2019 IEEE Milan PowerTech最新文献_第8页

Stochastic optimization framework for online scheduling of an EV charging station in a residential place with photovoltaics and energy storage system 光伏储能住宅电动汽车充电站在线调度的随机优化框架

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810912

Gustavo Aragón, Otilia Werner-Kytölä, E. Gümrükcü

{"title":"Stochastic optimization framework for online scheduling of an EV charging station in a residential place with photovoltaics and energy storage system","authors":"Gustavo Aragón, Otilia Werner-Kytölä, E. Gümrükcü","doi":"10.1109/PTC.2019.8810912","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810912","url":null,"abstract":"House and building energy management systems (HEMS) are becoming key when it comes to assure grid stability and to offer flexibility. At the same time, energy systems technology has evolved to enable energy storage systems and electric vehicles to be managed together with local generated energy taking into consideration the preferences of the household owner. Contributing to this tendency, this work presents a stochastic optimization platform (SOFW) for optimal control using dynamic programming and stochastic optimization models. A stochastic optimization model involving a household composed of photovoltaics, energy storage system and an electric vehicle is designed and tested within SOFW. The uncertainties of the plug-in time and state of charge of the battery of the electric vehicle are modeled using a Markovian process and a Monte-Carlo simulation. The results showed that the proposed stochastic optimization model can be solved using dynamic programming and deployed as a continuous optimal control within SOFW. The system will be deployed shortly in Italy within one use case of the Storage 4 Grid (S4G) project.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129291934","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: 3

A Data-driven Approach to Grid Impedance Identification for Impedance-based Stability Analysis under Different Frequency Ranges 一种数据驱动的网格阻抗识别方法，用于不同频率范围下基于阻抗的稳定性分析

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810402

Chendan Li, M. Molinas, O. Fosso, Nan Qin, Lin Zhu

引用次数: 5

Learning from power system data stream 从电力系统数据流中学习

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810950

Mauro Escobar, D. Bienstock, M. Chertkov

引用次数: 3

An optimization Model for Airport Infrastructures in Support to Electric Aircraft 支持电动飞机的机场基础设施优化模型

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810713

F. Salucci, L. Trainelli, R. Faranda, M. Longo

引用次数: 8

Economic Impact of the Active Power Droop Gain in Droop-Based Islanded Microgrids 孤岛微电网有功功率下垂增益的经济影响

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810513

P. Vergara, J. López, Silva Luiz C. P. da, M. J. Rider

引用次数: 2

Passivity-Based Control for a PV/Battery/Fuel Cell/Electrolyser Hybrid Power System 光伏/电池/燃料电池/电解槽混合动力系统的无源控制

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810688

S. Kong, M. Hilairet, R. Roche

引用次数: 8

A Smart Voltage Optimization Approach for Industrial Load Demand Response 工业负荷需求响应的智能电压优化方法

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810705

Adarsh Madhavan, Brian Lee, C. Cañizares, Kankar Bhattacharya

{"title":"A Smart Voltage Optimization Approach for Industrial Load Demand Response","authors":"Adarsh Madhavan, Brian Lee, C. Cañizares, Kankar Bhattacharya","doi":"10.1109/PTC.2019.8810705","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810705","url":null,"abstract":"This paper proposes a generic and comprehensive Voltage Optimization (VO) strategy for energy savings by industrial customers, to lower operating expenses through the implementation of an optimal process-based Demand Response (DR) program without affecting the real-time manufacturing process. This strategy takes into account the complex nature of industrial loads and their unique set of operating constraints, to reduce energy demand for industrial customers by means of varying the voltage at the utility service entrance to the plant. The proposed approach utilizes a Neural Network (NN) model of the industrial load, trained using historical operating data, to estimate the real power consumption of the load, based on the bus voltage and overall plant process. The NN load model is incorporated into the proposed VO model, whose objective is the minimization of the energy drawn from the substation and the number of switching operations of Load Tap Changers (LTC). The proposed VO framework is tested on a real plant model developed using actual measured data. The results demonstrate that the proposed technique can be successfully implemented by industrial customers and plant operators to enhance energy savings compared to Conservation Voltage Reduction (CVR) approaches, and also as a DR strategy that effectively manages the dependence of industrial loads on time-sensitive and critical manufacturing processes.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113733","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

Fault Detection and Localization in LV Smart Grids 低压智能电网故障检测与定位

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810799

N. Sapountzoglou, B. Raison, N. Silva

引用次数: 7

Coordination between an Aggregator and Distribution Operator to Achieve Network-Aware Load Control 聚合器和分配算子之间的协调以实现网络感知负载控制

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810635

Stephanie Ross, N. Ozay, J. Mathieu

引用次数: 10

Integrated Energy-Water Model for Interdependent Storage, Run-of-River and Pump Hydropower 蓄、流、泵相互依存的能量-水综合模型

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810468

E. A. Martínez Ceseña, M. Panteli, J. Mutale, P. Mancarella, J. Tomlinson, J. Harou

引用次数: 1