Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems

Ordiano, Jorge Ángel González; Doneit, Wolfgang; Waczowicz, Simon; Gröll, Lutz; Mikut, Ralf; Hagenmeyer, Veit

doi:10.48550/arxiv.1701.06463

Cited by 1 publication

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“…2) Sources of Uncertainty at the First Stage: The power output from uncontrolled generation {l(k)} k∈S is yet unknown at k 0 . However, a probabilistic forecast for {l(k)} k∈S can be calculated [14]. Hence, the inflexible power output at the first stage is represented by a random variable, L(k|k 0 ), whose realization is l(k) = 1 l(k).…”

Section: Three-stage Scheduling and Operationmentioning

confidence: 99%

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Reliable Dispatch of Renewable Generation via Charging of Time-Varying PEV Populations

Appino

Munoz-Ortiz

Ordiano

et al. 2019

IEEE Trans. Power Syst.

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The inherent storage of plug-in electric vehicles is likely to foster the integration of intermittent generation from renewable energy sources into existing power systems. In the present paper, we propose a three-stage scheme to the end of achieving dispatchability of a system composed of plug-in electric vehicles and intermittent generation. The main difficulties in dispatching such a system are the uncertainties inherent to intermittent generation and the time-varying aggregation of vehicles. We propose to address the former by means of probabilistic forecasts and we approach the latter with separate stagespecific models. Specifically, we first compute a dispatch schedule, using probabilistic forecasts together with an aggregated dynamic model of the system. The power output of the single devices are set subsequently, using deterministic forecasts and device-specific models. We draw upon a simulation study based on real data of generation and vehicle traffic to validate our findings.Index Terms-dispatch schedule, plug-in electric vehicle, probabilistic forecasting, renewable energy, stochastic programming, dynamic aggregation of energy storage All authors are with the

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Section: Three-stage Scheduling and Operationmentioning

confidence: 99%

“…4) Forecasts: The data of PV generation and radiation forecasts is used to train several quantile regressions based on a method described in [14] with the open-source MATLAB toolbox SciXMiner [21]. Thereafter, probabilistic forecasts for both power and energy are obtained using the procedure described in [10,Section 5.2].…”

Section: A Test Casementioning

confidence: 99%