scholarly journals A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Raphael Anaadumba ◽  
Qi Liu ◽  
Bockarie Daniel Marah ◽  
Francis Mawuli Nakoty ◽  
Xiaodong Liu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Mobile Devices ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Electrical Network ◽  
Effective Control ◽  
Energy Sources ◽  
Forecasting Model ◽  
Communication Overhead ◽  
Energy Forecasting

AbstractEnergy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

Development of a Basic Voltage Source for a Micro Grid Based on Renewable Energy Sources

2021 ◽  
Vol 1 (42) ◽  
pp. 39-43
Author(s):  
Leonid Yuferev ◽  
Keyword(s):  
Renewable Energy ◽  
Single Phase ◽  
Block Diagram ◽  
Renewable Energy Sources ◽  
Electrical Energy ◽  
Electrical Network ◽  
Voltage Source ◽  
Energy Sources ◽  
Micro Grid ◽  
Alternating Voltage

At the heart of a micro-grid that includes several sources of electrical energy, including renewable energy, there must be a base (reference) source that sets the voltage and frequency in the network. (Research purpose) The research purpose is in developing a basic voltage source of a micro-grid, which includes renewable energy sources. (Materials and methods) The article shows that the sources of electrical energy are different, but the main requirement for them is to be able to supply voltage to the electrical network at any time, there is a standard voltage scale in the network. For a micro-grid, which will include various sources of electrical energy with a total power of up to 25 kW, you can limit yourself to a voltage of 220 V and a single-phase electrical network. (Results and discussion) The article presents a block diagram; technical characteristics and electrical diagram of the inverter of a single-phase basic voltage source; a method for calculating the output filter of the inverter based on pulse-width modulation technology. (Conclusions) When using renewable energy sources in the micro-grid, it is necessary to take into account that there are periods of time in which they do not generate electrical energy and choose as the base one that will not have such moments, or you need to use a temporary electricity storage device. It is possible to use a chemical source or a battery of accumulators as a base source. When creating a micro-grid of alternating voltage, an inverter is added to these sources from constant voltage to alternating voltage. The article presents a block diagram of a base voltage source with an AC output reference voltage to create a micro grid with a power of up to 25 kW.

Experimental Study of the Sustainability of a Renewable-Energy-Driven Residential System With Heating Loads

2018 ◽  
Author(s):  
Takao Kakizaki ◽  
Kosuke Hirano ◽  
Norio Morohashi ◽  
Masahito Oguma
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Mixed Integer ◽  
Effective Control ◽  
Energy Sources ◽  
Independent System ◽  
Sustainable Operation ◽  
Residential House ◽  
Heating Loads ◽  
Large Heating

The sustainability of an energy-independent system with a relatively large heating load and that is driven by multiple renewable energy sources such as a photovoltaic battery and a biofuel generator has been investigated. The utilization of renewable energy has become one of the most important areas of interest for residential houses, as seen in zero energy house trends. In particular, technologies for energy-independent residential houses that can be categorized as off-grid systems have gained importance. In this paper, the design concept and the detail of the constructed pilot scale test system comprising a photovoltaic power (PV) generator and a biofuel power generator (BFG) are explained. Experimental results prove that continuous system operation is possible based on an effective control of these multiple renewable energy sources, even for relatively large heating loads. The results also imply that usage of multiple-source renewable energy is effective for the sustainable operation of an energy-independent residential house. Moreover, optimizing the energy consumption of the energy-independent system with heating is discussed. Here, mixed integer linear programming has been applied to the system driven by multiple renewable energy sources to optimize the sustainable operation of the system. The simulation results show that it is possible to reduce the cost incurred on biofuel by about 40% as compared with that of the system driven only by biofuel energy. Consequently, multiple sources of renewable energy are effective for the sustainable operation of an energy-independent residential house even with relatively large heating loads.

scholarly journals An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1438 ◽  
Author(s):  
Sunghyeon Choi ◽  
Jin Hur
Keyword(s):  
Renewable Energy ◽  
Fossil Fuels ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Energy Output ◽  
Energy Sources ◽  
Output Data ◽  
Past Data ◽  
Base Learner

As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

scholarly journals Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6405
Author(s):  
Muhammad Ahsan Zamee ◽  
Dongjun Won
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Gradient Descent ◽  
Renewable Energy Sources ◽  
Population Based ◽  
Energy Sources ◽  
Forecasting Model ◽  
Dynamic Learning ◽  
Artificial Neural

A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

scholarly journals Managing Energy Demand of Renewable Energy Sources by using Smart Grid Technology

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1234-1238
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Electrical Network ◽  
Energy Sources ◽  
Grid Technology ◽  
Renewable Sources ◽  
Smart Grid Technology ◽  
Day By Day

Day by day the non-renewable sources of power are getting depleted. Due to higher demands of energy the necessity of such sources is becoming more important in the field of electrical power according to the estimates of power sector the usage of renewable component has increased by 9% w.r.t to 2013. Even though non-renewable sources capacity increasing day by day the complexity of the power system network increasing which result in mismatch between voltage and power in electrical network. By using smart grid technology, we can minimize the unequal effect of voltage and power in the domain of renewable energy source. Smart grid technology uses digital transformation of electrical parameter to central coordination center at high sample rate, which uses effective and reliable operation in managing energy demand of renewable energy sources

scholarly journals Problems of electromagnetic compatibility of powerful energy associations during mass connection of renewable energy sources

2021 ◽  
pp. 34-45
Author(s):  
Yu.A. Papaika ◽  
O.G. Lysenko ◽  
A.V. Bublikov ◽  
I.G. Olishevskiy
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Power Supply ◽  
Electromagnetic Compatibility ◽  
Renewable Energy Sources ◽  
Electrical Equipment ◽  
Electrical Network ◽  
Energy Sources ◽  
Nonlinear Loads ◽  
Supply Systems

Purpose. Analysis of the problem of energy efficiency and electromagnetic compatibility of powerful energy associations with nonlinear loads and renewable energy sources. Finding promising ways to increase energy efficiency of power supply systems. Methodology. Mathematical modeling of electromagnetic compatibility. Findings. The analysis of the problem of energy efficiency and electromagnetic compatibility of powerful energy associations with nonlinear loads and renewable energy sources allows us to formulate the following provisions that determine the objectives of this study. A promising way to increase the energy efficiency of power supply systems is the introduction of refined methods of analysis and forecasting of electrical modes of industrial enterprises, as well as indicators of voltage quality and reliability of electrical equipment. Originality.  Although the problem of electromagnetic compatibility has been the subject of numerous domestic and foreign studies, it should be noted that most of these works consider the processes of generating electromagnetic interference in the electrical network without reference to the technological schedules of electrical equipment. Practical value.  One of the electromagnetic effects, which is manifested in the operation of frequency converters, are significant levels of interharmonics and higher harmonics, which are generated in the electrical network and contribute to the growth of electricity losses and reduce the service life of electrical equipment. However, the regularities connecting the parameters of the power system and the modes of powerful industrial converters have not been studied, and the substantiation of the parameters of a rational energy efficient mode of the power supply system, taking into account individual graphs of higher harmonics, has not been carried out until today.

scholarly journals Application of PSO for optimal coordination of directional over-current relays in distribution system with distributed renewable energy sources

2021 ◽  
Vol 10 (2) ◽  
pp. 188
Author(s):  
Lazhar Bougouffa ◽  
Abdelaziz Chaghi
Keyword(s):  
Renewable Energy ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Distribution Networks ◽  
Electrical Network ◽  
Energy Sources ◽  
Optimal Coordination

<p>The use of Distributed Renewable Energy Sources in the electrical network has expanded greatly. But, integration of these resources into distribution systems caused more problems in protection related issues such as mis-coordination, and changes the direction and value of fault currents. When connecting new D-RES to electrical power distribution networks, it is required to re-coordinate Directional Over-CurrentRelays (DOC-Relays) to ensure the continuity of the power transmission when the short circuits take place. This work presented a Particle Swarm Optimization (PSO) algorithm to determine two independent variables called Pickup current (Ip) and Time Dial Setting (TDS) for optimal setting of relays. From analysis result, the impacts of RES location in the distribution system on DOCRs had been observed on the optimal relays settings</p>

Renewable Energy Sources

IEE Review ◽  
1991 ◽  
Vol 37 (4) ◽  
pp. 152
Author(s):  
Kenneth Spring
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Energy Sources
Sign in / Sign up

Export Citation Format

Share Document