Adjustable resource ratio and energy storage distribution optimization method considering new energy bearing capacity

Abstract

The invention discloses a new energy bearing capacity-considered adjustable resource ratio and energy storage distribution point optimization method, which comprises the following steps of: establishing a wind power generation model, a photovoltaic power generation model and a storage battery model, and calculating the optimal energy storage system capacity, namely the storage battery capacity; taking the distribution proportion of wind power and photoelectricity in the optimal energy storage system capacity state as a constraint condition, taking the maximum value of the annual total generating capacity of wind power and photovoltaic energy in each region in the power grid as a target function, establishing a wind and light storage capacity optimal proportion model, and solving to obtain the optimal proportion of each distributed resource capacity; and determining the optimal layout position of the energy storage system in the power grid, establishing an energy storage stationing optimization model by taking the maximum energy storage utilization rate and the minimum energy storage construction and maintenance cost as objective functions, and solving to obtain the optimal energy storage stationing position. According to the method, the bearing capacity of the power grid to renewable resources is improved, the adaptability and flexibility are high, and system configuration can be dynamically adjusted according to resource conditions and load requirements of different regions.

Description

Technical Field

[0001] The present invention relates to an optimization method, and in particular to an adjustable resource allocation and energy storage point optimization method taking into account the new energy carrying capacity. Background Art

[0002] In the process of promoting sustainable development and reducing dependence on traditional energy, the use of renewable energy has received increasing attention. Wind and solar energy, as two major adjustable resources, have huge application potential. Therefore, the use of wind-solar-storage power generation is a very suitable solution, but whether the capacity ratio of each link in the system is reasonable is directly related to the reliability and economy of the entire power grid. Reasonable capacity ratio can reduce dependence on traditional energy, improve the utilization rate of renewable energy, and ensure the power supply quality and stability of the power grid.

[0003] In addition, based on the optimal allocation of distributed adjustable resources, energy storage equipment, as an important component of adjustable resources, has the characteristics of being able to absorb or release energy, provide power support during peak periods, and store energy during low periods, thereby achieving balanced regulation of the grid load. Therefore, through reasonable energy storage distribution points, multiple factors such as load demand, resource potential, operating costs, system stability, and power quality can be comprehensively considered to improve the new energy carrying capacity.

[0004] At present, traditional optimal allocation methods for distributed resources often ignore the impact of new energy access on the power grid, and lack designs for dynamic and harmonious coordinated optimization between distributed resources and energy storage systems.

[0005] At the same time, existing energy storage deployment optimization methods are usually based on a single load forecast or historical data, ignoring the real-time fluctuation characteristics of new energy and the flexibility of distributed resources. Therefore, the configuration and deployment of energy storage systems cannot accurately match the load demand of each region with the characteristics of new energy generation. Summary of the invention

[0006] In order to solve the shortcomings of the above-mentioned technology, the present invention provides an adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: an adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity, comprising the following steps:

[0008] S1. Establish wind power generation model, photovoltaic power generation model and battery model, and calculate the optimal energy storage system capacity, i.e., battery capacity;

[0009] S2. Taking the distribution ratio of wind power and photovoltaic power under the optimal energy storage system capacity as a constraint condition, and taking the maximum annual total power generation of wind power and photovoltaic energy in each region of the power grid as the objective function, an optimal ratio model of wind, photovoltaic and storage capacity is established to solve the optimal ratio of each distributed resource capacity;

[0010] S3. Determine the optimal layout location of the energy storage system in the power grid based on load demand, new energy output and energy storage response capability. Take maximizing energy storage utilization and minimizing energy storage construction and maintenance costs as the objective function, establish an energy storage layout optimization model, and solve for the optimal energy storage layout location.

[0011] As a further implementation of this plan:

[0012] In S1, the wind power generation model, photovoltaic power generation model and battery model are established to quantitatively evaluate the energy balance of the wind-solar-energy storage system;

[0013] The optimal energy storage system capacity is calculated by taking into account weather conditions, renewable energy generation efficiency and load demand factors.

[0014] As a further implementation of this plan:

[0015] The wind power generation model takes the vertical axis wind turbine as the research object, and the output power P of the wind turbine is Wt Indicates that the expression is:

[0016]

[0017] In the formula, v ci is the approximate wind speed; v t is the rated wind speed; v r is the optimal operating wind speed; v co is the cut-out wind speed; P r is the rated output power; A, B, C are the proportional gains of the wind turbine equipment.

[0018] As a further implementation of this plan:

[0019] The process of establishing the photovoltaic power generation model is as follows:

[0020] According to the total solar radiation H on the horizontal surface and the horizontal scattered amount H d , get the solar radiation on different inclined surfaces, that is:

[0021]

[0022] Where, T is the monthly average temperature; α is the tilt angle of the photovoltaic panel; ρ is the reflection coefficient of the object being measured; R b is the direct reflectivity ratio of the inclined surface to the horizontal surface, and the expression is:

[0023]

[0024] Where φ is the local latitude, δ is the solar declination angle; ω s and ω0 are the sunset hour angles on the inclined plane and the horizontal plane respectively;

[0025] in:

[0026]

[0027] ω0=arccos(-tanφtanδ)

[0028] ω s =min[ω0,arccos(-tan(φ-α)tanδ)]

[0029] Where, χ is any χ day in a year;

[0030] Photovoltaic power generation E s The expression is:

[0031]

[0032] Where P is the rated power of photovoltaic power generation.

[0033] As a further implementation of this plan:

[0034] The process of establishing the battery model is as follows:

[0035] After selecting the wind turbine and PV modules, the monthly power generation curves are calculated as f1 and f2 respectively;

[0036] The sum of the two parameters f1 and f2 is the monthly power curve of the power generation system. r Make comparisons;

[0037] When f3 is in f r When it is at the bottom, it means that the battery pack is needed to provide power; when f3 <f r When , it means that there must be a battery, a separate integral interval, the larger the absolute value of the integral, the smaller the battery capacity of that month;

[0038] Divide a year into different time periods, each time period corresponds to an integral interval, then the optimal battery capacity W b Under this condition, the distribution ratio of wind power and photovoltaic power is:

[0039]

[0040] Where DOD is the maximum depth of discharge.

[0041] As a further implementation of this plan:

[0042] In S2, the expression of the constructed objective function is:

[0043]

[0044] Where n is the partition number; N is the total number of partitions; d is the optimal number of days; D is the total number of days in a year; t is the optimal time period of each day; ψ is the total number of optimal cycles per day; Δt is the length of the optimal cycle of a unit; is the wind power generation power at the optimal day d at time t in zone n; is the photovoltaic power generation power in the optimal day d and optimal period t in zone n; is the reference load of the entire power grid.

[0045] As a further implementation of this plan:

[0046] In S2, the constraint conditions include: reference load matching constraint, daily load matching deviation value constraint, renewable energy output constraint, renewable energy and chemical energy storage installed capacity constraint, renewable energy curtailment constraint, renewable energy power generation ratio constraint, chemical energy storage charging and discharging power constraint, and chemical energy storage energy constraint.

[0047] As a further implementation of this plan:

[0048] The settings of each constraint are as follows:

[0049] 1) The reference load matching constraint expression is:

[0050]

[0051] Where: is the discharge power of chemical energy storage; Charging power for chemical energy storage; is the positive deviation of the reference load match; is the negative deviation of the reference load matching;

[0052] 2) The constraint expression of daily load matching deviation value is:

[0053]

[0054] In the formula, ε is the daily load matching deviation value, that is, within the optimal period, the matching deviation value of the output power is no more than 0.5% of the target power;

[0055] 3) The expression of renewable energy output constraint is:

[0056]

[0057] In the formula, is the optimal installed capacity of wind power generation in area n; is the optimal installed capacity of photovoltaic power generation in area n; Standardize output sequences for wind power generation; Standardize output sequences for photovoltaic power generation;

[0058] 4) The installed capacity constraints of renewable energy and chemical energy storage are:

[0059]

[0060] In the formula, is the minimum installed wind power capacity in area n; is the maximum installed wind power capacity in area n; is the minimum photovoltaic installed capacity in zone n; is the maximum installed photovoltaic capacity in zone n; is the optimal installed capacity of chemical energy storage in zone n; is the minimum installed capacity of chemical energy storage in zone n; is the maximum installed capacity of chemical energy storage in zone n;

[0061] 5) The constraints for renewable energy curtailment are:

[0062]

[0063] In the formula, θ is the maximum amount of renewable energy abandoned electricity;

[0064] 6) The constraints on the proportion of renewable energy generation are:

[0065]

[0066] In the formula, λ q The minimum share of renewable energy generation in the total load;

[0067] 7) The chemical energy storage charging and discharging power constraints are:

[0068]

[0069] Where: and They are the discharge state variable and charge state variable of chemical energy storage respectively.

[0070] In addition, since the chemical energy storage in the area cannot be fully utilized in one charge and discharge process, the constraints are set as follows:

[0071]

[0072] In the formula, is the chemical storage energy at time t; is the chemical energy storage capacity at t-1; γ B is the self-release rate of chemical energy storage; η ch is the charging efficiency; η dc is the discharge efficiency.

[0073] 8) Energy constraints of chemical energy storage

[0074] The energy constraint of chemical energy storage is:

[0075]

[0076] In the formula is the maximum energy of chemical energy storage, α B is the minimum energy coefficient of chemical energy storage.

[0077] As a further implementation of this plan:

[0078] After the energy storage installed capacity is determined, step S3 is performed. The objective function constructed in S3 is shown in the following formula:

[0079]

[0080] In the formula, C B,j is the construction cost of energy storage system j; C M,j is the annual maintenance cost of energy storage system j, P dc (t) and P ch (t) is the discharge and charging power of the energy storage system at time t, and α and β are the energy storage response efficiency coefficients.

[0081] As a further implementation of this plan:

[0082] The constraints of the energy storage optimization layout model are set as follows:

[0083] 1) Grid load balance constraints

[0084] L t +P ch (t)-P dc (t) = P grid (t)

[0085] Where, L t is the load demand in time period t; P grid (t) Electricity obtained from outside the grid;

[0086] 2) Energy storage power constraints

[0087] 0≤P ch (t)≤C j

[0088] 0≤Pdc (t)≤C j

[0089] In the formula, C j is the maximum response capability of energy storage system j;

[0090] 3) Energy storage capacity constraints

[0091]

[0092] In the formula, E j (t) is the energy storage state of energy storage system j at time t, and The minimum and maximum capacity limits of the energy storage system.

[0093] 4) Energy storage deployment decision variables

[0094] x j ∈{0,1}

[0095] Among them, 1 means that the energy storage system is deployed at position j, and 0 means that it is not deployed.

[0096] The present invention discloses an adjustable resource ratio and energy storage point optimization method considering the carrying capacity of new energy. By accurately establishing wind power, photovoltaic power generation and energy storage models, and comprehensively considering factors such as weather and load demand, the optimal ratio model of wind, solar and energy storage capacity is obtained, and the energy balance and energy storage capacity configuration of the wind, solar and energy storage system are optimized, and the utilization efficiency of renewable energy is maximized. According to the optimal energy storage capacity configuration, the proportion of wind, solar and energy storage resources is reasonably designed to ensure the stable operation of the power grid. In addition, by establishing an energy storage point optimization model, the layout position of the energy storage system is accurately determined, and the energy storage utilization rate is maximized and the cost is minimized, providing an efficient and economical distributed adjustable resource management solution for the power grid. This method not only improves the carrying capacity of the power grid for renewable resources and promotes green and low-carbon development, but also has strong adaptability and flexibility. It can dynamically adjust the system configuration according to the resource conditions and load demands of different regions, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is the overall technical roadmap of the present invention. DETAILED DESCRIPTION

[0098] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0099] The present invention firstly proposes a distributed adjustable resource optimal matching method taking into account the energy storage capacity limitation. Through intelligent matching, the optimal matching between distributed energy, energy storage and load demand is achieved, thereby improving the overall operating efficiency and economy of the system.

[0100] Then, based on the fluctuation characteristics of renewable energy power generation and changes in load demand, a dynamic optimization method for energy storage layout is designed. By comprehensively considering load forecasting, renewable energy output forecasting, and the responsiveness of the energy storage system, scientific layout of energy storage resources is achieved to avoid resource waste, while improving the stability of the power grid and energy utilization.

[0101] like Figure 1 As shown, the adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity proposed in the present invention has the following overall technical route:

[0102] S1. Establish wind power generation model, photovoltaic power generation model and battery model, quantitatively evaluate the energy balance of wind-solar-energy storage system, comprehensively consider factors such as weather conditions, renewable energy generation efficiency and load demand, and calculate the optimal energy storage system capacity (battery capacity).

[0103] S2. Taking the distribution ratio of wind power and photovoltaic power under the optimal energy storage system capacity state as a constraint condition, constructing the maximum annual total power generation of wind power and photovoltaic energy in each region of the power grid as the objective function, establishing an optimal ratio model of wind, photovoltaic and storage capacity, and solving the optimal ratio of each distributed resource capacity.

[0104] S3. Establish an energy storage point optimization model to determine the optimal layout location of the energy storage system in the power grid based on load demand, new energy output and energy storage response capability. Take maximizing energy storage utilization and minimizing energy storage construction and maintenance costs as the objective function, establish an energy storage point optimization model, and solve for the optimal energy storage point location.

[0105] The steps are as follows:

[0106] The first step is step 1: by establishing wind power generation model, photovoltaic power generation model and battery model, the energy balance of wind, solar and energy storage system is quantitatively evaluated. These models take into account factors such as weather conditions, renewable energy generation efficiency and load demand, and calculate the optimal battery capacity.

[0107] For the wind power generation model, the vertical axis wind turbine is selected as the research object. The output power P of the wind turbine is Wt With wind speed v t Related. The expression is:

[0108]

[0109] In the formula, v ci is the approximate wind speed; v t is the rated wind speed; v r is the optimal operating wind speed; v co is the cut-out wind speed; P ris the rated output power; A, B, C are the proportional gains of the wind turbine equipment.

[0110] The role of the photovoltaic power generation model is to predict and optimize the power output of the photovoltaic power generation system and provide a reference for the ratio of system capacity. In order to obtain the solar radiation, it is necessary to know the local solar radiation, geographical and meteorological data. The data obtained from the meteorological station generally only include the total solar radiation H on the horizontal surface, the horizontal scattered radiation H d According to these two data, the amount of solar radiation on different inclined surfaces can be obtained, namely:

[0111]

[0112] Where, T is the monthly average temperature; α is the tilt angle of the photovoltaic panel; ρ is the reflection coefficient of the object being measured; R b is the direct reflectivity ratio of the inclined surface to the horizontal surface, and the expression is:

[0113]

[0114] Where φ is the local latitude, δ is the solar declination angle; ω s and ω0 are the sunset hour angles on the inclined plane and the horizontal plane respectively. Among them:

[0115]

[0116] ω0=arccos(-tanφtanδ)

[0117] ω s =min[ω0, arcos(-tan(φ-α)tanδ)]

[0118] Where x is any x day in a year.

[0119] Photovoltaic power generation E s The expression is:

[0120]

[0121] Where P is the rated power of photovoltaic power generation.

[0122] The optimal capacity of the battery bank is related to the load demand. When a certain type of wind turbine and photovoltaic module is selected, the number is calculated and the monthly power generation curve is calculated based on the local weather data as f1 and f2. The sum of these two parameters f3 is the monthly power generation curve of the power generation system, which is then compared with the load power requirement curve f r For comparison. When f3 is at f r At the bottom, the battery pack is needed to provide power; when f3 <f rWhen the battery is used, there must be a separate integral interval. The larger the absolute value of the integral, the smaller the minimum capacity of the battery pack in that month. b .

[0123] Assuming that a year is divided into different time periods, each time period corresponds to an integral interval, then the optimal battery capacity W b Under this condition, the distribution ratio of wind power and photovoltaic power is:

[0124] W b =max[∫(f r -f3)dx] / E s DOD

[0125] Where DOD is the maximum depth of discharge.

[0126] Then step 2: Set the optimal battery capacity W b The distribution ratio of wind power and photovoltaic power under the state of crop constraints, with the maximum annual total power generation of wind power and photovoltaic energy in each region of the power grid as the objective function, the expression of the constructed model is:

[0127]

[0128] Where n is the partition number; N is the total number of partitions; d is the optimal number of days; D is the total number of days in a year; t is the optimal time period of each day; ψ is the total number of optimal cycles per day; Δt is the length of the optimal cycle of a unit; is the wind power generation power at the optimal day d at time t in zone n; is the photovoltaic power generation power in the optimal day d and optimal period t in zone n; is the reference load of the entire power grid.

[0129] The constraints are set up as follows:

[0130] 1) Reference load matching constraints

[0131] By accurately calculating the battery capacity, the discharge and charging capabilities of the energy storage system can be optimized to meet the matching requirements of the reference load, thereby achieving a better balance between load and supply. The reference load expression of the entire power grid is:

[0132]

[0133] Where: is the discharge power of chemical energy storage; Charging power for chemical energy storage; is the positive deviation of the reference load match; is the negative deviation of the reference load match.

[0134] 2) Daily load matching deviation value constraints

[0135] It is required that within the optimal period, the matching deviation value of the output power is not greater than 0.5% of the target power. The constraint condition expression of the daily load matching deviation value is:

[0136]

[0137] In the formula, ε is the daily load matching deviation value, that is, in the optimal period, the matching deviation value of the output power is no more than 0.5% of the target power, so that the total output power and load variation characteristics of renewable energy in different regions are consistent.

[0138] 3) Renewable energy output constraints

[0139] The renewable energy output constraint expression is:

[0140]

[0141] In the formula is the optimal installed capacity of wind power generation in area n; is the optimal installed capacity of photovoltaic power generation in area n; Standardize output sequences for wind power generation; Standardized output sequence for photovoltaic power generation.

[0142] 4) Constraints on installed capacity of renewable energy and chemical energy storage

[0143] The installed capacity constraints of renewable energy and chemical energy storage are:

[0144]

[0145] In the formula, is the minimum installed wind power capacity in area n; is the maximum installed wind power capacity in area n; is the minimum photovoltaic installed capacity in zone n; is the maximum installed photovoltaic capacity in zone n; is the optimal installed capacity of chemical energy storage in zone n; is the minimum installed capacity of chemical energy storage in zone n; The maximum installed capacity of chemical energy storage in zone n

[0146] 5) Constraints on the amount of renewable energy curtailment

[0147] The constraints for renewable energy curtailment are:

[0148]

[0149] Where θ is the maximum amount of renewable energy curtailment.

[0150] 6) Constraints on the proportion of renewable energy generation

[0151] Constraints on the proportion of renewable energy generation:

[0152]

[0153] In the formula, λ q The minimum proportion of renewable energy generation in the total load.

[0154] 7) Chemical energy storage charging and discharging power constraints

[0155] The chemical energy storage charging and discharging power constraints are:

[0156]

[0157] Where: and They are the discharge state variable and charge state variable of chemical energy storage respectively.

[0158] In addition, since the chemical energy storage in the area cannot be fully utilized in one charge and discharge process, the constraints are set as follows:

[0159]

[0160] In the formula, is the chemical storage energy at time t; is the chemical energy storage capacity at t-1; γ B is the self-release rate of chemical energy storage; η ch is the charging efficiency; η dc is the discharge efficiency.

[0161] 8) Energy constraints of chemical energy storage

[0162] The energy constraint of chemical energy storage is:

[0163]

[0164] In the formula is the maximum energy of chemical energy storage, α B is the minimum energy coefficient of chemical energy storage.

[0165] According to the above objective function and constraints, an optimal ratio model of wind, solar and energy storage capacity including photovoltaic, wind power and energy storage installed capacity is established. By solving the model, the optimal wind power installed capacity, photovoltaic power generation installed capacity, chemical energy storage installed capacity and energy storage installed capacity can be obtained to achieve the optimal ratio of wind, solar and energy storage.

[0166] After determining the installed capacity of energy storage, proceed to step 3 to establish an energy storage point optimization model. According to load demand, new energy output and energy storage response capability, the optimal layout location of the energy storage system in the power grid is determined. With maximizing energy storage utilization and minimizing energy storage construction and maintenance costs as the objective function, an energy storage point optimization model is established to solve the optimal energy storage point location.

[0167] The specific process of step 3 is as follows:

[0168] 3.1 The goal of this part is to optimize the location of the energy storage system based on load demand, new energy output and energy storage response capability. The objective function is to maximize the utilization of energy storage and minimize the construction and maintenance costs, as shown in the following formula:

[0169]

[0170] In the formula, C B,j is the construction cost of energy storage system j; C M,j is the annual maintenance cost of energy storage system j, P dc (t) and P ch (t) is the discharge and charging power of the energy storage system at time t, and α and β are the energy storage response efficiency coefficients.

[0171] 3.2 Set the constraints of the energy storage optimization model:

[0172] 1) Grid load balance constraints

[0173] L t +P ch (t)-P dc (t) = P grid (t)

[0174] Where, L t is the load demand in time period t; P grid (t) is the electricity obtained from the outside by the grid.

[0175] 2) Energy storage power constraints

[0176] 0≤P ch (t)≤C j

[0177] 0≤P dc (t)≤C j

[0178] Where: C j is the maximum response capability of energy storage system j.

[0179] 3) Energy storage capacity constraints

[0180]

[0181] In the formula, E j (t) is the energy storage state of energy storage system j at time t, and The minimum and maximum capacity limits of the energy storage system.

[0182] 4) Energy storage deployment decision variables

[0183] x j ∈{0,1}

[0184] Among them, 1 means that the energy storage system is deployed at position j, and 0 means that it is not deployed.

[0185] 3.3 Obtain the optimal energy storage location by solving the energy storage system optimization layout model.

[0186] Therefore, the method proposed in the present invention establishes an optimal ratio model of wind, photovoltaic and energy storage capacity by accurately modeling wind, photovoltaic power generation and energy storage systems, comprehensively considering factors such as weather and load demand, and optimizes the energy balance and energy storage capacity configuration of the wind, photovoltaic and energy storage system, maximizes the utilization efficiency of renewable energy, and reasonably designs the ratio of wind, photovoltaic and energy storage resources according to the optimal energy storage capacity configuration, ensuring the stable operation of the power grid. In addition, by establishing an energy storage point optimization model, the layout location of the energy storage system is accurately determined, the energy storage utilization rate is maximized and the cost is minimized, providing an efficient and economical distributed adjustable resource management solution for the power grid.

[0187] This method not only improves the grid's carrying capacity for renewable resources and promotes green and low-carbon development, but also has strong adaptability and flexibility. It can dynamically adjust the system configuration according to the resource conditions and load demands of different regions, and has broad application prospects.

[0188] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing adjustable resource ratio and energy storage point distribution considering the carrying capacity of new energy, characterized by: The following steps are involved: S1. Establish wind power generation model, photovoltaic power generation model and battery model, and calculate the optimal energy storage system capacity, i.e., battery capacity; S2. Taking the distribution ratio of wind power and photovoltaic power under the optimal energy storage system capacity as a constraint condition, and taking the maximum annual total power generation of wind power and photovoltaic energy in each region of the power grid as the objective function, an optimal ratio model of wind, photovoltaic and storage capacity is established to solve the optimal ratio of each distributed resource capacity; S3. Determine the optimal layout location of the energy storage system in the power grid based on load demand, new energy output and energy storage response capability. Take maximizing energy storage utilization and minimizing energy storage construction and maintenance costs as the objective function, establish an energy storage layout optimization model, and solve for the optimal energy storage layout location.

2. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 1 is characterized in that: In S1, the wind power generation model, photovoltaic power generation model and battery model are established to quantitatively evaluate the energy balance of the wind-solar-energy storage system; The optimal energy storage system capacity is calculated by taking into account weather conditions, renewable energy generation efficiency and load demand factors.

3. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 2 is characterized by: The wind power generation model takes the vertical axis wind turbine as the research object, and the output power P of the wind turbine is Wt Indicates that the expression is: In the formula, v ci is the approximate wind speed; v t is the rated wind speed; v r is the optimal operating wind speed; v co is the cut-out wind speed; P r is the rated output power; A, B, C are the proportional gains of the wind turbine equipment.

4. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 3 is characterized by: The process of establishing the photovoltaic power generation model is as follows: According to the total solar radiation H on the horizontal surface and the horizontal scattered amount H d , get the solar radiation on different inclined surfaces, that is: Where, T is the monthly average temperature; α is the tilt angle of the photovoltaic panel; ρ is the reflection coefficient of the object being measured; R b is the direct reflectivity ratio of the inclined surface to the horizontal surface, and the expression is: Where φ is the local latitude, δ is the solar declination angle; ω s and ω0 are the sunset hour angles on the inclined plane and the horizontal plane respectively; in: ω0=arccos(-tanφtanδ) oh s =min[ω0,arccos(-tan(φ-α)tanδ)] Where, χ is any χ day in a year; Photovoltaic power generation E s The expression is: Where P is the rated power of photovoltaic power generation.

5. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 4 is characterized in that: The process of establishing the battery model is as follows: After selecting the wind turbine and PV modules, the monthly power generation curves are calculated as f1 and f2 respectively; The sum of the two parameters f1 and f2 is the monthly power curve of the power generation system. r Make comparisons; When f3 is in f r When it is at the bottom, it means that the battery pack is needed to provide power; when f3 <f r When , it means that there must be a battery, a separate integral interval, the larger the absolute value of the integral, the smaller the battery capacity of that month; Divide a year into different time periods, each time period corresponds to an integral interval, then the optimal battery capacity W b Under this condition, the distribution ratio of wind power and photovoltaic power is: W b =max[∫(f r -f3)dx / E s DOD Where DOD is the maximum depth of discharge.

6. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 5 is characterized by: In S2, the expression of the constructed objective function is: Where n is the partition number; N is the total number of partitions; d is the optimal number of days; D is the total number of days in a year; t is the optimal time period of each day; ψ is the total number of optimal cycles per day; Δt is the length of the optimal cycle of a unit; is the wind power generation power at the optimal day d at time t in zone n; is the photovoltaic power generation power in the optimal day d and optimal period t in zone n; is the reference load of the entire power grid.

7. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 6 is characterized by: In S2, the constraint conditions include: reference load matching constraint, daily load matching deviation value constraint, renewable energy output constraint, renewable energy and chemical energy storage installed capacity constraint, renewable energy curtailment constraint, renewable energy power generation ratio constraint, chemical energy storage charging and discharging power constraint, and chemical energy storage energy constraint.

8. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 7 is characterized by: The settings of each constraint are as follows: 1) The reference load matching constraint expression is: Where: is the discharge power of chemical energy storage; Charging power for chemical energy storage; is the positive deviation of the reference load match; is the negative deviation of the reference load matching; 2) The constraint expression of daily load matching deviation value is: In the formula, ε is the daily load matching deviation value, that is, within the optimal period, the matching deviation value of the output power is no more than 0.5% of the target power; 3) The expression of renewable energy output constraint is: In the formula, is the optimal installed capacity of wind power generation in area n; is the optimal installed capacity of photovoltaic power generation in area n; Standardize output sequences for wind power generation; Standardize output sequences for photovoltaic power generation; 4) The installed capacity constraints of renewable energy and chemical energy storage are: In the formula, is the minimum installed wind power capacity in area n; is the maximum installed wind power capacity in area n; is the minimum photovoltaic installed capacity in zone n; is the maximum installed photovoltaic capacity in zone n; is the optimal installed capacity of chemical energy storage in zone n; is the minimum installed capacity of chemical energy storage in zone n; is the maximum installed capacity of chemical energy storage in zone n; 5) The constraints for renewable energy curtailment are: In the formula, θ is the maximum amount of renewable energy abandoned electricity; 6) The constraints on the proportion of renewable energy generation are: In the formula, λ q The minimum share of renewable energy generation in the total load; 7) The chemical energy storage charging and discharging power constraints are: Where: and They are the discharge state variable and charge state variable of chemical energy storage respectively. In addition, since the chemical energy storage in the area cannot be fully utilized in one charge and discharge process, the constraints are set as follows: In the formula, is the chemical storage energy at time t; is the chemical energy storage capacity at t-1; γ B is the self-release rate of chemical energy storage; η ch is the charging efficiency; η dc is the discharge efficiency. 8) Energy constraints of chemical energy storage The energy constraint of chemical energy storage is: In the formula is the maximum energy of chemical energy storage, α B is the minimum energy coefficient of chemical energy storage.

9. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 8 is characterized by: After the energy storage installed capacity is determined, step S3 is performed. The objective function constructed in S3 is shown in the following formula: In the formula, C B,j is the construction cost of energy storage system j; C M,j is the annual maintenance cost of energy storage system j, P dc (t) and P ch (t) is the discharge and charging power of the energy storage system at time t, and α and β are the energy storage response efficiency coefficients.

10. The adjustable resource ratio and energy storage point optimization method considering the new energy carrying capacity according to claim 9 is characterized in that: The constraints of the energy storage optimization layout model are set as follows: 1) Grid load balance constraints L t +P ch (t)-P dc (t)=P grid (t) Where, L t is the load demand in time period t; P grid (t) Electricity obtained from outside the grid; 2) Energy storage power constraints 0≤P ch (t)≤C j 0≤P dc (t)≤C j In the formula, C j is the maximum response capability of energy storage system j; 3) Energy storage capacity constraints In the formula, E j (t) is the energy storage state of energy storage system j at time t, and The minimum and maximum capacity limits of the energy storage system. 4) Energy storage deployment decision variables x j ∈{0,1} Among them, 1 means that the energy storage system is deployed at position j, and 0 means that it is not deployed.

Images