Multi-objective Dynamic Optimization Method for Capacity Configuration of Energy Storage System to Mitigate Transient Under-Frequency Load Shedding

doi:10.12204/j.issn.1000-7229.2021.03.010

Abstract

Abstract:

During the frequency decline process of a power system subject to a disturbance, unnecessary under-frequency load shedding will be triggered if the instantaneous frequency is too low. This will reduce the power system reliability. Energy storage can be used to mitigate the unnecessary transient under-frequency load shedding due to its fast power regulation ability. According to the equivalent frequency response model of power systems, this paper proposes a multi-objective dynamic optimization model with piecewise function constraints which is suitable for the configuration of energy storage capacity. This model aims to consider two conflict objectives at the same time, such as the cost of energy storage configuration and the performance of transient frequency regulation. The big-M method and implicit trapezoid integration method are used to transform the multi-objective dynamic optimization model into a multi-objective mixed integer quadratic programming model. Then the normalized normal constraint method and CPLEX solver are used to obtain the Pareto optimal solution. Finally, the effectiveness of the proposed multi-objective optimization model of energy storage capacity configuration is validated with the simulation result on the IEEE 24-bus system.

Key words: energy storage system, fast frequency regulation, multi-objective dynamic optimization, big-M method, energy storage capacity configuration

CLC Number:

TM74

LIU Qingkai, LIU Mingbo, LU Wentian. Multi-objective Dynamic Optimization Method for Capacity Configuration of Energy Storage System to Mitigate Transient Under-Frequency Load Shedding[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 81-88.

Figures/Tables 11

Fig.1 Equivalent frequency response model of power system

Fig.2 Flowchart of the multi-objective dynamic optimization

Fig.3 Connection diagram of IEEE 24-bus system

Table 1 Parameters of IEEE 24-bus System

Fig.4 Pareto frontier of bi-objective optimization model for energy storage configuration

Table 2 Comparison of three schemes for energy storage configuration

Fig.5 System frequency response curves under three schemes for energy storage configuration

Fig.6 Curves of rate of frequency change under three schemes for energy storage configuration

Fig.7 Output power curves of energy storage under three configuration schemes

Fig.8 SOC curve under energy storage configuration scheme 1

Fig.9 Comparison of system frequency-response curves from simulation and calculation model

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