Research on Optimal Weight Index Calculation for Renewable Energy Applying Multidisciplinary Collaborative Optimization Theory

doi:10.12204/j.issn.1000-7229.2021.01.006

Abstract

Abstract:

With the continuous improvement of the guarantee mechanism of renewable energy power accommodation, it is an important link to lead the long-term development of renewable energy power accommodation to scientifically and rationally set the weight of renewable energy accommodation for each province and region. Applying multidisciplinary collaborative optimization theory, the actual grid structure and grid operation in the region is comprehensively considered in this paper. The influence of large amount of volatile renewable energy on the traditional controllable generating units is analyzed. Under the background of the implementation of carbon emission quotas, a method for calculating the optimal weight index of renewable energy is proposed. By setting up the system objective function of the optimal comprehensive cost and the parallel sub-disciplines under different consideration aspects, the global optimal solution is found by means of cyclic iteration of the system discipline and sub-discipline. The proposed model and algorithm are verified by an example of IEEE 118-node system and an actual power grid in a province.

Key words: renewable energy, power consumption weight, multidisciplinary collaborative optimization, carbon emission quota, comprehensive cost

CLC Number:

TM61

CHEN Quan, WU Kecheng, QU Yi, HE Chungeng, HUANG Binbin, XIE Min. Research on Optimal Weight Index Calculation for Renewable Energy Applying Multidisciplinary Collaborative Optimization Theory[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 49-58.

Figures/Tables 13

Fig.1 The concept map of multidisciplinary collaborative optimization

Fig.2 Requirements on system capability for climbing and landslide

Fig.3 Flow chart of model calculation for optimal index of renewable energy

Fig.4 Load curve of IEEE 118-node test system

Fig.5 Forecast output of typical daily part of wind farm and photovoltaic field

Fig.6 The results of typical unit outputs

Table 1 Percentage of total electricity of each unit

Table 2 Results of multidisciplinary optimization

Table 3 Results of weight index under different permeability

Table 4 Comparison of optimization results between the two methods

Table 5 Comparison of time consuming of different calculation methods

Fig.7 Total load curves for four typical days

Table 6 Electricity, percentage and cost of each type of unit in four seasons

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