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Abstract: This paper focuses on the sizing optimization of a thermoelectric generator, combined with a potential heatsink, that harvests heat from the human body. To ensure success, energy models describing a thermoelectric module and a heatsink are introduced; these two models are then simplified for small temperature differences while respecting the continuity between them (both with and without the heatsink). In order to maximize the power produced by such a system, the dual thermal and electrical matching of the generator with its environment is studied in detail. Lastly, to optimize the sizing, a set of optimization parameters are defined, namely leg length, the capture surface area of the thermoelectric generator and, if a heatsink is present, its height. The results derived help draw a conclusion on the pertinence of adding a heatsink, for the purpose of maximizing electrical power while minimizing total volume. [Copyright &y& Elsevier]

Highlights • The energy conversion chain in a photovoltaic water pumping system was modelled. • Water collection by the inhabitants was included as a model input. • The model was validated by using data acquired on a system in rural Africa. • The accuracy of the model is higher than 95% when using local climatic data. • Accuracy drops of 1–3% when replacing local climatic data by satellite ones. Abstract The low electrification rate in rural sub-Saharan Africa prevents access to energy services which are essential to improve living conditions. One of these energy services is electrified water pumping, which is particularly relevant for these areas where water access continues being a significant challenge. Pumping systems powered by photovoltaic energy have emerged as an interesting solution in off-grid areas. This article presents a model of photovoltaic water pumping system (PVWPS) for providing domestic water to off-grid rural communities. The model simulates the pumped flow rate and the water level in the storage tank from the climatic data (irradiance, ambient temperature) and the profile of water collection by the users of the system. The modelling of the different stages of the energy conversion chain and a method for identifying the unknown parameters of PVWPS are presented in this article. The model is applied to a pilot PVWPS situated in a rural village of Burkina Faso. The comparison between the measurements performed on the system and the model outputs allows to validate the model experimentally. Results indicate that the model permits to accurately simulate the water height in the tank both when climatic data from local sensors and from satellite are inputted in the model. The model could therefore be applied to other off-grid areas to perform techno-economic optimization and size new PVWPS as well as to evaluate the performances of existing PVWPS. The originalities of this work include the consideration of the water collection profile as a model input and the monitoring of a PVWPS in a rural village of Sub-Saharan Africa, an area where no continuous measurements on these systems has been performed, to the best knowledge of the authors. Further, the comparison of the impact of inputting satellite climatic data instead of measured ones on the PVWPS model accuracy is also a novel contribution. [ABSTRACT FROM AUTHOR]

This paper proposes a flicker severity study for Wave Energy Converter farms. The flicker severity is introduced and the reason why it is an important constraint for a wave farm is explained. A new representation called intrinsic flicker severity is introduced which describes the flicker severity independently of the grid. The influence of device type, its control and the sea-state on average production, flicker severity and on the ratio between flicker and production are studied with three types of devices: an Oscillating Water Column and two Direct Wave Energy Converters (two point absorbers: a Heaving Buoy and the SEAREV). The influence of the size and the placement of each unit in the wave farm is presented with a farm-unit flicker ratio, compared with the square-root of unit hypothesis (noise behavior), as a function of wave direction by taking into account wave direction dispersion. Finally, solutions are presented to reduce the flicker produced to comply with grid code requirements in order to allow grid integration of wave farms. [ABSTRACT FROM AUTHOR]

• Optimally scheduling the cleaning of remote photovoltaic systems is challenging. • Low cost detection of cleaning interventions helps to decide on further operations. • 4 machine learning algorithms are applied on data from a remote photovoltaic system. • Reliability of 95% is reached with 3.5 mHz voltage, current and temperature signals. • 3 implementation strategies are proposed to meet low cost and accuracy goals. Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities. [ABSTRACT FROM AUTHOR]

• Design of stand-alone hybrid PV systems including a new real-time DSM strategy. • Simulation of 'real-time' domestic demand based on Bayesian network and Monte Carlo simulation. • Benefits of DSM in the enhancement of the system's performance, benefits for users, and user comfort. • Parametric study of the benefits of DSM with varying diesel prices and battery capacity. Demand-side management (DSM) represents a potential way to improve the profitability of renewable energy systems. In this paper, power management including a new DSM strategy in a stand-alone hybrid Photovoltaic (PV) Diesel/Battery system with multiple customers has been studied. A new probabilistic model of the consumer behavior based on Bayesian network and Monte Carlo simulation has been carried out so as to capture the real-time and stochastic aspect of the demand. The analysis has been made by means of a one-year period simulation of the whole system. Statistical data on consumers and meteorological observation data have been used to set the simulation's parameters. Numerical results showed that with the implementation of DSM, energy costs are reduced by 11.3% for equal total consumption, and the use of solar energy resources rose to 54%. This provides insight on the significant performance enhancement offered by a DSM scheme in such a system. [ABSTRACT FROM AUTHOR]