scholarly journals Open-source multi-year power generation, consumption, and storage data in a microgrid

2021 ◽  
Vol 13 (2) ◽  
pp. 025301
Author(s):  
Sushil Silwal ◽  
Colton Mullican ◽  
Yi-An Chen ◽  
Avik Ghosh ◽  
John Dilliott ◽  
...  
Keyword(s):  
Power Generation ◽  
Open Source ◽  
And Storage

scholarly journals Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution

2021 ◽  
Vol 13 (15) ◽  
pp. 8182
Author(s):  
José María Portalo ◽  
Isaías González ◽  
Antonio José Calderón
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Smart Grids ◽  
Raspberry Pi ◽  
Operator Interface ◽  
Open Source Hardware ◽  
Real Time Information ◽  
And Storage ◽  
Time Information ◽  
Pv Generator

Smart grids and smart microgrids (SMGs) require proper monitoring for their operation. To this end, measuring, data acquisition, and storage, as well as remote online visualization of real-time information, must be performed using suitable equipment. An experimental SMG is being deployed that combines photovoltaics and the energy carrier hydrogen through the interconnection of photovoltaic panels, electrolyser, fuel cell, and load around a voltage bus powered by a lithium battery. This paper presents a monitoring system based on open-source hardware and software for tracking the temperature of the photovoltaic generator in such an SMG. In fact, the increases in temperature in PV modules lead to a decrease in their efficiency, so this parameter needs to be measured in order to monitor and evaluate the operation. Specifically, the developed monitoring system consists of a network of digital temperature sensors connected to an Arduino microcontroller, which feeds the acquired data to a Raspberry Pi microcomputer. The latter is accessed by a cloud-enabled user/operator interface implemented in Grafana. The monitoring system is expounded and experimental results are reported to validate the proposal.

ENERGY EFFICIENCY EVALUATION FOR A “GREEN” POWER GENERATION PROCESS WITH MINIMUM EFFORT ON CARBON DIOXIDE CAPTURE AND STORAGE

2012 ◽  
Vol 199 (12) ◽  
pp. 1642-1651 ◽  
Author(s):  
Suttichai Assabumrungrat ◽  
Janewit Phromprasit ◽  
Siriporn Boonkrue ◽  
Worapon Kiatkittipong ◽  
Wisitsree Wiyaratn ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Power Generation ◽  
Energy Efficiency ◽  
Carbon Dioxide Capture ◽  
Efficiency Evaluation ◽  
Generation Process ◽  
Green Power ◽  
Energy Efficiency Evaluation ◽  
And Storage

scholarly journals Solar Tower Power Plants of Molten Salt External Receivers in Algeria: Analysis of Direct Normal Irradiation on Performance

2018 ◽  
Vol 8 (8) ◽  
pp. 1221 ◽  
Author(s):  
Abdelkader Rouibah ◽  
Djamel Benazzouz ◽  
Rahmani Kouider ◽  
Awf Al-Kassir ◽  
Justo García-Sanz-Calcedo ◽  
...  
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Thermal Energy ◽  
Greenhouse Effect ◽  
Energy Production ◽  
Power Plants ◽  
Storage Capacity ◽  
Real Data ◽  
Multiple Power ◽  
And Storage

The increase of solar energy production has become a solution to meet the demand of electricity and reduce the greenhouse effect worldwide. This paper aims to determine the performance and viability of direct normal irradiation of three solar tower power plants in Algeria, to be installed in the highlands and the Sahara (Béchar, El Oued, and Djelfa regions). The performance of the plants was obtained through a system advisor model simulator. It used real data gathered from appropriate meteorological files. A relationship between the solar multiple (SM), power generation, and thermal energy storage (TES) hours was observed. The results showed that the optimal heliostat field corresponds to 1.8 SM and 2 TES hours in Béchar, 1.2 SM and 2 TES hours for El Oued, and 1.5 SM and 4 TES hours for Djelfa. This study shows that there is an interesting relationship between the solar multiple, power generation, and storage capacity.

scholarly journals Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2097 ◽  
Author(s):  
Chenhua Ni ◽  
Xiandong Ma
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Convolutional Neural Network ◽  
Wave Height ◽  
Regression Models ◽  
Cost Effective ◽  
Wave Power ◽  
Marine Energy ◽  
Artificial Network ◽  
And Storage

Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

SunPower: A Path Toward Strategic Development

2021 ◽  
Vol 3 (2) ◽  
pp. 77-86
Author(s):  
Aarti Singh
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Mergers And Acquisitions ◽  
Energy Demand ◽  
Energy Sector ◽  
Growth Path ◽  
Strategic Choices ◽  
Strategic Development ◽  
Solar Power Generation ◽  
And Storage

SunPower Corporation is an American organization working in the solar energy sector specialized in power generation and solar energy storage. This case highlights the strategic growth path of SunPower corporation, which originated as an outcome of effective solar cells production. This case also discussed how the energy sector considers solar energy the vital energy source, and solar energy organizations grow to capture the energy demand. The case also highlights the various consolidations of SunPower in terms of different mergers and acquisitions as essential strategies to succeed. The case also discusses the competitors’ strategy of the top five solar power generation and storage firms, which give a clear view of how different solar firms are building their space in the solar energy sector. With competitors’ discussion, this helps to understand the challenge faced by SunPower to beat the competition among the other solar firms. In the end, the case focused on the ongoing and future strategic choices of SunPower as SunPower Corporation.

Sequential Combustion in Ansaldo Energia Gas Turbines: The Technology Enabler for CO2-Free, Highly Efficient Power Production Based on Hydrogen

2020 ◽  
Author(s):  
Andrea Ciani ◽  
John P. Wood ◽  
Anders Wickström ◽  
Geir J. Rørtveit ◽  
Rosetta Steeneveldt ◽  
...  
Keyword(s):  
Power Generation ◽  
Free Energy ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Power Plants ◽  
Carbon Capture And Storage ◽  
Combined Cycle ◽  
Fuel Flexibility ◽  
Combustion Technology ◽  
And Storage

Abstract Today gas turbines and combined cycle power plants play an important role in power generation and in the light of increasing energy demand, their role is expected to grow alongside renewables. In addition, the volatility of renewables in generating and dispatching power entails a new focus on electricity security. This reinforces the importance of gas turbines in guaranteeing grid reliability by compensating for the intermittency of renewables. In order to achieve the Paris Agreement’s goals, power generation must be decarbonized. This is where hydrogen produced from renewables or with CCS (Carbon Capture and Storage) comes into play, allowing totally CO2-free combustion. Hydrogen features the unique capability to store energy for medium to long storage cycles and hence could be used to alleviate seasonal variations of renewable power generation. The importance of hydrogen for future power generation is expected to increase due to several factors: the push for CO2-free energy production is calling for various options, all resulting in the necessity of a broader fuel flexibility, in particular accommodating hydrogen as a future fuel feeding gas turbines and combined cycle power plants. Hydrogen from methane reforming is pursued, with particular interest within energy scenarios linked with carbon capture and storage, while the increased share of renewables requires the storage of energy for which hydrogen is the best candidate. Compared to natural gas the main challenge of hydrogen combustion is its increased reactivity resulting in a decrease of engine performance for conventional premix combustion systems. The sequential combustion technology used within Ansaldo Energia’s GT36 and GT26 gas turbines provides for extra freedom in optimizing the operation concept. This sequential combustion technology enables low emission combustion at high temperatures with particularly high fuel flexibility thanks to the complementarity between its first stage, stabilized by flame propagation and its second (sequential) stage, stabilized by auto-ignition. With this concept, gas turbines are envisaged to be able to provide reliable, dispatchable, CO2-free electric power. In this paper, an overview of hydrogen production (grey, blue, and green hydrogen), transport and storage are presented targeting a CO2-free energy system based on gas turbines. A detailed description of the test infrastructure, handling of highly reactive fuels is given with specific aspects of the large amounts of hydrogen used for the full engine pressure tests. Based on the results discussed at last year’s Turbo Expo (Bothien et al. GT2019-90798), further high pressure test results are reported, demonstrating how sequential combustion with novel operational concepts is able to achieve the lowest emissions, highest fuel and operational flexibility, for very high combustor exit temperatures (H-class) with unprecedented hydrogen contents.

Coordinative Optimization Control of Microgrid Based on Model Predictive Control

2022 ◽  
pp. 233-252
Author(s):  
Changbin Hu ◽  
Lisong Bi ◽  
ZhengGuo Piao ◽  
ChunXue Wen ◽  
Lijun Hou
Keyword(s):  
Power Generation ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Unit Commitment ◽  
Feedback Mechanism ◽  
Battery Life ◽  
Operation Costs ◽  
Power Generation Technology ◽  
Reasonable Behavior ◽  
And Storage

This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.

scholarly journals A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

2020 ◽  
Vol 12 (17) ◽  
pp. 7035
Author(s):  
Pei Huang ◽  
Xingxing Zhang ◽  
Benedetta Copertaro ◽  
Puneet Kumar Saini ◽  
Da Yan ◽  
...  
Keyword(s):  
Power Generation ◽  
Energy Transition ◽  
Mobility Model ◽  
Solar Photovoltaics ◽  
Performance Improvements ◽  
Urban Energy ◽  
Advanced Controls ◽  
Energy Sharing ◽  
And Storage ◽  
Pv Power Generation

The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy storage, the development of the energy sharing concept and the associated advanced controls, the conventional solar mobility model (i.e., solar-to-vehicles (S2V), using solar energy in a different location) and context are becoming less compatible and limited for future scenarios. For instance, energy sharing within a building cluster enables buildings to share surplus PV power generation with other buildings of insufficient PV power generation, thereby improving the overall PV power utilization and reducing the grid power dependence. However, such energy sharing techniques are not considered in the conventional solar mobility models, which limits the potential for performance improvements. Therefore, this study conducts a systematic review of solar mobility-related studies as well as the newly developed energy concepts and techniques. Based on the review, this study extends the conventional solar mobility scope from S2V to solar-to-buildings, vehicles and storage (S2BVS). A detailed modeling of each sub-system in the S2BVS model and related advanced controls are presented, and the research gaps that need future investigation for promoting solar mobility are identified. The aim is to provide an up-to-date review of the existing studies related to solar mobility to decision makers, so as to help enhance solar power utilization, reduce buildings’ and EVs’ dependence and impacts on the power grid, as well as carbon emissions.

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