Wind Market Research Articles

Sub-technology market share strongly affects critical material constraints in power system transitions

Critical material constraints may limit and guide power system transitions towards net zero. Pathways to mitigate these constraints need to be evaluated and pursued to ensure successful transition. Here, we explore the material constraint mitigation pathways from the perspective of adjusting power generation sub-technology market shares, analysing nineteen critical materials that may cause material constraints. We find that the power generation system transition within China’s carbon neutrality scenario results in 52.2 megatonnes of cumulative material demand by 2060, approximately 2.7 times that of the business-as-usual scenario. Solar photovoltaic and wind power sub-technology market shares have the greatest impact on critical material demand. As progressive thin-film solar photovoltaic sub-technologies gain market share, the demand for gallium from solar photovoltaic may increase 56-fold. Material constraints are likely to occur for gallium, terbium, germanium, tellurium, indium, uranium and copper. The importance value is determined by the ratio of power sector to all-sector material demand; the importance value of gallium will increase to 50% due to increases in gallium arsenide and permanent magnet sub-technologies. Our study findings show that sub-technology market shares need to be considered when evaluating future material constraints. Our results provide insights for future research investigating mitigation pathways.

Impacts of Factors of Production and Public Policy on the US and China Offshore Wind Industries Analysis

In 2023, global carbon dioxide emissions climbed to a staggering new high of 37.4 metric tons, a grim situation driven primarily by the massive consumption of fossil fuels, particularly coal. In this context, the offshore wind power industry has emerged as a key part of tackling climate change and emission reduction challenges. This paper provides an in-depth analysis of how productive factors and public policy work together to shape the development of the offshore wind industry in the United States and China, highlighting the importance of land availability, labor market quality and size, and capital investment flows in shaping the future of the industry. At the same time, the paper also carefully examines how the government can effectively attract and promote investors to participate in offshore wind power projects through a series of public policies, such as tax incentives, subsidies and other financial incentives. The results show that China has a clear lead in the offshore wind market compared to the United States. This paper aims to make insightful suggestions for the sustainable development of offshore wind power industry in both countries through comparative analysis of the advantages and disadvantages of the two countries.

Benchmarking of liquid air energy storage with and without added firing against batteries and gas-fired power plants

Rapid deployment of variable renewables is broadly viewed as the primary mechanism for reducing the carbon intensity of electricity systems, motivating the development and implementation of technologies such as liquid air energy storage (LAES). This study investigates the thermodynamic performance of LAES and evaluates the improvements attained through natural gas added firing (AF), determining the coefficients of structural bonds to identify design optimization priorities. Subsequently, LAES configurations are benchmarked against mid-scale natural gas combined cycles (NGCC) and batteries through an economic optimization based on electricity price profiles from different European markets for the year 2023. At a CO2 emissions tax of 100 €/ton, results reveal that the LAES-AF concept located in Germany could compete with an NGCC plant if natural gas prices exceed 8.3 €/GJ, while for prices above 9.1 €/GJ batteries (with cost reduction projected to 2030) were the preferred option. In this range, the LAES-AF plant reduced fuel consumption by 24.0–27.2 % relative to the NGCC benchmark, which would reduce emissions when using natural gas or costs when using costlier carbon-neutral fuels. The standalone LAES plant did not prove economically viable in any location, being outperformed by batteries and requiring a CO2 price above 300 €/ton to compete with LAES-AF. As electricity price volatility keeps rising with growing wind and solar market shares, the economic attractiveness of LAES should improve as longer periods of near-zero electricity prices negate its lower round-trip efficiency and capitalize on its lower cost for energy storage. Therefore, further work on the development of innovative LAES cycles is recommended.

Holistic opportunistic maintenance scheduling and routing for offshore wind farms

Despite the high growth of the offshore wind market, the economic benefits of wind energy sources are still being undermined by its high operation and maintenance expenses. On the one hand, high maintenance expenses are a direct result of offshore-specific challenges such as complex ocean meteorology, varying vessel accessibility, and shifting transportation requirements. Besides, component degradation and weather conditions largely limit the ability of turbine operators to estimate a workable maintenance time window. This study aims to develop a holistic opportunistic maintenance strategy to address the practical challenges of offshore wind farms. The proposed strategy starts by deriving the preventive maintenance interval of each turbine component based on its degradation trend and predictable cost rates. Then, each turbine is maintained in groups based on the maintenance opportunities arising from preventive maintenance, unexpected failures, and cable damage. Following that, given weather condition forecasts of key parameters (wind speed and wave height), the proposed strategy optimizes the daily allocations and vessel routes to maintain turbines in a timely and cost-effective manner. Finally, experimental results based on real-world data from an actual offshore wind farm demonstrate that the proposed strategy outperforms several universal maintenance strategies in key metrics. Compared to the simple, widely employed, and easy-to-implement strategies, it can help reduce the total cost by 85.9 %, 65.9 %, and 41.6 %, respectively. This work helps turbine operators implement comprehensive and cost-effective maintenance schemes, which can lead to tariff duction and wind farm promotion benefits in the long term.

Beyond Jack-Ups: A Moonshot for Future Offshore Wind Turbine Installation Vessels for an Uncertain Market

This paper addresses the growing offshore wind market's demand for larger turbines in deeper waters by highlighting limitations in existing installation solutions and proposing a new concept with a floating monohull, named Moonshot, which will thus be different than traditional jack-up or semi-submersible crane installation vessel options. This paper discusses the design process, which combines Ulstein Rotterdam’s Controlled Innovation and Blended Design to develop the concept. This process is used to explore various market scenarios to determine optimal vessel parameters. Results demonstrate how optimizing for financial performance or seakeeping behavior impacts the design. Moonshot's initial parameters are established, and its performance is compared to existing installation solutions.

Wind Power Integration and Challenges in Low Wind Zones. A Study Case: Albania

High wind performance systems are influenced by many factors such as site wind resources and configuration, technical wind turbine features and many financial conditions. Scenario planning and modelling activities often focus on restricted parameters and numbers to justify wind power plant performance. To better understand possible pathways to scaling up the distributed wind market in Albania, a deep and multidimensional calculations based on Monte Carlo analysis, using RETScreen and wind JEDI model, to assess socio-economic impact as a function of turbine output power, operating and maintenance cost and many other financial inputs by testing different WT (i.e., VESTAS, GAMESA, W2E and NORDEX) with rated power from 3.45 MW up to 4.5MW applied on LCOE, NPV, SPP, equity payback, B-C, after-tax IRR on equity and effects of GHG credits extended at a sensitivity range of ±35% is scientifically performed. From the simulation results LCOE reaches a minimal value of €43.48/MWh, if the debt rate is 99 % and a debt interest rate of 5.0%, a TotCapEx of €828/MW (-35 % less expenditures) indexed as the best scenario. For the base case scenario LCOE results €62.79/MWh, when applying a debt rate of 80% and a TotCapEx of (€1274/MW), while in the worst-case scenario LCOE impart a maximal value of €87.63/MWh if a TotCapEx of €1720/MW (+35 % more expenditures) and a share of 52 % debt rate is applied. Local annual economic impact (m€) during construction period and operating period evaluated in the wind JEDI model result around m€ 89.92 and m€ 23.54, respectively. As a conclusion, wind power plants (WPP), installed in low wind zones (Albania and many other EU countries) would be of interest if an electricity export rate of 110€/MWh, and a GHG credit rate of €50/tCO2 were accepted.

Two-stage stochastic optimization of virtual power plant with wind power and uncertain demand

ABSTRACT The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively.

Development of a floating Vertical Axis Wind Turbine for the Mediterranean Sea

Differently from Horizontal Axis Wind Turbines (HAWTs), which are the reference technologies in the wind market for their reliability and maturity, Vertical Axis Wind Turbine (VAWT) applications are related to small-scale contexts, such as providing electricity in isolated areas or urban settings. Consequently, the capacity of VAWTs results significantly lower than the order of megawatts and does not exceed a few tens of kilowatts. A promising field of application for VAWTs is the floating offshore: among the main advantages there are an increased static stability, by placing the rotor-nacelle assembly (RNA) at the base of the VAWT and reduced operational and maintenance (O&M) costs. Moreover, the different wake dynamics allows to reduce the aerodynamic losses, allowing closer turbine installations. However, to be competitive with floating HAWTs, it is necessary to have numerical models for the analysis and simulation of multi-megawatt VAWTs. This paper aims to introduce a time domain model of a floating vertical axis wind turbine, developed within the Matlab-Simscape environment. The model comprises an aerodynamics module, based on Double Multiple Stream Tube theory while hydrodynamics is modelled using WEC-Sim. A case study, involving a Darrieus H-rotor VAWT tested in the Mediterranean Sea and supported by a semi-sub foundation, the OC4-DeepCwind, is introduced. The results obtained are compared with those from QBlade, an software developed by TU Berlin, demonstrating a good agreement between the two codes.

Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles

This research introduces a stochastic scheduling approach that incorporates risk constraints for an energy hub (EH), considering uncertainties related to renewable generation and load demands. The proposed method utilizes the Conditional Value at Risk (CVaR) technique to assess and quantify risks. By striking a balance between reducing operational and emissions costs and increasing risk aversion, the approach presents a trade-off. The EH comprises various components such as a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP), and plug-in electric vehicles (PEVs). Uncertain variables encompass factors such as wind speed, solar irradiation, different demands, and market prices. To optimize profits and enhance the consumption curve, demand response programs (DRPs) for electrical, thermal, and cooling demands are implemented. To address the uncertainties associated with input random variables, the efficient k-means data clustering method is employed. A new slime mold algorithm, based on coughing and chaos theory, has been proposed to enhance the problem's solution. The algorithm incorporates innovative operators to improve its capabilities. By utilizing the coughing mechanism and chaos theory, the algorithm explores the solution space more effectively, resulting in improved outcomes for the problem at hand. The results demonstrate significant flexibility in EH management and are extensively discussed. Simulation results indicate that integrating PEVs, FCPP, and DRPs can lead to reductions of 2 %, 7 %, and 11 % in the EH's operating costs, respectively. Furthermore, considering PEVs, FCPP, and DRPs can improve the EH's risk cost by 1.98 %, 6.7 %, and 10.5 %, respectively. Based on the numerical results, in Case 4 led to a remarkable 12.65 % reduction in operational costs while simultaneously achieving a 15.43 % decrease in emission costs, showcasing the effectiveness of the proposed approach in optimizing energy management in an energy hub system.

Numerical investigation of a new hybrid floating wind turbine concept

In today’s energy scenario dominated by the need to develop new methods of generating electricity, energy-intensive infrastructures are a promising solution towards energy self-sufficiency and environmental sustainability. Floating offshore wind turbine represents one of the major solution to exploit renewable energies. Currently, the increase of offshore wind market opens up the possibility of integrating different technologies to take advantage of marine energy potential, in particular wave energy.
 This work presents a new wave-wind hybrid floating platform with three Oscillating Water Columns (OWC) integrated in a floating offshore wind spar buoy. The design methodology is described showing the size of geometric characteristics of OWCs and the size of the energy conversion system. The hybrid system is investigated by a time-domain model that integrates the wind turbine model, the hydrodynamics of the floater, the thermodynamics of the OWC air chambers, and the damping effect induced by the OWC air turbine. The thermo-aero-hydro coupled numerical framework is described to highlight the implementation of the thermodynamic model in WEC-Sim/MATLAB environment. Specifically, the water column dynamics are solved as pistonrigid body representation, enabling the time-domain analysis of the OWC dynamic behaviour. Impulse turbines are considered as OWC Power Take-Offs (PTO). The resource scenario of Mediterranean Sea is considered as case study, in terms of both wind and wave conditions. The analysis focuses on the power extraction capabilities of the OWCs and on the impact of OWCs on the productivity of the wind turbine. Further developments on multi-objective design tools and on optimal PTO control design aiming power maximisation could be conducted for hybrid energy platforms based on this established numerical framework.

Pricing and hedging wind power prediction risk with binary option contracts

In markets with a high proportion of wind generation, high wind outputs tend to induce low market prices and, alternatively, high prices often occur under low wind output conditions. Wind producer revenues are affected adversely in both situations. Whilst it is not possible to directly hedge revenues, it is possible to hedge wind speed with weather insurance and market prices with forward derivatives. Thus combined hedges are offered to the wind producers through bilateral arrangements and as a consequence, the risk managers of wind assets need to be able to forecast fair prices for them. We formulate these hedges as binary option contracts on the combined uncertainties of wind speed and market price and provide a new analysis, based upon machine learning classification, to forecast fair prices for such hedges. The proposed forecasting model achieves a classification accuracy of 88 percent and could therefore aid the wind producers in their negotiations with the hedge providers. Furthermore, in a realistic example, we find that the predicted costs of such hedges are quite affordable and should therefore become more widely adopted by the insurers and wind generators.

Efficient energy management framework for enhancing the techno-economic-environmental performance of grid-connected microgrids under uncertain conditions

Due to the limited availability of fossil fuels and environmental concerns over greenhouse gas emissions, micro-grid systems (MGSs) have become crucial for integrating a high penetration of renewables. Nonetheless, the complexity of the operation of MGS increases in the actual operating process due to the irregular nature of renewables, electrical loads (load demands), and uncertain market prices. The core aim of this paper is to suggest a bi-objective optimization model for managing energy efficiently in a grid-connected MG while considering the unsteadiness of electrical load demands, market prices, and renewable output powers. Also, the suggested model tries to improve the MG’s scheduling while considering the wind turbine, photovoltaic, loads, and market pricing uncertainty. MG operators can develop strategies that balance cost savings with risk management and ensure long-term sustainability by considering the uncertainty of the different generation/demand resources and markets. The suggested optimization model includes two distinct objective functions – minimizing the 24-hour horizon’s total projected cost of the grid-connected MG and reducing MG emissions during the considered period. Further, a simultaneous reduction of the overall operating cost and the pollutant emission MG over 24 h is presented. The general algebraic modeling system (GAMS) has been used to solve the presented multi-objective problem. The findings clearly demonstrate the efficacy of the suggested energy management (EM) methodology for improving the techno-economic-environmental performance of grid-connected MGs under uncertain situations.

The offshore prefabrication and semi-wet towing of a bucket foundation for offshore wind turbines

In recent years, the bucket foundation as a new form of supporting structure of offshore wind turbines (OWTs) has attracted increasing attention with the rapid development of offshore wind power. A synthetical prefabrication and transportation (P&T) technique is proposed in this paper to enable a broader application of bucket foundations efficiently and economically in the offshore wind market. The novel P&T technology consists of two key auxiliary pieces of equipment, i.e., an offshore prefabrication platform (OPP) and a U–K barge. The process of the offshore prefabrication is elaborated, and the requirement that need to be met for the monopod-bucket foundation with subdivisions (MBFS) to achieve offshore prefabrication utilizing the OPP is analyzed. Then, the concept of the semi-wet towing with a U–K barge is proposed, which can provide sufficient buoyancy and stability to ensure the safety of the MBFS during the towing operation. To investigate the seakeeping of the combined semi-wet system in moored and towing scenarios, a series of experiments were carried out to prove the reliability of the U–K barge design. Sensitivity analysis regarding towing speed, wave height, and wave period for the disintegration risk is performed, and three methods are proposed to avoid the disintegration of the U–K barge and MBFS at extreme sea cases. The results show that the proposed offshore prefabrication is feasible, and it is suggested that as the MBFS floats up, the water depth of the construction site should not be less than 6 m. In addition, it is proved that the combined semi-wet towing system composed of the U–K barge and the MBFS has an exceptional seakeeping capability with regard to the pitching angles, accelerations, and towing resistances, and that the wave height and the towing speed have greater effects on the disintegration risk than the wave period.

Deep reinforcement learning for wind and energy storage coordination in wholesale energy and ancillary service markets

Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

What drives the change of capacity factor of wind turbine in the United States?

The capacity factor (CF) is a vital parameter used to quantify the performance and efficiency of a wind turbine. An increase in generation efficiency leads to higher wind power production, improving the economics within the growing global wind market. In this research, we use a data-driven statistical method to explore the contributions of the three main drivers of CF change: turbine aging, changes in wind speed, and technological improvements. We find that for the group of old turbines (operated before 2008) with an unchanging technical condition, wind increases contributed ∼10% to the increasing CF on average from 2010 to 2020. For new turbines (built from 2008 to 2020), technological improvements had a strong positive effect on CF from 2015 to 2020, exceeding the effect of wind increases and offsetting the effects of aging. On average, rising wind speeds increased CF by ∼5% per year, while technological improvements increased it by ∼12%. As the installed capacity of wind turbines grew, technological progress became the dominant driver in CF increase. However, poor site selection potentially compromised the positive effect on CF afforded by technology changes early in the decade.

Multi-objective long-term expansion planning of electric vehicle infrastructures integrated with wind and solar units under the uncertain environment considering demand side management: A real test case

The penetration of electric vehicles and renewable resources as highly influenced components in global warming and greenhouse gasses emission has increased in recent years. This paper determines the optimal allocation of battery charging and swapping stations integrated with wind and solar units to minimize total cost and voltage deviation based on a long-term expansion planning. According to the presence of uncertain parameters and in order to simulate realistic conditions, a scenario generation/reduction approach is also performed to model the fluctuations of wind speed, solar radiation, electrical loads, incoming vehicles and market price. As well, a time-of-use strategy is implemented to show the impact of flexible loads on objective functions. It is noteworthy that simulations are implemented on a real distribution network with actual geographical data. The results for the proposed model in the deterministic state show an improvement equal to 283.33% for the voltage deviation, while a profit equal to 2.2 million dollars per model lifetime is achieved. The uncertainties deteriorate the voltage deviation by 12.12%, in return, the profit is increased by 8.63%. It is also concluded that responsive loads increase the profit and enhance the voltage deviation by 7.94% and 6.3%, respectively.

On a road map for technology qualification, innovation and cost reduction in floating offshore wind: learning from Hywind and Norwegian approach

The main aim of this paper is to synthetize and to learn from the existing road maps and the Norwegian approach on the floating offshore wind. It targets to identify main priorities, key challenges, and technological qualification for the floating offshore wind development. The Norwegian approach is evaluated in the view of the key success factors of the offshore wind market highlighted by the World Bank. One particularity of the national Norwegian offshore landscape is the usage of the Norwegian floating wind for decarbonification and “greenification” of the oil and gas industry. Learning from the Hywind projects and Norwegian approaches and experiences for the floating offshore wind on the national and international level has potential in contributing to enhance the road maps for floating offshore wind in terms of technology qualification, innovation and cost reduction. This article offers a handful of key take away points for industry and decision makers about development of the floating offshore wind.

Tool for optimization of sale and storage of energy in wind farms

In this work we address the problem of energy management in a wind farm supported by an Energy Storage System (ESS) that operates in an electricity market with six intraday sessions and with penalty policies for imbalances between commitments and the energy really injected. We face it through a cascade of model predictive controllers that also require the design of predictors for wind and electricity market price forecasts. The master controller is executed synchronously with the market sessions and decides the commitments. The slave controller is executed each hour and decides the energy that should be sold to minimize the economical penalties if the commitment is not achievable. Finally, a real-time controller decides how to manage the energy storage in the ESS to sell the desired energy when possible. We use historical real data for the design and validation of the approach and show its benefits. The results show that the cascade structure helps to adequately adapt the energy committed in the intraday market. We also obtain the necessary prices on batteries so that their use is profitable.

Potential of wind energy and economic assessment in Egypt considering optimal hub height by equilibrium optimizer

In Egypt, the wind market increases quickly to make it one of the top countries in the Middle East. This study discusses the viability of wind resources and the economic assessment for four locations in Egypt: Ras El-Hekma, Farafra, Nuweiba, and Aswan through two stages. In the first stage, the optimal hub height for some wind turbines has been calculated by using Equilibrium Optimizer (EO) algorithm to achieve maximum wind energy with overall minimum cost. The second stage, the economic assessment has been evaluated by using such turbines to calculate the cost of energy (COE) compared to the global and Egyptian production costs of wind energy. Developed MATLAB programs are applied for statistical analysis of wind data. The results have shown that Ras El-Hekma’s average wind speed is higher than other sites and its wind energy potential is the best. Moreover, the economic assessment for selected locations turns out that Ras El-Hekma by using EWT-DW61/22 turbine has the lowest COE.

Offshore Wind Installation Vessels

To reach the sustainable targets and to reduce the investment risk, there is a need for more certainty and predictability regarding the requirements of the future offshore wind installation vessels. To capture the fast changing offshore wind market and its impact on vessel requirements, this paper generates scenarios using Epoch-era analysis. A parametric model is created to determine the performance of a set of vessels defined by their length, beam, depth, crane capacity, speed, and transport strategy in the different scenarios. The strength of combining Epoch-era analysis and parametric modelling is that the performance criteria and input variables can be tailor-made to a stakeholders strategy resulting in robust input variables for the concept vessel design.