The energy efficiency of a renewable energy system is inextricably linked to the energy storage technologies used in conjunction with it. The most extensively utilized energy storage technology for all purposes is electrochemical storage batteries, which have grown more popular over time because of their extended life, high working voltage, and low self-discharge rate. However, these batteries cannot withstand the very low temperatures encountered in cold regions, even with these very promising technical characteristics. The cold northern temperatures affect the batteries’ electromotive force and thus decrease their storage capacity. In addition, they affect the conductivity of the electrolyte and the kinetics of electrochemical reactions, thus influencing the capacity and speed of electrons in the electrolyte. In this article, which is intended as a literature review, we first describe the technical characteristics of charge–discharge rate of different electrochemical storage techniques and their variations with temperature. Then, new approaches used to adapt these electrochemical storage techniques to cold climates are presented. We also conduct a comparative study between the different electrochemical storage techniques regarding their performance in the harsh climatic conditions of the Canadian North.

The quality of prebaked carbon anodes, consumed in electrolysis during the primary aluminum production, has an important impact on the cell performance. The anode quality depends on the raw material quality and operating conditions in the anode plant. Development of simple, quick, and inexpensive techniques and tools for anode quality control will help industry identify the source of problems and take the necessary corrective actions rapidly. In this article, different quality control tools developed to find the wettability of coke by pitch, effect of mixing on coke particle size distribution, metallic impurity content, optimum vibration time, pitch content in green anode, and the measurement of green and baked anode electrical resistivities are presented. In parallel, data analysis using the artificial neural network (ANN), a powerful statistical tool for such applications, provides complementary information on quality and process. This article also presents the potential utilization of ANN in quality control.