GS

G. Strbac

66 records found

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historical records. Generating diverse operating conditions is highly relevant for the uncertain future of emerging power systems that are completely d ...

Generating quality datasets for real-time security assessment

Balancing historically relevant and rare feasible operating conditions

This paper presents a novel, unified approach for generating high-quality datasets for training machine-learned models for real-time security assessment in power systems. Synthetic data generation methods that extrapolate beyond historical data can be inefficient in generating fe ...
Developing innovative electricity market designs to facilitate a sustainable transition to (near) 100% renewable power systems while meeting societal needs is a crucial and actual topic of research. This article presents preliminary key findings from the H2020 European project Tr ...
Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computational time problem of convent ...
The classical formulation of the transmission switching problem as a mixed-integer problem is intractable for large systems in real-time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchabl ...
Machine learning has been used in the past to construct predictors, also known as classifiers, for dynamic security assessment. Although accurate classifiers can be trained for a single topology, often they do not work for another. However, the power system topology can change fr ...
Digitalization is one of the key drivers for energy system transformation. The advances in communication technologies and measurement devices render available a large amount of operational data and enable the centralization of such data storage and processing. The greater access ...
Machine Learning (ML) for real-time Dynamic Security Assessment (DSA) promises a probabilistic approach to secure lower safety margins and costs. However, future systems with a high share of renewables have low inertia and converter-interfaced devices resulting in faster dynamics ...
This paper proposes a method to compute corrective control actions for dynamic security in real-time and quantifies the economic value of corrective control. Lowered inertia requires fast control methods in real-time to correct system operation and maintain system security when e ...
Buildings are responsible for about a quarter of global energy-related CO 2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through incre ...
Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for some stability phenomena that are unpra ...
The integration of renewable energy sources increases the operational uncertainty of electric power systems and can lead to more frequent dynamic phenomena. The use of classifiers from machine learning is promising to include dynamics in the security assessment of the power syste ...
Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This article derives a decentralized load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achiev ...
Power systems transport an increasing amount of electricity, and in the future, involve more distributed renewables and dynamic interactions of the equipment. The system response to disturbances must be secure and predictable to avoid power blackouts. The system response can be s ...
Dynamic Security Assessment (DSA) for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources (RES) and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, ...
Accurately and efficiently estimating system performance under uncertainty is paramount in power system planning and operation. Monte Carlo simulation is often used for this purpose, but convergence may be slow, especially when detailed models are used. Previously published metho ...
Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) d ...
Concerns about system adequacy have led to the establishment of capacity mechanisms in a number of regulatory areas. Against this background, it is essential to accurately quantify the contribution to security of supply that results from interconnectors to neighbouring systems. T ...
We present a method to find the maximum magnitude of any supply-shortfall service that an aggregator of energy storage devices is able to sell to a grid operator. This is first demonstrated in deterministic settings, then applied to scenarios in which device availabilities are st ...