▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Mitigation and Adaptation Strategies for Global Change

Machine learning methods for solar radiation forecasting: A review

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

Solar photovoltaic generation forecasting methods: A review

Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.

Stochastic Optimization for Unit Commitment—A Review

http://pierrepinson.com/docs/pinsongirard11.pdf

Evaluating the quality of scenarios of short-term wind power generation

In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges, it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0–6 h). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when online updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques.

https://hal-mines-paristech.archives-ouvertes.fr/hal-01581946/document

Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production

Today, there is a growing interest in developing short-term wind power forecasting tools able to provide reliable information about particular, so-called ‘extreme’ situations. One of them is the large and sharp variation of the production a wind farm can experience within a few hours called ramp event. Developing forecast information specially dedicated to ramps is of primary interest because of both the difficulties that usual models have to predict and the potential risk they represent in the management of a power system. First, we propose a methodology to characterize ramps of wind power production with a derivative filtering approach derived from the edge detection literature. Then we investigate the skill of numerical weather prediction ensembles to make probabilistic forecasts of ramp occurrence. Through conditioning probability forecasts of ramp occurrence to the number of ensemble members forecasting a ramp in time intervals, we show how ensembles can be used to provide reliable forecasts of ramps with sharpness. Our study relies on 18 months of wind power measures from an 8 MW wind farm located in France and forecasts ensemble of 51 members from the Ensemble Prediction System of the European Center for Medium-Range Weather Forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

/pdf/forecasting-ramps-of-wind-power-production-with-numerical-1uixb4n73e.pdf

Forecasting ramps of wind power production with numerical weather prediction ensembles

https://hal-mines-paristech.archives-ouvertes.fr/hal-01617620/document

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

This study presents a generic methodology to produce simplified models able to provide a comprehensive life cycle impact assessment of energy pathways. The methodology relies on the application of global sensitivity analysis to identify key parameters explaining the impact variability of systems over their life cycle. Simplified models are built upon the identification of such key parameters. The methodology is applied to one energy pathway: onshore wind turbines of medium size considering a large sample of possible configurations representative of European conditions. Among several technological, geographical and methodological parameters, we identified the turbine load factor and the wind turbine lifetime as the most influent parameters. Greenhouse Gas (GHG) performances have been plotted as a function of these key parameters identified. Using these curves, GHG performances of a specific wind turbine can be estimated, thus avoiding the undertaking of an extensive Life Cycle Assessment (LCA). This methodology should be useful for decisions makers, providing them a robust but simple support tool for assessing the environmental performance of energy systems.

Robin Girard

Papers

Evaluating the quality of scenarios of short-term wind power generation

Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production

Forecasting ramps of wind power production with numerical weather prediction ensembles

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

From LCAs to simplified models: a generic methodology applied to wind power electricity.