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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10166">arXiv:2411.10166</a> <span> [<a href="https://arxiv.org/pdf/2411.10166">pdf</a>, <a href="https://arxiv.org/format/2411.10166">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Two-Stage Robust Optimal Operation of Distribution Networks using Confidence Level Based Distributionally Information Gap Decision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zhisheng Xiong</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+B">Bo Zeng</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10166v1-abstract-short" style="display: inline;"> This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10166v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10166v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10166v1-abstract-full" style="display: none;"> This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To account for the probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties, reducing reliance on precise probability distributions. Consequently, a two-stage robust optimal operation model for distribution networks using CL-DIGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing method, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10166v1-abstract-full').style.display = 'none'; document.getElementById('2411.10166v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00995">arXiv:2411.00995</a> <span> [<a href="https://arxiv.org/pdf/2411.00995">pdf</a>, <a href="https://arxiv.org/ps/2411.00995">ps</a>, <a href="https://arxiv.org/format/2411.00995">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00995v1-abstract-short" style="display: inline;"> The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution netw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00995v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00995v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00995v1-abstract-full" style="display: none;"> The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution network simulators, followed by real-time execution. However, DRL algorithms tend to converge to local optima due to limited exploration efficiency. Additionally, DRL algorithms can not enforce voltage magnitude constraints, leading to potential operational violations when implemented in the distribution network operation. This study addresses these challenges by proposing a novel safe imitation reinforcement learning (IRL) framework that combines IRL and a designed safety layer, aiming to optimize the operation of Energy Storage Systems (ESSs) in active distribution networks. The proposed safe IRL framework comprises two phases: offline training and online execution. During the offline phase, optimal state-action pairs are collected using an NLP solver, guiding the IRL policy iteration. In the online phase, the trained IRL policy's decisions are adjusted by the safety layer to maintain safety and constraint compliance. Simulation results demonstrate the efficacy of Safe IRL in balancing operational efficiency and safety, eliminating voltage violations, and maintaining low operation cost errors across various network sizes, while meeting real-time execution requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00995v1-abstract-full').style.display = 'none'; document.getElementById('2411.00995v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16743">arXiv:2409.16743</a> <span> [<a href="https://arxiv.org/pdf/2409.16743">pdf</a>, <a href="https://arxiv.org/format/2409.16743">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Event-Triggered Non-Linear Control of Offshore MMC Grids for Asymmetrical AC Faults </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cherat%2C+N">Naajein Cherat</a>, <a href="/search/eess?searchtype=author&query=Nougain%2C+V">Vaibhav Nougain</a>, <a href="/search/eess?searchtype=author&query=Majstorovi%C4%87%2C+M">Milovan Majstorovi膰</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Leki%C4%87%2C+A">Aleksandra Leki膰</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.16743v1-abstract-short" style="display: inline;"> Fault ride-through capability studies of MMC-HVDC connected wind power plants have focused primarily on the DC link and onshore AC grid faults. Offshore AC faults, mainly asymmetrical faults have not gained much attention in the literature despite being included in the future development at national levels in the ENTSO-E HVDC code. The proposed work gives an event-triggered control to stabilize th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16743v1-abstract-full').style.display = 'inline'; document.getElementById('2409.16743v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16743v1-abstract-full" style="display: none;"> Fault ride-through capability studies of MMC-HVDC connected wind power plants have focused primarily on the DC link and onshore AC grid faults. Offshore AC faults, mainly asymmetrical faults have not gained much attention in the literature despite being included in the future development at national levels in the ENTSO-E HVDC code. The proposed work gives an event-triggered control to stabilize the system once the offshore AC fault has occurred, identified, and isolated. Different types of control actions such as proportional-integral (PI) controller and super-twisted sliding mode control (STSMC) are used to smoothly transition the post-fault system to a new steady state operating point by suppressing the negative sequence control. Initially, the effect of a negative sequence current control scheme on the transient behavior of the power system with a PI controller is discussed in this paper. Further, a non-linear control strategy (STSMC) is proposed which gives quicker convergence of the system post-fault in comparison to PI control action. These post-fault control operations are only triggered in the presence of a fault in the system, i.e., they are event-triggered. The validity of the proposed strategy is demonstrated by simulation on a $\pm$525 kV, three-terminal meshed MMC-HVDC system model in Real Time Digital Simulator (RTDS). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16743v1-abstract-full').style.display = 'none'; document.getElementById('2409.16743v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ISGT 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08399">arXiv:2408.08399</a> <span> [<a href="https://arxiv.org/pdf/2408.08399">pdf</a>, <a href="https://arxiv.org/format/2408.08399">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xia%2C+W">Weijie Xia</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+G">Gao Peng</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Pauwels%2C+E">Eric Pauwels</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08399v2-abstract-short" style="display: inline;"> Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing numbers of various low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08399v2-abstract-full').style.display = 'inline'; document.getElementById('2408.08399v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08399v2-abstract-full" style="display: none;"> Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing numbers of various low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains with a moderate amount of data and thousands of target domains. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning, whereas ECP modeling requires more lightweight methods. (3) Deep learning models often lack explainability, hindering their application in industry. This paper proposes a novel FSL method that exploits Transformers and Gaussian Mixture Models (GMMs) for ECP modeling to address the above-described issues. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6\% of the complete domain dataset) while it outperforms state-of-the-art time series modeling methods, maintaining the advantages of being both lightweight and interpretable. The project is open-sourced at https://github.com/xiaweijie1996/TransformerEM-GMM.git. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08399v2-abstract-full').style.display = 'none'; document.getElementById('2408.08399v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03685">arXiv:2408.03685</a> <span> [<a href="https://arxiv.org/pdf/2408.03685">pdf</a>, <a href="https://arxiv.org/ps/2408.03685">ps</a>, <a href="https://arxiv.org/format/2408.03685">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+S">Shuyi Gao</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+W">Weijie Xia</a>, <a href="/search/eess?searchtype=author&query=Duque%2C+E+M+S">Edgar Mauricio Salazar Duque</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03685v2-abstract-short" style="display: inline;"> Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03685v2-abstract-full').style.display = 'inline'; document.getElementById('2408.03685v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03685v2-abstract-full" style="display: none;"> Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents. Additionally, RL-ADN incorporates the Laurent power flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy. The effectiveness of RL-ADN is demonstrated using in different sizes of distribution networks, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. This enhancement is particularly beneficial from the increased diversity of training scenarios. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: https://github.com/ShengrenHou/RL-ADN and https://github.com/distributionnetworksTUDelft/RL-ADN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03685v2-abstract-full').style.display = 'none'; document.getElementById('2408.03685v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.13538">arXiv:2407.13538</a> <span> [<a href="https://arxiv.org/pdf/2407.13538">pdf</a>, <a href="https://arxiv.org/format/2407.13538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lin%2C+N">Nan Lin</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.13538v1-abstract-short" style="display: inline;"> High-resolution time series data are crucial for operation and planning in energy systems such as electrical power systems and heating systems. However, due to data collection costs and privacy concerns, such data is often unavailable or insufficient for downstream tasks. Data synthesis is a potential solution for this data scarcity. With the recent development of generative AI, we propose EnergyD… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13538v1-abstract-full').style.display = 'inline'; document.getElementById('2407.13538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13538v1-abstract-full" style="display: none;"> High-resolution time series data are crucial for operation and planning in energy systems such as electrical power systems and heating systems. However, due to data collection costs and privacy concerns, such data is often unavailable or insufficient for downstream tasks. Data synthesis is a potential solution for this data scarcity. With the recent development of generative AI, we propose EnergyDiff, a universal data generation framework for energy time series data. EnergyDiff builds on state-of-the-art denoising diffusion probabilistic models, utilizing a proposed denoising network dedicated to high-resolution time series data and introducing a novel Marginal Calibration technique. Our extensive experimental results demonstrate that EnergyDiff achieves significant improvement in capturing temporal dependencies and marginal distributions compared to baselines, particularly at the 1-minute resolution. Additionally, EnergyDiff consistently generates high-quality time series data across diverse energy domains, time resolutions, and at both customer and transformer levels with reduced computational need. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13538v1-abstract-full').style.display = 'none'; document.getElementById('2407.13538v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14251">arXiv:2406.14251</a> <span> [<a href="https://arxiv.org/pdf/2406.14251">pdf</a>, <a href="https://arxiv.org/format/2406.14251">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Enhanced Optimal Power Flow Based Droop Control in MMC-MTDC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Du%2C+H">Hongjin Du</a>, <a href="/search/eess?searchtype=author&query=Prasad%2C+R">Rashmi Prasad</a>, <a href="/search/eess?searchtype=author&query=Lekic%2C+A">Aleksandra Lekic</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14251v1-abstract-short" style="display: inline;"> Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and D… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14251v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14251v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14251v1-abstract-full" style="display: none;"> Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and DC voltage deviations while ensuring the stable operation of the MTDC grid by dynamically adjusting the system operation points. The modified Nordic 32 test system with an embedded 4-terminal DC grid is modeled in Julia and the proposed control strategy is applied to the power model. The results demonstrate the feasibility and effectiveness of the proposed droop control strategy, affirming its potential value in enhancing the performance and reliability of hybrid AC-DC power systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14251v1-abstract-full').style.display = 'none'; document.getElementById('2406.14251v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.11963">arXiv:2405.11963</a> <span> [<a href="https://arxiv.org/pdf/2405.11963">pdf</a>, <a href="https://arxiv.org/format/2405.11963">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Diaz-Londono%2C+C">Cesar Diaz-Londono</a>, <a href="/search/eess?searchtype=author&query=Orfanoudakis%2C+S">Stavros Orfanoudakis</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Ruiz%2C+F">Fredy Ruiz</a>, <a href="/search/eess?searchtype=author&query=Gruosso%2C+G">Giambattista Gruosso</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.11963v1-abstract-short" style="display: inline;"> Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11963v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11963v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11963v1-abstract-full" style="display: none;"> Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules to balance supply and demand while minimizing operational costs and maximizing flexibility. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. Our research focuses on harnessing the potential of MPC in G2V and V2G applications, by providing a simulation tool that allows to maximize EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. In this paper, we propose an open-source MPC controller for G2V and V2G-enabled demand response management. The proposed approach is capable of tackling the uncertainties inherent in demand response operations. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, our controller enables Charge Point Operators (CPOs) to optimize EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11963v1-abstract-full').style.display = 'none'; document.getElementById('2405.11963v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.02180">arXiv:2405.02180</a> <span> [<a href="https://arxiv.org/pdf/2405.02180">pdf</a>, <a href="https://arxiv.org/format/2405.02180">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xia%2C+W">Weijie Xia</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.02180v3-abstract-short" style="display: inline;"> Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02180v3-abstract-full').style.display = 'inline'; document.getElementById('2405.02180v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02180v3-abstract-full" style="display: none;"> Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02180v3-abstract-full').style.display = 'none'; document.getElementById('2405.02180v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.02568">arXiv:2404.02568</a> <span> [<a href="https://arxiv.org/pdf/2404.02568">pdf</a>, <a href="https://arxiv.org/ps/2404.02568">ps</a>, <a href="https://arxiv.org/format/2404.02568">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> On Future Power Systems Digital Twins: A Vision Towards a Standard Architecture </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zomerdijk%2C+W">Wouter Zomerdijk</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=AlSkaif%2C+T">Tarek AlSkaif</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.02568v2-abstract-short" style="display: inline;"> The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02568v2-abstract-full').style.display = 'inline'; document.getElementById('2404.02568v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.02568v2-abstract-full" style="display: none;"> The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, we present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, we introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes, while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. We also discuss our vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasizing challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, we present our vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02568v2-abstract-full').style.display = 'none'; document.getElementById('2404.02568v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted for publication in a journal. This corresponds to the submitted version. After acceptance, it may be removed depending on the journal's requirements for copyright. Version 2 is the revised version after comments from reviewers. Updated contributions in the introduction, added section on TSO/DSO benefits and section on steps to implementation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.06293">arXiv:2311.06293</a> <span> [<a href="https://arxiv.org/pdf/2311.06293">pdf</a>, <a href="https://arxiv.org/ps/2311.06293">ps</a>, <a href="https://arxiv.org/format/2311.06293">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Quantum Neural Networks for Power Flow Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kaseb%2C+Z">Zeynab Kaseb</a>, <a href="/search/eess?searchtype=author&query=Moller%2C+M">Matthias Moller</a>, <a href="/search/eess?searchtype=author&query=Balducci%2C+G+T">Giorgio Tosti Balducci</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.06293v2-abstract-short" style="display: inline;"> This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum-classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) rob… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06293v2-abstract-full').style.display = 'inline'; document.getElementById('2311.06293v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.06293v2-abstract-full" style="display: none;"> This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum-classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum-classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06293v2-abstract-full').style.display = 'none'; document.getElementById('2311.06293v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 13 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03556">arXiv:2310.03556</a> <span> [<a href="https://arxiv.org/pdf/2310.03556">pdf</a>, <a href="https://arxiv.org/format/2310.03556">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.epsr.2024.110775">10.1016/j.epsr.2024.110775 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=B%C3%B6lat%2C+K">Kutay B枚lat</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S+H">Simon H. Tindemans</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03556v2-abstract-short" style="display: inline;"> 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 assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03556v2-abstract-full').style.display = 'inline'; document.getElementById('2310.03556v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03556v2-abstract-full" style="display: none;"> 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 assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to prevent the singular solutions that the regular MLL criterion gives rise to, and it is proven that LOO-MLL prevents these. Relying on this guaranteed robustness, the model is extended by adjustable weights for the kernels. In addition, a modified expectation-maximization algorithm is employed to accelerate the optimization speed reliably. The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models. Results show that the proposed models have promising performance, in addition to their singularity prevention guarantees. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03556v2-abstract-full').style.display = 'none'; document.getElementById('2310.03556v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15727">arXiv:2309.15727</a> <span> [<a href="https://arxiv.org/pdf/2309.15727">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ISGTEurope.2019.8905616">10.1109/ISGTEurope.2019.8905616 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Towards Scalable FMI-based Co-simulation of Wind Energy Systems Using PowerFactory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A+A">Arjen A van der Meer</a>, <a href="/search/eess?searchtype=author&query=Bhandia%2C+R">Rishabh Bhandia</a>, <a href="/search/eess?searchtype=author&query=Widl%2C+E">Edmund Widl</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Steinbrink%2C+C">Cornelius Steinbrink</a>, <a href="/search/eess?searchtype=author&query=Chodura%2C+P">Przemyslaw Chodura</a>, <a href="/search/eess?searchtype=author&query=Strasser%2C+T+I">Thomas I. Strasser</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15727v1-abstract-short" style="display: inline;"> Due to the increased deployment of renewable energy sources and intelligent components the electric power system will exhibit a large degree of heterogeneity, which requires inclusive and multi-disciplinary system assessment. The concept of co-simulation is a very attractive option to achieve this; each domain-specific subsystem can be addressed via its own specialized simulation tool. The applica… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15727v1-abstract-full').style.display = 'inline'; document.getElementById('2309.15727v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15727v1-abstract-full" style="display: none;"> Due to the increased deployment of renewable energy sources and intelligent components the electric power system will exhibit a large degree of heterogeneity, which requires inclusive and multi-disciplinary system assessment. The concept of co-simulation is a very attractive option to achieve this; each domain-specific subsystem can be addressed via its own specialized simulation tool. The applicability, however, depends on aspects like standardised interfaces, automated case creation, initialisation, and the scalability of the co-simulation itself. This work deals with the inclusion of the Functional Mock-up Interface for co-simulation into the DIgSILENT PowerFactory simulator, and tests its accuracy, implementation, and scalability for the grid connection study of a wind power plant. The coupling between the RMS mode of PowerFactory and MATLAB/Simulink in a standardised manner is shown. This approach allows a straightforward inclusion of black-boxed modelling, is easily scalable in size, quantity, and component type. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15727v1-abstract-full').style.display = 'none'; document.getElementById('2309.15727v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.14304">arXiv:2307.14304</a> <span> [<a href="https://arxiv.org/pdf/2307.14304">pdf</a>, <a href="https://arxiv.org/ps/2307.14304">ps</a>, <a href="https://arxiv.org/format/2307.14304">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Duque%2C+E+M+S">Edgar Mauricio Salazar Duque</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.14304v1-abstract-short" style="display: inline;"> The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality control models that adaptively respond to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14304v1-abstract-full').style.display = 'inline'; document.getElementById('2307.14304v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.14304v1-abstract-full" style="display: none;"> The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality control models that adaptively respond to distribution networks' stochastic nature. However, current DRL algorithms lack the capabilities to enforce operational constraints strictly, often even providing unfeasible control actions. To address this issue, we propose a DRL framework that effectively handles continuous action spaces while strictly enforcing the environments and action space operational constraints during online operation. Firstly, the proposed framework trains an action-value function modeled using DNNs. Subsequently, this action-value function is formulated as a mixed-integer programming (MIP) formulation enabling the consideration of the environment's operational constraints. Comprehensive numerical simulations show the superior performance of the proposed MIP-DRL framework, effectively enforcing all constraints while delivering high-quality dispatch decisions when compared with state-of-the-art DRL algorithms and the optimal solution obtained with a perfect forecast of the stochastic variables. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14304v1-abstract-full').style.display = 'none'; document.getElementById('2307.14304v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to a publication in a journal. This corresponds to the submitted version. After acceptance, it may be removed depending on the journal's requirements for copyright</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.10775">arXiv:2306.10775</a> <span> [<a href="https://arxiv.org/pdf/2306.10775">pdf</a>, <a href="https://arxiv.org/format/2306.10775">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/0.1109/PESGM52003.2023.10252603">0.1109/PESGM52003.2023.10252603 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Impact of Dynamic Tariffs for Smart EV Charging on LV Distribution Network Operation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Verbist%2C+F">Flore Verbist</a>, <a href="/search/eess?searchtype=author&query=Panda%2C+N+K">Nanda Kishor Panda</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.10775v1-abstract-short" style="display: inline;"> With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement. To circumvent the associated capacity limitations, this paper evaluates the effectiveness of different levels of network constraints and different dynamic tariffs, including a dynamic network tariff. A detailed optimisation model is first deve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10775v1-abstract-full').style.display = 'inline'; document.getElementById('2306.10775v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.10775v1-abstract-full" style="display: none;"> With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement. To circumvent the associated capacity limitations, this paper evaluates the effectiveness of different levels of network constraints and different dynamic tariffs, including a dynamic network tariff. A detailed optimisation model is first developed for public charging electric vehicles in a representative Dutch low voltage (LV) distribution network, susceptible to congestion and voltage problems by 2050 without smart charging of EVs. Later, a detailed reflection is made to assess the influence of the modelled features on the distribution system operator (DSO), charge point operator (CPO) costs, and the EVs' final state-of-charge (SOC) for both mono- (V1G) and bi-directional (V2G) charging. Results show that the dynamic network tariff outperforms other flat tariffs by increasing valley-filling. Consequently, compared to regular day-ahead pricing, a {significant} reduction in the frequency of congestion in the lines is achieved. In addition, V2G ensures the joint optimum for different stakeholders causing adequate EV user satisfaction, decreased CPO costs compared to conventional charging and fewer violations of grid constraints for the DSOs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10775v1-abstract-full').style.display = 'none'; document.getElementById('2306.10775v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in the proceedings of IEEE PES GM 2023, Orlando, Florida</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.05484">arXiv:2305.05484</a> <span> [<a href="https://arxiv.org/pdf/2305.05484">pdf</a>, <a href="https://arxiv.org/ps/2305.05484">ps</a>, <a href="https://arxiv.org/format/2305.05484">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shengren%2C+H">Hou Shengren</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a>, <a href="/search/eess?searchtype=author&query=Duque%2C+E+M+S">Edgar Mauricio Salazar Duque</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.05484v1-abstract-short" style="display: inline;"> The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL algorithms fail to enforce rigorous operational constraints… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05484v1-abstract-full').style.display = 'inline'; document.getElementById('2305.05484v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.05484v1-abstract-full" style="display: none;"> The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL algorithms fail to enforce rigorous operational constraints (e.g., power balance, ramping up or down constraints) limiting their implementation in real systems. To overcome this, in this paper, a DRL algorithm (namely MIP-DQN) is proposed, capable of \textit{strictly} enforcing all operational constraints in the action space, ensuring the feasibility of the defined schedule in real-time operation. This is done by leveraging recent optimization advances for deep neural networks (DNNs) that allow their representation as a MIP formulation, enabling further consideration of any action space constraints. Comprehensive numerical simulations show that the proposed algorithm outperforms existing state-of-the-art DRL algorithms, obtaining a lower error when compared with the optimal global solution (upper boundary) obtained after solving a mathematical programming formulation with perfect forecast information; while strictly enforcing all operational constraints (even in unseen test days). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05484v1-abstract-full').style.display = 'none'; document.getElementById('2305.05484v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.11410">arXiv:2303.11410</a> <span> [<a href="https://arxiv.org/pdf/2303.11410">pdf</a>, <a href="https://arxiv.org/format/2303.11410">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Sharifnia%2C+E">Ensieh Sharifnia</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S+H">Simon H. Tindemans</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.11410v1-abstract-short" style="display: inline;"> Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical data. The coordinates of the latent space codes of VAEs have been shown to correlate with conceptual features of the data, which can be leveraged to synthesize targeted data with desired features. However, the locations of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11410v1-abstract-full').style.display = 'inline'; document.getElementById('2303.11410v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.11410v1-abstract-full" style="display: none;"> Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical data. The coordinates of the latent space codes of VAEs have been shown to correlate with conceptual features of the data, which can be leveraged to synthesize targeted data with desired features. However, the locations of the VAEs' latent space codes that correspond to specific properties are not constrained. Additionally, the generation of data with specific characteristics may require data with corresponding hard-to-get labels fed into the generative model for training. In this paper, to make data generation more controllable and efficient, an oriented variation autoencoder (OVAE) is proposed to constrain the link between latent space code and generated data in the form of a Spearman correlation, which provides increased control over the data synthesis process. On this basis, an importance sampling process is used to sample data in the latent space. Two cases are considered for testing the performance of the OVAE model: the data set is fully labeled with approximate information and the data set is incompletely labeled but with more accurate information. The experimental results show that, in both cases, the OVAE model correlates latent space codes with the generated data, and the efficiency of generating targeted samples is significantly improved. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11410v1-abstract-full').style.display = 'none'; document.getElementById('2303.11410v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 10 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.04056">arXiv:2209.04056</a> <span> [<a href="https://arxiv.org/pdf/2209.04056">pdf</a>, <a href="https://arxiv.org/format/2209.04056">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ISGT-Europe54678.2022.9960309">10.1109/ISGT-Europe54678.2022.9960309 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Generating Contextual Load Profiles Using a Conditional Variational Autoencoder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S+H">Simon H. Tindemans</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.04056v2-abstract-short" style="display: inline;"> Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04056v2-abstract-full').style.display = 'inline'; document.getElementById('2209.04056v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.04056v2-abstract-full" style="display: none;"> Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate `realistic' data with satisfying univariate distributions and multivariate dependencies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04056v2-abstract-full').style.display = 'none'; document.getElementById('2209.04056v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 figures, conference</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> in 2022 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), IEEE, Novi Sad, Serbia, 2022, pp. 1-6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.00728">arXiv:2208.00728</a> <span> [<a href="https://arxiv.org/pdf/2208.00728">pdf</a>, <a href="https://arxiv.org/ps/2208.00728">ps</a>, <a href="https://arxiv.org/format/2208.00728">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shengren%2C+H">Hou Shengren</a>, <a href="/search/eess?searchtype=author&query=Salazar%2C+E+M">Edgar Mauricio Salazar</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.00728v1-abstract-short" style="display: inline;"> Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems' operational cost and technical constraints (e.g, generation-demand power balance) DRL algorithms must consider a trade-of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.00728v1-abstract-full').style.display = 'inline'; document.getElementById('2208.00728v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.00728v1-abstract-full" style="display: none;"> Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems' operational cost and technical constraints (e.g, generation-demand power balance) DRL algorithms must consider a trade-off when designing the reward function. This trade-off introduces extra hyperparameters that impact the DRL algorithms' performance and capability of providing feasible solutions. In this paper, a performance comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are presented. We aim to provide a fair comparison of these DRL algorithms for energy systems optimal scheduling problems. Results show DRL algorithms' capability of providing in real-time good-quality solutions, even in unseen operational scenarios, when compared with a mathematical programming model of the energy system optimal scheduling problem. Nevertheless, in the case of large peak consumption, these algorithms failed to provide feasible solutions, which can impede their practical implementation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.00728v1-abstract-full').style.display = 'none'; document.getElementById('2208.00728v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.11435">arXiv:2110.11435</a> <span> [<a href="https://arxiv.org/pdf/2110.11435">pdf</a>, <a href="https://arxiv.org/format/2110.11435">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.epsr.2022.108603">10.1016/j.epsr.2022.108603 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Generating Multivariate Load States Using a Conditional Variational Autoencoder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Sharifnia%2C+E">Ensieh Sharifnia</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Z">Zhi Gao</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S+H">Simon H. Tindemans</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.11435v2-abstract-short" style="display: inline;"> For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a multivariate load state generating model on the basis of a condi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.11435v2-abstract-full').style.display = 'inline'; document.getElementById('2110.11435v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.11435v2-abstract-full" style="display: none;"> For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed. Going beyond common CVAE implementations, the model includes stochastic variation of output samples under given latent vectors and co-optimizes the parameters for this output variability. It is shown that this improves statistical properties of the generated data. The quality of generated multivariate loads is evaluated using univariate and multivariate performance metrics. A generation adequacy case study on the European network is used to illustrate model's ability to generate realistic tail distributions. The experiments demonstrate that the proposed generator outperforms other data generating mechanisms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.11435v2-abstract-full').style.display = 'none'; document.getElementById('2110.11435v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 5 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Electric Power Systems Research Volume 213, December 2022, 108603 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.07158">arXiv:2005.07158</a> <span> [<a href="https://arxiv.org/pdf/2005.07158">pdf</a>, <a href="https://arxiv.org/format/2005.07158">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ISGT-Europe47291.2020.9248894">10.1109/ISGT-Europe47291.2020.9248894 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Training Strategies for Autoencoder-based Detection of False Data Injection Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+K">Kaikai Pan</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S">Simon Tindemans</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.07158v2-abstract-short" style="display: inline;"> The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.07158v2-abstract-full').style.display = 'inline'; document.getElementById('2005.07158v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.07158v2-abstract-full" style="display: none;"> The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.07158v2-abstract-full').style.display = 'none'; document.getElementById('2005.07158v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 6 figures, 1 table, conference</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), IEEE, Den Haag, the Netherlands, 2020, pp. 1-5 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.02229">arXiv:2003.02229</a> <span> [<a href="https://arxiv.org/pdf/2003.02229">pdf</a>, <a href="https://arxiv.org/format/2003.02229">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/PMAPS47429.2020.9183526">10.1109/PMAPS47429.2020.9183526 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Detection of False Data Injection Attacks Using the Autoencoder Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chenguang Wang</a>, <a href="/search/eess?searchtype=author&query=Tindemans%2C+S">Simon Tindemans</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+K">Kaikai Pan</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.02229v3-abstract-short" style="display: inline;"> State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.02229v3-abstract-full').style.display = 'inline'; document.getElementById('2003.02229v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.02229v3-abstract-full" style="display: none;"> State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.02229v3-abstract-full').style.display = 'none'; document.getElementById('2003.02229v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 5 figures, 1 table, conference</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, Liege, Belgium, 2020, pp. 1-6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.02062">arXiv:1903.02062</a> <span> [<a href="https://arxiv.org/pdf/1903.02062">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MSCPES.2018.8405401">10.1109/MSCPES.2018.8405401 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Design of experiments aided holistic testing of cyber-physical energy systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A">Arjen van der Meer</a>, <a href="/search/eess?searchtype=author&query=Steinbrink%2C+C">Cornelius Steinbrink</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Bondy%2C+D+M">Daniel Morales Bondy</a>, <a href="/search/eess?searchtype=author&query=Degefa%2C+M+Z">Merkebu Zenebe Degefa</a>, <a href="/search/eess?searchtype=author&query=Andren%2C+F+P">Filip Pr枚stl Andren</a>, <a href="/search/eess?searchtype=author&query=Strasser%2C+T">Thomas Strasser</a>, <a href="/search/eess?searchtype=author&query=Lehnhoff%2C+S">Sebastian Lehnhoff</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.02062v1-abstract-short" style="display: inline;"> The complex and often safety-critical nature of cyber-physical energy systems makes validation a key challenge in facilitating the energy transition, especially when it comes to the testing on system level. Reliable and reproducible validation experiments can be guided by the concept of design of experiments, which is, however, so far not fully adopted by researchers. This paper suggests a structu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.02062v1-abstract-full').style.display = 'inline'; document.getElementById('1903.02062v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.02062v1-abstract-full" style="display: none;"> The complex and often safety-critical nature of cyber-physical energy systems makes validation a key challenge in facilitating the energy transition, especially when it comes to the testing on system level. Reliable and reproducible validation experiments can be guided by the concept of design of experiments, which is, however, so far not fully adopted by researchers. This paper suggests a structured guideline for design of experiments application within the holistic testing procedure suggested by the European ERIGrid project. In this paper, a general workflow as well as a practical example are provided with the aim to give domain experts a basic understanding of design of experiments compliant testing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.02062v1-abstract-full').style.display = 'none'; document.getElementById('1903.02062v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2018 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.04131">arXiv:1710.04131</a> <span> [<a href="https://arxiv.org/pdf/1710.04131">pdf</a>, <a href="https://arxiv.org/format/1710.04131">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-319-64635-0_15">10.1007/978-3-319-64635-0_15 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Validating Intelligent Power and Energy Systems - A Discussion of Educational Needs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kotsampopoulos%2C+P">Panos Kotsampopoulos</a>, <a href="/search/eess?searchtype=author&query=Hatziargyriou%2C+N">Nikos Hatziargyriou</a>, <a href="/search/eess?searchtype=author&query=Strasser%2C+T+I">Thomas I. Strasser</a>, <a href="/search/eess?searchtype=author&query=Moyo%2C+C">Cyndi Moyo</a>, <a href="/search/eess?searchtype=author&query=Rohjans%2C+S">Sebastian Rohjans</a>, <a href="/search/eess?searchtype=author&query=Steinbrink%2C+C">Cornelius Steinbrink</a>, <a href="/search/eess?searchtype=author&query=Lehnhoff%2C+S">Sebastian Lehnhoff</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A+A">Arjen A. van der Meer</a>, <a href="/search/eess?searchtype=author&query=Bondy%2C+D+E+M">Daniel Esteban Morales Bondy</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Calin%2C+M">Mihai Calin</a>, <a href="/search/eess?searchtype=author&query=Khavari%2C+A">Ata Khavari</a>, <a href="/search/eess?searchtype=author&query=Sosnina%2C+M">Maria Sosnina</a>, <a href="/search/eess?searchtype=author&query=Rodriguez%2C+J+E">J. Emilio Rodriguez</a>, <a href="/search/eess?searchtype=author&query=Burt%2C+G+M">Graeme M. Burt</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.04131v1-abstract-short" style="display: inline;"> Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.04131v1-abstract-full').style.display = 'inline'; document.getElementById('1710.04131v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.04131v1-abstract-full" style="display: none;"> Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational and training needs addressing the higher complexity of intelligent energy systems. Education needs and requirements are discussed, such as the development of systems-oriented skills and cross-disciplinary learning. Education and training possibilities and necessary tools are described focusing on classroom but also on laboratory-based learning methods. In this context, experiences of using notebooks, co-simulation approaches, hardware-in-the-loop methods and remote labs experiments are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.04131v1-abstract-full').style.display = 'none'; document.getElementById('1710.04131v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2017)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.02315">arXiv:1710.02315</a> <span> [<a href="https://arxiv.org/pdf/1710.02315">pdf</a>, <a href="https://arxiv.org/format/1710.02315">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-319-64635-0_13">10.1007/978-3-319-64635-0_13 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Simulation-based Validation of Smart Grids - Status Quo and Future Research Trends </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Steinbrink%2C+C">Cornelius Steinbrink</a>, <a href="/search/eess?searchtype=author&query=Lehnhoff%2C+S">Sebastian Lehnhoff</a>, <a href="/search/eess?searchtype=author&query=Rohjans%2C+S">Sebastian Rohjans</a>, <a href="/search/eess?searchtype=author&query=Strasser%2C+T+I">Thomas I. Strasser</a>, <a href="/search/eess?searchtype=author&query=Widl%2C+E">Edmund Widl</a>, <a href="/search/eess?searchtype=author&query=Moyo%2C+C">Cyndi Moyo</a>, <a href="/search/eess?searchtype=author&query=Lauss%2C+G">Georg Lauss</a>, <a href="/search/eess?searchtype=author&query=Lehfuss%2C+F">Felix Lehfuss</a>, <a href="/search/eess?searchtype=author&query=Faschang%2C+M">Mario Faschang</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A+A">Arjen A. van der Meer</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Gehrke%2C+O">Oliver Gehrke</a>, <a href="/search/eess?searchtype=author&query=Guillo-Sansano%2C+E">Efren Guillo-Sansano</a>, <a href="/search/eess?searchtype=author&query=Syed%2C+M+H">Mazheruddin H. Syed</a>, <a href="/search/eess?searchtype=author&query=Emhemed%2C+A">Abdullah Emhemed</a>, <a href="/search/eess?searchtype=author&query=Brandl%2C+R">Ron Brandl</a>, <a href="/search/eess?searchtype=author&query=Nguyen%2C+V+H">Van Hoa Nguyen</a>, <a href="/search/eess?searchtype=author&query=Khavari%2C+A">Ata Khavari</a>, <a href="/search/eess?searchtype=author&query=Tran%2C+Q+T">Quoc Tuan Tran</a>, <a href="/search/eess?searchtype=author&query=Kotsampopoulos%2C+P">Panos Kotsampopoulos</a>, <a href="/search/eess?searchtype=author&query=Hatziargyriou%2C+N">Nikos Hatziargyriou</a>, <a href="/search/eess?searchtype=author&query=Akroud%2C+A">Akroud Akroud</a>, <a href="/search/eess?searchtype=author&query=Rikos%2C+E">Evangelos Rikos</a>, <a href="/search/eess?searchtype=author&query=Degefa%2C+M+Z">Merkebu Z. Degefa</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.02315v1-abstract-short" style="display: inline;"> Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical energy systems with a high(er) penetration of fluctuating renewable generation and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.02315v1-abstract-full').style.display = 'inline'; document.getElementById('1710.02315v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.02315v1-abstract-full" style="display: none;"> Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical energy systems with a high(er) penetration of fluctuating renewable generation and controllable loads. As a result of these developments the validation on the system level becomes much more important during the whole engineering and deployment process, today. In earlier development stages and for larger system configurations laboratory-based testing is not always an option. Due to recent developments, simulation-based approaches are now an appropriate tool to support the development, implementation, and roll-out of smart grid solutions. This paper discusses the current state of simulation-based approaches and outlines the necessary future research and development directions in the domain of power and energy systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.02315v1-abstract-full').style.display = 'none'; document.getElementById('1710.02315v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2017)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.02312">arXiv:1710.02312</a> <span> [<a href="https://arxiv.org/pdf/1710.02312">pdf</a>, <a href="https://arxiv.org/format/1710.02312">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-319-64635-0_12">10.1007/978-3-319-64635-0_12 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An Integrated Research Infrastructure for Validating Cyber-Physical Energy Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Strasser%2C+T+I">Thomas I. Strasser</a>, <a href="/search/eess?searchtype=author&query=Moyo%2C+C">Cyndi Moyo</a>, <a href="/search/eess?searchtype=author&query=Br%C3%BCndlinger%2C+R">Roland Br眉ndlinger</a>, <a href="/search/eess?searchtype=author&query=Lehnhoff%2C+S">Sebastian Lehnhoff</a>, <a href="/search/eess?searchtype=author&query=Blank%2C+M">Marita Blank</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A+A">Arjen A. van der Meer</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Gehrke%2C+O">Oliver Gehrke</a>, <a href="/search/eess?searchtype=author&query=Rodriguez%2C+J+E">J. Emilio Rodriguez</a>, <a href="/search/eess?searchtype=author&query=Merino%2C+J">Julia Merino</a>, <a href="/search/eess?searchtype=author&query=Sandroni%2C+C">Carlo Sandroni</a>, <a href="/search/eess?searchtype=author&query=Verga%2C+M">Maurizio Verga</a>, <a href="/search/eess?searchtype=author&query=Calin%2C+M">Mihai Calin</a>, <a href="/search/eess?searchtype=author&query=Khavari%2C+A">Ata Khavari</a>, <a href="/search/eess?searchtype=author&query=Sosnina%2C+M">Maria Sosnina</a>, <a href="/search/eess?searchtype=author&query=de+Jong%2C+E">Erik de Jong</a>, <a href="/search/eess?searchtype=author&query=Rohjans%2C+S">Sebastian Rohjans</a>, <a href="/search/eess?searchtype=author&query=Kulmala%2C+A">Anna Kulmala</a>, <a href="/search/eess?searchtype=author&query=M%C3%A4ki%2C+K">Kari M盲ki</a>, <a href="/search/eess?searchtype=author&query=Brandl%2C+R">Ron Brandl</a>, <a href="/search/eess?searchtype=author&query=Coffele%2C+F">Federico Coffele</a>, <a href="/search/eess?searchtype=author&query=Burt%2C+G+M">Graeme M. Burt</a>, <a href="/search/eess?searchtype=author&query=Kotsampopoulos%2C+P">Panos Kotsampopoulos</a>, <a href="/search/eess?searchtype=author&query=Hatziargyriou%2C+N">Nikos Hatziargyriou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.02312v1-abstract-short" style="display: inline;"> Renewables are key enablers in the plight to reduce greenhouse gas emissions and cope with anthropogenic global warming. The intermittent nature and limited storage capabilities of renewables culminate in new challenges that power system operators have to deal with in order to regulate power quality and ensure security of supply. At the same time, the increased availability of advanced automation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.02312v1-abstract-full').style.display = 'inline'; document.getElementById('1710.02312v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.02312v1-abstract-full" style="display: none;"> Renewables are key enablers in the plight to reduce greenhouse gas emissions and cope with anthropogenic global warming. The intermittent nature and limited storage capabilities of renewables culminate in new challenges that power system operators have to deal with in order to regulate power quality and ensure security of supply. At the same time, the increased availability of advanced automation and communication technologies provides new opportunities for the derivation of intelligent solutions to tackle the challenges. Previous work has shown various new methods of operating highly interconnected power grids, and their corresponding components, in a more effective way. As a consequence of these developments, the traditional power system is being transformed into a cyber-physical energy system, a smart grid. Previous and ongoing research have tended to mainly focus on how specific aspects of smart grids can be validated, but until there exists no integrated approach for the analysis and evaluation of complex cyber-physical systems configurations. This paper introduces integrated research infrastructure that provides methods and tools for validating smart grid systems in a holistic, cyber-physical manner. The corresponding concepts are currently being developed further in the European project ERIGrid. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.02312v1-abstract-full').style.display = 'none'; document.getElementById('1710.02312v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2017)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.00583">arXiv:1705.00583</a> <span> [<a href="https://arxiv.org/pdf/1705.00583">pdf</a>, <a href="https://arxiv.org/format/1705.00583">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MSCPES.2017.8064528">10.1109/MSCPES.2017.8064528 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Cyber-Physical Energy Systems Modeling, Test Specification, and Co-Simulation Based Testing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=van+der+Meer%2C+A+A">Arjen A. van der Meer</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Heussen%2C+K">Kai Heussen</a>, <a href="/search/eess?searchtype=author&query=Bondy%2C+D+E+M">Daniel Esteban Morales Bondy</a>, <a href="/search/eess?searchtype=author&query=Gehrke%2C+O">Oliver Gehrke</a>, <a href="/search/eess?searchtype=author&query=Steinbrink%2C+C">Cornelius Steinbrink</a>, <a href="/search/eess?searchtype=author&query=Blank%2C+M">Marita Blank</a>, <a href="/search/eess?searchtype=author&query=Lehnhoff%2C+S">Sebastian Lehnhoff</a>, <a href="/search/eess?searchtype=author&query=Widl%2C+E">Edmund Widl</a>, <a href="/search/eess?searchtype=author&query=Moyo%2C+C">Cyndi Moyo</a>, <a href="/search/eess?searchtype=author&query=Strasser%2C+T+I">Thomas I. Strasser</a>, <a href="/search/eess?searchtype=author&query=Nguyen%2C+V+H">Van Hoa Nguyen</a>, <a href="/search/eess?searchtype=author&query=Akroud%2C+N">Nabil Akroud</a>, <a href="/search/eess?searchtype=author&query=Syed%2C+M+H">Mazheruddin H. Syed</a>, <a href="/search/eess?searchtype=author&query=Emhemed%2C+A">Abdullah Emhemed</a>, <a href="/search/eess?searchtype=author&query=Rohjans%2C+S">Sebastian Rohjans</a>, <a href="/search/eess?searchtype=author&query=Brandl%2C+R">Ron Brandl</a>, <a href="/search/eess?searchtype=author&query=Khavari%2C+A+M">Ata M. Khavari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.00583v1-abstract-short" style="display: inline;"> The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00583v1-abstract-full').style.display = 'inline'; document.getElementById('1705.00583v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.00583v1-abstract-full" style="display: none;"> The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00583v1-abstract-full').style.display = 'none'; document.getElementById('1705.00583v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1601.07783">arXiv:1601.07783</a> <span> [<a href="https://arxiv.org/pdf/1601.07783">pdf</a>, <a href="https://arxiv.org/ps/1601.07783">ps</a>, <a href="https://arxiv.org/format/1601.07783">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICIT.2016.7474812">10.1109/ICIT.2016.7474812 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Stochastic Battery Model for Aggregation of Thermostatically Controlled Loads </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Khan%2C+S">Sohail Khan</a>, <a href="/search/eess?searchtype=author&query=Shahzad%2C+M">Mohsin Shahzad</a>, <a href="/search/eess?searchtype=author&query=Habib%2C+U">Usman Habib</a>, <a href="/search/eess?searchtype=author&query=Gawlik%2C+W">Wolfgang Gawlik</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1601.07783v1-abstract-short" style="display: inline;"> The potential of demand side as a frequency reserve proposes interesting opportunity in handling imbalances due to intermittent renewable energy sources. This paper proposes a novel approach for computing the parameters of a stochastic battery model representing the aggregation of Thermostatically Controlled Loads (TCLs). A hysteresis based non-disruptive control is used using priority stack algor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.07783v1-abstract-full').style.display = 'inline'; document.getElementById('1601.07783v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.07783v1-abstract-full" style="display: none;"> The potential of demand side as a frequency reserve proposes interesting opportunity in handling imbalances due to intermittent renewable energy sources. This paper proposes a novel approach for computing the parameters of a stochastic battery model representing the aggregation of Thermostatically Controlled Loads (TCLs). A hysteresis based non-disruptive control is used using priority stack algorithm to track the reference regulation signal. The parameters of admissible ramp-rate and the charge limits of the battery are dynamically calculated using the information from TCLs that is the status (on/off), availability and relative temperature distance till the switching boundary. The approach builds on and improves on the existing research work by providing a straight-forward mechanism for calculation of stochastic parameters of equivalent battery model. The effectiveness of proposed approach is demonstrated by a test case having a large number of residential TCLs tracking a scaled down real frequency regulation signal. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.07783v1-abstract-full').style.display = 'none'; document.getElementById('1601.07783v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IEEE ICIT 2016 conference</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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