Proceedings of the Eleventh ACM International Conference on Future Energy Systems最新文献_第3页

Economic Analysis of using Distributed Energy Storage for Frequency Regulation 分布式储能用于频率调节的经济分析

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397745

N. Mehmood, N. Arshad

{"title":"Economic Analysis of using Distributed Energy Storage for Frequency Regulation","authors":"N. Mehmood, N. Arshad","doi":"10.1145/3396851.3397745","DOIUrl":"https://doi.org/10.1145/3396851.3397745","url":null,"abstract":"The need for a high ramping energy resource for frequency regulation is increasing due to the high penetration of intermittent and variable renewable energy sources, such as wind and solar, in the electricity grid. Traditionally, special generators have been used for frequency regulation. These generators can provide high capacity but have a very slow response time. Battery energy storage (BES) has gotten tremendous attention due to the advancement in technology. BES has a very fast response time, which makes it suitable for frequency regulation. In this paper, we perform an economic analysis of a distributed energy storage participating in the PJM and NYISO regulation markets. The distributed storage consists of many small consumers' installed batteries. A centralized entity at a microgrid level controls the distributed storage using our proposed algorithms. The economic analysis is performed from the perspective of individual storage owners. Our results show that the five-year net-present-value (NPV) of the consumers' investment is positive if the utility shares 30% (or above) of the regulation revenue with the storage owners and keeps the rest of the 70%.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"78 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":"114698572","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

X-Leep: Leveraging Cross-Layer Pacing for Energy-Efficient Edge Systems X-Leep:利用跨层起搏实现节能边缘系统

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402924

S. Reif, Benedict Herzog, Pablo Gil Pereira, Andreas Schmidt, Tobias Büttner, Timo Hönig, Wolfgang Schröder-Preikschat, T. Herfet

引用次数: 1

A Lightweight Energy-Efficient Computational Offloading Scheme in Mobile Edge Computing 移动边缘计算中一种轻量级节能的计算卸载方案

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402922

Weigang Zhang, Biyu Zhou, Weixia Dang, Songlin Hu

引用次数: 0

Adaptive Control Using Machine Learning for Distributed Storage in Microgrids 基于机器学习的微电网分布式存储自适应控制

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402122

Ramachandra Rao Kolluri, J. Hoog

{"title":"Adaptive Control Using Machine Learning for Distributed Storage in Microgrids","authors":"Ramachandra Rao Kolluri, J. Hoog","doi":"10.1145/3396851.3402122","DOIUrl":"https://doi.org/10.1145/3396851.3402122","url":null,"abstract":"The falling costs of solar photovoltaic systems and energy storage mean that these are being increasingly deployed in microgrids across the globe. Distributed storage can provide benefits for its owner, but can also play a key role in improving microgrid stability and resilience. However, most approaches to date assume that a central authority can control multiple nodes or households in the network. This introduces significant communication and control requirements, and may introduce points of failure. In this work we provide an initial exploration of how a machine learning model, trained on optimal control solutions, can be used locally at each node in the network to emulate a similar behaviour. The aim is for the trained model to provide benefits both for the individual energy storage owners, while also enabling community-level cooperative behaviour - all in a low communication-overhead, privacy-preserving manner. It is experimentally shown that a neural network trained on limited data from optimal schedules can learn node interactions and network characteristics, and can achieve partial voltage regulation for the entire microgrid. This can be done while still achieving a small (3%) network-wide cost savings compared to a scenario in which no distributed storage is present, can be implemented only locally, and does not introduce any significant requirements for central control and communication.","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":"127132381","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: 5

Jointly Optimizing the IT and Cooling Systems for Data Center Energy Efficiency based on Multi-Agent Deep Reinforcement Learning 基于多智能体深度强化学习的数据中心IT和冷却系统能效联合优化

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402658

Ce Chi, Kaixuan Ji, Avinab Marahatta, Penglei Song, Fa Zhang, Zhiyong Liu

{"title":"Jointly Optimizing the IT and Cooling Systems for Data Center Energy Efficiency based on Multi-Agent Deep Reinforcement Learning","authors":"Ce Chi, Kaixuan Ji, Avinab Marahatta, Penglei Song, Fa Zhang, Zhiyong Liu","doi":"10.1145/3396851.3402658","DOIUrl":"https://doi.org/10.1145/3396851.3402658","url":null,"abstract":"With the development and application of cloud computing, the increasing amount of data centers has resulted in huge energy consumption and severe environmental problems. Improving the energy efficiency of data centers has become a necessity. In this paper, in order to improve the energy efficiency of both IT and cooling systems for data centers, a model-free deep reinforcement learning (DRL) based joint optimization approach MACEEC is proposed. To improve the cooperation between IT and cooling system while handling the high-dimensional state space and the large hybrid discrete-continuous action space, a hybrid AC-DDPG multi-agent structure is developed. A scheduling baseline comparison method is proposed to enhance the stability of the architecture. And an asynchronous control optimization algorithm is developed to solve the different responding time issue between IT and cooling system. Experiments based on real-world traces data validate that MACEEC can effectively improve the overall energy efficiency for data centers while ensuring the temperature constraint and service quality compared with existing joint optimization approaches.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"29 1 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":"131383602","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: 17

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing 小规模社区足以实现成本和数据效率高的点对点能源共享

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397741

Romaric Duvignau, V. Heinisch, L. Göransson, Vincenzo Gulisano, M. Papatriantafilou

{"title":"Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing","authors":"Romaric Duvignau, V. Heinisch, L. Göransson, Vincenzo Gulisano, M. Papatriantafilou","doi":"10.1145/3396851.3397741","DOIUrl":"https://doi.org/10.1145/3396851.3397741","url":null,"abstract":"Due to ever lower cost, investments in renewable electricity generation and storage have become more attractive to electricity consumers in recent years. At the same time, electricity generation and storage have become something to share or trade locally in energy communities or microgrid systems. In this context, peer-to-peer (P2P) sharing has gained attention, since it offers a way to optimize the cost-benefits from distributed resources, making them financially more attractive. However, it is not yet clear in which situations consumers do have interests to team up and how much cost is saved through cooperation in practical instances. While introducing realistic continuous decisions, through detailed analysis based on large-scale measured household data, we show that the financial benefit of cooperation does not require an accurate forecasting. Furthermore, we provide strong evidence, based on analysis of the same data, that even P2P networks with only 2--5 participants can reach a high fraction (96% in our study) of the potential gain, i.e., of the ideal offline (i.e., non-continuous) achievable gain. Maintaining such small communities results in much lower associated costs and better privacy, as each participant only needs to share its data with 1--4 other peers. These findings shed new light and motivate requirements for distributed, continuous and dynamic P2P matching algorithms for energy trading and sharing.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"56 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":"125308629","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: 5

Optimization-based energy sharing among customers for enhanced resilience in a community microgrid 基于优化的客户间能源共享，以增强社区微电网的弹性

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403515

Sk Razibul Islam, E. Ratnam, S. Chau, J. Ward

引用次数: 1

How does Load Disaggregation Performance Depend on Data Characteristics?: Insights from a Benchmarking Study 负载分解性能如何依赖于数据特征?:来自基准研究的见解

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3397691

A. Reinhardt, Christoph Klemenjak

{"title":"How does Load Disaggregation Performance Depend on Data Characteristics?: Insights from a Benchmarking Study","authors":"A. Reinhardt, Christoph Klemenjak","doi":"10.1145/3396851.3397691","DOIUrl":"https://doi.org/10.1145/3396851.3397691","url":null,"abstract":"Electrical consumption data contain a wealth of information, and their collection at scale is facilitated by the deployment of smart meters. Data collected this way is an aggregation of the power demands of all appliances within a building, hence inferences on the operation of individual devices cannot be drawn directly. By using methods to disaggregate data collected from a single measurement location, however, appliance-level detail can often be reconstructed. A major impediment to the improvement of such disaggregation algorithms lies in the way they are evaluated so far: Their performance is generally assessed using a small number of publicly available electricity consumption data sets recorded from actual buildings. As a result, algorithm parameters are often tuned to produce optimal results for the used data sets, but do not necessarily generalize to different input data well. We propose to break this tradition by presenting a toolchain to create synthetic benchmarking data sets for the evaluation of disaggregation performance in this work. Generated synthetic data with a configurable amount of concurrent appliance activity is subsequently used to comparatively evaluate eight existing disaggregation algorithms. This way, we not only create a baseline for the comparison of newly developed disaggregation methods, but also point out the data characteristics that pose challenges for the state-of-the-art.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"24 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":"116729571","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: 23

Machine Learning for Data-Driven Indicators Applied to Power Distribution System 数据驱动指标的机器学习在配电系统中的应用

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3403512

Diana Estefanía Chérrez, G. B. Archilli, L. C. P. Silva

{"title":"Machine Learning for Data-Driven Indicators Applied to Power Distribution System","authors":"Diana Estefanía Chérrez, G. B. Archilli, L. C. P. Silva","doi":"10.1145/3396851.3403512","DOIUrl":"https://doi.org/10.1145/3396851.3403512","url":null,"abstract":"Indicators play an important role as they offer a quick overview of the system performance. However, obtain each indicator for each component of the power distribution system is cumbersome using classical approaches because the number of devices and data that must be studied is extensive. The main purpose of this work is to take advantage of machine learning algorithms to: (i) learn patterns from our data, and (ii) compute prediction-based indicators (we called data-driven indicators), that can be used to understand and improve distribution network performance. Our proposed methodology, used a long short-term memory (LSTM) auto-encoder architecture as a feature extractor in order to reduce the dimensionality, and then we used an LSTM forecaster network to make a daily prediction using smart-meters measurements. Finally, we employed the predicted values to compute the standardized indicators and ranked them based on the critical state. We carried out this analysis using real-world data collected at the State University of Campinas (UNICAMP). Our findings suggest that our proposed methodology can be suitable for power distribution networks where we faced with the problem of modeling unbalanced three-phase systems and with low X/R ratios.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"7 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":"130892240","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

Explicitly Consider Server-Attached Fans for Thermal Modeling in Edge Data Centers 明确考虑服务器附加风扇在边缘数据中心的热建模

Proceedings of the Eleventh ACM International Conference on Future Energy Systems Pub Date : 2020-06-12 DOI: 10.1145/3396851.3402921

Xu Zhao, Yijun Lu, Zhan Li, Jian Tan, Youquan Feng, Yuanqing Tao

{"title":"Explicitly Consider Server-Attached Fans for Thermal Modeling in Edge Data Centers","authors":"Xu Zhao, Yijun Lu, Zhan Li, Jian Tan, Youquan Feng, Yuanqing Tao","doi":"10.1145/3396851.3402921","DOIUrl":"https://doi.org/10.1145/3396851.3402921","url":null,"abstract":"Edge data center has become an important data center type in recent years. Comparing to a conventional data center, an edge data center often lacks sophisticated cooling equipment and infrastructure support. In the resulting poor thermal environment, fans attached to individual servers have to work harder in an edge data center than those in a conventional data center. Research have shown that power generated by server-attached fans are quite significant to be ignored from thermal standpoint when fan speed is high. In this paper, as we consider power efficiency in edge data centers, we argue that power generated by fans attached to the servers should be explicitly considered for thermal modeling in the overall thermal optimization framework. We propose a design that incorporates fan power in a neural network to better predict a server's thermal state in an edge data center. We further propose a task scheduling algorithm that utilizes the improved neural network to enhance an edge data center's overall power efficiency. Based on the experimental results from a field edge data center, the improved neural network achieves better accuracy in predicting individual server's thermal state, outperforming other neural networks on precision. The proposed task scheduling algorithm, powered by the improved neural network, saves as much as 11% power consumption comparing to unoptimized algorithms.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"51 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":"132202119","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: 1