Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview

Wind energy is a fast evolving field that has attracted a lot of attention and investments in the last dec- ades. Being an increasingly competitive market, it is very important to minimize establishment costs and increase production profits already at the design phase of new wind parks. This paper is based on many years of collaboration with Vattenfall, a leading wind energy developer and wind power operator, and aims at giving an overview of the experience of using Mathematical Optimization in the field. The paper illustrates some of the practical needs defined by energy companies, showing how optimization can help the designers to increase production and reduce costs in the design of offshore parks. In particular, the study gives an overview of the individual phases of designing an offshore windfarm,andsomeoftheoptimizationproblemsinvolved. Finally it goes in depth with three of the most important optimization tasks: turbine location, electrical cable routing and foundation optimization. The paper is concluded with a discussion of future challenges

Optimization of reliable cyclic cable layouts in offshore wind farms

A novel approach for optimizing reliable cable layouts in offshore wind farms is presented. While optimization models traditionally are designed to suggest acyclic cable routes, those developed in this work recognize that cyclic layouts reduce the consequences of cable failures. The models under study take into account that cables cannot cross each other, which, particularly in instances with restrictive cable capacity, can make it attractive to let cables follow a joint trajectory, and visit turbines without connecting to them. A two-layered optimization process is developed. The outer layer is associated with an integer programming problem, which is subject to simultaneous generation of rows and columns representing cable paths. In the inner layer, a problem identifying feasible low cost paths is solved, guided by optimal dual variable values in the continuous relaxation of the former problem. Results from experimental applications to existing wind farms show good promise of the method.publishedVersio

Optimal Wind Farm Cabling

Wind farm cable length has a direct impact on the project cost, reliability and electrical losses. The optimum cable layout results in a lower unit cost of generating electricity offshore. This paper explores three cabling structures: the string structure, ring structures and multi-loop structure on a 3D seabed. The newly proposed multi-loop structure increases reliability and proves to be most economic when the failure rate and mean time to repair (MTTR) of cables are relatively high. Particle swarm optimization (PSO) is used to find the optimal substation location that minimizes the overall cable distance

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Stochastic optimization of offshore wind power plants operation for maximizing energy generation: focusing on the electric power system optimization and cost minimization

It is a reality that nowadays the demand for energy is increasing in the world, but at the same time conventional energy resources are becoming expensive, rare, and more pollutant. All these facts are leading the countries to focus on renewable energy sources, as they are cleaner and abundant. In this field Offshore Wind Power Plants (OWPPs) are becoming increasingly relevant in Europe, and worldwide mainly due to space limitations constraints (possibility of using larger wind turbines), the fact wind speeds are potentially higher and smoother at sea (leading to higher power generation), lower visual and noise impact than onshore farms, and finally because of the progressive saturation of propitious onshore sites. Currently, and because of the environmental and social legislation, OWPPs are forced to be constructed further from shore. There are three main factors to be covered when designing AC electric system of OWPPs: investment cost of components, system efficiency, and system reliability. The present project is focus on the first two key factors, and also considers a stochastic optimization of the electric system of an OWPP operation in order to minimize the investment and operational cost, and in this way try to get the best scenario obtaining the most of benefits. GAMS and MATLAB softwares have been used to implement the main program, obtaining a basic engineering tool to design and take decisions for the electric power system applied to real offshore wind farm

Stepwise investment plan optimization for large scale and multi-zonal transmission system expansion

This paper develops a long term transmission expansion optimization methodology taking the probabilistic nature of generation and demand, spatial aspects of transmission investments and different technologies into account. The developed methodology delivers a stepwise investment plan to achieve the optimal grid expansion for additional transmission capacity between different zones. In this paper, the optimization methodology is applied to the Spanish and French transmission systems for long term optimization of investments in interconnection capacity

Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm

Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.</p