Energy storage locating and sizing method

Abstract

The invention provides an energy storage locating and sizing method, and belongs to the technical field of energy storage power grids. An energy storage locating and sizing method comprises the following steps: establishing a mathematical model comprising a regional power grid transformer substation, a power supply and a power transmission line; formulating a control target of the peak-valley difference rate of the total active net load of the regional power grid; constructing a target function, and determining constraint conditions required by calculation of the target function; a grey correlation analysis method and a particle swarm algorithm are adopted to iteratively calculate power grid power flow and objective function values; and outputting the energy storage configuration capacity of each transformer substation. The energy storage locating and sizing method provided by the invention has the advantages of settable objective function value, fast convergence speed, high convergence precision and output energy storage capacity spatial distribution which better accords with power grid operation characteristics, and solves the problems of slow convergence speed, high convergence precision and the like of an existing energy storage locating and sizing intelligent algorithm. An expected output result can be obtained only by adjusting the relative weight of the sub-objective function in the objective function for multiple times, and the output result does not fully consider the operation characteristics of the power grid, so that the method has relatively high engineering application value.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power grids, and in particular to a method for energy storage site selection and capacity determination. Background Art

[0002] With the advancement of my country's "carbon peak and carbon neutrality" development strategy, the proportion of wind power and photovoltaics in the power supply structure is increasing, and the total installed capacity is gradually exceeding that of thermal power to become an important power source. However, the power they generate is random, volatile, and intermittent, and the power supply reliability is weaker than that of traditional thermal power units. New energy storage occupies a small area, has a short construction period, and is flexible in operation. In conjunction with wind power and photovoltaics, it can effectively improve the output characteristics of wind power and photovoltaics. New energy storage also has the characteristics of electrical energy time and space transfer. By controlling the charging and discharging power and charging and discharging time periods according to power dispatch instructions, it can effectively reduce the peak-to-valley difference in regional power grid power load and promote the consumption of new energy. It can be seen that it is of great significance to scientifically configure energy storage capacity and spatial distribution according to the operating characteristics of the regional power grid.

[0003] Existing energy storage site selection and sizing technologies often utilize intelligent algorithms such as the particle swarm optimization algorithm and the gray wolf algorithm. These algorithms optimize voltage regulation, peak regulation, and frequency regulation, and determine the spatial distribution of energy storage capacity through iterative calculations, subject to constraints such as energy storage charge and discharge balance and grid node voltage limits. Existing multi-objective optimization intelligent algorithms for energy storage site selection and sizing control the output by adjusting the weight ratios of multiple optimization sub-objectives. This requires multiple calculations to achieve the desired effect, resulting in a relatively concentrated spatial distribution of energy storage capacity, which is inconsistent with the dispersed deployment characteristics of distributed renewable energy sources such as wind power and photovoltaics, hindering the local consumption of renewable energy. Furthermore, some optimization algorithms directly use the algebraic sum of grid node load fluctuations as optimization sub-objectives, which cannot directly reflect the fluctuations in the total load of the regional power grid. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for energy storage site selection and capacity determination based on grey correlation analysis and particle swarm optimization in response to the deficiencies of the existing technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for energy storage site selection and sizing includes: establishing a mathematical model including regional power grid substations, power sources, and transmission lines; formulating a control target for the peak-to-valley difference rate of the regional power grid's total active net load; constructing an objective function and determining the constraints required for calculating the objective function; using a grey correlation analysis method and a particle swarm algorithm to iteratively calculate the power grid flow and the objective function value; and outputting the energy storage configuration capacity of each substation.

[0006] Furthermore, the objective function is expressed as Where λ1 and λ2 are the weight coefficients of the active net load peak-to-valley difference sub-objective function f1 and the energy storage configuration capacity sub-objective function f2, respectively; The active net load peak-to-valley difference sub-objective function f1 is expressed as , , Where P ij is the net active load of substation i in period j, i ranges from [1, N], N is the number of substations; j ranges from [1, T], T is the maximum value of the study period; The total active power consumed by users with voltage levels of 110 kV and below within the power supply range of substation i in time period j; The cumulative active power generated by wind farms with voltage levels of 110 kV and below within the power supply range of substation i during time period j; The cumulative active power generated by PV power stations with a voltage level of 110 kV or below within the power supply range of substation i during time period j; is the cumulative active power of all energy storages within the power supply range of substation i in time period j, with energy storage discharge taking positive values ​​and energy storage charging taking negative values; max(P ij ) is the active net load P of substation i in the period [1,T] ij The maximum value of min(P ij ) is the active net load P of substation i in the period [1,T] ij The minimum value of D i is the peak-to-valley difference rate of active net load of substation i in the period [1, T]; The energy storage configuration capacity sub-objective function f2 is expressed as ;Where t0 is the starting period of energy storage charging / discharging; is the duration of energy storage charge / discharge in period j; n is the duration of energy storage charge / discharge the number of It is the energy storage charging / discharging cut-off period.

[0007] Furthermore, the constraints include grid power balance constraints, energy storage charge and discharge power constraints, and energy storage charge and discharge capacity balance constraints; The grid power balance constraint is expressed as Where The sum of the active power injected by the 220 kV and higher voltage grids into the 220 kV grid during time period j; The sum of the active power generated by other power sources, excluding wind farms and photovoltaic power stations with a voltage level of 110 kV and below, supplying power to the regional 220 kV and below power grid in time period j; = The total active power consumed by 220 kV power users in time period j minus the sum of the active power generated by 220 kV wind power and photovoltaic power in time period j; The energy storage charging and discharging power constraint is expressed as Where 、 are the lower limit and upper limit of the cumulative sum of charging / discharging active power of all energy storage within the power supply range of substation i during the period [1, T], respectively. The absolute value of the upper limit and lower limit of the cumulative sum of charging / discharging active power is the absolute value of the peak-to-valley difference of active power of substation i during the period [1, T]. The energy storage charge and discharge capacity balance constraint is expressed as .

[0008] Furthermore, the method for iteratively calculating the power grid flow and the objective function value includes: S4-1: Initialize the substation operation data and other parameters, randomly generate particle populations, calculate the particle objective function value, obtain the initial optimal position of the particle and the initial global optimal position, and calculate the adaptive dynamic inertia weight of each particle; S4-2: The particle updates its velocity and position based on the historical optimal position calculated in the last iteration, the global optimal position of the population, and the adaptive dynamic inertia weight; S4-3: Calculate the objective function value of the particle, the grey correlation coefficient between the particle comparison sequence and the target reference sequence, update the particle's historical optimal position and the population's global optimal position, and update the particle's adaptive dynamic inertia weight; S4-4: Determine whether the particle iteration termination condition is met. If the termination condition is met, end the iterative calculation; otherwise, return to S4-2 and iterate again.

[0009] Furthermore, in step S4-1, the number of particles in the population is M, and a single particle includes a particle position and a particle velocity. The particle position is composed of the energy storage capacity configured in N substations and the output of the energy storage in each time period [1, T]. The particle velocity is the change in the particle position calculated in the next iteration. The initial optimal position of the particle is the particle position obtained by initialization. The initial global optimal position of the particle is the position of the particle in the current population whose gray correlation coefficient between the particle comparison sequence and the target reference sequence is the largest.

[0010] Furthermore, the target reference sequence is a sequence formed by decomposing the control target into each substation according to the peak-to-valley difference rate of the net active load of each substation.

[0011] Furthermore, the particle comparison sequence is composed of the peak-to-valley difference rate of each substation in the particle after considering the energy storage output. The grey correlation coefficient Z of the particle comparison sequence and the target reference sequence is expressed as Where is the target reference sequence; is the particle comparison sequence, y ranges from [1, M], M is the number of particles in the population; ξ is the resolution coefficient, which is 0.5; s is the number of iterations; and Grey relational degree Expressed as , N is the number of substations.

[0012] Furthermore, the adaptive dynamic inertia weight is expressed as , where ω y is the adaptive dynamic inertia weight of particle y; ω min is the minimum value of inertia weight, which is 0.4; ω max is the maximum value of inertia weight, which is 0.9; F y is the objective function value of particle y; F avg is the average value of the objective function of the M particles in the current population; F min is the minimum value of the objective function among the M particles in the current population; s is the number of iterations.

[0013] Furthermore, the method of updating the particle speed and position is expressed as , ; In the formula, c1 and c2 are learning factors; r1 and r2 are random numbers that obey U(0,1); is the d-th dimension component corresponding to the i-th substation in the s-th iteration individual optimal particle, and the value range of d is [1, T×N]; is the d-th dimension component corresponding to the i-th substation in the global optimal particle of the s-th iteration; is the update speed of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y, and its value range is [0, max(P ij )-min(P ij )]; is the update speed of the d-th dimension component corresponding to the i-th substation in the s+1-th iteration particle y; is the position of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y; is the position of the d-th dimension component corresponding to substation i in the s+1th iteration particle y.

[0014] Furthermore, the regional power grid substation is a 220 kV substation, the power source includes a wind farm, photovoltaic power station and thermal power plant with a voltage level of 220 kV and below, and the transmission line is a transmission line physically connected between the substation and the power plant.

[0015] Furthermore, the method for determining whether a particle iteration termination condition is met includes: determining whether a specified number of iterations is reached.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses the peak-to-valley difference of the regional power grid's total active net load, which to some extent characterizes the output fluctuation of thermal power units, as the control target. Compared with the existing multi-objective intelligent optimization algorithm for energy storage site selection and sizing, which directly uses the algebraic sum of the peak-to-valley difference of the active net load of each substation as the optimization target, it has greater engineering significance. 2. This invention proposes to directly set the objective function value of the particle swarm algorithm according to the engineering application requirements, solving the problem that existing multi-objective intelligent optimization algorithms for energy storage site selection and capacity determination require multiple adjustments to the relative weights of sub-objective functions in the objective function to obtain the expected output results; 3. This paper proposes an adaptive dynamic inertia weight calculation method based on the grey correlation coefficient. According to the closeness between the current particle objective function value and the control objective function value, the inertia weight is dynamically adjusted to improve the convergence speed of the algorithm. 4. The present invention uses the difference between the maximum and minimum values ​​of substation load fluctuation as the boundary to constrain the particle speed update range, so that the spatial distribution of energy storage capacity in the output result has better dispersion, matching the installed capacity scale of new energy and load power characteristics of each substation, and avoiding excessive concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0018] Figure 1 : Flowchart of the implementation of the present invention; Figure 2 : Algorithm iterative calculation flow chart; Figure 3 : Schematic diagram of particle velocity and position update. DETAILED DESCRIPTION

[0019] In order to better understand the present invention, the content of the present invention is further clearly described below in conjunction with the examples and drawings, but the protection content of the present invention is not limited to the following examples. In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.

[0020] Example 1: See Figure 1-Figure 3 The purpose of this embodiment is to provide a method for energy storage site selection and capacity determination.

[0021] See also Figure 1 As shown, the volume determination method includes the following steps: S1, establish a mathematical model including regional power grid substations, power sources, and transmission lines.

[0022] The present invention is based on a 220 kV power grid. In step S1, the regional power grid substation is a 220 kV substation. The power source includes wind farms, photovoltaic power stations, thermal power plants, etc. with a voltage level of 220 kV and below. The transmission line is a transmission line physically connecting the above-mentioned substations and power plants.

[0023] S2, formulate the control target of the peak-to-valley difference rate of the total active net load of the regional power grid.

[0024] In step S2, a peak-to-valley difference rate control target for the total active net load of the regional power grid is formulated, and the control target is decomposed into each substation according to the size of the peak-to-valley difference rate of the active net load of each substation, and used as a target reference sequence for calculation in subsequent steps.

[0025] S3, construct the objective function and determine the constraints required for the objective function calculation.

[0026] In step S3, the objective function is constructed and the constraints required for the objective function calculation are determined, including the following steps: S3-1: Constructing the objective function The objective function is expressed as Where λ1 and λ2 are the weight coefficients of the active net load peak-to-valley difference sub-objective function f1 and the energy storage configuration capacity sub-objective function f2, respectively; F is the objective function.

[0027] The sub-objective function f1 of the peak-to-valley difference rate of active net load is expressed as: Where, P ij is the net active load of substation i in period j, i ranges from [1, N], N is the number of substations; j ranges from [1, T], T is the maximum value of the study period; The total active power consumed by users with voltage levels of 110 kV and below within the power supply range of substation i in time period j; The cumulative active power generated by wind farms with voltage levels of 110 kV and below within the power supply range of substation i during time period j; The cumulative active power generated by 110 kV and below PV power stations within the power supply range of substation i during time period j; is the cumulative active power of all energy storages within the power supply range of substation i in time period j, with energy storage discharge taking positive values ​​and energy storage charging taking negative values; max(P ij ) is the active net load P of substation i in the period [1,T] ij The maximum value of min(P ij ) is the active net load P of substation i in the period [1,T] ij The minimum value of D iis the peak-to-valley difference rate of the net active load of substation i in the period [1, T]; f1 ​​is the cumulative sum sub-objective function of the peak-to-valley difference rate of the net active load of N substations in the period [1, T].

[0028] The energy storage configuration capacity sub-objective function f2 is expressed as: Where t0 is the starting period of energy storage charging / discharging; is the duration of energy storage charge / discharge in period j, which can be the duration of period j; n is the duration of energy storage charge / discharge the number of is the energy storage charging / discharging cut-off period; f2 is the cumulative sub-objective function of the energy storage configuration capacity of N substations in the [1, T] period.

[0029] S3-2: Determine the constraints required for objective function calculation The constraints include grid power balance constraints, energy storage charging and discharging power constraints, and energy storage charging and discharging capacity balance constraints.

[0030] The grid power balance constraint is expressed as Where, The sum of the active power injected by the 220 kV and higher voltage grids into the 220 kV grid during time period j; The sum of the active power generated by other power sources, excluding wind farms and photovoltaic power stations with a voltage level of 110 kV and below, supplying power to the regional 220 kV and below power grid in time period j; It is the total active power consumed by 220 kV power users in time period j minus the sum of the active power generated by 220 kV wind power and photovoltaic power in time period j.

[0031] The energy storage charging and discharging power constraint is expressed as Where, 、 are the lower limit and upper limit of the cumulative sum of charging / discharging active power of all energy storages within the power supply range of substation i during the period [1, T], respectively. The absolute value of the upper limit and lower limit of the cumulative sum of charging / discharging active power is the absolute value of the peak-to-valley difference of active power of substation i during the period [1, T].

[0032] The energy storage charge and discharge capacity balance constraint is expressed as .

[0033] S4, uses grey correlation analysis and particle swarm optimization to iteratively calculate the power flow and objective function value.

[0034] like Figure 2 As shown, step S4 uses grey correlation analysis and particle swarm optimization to iteratively calculate the objective function value, specifically: S4-1. Initialize the substation's operating data and other parameters, randomly generate a population of particles of a certain size, calculate the particle objective function value, obtain the particle's initial optimal position and the initial population's global optimal position, and calculate the adaptive dynamic inertia weight of each particle. S4-2, the particle updates its speed and position based on the historical optimal position calculated in the last iteration, the global optimal position of the population, and the adaptive dynamic inertia weight; S4-3, calculate the objective function value of the particle, the grey correlation coefficient between the particle comparison sequence and the target reference sequence, update the particle's historical optimal position and the population's global optimal position, and update the particle's adaptive dynamic inertia weight; S4-4, determine whether the particle iteration termination condition is met, if the termination condition is met, end the iterative calculation, otherwise, return to S4-2 and iterate again.

[0035] In S4-1, the number of particles in the population is M. Each particle has a particle position and particle velocity. The particle position is composed of the energy storage capacity configured by N substations and the output of the energy storage in each time period [1, T]. The particle velocity is the change in the particle position calculated by the next iteration. The initial optimal position of the particle is the particle position obtained by initialization. The initial global optimal position of the particle is the position of the particle in the current population with the largest grey correlation coefficient between the particle comparison sequence and the target reference sequence. The particle comparison sequence is composed of the peak-to-valley difference rate of each substation within the particle after taking into account the energy storage output. The grey correlation coefficient between the particle comparison sequence and the target reference sequence is expressed as: Where, Z is the grey correlation coefficient; is the reference sequence; is the comparison sequence, the value range of y is [1,M]; ξ is the resolution coefficient, which is 0.5.

[0036] Where, for and The grey relational degree of .

[0037] In S4-1, the adaptive dynamic inertia weight of each particle is calculated, which is specifically expressed as: Where, ω y is the adaptive dynamic inertia weight of particle y; ω min is the minimum value of inertia weight, which is 0.4; ω max is the maximum value of inertia weight, which is 0.9; F y is the objective function value of particle y; F avg is the average value of the objective function of the M particles in the current population; Fmin is the minimum value of the objective function among the M particles in the current population; s is the number of iterations.

[0038] like Figure 3 As shown, the speed and position of the particle are updated in S4-2, which is specifically expressed as: Where c1 and c2 are learning factors; r1 and r2 are random numbers that obey U(0,1); is the d-th dimension component corresponding to the i-th substation in the s-th iteration individual optimal particle, and the value range of d is [1, T×N]; is the d-th dimension component corresponding to the i-th substation in the global optimal particle of the s-th iteration; is the update speed of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y, and its value range is [0, max(P ij )-min(P ij )]; is the update speed of the d-th dimension component corresponding to the i-th substation in the s+1-th iteration particle y; is the position of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y; is the position of the d-th dimension component corresponding to substation i in the s+1th iteration particle y.

[0039] In S4-4, the termination condition of the algorithm iterative calculation is reached when the specified number of iterations is reached.

[0040] S5. Output the energy storage configuration capacity of each substation.

[0041] According to the calculation result of step S4, the energy storage configuration capacity of each substation is output.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for selecting a site and determining the capacity of energy storage, characterized in that: include: Build a mathematical model of the regional power grid including substations, power sources and transmission lines; Formulate control targets for the peak-to-valley difference rate of the regional power grid's total active net load; Construct the objective function and determine the constraints required for the objective function calculation; Grey correlation analysis and particle swarm optimization are used to iteratively calculate power flow and objective function values. Output the energy storage configuration capacity of each substation.

2. The method for selecting a site and determining the capacity of energy storage according to claim 1, wherein: The objective function is expressed as Where λ1 and λ2 are the weight coefficients of the active net load peak-to-valley difference sub-objective function f1 and the energy storage configuration capacity sub-objective function f2, respectively; The active net load peak-to-valley difference sub-objective function f1 is expressed as , , Where P ij is the net active load of substation i in period j, i ranges from [1, N], N is the number of substations; j ranges from [1, T], T is the maximum value of the study period; The total active power consumed by users with voltage levels of 110 kV and below within the power supply range of substation i in time period j; The cumulative active power generated by wind farms with voltage levels of 110 kV and below within the power supply range of substation i during time period j; The cumulative active power generated by PV power stations with a voltage level of 110 kV or below within the power supply range of substation i during time period j; is the cumulative active power of all energy storages within the power supply range of substation i in time period j, with energy storage discharge taking positive values ​​and energy storage charging taking negative values; max(P ij ) is the active net load P of substation i in the period [1,T] ij The maximum value of min(P ij ) is the active net load P of substation i in the period [1,T] ij The minimum value of D i is the peak-to-valley difference rate of active net load of substation i in the period [1, T]; The energy storage configuration capacity sub-objective function f2 is expressed as ;Where t0 is the starting period of energy storage charging / discharging; is the charging / discharging duration of the energy storage in time period j; n is the energy storage charge / discharge duration the number of It is the energy storage charging / discharging cut-off period.

3. The method for selecting a site and determining the capacity of energy storage according to claim 2, wherein: The constraints include grid power balance constraints, energy storage charge and discharge power constraints, and energy storage charge and discharge capacity balance constraints; The grid power balance constraint is expressed as Where The sum of the active power injected by the 220 kV and higher voltage grids into the 220 kV grid during time period j; The sum of the active power generated by other power sources, excluding wind farms and photovoltaic power stations with a voltage level of 110 kV and below, supplying power to the regional 220 kV and below power grid in time period j; = The total active power consumed by 220 kV power users in time period j minus the sum of the active power generated by 220 kV wind power and photovoltaic power in time period j; The energy storage charging and discharging power constraint is expressed as Where 、 are the lower limit and upper limit of the cumulative sum of charging / discharging active power of all energy storage within the power supply range of substation i during the period [1, T], respectively. The absolute value of the upper limit and lower limit of the cumulative sum of charging / discharging active power is the absolute value of the peak-to-valley difference of active power of substation i during the period [1, T]. The energy storage charge and discharge capacity balance constraint is expressed as .

4. The method for selecting a site and determining the capacity of energy storage according to claim 1, wherein: Methods for iteratively calculating power flow and objective function values ​​include: S4-1: Initialize the substation operation data and other parameters, randomly generate particle populations, calculate the particle objective function value, obtain the initial optimal position of the particle and the initial global optimal position, and calculate the adaptive dynamic inertia weight of each particle; S4-2: The particle updates its velocity and position based on the historical optimal position calculated in the last iteration, the global optimal position of the population, and the adaptive dynamic inertia weight; S4-3: Calculate the objective function value of the particle, the grey correlation coefficient between the particle comparison sequence and the target reference sequence, update the particle's historical optimal position and the population's global optimal position, and update the particle's adaptive dynamic inertia weight; S4-4: Determine whether the particle iteration termination condition is met. If the termination condition is met, end the iterative calculation; otherwise, return to S4-2 and iterate again.

5. The method for selecting a site and determining the capacity of energy storage according to claim 4, characterized in that: In step S4-1, the number of particles in the population is M. A single particle includes a particle position and a particle velocity. The particle position is composed of the energy storage capacity configured by N substations and the output of the energy storage in each period [1, T]. The particle velocity is the change in the particle position calculated by the next iteration. The initial optimal position of the particle is the particle position obtained by initialization. The initial global optimal position of the particle is the position of the particle in the current population whose gray correlation coefficient between the particle comparison sequence and the target reference sequence is the largest.

6. The method for selecting a site and determining the capacity of energy storage according to claim 5, characterized in that: The target reference sequence is a sequence formed after decomposing the control target into each substation according to the peak-to-valley difference rate of the net active load of each substation.

7. The method for selecting a site and determining the capacity of energy storage according to claim 5, characterized in that: The particle comparison sequence is composed of the peak-to-valley difference rate of each substation in the particle after considering the energy storage output. The grey correlation coefficient Z between the particle comparison sequence and the target reference sequence is expressed as Where is the target reference sequence; is the particle comparison sequence, y ranges from [1, M], M is the number of particles in the population; ξ is the resolution coefficient, which is 0.5; s is the number of iterations; and Grey relational degree Expressed as , N is the number of substations.

8. The method for selecting a site and determining the capacity of energy storage according to claim 5, characterized in that: The adaptive dynamic inertia weight is expressed as , where ω y is the adaptive dynamic inertia weight of particle y; ω min is the minimum value of inertia weight, which is 0.4; ω max is the maximum value of inertia weight, which is 0.9; F y is the objective function value of particle y; F avg is the average value of the objective function of the M particles in the current population; F min is the minimum value of the objective function among the M particles in the current population; s is the number of iterations.

9. The method for selecting a site and determining the capacity of energy storage according to claim 5, characterized in that: The method for updating the particle's velocity and position is expressed as , ; In the formula, c1 and c2 are learning factors; r1 and r2 are random numbers that obey U(0,1); is the d-th dimension component corresponding to the i-th substation in the s-th iteration individual optimal particle, and the value range of d is [1, T×N]; is the d-th dimension component corresponding to the i-th substation in the global optimal particle of the s-th iteration; is the update speed of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y, and its value range is [0, max(P ij )-min(P ij )]; is the update speed of the d-th dimension component corresponding to the i-th substation in the s+1-th iteration particle y; is the position of the d-th dimension component corresponding to the i-th substation in the s-th iteration particle y; is the position of the d-th dimension component corresponding to substation i in the s+1th iteration particle y.

10. The method for selecting a site and determining the capacity of energy storage according to claim 1, wherein: The regional power grid substation is a 220 kV substation, the power source includes a wind farm, photovoltaic power station and thermal power plant with a voltage level of 220 kV and below, and the transmission line is a transmission line physically connected between the substation and the power plant.

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