Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Feasibility assessment of hybrid solar photovoltaic-biogas generator based charging station : a case of easy bike and auto rickshaw scenario in a developing nation

The popularity of electric vehicles (EVs) is increasing day by day in the modern world. The charging of EVs from grid-connected charging stations causes a considerable power crisis in the grid. Integrating renewable energy resources (RESs) with conventional energy sources in the power grid is now considered feasible to reduce peak power demand and the inevitable emission effect. Hence, this paper presents an energy solution for EV charging with two RESs, namely, solar photovoltaic (PV) and biogas. HOMER software is utilized to analyze the potency and functionality of solar PV and biogas-based EV charging stations. The proposed system consists of a solar PV system, two biogas engine generators, and a bidirectional converter with battery storage. The variation of different costs, such as net present cost (NPC), initial cost, and cost of energy (COE) for different solar PV systems (3 kW, 4.5 kW, 6 kW, and 9 kW), are analyzed in HOMER software. The 4.5 kW solar PV system is finally selected as the NPC, initial cost, and COE are $93,530,$19,735, and $0.181, respectively, which is efficient. The system’s lifetime is 25 years, where an initial 12 years is required to overcome the system cost, and the remaining 13 years will provide financial benefits. The study also illustrates the effect of solar irradiance, biomass, and the change in the load of the energy management system. The techno−economic analysis shows that the proposed scheme can be an effective energy solution. The emission of greenhouse gases (GHGs), such as CO2, CO, SO2, and NOX, is reduced considerably compared to other existing techniques. The study is expected to be beneficial in renewables-based EV charging systems with techno−economic and environmental feasibility

Study of degradation of a grid connected photovoltaic system

Performance of photovoltaic (PV) systems degrades due to the technology and the operating conditions. The degradation of is one of the key indicators for reliability assessment of a PV system. This paper presents a degradation study of the grid connected PV system located in the campus of the University of Salento. A comparative analysis of actual and theoretical output power is carried out over a monitoring period of five years. PVsyst software is chosen to simulate the output power using actual meteorological data. The hourly expected power generation index is introduced to investigate on degradation and reliability

Forecasting of PV Power Generation using weather input data‐preprocessing techniques

Stochastic nature of weather conditions influences the photovoltaic power forecasts. The present work investigates the accuracy performance of data-driven methods for PV power ahead prediction when different data preprocessing techniques are applied to input datasets. The Wavelet Decomposition and the Principal Component Analysis were proposed to decompose meteorological data used as inputs for the forecasts. A time series forecasting method as the GLSSVM (Group Least Square Support Vector Machine) that combines the Least Square Support Vector Machines (LS-SVM) and Group Method of Data Handling (GMDH) was applied to the measured weather data and implemented for day-ahead PV generation forecast

A preliminary study of the degradation of large-scale c-Si photovoltaic system under four years of operation in semi-arid climates

We report the preliminary results of the degradation study of large-scale photovoltaic (PV) system installed using 1006.74 kWp crystalline silicon (c-Si) PV array. A linear least square (LLS) fitting method is adopted, and the degradation rate (DR) is calculated for the four years based on monitored operational data in semi-arid climates of India. Keywords: Crystalline silicon, PV modules, Degradation rate, Performance ratio, PV in semi-arid climates, Large-scale PV, Linear least squar

Data resulting from the CFD analysis of ten window frames according to the UNI EN ISO 10077-2

Data are related to the numerical simulation performed in the study entitled “CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077” (Malvoni et al., 2016) [1].The paper focuses on the results from a two-dimensional numerical analysis for ten frame sections suggested by the ISO 10077-2 and performed using GAMBIT 2.2 and ANSYS FLUENT 14.5 CFD code.The dataset specifically includes information about the CFD setup and boundary conditions considered as the input values of the simulations.The trend of the isotherms points out the different impacts on the thermal behaviour of all sections with air solid material or ideal gas into the cavities. Keywords: CFD, Thermal break, Window, Frame, 10077, EPB

Photovoltaic power forecasting using statistical methods: impact of weather data

An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP gridconnected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error

Analysis of energy consumption: A case study of an Italian winery

The European Directives promote the energy consumption assessment in residential and industrial sectors in order to identify specific measures for getting energy savings. This paper presents the results of the energy use analysis, carried out for a wine manufacturing firm located in Southern Italy. The energy consumptions of the main wine production processes are investigated, showing that the cooling is the most energy-intensive user. Potential actions as thermal insulation of storage tanks and integration of solar cooling system are proposed and analyzed in terms of energy saving to improve energy efficiency of the refrigeration process in the winery

CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077

The main goal of the EPBD (Energy Performance Buildings Directive) is the improvement of the energy performance of the European buildings. The internal comfort is critically dependent on the envelope that plays a key role in the thermal balance of the entire building. In particular, the windows are one of the most critical elements in terms of solar gains, heat losses and thermal bridges; therefore, the design of high efficiency frames is requested, both in cold and warm climate, but with different peculiarity. The UNI EN ISO 10077-2 provides a methodology to evaluate the frame thermal behaviour and it proposes the criteria to validate the numerical model. This paper presents a two-dimensional numerical method for the thermal behaviour evaluation of the frame sections using GAMBIT 2.2 and ANSYS FLUENT 14.5 CFD code. The results have been validated in accordance with the UNI EN ISO 10077-2. The standard ISO replaces the air gas with a fictitious material “air solid” into the cavities. Besides the simulation carried out with ideal gas entails higher internal surface temperature than the air solid case. Therefore, the standard ISO imposes preventive computational conditions. The proposed numerical method can be implemented for several frame profiles with different features in terms of geometry and materials, representing a valid support in the design of new high thermal performance frames

Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate

The advancement of photovoltaic (PV) energy into electricity market requires efficient photovoltaic power prediction systems. Furthermore the analysis of PV power forecasting errors is essential for optimal unit commitment and economic dispatch of power systems with significant PV power penetrations. This study is focused on the forecasting of the power output of a photovoltaic system located in Apulia - South East of Italy at different forecasting horizons, using historical output power data and performed by hybrid statistical models based on Least Square Support Vector Machines (LS-SVM) with Wavelet Decomposition (WD). Five forecasting horizons, from 1 h up to 24 h, were considered. A detailed error analysis, by mean error and statistical distributions was carried out to compare the performance with the traditional Artificial Neural Network (ANN) and LS-SVM without the WD. The decomposition of the RMSE into three contributions (bias, standard deviation bias and dispersion) and the estimation of the skewness and kurtosis statistical metrics provide a better understanding of the differences between prediction and measurement values. The hybrid method based on LS-SVM and WD out-performs other methods in the majority of cases. It is also evaluated the impact of the accuracy of the forecasting method on the imbalance penalties. The most accurate forecasts permit to reduce such penalties and thus maximize revenue. © 2015 Elsevier Ltd. All rights reserved