scholarly journals Easing the natural gas crisis: Reducing natural gas prices through increased deployment of renewable energy and energy efficiency

2004 ◽  
Author(s):  
Ryan Wiser ◽  
Mark Bolinger ◽  
Matt St. Clair
2020 ◽  
Vol 24 (1) ◽  
pp. 669-680
Author(s):  
Aiman Albatayneh ◽  
Mohammad N. Assaf ◽  
Dariusz Alterman ◽  
Mustafa Jaradat

Abstract The tremendous growth in the transportation sector as a result of changes in our ways of transport and a rise in the level of prosperity was reflected directly by the intensification of energy needs. Thus, electric vehicles (EV) have been produced to minimise the energy consumption of conventional vehicles. Although the EV motor is more efficient than the internal combustion engine, the well to wheel (WTW) efficiency should be investigated in terms of determining the overall energy efficiency. In simple words, this study will try to answer the basic question – is the electric car really energy efficient compared with ICE-powered vehicles? This study investigates the WTW efficiency of conventional internal combustion engine vehicles ICEVs (gasoline, diesel), compressed natural gas vehicles (CNGV) and EVs. The results show that power plant efficiency has a significant consequence on WTW efficiency. The total WTW efficiency of gasoline ICEV ranges between 11–27 %, diesel ICEV ranges from 25 % to 37 % and CNGV ranges from 12 % to 22 %. The EV fed by a natural gas power plant shows the highest WTW efficiency which ranges from 13 % to 31 %. While the EV supplied by coal-fired and diesel power plants have approximately the same WTW efficiency ranging between 13 % to 27 % and 12 % to 25 %, respectively. If renewable energy is used, the losses will drop significantly and the overall efficiency for electric cars will be around 40–70% depending on the source and the location of the renewable energy systems.


2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Gosia Stein-Brzozowska ◽  
Christian Bergins ◽  
Allan Kukoski ◽  
Song Wu ◽  
Michalis Agraniotis ◽  
...  

In terms of CO2 emissions, the year 2030 has been addressed as a very crucial deadline for both European Union (EU) and the U.S. Whereas the U.S. Clean Power Plan proposes the reduction of national CO2 emissions from the existing power stations by 30% with respect to 2005, the EU aims at cutback by 40% from their levels in 1990. Due to the restricted emission goals dictated by the European and U.S. energy policies, both energy markets witness currently drastic changes. Whereas the U.S. wants to shift away from coal, the EU shifts away from gas due to high natural gas prices in Europe while drastically increasing the feed-ins from renewable energy sources (RES). In some of the European countries constantly growing installation of renewable energy plants is superseding natural gas-fired power plants and thus causing the electrical grid stabilization to be overtaken by coal fired power stations. On the contrary, the U.S. market due to increasing extraction of shale gas and low natural gas prices puts the gas power plants in favor and poses increasing pressure on closing some coal fired plants. A solution that uses the potential of the existing site and reduces overall emissions is converting from coal into gas-fired power plants, so-called fuel switch. Whereas for the U.S. market the later solution is relevant, in the vast majority of EU Member States the focus is on increasing the flexibility of coal fired power plants. The challenges and technical solutions developed and applied according to the demands of the market in both EU and U.S. are addressed in this paper. Both currently applied technologies and technologies under development are shortly presented.


2020 ◽  
Vol 4 (1) ◽  
pp. 1-7
Author(s):  
Made Dirgantara ◽  
Karelius Karelius ◽  
Marselin Devi Ariyanti, Sry Ayu K. Tamba

Abstrak – Biomassa merupakan salah satu energi terbarukan yang sangat mudah ditemui, ramah lingkungan dan cukup ekonomis. Keberadaan biomassa dapat dimaanfaatkan sebagai pengganti bahan bakar fosil, baik itu minyak bumi, gas alam maupun batu bara. Analisi diperlukan sebagai dasar biomassa sebagai energi seperti proksimat dan kalor. Analisis terpenting untuk menilai biomassa sebagai bahan bakar adalah nilai kalori atau higher heating value (HHV). HHV secara eksperimen diukur menggunakan bomb calorimeter, namun pengukuran ini kurang efektif, karena memerlukan waktu serta biaya yang tinggi. Penelitian mengenai prediksi HHV berdasarkan analisis proksimat telah dilakukan sehingga dapat mempermudah dan menghemat biaya yang diperlukan peneliti. Dalam makalah ini dibahas evaluasi persamaan untuk memprediksi HHV berdasarkan analisis proksimat pada biomassa berdasarkan data dari penelitian sebelumnya. Prediksi nilai HHV menggunakan lima persamaan yang dievaluasi dengan 25 data proksimat biomassa dari penelitian sebelumnya, kemudian dibandingkan berdasarkan nilai error untuk mendapatkan prediksi terbaik. Hasil analisis menunjukan, persamaan A terbaik di 7 biomassa, B di 6 biomassa, C di 6 biomassa, D di 5 biomassa dan E di 1 biomassa.Kata kunci: bahan bakar, biomassa, higher heating value, nilai error, proksimat  Abstract – Biomass is a renewable energy that is very easy to find, environmentally friendly, and quite economical. The existence of biomass can be used as a substitute for fossil fuels, both oil, natural gas, and coal. Analyzes are needed as a basis for biomass as energy such as proximate and heat. The most critical analysis to assess biomass as fuel is the calorific value or higher heating value (HHV). HHV is experimentally measured using a bomb calorimeter, but this measurement is less effective because it requires time and high costs. Research on the prediction of HHV based on proximate analysis has been carried out so that it can simplify and save costs needed by researchers. In this paper, the evaluation of equations is discussed to predict HHV based on proximate analysis on biomass-based on data from previous studies. HHV prediction values using five equations were evaluated with 25 proximate biomass data from previous studies, then compared based on error value to get the best predictions. The analysis shows that Equation A predicts best in 7 biomass, B in 6 biomass, C in 6 biomass, D in 5 biomass, and E in 1 biomass. Key words: fuel, biomass, higher heating value, error value, proximate 


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