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The current COVID-19 pandemic and the preventive measures taken to contain the spread of the disease have drastically changed the patterns of our behavior. The pandemic and movement restrictions have significant influences on the behavior of the environment and energy profiles. In 2020, the reliability of the power system became critical under lockdown conditions and the chaining in the electrical consumption behavior. The COVID-19 pandemic will have a long-term effect on the patterns of our behavior. Unlike previous studies that covered only the start of the pandemic period, this paper aimed to examine and analyze electrical demand data over a longer period of time with five years of collected data up until November 2020. In this paper, the demand analysis based on the time series decomposition process is developed through the elimination of the impact of times series correlation, trends, and seasonality on the analysis. This aims to present and only show the pandemic’s impacts on the grid demand. The long-term analysis indicates stress on the grid (half-hourly and daily peaks, baseline demand and demand forecast error) and the effect of the COVID-19 pandemic on the power grid is not a simple reduction in electricity demand. In order to minimize the impact of the pandemic on the performance of the forecasting model, a rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in this paper. The proposed forecast model aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model. The proposed forecast model outperformed the benchmark forecast model ARIMAX and Artificial Neural Network (ANN) and reduced the forecast error by up to 23.7%.

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The incorporation of renewable energy microgrids brings along several new protection coordination challenges due to the new and stochastic behaviour of power flow and fault currents distribution. An optimal coordination scheme is a potential solution to develop an efficient protection system to handle the microgrid protection challenges. In this paper, new optimal Over Current (OC) relays coordination schemes have been developed using nonstandard tripping characteristics for power network connected to renewable energy resources. The International Electrotechnical Commission (IEC) microgrid and IEEE-9 bus systems have been used as benchmark networks to test and evaluate the coordination schemes. The proposed OC relays coordination approach delivers a fast and more reliable performance under different OC faults scenarios compared to traditional approaches. In addition, to improve and evaluate the performance of the proposed coordination approach, four modern and novel metaheuristic optimization algorithms are developed and employed to solve the OC relay coordination problem, namely: Modified Particle Swarm Optimization (MPSO), Teaching Learning (TL), Grey Wolf Optimizer (GWO) and Moth-Flame Optimization Algorithm (MFO). In this paper, the modern metaheuristic algorithms have been employed to handle the impact of renewable energy on the grid, enhance the sensitivity and selectivity of the protection system. The test cases, consider the impact of integrating the different levels of renewable energy resources (with a capacity increment of 25% and 50%) in the microgrid on the OC relays protection performance by using nonstandard and standard tripping characteristics. In addition, a comparison analysis for the modern metaheuristic algorithms with Particle Swarm Optimization (PSO) algorithm as a common and standard technique in solving coordination problems under different fault scenarios considering also the higher impedance faults are introduced. The results in all cases showed that the proposed optimal nonstandard approach successfully reduced the overall tripping time and improve the performance of the protection system in terms of sensitivity and selectivity.

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