Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Wind-storage coordination multi-objective optimal control method based on dynamic weighting

A multi-objective optimization and control method technology, applied in the field of wind-storage coordination multi-objective optimization control based on dynamic weighting, can solve the problems of relative importance changes, wind power scenarios that cannot adapt to changes, and difficult sub-objective weights, etc. The effect of improving the charging and discharging efficiency of energy storage, smoothing the fluctuation of wind power and improving the control effect

Inactive Publication Date: 2017-05-31
SHANGHAI JIAOTONG UNIV
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Although in the traditional power grid, the fixed weighting method has achieved many successful applications, but in the smart grid control with wind power access as a typical application, the fixed weighting method will be difficult to adapt to the inherent randomness characteristics of the smart grid, and cannot adapt to changes. Wind Power Scenarios
On the one hand, it is because the possible states of the system cannot be exhausted in the offline state, so it is difficult to rely on limited simulation scenarios to determine the weight of each sub-goal; on the other hand, in a time-varying environment, the relative importance of each index is also may be changing

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Wind-storage coordination multi-objective optimal control method based on dynamic weighting
  • Wind-storage coordination multi-objective optimal control method based on dynamic weighting
  • Wind-storage coordination multi-objective optimal control method based on dynamic weighting

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0146] Taking a 48MW wind farm in Chongming Island, Shanghai as the research object, an example model was established in MATLAB to compare the effects of wind storage coordination control using the fixed weighting method and the dynamic weighting method. The fixed weight coefficient values ​​are taken from the literature, ie α=1, β=2. The energy storage capacity of the wind farm configuration is 10MWh, accounting for about 20% of the wind farm capacity, and the rated charging and discharging power is 10MW. The ideal SOC is set to 60%, and the allowable variation range of SOC is [0.1,0.9]. The LPF filter time constant is 200s, and the energy storage control period is 20s. References for restrictions on ramp rate of wind farms: 10min and 1min active power change limits are 16MW and 4.8MW respectively. Select a wind power simulation scenario such as Figure 5 shown.

[0147] Set the initial value of (α, β) of the dynamic weighting method as (1, 1), set the initial SOC as 20%,...

Embodiment 2

[0158] In the coordinated control of wind storage, the control strategy not only needs to face different wind power fluctuation scenarios, but also needs to face different energy storage capacity configurations, SOC initial values, and different weight coefficient initial value settings. The robustness of the dynamic weighting method to these situations is further tested below.

[0159] A. Sensitivity to the initial situation of SOC and the initial value of the weight coefficient

[0160] The initial value of SOC is 20%, 60%, and 80%, respectively, and the initial value of weight coefficient is respectively selected from (1,1), (5,5), (8,8), a total of 3×3=9 different initial value situations . The weight coefficient change curve is as follows Figure 11 shown. In the figure, the definition of vertical and horizontal coordinates of each sub-graph is consistent with Figure 10 same.

[0161] Depend on Figure 11 It can be seen that the dynamic weighting method is basicall...

Embodiment 3

[0164] In this embodiment, the energy storage capacity is reduced to 5MWh, which is only about 10% of the capacity of the wind farm, and the rated charging and discharging power of the energy storage is correspondingly reduced to 5MW. Let the initial value of SOC be 20%, and the initial value of weight coefficient be (1,1).

[0165] Depend on Figure 16 It can be seen that with the decrease of the energy storage capacity, the control ability of the fixed weighting method on the SOC is obviously reduced, and the SOC exceeds the limit at 3h. In contrast, the dynamic empowerment method still maintains a good control effect on SOC.

[0166] At this time, the dynamic changes of energy storage output, wind power grid-connected power fluctuations, and weight coefficients α and β are shown in Figure 17-Figure 20 shown. In terms of energy storage output, the dynamic weighting method is still better than the fixed weighting method. In terms of wind power grid-connected power fluctu...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a wind power storage coordination multi-objective optimization control method based on dynamic weighting. The method comprises the following steps of: 1) establishing a MPC model according set multiple objects and a constraint condition; 2) acquiring a wind electricity power predicted value and a current energy storage SOC value within a set time scale; 3) acquiring the optimal weight coefficients of objectives in the MPC model by using an online adjusting and optimizing method; 4) solving the MPC model with the optimal weight coefficients in order to obtain an optimized value of wind electricity grid-connected power; 5) performing continuous control according to an energy storage control period by means of a LPF and compensating the output of LPF control by using the optimized value of wind electricity grid-connected power so as to obtain final grid-connected power and an energy storage output power set value; and 6) determining whether a MPC period is completed when the step 5) is executed, and returning to the step 2) if yes or returning to the step 6). Compared with a method in the prior art, the method is suitable for variable wind electricity power cases and obviously improve an energy storage SOC control effect.

Description

technical field [0001] The invention relates to a wind storage coordination control method, in particular to a wind storage coordination multi-objective optimization control method based on dynamic weighting. Background technique [0002] On-site deployment of a Battery Energy Storage System (BESS) in a wind farm is an important measure to improve the ability of the grid to accept wind power. Among various wind-storage coordinated control methods, low pass filter (Low Pass Filter, LPF) is a simple and practical control method. However, LPF control lacks predictability to future wind power changes, which limits its control effect when wind power fluctuates greatly. In addition, with the deepening of wind-storage coordination and application, the control has changed from single-objective to multi-objective, from the initial consideration of reducing wind farm output fluctuations to paying attention to energy storage charging and discharging efficiency and service life at the ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00H02J3/28H02J3/38
Inventor 李中豪张沛超马军
Owner SHANGHAI JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com