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Heat capacity is the ratio of heat absorbed by the substance corresponding to its temperature change. It is also called thermal capacity and the SI unit of heat capacity is J/K. Whereas, specific heat capacity is defined as the amount of heat necessary to change the temperature of 1 kg of a substance by 1 K and is also called massic heat capacity. Its SI unit is J/kg⋅K.
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For a system that undergoes a thermodynamic process at a constant volume condition, the heat absorbed is used only to increase the system's internal energy and not for doing any kind of work. While for a system undergoing a thermodynamic process under a constant pressure condition, the amount of heat absorbed is used not only for increasing the internal energy (as a function of temperature) but also for doing some work. The molar heat capacity is the amount of heat required to increase the...
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Optimizing gas entry-exit capacity utilization under uncertainty.

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Summary

Optimizing Norway's natural gas capacity allocation using stochastic programming enhances European energy security. Moderating operator risk aversion significantly boosts system welfare and identifies key network bottlenecks.

Keywords:
Capacity allocation under uncertaintyEntry–exit capacity marketsNorwegian natural gasStochastic programming

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Area of Science:

  • Energy Systems Analysis
  • Operations Research
  • Econometrics

Background:

  • Natural gas is crucial for Europe's energy supply, with Norway as a major provider.
  • Managing entry-exit capacity in Norway's gas network is complex due to demand and price uncertainties.
  • Network stability concerns often lead to risk-averse capacity allocation strategies.

Purpose of the Study:

  • To develop a scalable stochastic programming model for optimal natural gas capacity allocation under uncertainty.
  • To analyze the impact of risk aversion on capacity allocation and system welfare in Norway's gas network.
  • To provide insights into system bottlenecks and the value of flexibility for policymakers and stakeholders.

Main Methods:

  • Development of a scalable stochastic programming model for capacity allocation.
  • Case study application to Norway's gas pipeline network.
  • Analysis of risk aversion's influence on optimal capacity decisions and system outcomes.

Main Results:

  • The model successfully determines optimal capacity allocation under uncertainty.
  • Moderating risk aversion in capacity allocation leads to substantial system welfare gains.
  • Identification of critical system bottlenecks and quantification of flexibility's value.

Conclusions:

  • Stochastic programming offers an effective approach to optimize natural gas capacity allocation.
  • Reducing excessive risk aversion in capacity management can unlock significant economic benefits for the European gas market.
  • The study provides valuable data and insights for enhancing energy security and market efficiency.