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.. , Left: reference image Center left: borders and numbers are removed Center right: edge recognition. Right: final fitted surface, p.109

, Auto-correlation lengthscale computation In black are the points obtained from all the pairs from our original data, that is absolute wind speed differences as a function of the distance between the two points

.. , In red is the exponential fit In black are the derivatives of the fit at 0 and ?. In green is the recovered auto-correlation lengthscale. The unit for the lengthscale is in km, p.112

.. , Typical response curves of different wind turbines, p.114

, The origin of the hours is the first of, and the production is in MWh In blue: the observed wind production In dark red: the day-ahead predictions from the grid operator, All curves are hourly production data light red: the day-ahead predictions from weather data. . . . . . . . . . . . 114, 2011.

]. Daily, , p.115, 2012.

.. , Hourly solar production in MWh The time origin is the first of In blue: observed production by RTE In dark red: reconstructed production from observed weather data, p.116, 2011.

, A.1 Comparison of bandwidths: Large bandwidth in first stage, p.125

, A.2 Comparison of bandwidths: Small bandwidth in first stage, p.126

.. , A.3 Histogram of volume variation between points, p.127

.. , Illustrating the effect of increased uncertainty, p.138

, Traded volume plotted against total annual consumption, p.141

.. , Example aggregate demand and supply bid functions, p.145

.. , Aggregate bid functions for 20 consecutive days, p.145

.. , , p.149

.. Temperature-based-seasonality-controls, , p.161

.. Continuous-controls-for-daily-patterns, 161 List of Figures 3.B.1 Plotted average realised Volume (left) and Price (right) per Hour with 95% confidence intervals, p.185

.. , Distribution of observed market equilibria, p.186

.. , Distribution of minimum and maximum production volumes (and corresponding range) bid in an hourly auction, p.186

.. , Distribution of number of bid function steps, p.186

, Histogram of predicted wind (left) and predicted solar (right) generation 187

L. and .. , , p.37

.. The-day-ahead-markets, , p.40

, Heuristic Description of the Model 43

, Stochastic Dynamics, vol.47

P. The-stochastic, , p.47

C. The-ramping, , p.49

.. Discussion-of-the-approximations, , p.52

P. The-maximization, , p.52

, The Monopoly 53

R. , , p.54

, The Symmetric Oligopoly 57

D. , , p.62

, Dynamic Behavior of the Bids 63

T. and .. , , p.65

R. , , p.68

D. , , p.71

, Table of Contents -Long

.. , A.2 Outlier detection and removal, p.126

, Investigating the Impact of Uncertainty on Firms with Dynamic Costs: A Case Study of the French Electricity Market 129, Chapter

, 1 Introduction 131

L. Review and C. , , p.134

.. Theoretical-prediction, , p.137

, The EPEX Spot Market 140

.. General, , p.140

M. Auction-rules, , p.142

, Our Data Explained 144

, Methodology, vol.147

R. and .. , , p.150

.. Demand-estimation, , p.165

.. Final-regression, , p.171

F. , , p.176

E. Internal, , p.178

E. , , p.180

, Appendix, vol.183

, Appendix 3.A Summary Statistics of Selected Points 183

, Appendix 3.B Computational Details and Descriptives 184

.. , 1 Hard choices in the PLU computation, p.184

B. , D. Statistics, and .. , , p.185

, General conclusion References 203