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These actors in a value chain must gain non-negative value from the solution to ensure the adoption (Adner, 2012).
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Meanwhile, the adoption of a certain technological or behavioral solution to a problem often affects and is affected by other actors in the system (Ma et al., 2018). This system model can be subjected to different parameter configurations and stimuli to test how it behaves and responds under these circumstances and thereby get a better understanding of the real system (Christensen et al., 2019). To avoid risks and unnecessary costs, system operators must evaluate the performance of potential solutions before they are carried out in practice by modeling the system (Ma et al., 2016). This is expected to impact the electricity distribution system that supports the charging (Fatras et al., 2020), as personal transportation constituted the considerable share of 75.2% of the energy consumption within the Danish transport sector in 2019 (Data, tabeller, statistikker og kort Energistatistik, 2019). For instance, although 97% of the consumed energy for transportation was based on fossil fuels in Denmark 2017 (Basisfremskrivning, 2020), it is expected that the personal vehicle pool on the Danish roads will be comprised of 380,000 Electric Vehicles (EV) in 2030 (Basisfremskrivning, 2020). Due to the increasing prevalence of distributed energy resources (DER), distribution grids will face the challenge of insufficient grid capacity (Billanes et al., 2017). The 2015 United Nations Paris Agreement on climate change has caused a need for an energy sector that relies more on renewable energy resources and less on conventional fossil fuels, such as oil, coal, and natural gas.
Anylogic more than 5000 agent verification#
To verify the behavior of the developed agent-based simulation models, a verification procedure of the agent models is briefly discussed, which includes unit, integration, and system testing approaches similar to the ones used in software testing. The case study shows that the proposed agent-based modeling framework supports the development of agent-based models for simulating energy business ecosystems. The framework is demonstrated with a case study of an energy business ecosystem consisting of an electricity distribution grid with 137 connected domestic consumers.
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In addition, the framework depends on a pre-step that covers mapping and architecture development of the business ecosystem to be modeled. There are two steps in this proposed framework: Step 1 – Interface and role interactions design and Step 2 – Agent architecture and connections design. This paper proposes an agent-based modeling framework for developing agent-based simulation models of business ecosystems that can be applied to multiple evaluation scenarios by simple configuration of agents and roles. However, there is no efficient tool for system operators to quickly set up and perform simulations of alternative scenarios for system updates before planning their course of action, without much experience with programming or system modeling. With proper evaluation, distribution system operators can decide on a reasonable course of action for encouraging energy flexibility and make predictions on the recommended timing and magnitude of system updates under different scenarios. Modeling and simulation have been popularly used for system investigation and evaluation.