Cellular automata and energetics system formation

Abstract

Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agentoriented approach, where local interactions lead to a global behavior. This approach helps to understand how non‐deterministic behavior that is near self‐organized criticality (SOC) is used to explain natural and social phenomena can emerge from local interactions between agents. The basis of our decision to develop cellular automata (CA) as a model for energetics system formation and development in restricted region is its hypothetical dependence on and origin from the “urban slice” which is basis for “energetics slice”. In the urban CA model there we introduce five types of cells representing empty area, roads, houses, water and forest. Some types of cells are introduced only for the model better correspondence to the real system and don't have essentially influence to the modeling results. We assign all cell types certain weight, which affects the probability of new “houses” cells appearance replacing “empty area” cells. Usually all cells except the empty ones have much bigger weight, therefore the dynamic of houses distribution in restricted area is organized in clusters. A first step in model ‘reliability’ is an understanding how these systems behave over time. CA's are an alternative to differential equations on an attempt to model these systems. One of the most important features of CA models is its desirable capacity to capture quantitative micro‐level dynamics and relate them to qualitative macro‐level behavior. Energetics system formation is dynamic process that directly depends on houses conglomerate formation, energy production and transferring prices, energy consumption factor etc. Dynamical CA model can be used to realize certain purposes of energetic policy and to make decisions about volume of production as well as prices of generation and transmission. These decisions, in one's turn, influence successive urban and energetics system dynamics. There is analyzed how various policies influence urban system development as well as its stability after the new capacity is installed and prices in generation and transmission as well as system administration are changed, etc [3]. Also there is explored how energetics system dynamics obtained with help of dynamic model corresponds with CA obtained dynamics.

Ląstelių automatai ir energetinės sistemos susidarymas

Santrauka. Pasiūlytas efektyvus ląstelių automatų (LA) modelis, aprašantis sudėtingą urbanistinę sistemą (namų konglomeratų) krizinės savirangos aspektu. Krizinės savirangos būsena (KSB) yra būdinga daugeliui sudėtingų dinaminių sistemų, tame tarpe ir urbanistinei bei energetinei sistemai. Šiame straipsnyje nagrinėjamas urbanistinių klasterių formavimasis ir parametrų, kurie labiausiai atitinka KSB, nustatymas. Pažymima, kad energetinė sistema, kuri yra apibūdinama generatorių kiekio pagal galingumą pasiskirstymu, yra išvestinė iš urbanistinės sistemos, todėl jai tinka urbanistinės sistemos LA modelis.

First Published Online: 14 Oct 2010

Keyword : Cellular automata, urban system, energetic system, self‐organized criticality