Impact of increased renewables on natural gas markets in eastern United States

This paper explores the market structures of natural gas and electricity as well as the interdependence of natural gas prices and bids with increasing reliance on natural gas as the penetration of renewable energy resources increases in order to complement their intermittencies. In particular, the p...

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Bibliographic Details
Main Authors: Nandakumar, Neha (Contributor), Annaswamy, Anuradha M (Contributor)
Other Authors: MIT Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Springer Berlin Heidelberg, 2017-05-05T13:50:53Z.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Nandakumar, Neha  |e author 
100 1 0 |a MIT Institute for Data, Systems, and Society  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Mechanical Engineering  |e contributor 
100 1 0 |a Nandakumar, Neha  |e contributor 
100 1 0 |a Annaswamy, Anuradha M  |e contributor 
700 1 0 |a Annaswamy, Anuradha M  |e author 
245 0 0 |a Impact of increased renewables on natural gas markets in eastern United States 
260 |b Springer Berlin Heidelberg,   |c 2017-05-05T13:50:53Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/108687 
520 |a This paper explores the market structures of natural gas and electricity as well as the interdependence of natural gas prices and bids with increasing reliance on natural gas as the penetration of renewable energy resources increases in order to complement their intermittencies. In particular, the paper will attempt to answer the following two questions: What could the generation mix look like in 2030 with a renewable-rich generation landscape and how could this impact gas prices? How do gas-fired generator (GFG) generation volatility, their prices, and their bids for gas change between 2015 and 2030 with increased penetration of renewables? In order to answer these questions, computational models are derived using forecasting and regression analysis tools and an auction model. 
520 |a National Science Foundation (U.S.) (EFRI-1441301) 
546 |a en 
655 7 |a Article 
773 |t Journal of Modern Power Systems and Clean Energy