Modeling Smart Grids as Complex Systems through the Implementation of Intelligent Hubs

The electrical system is undergoing a profound change of state, which will lead to what is being called the smart grid. The necessity of a complex system approach to cope with ongoing changes is presented: combining a systemic approach based on complexity science with the classical views of electrical grids is important for an understanding the behavior of the future grid. Key issues like different layers and inter-layer devices, as well as subsystems are discussed and proposed as a base to create an agent-based system model to run simulations. 1 The electrical grid as a Complex System The electrical grid as a whole can be considered as a complex system (more properly a Complex Computer System) whose aim is to assure a reliable power supply to all its consumers. Only regarding the grid from a multi-disciplinary point of view can help us understand the behavior of these systems. Despite conceptual advances in concrete fields like chaos theory or emergence in non-linear or self-organized systems, which were studied in the last decades, a unified theory of complexity does not yet exist. Complex networks have been studied by several scientists. Erdos and Renyi (1959) suggested the modeling of networks as random graphs. In a random graph (Bollobas, 1998), the nodes are connected by a placing a random number of links among them. This leads to a Poisson distribution when considering the numbers of connections of the nodes, thus there are many nodes with a similar number of links. Watts and Strogatz (1998) defined β as the probability of rewiring an edge of a ring graph and called these networks small-world. Analyzing networks with values 0 < β < 1, they found that these systems can be highly clustered, with a relatively homogenous topology, and have small characteristic path lengths. However, the study of networks in the real world has shown that there are many examples where this is not true but they exhibit a common property: the number of links k originating from a given node exhibits a power law distribution P(k) ∝ k−γ, i.e. few nodes having a large number of links. These networks are called scale-free and they are located in between the range of random and completely regular wired networks. Many systems in the real world such as neural networks, social networks and also the power grid, fulfill these properties. Barabasi and Albert (2002) mapped the topology of a portion of the World Wide Web and found that some nodes, which they called hubs, have many more connections than others and that the network as a whole exhibits a power-law distribution for the number of links connecting to a node. Using the BarabasiAlbert network model, Chassin and Posse (2005) analyzed the topologies of the North American electric grid to estimate their reliability and calculated the exponent of scale-free power law as being λ = 3.04 for the U.S. eastern grid and λ = 3.09 for the western one. Considering all of the advancements in complexity science, in this paper we will show how an electricity grid can be represented through a model as a complex system that can be used for simulations. First, the smart grid will be presented and some key issues discussed. Then, the approach for modeling the grid is explained and in the last section the simulation model is presented.