4 research outputs found
Smarter Charging: Modeling optimal EV charging in solar parking lots for reducing peak demand, considering uncertainty in solar power forecasting and EV energy demand
No full textSmart charging offers the potential for electric vehicles to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many computer models have been developed to simulate the behavior of smart charging. Yet these models often assume that future information is known perfectly, including when vehicles will begin charging and how much solar energy will be available at that time. In reality, this information is subject to uncertainty, meaning the performance of smart charging may be worse than predicted by these models. This report details the development of an improved model which considers future uncertainty in smart charging behavior. It is determined that uncertainty does decrease the effectiveness of smart charging, but with strategies that are able to robustly consider this uncertainty smart charging can still offer tremendous benefits over traditional uncoordinated charging
PV System Monitoring and Fault Detection using Peer Systems
No full textSolar energy is an abundant, scalable, and clean source of energy. With an exponential drop in prices of PV modules, more and more rooftop photovoltaic (PV) systems are being installed worldwide. Since these small-scale PV systems do not use expensive sensors, it is difficult to detect malfunctions for these systems. This could lead to lower energy generation along with financial losses for the owners. Thus, a method for PV yield monitoring is developed for early and remote fault detection. This method does not use the conventional analytical approach as it depends on inaccurately extrapolated weather data. Instead, the proposed method uses data from similar or neighbouring or peer PV systems for estimating the expected energy generation. By comparing the expected energy generation with actual energy generation, a faulty system can be flagged. In this project, information from about 12000 PV systems is used, which includes system design information such as location, number of panels, panel orientation, etc. along with the historical daily energy generation for periods ranging from two months to up to seven years per system.In this thesis, a machine learning model was developed for predicting energy yields, which uses a Genetic Algorithm (GA) for optimization. This model splits the available data into system design, system location and system yield data. Thus, the model uses these as criteria for finding PV systems similar to the monitored system. Once good peer systems are located, system yield data of those systems are used for estimating the expected energy yields of the monitored system. The three criteria used by the model do not have equal influence on finding good peers, thus, the model had to be trained or optimization was done using the training data. Post optimization, the relative influence of system design: system yield: system location was found to be 0.125:0.875:0 with on average 16 good peers needed for accurate predictions. The proposed model has a mean normalized RMSE of 0.057 and about 95% of the systems tested had an R2 score higher than 0.85. The existing commercial software at Solar Monkey has a mean normalized RMSE of 0.082 and about 83% of the systems tested had an R2 score higher than 0.85.The predicted energy generation calculated by the proposed model is compared with the actual energy generation to detect any malfunctions that may have occurred in the monitored system. Thus, 120 randomly chosen PV systems were analysed for faults. Based on this, a semi-automatic categorization framework was created with the proposed model as one of the criteria to detect common faults in the system such as missing data, under-performance, over-performance and false positives. Using the categorization framework, certain PV systems were found as interesting examples for under-performance with broken panels or string, over-performance with system size change and false positives. The model is especially useful for separating system design mismatch from actual system malfunctions. With the framework, it was shown how the proposed peer-to-peer model can be used for fault detection along with certain other models.Electrical Engineering | Sustainable Energy Technolog
Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty
No full textScheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilizationEnergy TechnologyEnergie and Industri
Photovoltaic system monitoring and fault detection using peer systems
No full textMonitoring residential scale photovoltaic (PV) systems is important for maximizing the energy yield and detecting malfunctions. Analytical-based approaches are not reliable in these systems because of the lack of on-site measurements and detailed PV system specifications. In this paper, a collaborative approach is proposed which does not depend on weather data but on similar PV systems. Based on the so-called performance-to-peer approach, the aim of this work is to improve this baseline model by adding PV systems characteristics and by optimizing with machine learning techniques. The methodology has been tested in a fleet of more than 12,000 PV systems located in the Netherlands with up to 7 years of data per system. The proposed model achieves an average (Formula presented.) of 94.1% and a NRMSE of 0.05, outperforming in terms of (Formula presented.) the baseline model by 1.4 points, and the analytical approach by 3.8. The data requirements of this model are not high: With 1,700 years of PV system data with daily resolution, the maximum performance can be achieved as long as a minimum of 6 months of data per system and 100 PV systems are considered. The application of this model for fault detection and categorization has also been shown. The proposed approach has shown its strengths with respect to other methods through its ability of distinguishing between system mismatch and actual fault and of adapting to new situations via retraining.Photovoltaic Materials and Device
