Modelización de la demanda de energía eléctrica: más allá de la normalidad (Modeling of Electrical Energy Demand: Beyond Normality)

2019 ◽  
Author(s):  
Juan Fernando Rendón ◽  
Alfredo Trespalacios ◽  
Lina Cortes ◽  
Hernán Villada
Keyword(s):  
Energy Demand ◽  
Electrical Energy

scholarly journals A Sizing Method for PV–Battery–Generator Systems for Off-Grid Applications Based on the LCOE

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1988
Author(s):  
Ioannis E. Kosmadakis ◽  
Costas Elmasides
Keyword(s):  
Renewable Energy ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Temporal Distribution ◽  
Electrical Energy ◽  
Optimal Number ◽  
Diesel Generator ◽  
Levelized Cost Of Electricity ◽  
Sufficient Power ◽  
Battery Energy

Electricity supply in nonelectrified areas can be covered by distributed renewable energy systems. The main disadvantage of these systems is the intermittent and often unpredictable nature of renewable energy sources. Moreover, the temporal distribution of renewable energy may not match that of energy demand. Systems that combine photovoltaic modules with electrical energy storage (EES) can eliminate the above disadvantages. However, the adoption of such solutions is often financially prohibitive. Therefore, all parameters that lead to a functionally reliable and self-sufficient power generation system should be carefully considered during the design phase of such systems. This study proposes a sizing method for off-grid electrification systems consisting of photovoltaics (PV), batteries, and a diesel generator set. The method is based on the optimal number of PV panels and battery energy capacity whilst minimizing the levelized cost of electricity (LCOE) for a period of 25 years. Validations against a synthesized load profile produced grid-independent systems backed by different accumulator technologies, with LCOEs ranging from 0.34 EUR/kWh to 0.46 EUR/kWh. The applied algorithm emphasizes a parameter of useful energy as a key output parameter for which the solar harvest is maximized in parallel with the minimization of the LCOE.

scholarly journals A Theoretical Study on Reversible Solid Oxide Cells as Key Enablers of Cyclic Conversion between Electrical Energy and Fuel

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4517
Author(s):  
Saheli Biswas ◽  
Shambhu Singh Rathore ◽  
Aniruddha Pramod Kulkarni ◽  
Sarbjit Giddey ◽  
Sankar Bhattacharya
Keyword(s):  
Energy Demand ◽  
Electrode Materials ◽  
Scale Up ◽  
High Capacity ◽  
Electrical Energy ◽  
Solid Oxide ◽  
Lower Efficiency ◽  
Internal Reforming ◽  
Fuels And Chemicals ◽  
The Cost

Reversible solid oxide cells (rSOC) enable the efficient cyclic conversion between electrical and chemical energy in the form of fuels and chemicals, thereby providing a pathway for long-term and high-capacity energy storage. Amongst the different fuels under investigation, hydrogen, methane, and ammonia have gained immense attention as carbon-neutral energy vectors. Here we have compared the energy efficiency and the energy demand of rSOC based on these three fuels. In the fuel cell mode of operation (energy generation), two different routes have been considered for both methane and ammonia; Routes 1 and 2 involve internal reforming (in the case of methane) or cracking (in the case of ammonia) and external reforming or cracking, respectively. The use of hydrogen as fuel provides the highest round-trip efficiency (62.1%) followed by methane by Route 1 (43.4%), ammonia by Route 2 (41.1%), methane by Route 2 (40.4%), and ammonia by Route 1 (39.2%). The lower efficiency of internal ammonia cracking as opposed to its external counterpart can be attributed to the insufficient catalytic activity and stability of the state-of-the-art fuel electrode materials, which is a major hindrance to the scale-up of this technology. A preliminary cost estimate showed that the price of hydrogen, methane and ammonia produced in SOEC mode would be ~1.91, 3.63, and 0.48 $/kg, respectively. In SOFC mode, the cost of electricity generation using hydrogen, internally reformed methane, and internally cracked ammonia would be ~52.34, 46.30, and 47.11 $/MWh, respectively.

Benchmarks for the energy demand of nutrient removal plants

2003 ◽  
Vol 47 (12) ◽  
pp. 125-132 ◽  
Author(s):  
O. Nowak
Keyword(s):  
Nutrient Removal ◽  
Energy Demand ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Electrical Energy ◽  
Anaerobic Sludge ◽  
Operational Experience ◽  
Municipal Wastewater Treatment ◽  
Sludge Digestion ◽  
Municipal Wastewater Treatment Plants

The energy demand of municipal wastewater treatment plants for nutrient removal equipped with primary clarifiers, activated sludge system, anaerobic sludge digestion, and CHP is evaluated theoretically, on the basis of COD balances. Operational experience from energy-efficient Austrian treatment plants confirms that the demand on external electrical energy can be kept as low as 5 to 10 kWh/(pe.a) depending on the N:COD ratio in the raw wastewater. A low N:COD ratio helps to keep not only the effluent nitrogen load low, but also the energy demand. Measures to minimise the energy demand at treatment plants and to reduce the nitrogen load are discussed.

scholarly journals Research Proposal: Cryptocurrency User Attitudes Towards the Environmental Impact of Proof-of-Work

2021 ◽  
Author(s):  
Moritz Platt ◽  
Francesco Pierangeli
Keyword(s):  
Climate Change ◽  
Environmental Impact ◽  
Energy Demand ◽  
Electrical Energy ◽  
Consensus Protocol ◽  
Energy Needs ◽  
User Attitudes ◽  
Decentralised Systems ◽  
Consensus Models ◽  
Potential Impact

The consumption of electrical energy is a requisite for ‘proof-of-work’, a class of consensus protocols for decentralised systems. ‘Ethereum’ and ‘Bitcoin’, along with various other cryptocurrencies, use implementations of such a consensus protocol. Among experts, the vast energy demand associated with the rising popularity of cryptocurrencies and the potential impact on climate change have been discussed extensively. It is, however, unclear what attitudes the users of cryptocurrencies themselves have towards the consequences of its growing energy demand. The proposed study aims to answer this question through survey research, using ‘Bitcoin’ as an archetype of a proof-of-work cryptocurrency. Conducting the study will reveal whether cryptocurrency users themselves consider their energy needs to be problematic, and which stakeholders they hold accountable to reduce consumption. The outcome can provide a theoretical grounding in social science for the ongoing implementation of alternative consensus models, for example in the context of the ‘Eth2’ upgrade of the ‘Ethereum’ blockchain.

Impact of Rooftop PV Shading on Net Electrical Energy Demand of Buildings in Pakistan

2021 ◽  
Vol 3 (1) ◽  
pp. 45-49
Author(s):  
Muhammad Umar Maqbool ◽  
Arslan Dawood Butt ◽  
Abdul Rauf Bhatti ◽  
Yawar Ali Sheikh ◽  
Muhammad Waleed Asif
Keyword(s):  
Energy Demand ◽  
Heat Load ◽  
Layer Structure ◽  
Electrical Energy ◽  
Position Angle ◽  
Net Energy ◽  
Ambient Conditions ◽  
Pv System ◽  
Roof Structure ◽  
The Impact

This work performs a quantitative assessment of the impact of rooftop PV installation on building’s net energy demand using model of roof structure and steady state thermal simulations. For this purpose, roof structure typically used in Faisalabad, Pakistan is modeled with and without the shading effect due to a 395 W commercial rooftop PV setup. The simulated parameters include the impact of PV module’s dimensions, mounting position/angle alongside roof size and ambient conditions on heat load of air-conditioning system to maintain a temperature of 25 °C within building’s top floor. During the daylight hours of July, the heat load added by the roof on average reduces from 150.87 BTU/h/m2 without PV to 118.16 BTU/h/m2 with PV structure. This 20.05% reduction in energy demand has been achieved with July’s maximum daytime solar and infrared irradiances of 792.2 W/m2 and 466 W/m2 recorded at an average ambient temperature of 35.5 °C and wind speed of 2.75 m/s. This study provides valuable data on optimization of roof layer structure during building’s construction in anticipation of PV system installation at a later stage. Also developed techniques/methods to reduce building’s energy budget due to PV installation, can be valuable input for construction industry as well.

Mississippi County Community College Solar Photovoltaic Total Energy Project

1979 ◽  
Author(s):  
E. M. Henry
Keyword(s):  
Community College ◽  
Energy Demand ◽  
Storage System ◽  
Vital Signs ◽  
Electrical Energy ◽  
Redox System ◽  
Energy System ◽  
Energy Storage System ◽  
Total System ◽  
Efficient Operation

Mississippi County Community College at Blytheville, Arkansas, will derive its total electrical and thermal energy demand from an actively cooled photovoltaic energy system being developed under the management of TEAM, Inc. of Springfield, Virginia. The facility has a design peak electrical requirement for 240 kw to be supplied by a 26-sun concentrating collector field that fully tracks E-W. A 2.4 megawatt-hour electrical energy storage system under consideration is an iron redox system using FeCl2 electrolyte and pressure-molded carbon/PVC electrodes. The power conditioning system will include a 300-kw solid-state inverter to furnish 480-v, three-phase, 60-Hz ac to the College, and appropriate switching to acquire utility company power in emergencies. Process control includes the capability to gather vital signs on the collectors, thermal loop, electrical storage and building demands, and to provide closed-loop tracking and all control signals for energy efficient operation of the total system.

scholarly journals Metamaterial Concentrator for Thermal Energy Harvester

2020 ◽  
Vol 190 ◽  
pp. 00032
Author(s):  
Rapha Nichita Kaikatui ◽  
Adik Putra Andika ◽  
Vinsenius Letsoin ◽  
Paulus Mangera ◽  
Damis Hardiantono ◽  
...  
Keyword(s):  
Thermal Energy ◽  
Communication Systems ◽  
Energy Demand ◽  
Electrical Potential ◽  
Electrical Energy ◽  
Heat Energy ◽  
Small Scale ◽  
Alternative Source ◽  
Heating And Cooling ◽  
Technological Advances

Energy demand increases in line with rapid technological advances. Research on the harvesting of renewable energy continues to be done to make efforts to convert heat energy, which is very abundant in our daily environment. Thermoelectric technology is an alternative source in answering energy needs and can produce energy on a large and small scale. Thermoelectric technology works by converting heat energy into electricity directly, or from electricity to cold. This research presents an experimental study conducted to find out the thermoelectric characteristics of the TEC in the reversal function, with heating and cooling tests on each side of the TEC type thermoelectric element, carried out to obtain the voltage value as the electrical potential generated from this element. The result is thermoelectric potential to generate DC electricity but is very limited in the function of maintaining a heat source on the hot side element. This research then proposes thermal metamaterial that functions as a collector of thermal energy in the method of converting thermal energy into DC electrical energy for the application of low power consumption communication systems.

Spatial analysis of the electrical energy demand in Greece

Energy Policy ◽  
2017 ◽  
Vol 102 ◽  
pp. 340-352 ◽  
Author(s):  
Hristos Tyralis ◽  
Nikos Mamassis ◽  
Yorgos N. Photis
Keyword(s):  
Spatial Analysis ◽  
Energy Demand ◽  
Electrical Energy

Electric Load Forecasting with Deep Machine Learning

2019 ◽  
Vol 16 (8) ◽  
pp. 3404-3409
Author(s):  
Ala Adin Baha Eldin Mustafa Abdelaziz ◽  
Ka Fei Thang ◽  
Jacqueline Lukose
Keyword(s):  
Mobile Application ◽  
Energy Demand ◽  
Short Term Memory ◽  
Historical Data ◽  
Electrical Energy ◽  
Training Data ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Term Memory ◽  
Long Short Term Memory

The most commonly used form of energy in houses, factories, buildings and agriculture is the electrical energy, however, in recent years, there has been an increase in electrical energy demand due to technology advancements and rise in population, therefore an appropriated forecasting system must be developed to predict these demands as accurately as possible. For this purpose, five models were selected, they are Bidirectional-Long Short Term Memory (Bi-LSTM), Feed Forward Neural Network (FFNN), Long Short Term Memory (LSTM), Nonlinear Auto Regressive network with eXogenous inputs (NARX) and Multiple Linear Regression (MLR). This paper will demonstrate the development of these selected models using MATLAB and an android mobile application, which is used to visualize and interact with the data. The performance of the selected models was evaluated by performing the Mean Absolute Percent Error (MAPE), the selected historical data used to perform the MAPE was obtained from Toronto, Canada and Tasmania, Australia, where the year 2006 until 2016 was used as training data and the year 2017 was used to test the MAPE of the historical data with the models’ data. It is observed that the NARX model had the least MAPE for both the regions resulting in 1.9% for Toronto, Canada and 2.9% for Tasmania, Australia. Google cloud is used as the IoT (Internet of Things) platform for NARX data model, the 2017 datasets is converted to JavaScript Object Notation (JSON) file using JavaScript programming language, for data visualization and analysis for the android mobile application.

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