Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Hoekstra, AE Auke; Steinbuch, M Maarten; Verbong, Gpj Geert

doi:10.1155/2017/1967645

Cited by 58 publications

(47 citation statements)

References 270 publications

(282 reference statements)

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“…A major advantage of agent-based models is straightforward calibration with real-world data due to the intrinsic connection between real-world entities, such as people and vehicles, and the modelled agents, such as people-agents and vehicle-agents [34]. It is also an efficient method for both 'what-if' analyses and modifying the model-logic once a base model is developed.…”

Section: Agent-based Modelling To Study Mobility Demandmentioning

confidence: 99%

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

Hogeveen¹,

Steinbuch²,

Verbong³

et al. 2021

TOTJ

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Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.

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Section: Agent-based Modelling To Study Mobility Demandmentioning

confidence: 99%

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

Hogeveen¹,

Steinbuch²,

Verbong³

et al. 2021

TOTJ

Self Cite

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“…However, [9] find that only a few energy models account for co-evolutionary dynamics between policies, behaviour of actors and technologies [22], claiming that such dynamics are key and that energy models should not only look at technologies to be useful for effective climate-change mitigation efforts [23]. [16] and [24] argue that when studying sustainability transitions new modelling approaches are needed, which analyse policy interventions in the energy sector and their impacts on agents' investments, business models and social practices. This should be done by taking into account feedback loops between dimensions (or system elements), reinforcing mechanisms resulting from interactions between agents and the institutional dimensions, and co-evolution, which may lead to multiple solutions and transition pathways [24].…”

Section: Modelling Of Key Actors In Energy Transitions 21 Actors and mentioning

confidence: 99%

“…[16] and [24] argue that when studying sustainability transitions new modelling approaches are needed, which analyse policy interventions in the energy sector and their impacts on agents' investments, business models and social practices. This should be done by taking into account feedback loops between dimensions (or system elements), reinforcing mechanisms resulting from interactions between agents and the institutional dimensions, and co-evolution, which may lead to multiple solutions and transition pathways [24]. These new modelling approaches should also introduce agents which explicitly take decisions for sound climate policy-making [25].…”

Section: Modelling Of Key Actors In Energy Transitions 21 Actors and mentioning

confidence: 99%

The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors

Barazza

Strachan

2020

Energy Research & Social Science

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Achieving electricity sector transitions consistent with stringent climate change mitigation under the Paris Agreement requires a careful understanding both of the coordinating role of national governments and of its interactions with the heterogeneous market players who will make the lowcarbon investments in the electricity sector. However, traditional energy models and scenarios generally assume exogenous policy targets and fail to capture this co-evolution between policymakers and heterogeneous private and public investors. This paper uses BRAIN-Energy, a novel agentbased model of investment in electricity generation to simulate and contrast government and investor dynamics in the transition pathways of the UK, German and Italian electricity sectors. Key findings show that a successful transition -which achieves the energy policy "trilemma" (low carbon, secure, affordable) -requires the co-evolution of the policy dimension (strong and frequently updatable CO2 price, renewable subsidies and capacity market) with the strategies of the heterogeneous market players. If this dynamic balance is maintained then incentives are politically feasible and suppliers learn and evolve (in what we term a virtuous cycle). If either the incentives are too weak to drive learning or too expensive so the policy regime collapses, then the transition fails on one of its key dimensions (in what we term a vicious cycle). Getting this balance right is harder in risky markets that also have players with more pronounced bounded rationality and path dependence in how they make investments.

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“…ABMs have also been used to evaluate policies for promoting "climate clubs" oriented toward facilitating cooperation among institutions and states in response to the challenges of the Anthropocene [16,[51][52][53]. Social simulation techniques are increasingly applied in the study of sustainability transitions such as shifts toward low-carbon energy [19,54]. For reviews of other uses of ABMs to address issues related to human adaptation and sustainability, see [17,24,55,56].…”

Section: Advances In Sustainability Modelingmentioning

confidence: 99%

Human Simulation and Sustainability: Ontological, Epistemological, and Ethical Reflections

Shults

Wildman

2020

Sustainability

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This article begins with a brief outline of recent advances in the application of computer modeling to sustainability research, identifying important gaps in coverage and associated limits in methodological capability, particularly in regard to taking account of the tangled human factors that are often impediments to a sustainable future. It then describes some of the ways in which a new transdisciplinary approach within “human simulation” can contribute to the further development of sustainability modeling, more effectively addressing such human factors through its emphasis on stakeholder, policy professional, and subject matter expert participation, and its focus on constructing more realistic cognitive architectures and artificial societies. Finally, the article offers philosophical reflections on some of the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation, considered in light of the importance of human factors, including values and worldviews, in the modeling process. Based on this philosophical analysis, we encourage more explicit conversations about the value of naturalism and secularism in finding and facilitating effective and ethical strategies for sustainable development.

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Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Cited by 58 publications

References 270 publications

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors

Human Simulation and Sustainability: Ontological, Epistemological, and Ethical Reflections

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