arXiv subjects

Hendrik Lens

Publications and source records attributed to Hendrik Lens.

8 recordsLinked to original sources

arXiv2026

The Benefits of an Integrated Approach for Stability-Constrained Power System Planning

Increasing penetration of inverter-based resources in today's power systems requires substitution of the contribution from synchronous generators to dynamic voltage stability and inertial response. However, established approaches for power system planning are sequential, as stabilising measures are only considered at a later stage of the planning process. We investigate the advantages of an integrated approach for power system planning, where stabilising measures are considered simultaneously with the expansion of generation, transmission, and storage systems via simplified stability constraints on inertia and voltage stability. We find that system costs are reduced with the integrated approach and that the dual-use option of grid-forming battery energy storage systems is favoured over other stabilising measures like static synchronous compensators.

math.OC↗

arXiv2026

A Novel Two-Step Approach for Reactive Power Demand Calculation Using Integrated Voltage Stability Analysis

The assessment of reactive power demand plays an instrumental role in power system planning. This paper presents a methodology for calculating reactive power demand based on a two-step approach. Unlike existing methodologies in the literature that focus primarily on optimization of reactive power compensation equipment placement and sizing through single-simulation approaches, this methodology directly calculates the actual reactive power demand through a comprehensive back-to-back simulation framework. While existing methods address either long-term or short-term voltage stability using either steady-state analysis or individual dynamic simulations, the proposed approach integrates both stability assessments sequentially through iterative Quasi-Dynamic Simulation, Q-V analysis and dynamic simulation. Furthermore, this methodology employs comprehensive time-series analysis over a full annual period (8760 hours) with multi-criteria violation assessment (number, severity and duration of voltage violations). In the final section of this paper, a case study was conducted to demonstrate the application of the proposed methodology. Simulations were performed to validate the effectiveness of the methodology, with the results showing that all buses with voltage issues were successfully addressed and finally the total reactive power demand across the network was calculated.

eess.SY↗

arXiv2026

Improving Operational Feasibility in Large-Scale Power System Planning

Large-scale power system planning mostly uses linearized, active power only approximations of the power flow equations, ignores many operational constraints, and tests the operational feasibility of the resulting systems only under strongly simplifying assumptions. We propose an approach to obtain solutions to large instances of the alternating current capacity expansion problem via redispatch and reinforcement of an initial solution. The problem formulation considers simultaneous expansion of generators, reactive compensation devices, storage systems, and transmission. Furthermore, it includes operational constraints via startup procedures and capability curves of power sources and simplified stability limits via constraints on voltage angle differences and voltage magnitudes. To obtain initial solutions, we test several established and partly modified power flow approximations and integrate them into an approach for iterative transmission expansion planning, thereby obtaining convex formulations. We demonstrate the approach on large problem instances covering the islands of Great Britain and Ireland at the transmission level, for which we extend the open data source to model reactive power. We find that including transmission losses to determine the initial solution is most decisive, as the amount of redispatch and reinforcements necessary to obtain an alternating current feasible solution is reduced, whereas incorporating reactive power constraints did not lead to further improvements. Our approach ensures an alternating current feasible system under weak assumptions, thus guaranteeing steady-state voltage stability and allowing subsequent dynamic grid simulations, which is instrumental for planning stable future inverter-dominated power systems.

math.OC↗

arXiv2025

Agent-Based Analysis of the Impact of Near Real-Time Data and Smart Balancing on the Frequency Stability of Power Systems

Single imbalance pricing provides an incentive to balance responsible parties (BRPs) to intentionally introduce power schedule deviations in order to reduce the control area imbalance and receive a remuneration through the imbalance settlement mechanism. This is called smart balancing or passive balancing and is actively encouraged in, e.g., the Netherlands and Belgium through the publication of near real-time (NRT) data on the control area imbalance by the transmission system operator. It is known that under certain conditions, smart balancing can deteriorate the frequency stability of the power system. This paper examines how the publication of different types of NRT data affects smart balancing and the frequency stability. A Monte-Carlo simulation of a dynamic multi-agent model is performed to analyse the effects of smart balancing with different parameters for the agents and the environment, using historical time series of the power imbalance of the German control block as a basis. It is found that smart balancing can significantly reduce the amount and cost of frequency restoration reserve activation, but leads to a general increase of the frequency variability. Depending on the type of NRT data and agent parameters, the frequency stability margins are also reduced. The negative effects on the frequency stability are stronger when NRT data is published using large bins and with long delays.

eess.SY↗

arXiv2025

Insights from Game Theory into the Impact of Smart Balancing on Power System Stability

Smart balancing, also called passive balancing, is the intentional introduction of active power schedule deviations by balance responsible parties (BRPs) to receive a remuneration through the imbalance settlement mechanism. From a system perspective, smart balancing is meant to reduce the need for, and costs of, frequency restoration reserves (FRR), but it can also cause large oscillations in the FRR and jeopardize the system stability. Using a dynamic control area model, this work defines a 2x2 game in which two BRPs can choose to perform smart balancing. We study the impact of time delay, ramp rates, and pricing mechanisms on Nash equilibria and Experience-weighted Attraction (EWA) learning. It is found that, even in an idealized setting, a significant fraction of games in a learned equilibrium results in an overreaction relative to the baseline disturbance, creating an imbalance in the opposite direction. This suggests that the system stability risks are inherent to smart balancing and not a question of implementation. Recommendations are given for implementation choices that can reduce (but not eliminate) the risk of overreactions.

eess.SY↗

arXiv2024

An Open Source Stochastic Unit Commitment Tool using the PyPSA-Framework

This paper presents an open source stochastic unit commitment (UC) optimization tool, which is available on GitHub. In addition, it presents an example use case in which UC optimization is done for a waste-to-energy plant with heat storage and a battery energy storage system (BESS) in Germany, under uncertain day-ahead and balancing power (aFRR) market prices as well as heat load uncertainty. The tool consists of multiple modular extensions for the Python for Power System Analysis (PyPSA) framework, namely the implementation of market and bidding mechanisms, stochastic optimization and multistaging.

math.OC↗

arXiv2024

Closed-loop Identification of a MSW Grate Incinerator using Bayesian Optimization for Selecting Model Inputs and Structure

The creation of low-order dynamic models for complex industrial systems is complicated by disturbances and limited sensor accuracy. This work presents a system identification procedure that uses machine learning methods and process knowledge to robustly identify a low-order closed-loop model of a municipal solid waste (MSW) grate incineration plant. These types of plants are known for their strong disturbances coming from fuel composition fluctuations. Using Bayesian optimization, the algorithm ranks and selects inputs from the available sensor data and chooses the model structure. This results in accurate models with low complexity while avoiding overfitting. The method is applied and validated using data of an industrial MSW incineration plant. The obtained models give excellent predictions and confidence intervals for the steam capacity and intermediate quantities such as supply air flow and flue gas temperature. The identified continuous-time models are fully given, and their step-response dynamics are discussed. The models can be used to develop model-based unit control schemes for grate incineration plants. The presented method shows great potential for the identification of over-actuated systems or disturbed systems with many sensors.

eess.SY↗

arXiv2022

An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting

PV power forecasting models are predominantly based on machine learning algorithms which do not provide any insight into or explanation about their predictions (black boxes). Therefore, their direct implementation in environments where transparency is required, and the trust associated with their predictions may be questioned. To this end, we propose a two stage probabilistic forecasting framework able to generate highly accurate, reliable, and sharp forecasts yet offering full transparency on both the point forecasts and the prediction intervals (PIs). In the first stage, we exploit natural gradient boosting (NGBoost) for yielding probabilistic forecasts, while in the second stage, we calculate the Shapley additive explanation (SHAP) values in order to fully comprehend why a prediction was made. To highlight the performance and the applicability of the proposed framework, real data from two PV parks located in Southern Germany are employed. Comparative results with two state-of-the-art algorithms, namely Gaussian process and lower upper bound estimation, manifest a significant increase in the point forecast accuracy and in the overall probabilistic performance. Most importantly, a detailed analysis of the model's complex nonlinear relationships and interaction effects between the various features is presented. This allows interpreting the model, identifying some learned physical properties, explaining individual predictions, reducing the computational requirements for the training without jeopardizing the model accuracy, detecting possible bugs, and gaining trust in the model. Finally, we conclude that the model was able to develop complex nonlinear relationships which follow known physical properties as well as human logic and intuition.

stat.AP↗