Analysis Model and Empirical Analysis of Power Grid Evolution Path Considering Multiple Interactive Developments

doi:10.12204/j.issn.1000-7229.2023.07.007

Abstract

Abstract:

Power grids in the future will evolve from a single mode driven by a load to a dual-driven mode of a power source and a load. Diversified and flexible resources are urgently required for multiple interactive developments to realize a scenario with a high proportion of renewable energy. The effective identification of evolution-driven paths and the comprehensive optimization of evolution paths have an important guiding significance for clarifying the development direction of future power grids and constructing specific implementation paths. This study analyzed the uncertainty faced by power grid evolution from the aspects of technological maturity, potential, and energy cost and proposed a method for generating massive evolution paths. Subsequently, a data-driven evolution path analysis method was proposed, including path dimensionality reduction and visualization, driving factor identification based on time-varying patterns, and optimal path proposal generation based on the Pareto frontier. Finally, the evolution path of a high-proportion renewable energy system was analyzed using North China as an example. The analysis results indicated that photovoltaics in North China will gradually surpass wind power to become the most important power generation resource in the future and that carbon emissions in 2060 will be 81% lower than those in 2030. The relative importance of each factor differed marginally. At the economic and environmental levels, the most important factor was the price of coal, while the maximum investable capacity of battery energy storage was the main factor at the technical level. Efforts should be made to reduce unit investment in renewable and battery energy storage and coal prices and increase the upper limit of battery energy storage allocation to achieve an evolutionary path that considers both low cost and low carbon emissions.

Key words: flexibility resources, evolution uncertainty, massive evolution paths, path visualization, driving factor identification, optimal path

CLC Number:

TM715

JI Jie, LU Zongxiang, LIANG Mingliang, LI Haibo, LI Fuqiang, JIANG Zongnan. Analysis Model and Empirical Analysis of Power Grid Evolution Path Considering Multiple Interactive Developments[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(7): 57-69.

Figures/Tables 15

Fig.1 Uncertain factors in power grid evolution

Fig.2 Massive evolution path generation and analysis framework

Fig.3 Schematic diagram of key driver identification based on time-varying patterns

Table 1 Parameter of installed capacity and load in North China 2025

Fig.4 Evolution path and distribution of renewable energy penetration rate

Fig.5 System evolution path and distribution

Fig.6 Visualization of generation and flexible resource structures at the technical level

Fig.7 Clustering results of renewable energy electricity penetration rate paths

Fig.8 Input factor mean curve corresponding to different category paths

Fig.9 Average distance between classes for each input factor

Fig.10 Economic-environmental measurement of evolution path and identification of optimal evolution path

Fig.11 Normalized mean curve of output conditions corresponding to optimal path group and non-optimal path group

Table A1 The range of values of uncertainty factors considered in evolution

Table A2 Principal component analysis results of resource structure at different development stages

Table A3 Average distance between classes for each input factor

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