Pierre Courbin

@article{addisu_1122,

title = {Smoothing of renewable energy generation using Gaussianbased method with power constraints},

author = {Alemayehu Addisu and Laurent George and Pierre Courbin and Vincent Sciandra},

url = {https://www.sciencedirect.com/science/article/pii/S1876610217346866},

year  = {2017},

date = {2017-10-01},

journal = {Energy Procedia},

volume = {134},

pages = {171-180},

abstract = {Integration of renewable energy resources to a power system can cause power fluctuations due to their intermittent nature. One way to reduce these effects is to smooth power production using energy storage systems (ESS). A typical approach to tackle the intermittency problem is to use ESS with traditional moving average method. Although it is easy to implement, the moving average method is affected by peaks and troughs during power generations which results in bigger battery sizes. In this paper, we propose a Gaussian-based smoothing algorithm that solves the pitfalls of the moving average methods. Besides smoothing, in big solar plants and wind farms such as in La Réunion island, the grid operator asks energy providers to provide power with a minimum difference from one time to another. We add this constraint on top of the smoothing problem. Then, we determine a minimum possible size of ESS so that the smoothed output power is maintained during the day-ahead forecast period. To test our approach, we use a day-ahead forecast and real data of an industrial site located in France. Bench-marking the moving average methods, we compare performances of the proposed algorithm using two metrics. Based on our simulation results, we obtained at least 34% improvement in smoothness measure and at least 19% reduced ESS size by using the proposed algorithm},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{addisu_1121,

title = {Gaussian-Based Smoothing of Wind and Solar Power Productions Using Batteries},

author = {Alemayehu Addisu and Laurent George and Pierre Courbin and Vincent Sciandra},

url = {http://www.ijmerr.com/index.php?m=content&c=index&a=show&catid=147&id=807},

year  = {2017},

date = {2017-03-01},

journal = {International Journal of Mechanical Engineering and Robotics Research},

volume = {6},

number = {2},

pages = {154-159},

abstract = {Intermittent nature of Distributed Energy Resources (DER) such as solar and wind cause significant power fluctuations and integrating them to a power systems requires control mechanisms to reduce fluctuations. One way to control the effect of fluctuation is to use Energy Storage Systems (ESS) for smoothing out their power productions. A typical method to attain this goal is to use ESS with classical moving average approaches. However, these methods are affected by peaks and troughs in power production due to cloud passing and wind gust effects on solar panels and wind turbines, respectively. In this paper, we propose a Gaussian-based smoothing method to alleviate pitfalls of moving average methods to smooth out forecasted values of solar and wind powers. Then, we determine a minimum ESS size required to maintain a smoothed power curve for a day-ahead period. From our experiments, the proposed algorithm requires smaller ESS size than the classical approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}