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Simon H. Tindemans

102 records found

To optimize the dispatch of batteries, a model is required that can predict the state of energy (SOE) trajectory for a chosen open-loop power schedule to ensure admissibility (i.e., that schedule can be realized). However, battery dispatch optimization is inherently challenging w ...

EnergySHR

A platform for energy dataset sharing and communications

Because the energy transition is a critical and urgent issue that is increasingly reliant on data, the Center for Energy System Intelligence (CESI), a Convergence collaboration between TU Delft and Erasmus University Rotterdam, has developed a platform where researchers on the en ...
The decarbonisation of electricity supply through variable renewable energy (VRE) is causing increasing congestion in electricity transmission and distribution grids. Redispatching after the closure of the day ahead market has been the most common congestion management instrument ...
The sudden proliferation of Electric Vehicles (EVs), batteries and photovoltaic cells in power networks can lead to congested distribution networks. A substitute for upgrading network capacity is a redispatch market that enables the Distribution System Operators (DSOs) to mitigat ...
Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact. This research proposes a novel method to quantify and represent the aggregate charging flexi ...
Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method that effectively captures both the intraday and interday chronology of real data considering extreme ...
Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to a ...
Smart charging of electric vehicles can alleviate grid congestion and reduce charging costs. However, various electric vehicle models currently lack the technical capabilities to effectively implement smart charging since they cannot handle charging pauses or delays. These models ...
Anomaly detection is of considerable significance in engineering applications, such as the monitoring and control of large-scale energy systems. This article investigates the ability to accurately detect and localize the source of anomalies, using an autoencoder neural network-ba ...

Aggregate Peak EV Charging Demand

The Influence of Segmented Network Tariffs

Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asse ...
Quantitative risk analysis is essential for power system planning and operation. Monte Carlo methods are frequently employed for this purpose, but their inherent sampling uncertainty means that accurate estimation of this uncertainty is essential. Basic Monte Carlo procedures are ...

C2 - GridOptions Tool

Real-World Day-Ahead Congestion Management using Topological Remedial Actions

Congestion is one of the major system risks for transmission system operators. At the same time, topological remedial actions still represent a largely unexploited form of non-costly exibility due to the combinatorial explosion in the number of possible actions. The GridOptions T ...
The electrification of end-energy use and the increasing integration of distributed energy resources (DERs) are significantly reshaping the landscape of low voltage (LV) distribution grids. However, many LV networks were originally designed without considering these transformativ ...
Electrification of energy end-uses brings an increasing load on electric distribution grids with load peaks that can cause network congestion. However, many new end-uses like electric vehicles, heat pumps, and electrified industrial processes have some flexibility to move their p ...
This paper proposes a multi-level segmented tariff to encourage consumers to provide demand response using a battery. The aim of the tariff is to (i) properly reflect consumers’ contribution to the distribution grid cost while ensuring cost recovery for the distribution network o ...
Electrical faults in the distribution system can lead to interruptions in customer power supply resulting in penalties that are borne by the distribution system operator. Accurate fault classification is an important step in locating the fault to achieve faster network restoratio ...
The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming more critical due to the need for modeling the short-term variability of RES output power and load de ...
Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward- ...
The proliferation of batteries, photovoltaic cells and Electric Vehicles (EVs) in electric power networks can result in network congestion. A redispatch market that allows the Distribution System Operators (DSOs) to relieve congested networks by asking the energy consumers to adj ...