2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

A review of computer tools for analysing the integration of renewable energy into various energy systems

Introduction to stochastic programming

http://cepac.cheme.cmu.edu/pasilectures/sahinidis/NVSpaperthree.pdf

Optimization under uncertainty: state-of-the-art and opportunities

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

/pdf/arima-models-to-predict-next-day-electricity-prices-1udz7ihu0r.pdf

ARIMA models to predict next-day electricity prices

The paper givas supply system in Norway, approach. Procedures ard scheduling are described. Scheduling is carried out is based on profit important factor; this price of the paper describes market price as an how to extend the common this case. Implications to also discussed.

scheduling in a deregulated system. The Norwegian case

The paper gives an overview of the deregulated electricity supply system in Norway, with emphasis on the generation scheduling approach. Procedures and tools for long-, medium- and short-term scheduling are described. Scheduling is carried out by each individual utility. Since scheduling is based on profit maximizing, the spot market price is a very important factor; this price is varying and uncertain, however. A part of the paper describes how to deal with this by considering the spot market price as an exogenously given stochastic variable. It is shown how to extend the common water value computations so as to handle this case. Implications to the medium- and short-term scheduling are also discussed.

Generation scheduling in a deregulated system. The Norwegian case

Hydro reservoir handling in Norway before and after deregulation

The paper describes the implementation of a new integrated tool for risk management in hydropower systems. Earlier practice in Scandinavia has been to separate operations scheduling and contract management. In the present approach operation scheduling and hedging by future contacts are integrated in one model. The risk level is controlled by setting revenue targets. Revenues below target are penalized; this implicitly defines a revenue utility function to reduce risk. The possibility of dynamically changing the future contract portfolio is now represented. The resulting large stochastic dynamic optimization problem is solved using a combination of stochastic dynamic programming and stochastic dual dynamic programming. Simulations for a test case show that the profit in the lower range is considerably improved with the new tool. The approach can be useful for hydropower companies that face price risks in addition to the inflow uncertainty, as is the case in a deregulated system.

Integrated risk management of hydro power scheduling and contract management

Most generation expansion planning tools do not model uncertainties in important variables such as energy demand and prices of energy carriers together with the dynamics of the system. A method for handling these uncertainties in generation expansion problems is described. The method is based on stochastic dynamic programming. As the uncertain variables are modeled by Markov chains they give a natural year-to-year dependence of the variables. This modeling makes it possible to describe the connection between investment decisions, time, construction periods, and uncertainty. The importance of modeling these connections is demonstrated by a realistic example. >

Birger Mo

Papers

scheduling in a deregulated system. The Norwegian case

Generation scheduling in a deregulated system. The Norwegian case

Hydro reservoir handling in Norway before and after deregulation

Integrated risk management of hydro power scheduling and contract management

Stochastic generation expansion planning by means of stochastic dynamic programming