Pedagogical outcomes of the nanogrid

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Table of contents

List of Figures
List of Tables
Nomenclature
1 General introduction
1.1 A building-size microgrid emulated in a experimental lab
1.2 The uncertainty associated to a deterministic solar forecast
1.3 Energy management of a microgrid under solar production uncertainty .
2 Experimental nanogrid development with pedagogical and demonstrator purposes
2.1 Introduction
2.2 Materials and methods
2.3 Nanogrid data output
2.4 Pedagogical outcomes of the nanogrid
2.4.1 Experimental procurement of Joule losses and the equivalent resistance of the circuit
2.4.2 Understanding the basics of power flows and its manipulation .
2.4.3 Recapitulation of learning outcomes
2.5 The load scheduling on-line game
2.5.1 The game Dashboard
2.5.2 The scheduling interactive table
2.5.3 Performance indicators calculation as the game scores
2.6 Conclusions
2.7 Current and future developments and functionalities
3 Uncertainty estimation for deterministic solar irradiance forecasts based on analogs ensembles
3.1 Introduction
3.2 From deterministic to probabilistic forecasts
3.3 Evaluation metrics considerations
3.3.1 Required properties for skillful probabilistic forecasts
3.3.2 Proposed evaluation framework
3.4 The Analog Ensemble method retrieval
3.4.1 The Analogs method principle
3.4.2 Considered datasets
3.4.3 Predictors selection
3.4.4 Similarity criteria for analog selection
3.5 Benchmark methods
3.5.1 Climatology and persistence
3.5.2 Monthly Climatology
3.5.3 ECMWF ensembles
3.6 Results
3.6.1 Performance of the AnEn regarding the number of members
3.6.2 Ensembles dispersion analysis and interpretation
3.6.3 Forecasts comparison
3.7 Conclusions
3.8 Future perspectives
4 Forecasts and energy management in a microgrid: Impact on services provided by a smart building
4.1 Introduction
4.2 Objectives
4.3 Use case description
4.4 Services and performance indicators
4.4.1 Service 1: Reduction in energy costs
4.4.2 Service 2: Reduction in electricity carbon footprint
4.4.3 Service 3: Day-ahead grid power commitment
4.4.4 Service 4: Reduction of grid peak power
4.5 Proposed two-step energy management system
4.5.1 The optimization algorithms
4.5.2 The scheduling module
4.5.3 The balancing module
4.5.4 Reference strategies
4.6 Performance evaluations
4.6.1 Added value of scheduling
4.6.2 Impact of deterministic forecasts uncertainty
4.6.3 Contribution of using quantile forecasts
4.6.4 How optimizing for one service affect the others
4.6.5 Seasonal performance optimization and analyses
4.7 Conclusions
4.8 Perspectives for further research
5 General conclusions and perspectives
Bibliography