search space, thereby improving the efficiency of the optimization process. Based on the above conclusions, it can be concluded that the coordinated optimization control method of power grid energy storage under multi-load conditions based on the GBO algorithm has good convergence characteristics under the CEC standard function and has certain advantages.

[0056] In summary, this paper proposes a multi-temporal and spatial scale optimization method for multi-load grid energy storage using the GBO algorithm. This method takes into account the grid characteristics and energy storage constraints across temporal and spatial scales. Utilizing the global search and optimization functions of GBO, it achieves efficient control and formulates the optimal charging and discharging strategy. In short, this research is crucial for energy transformation and achieving the "dual carbon" goals.

[0057] The present invention is a multi-time and space scale coordinated optimization control method for grid-side energy storage under multi-load conditions. In use, in view of the problem that the stability of grid operation is difficult to effectively ensure under different load conditions, the present invention proposes a multi-time and space scale coordinated optimization control method for grid energy storage under multi-load conditions based on the GBO algorithm; first, a multi-time and space scale model of grid load and energy storage system is constructed; then, the objective function of multi-time and space scale multi-load grid energy storage coordinated optimization control is constructed, and the constraint conditions are determined; finally, the GBO algorithm is used to optimize the capacity configuration, charging and discharging strategy and scheduling of the energy storage system under different time and space scales, so as to realize the coordinated control of the energy storage system under multiple time and space scales; through simulation verification and case analysis, it is proved that the proposed method is effective in improving the flexibility, reliability and economy of grid operation, and the method provides a new method and solution for the multi-time and space scale coordinated optimization control of grid energy storage system; the present invention has the advantages of adopting the GBO algorithm, combining multiple time and space scales, and realizing refined and intelligent control.

Claims

1. A multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions, characterized by: The method comprises the following steps: Step 1: Multi-timescale and multi-load grid output model; Step 2: Adopt the coordinated optimization control of power grid energy storage based on the GBO algorithm to achieve multi-load mode power grid energy storage multi-temporal and multi-spatial scale coordinated optimization control; Step 3: Example analysis.

2. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions according to claim 1, characterized in that: The multi-time scale and multi-load power grid output model in step 1 comprises the following steps: Step 1.1: Multi-time-scale grid output modeling; Step 1.2: Constraints.

3. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions as claimed in claim 2, characterized in that: The multi-time-scale grid output modeling in step 1.1 is specifically as follows: using the DFT analysis method to perform time-frequency transformation and inverse transformation on the original wind power output, and decomposing the wind power into power components of different time scales; according to the DFT characteristics, the spectral characteristics of the wind power output are consistent with the Nyquist frequency f N Symmetric, so we only need to select f N The amplitude-frequency characteristics are studied as the spectrum characteristics of wind power output. According to the sampling theorem and the symmetry of DFT data, f N is the highest resolution frequency of the spectrum analysis result, expressed as follows: In the formula, f s is the data sampling frequency; T s is the sampling period; according to the above formula, for a certain sampling frequency, f N is fixed and corresponds to the upper frequency limit of the wind energy output spectrum characteristics.

4. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions as claimed in claim 3, characterized in that: The step 1.1 is based on a multi-time scale wind power output model, taking into account the grid connection requirements and peak load demand of the wind farm, and decomposing the wind power output into two changing components, short-term and long-term, according to the frequency of change. According to the technical requirements of the volatility of wind power grid connection, the separation frequency f is selected. D , f D The smaller the value, the larger the high-frequency fluctuation component that the energy storage needs to compensate for, and the more obvious the improvement effect on wind power output fluctuation.

5. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions as claimed in claim 2, characterized in that: The constraints in step 1.2 include: system power balance constraint, system reserve capacity limitation and battery energy storage capacity balance constraint; wherein the system power balance constraint is: Where N G N is the number of installed fans; H is the number of installed energy storage units; are the energy storage and power generation of the energy storage power station unit j at time t; P i,t Generate electricity for wind farm; W_B The combined output of wind power and battery energy storage; P L,t is the load value of the system at time t; ρ and σ are the power consumption rate of the power station and the system line loss rate respectively.

6. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions as claimed in claim 5, characterized in that: The system reserve capacity is limited to: Where P i,max is the maximum technical output power of unit i; x i,t is the operating status of unit i at time t; R H,T is the reserve capacity provided by the energy storage unit at time t; R t is the reserve capacity value required by the system at time t.

7. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions according to claim 5, characterized in that: The battery energy storage capacity balance constraint is: E B,min ≤E B,n ≤E B (5), where E B,n is the amount of electricity stored in the battery during the n sampling cycles; η c , η d E is the charging and discharging efficiency of battery energy storage; B,min is the lower limit of battery energy storage capacity; B,n is the actual output power of the battery energy storage within n sampling cycles; E B The rated capacity of the battery energy storage; It is the charging symbol; It is the discharge sign.

8. The multi-time and space scale coordinated optimization control method for grid-side energy storage under multiple load conditions as claimed in claim 1, characterized in that: The grid energy storage coordinated optimization control based on the GBO algorithm in step 2 includes the following steps: Step 2.1: Initialization: In the GBO algorithm, the population of individuals is called a "vector" and can be represented as a D-dimensional search space. And randomly generate an initial vector X in the D-dimensional search space n , D represents the number of search space dimensions; N represents the number of individuals in the population; X n,d Indicates the position of individual n in the dth dimension: X n =X min +rand(0,1)×(X max -X min )(6), where X max , X min Represents the upper and lower limits of the search space; rand is a random number in the range of [0,1]; Step 2.2: Gradient search rule GSR: The GSR rule can help the GBO algorithm solve the random behavior in the optimization process. The motion direction DM is used to create an appropriate local search trend and use the global search capability of the GBO algorithm to find the optimal or approximately optimal solution that meets the solution space conditions.

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