document

More than accuracy: end-to-end wind power forecasting that optimises the energy system

Wahdany, D. (author), Schmitt, Carlo (author), Cremer, Jochen (author)

Weather forecast models are essential for sustainable energy systems. However, forecast accuracy may not be the best metric for developing forecast models. A more or less conservative forecast may be preferred over pure accuracy. For example, forecasting accurately in times of energy-deprived situations may be more important than in times of...

journal article 2023

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Deep Statistical Solver for Distribution System State Estimation

Habib, B. (author), Isufi, E. (author), Breda, Ward van (author), Jongepier, Arjen (author), Cremer, Jochen (author)

Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in...

journal article 2023

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Generating quality datasets for real-time security assessment: Balancing historically relevant and rare feasible operating conditions

Bugaje, A.-.A.B. (author), Cremer, Jochen (author), Strbac, Goran (author)

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 feasible and rare operating conditions (OCs). The proposed...

journal article 2023

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MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-Based Residential Demand Response

van Tilburg, Jasper (author), Cavalcante Siebert, L. (author), Cremer, Jochen (author)

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach...

conference paper 2023

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End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Vohra, Rushil (author), Rajaei, A. (author), Cremer, Jochen (author)

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM...

conference paper 2023

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Exploring Operational Flexibility of Active Distribution Networks with Low Observability

Chrysostomou, D. (author), Rueda, José L. (author), Cremer, Jochen (author)

Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices can effectively provide requires estimation and quantification so the network operators can plan operations close to real- time. Existing approaches estimating the...

conference paper 2023

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Regularised Learning with Selected Physics for Power System Dynamics

Xie, H. (author), Bellizio, Federica (author), Cremer, Jochen (author), Strbac, Goran (author)

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 conventional dynamic security assessment tools, many machine learning ...

conference paper 2023

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Machine learning and digital twins: monitoring and control for dynamic security in power systems

Brosinsky, Christoph (author), Karaçelebi, M. (author), Cremer, Jochen (author)

The reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the...

book chapter 2023

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Split-based sequential sampling for realtime security assessment

Bugaje, A.-.A.B. (author), Cremer, Jochen (author), Strbac, Goran (author)

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 different to historical power systems. In response, for the first...

journal article 2023

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Dynamic Incremental Learning for real-time disturbance event classification

Veerakumar, Nidarshan (author), Cremer, Jochen (author), Popov, M. (author)

With recent telemetric advancements, the real-time availability of power grid measurements has opened challenging opportunities for the design of advanced protection and control schemes. Artificial neural networks (ANN) are promising approaches for detecting and classifying disturbance events from measurement data. Numerous offline ANN-based...

journal article 2023

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Learning to run a power network with trust

Marot, Antoine (author), Donnot, Benjamin (author), Chaouache, Karim (author), Kelly, Adrian (author), Huang, Qiuhua (author), Hossain, Ramij Raja (author), Cremer, Jochen (author)

Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing...

journal article 2022

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Transient Stable Corrective Control Using Neural Lyapunov Learning

Bellizio, Federica (author), Cremer, Jochen (author), Strbac, Goran (author)

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 equipment fails. However, using corrective control beyond such...

journal article 2022

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Machine-learned security assessment for changing system topologies

Bellizio, Federica (author), Cremer, Jochen (author), Strbac, Goran (author)

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 frequently during operation due to maintenance and control actions...

journal article 2022

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Real-time transmission switching with neural networks

Bugaje, A.-.A.B. (author), Cremer, Jochen (author), Strbac, Goran (author)

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 switchable lines. In this paper, a real-time switching heuristic based on...

journal article 2022

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State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Segundo Sevilla, Felix Rafael (author), Liu, Yanli (author), Barocio, Emilio (author), Korba, Petr (author), Andrade, Manuel (author), Chaudhuri, Balarko (author), Cremer, Jochen (author), Rueda, José L. (author), Tindemans, Simon H. (author)

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of...

journal article 2022

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Perspectives on Future Power System Control Centers for Energy Transition

Marot, Antoine (author), Kelly, Adrian (author), Naglic, M. (author), Barbesant, Vincent (author), Cremer, Jochen (author), Stefanov, Alexandru (author), Viebahn, Jan (author)

Today's power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new...

journal article 2022

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Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market

Bellizio, Federica (author), Xu, Wangkun (author), Qiu, Dawei (author), Ye, Yujian (author), Papadaskalopoulos, Dimitrios (author), Cremer, Jochen (author), Teng, Fei (author), Strbac, Goran (author)

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 to data opens up new opportunities for a more efficient and...

journal article 2022

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Verifying Machine Learning conclusions for securing Low Inertia systems

Bellizio, Federica (author), Bugaje, Al Amin B. (author), Cremer, Jochen (author), Strbac, Goran (author)

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. Past research on ML-based DSA used high inertia systems to...

journal article 2022

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A causality based feature selection approach for data-driven dynamic security assessment

Bellizio, Federica (author), Cremer, Jochen (author), Sun, Mingyang (author), Strbac, Goran (author)

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 system. The training of these classifiers is typically performed offline...

journal article 2021

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Selecting decision trees for power system security assessment

Bugaje, Al-Amin B. (author), Cremer, Jochen (author), Sun, Mingyang (author), Strbac, Goran (author)

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 simulated in the time domain. However, this dynamic security...

journal article 2021