Several large offshore wind power plants (WPP) are planned in the seas around Europe. VSC-HVDC is a suitable means of integrating such large and distant offshore WPP which need long submarine cable transmission to the onshore grid. Recent trend is to use large wind turbine generators with full scale converters to achieve an optimal operation over a wide speed range. The offshore grid then becomes very much different from the conventional power system grid, in the sense that it is connected to power electronic converters only. A model of the wind power plant with VSC-HVDC connection is developed in PSCAD for time-domain dynamic simulation. This paper presents the modeling and simulation of such a system. A single line to ground fault has been simulated and fault currents for the grounded and ungrounded offshore grid system are obtained through simulation and then compared.
The following report presents the results of the experimental testing of the Exowave wave energy converter (WEC) performed in September 2023 at the Ocean and Coastal Engineering Laboratory at Aalborg University, Denmark. The model tests are performed based on the current design of the WEC35 Exowave floater as part of the project 250 MW bølgekraft I den danske Nordsø før 2030 – fase 1 supported by the Danish Energy Agency under the Energy Technology Development and Demonstration Program (EUDP) contract number 64022-1062.
With increasing demand for renewable energy resources, the development of alternative concepts is still ongoing. The wave energy sector is still in vast development on the way to contribute to the energy production world wide. The present study presents the development of the Exowave wave energy converter made so far. A numerical model has been established supported by wave flume tests performed at Aalborg University during the first phase of the development. Furthermore, a successful open sea demonstration has been performed on 7 meters of water at Blue Accelerator, Belgium, from which the concept has been proven. As part of the ongoing research, verification of the numerical model will be made through experimental testing in the wave tank of Aalborg University, and an open sea demonstration at 14 meters of water depth will be executed off the coast of Hanstholm, Denmark.
Large and remote offshore wind farms (OWFs) usually use voltage source converter (VSC) systems to transmit electrical power to the main network. Submarine high-voltage direct current (HVDC) cables are commonly used as transmission links. As they are liable to insulation breakdown, fault location in the HVDC cables is a major issue in these systems. Exact fault location can significantly reduce the high cost of submarine HVDC cable repair in multi-terminal networks. In this paper, a novel method is presented to find the exact location of the DC faults. The fault location is calculated using extraction of new features from voltage signals of cables' sheaths and a trained artificial neural network (ANN). The results obtained from a simulation of a three-terminal HVDC system in power systems computer-aided design (PSCAD) environment show that the maximum percentage error of the proposed method is less than 1%.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
This report provides a summary on the prospects for developing offshore logistics hubs and their evaluation as opportunities for the maritime and offshore industries. The report’s findings are based on respondents’ answers to surveys and focuses on when offshore logistic hubs will come into operation and their business potential. The data for this report is based on desk research and an analysis of survey responses. The report is produced by the PERISCOPE network.
This report provides an assessment on the prospects for offshore energy hubs. Four use cases have been developed and evaluated by respondents in a survey instrument for their forecasted time horizon to implementation and their business potential as opportunities for the maritime and offshore
industries. The report is produced by the PERISCOPE Group at Aarhus University for the PERISCOPE network.
This report provides an assessment on the prospects for the microgrids at large ports. A survey has been developed to this end and has been evaluated by respondents to crowdsource a forecasted time horizon to implementation and its potential as an opportunity for the maritime and offshore industries. The report is produced by the PERISCOPE Group at Aarhus University for the PERISCOPE network.
This paper considers the problem of determining the optimal vessel fleet to support maintenance operations at an offshore wind farm. We propose a two-stage stochastic programming (SP) model of the problem where the first stage decisions are what vessels to charter. The second stage decisions are how to support maintenance tasks using the chartered vessels from the first stage, given uncertainty in weather conditions and the occurrence of failures. To solve the resulting SP model we perform an ad-hoc Dantzig–Wolfe decomposition where, unlike standard decomposition approaches for SP models, parts of the second stage problem remain in the master problem. The decomposed model is then solved as a matheuristic by apriori generating a subset of the possible extreme points from the Dantzig–Wolfe subproblems. A computational study in three parts is presented. First, we verify the underlying mathematical model by comparing results to leading work from the literature. Then, results from in-sample and out-of-sample stability tests are presented to verify that the matheuristic gives stable results. Finally, we exemplify how the model can help offshore wind farm operators and vessel developers improve their decision making processes.
The EU Green Deal calls for a rapid and efficient green transition. On-going climate change and an increasing need for secure and sustainable energy means ambitious projects and goals are accelerated. To expand and exchange offshore wind energy across North Sea neighbouring countries, the Danish government presented in 2020 the Danish North Sea Energy Island (NSEI) project. This pilot project illustrates the shift from ‘nationally individualistic’ modes of connecting offshore wind energy projects, to supplying a multi-lateral renewable offshore energy grid. The Energy Island project builds on the Hub-and-Spoke (H&S) approach, which introduces a new level of complexity to governing the next generation of offshore wind energy projects. This paper analyses the political motivations for the Danish project and the planning and implementation of the Energy Islands, integrating a combination of collaborative and transboundary governance perspectives. The qualitative analysis is based on a document analysis and a literature review. Findings show how planning for the Danish Energy Island has faced delays and challenges, causing uncertainties about the Island’s capability to support Green Deal goals, as well as a mismatch between political ambitions and practical implementation. The artificial offshore island is currently under reconsideration due to costs and is, as of March 2024, still in its planning phase. This case study on the Danish NSEI serves as an introduction to the general functionalities and development of the Island and defines a Danish Energy Island. Results indicate that the combination of transboundary and collaborative governance structures are necessary as part of a successful implementation of Energy Islands.