Optimization Scheme for Energy Storage Capacity of Large Grid Connected with Wind Farm in New Power System

In this study, our foremost focus was on mitigating the significant impacts on system frequency due to power output variations from large-scale wind farms integrated into the grid. We conducted in-depth simulations using Matlab to meticulously analyze the relationship between the output power of wind turbines and wind speed. Taking into account the power features of wind turbines and the probability distribution of wind velocities, we proposed an innovative calculation method to determine the energy storage requirements to maintain consistent and stable output from wind energy systems over long durations. By rigorously testing with real wind farm data, the feasibility of this method was validated, highlighting its pivotal role as a reference in future wind power project designs.


Introduction
Wind power generation stands as a widely acknowledged clean and sustainable energy source.The inherent instability of wind, characterized by variations in speed and direction, poses distinct challenges.Incorporating a substantial number of wind farms into the electrical grid is vital for ensuring smooth system operations and superior power quality [1] [2].Negligence in addressing these concerns might compromise power delivery and, in dire scenarios, cause grid shutdowns, which would limit the potential utilization and expansion of wind energy [3].China is set to prioritize the establishment of over 30 large-scale power stations, each surpassing a capacity of 100,000 kilowatts, along with five wind energy centers of a megawatt scale [4].The grid integration of these significant facilities profoundly impacts frequency stability.Hence, adjusting the output power of wind farms to guarantee the consistent operation of the electrical grid is imperative.
The flywheel energy storage techniques are employed to balance the output power of wind turbines, showcasing benefits like high energy storage density, swift charge/discharge capabilities, and ecofriendliness [5].
The role of series-parallel super-capacitors is delved into stabilizing wind power output, highlighting attributes such as high power density, rapid charging and discharging, user-friendly control, efficient conversion, and environmental sustainability [6].
The battery energy storage system (BESS) is evaluated for enhancing the electrical quality of gridconnected wind farms, emphasizing its rapid power transfer and versatile 4-quadrant modulation [3].
Studies in [7][8][9] comprehensively investigate the application of superconducting magnetic energy storage in wind power integration, revealing its exemplary dynamic response, 4-quadrant operation, and stable energy retention capabilities.Scholars have extensively explored the application of energy storage in wind farm grid integration, aiming to master high-capacity storage solutions and consistently reduce the cost-per-unit of energy storage.
Drawing upon the probability density curve of wind speeds in wind farms, this article explores the methodology for determining the energy storage needed to maintain consistent power output over extended periods in large-scale wind farms.Our objective is to judiciously define the scale of energy storage, ensuring continuous power delivery from the wind farm and optimizing wind energy harnessing.

Modeling process
This article builds a simplified system model with large wind farms as shown in Figure 1 on the Simulink platform of Matlab.This system simulates A wind farm consisting of 50 double-fed wind turbines with a capacity of 1.5 MW, each with a parallel capacitor compensation capacity of 150 kvar as shown in Figure 2.These generators are boosted through 690 V/10 kV transformers and then connected to the system through 10 kV/220 kV step-up transformers.The aerodynamic mathematical model of wind turbines is Formula 1. (1) In the formula: is the rated power of the wind turbine; Is the air density; is the wind energy conversion efficiency coefficient; R is the radius of the wind turbine impeller; Injecting wind speed; Is the tip speed ratio; Is the pitch angle.The wind turbine wind speed power characteristic curve is Formula 2. (2) Figure 3 shows a histogram of the probability distribution of wind speed for a certain wind farm in 2022.The probability distribution curve of the curve fitted based on Figure 3 is Formula 3.
(3)  Currently, there are no discernible technical barriers in high-capacity energy storage, and the associated costs remain prohibitive.Investigating ways to utilize minimal energy storage mechanisms to guarantee consistent output from wind farms is a pertinent issue.The detailed procedure includes: 1) Using the probability density curve of wind speed, the mathematical expectation for the power output of a wind farm is delineated as: (4) 2) Set the anticipated output power derived earlier as the standard power level for the wind farm.
3) Identify the wind speed value V1 which aligns with this standard power level and is below the turbine's nominal wind speed.
4) With V1 as the benchmark, when actual wind speed surpasses V1, the wind farm maintains the output corresponding to V1, storing the surplus energy in energy storage systems; if the wind speed is below V1, it keeps the V1 output, with the shortfall supplied by the energy storage system.5) Determine the size of the energy storage mechanism using the formula S=EH, where H symbolizes the projected continuous operation hours of the wind farm at its minimum start-up wind speed.In selecting the storage size, a holistic approach should be adopted, considering elements such the specific duration of sustained high winds or calm periods as provided by meteorological insights, the cost implications of energy storage in wind farm developments, the relative contribution of wind farms to the overall grid, and the grid's frequency modulation capabilities.
6) In prolonged periods of insufficient wind speeds for initiation, dispatch centers should be prepped to manage potential non-production scenarios of the wind farm.
7) Orchestrating collaboration and complementary strategies among various wind farms can further optimize the requisite storage capacity.
According to Formula (2), E=0.57pu is calculated, indicating that the expected value of a 100 MW wind farm after calculation is 57 MW.The dispatch center can consider this wind farm as a power plant with an installed capacity of 57 MW.
The selection of H is mainly determined by the accuracy of the meteorological department's prediction of no wind (below the starting wind speed).If the proportion of wind power in the system is not large, the system has strong frequency regulation ability, or the construction cost of wind power is not allowed, then H can be taken as a smaller value, otherwise it should be taken as a larger value to ensure that the wind farm can continuously and stably output.When H is taken as 5 h, a 100000 kW wind farm should have an installed energy storage capacity of 59 MW×5 h=295 MW•h.As observed from Figure 4, with the support of energy storage systems, the wind farm is capable of maintaining consistent output over extended durations.However, there are intervals when the output is unstable, primarily due to prolonged periods of low wind speeds depleting the energy storage system, with no immediate recovery in wind conditions.
Under such circumstances, the wind farm can only produce power proportional to the available wind strength.Based on the outlined storage strategy, from a systemic viewpoint, the wind farm appears to be operating in a "derated generation" mode, generating at 59% of its peak capacity.In reality, however, the turbines within the wind farm are operating at full capacity, transmitting 59% of the active power and storing any excess.Ideally, the storage values should oscillate between 0 and their maximum, indicating that the storage system is continuously cycling between charging and discharging.Persistent values of 0 or at the maximum suggest either inadequate storage capacity or potential wastage of wind energy.
In Figure 4, the storage values remain at their peak for a significant duration, signifying several days of exceptionally high wind speeds, resulting in a fully charged storage system.Yet, this pattern is predictable, allowing for adjustments in the average output of the wind farm to ensure optimal wind resource utilization.

Conclusions 1)
This study presents a strategy to determine the energy storage scale necessary for sustaining consistent outputs in large-scale wind farms.Implementing this approach can assist wind farms in achieving more uniform outputs while minimizing the demand for energy storage infrastructure.
2) Considering the current technological landscape and cost implications, the derived energy storage reserve scale from our method is relatively extensive.During wind farm planning, a comprehensive evaluation is crucial to strike a balance between investments in expansive energy storage reserves and the pursuit of output stability.
3) Recognizing that the wind conditions of multiple wind farms will not align perfectly within a given time-frame, the likelihood of them simultaneously encountering windless periods is minimal.This suggests that multiple wind farms can operate synergistically, achieving a dynamic equilibrium and further mitigating the need for large energy storage solutions.In-depth exploration is required to ascertain the extent to which storage demands can be optimized.

Figure 1 .
Figure 1.Large wind farm grid connection model.

Figure 4 .
Figure 4. Wind farm output power curve before and after allocation and storage.