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Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks

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arXiv 2023年

作者： Phipps, Kaleb Heidrich, Benedikt Turowski, Marian Wittig, Moritz Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute for Technology Institute for Automation and Applied Informatics Eggenstein-Leopoldshafen76344 Germany

In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these deterministic forecasts into probabilistic forecasts is often challenging and based on numerous assumptions that may not hold in real-world situations. Therefore, the present article proposes a novel approach for creating probabilistic forecasts from arbitrary deterministic forecasts. In order to implement this approach, we use a conditional Invertible Neural Network (cINN). More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions. Besides showing the mathematical validity of our approach, we empirically show that our approach noticeably outperforms traditional methods for including uncertainty in deterministic forecasts and generally outperforms state-of-the-art probabilistic forecasting benchmarks. © 2023, CC BY.

关键词： Forecasting

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information

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arXiv 2023年

作者： Heidrich, Benedikt Phipps, Kaleb Neumann, Oliver Turowski, Marian Mikut, Ralf Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs. © 2023, CC BY.

关键词： Time series

Open Data-Driven automation of Residential Distribution Grid Modeling with Minimal Data Requirements

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arXiv 2023年

作者： Weber, Moritz Janecke, Luc Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

In the present paper, we introduce a new method for the automated generation of residential distribution grid models based on novel building load estimation methods and a two-stage optimization for the generation of the 20 kV and 400 V grid topologies. Using the introduced load estimation methods, various open or proprietary data sources can be utilized to estimate the load of residential buildings. These data sources include available building footprints from OpenStreetMap, 3D building data from OSM Buildings, and the number of electricity meters per address provided by the respective distribution system operator (DSO). For the evaluation of the introduced methods, we compare the resulting grid models by utilizing different available data sources for a specific suburban residential area and the real grid topology provided by the DSO. This evaluation yields two key findings: First, the automated 20 kV network generation methodology works well when compared to the real network. Second, the utilization of public 3D building data for load estimation significantly increases the resulting model accuracy compared to 2D data and enables results similar to models based on DSO-supplied meter data. This substantially reduces the dependence on such normally proprietary data. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

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arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization 63

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Real-time Pricing Mechanism for V2G Using Distributed Bileve...

63rd IEEE Conference on Decision and Control, CDC 2024

作者： You, Jiawei Dai, Xinliang Jiang, Yuning Yin, Haoyu Shi, Yuanming Jones, Colin N. ShanghaiTech University School of Information Science and Technology China Epfl Switzerland Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Epfl Automatic Control Laboratory Switzerland Washington University in St. Louis Department of Electrical and Systems Engineering United States

ISBN: (纸本)9798350316339

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Predictive control systems

Lowering barriers to a large-scale implementation of grid booster projects by introducing a hybrid concept

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Lowering barriers to a large-scale implementation of grid bo...

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Jonas Lotze Marvin Dorn Veit Hagenmeyer Strategic Asset Management TransnetBW GmbH Stuttgart Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The future of power systems belongs to renewables. And to integrate them, the expansion of the electricity grids is undoubtedly necessary. Furthermore, power systems cannot operate without reserves and flexibilities. For these to be efficient, system-serving, and economical, holistic concepts are needed. This paper describes the grid booster concept and presents the major barriers to a large-scale implementation in electric power systems. A proposal for enhancing economic viability and multi-applicability is presented through the discussion of a hybrid grid booster concept.

关键词： Hands Renewable energy sources Costs Wind energy Electricity Systems operation Europe Batteries Smart grids Turbines

Occupant-Oriented Demand Response with Multi-Zone Thermal Building Control

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arXiv 2023年

作者： Frahm, Moritz Dengiz, Thomas Zwickel, Philipp Maaß, Heiko Matthes, Jörg Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Eggenstein-Leopoldshafen Germany Karlsruhe Institute of Technology Institute for Industrial Production Karlsruhe Germany

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants’ Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a heuristic control approach from the literature (PC), and a conventional hysteresis-based two-point control as upper and lower benchmarks. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters concerning the electricity costs and OTS. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems. © 2023, CC BY.

关键词： Model predictive control

A Spatial Decomposition Method for Solving the Security-Constrained Unit Commitment Problem

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A Spatial Decomposition Method for Solving the Security-Cons...

IEEE Canada International Humanitarian Technology Conference (IHTC)

作者： Arjun Kumar Madhusoodhanan Marco Giuntoli Susanne Schmitt Iiro Harjunkoski Jan Poland Thomas Leibfried Institute of Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Electrical Energy Systems and High Voltage Technology Karlsruhe Institute of Technology Karlsruhe Germany Hitachi Energy Research Mannheim Germany

ISBN: (数字)9798350354645

ISBN: (纸本)9798350354652

Today, we are experiencing exponential growth in distributed energy sources and storage systems, such as Battery Energy Storage Systems (BESS), which participate in the day-ahead energy market. Moreover, the need for energy market coupling to ensure social welfare maximization and grid security makes the Security-Constrained Unit Commitment (SCUC) problem more challenging due to the increasing problem size. This work proposes a novel approach for solving the SCUC problem in a distributed manner by decomposing the grid into multiple areas connected by tie-lines. The solution leverages the Alternating Direction Method of Multipliers (ADMM) to solve the problem efficiently. The proposed Spatially Decomposed SCUC method significantly reduces the computational burden arising from the combinatorial nature of the problem. The speed-up and optimality of the proposed method is validated on the IEEE 118-bus and RTE 6468-bus systems.

关键词： Couplings Battery energy storage system Convex functions Security

Dissolved ozone sensing in water and blood based on commercial screen-printed and BDD electrodes

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Dissolved ozone sensing in water and blood based on commerci...

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Lisa Petani Lea Llupa Zhen Peng Genki Ogata Liane Koker Ulrich Gengenbach Yasuaki Einaga Christian Pylatiuk Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Chemistry Keio University Yokohama Japan

Dissolved ozone sensing in water is crucial for a variety of applications, including water treatment, food processing, and medical therapies, as ozone is widely used as an oxidising agent in these processes. Furthermore, dissolved ozone sensing in blood is important for medical therapies. We present a novel dissolved ozone sensor for measurement in water, based on commercially available screen-printed electrodes covered with different porous membranes made of polytetrafluoroethylene (PTFE) and non porous membranes made of polydimethylsiloxane (PDMS). With PTFE, a root mean squared error (RMSE), which is the difference between the novel sensor and the reference spectrometer, of 3.62 mg l −1 can be achieved, while with PDMS a RMSE of 6.34 mg l −1 is obtained. Also, we investigated the measurement of dissolved ozone in blood with membrane-free and electrolyte-free boron-doped diamond (BDD) electrodes. The proposed dissolved ozone sensors offer a promising solution for inexpensive monitoring of dissolved ozone concentrations in water and blood with a compact design.

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