scholarly journals Analyzing long-term empirical interactions between renewable energy generation, energy use, human capital, and economic performance in Pakistan

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nousheen Fatima ◽  
Yanbin Li ◽  
Munir Ahmad ◽  
Gul Jabeen ◽  
Xiaoyu Li
Keyword(s):  
Human Capital ◽  
Renewable Energy ◽  
Economic Performance ◽  
Energy Use ◽  
Energy Generation ◽  
Causal Connection ◽  
Causality Analysis ◽  
Short Term ◽  
Renewable Energy Generation

Abstract Background The current research attempts to systematically investigate the causal interactions between renewable energy generation, aggregated energy use, human capital, and economic performance in Pakistan both in a short-term and long-term test for the period of 1990–2016. Methods As a primary step, a unit root analysis was conducted employing, among others, an augmented Dickey-Fuller-generalized least squares (ADF-GLS) test. Based on the order of integration I(1), the Johansen and Juselius (JJ) co-integration testing was employed to confirm a long-term causality analysis, which was followed by a vector error correction model (VECM) to calculate the short-run Granger causality analysis. Furthermore, the vector autoregressive (VAR)-based Cholesky test allowed the standard deviation impulse response functions to be generated to explain the responses of variables to arbitrary shocks in the data series under analysis. Results The empirical findings unearthed the bilateral causal connection between aggregated energy use and economic performance, renewable energy generation and economic performance, and human capital and economic performance. Thus, it confirmed the existence of feedback effects for aggregated energy use, renewable energy generation, and human capital in their relation to economic performance. Likewise, a unilateral positive causal connection was revealed running from renewable energy generation and human capital to aggregated energy use, and from human capital to renewable energy generation in both a long-term and short-term test. Additionally, the causal association running from aggregated energy use and renewable energy generation to economic performance was exposed in a long-term as well as short-term test, hence supporting the growth hypothesis. Conclusions The findings signified the importance of an enhanced generation of renewable energy along with the promotion of an aggregated energy use for the economic performance in Pakistan.

scholarly journals The Impact of Economic Growth, Trade Openness and Technological Progress on Renewable Energy Use in Organization for Economic Co-Operation and Development Countries

2021 ◽  
Author(s):  
Md. Mahmudul Alam ◽  
Wahid Murad
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Least Squares ◽  
Technological Progress ◽  
Energy Use ◽  
Trade Openness ◽  
Oecd Countries ◽  
Ordinary Least Squares ◽  
Short Term

This study investigates the short-term and long-term impacts of economic growth, trade openness and technological progress on renewable energy use in Organization for Economic Co-operation and Development (OECD) countries. Based on a panel data set of 25 OECD countries for 43 years, we used the autoregressive distributed lag (ARDL) approach and the related intermediate estimators, including pooled mean group (PMG), mean group (MG) and dynamic fixed effect (DFE) to achieve the objective. The estimated ARDL model has also been checked for robustness using the two substitute single equation estimators, these being the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS). Empirical results reveal that economic growth, trade openness and technological progress significantly influence renewable energy use over the long-term in OECD countries. While the long-term nature of dynamics of the variables is found to be similar across 25 OECD countries, their short-term dynamics are found to be mixed in nature. This is attributed to varying levels of trade openness and technological progress in OECD countries. Since this is a pioneer study that investigates the issue, the findings are completely new and they make a significant contribution to renewable energy literature as well as relevant policy development.

Net Zero Energy Analysis Approach for Military Installations

2011 ◽  
Author(s):  
Kate Anderson ◽  
Samuel Booth ◽  
Kari Burman ◽  
Michael Callahan
Keyword(s):  
Renewable Energy ◽  
Energy Use ◽  
Energy Generation ◽  
Lessons Learned ◽  
Renewable Energy Resources ◽  
Zero Energy ◽  
Net Zero Energy ◽  
Military Installation ◽  
Net Zero ◽  
Renewable Energy Generation

Net zero energy is a concept of energy self-sufficiency based on minimized demand and use of local renewable energy resources. A net zero energy military installation is defined as: “A military installation that produces as much energy on-site from renewable energy generation or through the on-site use of renewable fuels, as it consumes in its buildings, facilities, and fleet vehicles.” [1] The National Renewable Energy Laboratory (NREL) developed a comprehensive, first-of-its-kind strategy for evaluating a military installation’s potential to achieve net zero energy status, including an assessment of baseline energy use, energy use reduction opportunities from efficiency or behavior changes, renewable energy generation opportunities, electrical systems analysis of renewable interconnection, microgrid potential, and transportation energy savings. This paper describes NREL’s net zero energy assessment strategy and provides a planning guide for other organizations interested in evaluating net zero potential. We also present case studies and describe lessons learned from NREL’s net zero energy assessments at seven installations, including the importance of enforcing and funding mandates, providing leadership support, collecting accurate data, and selecting appropriate technologies. Finally, we evaluate whether the net zero concept is a useful framework for analyzing an energy strategy and a reasonable goal.

scholarly journals Climate Change and Renewable Energy Generation in Europe—Long-Term Impact Assessment on Solar and Wind Energy Using High-Resolution Future Climate Data and Considering Climate Uncertainties

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 302
Author(s):  
Yuchen Yang ◽  
Kavan Javanroodi ◽  
Vahid M. Nik
Keyword(s):  
Climate Change ◽  
Renewable Energy ◽  
Wind Energy ◽  
Energy Production ◽  
Climate Models ◽  
Regional Climate ◽  
Energy Generation ◽  
Energy Potential ◽  
Renewable Energy Generation

Climate change can strongly affect renewable energy production. The state of the art in projecting future renewable energy generation has focused on using regional climate prediction. However, regional climate prediction is characterized by inherent uncertainty due to the complexity of climate models. This work provides a comprehensive study to quantify the impact of climate uncertainties in projecting future renewable energy potential over five climate zones of Europe. Thirteen future climate scenarios, including five global climate models (GCMs) and three representative concentration pathways (RCPs), are downscaled by the RCA4 regional climate model (RCM) over 90 years (2010–2099), divided into three 30-year periods. Solar and wind energy production is projected considering short-/long-term climate variations and uncertainties in seven representative cities (Narvik, Gothenburg, Munich, Antwerp, Salzburg, Valencia, and Athens). The results showed that the uncertainty caused by GCMs has the most substantial impact on projecting renewable energy generation. The variations due to GCM selection can become even larger than long-term climate change variations over time. Climate change uncertainties lead to over 23% and 45% projection differences for solar PV and wind energy potential, respectively. While the signal of climate change in solar radiation is weak between scenarios and over time, wind energy generation is affected by 25%.

scholarly journals A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

2020 ◽  
Vol 12 (4) ◽  
pp. 1653 ◽  
Author(s):  
Fatma Yaprakdal ◽  
M. Berkay Yılmaz ◽  
Mustafa Baysal ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Energy Generation ◽  
Electrical Load ◽  
Power Losses ◽  
Short Term ◽  
Real Power ◽  
Load Demand ◽  
Photovoltaic Power ◽  
Renewable Energy Generation

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

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