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Item type:Publication, Access status: with files , Towards grid-aware multi-period flexibility aggregation — a constrained zonotope approach(Elsevier, 2027-07-10); ; ; ; Aggregation schemes provide a means to reduce the computational complexity of power system operation by reducing the number of devices that are considered individually. This can be achieved with tools of computational geometry, where the feasible set is projected onto the decision variables of the point of interconnection. Set projection is computationally expensive, especially in the context of multi-period power system operation. This calls for efficiency improvements via structure exploitation of set representations. This paper proposes efficient flexibility aggregation via constrained zonotopes. We evaluate the performance of the proposed method on a 15-bus distribution grid with time-dependent elements for up to 96 timesteps. The results suggest that the presented method significantly improves computation times compared to classic polytope projection approaches.Publicationtype: Journal Article11 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Analysis of DC–DC converters as critical components for maritime DC grid simulationThe level of simulation detail accounts for system stability and dynamic response performance and is key to optimize between simulation accuracy and complexity. This study investigate its impact in shipboard DC grids focusing on power electronic converters as a critical component. A DC–DC forward converter model was analyzed with varying levels of complexity incorporating parasitic elements and compared to empirical data recorded at 100 kHz from a real DC–DC converter. The comparison was made regarding their time domain response to a load steps and their frequency domain characteristics. Results indicate that key characteristics like settling time and steady-state behavior can be accurately represented at moderate complexity, whereas capturing full system dynamics in time as well as frequency domain requires coupled hardware and digital simulations (Hardware-in-the-loop - HiL) due to component intricacies and parameter uncertainties.Publicationtype: Journal Article79 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems(Elsevier, 2024-10); ; ; While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior based on noisy data. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN performs in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noise. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 75%.Publicationtype: Journal Article44 236 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Toward data-driven predictive control of multi-energy distribution systemsThe necessity to obtain, to parametrize, and to maintain models of the underlying dynamics impedes predictive control of energy systems in many real-world applications. To alleviate the need for explicit model knowledge this paper proposes a framework for the operation of distributed multi-energy systems via data-driven predictive control with stochastic uncertainties. Instead of modeling the dynamics of the individual distributed energy resources, our approach relies on measured input–output data of the distributed resources only. Moreover, we combine data-driven predictive control with forecasts of exogenous signals (renewable generations and demands) by Gaussian processes. A simulation study based on realistic data illustrates the efficacy of the proposed scheme to handle mild non-linearities and measurement noise.Publicationtype: Journal Article9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Energy-based stochastic MPC for integrated electricity-hydrogen VPP in real-time markets(Elsevier, 2021-06); Mikut, RalfVirtual Power Plants (VPPs) comprising renewables and hydrogen production through power-to-gas technologies can help to increase renewable penetration and to improve operational flexibility and economic performance. However, the uncertainty inherent to forecasts of renewable generation and energy prices renders cost effective operation difficult. The present paper approaches the issue by means of receding-horizon stochastic optimization (i.e. by stochastic Model Predictive Control (MPC)). Differently from previous works, we do not tackle computational tractability with a sampling-based approach, but by mapping quantile forecasts of virtual energy profiles to the mode of operation that has the highest probability of being optimal. This way, we reduce the computational load and the forecasting burden. Furthermore, simulation studies show that the proposed algorithm can attain a significant percentage of the revenue of optimal control with perfect forecasts.Publicationtype: Journal Article11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On polynomial real-time control policies in stochastic AC optimal power flow(Elsevier, 2020-12); 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Selection of DC voltage controlling station in an HVDC grid(2017-03-01); ; ; ; This paper proposes a real-time quantitative evaluation of HVDC converters’ in an HVDC grid to select the suitable DC slack converter. This real-time evaluation considers the strength of connecting AC grid and the converter's on-line capacity margin as selection metrics. The strength of AC grid is evaluated in real-time by the estimation of grid short circuit capacity using recursive lease square algorithm. Given these selection metrics, the credibility of HVDC stations in controlling the DC voltage can be offered to the system operator in real-time for further operational decisions. This paper also studies the practical aspect of the estimation algorithm regarding selection of the operating points. As major contribution, it suggests to intelligently use a naturally occurring droop response in HVDC grids as a second operating point in the estimation algorithm to calculate the short circuit capacity. The method has been tested through set of scenarios using a real-time co-simulation platform. This platform includes real-time power system simulator to model AC/DC grid, industrial HVDC controllers and corresponding ICT systems. The results show that the proper selection of DC slack station can improve the AC system response and DC voltage drops during disturbances.Publicationtype: Journal Article44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Study of centralized and distributed coordination of power injection in multi-TSO HVDC grid with large off-shore wind integrationThis paper studies centralized and distributed schemes for the coordination of power injection in an HVDC grid connected to large wind generation. This coordination of power injection aims to keep the power balanced within the HVDC grid especially during large wind disturbances. Furthermore, the coordination tries to follow the converters' schedules set by the connecting AC-TSOs every 15-min. This schedule comes from an overall combined AC/DC economic dispatch calculation with lower resolution compared to this coordination. In this paper, the coordination of power injection has been formulated as a non-linear constrained optimization problem for the centralized architecture. Next, the centralized optimization problem is decomposed to sub-problems using the Auxiliary Problem Principle (APP) method for the distributed architecture. This distributed optimization problem is solved by exchanging the required information between the AC TSOs. These two different approaches have been evaluated for a 5-terminal HVDC grid. Furthermore, a sensitivity analysis has been carried out to find the optimal updating rate of power injection set-points. The result shows that the more frequent updating of power injection coordination in the centralized architecture improves the generation reserves in each AC area.Publicationtype: Journal Article46