Among the many potential future energy sources, hydrogen stands out as particularly promising. Because it is a green and renewable chemical process, water electrolysis has earned much interest among the different hydrogen production techniques. Seawater is the most abundant source of water and the ideal and cheapest electrolyte. The first part of this review includes the description of the general theoretical concepts: chemical, physical, and electrochemical, that stands on the basis of water electrolysis. Due to the rapid development of new electrode materials and cell technology, research has focused on specific seawater electrolysis parameters: the cathodic evolution of hydrogen; the concurrent anodic evolution of oxygen and chlorine; specific seawater catalyst electrodes; and analytical methods to describe their catalytic activity and seawater electrolyzer efficiency. Once the specific objectives of seawater electrolysis have been established through the design and energy performance of the electrolyzer, the study further describes the newest challenges that an accessible facility for the electrochemical production of hydrogen as fuel from seawater must respond to for sustainable development: capitalizing on known and emerging technologies; protecting the environment; utilizing green, renewable energies as sources of electricity; and above all, economic efficiency as a whole.

Summary This paper presents a modified whale optimization (MWO) algorithm for solving the problem of parameters estimation for input‐output curves of thermal and hydro generating units that operates in power plants. In order to improve the MWO algorithm, a chaotic component based on logistic map is added. This new algorithm is called chaotic MWO (CMWO). Both MWO and CMWO algorithms have kept the structure of original whale optimization (WO) algorithm, but some characteristics regarding how the solutions positions are updated inside CMWO and MWO phases were modified. Estimation of curve parameters is an optimization problem. Its objective is to minimize the sum of absolute errors between actual values and estimated values. The efficiency of CMWO, MWO, and WO algorithms is tested either on polynomial models or on models that use nonsmooth functions. The results of CMWO algorithm are then compared with the ones obtained by other recently published algorithms.

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Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.