Abstract
Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions.
Highlights
Renewable-based power plants are becoming more popular over the year due to the need for clean and sustainable energy
This paper proposed a method for designing virtual inertia controller (VIC) on battery energy storage system (BESS) named virtual synchronous machine (VISMA), which is adaptable to the fluctuation of operating conditions
The power coefficient can be expreIsnseEdquaastiinonE(q9u),aωtiognis(6th).e mechanical speed of the generator, Bm denotes the damping coefficient, aerodynamic torque is represented by τw_g, equivalent inertia is given by Jeq, and τe is the is described eblyecsturobm-inedchecxapn,(giμc.,aθTl )tho=erqccuo1me(.cpF2ruβ1erh−tehnce3srθmiv−oe rmce4,aθtthxhee−pmcaa5r)taimeca−elct6meβ1roodfetlhoefgPeMneSrGatocransi(db6ee) expreInsseEdqubaytiEoqnu(a6t)io, nc1s −(10c)6–(a1r3e) t[h21e].coefficient, x represents the function related to the wind turbine rotor, and from Equation (7) [21]
Summary
Renewable-based power plants are becoming more popular over the year due to the need for clean and sustainable energy. The impact of wind power plant integration on low-frequency oscillation is reported in [10]. The Redox flow battery is reported to influence the frequency regulation performance of the power system significantly All these prior mentioned technologies are still evolving compared to the battery energy storage system (BESS). The state-space model of the synchronous generator based on the park transformation for the oscillatory stability can be expressed as Equation (1). The power coefficient can be expreIsnseEdquaastiinonE(q9u),aωtiognis(6th).e mechanical speed of the generator, Bm denotes the damping coefficient, aerodynamic torque is represented by τw_g, equivalent inertia is given by Jeq, and τe is the is described eblyecsturobm-inedchecxapn,(giμc.,aθTl )tho=erqccuo1me(.cpF2ruβ1erh−tehnce3srθmiv−oe rmce4,aθtthxhee−pmcaa5r)taimeca−elct6meβ1roodfetlhoefgPeMneSrGatocransi(db6ee) expreInsseEdqubaytiEoqnu(a6t)io, nc1s −(10c)6–(a1r3e) t[h21e].coefficient, x represents the function related to the wind turbine rotor, and from Equation (7) [21]. The detailed presentation of the PV plant for the electromechanical time-frame analysis including the dynamic parameters can be found in [24]
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