Agent-Based Modeling as Part of Biomass Supply System Research
Aalto, Mika (2019-06-14)
Väitöskirja
14.06.2019
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Energiatekniikka
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-383-1
https://urn.fi/URN:ISBN:978-952-335-383-1
Tiivistelmä
Interest in the use of agent-based modeling (ABM) for studying biomass supply systems has increased because of its flexible and cost-efficient nature. While ABM has been around for a long time, recent developments in computing technology and modeling software have enabled more capable and complex models. This powerful dynamic simulation tool permits studying complex systems that feature interaction elements.
Simulation-based study can be used to support decision-making and increase understanding of the supply-system mechanisms involved with the various sources of biomass and the various technologies for utilizing it. While the modern use of biomass is often considered carbon-neutral and pressure to limit greenhouse-gas emissions has led the European Union to encourage this use, activities related to biomass supply systems may still cause greenhouse-gas emissions, so it is important to plan the system well and take dynamic elements into account. There are several complicating factors: supply systems are often considered complex systems, and biomass, with its variations in supply and demand, low energy-density, and high impact of transportation on usage costs, is a challenging study subject of a highly dynamic nature.
Accordingly, the possibilities and challenges of using ABM for studying biomass supply systems were assessed. The current use of simulation, especially ABM, was evaluated by means of bibliographic analysis with regard to three distinct modeling methods. Practical use of ABM in biomass supply chain study was examined with three models, which differed in geographical scale and level of abstraction: a model focusing on effects of policy changes, one centered on applying Big Data for simulation purposes, and a model integrating simulations with Geographical Information System data and ABM.
ABM was found to display the method-related problem of disparate terminology and reporting methods. There have been advances toward greater commonality in term use and efforts to standardize reporting, but uniform practice must be achieved before awareness and interest can grow. Also, while ABM proved to be good at handling large datasets and was able to generate huge result sets, their careful analysis is required if the conclusions are to be correct. Toward this end, however, some solutions involving design of experiments have been offered as a tool to select scenarios that better reveal the causes and consequences of the relevant events.
ABM’s good handling of data and its cost-efficient, flexible, and fast scenario analysis prove it to be highly suitable as a biomass supply system research tool. Models’ credibility and usefulness in this field is especially important because academic research uses simulation methods as a form of prototyping that may be used to focus study on certain scenarios, producing more precise results in line with real-life applications.
Simulation-based study can be used to support decision-making and increase understanding of the supply-system mechanisms involved with the various sources of biomass and the various technologies for utilizing it. While the modern use of biomass is often considered carbon-neutral and pressure to limit greenhouse-gas emissions has led the European Union to encourage this use, activities related to biomass supply systems may still cause greenhouse-gas emissions, so it is important to plan the system well and take dynamic elements into account. There are several complicating factors: supply systems are often considered complex systems, and biomass, with its variations in supply and demand, low energy-density, and high impact of transportation on usage costs, is a challenging study subject of a highly dynamic nature.
Accordingly, the possibilities and challenges of using ABM for studying biomass supply systems were assessed. The current use of simulation, especially ABM, was evaluated by means of bibliographic analysis with regard to three distinct modeling methods. Practical use of ABM in biomass supply chain study was examined with three models, which differed in geographical scale and level of abstraction: a model focusing on effects of policy changes, one centered on applying Big Data for simulation purposes, and a model integrating simulations with Geographical Information System data and ABM.
ABM was found to display the method-related problem of disparate terminology and reporting methods. There have been advances toward greater commonality in term use and efforts to standardize reporting, but uniform practice must be achieved before awareness and interest can grow. Also, while ABM proved to be good at handling large datasets and was able to generate huge result sets, their careful analysis is required if the conclusions are to be correct. Toward this end, however, some solutions involving design of experiments have been offered as a tool to select scenarios that better reveal the causes and consequences of the relevant events.
ABM’s good handling of data and its cost-efficient, flexible, and fast scenario analysis prove it to be highly suitable as a biomass supply system research tool. Models’ credibility and usefulness in this field is especially important because academic research uses simulation methods as a form of prototyping that may be used to focus study on certain scenarios, producing more precise results in line with real-life applications.
Kokoelmat
- Väitöskirjat [1068]