Optimal Battery Storage Configuration for High-Proportion Renewable Power Systems Considering Minimum Inertia Requirements

Xu Guo, Yang Li (), Feng Wu, Linjun Shi, Yuzhe Chen and Hailun Wang
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Xu Guo: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Yang Li: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Feng Wu: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Linjun Shi: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Yuzhe Chen: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Hailun Wang: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China

Sustainability, 2024, vol. 16, issue 17, 1-23

Abstract: With the continuous development of renewable energy worldwide, the issue of frequency stability in power systems has become increasingly serious. Enhancing the inertia level of power systems by configuring battery storage to provide virtual inertia has garnered significant research attention in academia. However, addressing the non-linear characteristics of frequency stability constraints, which complicate model solving, and managing the uncertainties associated with renewable energy and load, are the main challenges in planning energy storage for high-proportion renewable power systems. In this context, this paper proposes a battery storage configuration model for high-proportion renewable power systems that considers minimum inertia requirements and the uncertainties of wind and solar power. First, frequency stability constraints are transformed into minimum inertia constraints, primarily considering the rate of change of frequency (ROCOF) and nadir frequency (NF) indicators during the transformation process. Second, using historical wind and solar data, a time-series probability scenario set is constructed through clustering methods to model the uncertainties of wind and solar power. A stochastic optimization method is then adopted to establish a mixed-integer linear programming (MILP) model for the battery storage configuration of high-proportion renewable power systems, considering minimum inertia requirements and wind-solar uncertainties. Finally, through a modified IEEE-39 bus system, it was verified that the proposed method is more economical in addressing frequency stability issues in power systems with a high proportion of renewable energy compared to traditional scheduling methods.

Keywords: high-proportion renewable power systems; inertia requirement; mixed-integer linear programming; battery storage configuration; uncertainty (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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