Wind Capacity Research Articles

Role of green hydrogen in the decarbonization of the energy system in Thailand

High dependence on fossil fuels to meet the energy demand is the major source of greenhouse gas (GHG) emissions in Thailand. Decarbonization of the energy system to achieve the pledged climate targets is a challenging task for Thailand. The role of green hydrogen and hydrogen-based technologies in Thailand's energy transition needs to be explored. A bottom-up energy system model developed using AIM/Enduse framework has been used to assess the energy system transformation required with the focus on the role of hydrogen in achieving net zero GHG emissions. A business-as-usual (BAU) scenario and two net zero emissions (NZE) scenarios with low and high-hydrogen use have been assessed. This study finds that GHG emission reduction in the power sector and the transport sector would be the major contributor in achieving net zero emission in 2050 compared to the projected emissions in the BAU level. Energy efficiency improvement in the demand side would lower the electricity consumption compared to the BAU level despite the higher electrification of the end-use technologies. However, the demand to produce green hydrogen in the NZE scenarios would increase the electricity generation in 2050 b y up to 54 % compared to the BAU level. The capacity of electrolyser would reach 50 GW by 2050 in the high hydrogen use scenario. This would require additional solar and wind power capacity of 107 GW and 43 GW, respectively. In addition, it would require 50 million cubic meters of water. Cumulative investments in electrolysers to produce green hydrogen and renewable based power generation during 2031–2050 would be USD 122 billion.

Price Responsiveness of Solar and Wind Capacity Demands

Price Responsiveness of Solar and Wind Capacity Demands

Hybrid Renewable Energy Microgrids: A Genetic Algorithm Approach to System Design

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.

Transition Energétique - Remplacement d’une Microcentrale Diesel par un Système Renouvelable Hybride : Etude de cas.

This paper aims to study the replacement of a Diesel Generator based plant by a hybrid renewable system. To overcome both source and load fluctuations, a battery-based storage system is used. The renewable energy resources available in southern Algeria is considerable. Actually, this region has one of the largest wind capacity in the country with wind speeds up to 4 to 6 m/sec, the wind energy density is 280-Watt/m2/ day. Meanwhile, the solar potential is characterized by an average duration of sunshine above 11 hours/day and the energy they can provide for a large-scale industry can change the development of the region and of the whole country.Genetic algorithms under optimtool is used to size hybrid systems. The resulting levelized cost of electricity (LPSP) of $0.262/kWh, is competitive and economical if a large-scale electricity production is undertaken. The loss of power supply probability or customer dissatisfaction rate (LPSP) of 1.2545 %, which is within standard limits.

Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization

With rapid development of data center industry, achieving low energy consumption and costs become important. How to provide an optimal configuration on renewable distributed energy systems combining data centers is challenging. This work discussed possible hybrid configurations by combining diesel, photovoltaic, wind, and battery capacity in data center. For each mode, renewable penetration (RP) and the Levelized Cost of Electricity (LCOE) were used to evaluate the performance of system. A multi-objective optimization analysis of the case is performed based on the entropy weighted-TOPSIS method, genetic algorithm, and NSGA-II aiming to obtain both a high RP and a low LCOE. Then, comparative research on optimal power configuration with conventional or renewable energy was conducted. The results show that it is recommended to take a high-rated PV power. When wind power is taken, it is only meaningful when combined with PV power system. For the hybrid renewable power system without an energy storage unit, it's easy to realize a lower LCOE compared to Diesel mode, and its realizable maximum RP is 28.31 %. When battery energy storage is taken in the hybrid system, six optimal configurations are recommended which can achieve the lowest cost of 0.21 $/kWh and the highest RP of 44.5 %.

Techno-economic analysis and optimal sizing of hybrid PV-wind systems for hydrogen production by PEM electrolysis in California and Northern Germany

The aim of this study is to assess the economic potentials of power and hydrogen generation via solar and wind energy resources at locations in Northern Germany and California, both of these (geographical) regions being pioneers in terms of a sustainable energy transition. Based on extensive research and electrolyzer manufacturer interviews, green hydrogen generation plants are modeled in a MATLAB® environment. All relevant inputs and outputs of the systems studied (wind power plants, photovoltaic power plants, and polymer electrolyte membrane electrolyzers, among others) are considered for different locations and framework conditions. In contrast to the existing literature, special attention is paid to the part-load behavior of electrolysis systems, which becomes particularly relevant in their interplay with volatile renewables. Metrics such as the levelized costs of electricity (LCOE) of the wind and photovoltaic power plants and the resulting levelized costs of hydrogen (LCOHy) are determined. With the help of the developed model, dimensioning of a whole system's components can be determined for different locations. A case study is conducted for a Northern German site and another one for a site in the Californian Mojave Desert. Both the optimal dimensioning of the renewable energy plants and the ratio of installed wind and photovoltaic power plant capacity are strongly location-dependent. In Northern Germany, for example, lower LCOHy can be generated at higher wind power plant capacity shares and, in the Mojave Desert, be produced at higher photovoltaic power plant capacity shares. In general, with larger plants and correspondingly larger polymer electrolyte membrane electrolyzers, LCOHy are lower due to scaling effects. Following this dimensioning recommendation, the LCOHy vary between 4.5 €/kg and 5.2 €/kg in the Northern German case study and between 4.6 US$/kg and 5.3 US$/kg in the Californian one. With costs of 1–2 €/kg, gray hydrogen is still economically superior.

Hydrogen energy storage requirements for solar and wind energy production to account for long-term variability

Wind and solar energy production are plagued, in addition to short-term variability, by significant seasonal variability. The aim of this work is to show the variability of wind and solar energy production, and to compute the hydrogen energy storage needed to address this variability while supplying a stable grid. This is the very first work where the extent of the hydrogen energy storage needed to make stable a grid only supplied by wind and solar energy in Australia is computed. An annual grid demand of 570 TWh in Australia may require 327 GW of installed capacity of wind and solar, 809 TWh of non-dispatchable electricity, and 48 TWh of hydrogen energy storage. While the theoretical maximum power of the electrolysers is 267 GW, the average power is only 46 GW, permitting huge savings in electrolysers capacity adopting a high efficiency energy storage such as flow batteries in between wind and solar producers and electrolysers. The power of the hydrogen generators is 167 GW. The outcome aligns seamlessly with the recent evaluation conducted by the Royal Society regarding the energy storage requirements in the United Kingdom by 2050 to fulfil the net zero commitment in a 570 TWh annually stable grid, with hydrogen similarly serving as the primary source.

Solutions for recycling emerging wind turbine blade waste in China are not yet effective

Wind power supply chains are evolving as markets expand to reach climate goals. With the largest installed wind power capacity globally, China must deal with increasing composite turbine waste and anticipate its associated costs. Here we predict the quantity and composition of wind turbine blade waste based on historic deployment. A high-resolution database containing 14 turbine capacities (150–5500 kilowatts) was compiled based on 104 turbine models. The environmental and financial costs of waste treatment options were evaluated using a bottom-up approach. Based on current installations and future projections, 7.7 to 23.1 million tonnes of blade waste will be generated in China by 2050. Technologies exist to recycle glass fibre from blade waste, but these solutions vary in level of maturity and are not always commercially available, cost-competitive, or environmentally sustainable. Our findings can inform decision-makers in governments and industry on the pathways to carbon neutrality.

Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway

In this article, we investigate the mismatch of renewable electricity production to demand and how flexibility options enabling spatial and temporal smoothing can reduce risks of variability. As a case study we pick a simplified (partial) 2-region representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein–Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission are available. Our analysis shows that simulated loss values are reduced significantly by cooperation between regions and either mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus reduces likelihoods of inadequacy of renewable power systems.

Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations

Urban wind power is an appealing alternative for electricity supply. Comprehensive urban wind resource assessment is a prerequisite for cost-efficient deployment of wind turbines. Based on observations from multiple instruments, including a Doppler lidar (light detection and ranging) system, a microwave radiometer, and a cup anemometer/wind vane set, this study investigates the temporal (interannual, seasonal, and diurnal) variations of wind resources at various heights in a metropolitan city, Hong Kong. The long-term statistical distributions of wind speed, wind shear coefficient, and air density are analyzed, and their variations with height, wind direction, season, and time of day are thoroughly examined. Moreover, the wind power density and capacity factor of prototype wind turbines are estimated. It is observed that the statistical distributions of wind speed and air density are best represented by the Kappa and Normal-Weibull mixture distributions, respectively. There is a significant diurnal variation of wind shear coefficient, from 0.1 in the daytime to 0.4 at night. Moreover, a moderate potential for wind energy harvesting with wind power density of 110 W/m2 is found at the height of 120 m. This is the first known study that jointly uses lidar, microwave radiometer, and anemometer/wind vane for urban wind resource assessment. These instruments enable the investigation of the temporal variations of wind shear coefficient, air density, and wind power density at multiple heights, which were rarely examined in urban wind energy studies before.

Five grand challenges of offshore wind financing in the United States

Offshore wind energy has the potential to play a critical role in fostering a renewable energy transformation in the United States. This owes to its massive technical potential, strategic location near densely populated coastlines, and—relative to onshore wind and solar—high capacity factors and consistent production. The Biden Administration's target to build 30 GW of offshore wind capacity by 2030 (from 0.04 GW today) requires the creation and swift development of a new industry that interlinks the wind and power industries with the maritime sector. Critical to its success is financing. While financial capital is abundant, deploying it for offshore wind faces major challenges. We identify and describe five grand challenges affecting offshore wind finance in the U.S. Failing to address these challenges may put deployment targets at risk. The challenges include (1) Early years financing: navigating the complexities, timing mismatches, and high costs of projects in the development phase; (2) Policy support for project financial solvency: addressing the uncertainty and systematic transfers of tax credits away from offshore wind, characteristic of the U.S. Investment Tax Credit; (3) Workforce development: building a skilled workforce for an emerging market; (4) Transmission and integration barriers: upgrading the power grid to reliably support large scale offshore wind integration; and (5) Floating wind development: financing the development and scale-up of floating offshore wind technologies. The second challenge has already been solved to a large extent by the Inflation Reduction Act.

Farm size, spatial externalities, and wind energy development

AbstractThe global push for renewable energy must overcome the local challenge of convincing neighboring landowners to lease their properties for wind power. Is this challenge more or less pronounced in rural landscapes with small landholdings? Our theoretical model combines ideas from literatures on the commons, anticommons, and spatial externalities to explain conditions when small landholdings could promote or inhibit voluntary leasing. Empirically, we estimate the effects of landholding size and landscape fragmentation on wind farm uptake across rural areas of the United States over the past 20 years. Evidence from three spatial levels of analysis (counties, square‐mile sections, and individual parcels) indicates that areas with more landowners have less installed wind capacity after controlling for windiness, access to transmission lines, and other relevant factors that vary across and within counties. The findings imply that fragmented ownership, which is an overlooked factor in studies of the feasibility of decarbonization, will pose an impediment to future wind expansion on private land as remaining areas without wind development become disproportionately fragmented.

Investigating a Renewable-Resource-Targeting Mobile Aquaculture System Using Route Optimization Based on Optimal Foraging Theory

Aquaculture systems require careful consideration of location, which determines water conditions, pollution impacts, and hazardous conditions. Mobility may be able to address these factors while also supporting the targeting of renewable energy sources such as wind, wave, and solar power throughout the year. In this paper, a purpose-built mobile aquaculture ship is identified and modeled with a combination of renewable energy harvesting capabilities as a case study with the objective of assessing the potential benefits of targeting high renewable energy potentials to power aquaculture operations. A route optimization algorithm is created and tuned to simulate the mobility of the aquaculture platform and cost-basis comparisons are made to a stationary system. The small spatial variability in renewable energy potential when combining multiple resources significantly limits the benefits of a mobile, renewable-targeting aquaculture system. On the other hand, the consistent energy harvest from a blend of renewable energy types (13 kW installed wind capacity, 661 m2 installed solar, and 1 m characteristic width wave-energy converter) suggests that the potential benefits of a mobile platform for offshore aquaculture (mitigation of environmental and social concerns, any potential positive impact on yields, hazard avoidance, etc.) can likely be pursued without significant increases in energy harvester costs.

Nigerian wind energy status

This review paper evaluates the current status of the Nigeria wind energy sector to identify the available Wind Energy Potential, Installed Wind Energy Capacity and Renewable Energy Policies in a bid to provide accurate information to aid the exploration and exploitation of wind energy across Nigeria. Consequently, the huge expanse between the wind energy Potential across Cities in Nigeria, the poor numbers of installed wind capacity and Nigeria’s Renewable Energy policies show low interest in Wind Energy in comparison to other RE sources. All this calls for further investment in the Nigerian wind energy sector

Quantifying power system flexibility for the energy transition in Colombia

Multiple factors drive the energy transition toward power systems based on renewable energies. The use of renewable energies brings new challenges for operators since they are difficult to predict. Power system flexibility is the ability to handle differences between supply and load and can be quantified to measure the effects of renewable energies on power systems. Colombia expects to triple the current solar and wind power capacity by 2030; therefore, it is essential to evaluate the flexibility of the Colombian power. This paper presents a pioneering analysis in the local context, filling a local research gap, by quantifying the flexibility requirements of the Colombian power system based on changes in the net load and its ramps and considering wind and solar generation scenarios and load projections. This analysis has not been conducted locally and can be used by policymakers to determine the country’s energy transition path. The results show that renewable power impacts conventional generators. Hydro generation handles high ramp deviations well under normal conditions but faces challenges during low hydrology periods. The Colombian power system is projected to be flexible in 2030 but less so in 2050 due to overproduction and higher net load ramps.

Optimization of wind and solar energy storage system capacity configuration based on the Parzen window estimation method

Compressed air energy storage (CAES) effectively reduces wind and solar power curtailment due to randomness. However, inaccurate daily data and improper storage capacity configuration impact CAES development. This study uses the Parzen window estimation method to extract features from historical data, obtaining distributions of typical weekly wind power, solar power, and load. These distributions are compared to Weibull and Beta distributions. The wind–solar energy storage system's capacity configuration is optimized using a genetic algorithm to maximize profit. Different methods are compared in island/grid-connected modes using evaluation metrics to verify the accuracy of the Parzen window estimation method. The results show that it surpasses parameter estimation for real-time series-based configuration. Under grid-connected mode, rated power configurations are 1107 MW for wind, 346 MW for solar, and 290 MW for CAES. The CAES system has a rated capacity of 2320 MW·h, meeting average hourly power demand of 699.26 MW. It saves $6.55 million per week in electricity costs, with a maximum weekly profit of $0.61 million. Payback period for system investment is 5.6 years, excluding penalty costs.

Topological Design Optimization Approach for Winding Oil Flow Paths in Oil-Natural Air-Natural Transformers Based on a Fluidic-Thermal Coupled Model

Power transformers are a critical component in transmission and distribution systems. To limit the temperature rise to extend the life-cycle while operating with enough reliability and controllability of a transformer, the design of the cooling system has attracted extensive research attention. In this paper, a topology optimization (TO) methodology is presented for designing the coolant flow paths in a prototype oil-natural air-natural (ONAN) power transformer to maximize the winding heat dissipation capacity without any compromise on the flow resistance. First, the thermal and fluid flow characteristics in both the winding and the radiator of the prototype transformer are numerically investigated through a fluidic-thermal coupled field approach. The preliminary cooling effectiveness of the oil circulation system is initialized. Second, a material-density-based TO scheme is employed to realize the optimal design of the oil baffle structures. In the TO optimization, artificial Darcy frictional forces are applied to the solidified cells to block their internal flows in the fluidic calculations, while the thermal conduction coefficients in those cells are enhanced to separate the baffles from the flowing oil in thermal analyses. Finally, the fluidic and thermal coupled fields of the prototype transformer winding region with structurally optimized oil baffles are numerically investigated to validate the TO effectiveness.

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

AbstractWind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

Cheaper solar, cleaner grid?

Although wind capacity cost is not expected to experience large changes in the near future, solar capacity cost is projected to drop by 45% in roughly thirty years. Using an analytical model and a dynamic structural simulation model, we show that wind energy capacity investment first increases but then decreases under the projected solar cost decline. Results indicate that when solar cost drops from $880/kW to $700/kW, CO2 emissions increase by 12.9% due to the decline in wind energy investment, indicating that cheaper solar cost does not necessarily imply a cleaner grid without any carbon policy intervention. However, if the regulator requires a minimum of 36% renewable penetration rate, social welfare from renewable investment when solar cost arrives at $700/kW would increase by $0.74 billion/year. This study illustrates the importance of policies such as the renewable portfolio standards under future decline in solar capacity cost if a carbon tax is politically infeasible.

Firming 100% renewable power: Costs and opportunities in Australia's National Electricity Market

The decreasing cost of solar photovoltaic (PV) and wind power technologies makes 100% renewable energy systems economically viable. Building more capacity to mitigate periods of low solar and wind supply results in systems that produce surplus energy at other times. As these surpluses vary across time and space, it becomes crucial to map their geographic distribution and quantify their magnitude. This paper presents the largest geographic and longest time series analysis of oversizing solar PV and wind in the context of the Australian National Electricity Market. We examine the additional solar, wind and battery capacity required to meet 100% of demand between 2010 and 2020. Without battery storage, this is achieved by generating approximately four times demand at an average production cost 28% lower than recent wholesale electricity prices. The addition of 1–8 h of storage reduces the average production cost by 55% compared to recent prices. Our analysis also investigates the spatiotemporal characteristics of surplus energy generation under different scenarios. We identify which regions are most suitable for the expansion of industrial, commercial, and business operations using surplus renewable generation.