An Optimized Combination of a Large Grid Connected PV System along with Battery Cells and a Diesel Generator

Environmental, economical and technical benefits of photovoltaic (PV) systems make them to be used in many countries. The main characteristic of PV systems is the fluctuations of their output power. Hence, high penetration of PV systems into electric network could be detrimental to overall system performance. Furthermore, the fluctuations in the output power of PV systems make it difficult to predict their output, and to consider them in generation planning of the units. The main objective of this paper is to propose a hybrid method which can be used to control and reduce the power fluctuations generated from large grid- connected PV systems. The proposed method focuses on using a suitable storage battery along with curtailment of the generated power by operating the PV system below the maximum power point (MPP) and deployment of a diesel generator. These methods are analyzed to investigate the impacts of implementing them on the economical benefits that the PV system owner could gain. To maximize the revenues, an optimization problem is solved

Urban Drainage Infrastructures Toward a Sustainable Future

Urban drainage infrastructures (UDIs), as one of the main urban infrastructures, serve some important functions in urban areas and can be considered vital to reach the global goals that were set out by the United Nations to tackle current problems and make a more sustainable future. However, climate change and other drivers such as population growth, infrastructure aging, and rapid urbanization are exerting pressure on UDIs. This can not only undermine the expected performance of UDIs but also deviate from their role in the global goals. This chapter aims to shed light on the probable impacts of climatic change, urbanization, etc., on UDIs, and to propose measures to make them more resilient. Urbanization and climate change can have different negative impacts on deteriorating the performance of UDIs through an increase in flood risk and water pollution-related problems, which highlight the significance of incorporating these stressors into any adaptation and rehabilitation strategies in UDIs

Solar Irradiance Forecasting using Deep Neural Networks

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying what form the variation should take and allow the extraction of high-level features. The DRNN is used to predict the irradiance. The data utilized in this study is real data obtained from natural resources in Canada. The simulation of this method will be compared to several common methods such as support vector regression and feedforward neural networks (FNN). The results show that deep learning neural networks can outperform all other methods, as the performance tests indicate

Modeling and Simulation of Microgrid

Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control and production. To deal with this change, one needs to interpret the electrical grid as a system of systems (SoS) and build new models that capture the dynamic behavior of the microgrid. In this paper, different models of electric components in a microgrid are presented. These models use complex system modeling techniques such as agent-based methods and system dynamics, or a combination of different methods to represent various electric elements. Examples show the simulation of the solar microgrid is presented to show the emergent properties of the interconnected system. Results and waveforms are discussed

Chaotic Behavior in High-Gain Interleaved DC-DC Converters

In this paper, chaotic behavior in high gain dc-dc converters with current mode control is explored. The dc-dc converters exhibit some chaotic behavior because they contain switches. Moreover, in power electronics (circuits with more passive elements), the dynamics become rich in nonlinearity and become difficult to capture with linear analytical models. Therefore, studying modeling approaches and analysis methods is required. Most of the high-gain dc-dc boost converters cannot be controlled with only voltage mode control due to the presence of right half plane zero that narrows down the stability region. Therefore, the need of current mode control is necessary to ensure the stability of this type of boost converter. A significant number of the work reported so far has concentrated on explaining the chaos phenomena in the language of the nonlinear dynamics literature. In addition to analyzing and studying chaotic behaviors, this presents some ideas about moving toward gainful utilization of the nonlinear properties of power electronics. Simulation and experimental studies are included to validate the theory, and results will be discussed

Development of Dam-Break Model Considering Real Case Studies with Asymmetric Reservoirs

Dam-break flow is known as one of the most horrible phenomena. Some hypothetical reservoir geometries were evaluated in literature, but in nature, each reservoir has a unique geometry. In the present research, dam-break flow was studied based on different reservoir geometries using FLOW-3D. Six reservoirs were considered: reservoirs R1 and R2 belonged to Mahabad Dam (Iran) and Tignes Dam (France), with asymmetric reservoirs, respectively; reservoirs R3 and R4 had symmetrical trapezoidal reservoirs with angles 30 and 45 degrees, respectively; reservoir R5 had a rectangular shape, extending from one side; and reservoir R6 had a long reservoir, which also was used to verify FLOW-3D. The model performance was verified by experimental results and FLUENT model in literature. Results showed FLOW-3D with mesh sizes 30×30×30 mm and k-ɛ turbulence model outperformed FLUENT, based on R2, RMSE, and MAE. The results of water levels and flow velocities at five points proved that dam-break flow could vary from one dam to another, considering reservoir geometry. Peak water levels and velocities have been measured to show how reservoir geometry could cause catastrophic flow

Financial Performance Model of Sports Product Manufacturers Based on Entrepreneurial Marketing and Market Orientation with the Mediating Role of Information Technology

Today, the sports industry is known through modern technology, especially in the field of manufacturing sports tools and equipment. The present study uses correlation and applied methodology, which was done in the field using Structural Equations Modeling (SEM). The statistical population included producers of sports products, of which 320 producers from Iran were considered through available sampling. Morris (2002) Standard Entrepreneurial Marketing Questionnaire, Moghimi Market Orientation (2011), Noh and Fitzsimmons Information Technology (1999), and Azizi (2011) Financial Performance Questionnaire were used to collect data. Finally, 292 questionnaires were received physically and electronically. The results showed that entrepreneurial marketing and market orientation had a direct and indirect effect (mediated by technology) on financial performance and they significantly affected the financial performance. Finally, the model had the goodness of fit. In general, manufacturers who adopted a combination of entrepreneurial marketing strategy and information technology had better and more rational financial performance by taking advantage of attractive entrepreneurial opportunities. According to the research results, it is suggested that manufacturers provide the consumers with superior value by paying attention to the market orientation and innovation, to enable the company to achieve a competitive advantage

A Family of Scalable Non-Isolated Interleaved DC-DC Boost Converters with Voltage Multiplier Cells

In this paper, a family of non-isolated interleaved high-voltage-gain DC-DC converters is presented. This family can be used in a wide variety of applications, such as in a photovoltaic systems interface to a high voltage DC distribution bus in a microgrid and an X-ray system power supply. The general structure of this family is illustrated and consists of two stages: An interleaved boost stage and a voltage multiplier stage. The interleaved boost stage is a two-phase boost converter, and it converts the input DC voltage to an AC square waveform. Moreover, using the interleaved boost stage increases the frequency of the AC components so that it can be easily filtered with smaller capacitors and, therefore, makes the input current smoother than the one from the conventional boost converter. The voltage multiplier cell (VMC) can be a Dickson cell, Cockcroft-Walton (CW), or a combination of the two. The VMC stage rectifies the square-shaped voltage waveform coming from the interleaved boost stage and converts it to a high DC voltage. Several combinations of VMCs and how they can be extended are illustrated, and the difference between them is summarized so that designers can be able to select the appropriate topology for their applications. An example of this converter family is illustrated with detailed modes of operation, a steady-state analysis, and an efficiency analysis. The example converter was simulated to convert 20 VDC to 400 VDC , and a 200 W hardware prototype was implemented to verify the analysis and simulation. The results show that the example has a peak efficiency of 97% of this family of converters and can be very suitable for interfacing renewable energy sources to a 400 VDC DC distribution system

Real-Time Urban Flood Forecasting: Application of Hybrid Modelling Using Both Physically based and Data-Driven models

© 2023 The Author(s).Today, urban flood forecasting is modelled well by hydrologists through physically based models in which different weather data, characteristics of catchments and streams/conduits are used as inputs to provide water level in UDS or water depth of surface runoff [1]. However, continuous access to these data can be challenging and demanding for model calibration or real-time applications that result in lack of providing accurate forecasting [2]. Alternatively, data-driven models can be used in hydrology and data sciences to provide high-speed, less data demanding, and more accuracy for especially short-term water level/depth of urban flooding [3]. However, these models can be inaccurate when new situations, especially new climate change based extreme events, occurred because these models are unable to understand and be adapted with different physical and hydrological situation of catchments [4]. Therefore, while both approaches are well-explored in simulating rainfall-runoff relationship over the urban catchments of interest, coupling these models sound promising and worth investigating in real-time applications of flood warning systems. The present study critically investigates the applications of real-time hybrid models in which physically based and data-driven models are coupled together as integrated platform to take advantages of each type of modelling. The results show three different approaches have been highlighted in this area: (1) using physically-based models to provided up-to-date input data for machine-learning based modelling, (2) applying data-mining techniques to extract the rainfall-runoff features that are used for physically-based models, particularly different types of storm water management model, (3) error bias adjustment or interpolation of forecasts by using both physically-based and data-drive modelling. Results also indicate that the first approach have been usually expressed when input database faces missing data problem, high value uncertainty or highly impacted by climate-related extreme events. This approach was also used for small-scale but dense city area without flexibility in surface lands or underground modifications. On the other hand, the second approach have been presented where big database are available and data screening are required. Furthermore, this modelling approach is more appropriate for high variability and high coverage catchment areas. Finally, the last modelling approach outperforms other approaches in covering both quality and quantity of data resources. However, integration of interpolation and bias adjustment of individual models still have remains as open case than should be more tested in the future.Peer reviewe