In this paper, a new approach that is based on a collaborative system of MicroGrids (MGs) is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible deferrable loads (DLs), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DLs. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multiobjective optimization (MOO) problems. The first one targets the activation schedule of non-flexible DLs and the second one deals with the power profiles of flexible DLs. The MOO problems are resolved by using a fast and elitist multiobjective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MGs registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach.

This paper presents a proof-of-concept for a novel dq droop control technique that applies DC droop control methods to fixed frequency inverter-based AC microgrids using the dq0 transformation. Microgrids are usually composed of distributed generation units (DGUs) that are electronically coupled to each other through power converters. An inherent property of inverter-based microgrids is that, unlike microgrids with spinning machines, the frequency of the parallel-connected DGUs is a global variable independent from the output power since the inverters can control the output waveform frequency with a high level of precision. Therefore, conventional droop control methods that distort the system frequency are not suitable for microgrids operating at a fixed frequency. It is shown that the proposed distributed droop control allows accurate sharing of the active and reactive power without altering the microgrid frequency. The simulation and hardware-in-the-loop (HIL) results are presented to demonstrate the efficacy of the proposed droop control. Indeed, following a load change, the dq droop controller was able to share both active and reactive power between the DGUs, whereas maintaining the microgrid frequency deviation at 0% and the bus voltage deviations below 6% of their respective nominal values.

The outdoor terahertz communication channel imposes challenging constraints in the sixth genera- tion (6G), which has attracted researchers to investigate this piece of spectrum band 0.3-10 Thz. In this paper, we deploy a new transformer architecture called HA02 to attain enhanced channel estimation in Orthogonal Frequency-Division Multiplexing (OFDM) systems. This method is based on the self-attention mechanism which focuses on the most important elements of the Least–Squares (LS) method, it utilizes a transformer en- coder block as the encoder and a residual neural network as the decoder. Using the Rician channel model while considering the presence of Free Space Path Loss (FSPL) and the influence of weather conditions on the Thz link’s performance. Our simulations demonstrate high estimation performance compared with some channel estimation techniques.