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Author

Binghui Li

Other affiliations: North Carolina State University
Bio: Binghui Li is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Electric power system & Probabilistic logic. The author has an hindex of 6, co-authored 15 publications receiving 133 citations. Previous affiliations of Binghui Li include North Carolina State University.

Papers
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Journal ArticleDOI
TL;DR: This paper employs an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space, to explore different cost- and CO2-constrained futures.
Abstract: Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. Modeling to generate alternatives (MGA) is applied to an energy system model.Temoa, an open source energy model, and a two sector U.S. dataset are utilized.MGA is used to explore different cost- and CO2-constrained futures.Results highlight the value of iterative analysis to probe the model decision space.

76 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive review on how probabilistic solar forecasts are utilized in power systems to address the challenges is presented, such as probabilistically load flow models, stochastic optimization, robust optimization, and chance constraints.

65 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provided the first economic characterization of OCAES performance when coupled to an offshore wind farm by employing a mixed integer programming model, which seeks the minimum levelized cost of electricity by optimizing the grid-tied cable capacity and OCAE component sizes across a range of specified cable capacity factors.

42 citations

Journal ArticleDOI
TL;DR: A clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations, and can reduce the total computational time for scenario generation and reduction significantly.
Abstract: A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

18 citations

Journal ArticleDOI
04 Mar 2021
TL;DR: Findings show that local topological summaries can successfully reflect changes in the grid resilience, and compare the TDA summaries with the power system reliability metrics, to show this.
Abstract: Due to increasing threats on power systems from various extreme events such as adverse weather and cyber/physical attacks, research on power grid resilience is recently gaining a substantial tracti...

17 citations


Cited by
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01 Jan 2015

976 citations

01 Jan 2011
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

585 citations

01 Jan 2016
TL;DR: The fundamentals of engineering thermodynamics is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Abstract: Thank you for downloading fundamentals of engineering thermodynamics. Maybe you have knowledge that, people have look numerous times for their favorite books like this fundamentals of engineering thermodynamics, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their computer. fundamentals of engineering thermodynamics is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the fundamentals of engineering thermodynamics is universally compatible with any devices to read.

330 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a set of guiding principles for energy system optimization models (ESOMs) that can be used to guide ESOM-based analysis, including how to formulate research questions, set spatio-temporal boundaries, consider appropriate model features, conduct and refine the analysis, quantify uncertainty, and communicate insights.

230 citations