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Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. This approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time. • RTHS is extended to offshore wind turbine monopile structures. • OpenFAST software is modified and integrated into a RTHS framework. • Validated RTHS framework that incorporates the complete system into the simulation. • RTHS enables experimental assessment of offshore wind turbine foundation behavior. • RTHS computational challenges of offshore wind turbine structures are presented. [ABSTRACT FROM AUTHOR]

This paper presents an inerter-integrated wave energy converter (WEC) for vibration mitigation of offshore floating structures. The proposed WEC complies a floating buoy and an upper structure placed over the floating buoy connected by a spring and a ball screw with a rotational mass, which converts linear motion to rotational one, and the ball screw is connected to a rotating generator. As the two bodies move separately, the inertial force proportional to the relative acceleration of the two bodies called inerter is produced. This mechanism, similar to passive vibration reduction systems in automobiles and civil structures, not only produces electric power but also mitigates the vibration induced by the wave disturbances acting on the floating buoy. In this research, its analytical model was developed based on the two-degree-of-freedom dynamic model and the circuit equation of the generator. Numerical studies considering only the heave direction showed that the proposed WEC can achieve electric power generation and vibration mitigation of the upper structure at the same time. Moreover, to verify the efficacy experimentally, a small-scaled prototype was designed, and wave flume testing was conducted, which demonstrated a response reduction of more than 40% compared to the device without the proposed mechanism. • An WEC with inerter is proposed for vibration mitigation of floating structures. • The WEC achieves both vibration suppression and power generation simultaneously. • An analytical model is developed to evaluate the performance numerically. • Wave flume testing is conducted to validate the concept of the proposed WEC. [ABSTRACT FROM AUTHOR]

Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches. The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts, and incorporate changing policy objectives into the constraints to meet policy makers' expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system. The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers. Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made. [Display omitted] • Planning for the expansion of power generation under uncertainties is determined. • Strategies for the expansion of power generation towards renewable energy are optimised. • A novel multi-objective evolutionary algorithm with a constraint handling method is developed. • The impact of multiple renewable energy policies on power generation expansion planning is discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

• This paper presents the Survey of AI Methods for Renewable Energy Forecasting. • Taxonomy of AI for solar and wind renewable energy is presented. • Combinations of ML and metaheuristic methods for renewable energy are discussed. • Prospects of improved metaheuristic algorithms for renewable energy forecasting are presented. The efforts to revolutionize electric power generation and produce clean and sustainable electricity have led to the exploration of renewable energy systems (RES). This form of energy is replenished and cost-effective in terms of production and maintenance. However, RES, such as solar and wind energies, is intermittent; this is one of the drawbacks of its usage. In order to overcome this limitation, studies have been undertaken to forecast its availability and power output. The current trending method of forecasting availability and the power generated by the RES is the artificial intelligence (AI) method. However, with all its potential, traditional AI, such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and many more, does not have it all. Because of this, metaheuristic algorithms are being explored as optimization techniques to increase the performance accuracy of these AI forecasting methods and overcome some challenges of traditional AI models. This study presents an insightful survey of AI (traditional AI and metaheuristic) methods explored to forecast the availability of solar and wind renewable energy systems. A survey of the existing surveyed literature was presented. The taxonomy of the explored AI in RES was formulated, and the theoretical backgrounds of some of the traditional AI algorithms were presented. Also, the various forms of metaheuristic algorithms and the improved versions applied to optimize classical AI methods to forecast RES systems' availability and power output were surveyed. A conceptual framework of the hybrid AI application in RES was formulated. Finally, the survey discussion, insight, challenges of the existing models and future directions were presented. [ABSTRACT FROM AUTHOR]