Solar Engineering Of Thermal Processes

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

The purpose of this review article is to investigate the usefulness of different types of life cycle assessment (LCA) studies of electrified vehicles to provide robust and relevant stakeholder information. It presents synthesized conclusions based on 79 papers. Another objective is to search for explanations to divergence and “complexity” of results found by other overviewing papers in the research field, and to compile methodological learnings. The hypothesis was that such divergence could be explained by differences in goal and scope definitions of the reviewed LCA studies. The review has set special attention to the goal and scope formulation of all included studies. First, completeness and clarity have been assessed in view of the ISO standard’s (ISO 2006a, b) recommendation for goal definition. Secondly, studies have been categorized based on technical and methodological scope, and searched for coherent conclusions. Comprehensive goal formulation according to the ISO standard (ISO 2006a, b) is absent in most reviewed studies. Few give any account of the time scope, indicating the temporal validity of results and conclusions. Furthermore, most studies focus on today’s electric vehicle technology, which is under strong development. Consequently, there is a lack of future time perspective, e.g., to advances in material processing, manufacturing of parts, and changes in electricity production. Nevertheless, robust assessment conclusions may still be identified. Most obvious is that electricity production is the main cause of environmental impact for externally chargeable vehicles. If, and only if, the charging electricity has very low emissions of fossil carbon, electric vehicles can reach their full potential in mitigating global warming. Consequently, it is surprising that almost no studies make this stipulation a main conclusion and try to convey it as a clear message to relevant stakeholders. Also, obtaining resources can be observed as a key area for future research. In mining, leakage of toxic substances from mine tailings has been highlighted. Efficient recycling, which is often assumed in LCA studies of electrified vehicles, may reduce demand for virgin resources and production energy. However, its realization remains a future challenge. LCA studies with clearly stated purposes and time scope are key to stakeholder lessons and guidance. It is also necessary for quality assurance. LCA practitioners studying hybrid and electric vehicles are strongly recommended to provide comprehensive and clear goal and scope formulation in line with the ISO standard (ISO 2006a, b).

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Environmental impacts of hybrid, plug-in hybrid, and battery electric vehicles—what can we learn from life cycle assessment?

https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=3863&context=ele_comeng_facwork

Solar Irradiance Forecasting Using Deep Neural Networks

In this paper, an economic dispatch (ED) problem for a community microgrid is studied. In this microgrid, each agent pursues an ED for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, an energy market operating in the presence of the grid is introduced. The proposed market is mainly developed for an experimental community microgrid at the Missouri University of Science and Technology, Rolla, MO, USA, and can be applied to other distribution level microgrids. To develop the algorithm, first, the microgrid is modeled and a dynamic ED algorithm for each agent is developed. Afterwards, an algorithm for handling the market is introduced. Lastly, simulation results are provided to demonstrate the proposed community market, and show the effectiveness of the market in reducing the operation costs of passive and active agents.

Economic Dispatch for an Agent-Based Community Microgrid

This paper introduces a distributed economic dispatch strategy for microgrids with multiple energy storage systems. This strategy overcomes the challenges of dynamic couplings among all decision variables and stochastic variables in a centralized dispatching formulation. The proposed strategy can be implemented in the microgrid central controller as multiple problems with simplified and decomposed formulations. It can also be implemented in the local controllers and managed by the central controller. Numerical studies and experimental results illustrate the effectiveness of the proposed dispatching systems.

Economic Dispatch of a Hybrid Microgrid With Distributed Energy Storage

This paper investigates application of an unconstrained model predictive controller (MPC) known as a finite horizon linear quadratic regulator (LQR) for current control of a switched reluctance motor (SRM). The proposed LQR can cope with the measurement noise as well as uncertainties within the machine inductance profile. This paper utilizes MPC to generate the optimal duty cycles for drive of SRMs using pulse-width modulation (PWM) in oppose to delta-modulation. In this paper, first a practical MPC scheme for embedded implementation of the system is introduced. Afterward, Kalman filtering is used for state estimation while an adaptive controller is used to dynamically tune and update both MPC and Kalman models. Hence, the overall control structure is considered as a stochastic MPC with adaptive model calibration. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed method.

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Model Predictive Current Control of Switched Reluctance Motors With Inductance Auto-Calibration

This paper presents an interleaved boost converter with a bifold Dickson voltage multiplier suitable for interfacing low-voltage renewable energy sources to high-voltage distribution buses and other applications that require a high-voltage-gain conversion ratio. The proposed converter was constructed from two stages: an interleaved boost stage, which contains two inductors operated by two low-side active switches, and a voltage multiplier cell (VMC) stage, which mainly consists of diodes and capacitors to increase the overall voltage gain. The proposed converter offers a high-voltage-gain ratio with low voltage stress on the semiconductor switches as well as the passive components. This allows the selection of efficient and compact components. Moreover, the required inductance that ensures operation in the continuous conduction mode (CCM) is lower than the one in the conventional interleaved boost converter. The distinction of the proposed converter is that the inductors’ currents are equal, regardless of the number of VMCs. Equal sharing of interleaved boost-stage currents reduces the conduction loss in the active switches as well as the inductors and thus improves the overall efficiency, as the conduction power loss is a quadratic function. In this paper, the theory of operation and steady-state analysis of the proposed converter are illustrated and verified by simulation results. A $\text{200-W}$ hardware prototype was implemented to convert a $\text{20-V}$ input source to a $\text{400-V}$ dc load and validate both the theory and the simulation.

Pourya Shamsi

Papers

Solar Irradiance Forecasting Using Deep Neural Networks

Economic Dispatch for an Agent-Based Community Microgrid

Economic Dispatch of a Hybrid Microgrid With Distributed Energy Storage

Model Predictive Current Control of Switched Reluctance Motors With Inductance Auto-Calibration

High-Voltage-Gain DC–DC Step-Up Converter With Bifold Dickson Voltage Multiplier Cells