A Method and System for Optimized Configuration of Energy Storage for Wind and Photovoltaic Accommodation in Regional Power Grids

Abstract

The present invention discloses a method and system for optimizing the configuration of energy storage for wind and solar power consumption in a regional power grid. The method includes: generating wind and solar power output scenarios for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid; calculating the new energy consumption space of the regional power grid in the target year by using a preset new energy consumption evaluation model; optimizing the wind and solar planned installed capacity ratio with the goal of minimizing the wind and solar curtailment electricity within the statistical time to obtain the optimal wind and solar planned installed capacity ratio in the target year of the regional power grid; based on the calculated new energy consumption space, optimizing the configuration of the energy storage capacity under the optimal wind and solar planned installed capacity ratio with the goal of minimizing the curtailment electricity within the statistical time to obtain the optimal configuration of the energy storage capacity. The present invention can further reduce the wind and solar curtailment electricity, effectively reduce the waste of new energy resources, and provide a reference for the subsequent energy storage capacity configuration of the regional power grid.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing the configuration of energy storage for wind and solar power consumption in a regional power grid, belonging to the technical field of capacity configuration of wind, solar and energy storage. Background Art

[0002] Reasonably planning the installed capacity ratio of wind power and photovoltaic power according to the characteristics of local wind and solar resources, and configuring energy storage devices to reduce the amount of abandoned wind and solar power has important practical significance in the development and utilization of new energy.

[0003] Currently, for the research on the optimal capacity ratio of wind power and photovoltaic power generation, some aim at the maximum comprehensive benefit of co-constructing wind power and photovoltaic power in the same field, establish indicators reflecting the constraints of combined wind and photovoltaic power generation and the total economic benefits, and obtain the optimal ratio of wind power and photovoltaic power through production simulation; some establish an optimal capacity model of wind power and photovoltaic power with the lowest construction cost of wind and solar farms, and design different constraint scenarios to conduct capacity optimization respectively; some establish an equivalent load peak-valley difference index, and study the impact of different wind and solar capacity ratios on this index through the simulation of wind power and photovoltaic power output, and obtain the wind and solar installed capacity ratio that minimizes the change in the equivalent load peak-valley difference; some analyze the output characteristics of wind power and photovoltaic power in a certain regional power grid based on the analysis of new energy output data, and propose to use the Spearman correlation coefficient to study the complementary characteristics of the output of the two fluctuating energy sources and their reasonable capacity ratio. Some construct a weather type classification model based on KPCA and SOFM neural networks based on weather forecast data, quantitatively analyze the complementary degree of wind and solar power output under different weather types from two perspectives of volatility and rampability, and finally determine the optimal grid-connected capacity ratio of wind and solar under different weather types.

[0004] For the research on energy storage planning, some mainly consider the power supply reliability of the system when planning the energy storage capacity, find all capacity combinations that meet the threshold constraints, and find the combination with the lowest economic cost from them, which is the final energy storage capacity configuration decision; some obtain the energy storage capacity configuration through the method of pattern search. In each search, according to the autoregressive moving average (ARMA) model, probabilistic parameters such as wind power and photovoltaic power output are obtained, and then the corresponding energy storage capacity and load loss rate are calculated based on some constraints. Finally, through multiple rounds of search, the optimal energy storage capacity configuration and the optimal cost are obtained; some use the method of solving the mixed integer linear programming problem (MILP) to determine the energy storage capacity, consider the operating cost of the system, and obtain the optimal energy storage capacity by solving the unit commitment problem including wind power, photovoltaic power and energy storage.

[0005] Generally speaking, most of the current optimal capacity planning for regional wind power, photovoltaic power, and energy storage aims to maximize the benefits of each entity and is obtained through unified optimization and allocation of production simulation. Currently, there is a lack of a method that combines the optimal ratio of wind power and photovoltaic power with the optimized allocation of energy storage, and a method for optimizing the allocation of energy storage based on the optimal ratio of wind power and photovoltaic power on the basis of analyzing the output characteristics of wind power and photovoltaic power. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and system for optimizing the allocation of energy storage for wind and photovoltaic power consumption in a regional power grid, which can further reduce the amount of abandoned wind and photovoltaic power, effectively reduce the waste of new energy resources, and provide a reference for the subsequent energy storage capacity allocation in the regional power grid. To achieve the above purpose, the present invention is implemented by the following technical solutions:

[0007] In the first aspect, the present invention provides a method for optimizing the allocation of energy storage for wind and photovoltaic power consumption in a regional power grid, which is characterized by including:

[0008] Generate the wind and photovoltaic output scenarios for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid;

[0009] Calculate the new energy consumption space of the regional power grid for the target year by using a preset new energy consumption evaluation model for the wind and photovoltaic output scenarios of the target year;

[0010] Optimize the wind and photovoltaic planned installed capacity ratio with the goal of minimizing the wind and photovoltaic abandoned power during the statistical time to obtain the optimal wind and photovoltaic planned installed capacity ratio for the target year of the regional power grid;

[0011] Based on the calculated new energy consumption space, with the goal of minimizing the abandoned power during the statistical time, optimize the energy storage capacity under the optimal wind and photovoltaic planned installed capacity ratio to obtain the optimal configuration of the energy storage capacity.

[0012] In combination with the first aspect, further, the generation of the wind and photovoltaic output scenarios for the target year includes:

[0013] Based on the historical annual wind power and photovoltaic output data of the regional power grid, calculate the probability distribution of the wind power and photovoltaic output intervals at each moment, and randomly sample to generate the initial wind power and photovoltaic output scenario set;

[0014] Reconstruct the initial output scenario set in combination with the average fluctuation sequence of the theoretical power of wind power and photovoltaic power over the years to generate the wind power and photovoltaic output scenario set, which is the wind and photovoltaic output scenario for the target year.

[0015] In combination with the first aspect, further, the preset new energy consumption evaluation model includes:

[0016] According to the above wind and photovoltaic output simulation, calculate the total output P of wind power and photovoltaic power at time t

[0016] ,

[0015] , H , Calculation formula of P(t):

[0017] P H P(t) = P W P(t) + P S P(t)(1)

[0018] In formula (1), time t = 1, 2, ···, N, P W P(t) is the wind power output at time t; P S P(t) is the photovoltaic power output at time t;

[0019] Then, the new energy consumption space P ACC Calculation formula of P(t) at time t on the d-th day:

[0020]

[0021] In formula (2), P L P(t) is the load at the dispatching level at time t; P L,地调 is the load of the local dispatching; is the average value of the tie-line power at time t of the current month; α is the peak regulation capacity coefficient; is the maximum load at the dispatching level on the d-th day; is the average value of the tie-line power corresponding to the maximum load moment of the current month, negative for external power transmission and positive otherwise; P W,95％ is the wind power output at a 95% probability; P RES is the reserved reserve.

[0022] Combined with the first aspect, preferably, the calculation formula of the new energy curtailment data is as follows:

[0023] The new energy curtailment power P R Calculation formula of P(t):

[0024]

[0025] In formula (3), P H P(t) is the total output of wind power and photovoltaic power at time t; P ACC P(t) is the new energy consumption space at time t;

[0026] Calculation formula of the new energy curtailment electricity E R during the statistical period T:

[0027]

[0028] In formula (4), Δt is the sampling frequency within the statistical time;

[0029] Calculation formula of the new energy curtailment rate β during the statistical period:

[0030]

[0031] In formula (5), E H is the theoretical power generation of new energy power generation during the statistical period.

[0032] Combined with the first aspect, further, obtaining the optimal ratio of installed capacity of wind and photovoltaic power in the regional power grid in the target year includes:

[0033] Initialize the ratio of wind power installed capacity to the total installed capacity of wind and photovoltaic power as α;

[0034] Calculate the total output P H,α (t) of wind and photovoltaic power at time t when the ratio of wind power installed capacity to the total installed capacity of wind and photovoltaic power is α;

[0035] Calculate the curtailment power P R,α (t) of wind and photovoltaic power at time t when the ratio of wind power installed capacity to the total installed capacity of wind and photovoltaic power is α, which is calculated by the following formula:

[0036]

[0037] In formula (6), P H,α (t) is the total output of wind and photovoltaic power at time t, and P ACC (t) is the new energy consumption space at time t;

[0038] Adjust the ratio of installed capacity of wind and photovoltaic power planning to minimize the curtailment power γ of wind and photovoltaic power within the statistical time T. Then the optimization expression is:

[0039]

[0040] In formula (7), Δt is the sampling frequency within the statistical time;

[0041] Solve formula (7) to obtain the optimal ratio of installed capacity of wind and photovoltaic power planning in the regional power grid in the target year.

[0042] Combined with the first aspect, further, obtaining the optimal configuration of energy storage capacity includes:

[0043] Calculate the total output P H,α (t) of wind and photovoltaic power at time t when the ratio of wind power installed capacity to the total installed capacity of wind and photovoltaic power is α;

[0044] Taking the minimum curtailment power within the statistical time T as the goal, the optimization configuration expression of energy storage capacity is

[0045]

[0046] In formula (8), γ α,BESS is the curtailment power after adding energy storage under the optimal ratio of wind and photovoltaic power; P ACC(t) is the new energy consumption space at time t; Δt is the sampling frequency within the statistical time; P es (t) is the energy storage output power at time t, with discharging being positive and charging being negative, and is expressed by the following formula:

[0047] P es (t) = P es_DIS (t) - P es_C (t) (9)

[0048] In formula (9), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t;

[0049] Taking the energy storage charge and discharge constraint, the energy storage SOC constraint, and the energy storage daily charge and discharge times constraint as constraint conditions, the energy storage capacity optimization configuration expression is calculated to obtain the optimal configuration of the energy storage capacity.

[0050] Combined with the first aspect, further, the energy storage charge and discharge constraint is expressed by the following formula:

[0051]

[0052] In formula (10), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t; S es_C (t) is the energy storage charging state at time t, 0 means not charging, 1 means charging; S es_DIS (t) is the energy storage discharging state at time t, 0 means not discharging, 1 means discharging; η C is the energy storage charging efficiency; is the energy storage discharging efficiency; P es_C is the rated charging power of the energy storage; P es_DIS [[ID=4,6]]is the rated discharging power of the energy storage.

[0053] Combined with the first aspect, further, the energy storage SOC constraint is expressed by the following formula:

[0054]

[0055] In formula (11), SOC(t) is the state of charge of the energy storage at time t; SOC(t - 1) is the state of charge of the energy storage at time t - 1; SOC min is the lowest state of charge allowed for the energy storage device; SOC max is the highest state of charge allowed for the energy storage device; P es (t) is the energy storage output power at time t; E es is the configured energy storage capacity;

[0056] And for the energy storage device in terms of the energy stored at the end and start of the statistical time T, then:

[0057] SOC(0) = SOC(T) (12)

[0058] In Equation (12), SOC(0) is the initial state of charge, and SOC(T) is the state of charge at the end of the statistical time T.

[0059] In combination with the first aspect, further, the daily charge-discharge times constraint of the energy storage device is expressed by the following formula:

[0060]

[0061] In Equation (13), N C (day) is the daily charge times of the energy storage device; N C,max is the maximum daily charge times of the energy storage device; N DIS (day) is the daily discharge times of the energy storage device; N DIS,max is the maximum daily discharge times of the energy storage device.

[0062] In combination with the first aspect, further, when calculating the proportion α of the installed capacity of wind power in the total installed capacity of wind and photovoltaic power, the total output P H,α (t) of wind and photovoltaic power at time t is calculated by the following formula:

[0063]

[0064] In Equation (14), P N is the total installed capacity of wind and photovoltaic power in the regional power grid, with the unit of MW; is the per-unit value of wind power output at time t; is the per-unit value of photovoltaic power output at time t.

[0065] In the second aspect, the present invention provides an energy storage optimization configuration system for wind and photovoltaic power accommodation in a regional power grid, including:

[0066] A generation module: used to generate the wind and photovoltaic power output scenarios for the target year based on the historical annual wind power and photovoltaic power output data of the regional power grid;

[0067] A calculation module: used to calculate the new energy accommodation space of the regional power grid for the target year by using a preset new energy accommodation evaluation model for the wind and photovoltaic power output scenarios of the target year;

[0068] A first optimization module: used to optimize the wind and photovoltaic planned installed capacity ratio with the goal of minimizing the wind and photovoltaic power curtailment within the statistical time, so as to obtain the optimal wind and photovoltaic planned installed capacity ratio for the target year of the regional power grid;

[0069] Second optimization module: For the purpose of minimizing the curtailed power within the statistical time based on the calculated new energy accommodation space, optimize the configuration of the energy storage capacity under the optimal ratio of wind and photovoltaic planned installed capacity, and obtain the optimal configuration of the energy storage capacity.

[0070] Compared with the prior art, the beneficial effects achieved by the energy storage optimization configuration method and system for wind and photovoltaic accommodation in a regional power grid provided by the embodiments of the present invention include:

[0071] The present invention generates the wind and photovoltaic output scenarios for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid; calculates the new energy accommodation space of the regional power grid for the target year by using a preset new energy accommodation evaluation model; optimizes the ratio of wind and photovoltaic planned installed capacity with the goal of minimizing the curtailed wind and photovoltaic power within the statistical time, and obtains the optimal ratio of wind and photovoltaic planned installed capacity for the target year of the regional power grid; the present invention effectively increases the new energy accommodation space and reduces the curtailed wind and photovoltaic power by optimizing the wind and photovoltaic ratio, providing a reference for the new energy accommodation of the regional power grid and the subsequent planning of wind and photovoltaic installed capacity;

[0072] Based on the calculated new energy accommodation space, the present invention optimizes the configuration of the energy storage capacity under the optimal ratio of wind and photovoltaic planned installed capacity with the goal of minimizing the curtailed power within the statistical time, and obtains the optimal configuration of the energy storage capacity; the present invention further reduces the curtailed wind and photovoltaic power by reasonably configuring the energy storage, which can effectively reduce the waste of new energy resources and provide a reference for the subsequent energy storage capacity configuration of the regional power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flowchart of an energy storage optimization configuration method for wind and photovoltaic accommodation in a regional power grid provided by an embodiment of the present invention;

[0074] Figure 2 is a schematic diagram of the wind and photovoltaic output scenarios for the target year of a certain regional power grid in Embodiment 1 of the present invention; Fig. 2(a) is the per-unit value curve of the daily average wind power and photovoltaic output in spring and autumn of the wind and photovoltaic output scenarios for the target year, Figure 2 (b) is the per-unit value curve of the wind power output in spring and autumn of the wind and photovoltaic output scenarios for the target year, Figure 2 (c) is the per-unit value curve of the photovoltaic output in spring and autumn of the wind and photovoltaic output scenarios for the target year;

[0075] Figure 3 is the influence of different ratios of wind and photovoltaic planned installed capacity on the curtailed wind and photovoltaic power for the target year of a certain regional power grid in Embodiment 1 of the present invention; Figure 3 (a) is the influence of different ratios of wind and photovoltaic planned installed capacity on the curtailed wind and photovoltaic power in 2021, Figure 3 (b) is the influence of different ratios of wind and photovoltaic planned installed capacity on the curtailed wind and photovoltaic power in 2022;

[0076] Figure 4 It is the relationship between different energy storage power capacities and the corresponding curtailed power under the existing installed capacity ratio of wind and light in a certain regional power grid in the target year in Embodiment 1 of the present invention; Figure 4 (a) It is the relationship between different energy storage power capacities and the corresponding curtailed power under the existing installed capacity ratio of wind and light in 2021, Figure 4 (b) It is the relationship between different energy storage power capacities and the corresponding curtailed power under the existing installed capacity ratio of wind and light in 2022;

[0077] Figure 5 It is the relationship between different energy storage power capacities and the corresponding curtailed power under the optimal installed capacity ratio of wind and light in a certain regional power grid in the target year in Embodiment 1 of the present invention; Figure 5 (a) It is the relationship between different energy storage power capacities and the corresponding curtailed power under the optimal installed capacity ratio of wind and light in 2021, Figure 5 (b) It is the relationship between different energy storage power capacities and the corresponding curtailed power under the optimal installed capacity ratio of wind and light in 2022. Detailed implementation manners

[0078] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0079] Embodiment 1:

[0080] As Figure 1 shown, a method for generating an output scenario of a new energy power station includes:

[0081] Generating a wind and light output scenario for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid;

[0082] Calculating the new energy consumption space of the regional power grid in the target year by using a preset new energy consumption evaluation model for the wind and light output scenario of the target year;

[0083] Optimizing the installed capacity ratio of wind and light planning with the goal of minimizing the curtailed power of wind and light within the statistical time to obtain the optimal installed capacity ratio of wind and light planning in the target year of the regional power grid;

[0084] Based on the calculated new energy consumption space, with the goal of minimizing the curtailed power within the statistical time, optimizing the configuration of the energy storage capacity under the optimal installed capacity ratio of wind and light planning to obtain the optimal configuration of the energy storage capacity.

[0085] The specific steps include:

[0086] Step 1: Generating a wind and light output scenario for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid.

[0087] Step 1.1: Generate the average fluctuation sequences of wind power and photovoltaic power based on the historical annual wind power and photovoltaic power output data of the regional power grid.

[0088] Under the determined time granularity (15 min), calculate the theoretical power fluctuation amounts of wind and light over the years and take the average value at each moment to obtain the average fluctuation sequence {μ t |t = 1, 2,..., T - 1}.

[0089] Step 1.2: Calculate the probability distributions of the output intervals of wind power and photovoltaic power at each moment, and randomly sample to generate the initial output scenario set of wind power and photovoltaic power.

[0090] Calculate the probability distributions of wind power and photovoltaic power in each output interval at each moment, and randomly sample according to the probability distributions to generate the initial output scenario set Ω of wind power and photovoltaic power:

[0091]

[0092] In Equation (1), x m,t is the output value of wind power and photovoltaic power at the t-th moment in the m-th output scenario. The row vector represents an output scenario randomly sampled by wind power and photovoltaic power within the time period T; the column vector represents the output of the new energy power station under different scenarios at the same moment. The initial output scenario set Ω of wind power and photovoltaic power reflects the probability distributions of the output intervals of wind power and photovoltaic power at each moment, but does not satisfy the time correlation of the output of the new energy power station itself. Therefore, it is necessary to further reconstruct the data of each row vector in Ω.

[0093] Step 1.3: Reconstruct the initial output scenario set by combining the average fluctuation sequence of the theoretical power of wind power and photovoltaic power over the years to generate the output scenario set of wind power and photovoltaic power, which is the wind-solar output scenario for the target year.

[0094] Combine the fluctuation amount μ t at this moment in the average fluctuation amount sequence, and find the value x 1,t+1 x 2,t+1 ··· x m,t+1 T in the wind power and photovoltaic power output set [x m,t + μ t at the t + 1-th moment that is closest to x k,t+1 (k ≤ m), and use it as the output value of wind power and photovoltaic power at the t + 1-th moment in this output scenario. Determine the output values of the new energy power station at each moment in this output scenario in turn according to the above method.

[0095] ​By traversing the initial output scenario set Ω, the reconstructed wind power and photovoltaic output scenario set Ω' is obtained. The reconstructed wind power and photovoltaic output scenario set Ω' not only satisfies the probability distribution of the output intervals at each moment but also can more accurately reflect the time correlation of wind power and photovoltaic output. The output scenario sets of wind power and photovoltaic are simulated and generated respectively according to this method, so as to generate the wind-solar output scenarios.

[0096] Step 2: Calculate the wind-solar output scenarios of the target year by using a preset new energy consumption assessment model to obtain the new energy consumption space of the regional power grid in the target year.

[0097] The system regulation space between the load and the minimum starting capacity of thermal power units is the theoretically maximum new energy consumption space. When the new energy output is less than its consumption space, the new energy output system can fully consume it; while when the new energy output is greater than its consumption space, the excess part will cause the problem of new energy abandonment due to the inability of the system to consume it.

[0098] The preset new energy consumption assessment model includes:

[0099] The calculation formula for the new energy consumption space P ACC (t) at the t-th moment on the d-th day:

[0100]

[0101] In formula (2), the moment t = 1, 2, ···, N; P L (t) is the dispatching-caliber load at the t-th moment; P L,地调 is the local dispatching load; is the average value of the tie-line power at the t-th moment of the current month; α is the peak regulation ability coefficient; is the maximum dispatching-caliber load on the d-th day; is the average value of the tie-line power corresponding to the moment of the maximum load of the current month, negative for external transmission and positive otherwise; P W,95％ is the output of wind power at 95% probability; P RES is the reserved reserve.

[0102] According to the above wind-solar output simulation, calculate the total output P H (t) of wind power and photovoltaic at the t-th moment:

[0103] P H (t) = P W (t) + P S (t) (3)

[0104] In formula (3), the moment t = 1, 2, ···, N, P W (t) is the wind power output at the t-th moment; P S (t) is the photovoltaic output at the t-th moment.

[0105] New energy curtailment power P at time t R (t) calculation formula:

[0106]

[0107] In formula (4), P H (t) is the total output of wind power and photovoltaic power at time t; P ACC (t) is the new energy accommodation space at time t.

[0108] Calculate the new energy curtailment electricity E within the statistical period T R Calculation formula:

[0109]

[0110] In formula (5), Δt is the sampling frequency within the statistical time.

[0111] Calculation formula for the new energy curtailment rate β within the statistical period:

[0112]

[0113] In formula (6), E H is the theoretical power generation of new energy within the statistical period.

[0114] Step 3: Optimize the installed capacity ratio of wind power and photovoltaic power with the goal of minimizing the curtailment electricity of wind power and photovoltaic power within the statistical time, and obtain the optimal installed capacity ratio of wind power and photovoltaic power for the target year of the regional power grid.

[0115] Step 3.1: Initialize the proportion of wind power installed capacity in the total installed capacity of wind power and photovoltaic power as α.

[0116] Step 3.2: Calculate the total output P of wind power and photovoltaic power at time t when the proportion of wind power installed capacity in the total installed capacity of wind power and photovoltaic power is α H,α (t), which is calculated by the following formula:

[0117]

[0118] In formula (7), P N is the total installed capacity of wind power and photovoltaic power in the regional power grid, with the unit of MW; is the per-unit value of wind power output at time t; is the per-unit value of photovoltaic power output at time t.

[0119] Step 3.3: Calculate the curtailment power P of wind power and photovoltaic power at time t when the proportion of wind power installed capacity in the total installed capacity of wind power and photovoltaic power is α R,α (t), which is calculated by the following formula:

[0120]

[0121] In formula (8), P H,α (t) is the total wind and solar power output at time t, and P ACC (t) is the new energy consumption space at time t.

[0122] Step 3.4: Adjust the proportion of wind and solar planned installed capacity to minimize the wind and solar curtailment amount γ within the statistical time T. Then the optimization expression is:

[0123]

[0124] In formula (9), Δt is the sampling frequency within the statistical time.

[0125] Step 3.5: Solve formula (9) to obtain the optimal ratio of wind and solar planned installed capacity for the regional power grid in the target year.

[0126] The present invention effectively increases the new energy consumption space and reduces the wind and solar curtailment amount by optimizing the wind-solar ratio, providing a reference for new energy consumption in the regional power grid and subsequent wind-solar installed capacity planning.

[0127] Step 4: Based on the calculated new energy consumption space, with the goal of minimizing the curtailment amount within the statistical time, optimize the configuration of the energy storage capacity under the optimal ratio of wind and solar planned installed capacity to obtain the optimal configuration of the energy storage capacity.

[0128] Step 4.1: Obtain the total wind and solar power output P H,α (t) at time t when the proportion of wind power installed capacity in the total wind and solar installed capacity obtained in Step 3.2 is α.

[0129] Step 4.2: With the goal of minimizing the curtailment amount within the statistical time T, the energy storage capacity optimization configuration expression is

[0130]

[0131] In formula (10), γ α,BESS is the curtailment amount after adding energy storage under the optimal wind-solar ratio; P ACC (t) is the new energy consumption space at time t; Δt is the sampling frequency within the statistical time; P es (t) is the energy storage output power at time t, with discharging being positive and charging being negative, and is expressed by the following formula:

[0132] P es (t) = P es_DIS (t) - P es_C (t) (11)

[0133] In formula (11), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t.

[0134] Step 4.3: Taking the energy storage charge and discharge constraint, the energy storage SOC constraint, and the energy storage daily charge and discharge times constraint as the constraint conditions, calculate the optimal configuration expression of the energy storage capacity to obtain the optimal configuration of the energy storage capacity.

[0135] Constraint 1: The energy storage charge and discharge constraint, which is expressed by the following formula:

[0136]

[0137] In formula (12), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t; S es_C (t) is the charging state of the energy storage at time t, 0 means not charging, 1 means charging; S es_DIS (t) is the discharging state of the energy storage at time t, 0 means not discharging, 1 means discharging; η C is the energy storage charging efficiency; is the energy storage discharging efficiency; P es_C is the rated charging power of the energy storage; P es_DIS is the rated discharging power of the energy storage.

[0138] Constraint 2: The energy storage SOC constraint, which is expressed by the following formula:

[0139]

[0140] In formula (13), SOC(t) is the state of charge of the energy storage at time t; SOC(t - 1) is the state of charge of the energy storage at time t - 1; SOC min is the lowest state of charge allowed for the energy storage device. In this embodiment, SOC min = 0.1; SOC max is the highest state of charge allowed for the energy storage device. In this embodiment, SOC max = 0.9; P es (t) is the output power of the energy storage at time t; E es is the configured energy storage capacity.

[0141] And for the energy storage device, the stored energy system at the end and the start of the statistical time T, then:

[0142] SOC(0) = SOC(T) (14)

[0143] In formula (14), SOC(0) is the initial state of charge. In this embodiment, SOC(0) = 50%; SOC(T) is the state of charge at the end of the statistical time T. In this embodiment, T = 24h.

[0144] Constraint 3: The energy storage daily charge and discharge times constraint, which is expressed by the following formula:

[0145]

[0146] In formula (15), N C (day) is the daily charging times of the energy storage device; N C,max is the maximum daily charging times of the energy storage device; N DIS (day) is the daily discharging times of the energy storage device; N DIS,max is the maximum daily discharging times of the energy storage device; In this embodiment, it is stipulated that the daily charging and discharging times do not exceed 2 times.

[0147] The present invention further reduces the amount of abandoned wind and light power by reasonably configuring energy storage, can effectively reduce the waste of new energy resources, and provides a reference for the subsequent energy storage capacity configuration of the regional power grid.

[0148] Embodiment 2:

[0149] This embodiment adopts the method described in Embodiment 1, and combines the wind and light output data of a certain regional power grid from 2018 to 2020 to optimize the energy storage configuration of the regional power grid in 2021 and 2022.

[0150] Step 1: Generate the wind and light output scenarios of the target year according to the wind power and photovoltaic output data of the historical years of the regional power grid.

[0151] Based on the wind and light output scenario generation method described in Embodiment 1, according to the wind and light output data of the regional power grid from 2018 to 2020, generate Figure 2 the per-unit value curves of the daily average wind power and photovoltaic output in spring and autumn of the wind and light output scenario of the target year shown in Figure 2 (a), the per-unit value curve of the wind power output in spring and autumn of the wind and light output scenario of the target year shown in Figure 2 (c), the per-unit value curve of the photovoltaic output in spring and autumn of the wind and light output scenario of the target year shown in

[0152] Step 2: Calculate the wind and light output scenarios of the target year by using a preset new energy consumption evaluation model to obtain the new energy consumption space of the regional power grid in the target year.

[0153] It is expected that by the end of 2021, the installed capacities of wind power and photovoltaic power in the whole province will be 4.76 million kilowatts and 14.54 million kilowatts respectively, and the existing wind-light capacity ratio is 1:3.05. According to the existing wind-light capacity ratio, it is expected that by the end of 2022, the installed capacities of wind power and photovoltaic power will be 5.25 million kilowatts and 16.04 million kilowatts respectively. Assume that the annual growth rate of the spring and autumn load of a certain regional power grid is 8%, and the average power curve of the tie line in a certain month of the target year is obtained from the average value of the tie line power at the corresponding time of each day in the historical year. The consumption space of a certain regional power grid in the target year is obtained from the new energy consumption model, and combined with the simulated wind and light output data of the regional power grid in the target year, the abandoned power situation of the regional power grid in the target year is analyzed.

[0154] Analysis of Wind and Solar Curtailment in a Certain Regional Power Grid in Spring and Autumn of 2021:

[0155] By the end of 2021, the total installed capacity of wind and solar in a certain regional power grid was 19.3 million kilowatts, and the wind and solar penetration rate (the proportion of wind and solar installed capacity in the total installed capacity of all power sources in the power system) reached 24.4%. P W，95％ Take it as 4% of the installed capacity of wind power, P L,area Take it as 2.5 million kilowatts, P RES Take it as 2.5 million kilowatts, and set the peak shaving capacity coefficient α to 0.46.

[0156] It can be calculated that in spring and autumn of 2021, the wind and solar power generation in a certain regional power grid was 12,131,704,000 kWh, the maximum acceptable power generation was 17.089 million kilowatts, the minimum acceptable power generation was 3.776 million kilowatts, the maximum curtailment power was 1.785 million kilowatts, the curtailment electricity in spring and autumn was 24,433,000 kWh, and the curtailment rate was 0.21%. The total number of curtailment days in spring and autumn of 2021 in a certain regional power grid was 16 days, including 14 days of curtailment in spring and 2 days of curtailment in autumn.

[0157] Analysis of Wind and Solar Curtailment in a Certain Regional Power Grid in Spring and Autumn of 2022 (Based on the Wind and Solar Installed Capacity Ratio at the End of 2022):

[0158] By the end of 2022, the total installed capacity of wind and solar in a certain regional power grid was 21.3 million kilowatts, and the wind and solar penetration rate reached 25.6%. P W,95％ Take it as 4% of the installed capacity of wind power, P L,area Take it as 2.8 million kilowatts, P RES Take it as 2.5 million kilowatts, and set the peak shaving capacity coefficient α to 0.46.

[0159] It can be calculated that in spring and autumn of 2022, the wind and solar power generation in a certain regional power grid was 13,382,259,000 kWh, the maximum acceptable power generation was 18.523 million kilowatts, the minimum acceptable power generation was 4.086 million kilowatts, the maximum curtailment power was 2.193 million kilowatts, the curtailment electricity in spring and autumn was 35,230,000 kWh, and the curtailment rate was 0.26%. The total number of curtailment days in spring and autumn of 2022 in a certain regional power grid was {18} days, including 16 days of curtailment in spring and 2 days of curtailment in autumn.

[0160] Step 3: Optimize the wind and solar planned installed capacity ratio with the goal of minimizing the wind and solar curtailment electricity during the statistical period to obtain the optimal wind and solar planned installed capacity ratio for the target year of the regional power grid.

[0161] Based on the predicted data of wind power and photovoltaic power output and the predicted data of consumption space in a certain regional power grid at the end of 2021 and 2022, analyze the impact of different wind power installed capacity ratios on the curtailment of wind and light, optimize the installed capacity ratio of wind and light planning with the goal of minimizing the curtailment of wind and light within the statistical time, and obtain the optimal installed capacity ratio of wind and light planning for the target year of the regional power grid.

[0162] Figure 3 (a) shows the impact of different installed capacity ratios of wind and light planning on the curtailment of wind and light in 2021. When the optimal installed capacity ratio of wind and light planning in a certain regional power grid in 2021 is 1:1.86, that is, when the wind power installed capacity is 6.755 million kilowatts and the photovoltaic installed capacity is 12.545 million kilowatts, the curtailment of wind and light is the smallest at this time, which is 8.414 million kilowatt-hours. Compared with the existing installed capacity ratio of wind and light (1:3.05), 16.019 million kilowatt-hours more is consumed, and the curtailment rate is reduced from 0.21% to 0.06%.

[0163] If the optimal installed capacity ratio of wind and light planning 1:1.86 obtained by calculation is used to configure the installed capacity of wind and light new energy in 2021, 1.8 million kilowatts of wind and light installed capacity can be added on the basis of the original 19.3 million kilowatts of wind and light installed capacity, that is, the total wind and light installed capacity reaches 21.1 million kilowatt-hours, and the curtailment rate of wind and light is only 0.21% of the curtailment rate under the existing installed capacity ratio of wind and light (1:3.05). Obviously, optimizing the existing installed capacity ratio of wind and light can greatly improve the consumption of new energy.

[0164] Figure 3 (b) shows the impact of different installed capacity ratios of wind and light planning on the curtailment of wind and light in 2022. When the optimal installed capacity ratio of wind and light planning in a certain regional power grid in 2022 is 1:1.78, that is, when the wind power installed capacity is 7.668 million kilowatts and the photovoltaic installed capacity is 13.632 million kilowatts, the curtailment of wind and light is the smallest at this time, which is 12.39 million kilowatt-hours. Compared with the existing installed capacity ratio of wind and light in 2022, 22.835 million kilowatt-hours more is consumed, and the curtailment rate is reduced from 0.26% to 0.09%.

[0165] If the optimal installed capacity ratio of wind and light planning 1:1.78 obtained by calculation is used to configure the installed capacity of wind and light new energy in 2022, 2.08 million kilowatts of wind and light installed capacity can be added on the basis of the original 21.3 million kilowatts of wind and light installed capacity, that is, the total wind and light installed capacity reaches 23.38 million kilowatt-hours, and the curtailment rate of wind and light is only 0.26% of the curtailment rate under the existing installed capacity ratio of wind and light (1:3.05).

[0166] It can be seen from the above results that:

[0167] (1) According to the optimal installed capacity ratio of wind and light planning for the target year of the regional power grid, configuring the installed capacity of wind and light according to this can effectively increase the consumption space of new energy and reduce the curtailment rate of wind and light.

[0168] (2) From the historical operation of a certain regional power grid from 2018 to 2020, since the peak-valley difference of the load in this regional power grid is small, the ability to absorb wind power at night is strong, and there is no curtailment of electricity. However, curtailment of electricity has occurred many times during the peak period of photovoltaic power generation at noon. This shows that from the perspective of reducing the curtailment of wind and light electricity, the current wind-light ratio of 1:3.05 is not reasonable, and the proportion of photovoltaic is significantly too high. Using the method described in Embodiment 1, the optimal wind-light planned installed capacity ratios for 2021 and 2022 are calculated to be 1:1.86 and 1:1.78 respectively, which is consistent with the above analysis results, that is, significantly increasing the capacity ratio of wind power in the existing wind-light installed capacity structure of a certain regional power grid will better improve its ability to absorb new energy power generation.

[0169] (3) As the load and wind-light penetration rate increase, the optimal solution of the wind power installed capacity ratio in 2022 increases slightly compared with that in 2021. The minimum curtailment of wind and light electricity is reached at the optimal wind-light planned installed capacity ratio of 1:1.78. If the wind power installed capacity ratio is further increased, the curtailment of electricity at night brought by the unit wind power installed capacity will be greater than the curtailment of electricity during the day of photovoltaic, resulting in an increase in the total curtailment of electricity; on the contrary, if the wind power installed capacity ratio is reduced, the curtailment of electricity at night brought by the unit wind power installed capacity will be less than the curtailment of electricity during the day of photovoltaic, which will also increase the total curtailment of electricity.

[0170] Step 4: Based on the calculated new energy absorption space, with the goal of minimizing the curtailment of electricity within the statistical time, optimize the configuration of the energy storage capacity under the optimal wind-light planned installed capacity ratio to obtain the optimal configuration of the energy storage capacity.

[0171] In this embodiment, the rated parameters of the battery module selected as the energy storage medium are: charge and discharge power of 3 MW, energy storage module capacity of 6 MWh, charge and discharge efficiency of 90% each, and the cycle service life is selected as 5000 times.

[0172] As Figure 4 (a) shows the relationship between different energy storage power capacities and the corresponding curtailment of electricity under the existing wind-light installed capacity ratio of 1:3.05 in a certain regional power grid in 2021. It can be seen from the figure that if a certain regional power grid configures energy storage according to 2.91 million kilowatts / 5.82 million kWh in 2021 (accounting for 15.1% of the new energy installed capacity), the curtailment rate can be reduced from 0.21% to 0%. In addition, as the energy storage configuration capacity increases, the change range of the curtailment rate first decreases significantly and then gradually decreases. Therefore, when planning the energy storage configuration, the economic benefits per unit capacity of the energy storage investment should be considered.

[0173] As Figure 4(b) shows the relationship between different energy storage power capacities and the corresponding curtailed power under the existing wind-solar installed capacity ratio of 1:3.05 in a certain regional power grid in 2022. It can be seen from the figure that if the energy storage in a certain regional power grid is configured according to 3.59 million kilowatts / 7.18 million kWh in 2022 (accounting for 16.9% of the new energy installed capacity), the curtailment rate can be reduced from 0.26% to 0%. In addition, due to the increase in the installed capacity of wind and solar in 2022, the total output of wind and solar increases, while the increase in the accommodation space is not large, resulting in an increase in the corresponding curtailed power. Therefore, at the same curtailment rate, the energy storage capacity configured in 2022 is higher than that in 2021.

[0174] As Figure 5 (a) shows the relationship between different energy storage power capacities and the corresponding curtailed power under the optimal wind-solar installed capacity ratio of 1:1.86 in a certain regional power grid in 2021. It can be seen from the figure that if the energy storage in a certain regional power grid is configured according to 2.34 million kilowatts / 4.86 million kWh in 2021 (accounting for 12.1% of the new energy installed capacity), the curtailment rate can be reduced from 0.06% to 0%. Comparing Figure 4 (a) and Figure 5 (a), it can be known that if the curtailment rate in 2021 is also reduced to 0%, the energy storage power capacity can be configured 570,000 kilowatts less (accounting for 3.0% of the new energy installed capacity) when the installed capacity of wind and solar is at the optimal ratio of the planned installed capacity of wind and solar (1:1.86) than when the existing wind-solar installed capacity ratio is (1:3.05). Therefore, a reasonable wind-solar installed capacity ratio is beneficial to reducing the configured capacity of energy storage, thereby reducing the configuration cost of energy storage devices.

[0175] As Figure 5 (b) shows the relationship between different energy storage power capacities and the corresponding curtailed power under the optimal wind-solar installed capacity ratio of 1:1.78 in a certain regional power grid in 2022. It can be seen from the figure that if the energy storage in a certain regional power grid is configured according to 2.85 million kilowatts / 5.7 million kWh in 2022 (accounting for 13.4% of the new energy installed capacity), the curtailment rate can be reduced from 0.09% to 0%. Comparing Figure 4 (b) and Figure 5 (b), it can be known that if the curtailment rate in 2022 is also reduced to 0%, the energy storage power capacity can be configured 540,000 kilowatts less (accounting for 2.5% of the new energy installed capacity) when the installed capacity of wind and solar is at the optimal ratio of the planned installed capacity of wind and solar (1:1.78) than when the existing wind-solar installed capacity ratio is (1:3.05).

[0176] Example 3:

[0177] The embodiment of the present invention provides an energy storage optimization configuration system for wind-solar accommodation in a regional power grid, including:

[0178] A generation module: used to generate the wind-solar output scenarios in the target year according to the historical annual wind power and photovoltaic output data of the regional power grid;

[0179] Calculation module: used to calculate the wind and light output scenarios of the target year by adopting a preset new energy consumption assessment model, and obtain the new energy consumption space of the regional power grid in the target year;

[0180] The first optimization module: used to optimize the installed capacity ratio of wind and light planning with the goal of minimizing the wind and light abandonment power within the statistical time, and obtain the optimal installed capacity ratio of wind and light planning in the target year of the regional power grid;

[0181] The second optimization module: used to optimize the configuration of the energy storage capacity under the optimal installed capacity ratio of wind and light planning based on the calculated new energy consumption space, with the goal of minimizing the abandonment power within the statistical time, and obtain the optimal configuration of the energy storage capacity.

[0182] Embodiment 4:

[0183] An embodiment of the present invention provides an operation and maintenance risk analysis system for a power monitoring system, including a processor and a storage medium;

[0184] The storage medium is used to store instructions;

[0185] The processor is used to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0186] Embodiment 5:

[0187] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method described in Embodiment 1.

[0188] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the process Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks.

[0190] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the processes Figure 1 one or more processes and / or blocks Figure 1 functions specified in one or more blocks.

[0191] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more processes and / or blocks Figure 1 one or more blocks.

[0192] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An energy storage optimization configuration method for wind and solar power consumption in regional power grids, characterized in that Including: Generating the wind and photovoltaic output scenarios for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid; Calculating the new energy consumption space of the regional power grid for the target year by using a preset new energy consumption evaluation model for the wind and photovoltaic output scenarios of the target year; Optimizing the wind and photovoltaic planned installed capacity ratio with the goal of minimizing the wind and photovoltaic curtailment electricity within the statistical time to obtain the optimal wind and photovoltaic planned installed capacity ratio for the regional power grid in the target year; wherein, obtaining the optimal wind and photovoltaic planned installed capacity ratio for the regional power grid in the target year includes: Initializing the proportion of the wind power installed capacity in the total wind and photovoltaic installed capacity as α; When calculating the proportion of wind power installed capacity in the total installed capacity of wind and solar power as α, the total output of wind and solar power at time t is P H,α (t); When calculating the proportion of wind power installed capacity in the total installed capacity of wind and solar power as α, the curtailment power of wind and solar power at time t, P R,α (t), is calculated by the following formula: In formula (3), P H,α (t) is the total output of wind and light at time t when the proportion of wind power installed capacity in the total installed capacity of wind and light is α, and P ACC (t) is the new energy consumption space at time t; Adjusting the wind and photovoltaic planned installed capacity ratio to minimize the wind and photovoltaic curtailment electricity γ within the statistical time T, and the optimization expression is: In formula (4), Δt is the sampling frequency within the statistical time; Solving formula (4) to obtain the optimal wind and photovoltaic planned installed capacity ratio for the regional power grid in the target year; Based on the calculated new energy consumption space, with the goal of minimizing the curtailment electricity within the statistical time, optimizing the configuration of the energy storage capacity under the optimal wind and photovoltaic planned installed capacity ratio to obtain the optimal configuration of the energy storage capacity; wherein, obtaining the optimal configuration of the energy storage capacity includes: With the goal of minimizing the curtailment electricity within the statistical time T, the energy storage capacity optimization configuration expression is In formula (5), γ α,BESS is the amount of curtailed power after adding energy storage under the optimal wind-solar ratio; P ACC (t) is the new energy consumption space at time t; Δt is the sampling frequency within the statistical time; P es (t) is the energy storage output power at time t, with discharging being positive and charging being negative, and is expressed by the following formula: P es P(t) = P es_DIS P(t) - P es_C P(t)(6) In Equation (6), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t; Calculating the energy storage capacity optimization configuration expression with the energy storage charge and discharge constraint, the energy storage SOC constraint, and the energy storage daily charge and discharge times constraint as the constraint conditions to obtain the optimal configuration of the energy storage capacity.

2. The energy storage optimization configuration method for wind and light accommodation in a regional power grid according to claim 1, characterized in that, The generating the wind and photovoltaic output scenarios for the target year includes: Based on the historical annual wind power and photovoltaic output data of the regional power grid, calculating the probability distribution of the wind power and photovoltaic output intervals at each moment, and randomly sampling to generate the initial wind and photovoltaic output scenario set; Reconstructing the initial output scenario set in combination with the average fluctuation sequence of the theoretical power of wind and photovoltaic over the years to generate the wind and photovoltaic output scenario set, which is the wind and photovoltaic output scenario for the target year.

3. The energy storage optimization configuration method for wind and light consumption in a regional power grid according to claim 1, characterized in that The preset new energy consumption evaluation model includes: According to the above simulation of the wind and solar power output, calculate the total output P of wind power and photovoltaic power at time t H (Calculation formula of (t): P H P(t) = P W + P(t) S (t)(1) In Equation (1), at times t = 1, 2, ···, N, P W (t) is the wind power output at time t; P S (t) is the photovoltaic power output at time t; Then the calculation formula for the new energy consumption space P ACC (t) at the t-th moment on the d-th day is as follows: In formula (2), P L (t) is the dispatching caliber load at time t; P L,地调 is the load of the local dispatching; is the average value of the tie-line power at time t of the current month; α is the peak regulation capacity coefficient; is the maximum dispatching caliber load on the d-th day; is the average value of the tie-line power corresponding to the moment of the maximum load of the current month, negative for external power output and positive otherwise; P W,95％ is the output of wind power at a 95% probability; P RES is the reserved reserve.

4. The energy storage optimization configuration method for wind and light accommodation in a regional power grid according to claim 1, wherein The energy storage charge and discharge constraint is expressed by the following formula: In formula (7), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t; S es_C (t) is the charging state of the energy storage at time t, 0 means not charging, 1 means charging; S es_DIS (t) is the discharging state of the energy storage at time t, 0 means not discharging, 1 means discharging; η C is the energy storage charging efficiency; is the energy storage discharging efficiency; P es_C is the rated charging power of the energy storage; P es_DIS is the rated discharging power of the energy storage.

5. The energy storage optimization configuration method for wind and light consumption in a regional power grid according to claim 1, characterized in that The energy storage SOC constraint is expressed by the following formula: In Equation (8), SOC(t) is the state of charge of the energy storage at time t; SOC(t-1) is the state of charge of the energy storage at time t-1; SOC min is the minimum state of charge allowed for the energy storage device; SOC max is the maximum state of charge allowed for the energy storage device; P es (t) is the energy storage output power at time t; E es is the configured energy storage capacity; And for the energy storage device, the stored energy system at the end and the start of the statistical time T, then: SOC(0) = SOC(T) (9) In formula (9), SOC(0) is the initial state of charge, and SOC(T) is the state of charge at the end of the statistical time T.

6. The energy storage optimization configuration method for wind and light consumption in a regional power grid according to claim 1, wherein The energy storage daily charge and discharge times constraint is expressed by the following formula: In formula (10), N C (day) is the daily charging times of the energy storage device; N C,max is the maximum daily charging times of the energy storage device; N DIS (day) is the daily discharging times of the energy storage device; N DIS,max is the maximum daily discharging times of the energy storage device.

7. The energy storage optimization configuration method for wind and light accommodation in a regional power grid according to claim 1, characterized in that, When the proportion of the installed wind power capacity in the total installed capacity of wind and solar power is α, the total output P H,α (t) of wind and solar power at time t is calculated by the following formula: In formula (11), P N is the total installed capacity of wind and solar power in the regional power grid, with the unit of MW; is the per-unit value of wind power output at time t; is the per-unit value of photovoltaic power output at time t.

8. An energy storage optimization configuration system for wind and solar power accommodation in regional power grids, characterized in that, Including: Generating module: used for generating the wind and photovoltaic output scenarios for the target year based on the historical annual wind power and photovoltaic output data of the regional power grid; Calculating module: used for calculating the new energy consumption space of the regional power grid for the target year by using a preset new energy consumption evaluation model for the wind and photovoltaic output scenarios of the target year; First optimization module: used for optimizing the wind and photovoltaic planned installed capacity ratio with the goal of minimizing the wind and photovoltaic curtailment electricity within the statistical time to obtain the optimal wind and photovoltaic planned installed capacity ratio for the regional power grid in the target year; wherein, obtaining the optimal wind and photovoltaic planned installed capacity ratio for the regional power grid in the target year includes: Initializing the proportion of the wind power installed capacity in the total wind and photovoltaic installed capacity as α; When calculating the proportion of wind power installed capacity in the total installed capacity of wind and solar power as α, the total output of wind and solar power at time t is P H,α (t); When calculating the proportion of wind power installed capacity in the total installed capacity of wind and solar power as α, the curtailment power of wind and solar power at time t, P R,α (t), is calculated by the following formula: In formula (3), P H,α (t) is the total output of wind and light at time t when the proportion of wind power installed capacity in the total installed capacity of wind and light is α, and P ACC (t) is the new energy consumption space at time t; Adjusting the wind and photovoltaic planned installed capacity ratio to minimize the wind and photovoltaic curtailment electricity γ within the statistical time T, and the optimization expression is: In formula (4), Δt is the sampling frequency within the statistical time; Solve formula (4) to obtain the optimal ratio of installed capacity of wind and light in the regional power grid in the target year; The second optimization module: used to optimize the configuration of the energy storage capacity in the case of the optimal ratio of installed capacity of wind and light with the goal of minimizing the curtailed power within the statistical time based on the calculated new energy accommodation space, and obtain the optimal configuration of the energy storage capacity; wherein, the obtaining of the optimal configuration of the energy storage capacity includes: With the goal of minimizing the curtailed power within the statistical time T, the expression for optimizing the configuration of the energy storage capacity is In formula (5), γ α,BESS is the curtailment power after adding energy storage under the optimal wind-solar ratio; P ACC (t) is the new energy accommodation space at time t; Δt is the sampling frequency within the statistical time; P es (t) is the energy storage output power at time t, with discharging being positive and charging being negative, and is expressed by the following formula: P es P(t) = P es_DIS P(t) - P es_C P(t)(6) In Equation (6), P es_C (t) is the charging power of the energy storage at time t; P es_DIS (t) is the discharging power of the energy storage at time t; Using the energy storage charge and discharge constraint, the energy storage SOC constraint, and the daily energy storage charge and discharge times constraint as constraint conditions, calculate the expression for optimizing the configuration of the energy storage capacity to obtain the optimal configuration of the energy storage capacity.

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