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Extreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation. [ABSTRACT FROM AUTHOR]

• We propose an approach to identify solar prosumers from net energy consumption data. • The proposed approach yielded high accuracy when tested on real smart meter data. • Data needed to identify solar prosumers can be reduced to a single annual point. • Solar Prosumer Identification-Duration Curves can support decision making. With the continued growth of distributed power generation, the number of customers who consume and produce energy via solar photovoltaic (PV) power generation (solar prosumers) has been increasing. Unfortunately, electric utilities are not aware of the location of all solar prosumers due to unauthorized or unreported installations and lack of separate PV metering in areas with Net Energy Metering policies. Knowledge of the location of solar prosumers can inform circuit protection and voltage regulation settings, and help grid operators improve situational awareness and better plan for daily variations in demand, which are further magnified by generation intermittency. This paper proposes an innovative approach to identify customers with PV power generation using net energy consumption data from smart meters. Methods of dimensionality reduction are evaluated to propose an effective means of reducing the amount of data needed to accurately identify solar prosumers to a single data point and improve the clustering time by a significant factor. The proposed advanced classification approach for solar prosumer identification, based on agglomerative clustering, demonstrates very significant classification accuracy and outperforms k-means and neural network based approaches on real consumption data from a large number of residential customers. As the number of solar prosumers is growing daily, a novel Solar Prosumer Identification-Duration Curve is also proposed to give operators and distribution planners insight into how quickly new solar prosumers can be accurately identified. [ABSTRACT FROM AUTHOR]

As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential to reduce the influence of disturbances, enhancing critical clearance time before frequency collapse and supporting secure power system operation. [ABSTRACT FROM AUTHOR]