Introduction to stochastic programming

Annual Energy Outlook

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.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

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Fundamentals Of Engineering Thermodynamics

Formalizing best practice for energy system optimization modelling

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.

/pdf/modelling-to-generate-alternatives-with-an-energy-system-3w5qor8bc9.pdf

Modelling to generate alternatives with an energy system optimization model

A review on the integration of probabilistic solar forecasting in power systems

A techno-economic assessment of offshore wind coupled to offshore compressed air energy storage

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.

A clustering-based scenario generation framework for power market simulation with wind integration

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...

Binghui Li

Papers

Modelling to generate alternatives with an energy system optimization model

A review on the integration of probabilistic solar forecasting in power systems

A techno-economic assessment of offshore wind coupled to offshore compressed air energy storage

A clustering-based scenario generation framework for power market simulation with wind integration

A hybrid approach for transmission grid resilience assessment using reliability metrics and power system local network topology