Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events
- URL: http://arxiv.org/abs/2503.05735v1
- Date: Wed, 19 Feb 2025 12:25:29 GMT
- Title: Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events
- Authors: Diederik Coppitters, Gabriel Wiest, Leonard Göke, Francesco Contino, André Bardow, Stefano Moret,
- Abstract summary: Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning.<n>We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.
Related papers
- From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
Much of this debate has concentrated on direct impact without addressing the significant indirect effects.<n>This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption.<n>We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
arXiv Detail & Related papers (2025-01-27T22:45:06Z) - OpenEP: Open-Ended Future Event Prediction [57.63525290892786]
We introduce OpenEP (an Open-Ended Future Event Prediction task), which generates flexible and diverse predictions aligned with real-world scenarios.
For question construction, we pose questions from seven perspectives, including location, time, event development, event outcome, event impact, event response, and other.
For outcome construction, we collect free-form text containing the outcomes as ground truth to provide semantically complete and detail-enriched outcomes.
arXiv Detail & Related papers (2024-08-13T02:35:54Z) - 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) - Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
"Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation.
We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations.
We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
arXiv Detail & Related papers (2023-10-18T18:38:59Z) - Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch.
In this paper, we collected large-scale load datasets and released a new renewable energy dataset.
We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z) - Sustainable Edge Intelligence Through Energy-Aware Early Exiting [0.726437825413781]
We propose energy-adaptive dynamic early exiting to enable efficient and accurate inference in an EH edge intelligence system.
Our approach derives an energy-aware EE policy that determines the optimal amount of computational processing on a per-sample basis.
Results show that accuracy and service rate are improved up to 25% and 35%, respectively, in comparison with an energy-agnostic policy.
arXiv Detail & Related papers (2023-05-23T14:17:44Z) - 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) - Weather-based forecasting of energy generation, consumption and price
for electrical microgrids management [0.0]
The transition towards a carbon-free society goes through an inevitable increase of the share of renewable generation in the energy mix.
This thesis studies the integration of renewables in power systems by investigating forecasting and decision-making tools.
arXiv Detail & Related papers (2021-07-01T09:02:36Z) - The role of quantum coherence in energy fluctuations [0.0]
We discuss the role of quantum coherence in the energy fluctuations of open quantum systems.
We introduce a protocol to define the statistics of energy changes as a function of energy measurements performed only after the evolution of the initial state.
We demonstrate our findings by running an experiment on the IBM Quantum Experience superconducting qubit platform.
arXiv Detail & Related papers (2021-06-11T15:32:24Z) - ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of
Harvested Energy [0.8774604259603302]
We present a runtime-based energy-allocation framework to optimize the utility of the target device under energy constraints.
The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day.
We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users.
arXiv Detail & Related papers (2021-02-26T17:21:25Z) - 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)
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.