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This paper analyzes the effect of meteorological variables such as solar irradiance and ambient temperature in addition to cultural factors such as consumer behavior levels on energy consumption in buildings. Reducing demand peaks to achieve a stable daily load and hence lowering electricity bills is the goal of this work. Renewable generation sources, including wind and Photovoltaics systems (PV) as well as battery storage are integrated to supply the managed home load. The simulation model was conducted using Matlab R2019b on a personal laptop with an Intel Core i7 with 16 GB memory. The model considered two seasonal scenarios (summer and winter) to account for the variable available energy sources and end-user electric demand which is classified into three demand periods, peak-demand, mid-demand, and low-demand, to evaluate the modeled supply-demand management strategy. The obtained results showed that the surrounding temperature and the number of family members significantly impact the rate of electricity consumption. The study was designed to optimize and manage electricity consumption in a building fed by a standalone hybrid energy system.

The main motivation for the study of superconducting magnetic energy storage (SMES) integrated into the electrical power system (EPS) is the electrical utilities' concern with eliminating Power Quality (PQ) issues and greenhouse gas emissions. This article aims to provide a thorough analysis of the SMES interface, which is crucial to the EPS. This article also discusses the development of SMES as a reliable energy storage system (ESS). Delivering outstanding performance to support the EPS in any upsetting scenario can help SMES achieve its goals. A few of the fascinating aspects of the application of SMES in this context are microgrids, transmission and distribution (T&D) grids, renewable energy sources (RES), and plug-in hybrid energy storage systems (HESS). However, power utilities must evaluate the effectiveness and enhance a better performance on PQ when presenting a highly efficient energy technology. This article examines the difficulties of integrating SMES into the electricity network as well as the performance effects of HESS and RES. Finally, by outlining the current state of knowledge and highlighting any gaps in the literature, the power protection strategies and difficulties for power networks using SMES are evaluated.

In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC.

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.