This research investigates the positive changes in the system frequency response indicators caused by the implementation of a set of optimal settings of an under-frequency load shedding (UFLS) scheme. The optimal under-frequency load shedding (UFLS) scheme is optimised by minimising the total amount of load shedding and taking into account the recovery process of the system frequency into its operational values after several losses of generation and satisfies the requirements of the under-frequency load shedding standard (PRC-006-SERC-02). The idea of implementing the optimal UFLS scheme is to identify how changes the minimum frequency, minimum time, rate of change of frequency and steady-state frequency when the amount of load shedding change. The optimal UFLS scheme formulation starts with identifying the variables to control with the optimisation and its respective bounds. Then, the objective function is formulated in terms of the total load shedding, and finally, the restrictions and requirements of the systems are written as inequality constraints. The optimal UFLS is evaluated in the IEEE 39-bus system. The simulations results demonstrate the suitability of the optimal UFLS to improve the frequency response indicators.

The Altai-Uliastai regional power system (AURPS) is a regional power system radially interconnected to the power system of Mongolia. The 110 kV interconnection is exceptionally long and susceptible to frequent trips because of weather conditions. The load-rich and low-inertia AURPS must be islanded during interconnection outages, and the under-frequency load shedding (UFLS) scheme must act to ensure secure operation. Traditional UFLS over-sheds local demand, negatively affecting the local population, especially during the cold Mongolian winter season. This research paper proposes a novel methodology to optimally calculate the settings of the UFLS scheme, where each parameter of the scheme is individually adjusted to minimise the total amount of disconnected load. This paper presents a computationally efficient methodology that is illustrated in a specially created co-simulation environment (DIgSILENT^® PowerFactory^TM + Python). The results demonstrate an outstanding performance of the proposed approach when compared with the traditional one.