Monitoring the OffGrid Photovoltaic Charging of Motorized Shades through IoT Networks Motorized window shades are widely used with embedded lithiumion batteries that are charged by solar energy via photovoltaic (PV) arrays. The complex models of sun irradiance and glass light absorption are developed to forecast the energy balance for long term operation of those offgrid shades. However, the batteries are often depleted due to partial shading and low illumination. The paper presents a new method of monitoring the energy balance of those motorized shades via IoT networks. The battery voltage variations over time are periodically measured to calculate the input solar energy and the motor power consumption. The result can inform users to either add more PV arrays or charge battery using a power adaptor before it is completely discharged. It guarantees the continuous operation of motorized shades.

Comparison of Charging Strategies of Electric Vehicles Using Local Power Production to Minimize Carbon Emissions The German WINNER research project aims at controlling and optimizing local power production, distribution, and consumption in residential areas. Beside residents, who consume electrical energy uncontrollable, there are electric vehicles of a car sharing provider as well. Their charging process can be monitored and controlled. Furthermore, we have a midsize photovoltaic (PV) power plant on the roof of the residential building. Within this local area, we measure energy consumptions and productions. This paper covers subsequent investigations on the possibility of using different charging strategies of electric vehicles from the view of the grid side. Thus, we have to measure energy consumption and production, calculate the surplus and store this excessive energy in the vehicle accumulator. We aim to be neutral against the local energy grid or at least not unhelpful, i. e., we have to avoid power withdrawal if possible. In this paper, we abstract from real use case with electric vehicles. We are using a simple battery model and make some assumptions on the energy consumption and PV production referring to our measurements. Afterwards, a recurrent approach is used to schedule charging and discharging processes along the day. We consider various charging aims over time, i. e., we vary the charging states and times, at which the aimed battery charging state should be reached. Finally, we use our system to answer the question, if our approach to lower the grid load was worth it. For low PV power days and cloudy days, we found usable strategies. Referring to PV peak days we had to do more intensive investigations.