Desent
The project aims at the development, application and dissemination of an integrated decision support system for energy use of buildings and transport, ensuring maximum efficiency with respect to supply and distributed energy generation.
The specific goal is to develop a smart energy control concept of household/vehicle energy use through the implementation of advanced ICT technology. Using multi-agent simulation, household preferences, energy use and responses to policies/services will be incorporated an integrated activity-travel demand model to predict the energy demand at a high level of temporal and spatial resolution. This system will be complemented with a model of distributed energy generation, which represents the possible interactions among local energy suppliers, grid operators and customers. The system will be implemented in several pilot cities. Experiences will provide the basis to evaluate whether such innovative high-resolution demand predictions provide improved insights into demand flexibility that can be used to balance services in the power grid. This is particularly important in the view of increased penetration of variable renewable generation, electrical mobility and distributed energy generation schemes in cities. Sub-project 1: Life Trajectory, Household Car Ownership Dynamics and Home Renewable Energy Equipment Adoption
Sub-project 1: Life Trajectory, Household Car Ownership Dynamics and Home Renewable Energy Equipment Adoption
This project focused on exploring the complex relationship between life course events, household car ownership, and decisions regarding home renewable energy equipment. With car ownership closely linked to urban issues like congestion, energy consumption, and pollution, understanding the impact of life course events on car ownership decisions and the subsequent choices of mobility tools and renewable energy solutions is crucial for advancing urban sustainability.
Objectives:
1. Examine Car Ownership and Mobility Tool Choices: The study aimed to investigate how life course events influence household car ownership decisions and choices of mobility tools, such as electric cars, electric bikes, and car sharing.
2. Analyze the Impact on Home Renewable Energy Equipment Decisions: The research sought to understand the potential effects of life course events and mobility tool choices on households' decisions to invest in home renewable energy equipment, like solar panels and heat pumps.
Methodology:
To achieve the research objectives, two types of data were collected: life trajectory data and stated choice data of mobility tools and home renewable energy equipment. The life trajectory data, collected in the Netherlands, provided insights into the influence of socio-demographics and life course events on car transaction behavior using a latent class competing risks model. The stated choice data, collected in Austria, allowed for the estimation of the impact of life course events on car ownership and mobility tool choices using a random component random parameter logit model.
Additionally, mixed logit and latent class logit models were employed to analyze the effects of mobility decisions on investment in home renewable energy equipment, considering heterogeneity among households.
Findings and Outcomes:
The research revealed significant heterogeneity in household car ownership decisions, with different life course events having varied effects on car ownership choices among different latent classes. Notably, life course events like baby birth influenced car ownership decisions and the choice of mobility tools, with people being inclined not to sell their current car when opting for electric bikes or car sharing.
Regarding home renewable energy equipment decisions, the purchase of battery electric vehicles (BEV) had a positive influence on the probability of investing in solar panels or heat pumps. Additionally, latent attitudes related to the environment, energy-saving, and technology innovation significantly influenced the intention to purchase energy equipment, particularly solar panels.
Conclusion:
This thesis provided valuable insights into the interplay between life course events, car ownership, and home renewable energy equipment decisions. By understanding the diffusion of new mobility tools and their long-term effects on investment in renewable energy solutions, the findings offer valuable guidelines for policymakers and urban planners to encourage sustainable mobility and energy product adoption. The research contributes to enhancing urban sustainability, addressing environmental concerns, and fostering energy-efficient, resilient cities.
Sub-project 2: Locating electric vehicle charging stations: a multi-agent based dynamic simulation
The electrification of transportation faces a significant barrier due to limited charging opportunities, often referred to as the chicken-or-egg problem. To encourage widespread adoption of plug-in electric vehicles (PEVs), it is crucial to ensure sufficient charging availability in urban areas. However, this requires understanding the heterogeneity in PEV users' charging behavior and optimizing the number and locations of charging stations.
Objectives:
1. Understanding PEV Charging Behavior: The research begins with a hazard-based duration analysis to examine the regularity and frequency of charging events at public charging stations. By segmenting PEV users based on charging regularity, valuable insights into frequent users' characteristics and charging patterns are gained.
2. Developing an Activity-Based Model: A dynamic activity-based model of travel demand, incorporating longitudinal effects, is proposed to depict complex decision-making processes influencing PEV users' charging patterns.
3. Optimizing Charging Station Locations: An integrated framework is developed for optimal planning of public charging stations, combining the activity-based demand model and a scenario-based stochastic programming approach to address uncertainty.
Methodology:
The hazard-based duration analysis models inter-charging times between consecutive charging events, identifying regular and erratic charging patterns. The longitudinal travel data of PEV users are collected through a smartphone-based prompted-recall survey, enabling the use of mixed-effects models to account for repeated measures and individual variances. The activity-based model, Albatross, is adapted to consider dynamic decision trees, accounting for longitudinal structure and charging behavior. The framework for charging station optimization utilizes a two-stage stochastic mixed-integer programming (TSMIP) model to address model uncertainty.
Findings and Outcomes:
The hazard-based analysis reveals two distinct groups of PEV users based on charging regularity, providing insights into charging frequency and public charging station usage. The activity-based model captures the complexities of PEV users' charging patterns, enabling a better understanding of their charging behavior under various constraints. The integrated framework for charging station optimization considers uncertainty and identifies the optimal number and locations of charging stations to cater to PEV users' demand effectively.
Conclusion:
By understanding PEV users' charging behavior and developing an activity-based model, the research contributes to enhancing the planning of public charging stations in urban areas. The integrated framework aids decision-making, promoting the widespread adoption of PEVs and facilitating the transition to a more sustainable urban transportation system.
Project information
- Category Energy transition
- Authors Gaofeng Gu & Seheon Kim
- Project date 03/2016 - 05/2019