AN AGENT-BASED MODELING APPROACH TO NON-EQUILIBRIUM
DYNAMICS OF NATURAL GAS SUPPLY SHOCK PROPAGATION
Alexander V. Outkin, Sandia National Laboratories,505-844-5187, avoutki@sandia.gov
Vanessa N. Vargas, Sandia National Laboratories, vnvarga@sandia.gov
Peter H. Kobos, Sandia National Laboratories, phkobos@sandia.gov
Melissa M. Myerly, Sandia National Laboratories, mmmyerl@sandia.gov
Garrett Barter, Sandia National Laboratories, gbarter@sandia.gov
Overview and Modeling Goals
The abundance of natural gas (NG) in the United States requires understanding of how this change will affect the
natural gas industry, downstream consumer, economic growth, and the society at large. This abundance may
propagate through changes to manufacturing, electric power generation, private and fleet vehicles, and increased
exports of liquefied natural gas. This paper presents the methodological foundations for a prototype model of the
U.S. natural gas system model (NGSM) that will allow comprehensive evaluation of the future states of the NG
system, regulatory dependencies, and economic consequences. We aim to develop a framework that will reveal how
decisions by producers, distributors, regulators, industries, and consumers can shape possible futures of the U.S. NG
system. Our approach uses agent-based and network modeling to represent the investment decisions and the
resulting NG price dynamics. Using this approach possible path dependencies and lock-in effects can be represented
and analyzed. We conceptualize the overall problem as supply shock propagation that pushes the NG system and the
economy away from its current state. We illustrate our approach with an early prototype model of the NG network
operation and supply shock propagation, where new supply and demand nodes can be added over time. We illustrate
the model with a stylized scenario where a large new demand node representing the LNG exports is added to the
network. Our preliminary results demonstrate that such addition is likely to have substantial effects on NG price and
on the pipelines capacity utilization.
Methods
Our approach combines an the agent-based and network modeling to represent the investment decisions and the
resulting natural gas (NG) price dynamics, as well as possible path dependencies and lock-in effects. Changes to the
NG will be instigated by heterogeneous agents imbued with different objectives, circumstances, and strategies.
Complex systems, such as NG, undergoing sudden changes may exhibit novel behaviors and phase transitions.
While results of such changes may not be entirely predictable or observable in real-time, agent-based models allow
for exploration of a myriad set of decisions and resulting novel system behaviors.
An Agent-Based modeling approach is ideal for this domain for a variety of reasons. The NG system under
consideration is spatially distributed with decentralized decision making. The agents within the NG system
(production, storage, market centers) are heterogeneous with different characteristics, objectives, and decision
strategies. The environment in which the system operates is complex: different laws and regulations, geographically
different infrastructures, different consumer preferences. Different entities
Supply shock propagation,
operate at different time-scales, for example, an individual consumer
investment decisions, SLOW
purchasing a car versus a corporate entity deciding whether to invest in an
LNG terminal for export.
+ New
supply
Relaxation
processes, MEDIUM
+ New
demand
More
supply
NG network, FAST
Figure 1. Three inter-conneted
models operating at different time
scales.
The key feature of the modeling approach we propose is to separate and
analyze semi-independently the processes operating at different time scales.
As the initial hypothesis, we split the NG supply shock propagation
modeling into three sub-components: day-to-day operation of NG network
(Fast), shock propagation (Slow), relaxation processes (Medium).
The Fast component includes supply, consumption, transmission decisions;
LNG, GTL day to day operation; and constraints imposed by the network
transmission, supply, and geographic availability. The Slow component
includes regulatory decisions (LNG exports approval); economics of LNG and
GTL investment decisions; construction resulting in new states of NG network.
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of
Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC0494AL85000. 37th IAEE Conference, Working Results.
The Medium component represents the NG network adaptation to executed investments and regulatory changes.
The Error! Reference source not found. represents the three time scales and models schematically. Not all models,
or their subcomponents need to be represented as ABMs. Given the separation of time-scales and other concerns, the
agent-based paradigm represents parts of the system and serves as glue to combine other models (such as systems
dynamics representing the supply) into a cohesive framework. Agent based models capture the evolution of system,
in this case the evolution of the NG system in the U.S. Our approach is conceptually based on our earlier work in
agent-based modelling (Darley and Outkin, 2007), energy modelling work (Barter et al., 2012 and Kobos et al.,
2011), and economic disruption modelling work (Vargas and Ehlen, 2013).
Generically, at a high level of abstraction, we represent a model as following:
- A set of agents: 𝐼 = {1, … , 𝑁}
- A set of agent strategies: 𝑆 = {𝑆1 , … , 𝑆𝑁 }, where 𝑆𝑖 represents a set of possible strategies for the agent 𝑖. We treat
strategy as a decision making-rule, which application results in decisions made and actions performed by the agent.
- A set of agent actions: 𝐴 = {𝐴1 , … , 𝐴𝑁 }, where 𝐴𝑖 represents a set of possible actions available to the agent 𝑖. Here,
the actions available to the agent 𝑖 are time and context-dependent: for example an LNG terminal can only be
constructed in a location with access to a sufficiently deep port and a decision to start the LNG shipments can only
be effective if LNG supply for example via an NG network is available.
- The interaction structures and the simulation environment where the agent’s actions are translated into the updated
states for the whole system and individual agents.
On a more detailed level, different subcomponents in Figure 1 are represented by semi-autonomous models. For
example, the NG network operations are represented by an existing NG network model, developed at Sandia (e.g.,
Gas Allocation Method, Mitchell et al., 2010).
Results
Our prototype model investigates the effects on the NG network of both excess supply and increasing demand. We
illustrate our approach with an example where a node representing the liquefied NG exports is added to the existing
NG network. Preliminary results highlight the NG network utilization, congestion, and price levels.
Conclusions
This paper demonstrates the applicability of agent-based modeling to understanding various questions in the Natural
Gas supply chain. Our starting premise is that the NG system has undergone an abrupt change due to increases in
supply driven by novel extraction methods. This has created a system where no historic data exists to represent the
possible system evolution and emergent behaviors. The goal of the proposed modeling effort is to understand in the
qualitative and quantitative fashion possible future states of the NG system, effects of possible regulatory actions,
and the emergent properties of the system. This paper outlines a conceptual model as well as the prototype
implementation and illustrates it on an example of a stylized network shock caused by an addition of significant
LNG exporting capacity.
References
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