Integrating reliability and resilience to support microgrid design

Quantifying the potential benefits of microgrids in the design phase can support the transition of passive distribution networks into microgrids. At current, reliability and resilience are the main drivers for this transition. Therefore, this paper presents a mathematical optimization model to support the retrofitting of distribution networks into microgrids integrating techno-economic, resilience and reliability objectives. Storage and distributed generation are optionally installed to complement renewable generation, enabling the microgrid to supply priority demands during stochastic islanding events with uncertain duration. For a comprehensive quantification and optimization of microgrid resilience and reliability, islanding due to external events is combined with a detailed model of internal faults. Minimizing the interruption costs yields optimal capacities and placements of distributed energy resources and new lines for reconfiguration. The proposed method produces microgrid designs with up to 95% reliability and resilience gain and moderate cost increase in two benchmark distribution networks using data from the US Department of Energy. The developed methodology is scalable to large networks owing to the tailored Column-and-Constraint-Generation approach

Anomaly detection for automated inspection of power line insulators

Inspection of insulators is important to ensure reliable operation of the power system. Deep learning has recently been explored to automate the inspection process by leveraging aerial images captured by drones along with powerful object detection models. However, a purely object detection-based approach exhibits class imbalance-induced poor detection accuracy for faulty insulators, especially for incipient faults. In order to address this issue in a data-efficient manner, this article proposes a two-stage approach that leverages object detection in conjunction with anomaly detection to reliably detect faults in insulators. The article adopts an explainable deep neural network-based one-class classifier for anomaly detection, that reduces the reliance on plentifully available images of faulty insulators, that might be difficult to obtain in real-life applications. The anomaly detection model is trained with two datasets -- representing data abundant and data scarce scenarios -- in unsupervised and semi-supervised manner. The results suggest that including as few as six real anomalies in the training dataset significantly improves the performance of the model, and enables reliable detection of rarely occurring faults in insulators. An analysis of the explanations provided by the anomaly detection model reveals that the model is able to accurately identify faulty regions on the insulator disks, while also exhibiting some false predictions

Optimizing protections against cascades in network systems: A modified binary differential evolution algorithm

International audienceThis paper addresses the optimization of protection strategies in critical infrastructures within a complex network systems perspective. The focus is on cascading failures triggered by the intentional removal of a single network component. Three different protection strategies are proposed that minimize the consequences of cascading failures on the entire system, on predetermined areas or on both scales of protective intervention in a multi-objective optimization framework. We optimize the three protection strategies by devising a modified binary differential evolution scheme that overcomes the combinatorial complexity of this optimization problem. We exemplify our methodology with reference to the topology of an electricity infrastructure, i.e. the 380 kV Italian power transmission network. We only focus on the structure of this network as a test case for the suggested protection strategies, with no further reference on its physical and electrical properties

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

International audienceThe purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout

A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection

International audienceReliable and safe power grid operation requires the anticipation of cascading failures and the establishment of appropriate protection plans for their management. In this paper, we address this latter problem by line switching and propose a multi-objective memetic algorithm (MOMA), which combines the binary differential evolution algorithm with the non-dominated sorting mechanism and the Lamarckian local search. The 380 kV Italian power transmission network is used as a realistic test case

Optimization of low-carbon multi-energy systems with seasonal geothermal energy storage: The Anergy Grid of ETH Zurich

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Modelling long term EU decarbonization policies along with detailed country energy system adequacy and security assessments

The assessment of adequacy and security of the energy system requires the detailed knowledge of physical and operational characteristics. In contrast, studies concerning energy transitions employ stylized models that oftentimes ignore the technical properties but have a lasting influence on longterm energy policies. This paper investigates the gap between energy system planning and operational models by linking these two perspectives: (1) a long-term investment model with low spatial resolution and high level of aggregation, and (2) a spatially resolved system security model that captures the interdependences between the backbone of the electric power sector, i.e., the electricity and the gas infrastructures. We assess EU decarbonisation pathways of the electricity sector towards 2050 by integrating the investment decisions of the long-term planning model and the safety performance of the resulting system operations via the security model. In a large RES deployment scenario, we investigate two flexibility options: gas power plants and cross-country transmission expansion. Using the integrated model, we analyze how the adequacy and security of supply under extreme short-term operational conditions impact the long-term planning of the energy system and the investment decision-making. We provide country specific recommendations for UK. Results indicate weaknesses in the gas-electricity system and suggest improvements on capacity allocation