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Coordination of power and natural gas markets via financial instruments.
- Source :
- Computational Management Science; Oct2021, Vol. 18 Issue 4, p505-538, 34p
- Publication Year :
- 2021
-
Abstract
- Current electricity and natural gas markets operate with deterministic description of uncertain supply, and in a temporally and sectorally decoupled way. This practice in energy systems is being challenged by the increasing integration of stochastic renewable energy sources. There is a growing need for exchanging operational flexibility among energy sectors, which requires to improve the sectoral coordination between electricity and natural gas markets. In addition, the dispatch of flexible units in both sectors needs to be made in a more uncertainty-aware manner, requiring to strengthen the temporal coordination between day-ahead and real-time energy markets. We explore the use of existing financial instruments in the form of virtual bidding (VB) as a market-based solution to enhance both sectoral and temporal coordination in energy markets. It is established in the literature that VB by purely financial players is able to enhance the temporal coordination between deterministic day-ahead and real-time markets. By developing various stochastic equilibrium and optimization models, we show that VB by physical players, i.e., gas-fired power plants, at the interface of power and natural gas systems is of great potential to improve not only the temporal coordination between deterministic day-ahead and real-time markets, but also the sectoral coordination between deterministic electricity and natural gas markets. We exploit a fully stochastic co-optimization model as an ideal benchmark, and numerically illustrate the benefits of VB for increasing the overall market efficiency in terms of reduced expected operational cost of the entire energy system. We eventually show that flexible resources in both electricity and natural gas markets are dispatched more efficiently in the day-ahead stage when VB exists. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1619697X
- Volume :
- 18
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Computational Management Science
- Publication Type :
- Academic Journal
- Accession number :
- 152894146
- Full Text :
- https://doi.org/10.1007/s10287-021-00403-x