Models are commonly used for various power system studies. This article presents a new framework to estimate and validate the parameters of the turbine-governor and also the load model in a multi-machine power system environment. The proposed method utilizes the center of inertia frequency and generating unit signals (especially active power) recorded by measurement devices to validate the parameters. The objective function is to minimize the total squared error between the simulated and the measured responses for some events. Particle swarm optimization is used to estimate the parameters. The accuracy of the estimated parameters is investigated through sensitivity analysis. Moreover, in order to improve the efficiency of the identification procedure for large-scale networks, a new scheme is proposed, which is based on the impact of each estimated parameter during the simulation time frames. The simulation results on the IEEE 9-bus and IEEE 118-bus systems show the validity and benefit of the proposed method.
The integration of Renewable Energy Sources (RESs) into distribution networks has increased in recent years due to numerous advantages. However, the RESs are intermittent and uncertain therefore may cause various limitations such as high lines loading and large voltage deviations, especially during high generation and low demand periods. Thus, this leads to an upper limit for the integrated capacity of RESs into the network, entitled Hosting Capacity (HC). In this paper, the complementarity of wind-PV along with the Demand Flexibility Program (DFP) are utilized for alleviating the limitations and increasing the HC in a hybrid AC/DC network. Moreover, an important feature of the AC/DC network, i.e., reactive control of Voltage Source Converters (VSCs) is investigated for increasing the HC. Additionally, a tradeoff is made between two conflicting objectives, i.e., HC and energy losses, which will be increased due to an excessive increase of the HC. Generally speaking, the paper proposes a multi-objective, multi-source, and multi-period extended optimal linear power flow model for simultaneously increasing the HC and decreasing the energy losses, utilizing stochastic programming for managing uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives.
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