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Higher Education Technical Challenges Hub: Module Specification

RE6M1- Optimization & Prevision Methods

pdf version of module specification

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pdf version of module specification








Module name:

Optimization and Precision Methods

Scope and form:

To enable the students approach optimization and decision problems and apply computational intelligence techniques to electric power systems integrating renewables
Give students the knowledge to implement forecasting models based on neural networks. Competences on the performance evaluation of forecasting models. Knowledge and practice on available computational applications for building forecasting models.
Give students Knowledge on different forecasting techniques and on the application specificity of forecasting electricity consumption, electricity markets prices and energy production considering renweables.

Duration (weeks; Hours/week):

15 weeks, 4h/week

Type of assessment:

Distributed evaluation with final exam. Work groups of 2-3 students.Practical classes based on the analysis of typical examples and development of field works

Qualified Prerequisites:

Algebra and Numerical Analysis

General module objectives:

The aim of this module is to instill confidence and understanding the basics of the concepts of optimization and forecasting techniques in the view of the renewable energy paradigms.The module spans a wide range of topics.

Topics and short description:

General concepts related to multiple criteria analysis, risk and uncertainty. Decision aid methods.Non-linear programming.Gradient methods.Non-linear programming with constraints.Linear and non-linear DC model for the optimal power flow problem with constraints.Evolutionary algorithms, particle swarm algorithms and other meta-heuristics.Neural Networks.
Forecasting techniques: Regression models. Time series analysis. Forecasting based on computational intelligence techniques. Load forecasting for short, medium and long term. Wind and solar forecasting. Energy market prices forecasting.Competences on building forecasting model based on temporal series analysis.
The optimization problem. Linear and nom linear methods. The simplex  method. Dual  variables. The importance of the dual variables for the resolution of economic problems.Linear sensitivity analysis.
Nonlinear problems.The gradient method.Newton's method.Dynamic programming.Interior poinalgoriths.
Fundamentals of quantitative forecasting. Least square estimates. Smoothing and time series methods.
Regression methods.Comparison and selection of forecasting methods.
Application of the optimization and forecasting methods to the renewable energy paradigms

Learning outcomes:




Optimization methods  related to renewables

Able to comprehend the fundamentals of optimization and forecasting techniques related to power systems

Students must comprehend the fundamentals of optimization and forecasting  techniques related to power systems

The various methods of optimization (linear and non linear functions) and forecasting

Able to use the different methods of optimization and forecasting to the renewables paradigms

Discuss the various optimization and forecasting methods and competence to apply them to the different problems during the planning and operation of power systems with renewable energy

Recommended literature:

Grainger, John J.; Power System Analysis. New York : McGraw Hill, cop. 1994, ISBN: 0-07-113338-0
Power generation, operation, and control ,Allen J. and Wollenberg, Bruce F., New York : John Wiley, cop. 1996, ISBN 0-471-58699-4
Forecasting : methods and applications, Makridakis, Spyros,Wheelwright, Steven C., Hyndman, Rob J. and John Wiley & Sons ed.. ISBN 0-471-53233-9