Proceedings of the Eleventh ACM International Conference on Future Energy Systems最新文献

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Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402364

J. Park, Zoltán Nagy

引用次数: 1

Multi-User Coalition Formation for Peer-to-Peer Energy Sharing 面向点对点能源共享的多用户联盟形成

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403511

Yue Zhou, S. Chau

引用次数: 5

Synchrophasor Measurements-based Events Detection Using Deep Learning 基于同步量测量的深度学习事件检测

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403513

H. Ren, Z. Hou, Heng Wang, P. Etingov

引用次数: 0

Fairness in Power Flow Network Congestion Management with Outer Matching and Principal Notions of Fair Division 外匹配与公平划分原则下潮流网络拥塞管理中的公平性

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397701

Brinn Hekkelman, H. L. Poutré

引用次数: 2

Understanding Differential Privacy in Non-Intrusive Load Monitoring 理解非侵入式负载监控中的差异隐私

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403508

Haoxiang Wang, Chenyu Wu

引用次数: 4

Prospective Experiment for Reinforcement Learning on Demand Response in a Social Game Framework 社交游戏框架下需求反应强化学习的前瞻性实验

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402365

Lucas Spangher, Akash Gokul, Manan Khattar, Joseph Palakapilly, A. Tawade, Adam Bouyamourn, Alex Devonport, C. Spanos

引用次数: 11

Proceedings of the Eleventh ACM International Conference on Future Energy Systems 第十一届美国计算机学会未来能源系统国际会议论文集

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851

引用次数: 1

FlexAbility - Modeling and Maximizing the Bidirectional Flexibility Availability of Unidirectional Charging of Large Pools of Electric Vehicles 灵活性——大型电动汽车池单向充电的双向灵活性建模与最大化

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397697

Jonas Schlund, M. Pruckner, R. German

{"title":"FlexAbility - Modeling and Maximizing the Bidirectional Flexibility Availability of Unidirectional Charging of Large Pools of Electric Vehicles","authors":"Jonas Schlund, M. Pruckner, R. German","doi":"10.1145/3396851.3397697","DOIUrl":"https://doi.org/10.1145/3396851.3397697","url":null,"abstract":"We propose a new methodology for modeling flexibility availability (FlexAbility) of decentralized electric loads, e.g., electric vehicle charging, with an intuitive visualization method. The approach includes a novel method for aggregating and disaggregating flexibility that is more accurate and less complex than existing approaches. In addition, it is suitable for online flexibility determination and dispatch. It is the first which enables to consider a total energy constraint per individual load. We enable the determination of guaranteed aggregated FlexAbility over a time horizon by means of calculating flexibility dispatch paths. We then propose a method for maximizing the bidirectional power flexibility of unidirectional charging for generic applications in the power grid. We combine both new methods in a simulation model of electric vehicles with realistic mobility behavior. We are the first to provide an evaluation of the bidirectional power flexibility from unidirectional charging of electric vehicles, which is found to be bounded by the minimal capability to decrease charging power. We show that there is a trade-off between power and energy flexibility. Today, 20 thousand of the typical electric vehicles in Germany are able to keep bidirectional power flexibility of at least 1.3 MW available during a whole year. The general modeling approach is applicable for other flexible loads with flexible profiles and a total energy constraint as well.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Defining a synthetic data generator for realistic electric vehicle charging sessions 为现实的电动汽车充电过程定义一个合成数据生成器

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403509

Manu Lahariya, Dries F. Benoit, Chris Develder

引用次数: 5

Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives 能源管理优化目标下基于置换的住宅短期负荷预测

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397731

M. Voss

{"title":"Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives","authors":"M. Voss","doi":"10.1145/3396851.3397731","DOIUrl":"https://doi.org/10.1145/3396851.3397731","url":null,"abstract":"What makes a household-level short-term load forecast \"good\"? Individual household load profiles are intermittent, as distinct peaks correspond to specific activities in the household. Using traditional point-wise error metrics to assess household-level forecasts may lead to, for instance, double-digit mean absolute percentage errors. One reason is a double penalty incurred if a peak is forecasted correctly in amplitude, but with a small delay in time. An adjusted forecast error measure based on local permutations was proposed to assess household-level forecasts by optimally aligning the peaks bounded by a displacement limit. This work shows how the choice of this parameter leads to different \"best\" forecasts in terms of specific applications, namely the optimization objectives of an energy management system. For that, different parameterizations of the Local Permutation Invariant (LPI) distance are compared within k-Nearest Neighbors as a forecasting model for three different optimization objectives. A simulation study on 100 households of the CER dataset shows that the optimal parameterization can decrease the peak load on average by over 22.5% compared to the Euclidean distance. However, for increasing self-sufficiency and minimizing costs, no significant improvements can be achieved. This implies that household-level forecasts should generally be evaluated in terms of their application, as traditional metrics as a proxy may not express its \"goodness\" adequately.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2