Energy Production Data Research Articles

Optimization and design of energy monitoring system in fuels terminal: a case study in North Sumatera province of Indonesia

Optimization of energy use in the PERTAMINA’s fuel terminal (TBBM) was carried out by the Energy Audit method. The data obtained during energy audit activities then processed to carry out the Energy Performance Indicators (EnPis) which produces Specific Energy Consumption (SEC) and energy baselines. The energy cost center that creates a web-based energy monitoring system is carried out to facilitate monitoring and control and will become supporting tools. The information provided from the energy monitoring system displays the profile of total energy consumption, distribution of energy consumption, the energy consumption of equipment in production facilities in the form of daily, monthly, and annual data. Energy intensity and baseline values obtained from energy consumption and production data for the past several years will be the target and warning system to maintain energy usage that exceeds reasonable limits. The results obtained by the value of energy intensity of 0.0023 GJ / KL, while the total emissions generated from energy consumption amounted to 1,144.4 tons-CO2 / year. 51% of energy is used for production facilities and the rest is used for supporting facilities. The use of electricity as well as thermal energy does not have a strong correlation with the level of fuel sales throughout the year and the use of NGS (New Gantry System) technology does not show a significant decrease in energy intensity, where IKE values fluctuate throughout the year. This reinforces the need for continuous and real time energy monitoring. By implementing the potential for energy savings, an energy use optimization is obtained by reducing energy consumption by an average of 10% - 15% per month or as much as 18,057 - 27,086 kWh per month, equivalent to 25 - 38 million rupiah per month.

Performance to Peers (P2P): A benchmark approach to fault detections applied to photovoltaic system fleets

The fault detection applied to a large amount of small distributed PV systems needs to be simple, cost-effective, and reliable. This work presents a fault detection procedure applied to distributed PV system fleets, based on a novel performance indicator, designated as Performance to Peers (P2P), that can be constructed on the sole basis of the comparison of the energy production data of several neighboring PV systems. This article explains how to construct this performance indicator and how to use it to carry out automatic fault detections. This fault detection procedure has been developed in the context of the performance analysis carried out on approximately 6000 PV installations located in Europe, and it is illustrated and discussed through real application cases. The P2P has been shown to be more stable than the Performance Ratio (PR), in particular in the presence of sub-par metadata on the PV systems, and it thus constitutes a more robust performance indicator for fault detection. The stability of P2P is characterized by an Absolute Median Deviation (MAD) that is typically of 10% for hourly data and 5% for daily data. The application of P2P to fault diagnosis is illustrated on four categories of faults that are among the most frequently observed on PV systems. The main limitations of this novel methodology are discussed, and several future lines of research are suggested.

Shale boom and cross-county commuting in the Permian Basin region

This study is set to quantify the impact of oil and gas production on the commuting pattern in the Permian Basin region. In particular, we focus on cross-county commuting that has shown a significant increase during the recent Permian Basin shale boom. Anecdotal evidence suggests that the increase is mainly driven by extended daily commuting and long-distance commuting (LDC) tied to the oil and gas industry. Our regression analysis integrating county-level commuting data and shale energy production data confirms that the impact is statistically significant. Using data from 67 counties in New Mexico and Texas between 2002 and 2017, we find that on average a one-million-BBLs increase in annual oil production leads to an increase of 100 inward commuters and an increase of 15 outward commuters. The net impact (inward – outward) is an increase of 85 commuters. By linking the empirical findings to the potential health, environmental, and socio-economic impacts of shale development with commuting being the channel of influence, further exploration suggests that the health and environmental impacts of extended daily commuting are much more significant compared to LDC. As far as the socio-economic aspect is concerned, both extended daily commuting and LDC can have significant impacts.

Efficiency of functioning solar collectors in selected households

The article presents the results of research on the efficiency of the solar collector system in south-eastern Poland. The basis of the article are the results of actual measurements recorded by the system measuring in 5 years (2014-2018). Three solar collectors with a total gross area of 6.3 m2 are used to heat utility water in a single-family house. The average annual heat production in this system was 1916 kWh. The system’s work was characterized by high seasonal and daily variability, which is related to the Polish climate’s specificity. Using the energy production data, the reduction of pollutant emissions achieved by changing the energy carrier was calculated: from electricity to heat from a renewable source, which is solar radiation. The installation of solar collectors allowed for satisfying part of the energy demand as well as reducing the emission of greenhouse gases and other pollutants into the atmosphere.

Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

Day‐ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed‐forward neural network‐based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, −0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.

Maximizing the degree of autarky of a 16 house neighbourhood by locally produced energy and smart control

Abstract In this work we investigate the possibilities of ”soft-islanding” (near autarkic behaviour) a microgrid with 16 houses in the Netherlands, using the DEMKit simulation toolkit. We consider a situation where energy is generated by PV-panels as well as by a CHP and energy is stored using batteries as well as a heat buffer. This research is inspired by the Aardehuizen, which is a community in the Netherlands where the inhabitants strive to use as much as possible of their sustainably produced energy locally, motivated by the ideology that their community must act sustainably and self-sufficient, rather than by financial incentives. In a first step the proper size for the equipment is determined based on energy production and consumption data of several weeks in the year. Secondly one year simulations for several scenarios are presented and the degree of autarky for each scenario is compared. We demonstrate that a (nearly) autarkic operating microgrid can be achieved, with a proper but also with a realistic sizing of the components. Combining this proper sizing of energy generation and storage assets, with an advanced control, it is possible to achieve a degree of autarky of 99.1% over a year, meaning that less than one percent of the energy has to be imported from the main grid.

Environmental Life Cycle Assessment of Grid-Integrated Hybrid Renewable Energy Systems in Northern Nigeria

Life cycle assessment is a crucial tool in evaluating systems performances for sustainability and decision-making. This paper provided environmental impact of integrating renewable energy systems to the utility-grid based on a baseline optimized energy production data from “HOMER” for renewable systems modelling of a site in northern Nigeria. The ultimate goal was to ascertain the best hybrid option(s) in sustaining the environment. Different assumptions and scenarios were modelled and simulated using Ganzleitlichen Bilanz (GaBi). Uncertainty analysis was ensured to the impact data based on pedigree-matrix and Excel-program, as well as overall policy relevance. The results of the impact categories revealed first scenario (i.e., conventional path-based) with the highest impacts on global warming potential (GWP), acidification potential (AP), human toxicity potential (HTP), and abiotic depletion potential (ADPfossils). The lowest impacts arise in the renewable-based scenarios for all the considered categories except the Ozone-layer depletion potential Category where the highest contribution falls in the third scenario (i.e., photovoltaic (PV)/biomass-biogas system) although all values being infinitesimal. In quantitative terms, the reduction in the GWP from the highest being the first scenario to the lowest being the fourth scenario (i.e., wind/biomass-biogas system) was 96.5%. Hence, with the outstanding contributions of the hybrid renewable systems, adopting them especially the lowest impact scenarios with expansions is relevant for environmental sustainability.

Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India

The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry.

Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on shannon entropy

An assessment model is built to investigate the degree of order in the organisational structure of the electricity regulatory institution (ERI) in China. The model is based on Shannon entropy and is constructed from the perspective of timeliness and accuracy of the flow of information. The model is then used to evaluate the degree of order in the organisational structures of the ERI during three stages of reform that occurred during 2002–13. The results indicate that the reforms and improvements made in the organisational structure of China's ERI have resulted in a stepwise increase in their degree of order (corresponding to 0.3156, 0.3277, and 0.3324 in the three stages, respectively). On this basis, a scheme is put forward to optimise the degree of order in the structure of the energy regulatory institution in the current stage. The results show that downsizing the internal and subordinate departments appropriately and creating more governmental agencies to regulate energy are conducive to further improving the degree of order of the energy regulatory institution. Finally, we use principal component analysis to propose a priority scheme for adding more regulatory governmental agencies based on sorted energy production and consumption data.

Uncertainty Analysis of Energy Production for a 3 × 50 MW AC Photovoltaic Project Based on Solar Resources

Access to solar energy is a prerequisite to remedy CO2 and improve the standard of human living. Green solar energy is only an immediate solution to add its zero emission profile and provide carbon footprint reduction benefits. This energy does not emit greenhouse gases which means it is a renewable free source of energy when producing electricity. The purpose of this paper is to investigate the accurate annul energy production data reducing uncertainty in solar energy estimates. This work investigated the solar energy assessment taking into account a detailed solar resource and energy production assessment for a 3 × 50 MW PV project with uncertainty analysis. The authors defined a total uncertainty of energy production which is estimated at 9.5% for one year and 8.9% for ten years as well as future variability of 3.4% for one year and 1.1% for ten years. The annual power degradation and expected energy production over the plant lifespan at dissimilar 99%, 90%, 75%, 60%, 50%, and 25% probability of surplus are also observed in this paper.

PLC Application as an Automatic Transfer Switch for on-grid PV System; Case Study Jakabaring Solar Power Plant Palembang

Programmable Logic Control (PLC) is an industrial computer specially designed to control the manufacturing process, such as robotics, assembly line, power plant, etc. PLC control program analyzes the input signal to decide the output state according to what has been pre-programmed by the user. This paper discussed the application of PLC as an ATS for on-grid PV system at PLTS Jakabaring which is the biggest PV power plant in Palembang. Therefore, it is interesting to discuss the application of PLC as an ATS in controlling which electric power source used to power Jakabaring Sports City to ensure there is no power outage during Asian Games 2018. The data is taken from July 24-27, 2018 before the event, however, the loads needed to power are more-less the same. The application of PLC as an ATS at PLTS Jakabaring is to automate the switching proses of exporting and importing electrical energy. The energy production data is also compared with the weather forecast to show the effect of sunny days on energy production. Data results show the application of PLC as an ATS for PV system; the ATS hold an essential role for the system due to the system independence of solar irradiance.

Techno-economic evaluation of installing EV and PV combined infrastructure on Academic Institution’s Parking Garages in Illinois, USA

The need for clean energy production is increasing due to our society’s high energy consumption, decreasing fossil fuel reserves, and growing environmental issues. Implementation of renewable energy systems on university campuses would best demonstrate academic institutions’ efforts to promote sustainability. In this research study, multiple locations were assessed to determine the feasibility of installing parking canopies coupled with solar photovoltaic (PV) systems and electric vehicle (EV) charging stations and to show these two systems’ effectiveness if they are implemented together. We reviewed the trend and growing demand towards electric vehicles and examined different solar panels and mounts to find the most optimal combination for the suggested study sites. Through the data collection on the study sites and the utilization of simulation tools we were able to obtain estimations of these two systems at different sites, the energy production of the systems, and carbon emissions reduction potential. As a result of the proposed study, three viable locations were selected to offer PV-EV parking canopies. A cost-benefit analysis was performed at each location to provide useful energy production and financial data to determine the optimal location. The results provided insightful implications to decision makers at the university. The implementation of solar PV and EV charging stations combined gives the university the ability to implement clean, renewable energy in order to show its commitment to sustainability as well as supporting the ability of students, commuters and faculty to switch from gas powered vehicles to electric vehicles.

Algeria energy production, population growth based on Olduvai Theory

During the last two centuries we have known nothing but exponential growth and in parallel we haveevolved what amounts to an exponential-growth culture, a culture so heavily dependent upon the continuance of exponential growth for its stability that it is incapable of reckoning with problems of no growth. So the Olduvai Theory is based on time-series data of world energy production and population; the data are arrays of discrete numbers year-on-year, not continuous functions of time. Hence the difference calculus must be used, not the infinitesimal calculus. Postulates 1 and 2 require that we distinguish intervals of linear growth from those of exponential growth. Global population has grown to 8.3 billion. Most, though not all, of that growth has been concentrated in South Asia and sub-Saharan Africa, in Algeria will growth to 52 million in 2050 and in 2100 will be 72 million, in Africa and in the same year 2050 will touched 1787 million and in the world will be 9655 million in 2050. The most important measure in the energy balance of Algeria is the total consumption of 42.87 billion kWh per year. Per capita this is an average of 1.065 kWh. Algeria could provide itself completely with self-produced energy. The total production of all energy producing facilities is 54 bn kWh. Thats 126 % of the countries own usage. Despite this Algeria trades its energy with foreign countries. Along with pure consumptions the production, imports and exports play an important role.

GIS-Based Software Infrastructure to Model PV Generation in Fine-Grained Spatio-Temporal Domain

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists in reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such renewable systems by modeling variability and uncertainty of their generation profiles. In this paper, we present a distributed software infrastructure for modeling and simulating energy production of photovoltaic (PV) systems in urban context. In its core, it performs simulations in a spatio-temporal domain exploiting geographic information systems together with meteorological data to estimate PV generation profiles in real operating conditions. This solution provides results in real-sky conditions with different time intervals: yearly, monthly, and subhourly. To evaluate the accuracy of our simulations, we tested the proposed software infrastructure in a real-world case study. Finally, experimental results are presented and compared with real energy production data collected from PV systems deployed in the case study area.

The impact of predicted demand on energy production

Energy is crucial for human life, a secure and accessible supply of power is essential for the sustainability of societies. Economic development and demographic progression increase energy demand, prompting countries to conduct research and studies on energy demand and production. Although, increasing in energy demand in the future requires a correct determination of the amount of energy supplied. Our article studies the impact of demand on energy production to find the relationship between the two latter and managing properly the production between the different energy sources. Historical data of demand and energy production since 2000 are used. The data are processed by the regression model to study the impact of demand on production. The obtained results indicate that demand has a positive and significant impact on production (high impact). Production is also increasing but at a slower pace. In this work, Morocco is considered as a case study.

Complementary‐based coalition formation for energy microgrids

AbstractIn recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.

Airborne remote sensing and in situ measurements of atmospheric CO<sub>2</sub> to quantify point source emissions

Abstract. Reliable techniques to infer greenhouse gas emission rates from localised sources require accurate measurement and inversion approaches. In this study airborne remote sensing observations of CO2 by the MAMAP instrument and airborne in situ measurements are used to infer emission estimates of carbon dioxide released from a cluster of coal-fired power plants. The study area is complex due to sources being located in close proximity and overlapping associated carbon dioxide plumes. For the analysis of in situ data, a mass balance approach is described and applied, whereas for the remote sensing observations an inverse Gaussian plume model is used in addition to a mass balance technique. A comparison between methods shows that results for all methods agree within 10 % or better with uncertainties of 10 to 30 % for cases in which in situ measurements were made for the complete vertical plume extent. The computed emissions for individual power plants are in agreement with results derived from emission factors and energy production data for the time of the overflight.

A Multifactor Approach to Modelling the Impact of Wind Energy on Electricity Spot Prices

We introduce a three-factor model of electricity spot prices, consisting of a deterministic seasonality and trend function as well as short- and long-term stochastic components, and derive a formula for futures prices. The long-term component is modelled as a Levy process with increments belonging to the class of generalised hyperbolic distributions. We describe the short-term factor by Levy semistationary processes: we start from a CARMA(2,1), i.e. a continous-time ARMA model, and generalise it by adding a short-memory stochastic volatility. We further modify the model by including the information about the wind energy production as an exogenous variable. We fit our models to German and Austrian data including spot and futures prices as well as the wind energy production and total load data. Empirical studies reveal that taking into account the impact of the wind energy generation on the prices improves the goodness of fit.

Analyses of the Extensible Blade in Improving Wind Energy Production at Sites with Low-Class Wind Resource

This paper describes the feasibility analysis of an innovative, extensible blade technology. The blade aims to significantly improve the energy production of a wind turbine, particularly at locations with unfavorable wind conditions. The innovative ‘smart’ blade will be extended at low wind speed to harvest more wind energy; on the other hand, it will be retracted to its original shape when the wind speed is above the rated wind speed to protect the blade from damages by high wind loads. An established aerodynamic model is implemented in this paper to evaluate and compare the power output of extensible blades versus a baseline conventional blade. The model was first validated with a monitored power production curve based on the wind energy production data of a conventional turbine blade, which is subsequently used to estimate the power production curve of extended blades. The load-on-blade structures are incorporated as the mechanical criteria to design the extension strategies. Wind speed monitoring data at three different onshore and offshore sites around Lake Erie are used to predict the annual wind energy output with different blades. The effects of extension on the dynamic characteristics of blade are analyzed. The results show that the extensive blade significantly increases the annual wind energy production (up to 20% to 30%) with different blade extension strategies. It, therefore, has the potential to significantly boost wind energy production for utility-scale wind turbines located at sites with low-class wind resource.

A method for detecting malfunctions in PV solar panels based on electricity production monitoring

In this paper a new method is developed for automatically detecting outliers or faults in the solar energy production of identical sets (sister arrays) of photovoltaic (PV) solar panels. The method involves a two-stage unsupervised approach. In the first stage, “in control” energy production data are created by using outlier detection methods and functional principal component analysis in order to remove global and local outliers from the data set. In the second stage, control charts for the “in control” data are constructed using both a parametric method and three non-parametric methods. The control charts can be used to detect outliers or faults in the production data in real-time or at the end of the day. As an illustration, the method is applied to analysis of the real energy production data of six sets of “identical” PV solar panels over a period of three years. Tests indicate that the proposed method is able to successfully detect a reduction in efficiency in one of the solar panel sets by up to 5%. Control charts based on parametric and non-parametric methods both show good performance results.