Recovering Power Grids Using Strategies Based on Network Metrics and Greedy Algorithms.

For this study, we investigated efficient strategies for the recovery of individual links in power grids governed by the direct current (DC) power flow model, under random link failures. Our primary objective was to explore the efficacy of recovering failed links based solely on topological network metrics. In total, we considered 13 recovery strategies, which encompassed 2 strategies based on link centrality values (link betweenness and link flow betweenness), 8 strategies based on the products of node centrality values at link endpoints (degree, eigenvector, weighted eigenvector, closeness, electrical closeness, weighted electrical closeness, zeta vector, and weighted zeta vector), and 2 heuristic strategies (greedy recovery and two-step greedy recovery), in addition to the random recovery strategy. To evaluate the performance of these proposed strategies, we conducted simulations on three distinct power systems: the IEEE 30, IEEE 39, and IEEE 118 systems. Our findings revealed several key insights: Firstly, there were notable variations in the performance of the recovery strategies based on topological network metrics across different power systems. Secondly, all such strategies exhibited inferior performance when compared to the heuristic recovery strategies. Thirdly, the two-step greedy recovery strategy consistently outperformed the others, with the greedy recovery strategy ranking second. Based on our results, we conclude that relying solely on a single metric for the development of a recovery strategy is insufficient when restoring power grids following link failures. By comparison, recovery strategies employing greedy algorithms prove to be more effective choices.

Transitioning to Low-Carbon Residential Heating: The Impacts of Material-Related Emissions.

To achieve climate neutrality, future urban heating systems will need to use a variety of low-carbon heating technologies. The transition toward low-carbon heating technologies necessitates a complete restructuring of the heating system, with significant associated material requirements. However, little research has been done into the quantity and environmental impact of the required materials for this system change. We analyzed the material demand and the environmental impact of the transition toward low-carbon heating in the Netherlands across three scenarios based on the local availability and capacity for sources of low-carbon heat. A wide range of materials are included, covering aggregates, construction materials, metals, plastics, and critical materials. We find that while the Dutch policy goal of reducing GHG emissions by 90% before 2050 can be achieved if only direct emissions from the heating system are considered, this is no longer the case when the cradle-to-gate emissions from the additional materials, especially insulation materials, are taken into account. The implementation of these technologies will require 59-63 megatons of materials in the period of 2021-2050, leading to a maximum reduction of 62%.

Nodal vulnerability to targeted attacks in power grids.

Due to the open data policies, nowadays, some countries have their power grid data available online. This may bring a new concern to the power grid operators in terms of malicious threats. In this paper, we assess the vulnerability of power grids to targeted attacks based on network science. By employing two graph models for power grids as simple and weighted graphs, we first calculate the centrality metrics of each node in a power grid. Subsequently, we formulate different node-attack strategies based on those centrality metrics, and empirically analyse the impact of targeted attacks on the structural and the operational performance of power grids. We demonstrate our methodology in the high-voltage transmission networks of 5 European countries and in commonly used IEEE test power grids.