Energy markets and natural gas price formation

2019 ◽  
pp. 1-7
Author(s):  
Keyword(s):  
Natural Gas ◽  
Energy Markets ◽  
Price Formation ◽  
Gas Price

Peak-Shaving Ratio Analysis of the Natural Gas Combined Heat and Power Plant With Distributed Peak-Shaving Heat Pumps

2017 ◽  
Author(s):  
Xiling Zhao ◽  
Xiaoyin Wang ◽  
Tao Sun
Keyword(s):  
Natural Gas ◽  
Heat Source ◽  
Heat Pump ◽  
Gas Turbines ◽  
Heat Load ◽  
Operating Cost ◽  
Heat Pumps ◽  
Optimized Design ◽  
Peak Shaving ◽  
Gas Price

Distributed peak-shaving heat pump technology is to use a heat pump to adjust the heat on the secondary network in a substation, with features of low initial investment, flexible adjustment, and high operating cost. The paper takes an example for the system that uses two 9F class gas turbines (back pressure steam) as the basic heat source and a distributed heat pump in the substation as the peak-shaving heat source. The peak-shaving ratio is defined as the ratio of the designed peak-shaving heat load and the designed total heat load. The economic annual cost is taken as a goal, and the optimal peak-shaving ratio of the system is investigated. The influence of natural gas price, electricity price, and transportation distance are also analyzed. It can provide the reference for the optimized design and operation of the system.

scholarly journals News-Based Research on Forecast of International Natural Gas Price Trend

2020 ◽  
Author(s):  
Tianxiang Li ◽  
Xiaosong Han ◽  
Aoqing Wang ◽  
Hui Li ◽  
Guosheng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Natural Gas ◽  
Predictive Accuracy ◽  
Price Trend ◽  
Learning Network ◽  
Natural Gas Prices ◽  
Deep Learning Network ◽  
Sentiment Score ◽  
Gas Price

In this paper, we build a deep learning network to predict the trends of natural gas prices. Given a time series, for each day, the gas price trend is classified as “up” and “down” according to the price compared to the last day. Meanwhile, we collect news articles as experimental materials from some natural gas related websites. Every article was then embedded into vectors by word2vec, weighted with its sentiment score, and labeled with corresponding day’s price trend. A CNN and LSTM fused network was then trained to predict price trend by these news vectors. Finally, the model’s predictive accuracy reached 62.3%, which outperformed most of other traditional classifiers.

scholarly journals Energetic and Economic Analyses of an LCPV/T Solar Hybrid Plant for a Sports Center Building in Mexico

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5681
Author(s):  
Iván Acosta-Pazmiño ◽  
Carlos Rivera-Solorio ◽  
Miguel Gijón-Rivera
Keyword(s):  
Natural Gas ◽  
Winter Season ◽  
Hybrid Plant ◽  
Hot Water ◽  
Electrical Performance ◽  
Payback Time ◽  
Economic Analyses ◽  
Levelized Cost Of Heat ◽  
Solar Field ◽  
Gas Price

This study presents a techno-economic performance evaluation of a hybrid low-concentrating photovoltaic/thermal (LCPV/T) plant, which operates in a student sports and wellness center building situated at a university campus in Mexico. The solar plant comprises 144 LCPV/T collectors based on a hybridized version of a local parabolic trough technology. Dynamic thermal and electrical performance analyses were performed in the TRNSYS simulation studio. The results showed that the solar field could cover up to 72% of the hot water demand of the building during the summer season and 24% during the winter season. The hybrid system could annually save 7185 USD, accounting for heat (natural gas boiler) and electricity generation. However, the payback time was of 19.23 years, which was mainly attributed to a reduced natural gas price in Monterrey, Mexico. A new approach to evaluating the equivalent levelized cost of heat (LCOHeq), is proposed. This results in an LCOHeq of 0.065 USD/kWh, which is nearly equivalent to the LCOH of a natural gas-fired boiler (0.067 USD/kWh). Finally, the hybrid plant could achieve a specific CO2e emission reduction of 77.87 kg CO2e per square meter of the required installation area.

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