By Tobias Wittmann
This e-book demonstrates how bounded rational choice versions may be standardized and parameterized via socio-economic info. concentrating on deepest strength know-how funding judgements, the writer exhibits how assorted consultant brokers might be built utilizing seek principles, research instruments and selection innovations. Diffusion curves for strength applied sciences equivalent to sun creditors, boilers and potency enhancements for structures are calculated. extra, the version is prolonged to check the effect of agencies’ festival on know-how diffusion. The modeling strategy offered during this ebook may perhaps function a template for functions in different domain.
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Extra resources for Agent-Based Models of Energy Investment Decisions
Presently, the software is maintained and further developed at the Technical University of Berlin, Germany. Other energy system models providing similar features to deeco could be used as well. 4 The Layer Concept 23 conditions like temperature, wind speed, and insulation on the performance of technologies may also be included18. 4 Agent Layer The agent layer comprises models of all actors who demand or supply energy in the distribution system. Each agent is associated with one control domain, located in the technical layer.
The operational decisions, such as unit commitment and market bid-set formulation, of commercial actors are often supported by sophisticated software tools 21. Such software holds the technical data of all demands, conversion units, storage, and infrastructure in a control domain, and estimates future demand profiles and market prices, taking ambient conditions and consumer behaviors into account. Some structural decisions such as the investment in small plants like cogeneration units, boilers, and other distributed technologies, small infrastructure extensions, and plant decommissioning are regularly made by commercial actors.
1. 2 Aggregation of Technology and Infrastructure Information The model is designed to project the future residential energy demand in cities. It is therefore assumed that each district of a city can be assigned to one of the neighborhood types described in Roth et al. (1980). If a district to be studied is composed of different neighborhood prototypes, then further subdivision will be necessary. This typology provides information about the size of and distance between buildings, the availability of different infrastructures (gas grid, district heating grid, electricity grid) and the costs associated with connecting to them.