2022 IEEE Power & Energy Society General Meeting (PESGM)最新文献

Optimal HVAC Scheduling under Temperature Uncertainty using the Wasserstein Metric 基于Wasserstein度量的温度不确定性下暖通空调调度优化

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9916922

Guanyu Tian, Q. Sun

{"title":"Optimal HVAC Scheduling under Temperature Uncertainty using the Wasserstein Metric","authors":"Guanyu Tian, Q. Sun","doi":"10.1109/PESGM48719.2022.9916922","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916922","url":null,"abstract":"The heating, ventilation and air condition (HVAC) system consumes the most energy in commercial buildings, consisting over 60% of total energy usage in the U.S. Flexible HVAC system setpoint scheduling could potentially save building energy costs. This paper proposes a distributionally robust optimal (DRO) HVAC scheduling method that minimizes the daily operation cost with constraints of indoor air temperature comfort and mechanic operating requirement. Considering the uncertainties from ambient temperature, a Wasserstein metric-based ambiguity set is adopted to enhance the robustness against probabilistic prediction errors. The schedule is optimized under the worst-case distribution within the ambiguity set. The proposed DRO method is initially formulated as a two-stage problem and then reformulated into a tractable mixed-integer linear programming (MILP) form. The paper evaluates the feasibility and optimality of the optimized schedules for a real commercial building. The numerical results indicate that the costs of the proposed DRO method are up to 6.6% lower compared with conventional techniques of optimization under uncertainties. They also provide granular risk-benefit options for decision-makinz in demand response programs.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"58-60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123127629","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: 0

Smart Sampling for Reduced and Representative Power System Scenario Selection 简化和代表性电力系统场景选择的智能采样

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9916835

Xueqing Sun, Xinya Li, Sohom Datta, Xinda Ke, Qiuhua Huang, Renke Huang, Z. Hou

{"title":"Smart Sampling for Reduced and Representative Power System Scenario Selection","authors":"Xueqing Sun, Xinya Li, Sohom Datta, Xinda Ke, Qiuhua Huang, Renke Huang, Z. Hou","doi":"10.1109/PESGM48719.2022.9916835","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916835","url":null,"abstract":"With increasing penetration of renewable energy and active market participation, power system operation scenarios and patterns have increased exponentially. This has led to challenges in identifying a good subset of scenarios for routine planning, operation, and emerging machine learning applications. To address these challenges, we develop an approach integrating comprehensive exploratory data analyses and smart sampling techniques to identify and select a small subset of representative power system scenarios that maintain the coverage of system scenarios and operation envelope, therefore, leading to very efficient, yet representative studies and analysis. We propose a hierarchical Latin Hypercube Sampling (LHS) technique for smart sampling, which allows free-form distributions of system load and considers generator commitment status along with generation levels. A set of performance metrics are also defined for systematic evaluation of the adequacy and efficiency of the sampled cases. The developed approach and metrics are demonstrated using the Texas 2000 bus system in this paper and will be extended to the more complex real world systems such as Western Interconnect System.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121825324","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

Online Health Monitoring of Photovoltaic Distributed Energy Resources 光伏分布式能源在线健康监测

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9916726

Rojan Bhattarai, H. Garcia

引用次数: 0

Creation of Simulated Test Cases for the Oscillation Source Location Contest 为振荡源定位竞赛创建模拟测试用例

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917120

S. Maslennikov, Bin Wang

引用次数: 3

Modeling Discrete Random Variables with Linear and Nonlinear Dependence for Probabilistic Load Flow 概率潮流中线性和非线性依赖的离散随机变量建模

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9916896

A. C. Melhorn, J. Taylor

引用次数: 0

EPRI-VCA: Optimal Reactive Power Dispatch Tool EPRI-VCA:优化无功功率调度工具

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917236

Tamer Ibrahim, A. del Rosso, Swaroop S. Guggilam, Kevin Dowling, Mahendra Patel

{"title":"EPRI-VCA: Optimal Reactive Power Dispatch Tool","authors":"Tamer Ibrahim, A. del Rosso, Swaroop S. Guggilam, Kevin Dowling, Mahendra Patel","doi":"10.1109/PESGM48719.2022.9917236","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917236","url":null,"abstract":"An adequate dynamic reactive reserve is essential for the operational reliability of a power system. Transmission system operators have recognized the need for improved voltage control and reactive power management approaches to handle the more complex coordination and interactions among controllers under the stringent operating conditions imposed by the emerging generation landscape and other system changes. The Electric Power Research Institute (EPRI) has developed a methodology and software tool to help transmission operators schedule and control static and dynamic reactive power (var) resources in a systematic manner. The software tool, called VCA Studio, identifies voltage control areas and schedules var resources solving a voltage secure multi-period optimal reactive power dispatch optimization (MP-ORPD) problem. The MP-ORPD problem considers the critical buses and critical contingencies and finds the set of preventive control set-points to ensure the system is secure under critical contingencies. This paper describes the methodology and main characteristics of the VCA Studio tool and a sample set of results obtained from case studies on various utility systems in Eastern Interconnection.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127264776","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: 0

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation 基于线性不确定性传播的中低压联合状态估计器

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917189

Mengmeng Cai, Xin Fang, A. Florita

引用次数: 0

Scalable Predictive Control and Optimization for Grid Integration of Large-scale Distributed Energy Resources 大规模分布式能源电网集成的可扩展预测控制与优化

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917010

Abinet Tesfaye Eseye, Bernard Knueven, Deepthi Vaidhynathan, J. King

{"title":"Scalable Predictive Control and Optimization for Grid Integration of Large-scale Distributed Energy Resources","authors":"Abinet Tesfaye Eseye, Bernard Knueven, Deepthi Vaidhynathan, J. King","doi":"10.1109/PESGM48719.2022.9917010","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917010","url":null,"abstract":"Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125925847","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

A Platform for Deploying Multi-agent Deep Reinforcement Learning in Microgrid Distributed Control 微电网分布式控制中多智能体深度强化学习的应用平台

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917136

T. Nguyen, Yu Wang, Q. Duong, Q. Tran, Ha Thi Nguyen, O. Mohammed

引用次数: 0

Power System State Recovery using Local and Global Smoothness of its Graph Signals 基于图信号局部和全局平滑的电力系统状态恢复

2022 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2022-07-17 DOI: 10.1109/PESGM48719.2022.9917018

Md Abul Hasnat, M. Rahnamay-Naeini

引用次数: 1