Abstract. This paper focuses on the production of electricity using a thermoelectric generator placed on the human body connected to a dc-dc converter. The small difference in temperature between the hot heat source (e.g. the human body, T b = 37• C) and the cold heat source (e.g. ambient air, Ta = 22• C), associated with a poor quality thermal coupling (mainly with the cold source), leads to a very low temperature gradient at the thermoelectric generator terminals and hence low productivity. Under these use conditions, the present article proposes an analysis of various ways to improve productivity given a surface capture system. Furthermore, we demonstrated, in this particular context, that maximizing the recovered electric power proves to be a different problem from that of maximizing efficiency, e.g. the figure of merit Z. We therefore define a new factor ZE, depending on the physical characteristics of thermoelectric materials, that maximizes electric power in the particular case where the thermal coupling is poor. Finally, this study highlights the benefit of sub-optimization of the power extracted from the thermoelectric generator to further improve efficiency of the overall system. We show that, given the conversion efficiency of the dc-dc converter, the maximum power point of the overall system is no more reached when the output voltage of the thermoelectric generator is equal to half of its electromotive force.

International audienceAvailability of day-ahead production forecast is an important step towards better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a firm production commitment. In order to mitigate the deviation from the commitment, an energy storage system connected to the wind farm is considered. One statistical characteristic of day-ahead forecast errors has a major impact on storage performance: errors are significantly correlated along several hours. We thus use a data-fitted autoregressive model that captures this correlation to quantify the impact of correlation on storage sizing. With a Monte Carlo approach, we study the behavior and the performance of an energy storage system (ESS) using the autoregressive model as an input. The ability of the storage system to meet a production commitment is statistically assessed for a range of capacities, using a mean absolute deviation criterion. By parametrically varying the correlation level, we show that disregarding correlation can lead to an underes- timation of a storage capacity by an order of magnitude. Finally, we compare the results obtained from the model and from field data to validate the model