This is a discrete-time model that partitions the ecosystem into a spatially-realistic grid with a spatial resolution of 10 km, and tracks the dynamics of grass, wildebeest movement and population dynamics, fire, and tree dynamics in each lattice cell.
The average degree (i.e. the average value of the number of lines connected to each node) of a realistic power grid ranges from 2 to 5, normally between 2 to 3, and does not scale with the grid size [8].
In (2), P x) and Q x) represent the degree distribution of the realistic power grid topology to be simulated and the generated RT-Smallworld network, respectively.
The purpose is to select the RT-Smallworld network with the most similar degree distribution to the realistic power grid topology.
There are four different values of n, the first is equal to the node number of the realistic power grid, and the others are 1000, 3000 and 5000.
Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.
We provide the bar charts of the degree distributions in Fig. 3, from which can be seen that SW networks have the greatest difference compared to realistic power grid topologies.
From the above analyses, we conclude that DSCRG is superior to SW and PreRTNSW with respect to execution time, average degree and distribution degree for simulating the realistic power grid topologies.
In order to substitute generated random graphs for realistic power grid topologies in simulations, we have studied several existing random graph generation algorithms and pointed out their shortcomings clearly.
Due to the existing uncertainty and the absence of specific plans, for the purpose of our analysis we projected a realistic smart grid design for Bothoven-Noord, which is based on the interviews and moderate participant observation.
