Fr. 158.00

Decision-Making in Design, Maintenance, Planning, and Investment of Wind Energy

English · Hardback

Shipping usually within 6 to 7 weeks

Description

Read more

This book demonstrates how decision-making models can be applied to solve specific real-life problems, with a particular emphasis on wind energy. In a step-by-step manner, it guides the reader through decision-making, the formulation of optimization models, and the methods for solving them. After providing an overview of various models for the design of wind farms, it presents an optimization model for deciding which economy (country) to invest in and models for selecting suppliers. A dedicated chapter focuses on different models for monitoring and predictive maintenance for wind turbines (farms) due to the construction of turbine blades and vibration. It shows how combinatorial optimization models can help to make optimal decisions for one-dimensional cutting stock of blanks, their processing, and determining the optimal composition for production. Moreover, it discusses how the energy consumption balance index formed by conventional and renewable sources can be determined and presents a means of identifying the relative share of wind energy consumption among the other renewable sources. 

Operations research professionals, students, and decision-makers alike will find this book to be a valuable resource for tackling real-world challenges and driving sustainable advances in wind energy solutions.

List of contents

General Approaches to Decision-Making.- Decision-Making in Planning and Investing in Wind Energy.- Decision-Making in Wind Farm Design.- Decision-Making in 1D Cutting of Blanks for Wind Turbine Manufacturing and Processing Planning.- Decision-Making in Structural Health Monitoring and Predictive Maintenance of Wind Turbines.- Economics Aspects and Social Impact of Wind Energy: Determining the Cost of Wind Electricity and the Relative Share of Wind Energy Consumption

About the author

Daniela Borissova is a full Professor at the Institute of Information and Communication Technologies at the Bulgarian Academy of Sciences. Her research interests include the area of integer programming, combination optimization, linear & nonlinear programming, multi-criteria optimization, multi-criteria decision analysis, group decision-making, e-learning, and digital transformation. She is also a Professor at the University of Library and Information Technologies, Bulgaria.

Summary

This book demonstrates how decision-making models can be applied to solve specific real-life problems, with a particular emphasis on wind energy. In a step-by-step manner, it guides the reader through decision-making, the formulation of optimization models, and the methods for solving them. After providing an overview of various models for the design of wind farms, it presents an optimization model for deciding which economy (country) to invest in and models for selecting suppliers. A dedicated chapter focuses on different models for monitoring and predictive maintenance for wind turbines (farms) due to the construction of turbine blades and vibration. It shows how combinatorial optimization models can help to make optimal decisions for one-dimensional cutting stock of blanks, their processing, and determining the optimal composition for production. Moreover, it discusses how the energy consumption balance index formed by conventional and renewable sources can be determined and presents a means of identifying the relative share of wind energy consumption among the other renewable sources. 


Operations research professionals, students, and decision-makers alike will find this book to be a valuable resource for tackling real-world challenges and driving sustainable advances in wind energy solutions.

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.