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This article presents internal model control (IMC) based decentralized reinforce‐control of renewable dynamic virtual power plant (DVPP) so that, it can be integrated into the power system as a substitution of fuel‐based conventional generators. Such grid integration towards net‐zero targets could not be possible without providing additional ancillary service (AS) to the power system, as the traditional AS would fall short with the retirement/substitution of conventional generators. The theory of DVPP from a technical perspective (i.e., TDVPP) is presented in a detailed and simplified manner, including the formulation of a generalized control objective (desired specification) for DVPP integration. The solution approach includes two steps: (1) disaggregation of desired specification and (2) decentralized reinforce‐control to match the disaggregated specification. The theory and solution approach for DVPP integration is presented in a generalized manner enabling the DVPP to offer multiple ASs, but the case study is limited only to frequency control AS (FCAS) in this article. The study is performed on the 'western system coordinating council (WSCC)' test system, in which an attempt is made towards net‐zero targets by substituting the largest thermal generator with renewable DVPP ensuring the grid's operation or dynamics safe. [ABSTRACT FROM AUTHOR]

The fundamental voltage, current, and phase angle are required for a wide variety of power system applications. An algorithm that is capable of calculating or estimating these quantities in real time, in the presence of distorted waveforms, finds application in diverse areas of power systems. Techniques to detect voltage sag include the root mean square (rms), Fourier transform, and peak voltage detection methods. The problem with these methods is that they use a windowing technique and can therefore be too slow when applied to detect voltage sags for mitigation since they use historical data. Recent work in the field of signal processing has led to an algorithm that can extract a single non-stationary sinusoidal signal out of a given multi-component input signal. The algorithm is capable of estimating the amplitude, phase and frequency. In this paper, the algorithm is compared to existing methods of sag detection. Index Terms--Mitigation, nonlinear filter, power quality (PQ), sags.

An adaptive real-time electricity pricing and optimal control system based on energy management of buildings is presented. This is made in the framework of a smart-grid according to energy demand and time-of-use (TOU) electricity tariff in conjunction with a model predictive control (MPC). The developed model is considered as a multiple single input single output MPC system that acts at a different rate of TOU- electricity tariffs (off-peak, standard and peak). A smart selective timer switching system links one to the other. The performance of the model design is expressed through the simulation results of the adaptive TOU-MPC for commercial buildings. [ABSTRACT FROM AUTHOR]