PV power forecast using a nonparametric PV model

Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

The penetration of stochastic renewable generation in modern power systems requires to reconsider conventional practices to ensure the reliable functioning of the electrical network. Decentralized control schemes for distributed energy resources (DERs) have gained attention to support the grid operation. In order to cope with the uncertainties of the DERs, predictive schemes that leverage on forecast of renewable generation recently came into prominence. The period of the control action typically depends on the availability of the reserve in the grid. For the case of microgrids, their limited physical extension and the lack of spatial smoothing imply fast power fluctuations and the necessity of coupling energy management strategies with real-time control. Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future available capacity, and consequently, solar resource assessment and power forecasting are of fundamental importance. This thesis focuses on developing forecasting methods and generation models to support the integration of photovoltaic systems in microgrids, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). In particular, we aim at computing probabilistic prediction intervals (PIs), i.e. we include information accounting for the intrinsic uncertainty of the prediction. In this respect, nonparametric tools to deliver PIs from sub-second to intra-hour forecasting horizons are proposed and benchmarked. They forecast the AC power and/or the global horizontal irradiance (GHI) by extracting selected endogenous influential variables from historical time series. The methods are shown to outperform available state-of-the-art techniques, and are able to capture the fastest fluctuations of small-scale PV plants. Then, we investigate how the inclusion of features from ground all-sky images can be used to improve time-series-based forecasting tools, thanks to identifying clouds movement. In this respect, we define a toolchain that allows predicting the cloud cover of the sun disk, through image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using an all-sky camera as an irradiance sensor. Next, we consider the problem of having power measurements that are corrupted by exogenous control actions (e.g. curtailment) and, therefore, not representative of the true potential of the PV plant. We propose a model-based strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and current. The strategy can improve time series-based direct power forecasting techniques when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available. The proposed methods to model and forecast the PV generation are then integrated in a single chain that allows to deliver power PIs that are able to account for the overall uncertainty of a PV system at a predefined confidence level. In the last part of the thesis, the proposed methods are experimentally validated in a real microgrid by considering possible applications in modern power systems

Engineering User-Centric Smart Charging Systems

Die Integration erneuerbarer Energiequellen und die Sektorenkopplung erhöhen den Bedarf an Flexibilität im Elektrizitätssystem. Elektrofahrzeuge koordiniert zu Laden bietet die Chance solche Flexibilität bereitzustellen. Allerdings hängt das Flexibilitätspotential von Elektrofahrzeugen davon ab in welchem Umfang sich die Nutzer der Fahrzeuge dazu entschließen intelligentes Laden zu nutzen. Ziel dieser Dissertation ist es Lösungen für intelligente Ladesysteme zu entwickeln, welche die Nutzer zu flexiblerem Laden anreizen und diese dabei zu unterstützen. Anhand eines Literaturüberblicks und einer Expertenbefragung werden zunächst Ziele identifiziert, welche Nutzer zu einer flexiblen Ladung motivieren können. Die Ergebnisse zeigen, dass neben finanziellen Anreizen auch die Integration erneuer-barer Energien und die Vermeidung von Netzengpässen einen Anreiz für das flexible La-den darstellen können. In der Folge wird untersucht, ob das Framing der Ladesituation hinsichtlich dieser Ziele die Ladeflexibilität von Elektrofahrzeugnutzern beeinflussen kann. Hierzu wird ein Online-Experiment mit Elektrofahrzeugnutzern evaluiert. Das sich ein Teil der Nutzer bei einem Umwelt-Framing flexibler verhält, macht Feedback darüber, wie die CO2-Emissionen von der bereitgestellten Flexibilität abhängen zu einem vielversprechenden Anreiz intelligentes Laden zu nutzen. Um solches Feedback zu er-möglichen werden als Nächstes die CO2-Einsparpotenziale eines optimierten Ladens im Vergleich zu unkontrolliertem Laden untersucht. Dazu werden die marginalen Emissions-faktoren im deutschen Stromnetz mithilfe eines regressionsbasierten Ansatzes ermittelt. Um Echtzeit-Feedback in realen Systemen zu ermöglichen wird darauf aufbauend eine Prognosemethode für Emissionsfaktoren entwickelt. Die Zielerreichung intelligenten Ladens hängt hauptsächlich von der zeitlichen und energetischen Flexibilität der Elektrofahrzeuge ab. Damit Nutzer diese Ladeeinstellungen nicht bei jeder Ankunft an der Ladestation von Hand eingeben zu müssen, könnten sie durch intelligente Assistenten unterstützt werden. Hierfür werden probabilistische Prognosen für die Flexibilität einzelner Ladevorgänge basierend auf historischen Ladevorgängen und Mobilitätsmustern entwickelt. Darüber hinaus zeigt eine Fallstudie, dass probabilistische Prognosen besser als Punktprognosen dazu geeignet sind die Ladung mehrerer Elektrofahrzeuge zu koordinieren