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Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed. The financial support from EPSRC for Liz Varga on project entitled "Transforming Utilities’ Conversion Points" (no. EP/J005649/1) is gratefully acknowledged.
- Karlsruhe Institute of Technology Germany
- European Institute for Energy Research Germany
- University of the Basque Country Spain
- UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA Spain
- Cranfield University United Kingdom
Complex systems, Smart grid, Microgrids modeling, NUCLEAR ENERGY AND ENGINEERING, agent based modeling, ENERGY AND FUELS, electrical grid, Electric grid, complex systems, smart grid, Multi-carrier energy systems, 600, multi-carrier energy systems, Renewable energy systems, microgrids modeling, Agent based modeling, ENERGY ENGINEERING AND POWER TECHNOLOGY, RENEWABLE ENERGY, SUSTAINABILITY AND THE ENVIRONMENT, renewable energy systems
Complex systems, Smart grid, Microgrids modeling, NUCLEAR ENERGY AND ENGINEERING, agent based modeling, ENERGY AND FUELS, electrical grid, Electric grid, complex systems, smart grid, Multi-carrier energy systems, 600, multi-carrier energy systems, Renewable energy systems, microgrids modeling, Agent based modeling, ENERGY ENGINEERING AND POWER TECHNOLOGY, RENEWABLE ENERGY, SUSTAINABILITY AND THE ENVIRONMENT, renewable energy systems
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).48 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
