Revealing Robust Oil and Gas Company Macro-Strategies using Deep
Multi-Agent Reinforcement Learning
- URL: http://arxiv.org/abs/2211.11043v1
- Date: Sun, 20 Nov 2022 17:52:59 GMT
- Title: Revealing Robust Oil and Gas Company Macro-Strategies using Deep
Multi-Agent Reinforcement Learning
- Authors: Dylan Radovic, Lucas Kruitwagen, Christian Schroeder de Witt, Ben
Caldecott, Shane Tomlinson, Mark Workman
- Abstract summary: The energy transition potentially poses an existential risk for major international oil companies (IOCs) if they fail to adapt to low-carbon business models.
We used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making.
Strategys emerged in the form of low-carbon business models as a result of early transition-oriented movement.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The energy transition potentially poses an existential risk for major
international oil companies (IOCs) if they fail to adapt to low-carbon business
models. Projections of energy futures, however, are met with diverging
assumptions on its scale and pace, causing disagreement among IOC
decision-makers and their stakeholders over what the business model of an
incumbent fossil fuel company should be. In this work, we used deep multi-agent
reinforcement learning to solve an energy systems wargame wherein players
simulate IOC decision-making, including hydrocarbon and low-carbon investments
decisions, dividend policies, and capital structure measures, through an
uncertain energy transition to explore critical and non-linear governance
questions, from leveraged transitions to reserve replacements. Adversarial play
facilitated by state-of-the-art algorithms revealed decision-making strategies
robust to energy transition uncertainty and against multiple IOCs. In all
games, robust strategies emerged in the form of low-carbon business models as a
result of early transition-oriented movement. IOCs adopting such strategies
outperformed business-as-usual and delayed transition strategies regardless of
hydrocarbon demand projections. In addition to maximizing value, these
strategies benefit greater society by contributing substantial amounts of
capital necessary to accelerate the global low-carbon energy transition. Our
findings point towards the need for lenders and investors to effectively
mobilize transition-oriented finance and engage with IOCs to ensure responsible
reallocation of capital towards low-carbon business models that would enable
the emergence of fossil fuel incumbents as future low-carbon leaders.
Related papers
- InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma [8.867831781244575]
InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of ESG disclosure mandates on corporate climate investments.
Our experiments show that without ESG-conscious investors with sufficient capital, corporate mitigation efforts remain limited under the disclosure mandate.
Providing more information about global climate risks encourages companies to invest more in mitigation, even without investor involvement.
arXiv Detail & Related papers (2024-11-15T00:31:45Z) - Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility.
We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL)
Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
arXiv Detail & Related papers (2024-06-12T05:08:51Z) - Streamlining Energy Transition Scenarios to Key Policy Decisions [3.737361598712633]
We derive interpretable storylines from stakeholder discussions using decision trees.
Our results show that choosing a high deployment of renewables makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand.
Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition.
arXiv Detail & Related papers (2023-11-11T18:10:32Z) - Proximal Policy Optimization Based Reinforcement Learning for Joint
Bidding in Energy and Frequency Regulation Markets [6.175137568373435]
Energy arbitrage can be a significant source of revenue for the battery energy storage system (BESS)
It is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions.
This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit.
arXiv Detail & Related papers (2022-12-13T13:07:31Z) - Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - Power Grid Congestion Management via Topology Optimization with
AlphaZero [0.27998963147546135]
We propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative.
Our approach ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition.
arXiv Detail & Related papers (2022-11-10T14:39:28Z) - (Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach
for Climate Finance and Risk Assessment [64.83786252406105]
Retrospective Social Cost of Carbon Updating (ReSCCU) is a novel mechanism that corrects for limitations as empirically measured evidence is collected.
To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP)
To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market.
arXiv Detail & Related papers (2022-05-02T06:02:13Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z) - A Deep Reinforcement Learning Framework for Continuous Intraday Market
Bidding [69.37299910149981]
A key component for the successful renewable energy sources integration is the usage of energy storage.
We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market.
An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency.
Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
arXiv Detail & Related papers (2020-04-13T13:50:13Z) - Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A
Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network.
We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.