The Smart Grid is not just about the digitalization of the Power Grid. In its more visionary acceptation, it is a model of energy management in which the users are engaged in producing energy as well as consuming it, while having information systems fully aware of the energy demand-response of the network and of dynamically varying prices. A natural question is then: to make the Smart Grid a reality will the Distribution Grid have to be updated? We assume a positive answer to the question and we consider the lower layers of Medium and Low Voltage to be the most affected by the change. In our previous work, we have analyzed samples of the Dutch Distribution Grid in our previous work and we have considered possible evolutions of these using synthetic topologies modeled after studies of complex systems in other technological domains in another previous work. In this paper, we take an extra important further step by defining a methodology for evolving any existing physical Power Grid to a good Smart Grid model thus laying the foundations for a decision support system for utilities and governmental organizations. In doing so, we consider several possible evolution strategies and apply then to the Dutch Distribution Grid. We show how more connectivity is beneficial in realizing more efficient and reliable networks. Our proposal is topological in nature, and enhanced with economic considerations of the costs of such evolutions in terms of cabling expenses and economic benefits of evolving the Grid.
The concept of network evolution of a technological infrastructure is considered in @cite_31 where a model for the evolution of the Internet Autonomous System (AS) is provided. As most works (e.g., @cite_16 @cite_37 ), this one focuses on creating a model of the existing evolution of the Internet rather than proposing new ways of evolving networks. The interesting aspect on the modeling of the Internet is the better results of the proposed model Parallel Addition and Rewiring Growth (PARG) compared to other models in the literature to capture specific features of the AS topology. In particular, the model is able to capture the dissortativeness of hubs (i.e., the most connected nodes in the network) and linear local assortativeness of nodes. The model proposed proves successful, however one must recognize that the AS connection are mainly logical and are due more to business and contractual agreements than physical interconnections, therefore missing part of the problem that is essential in the case of the topology.
@cite_31 @cite_16 @cite_37
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Network generators that capture the Internet's large-scale topology are crucial for the development of efficient routing protocols and modeling Internet traffic. Our ability to design realistic generators is limited by the incomplete understanding of the fundamental driving forces that affect the Internet's evolution. By combining several independent databases capturing the time evolution, topology, and physical layout of the Internet, we identify the universal mechanisms that shape the Internet's router and autonomous system level topology. We find that the physical layout of nodes form a fractal set, determined by population density patterns around the globe. The placement of links is driven by competition between preferential attachment and linear distance dependence, a marked departure from the currently used exponential laws. The universal parameters that we extract significantly restrict the class of potentially correct Internet models and indicate that the networks created by all available topology generators are fundamentally different from the current Internet. Our goal is to understand the evolution of the Autonomous System (AS) ecosystem over the last decade. Instead of focusing on abstract topological properties, we classify ASes into a number of "species" depending on their function and business type. Further, we consider the semantics of inter-AS links, in terms of customer-provider versus peering relations. We find that the available historic datasets from RouteViews and RIPE are not sufficient to infer the evolution of peering links, and so we restrict our focus to customer-provider links. Our findings highlight some important trends in the evolution of the Internet over the last decade, and hint at what the Internet is heading towards. After an exponential increase phase until 2001, the Internet now grows linearly in terms of both ASes and inter-AS links. The growth is mostly due to enterprise networks and content access providers at the periphery of the Internet. The average path length remains almost constant mostly due to the increasing multihoming degree of transit and content access providers. In recent years, enterprise networks prefer to connect to small transit providers, while content access providers connect equally to both large and small transit providers. The AS species differ significantly from each other with respect to their rewiring activity; content access providers are the most active. A few large transit providers act as "attractors" or "repellers" of customers. For many providers, strong attractiveness precedes strong repulsiveness by 3-9 months. Finally, in terms of regional growth, we find that the AS ecosystem is now larger and more dynamic in Europe than in North America. We study the large-scale topological and dynamical properties of real Internet maps at the autonomous system level, collected in a 3-yr time interval. We find that the connectivity structure of the Internet presents statistical distributions settled in a well-defined stationary state. The large-scale properties are characterized by a scale-free topology consistent with previous observations. Correlation functions and clustering coefficients exhibit a remarkable structure due to the underlying hierarchical organization of the Internet. The study of the Internet time evolution shows a growth dynamics with aging features typical of recently proposed growing network models. We compare the properties of growing network models with the present real Internet data analysis.