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The generation and supply of electricity is currently about to undergo a fundamental transition that includes extensive development of smart grids. Smart grids are huge and complex networks consisting of a vast number of devices and entities which are connected with each other. This opens new variations of disruption scenarios which can increase the vulnerability of a power distribution network. However, the network topology of a smart grid has significant effects on urban resilience particularly referring to the adequate provision of infrastructures. Thus, topology massively codetermines the degree of urban resilience, i.e. different topologies enable different strategies of power distribution. Therefore, this article introduces a concept of criticality adapted to a power system relying on an advanced metering infrastructure. The authors propose a two-stage operationalization of this concept that refers to the design phase of a smart grid and its operation mode, targeting at an urban resilient power flow during power shortage.

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Systems of critical infrastructures are characterized by strong interdependencies and the developments of urban areas towards Smart Cities even increase the underlying complexity due to growing automation and interconnectedness. A system of highly cross-linked components is especially prone to systemic risks making concepts of resilience accordingly important. One way for being able to withstand in times of stress, maintain security of supply, and promote adaptive and anticipative capabilities, is to establish early warning capabilities. As cities are complex and rather chaotic socio-technical systems reigned by randomness, the caused parametric uncertainties challenge modeling approaches that are intended to support robust decision-making. Sophisticated methods based on artificial intelligence can play an essential role in this case, as they perform well on highly complex environments and large data set. To study resilience, the urban area is split into zones where the city's state is determined by the states of these zones and the state of a zone is characterized by the criticalities of infrastructures accommodated there. Considering criticality as an atomic building block for urban performance assessments, this paper proposes a zone-based state forecast methodology by applying deep convolutional neural networks for learning state evolution that is influenced by non-linear demand dynamics. Furthermore, a case study is presented that applies agent-based simulations and underlines the relevance of deep learning approaches for Smart City early warning systems.

The generation and supply of electricity is currently about to undergo a fundamental transition that includes extensive development of smart grids. Smart grids are huge and complex networks consisting of a vast number of devices and entities which are connected with each other. This opens new variations of disruption scenarios which can increase the vulnerability of a power distribution network. However, the network topology of a smart grid has significant effects on urban resilience particularly referring to the adequate provision of infrastructures. Thus, topology massively codetermines the degree of urban resilience, i.e. different topologies enable different strategies of power distribution. Therefore, this article introduces a concept of criticality adapted to a power system relying on an advanced metering infrastructure. The authors propose a two-stage operationalization of this concept that refers to the design phase of a smart grid and its operation mode, targeting at an urban resilient power flow during power shortage.

In the context of the energy transition, sound decision making regarding the development of renewable energy systems faces various technical and societal challenges. In addition to climate-related uncertainties affecting technical issues of reliable grid planning, there are also subtle aspects and uncertainties related to the integration of energy technologies into built environments. Citizens’ opinions on grid development may be ambiguous or divergent in terms of broad acceptance of the energy transition in general, and they may have negative attitudes towards concrete planning in their local environment. First, this article identifies the issue of discrepancies between preferences of a fixed stakeholder group with respect to the question of the integration of renewable energy technology, posed from different perspectives and at different points in time, and considers it as a fundamental problem in the context of robust decision making in sustainable energy system planning. Second, for dealing with that issue, a novel dynamic decision support methodology is presented that includes multiple surveys, statistical analysis of the discrepancies that may arise, and multicriteria decision analysis that specifically incorporates the opinions of citizens. Citizens are considered as stakeholders and participants in smart decision-making processes. A case study applying agent-based simulations underlines the relevance of the methodology proposed for decision making in the context of renewable energies.