Wind Power Electricity Research Articles

Korean Offshore Wind Electrical Power Generation: Current Status and Prospects

Offshore wind power generation is recognized as an environmentally friendly and sustainable energy source, rapidly advancing with governmental support in many countries such as Denmark, China, Germany, and across Europe due to its high energy stability, potential, and economic benefits. South Korea has set a target of 17.7GW for wind power generation through its Renewable Energy 3020 Implementation Plan, aiming to supply 12GW of offshore wind power by 2030. However, Korea faces slower development compared to overseas counterparts due to various obstacles including lack of community acceptance, low business feasibility, complex approval processes, and insufficient supply chain. This study identifies fundamental reasons behind Korea's sluggish growth in offshore wind power generation and proposes the following solutions through comparison with leading international cases. 1) Strengthening positive communication with residents by clearly articulating the value and benefits of offshore wind power projects to address community acceptance issues. 2) Resolving low business feasibility issues through the establishment of government-led incentive programs and support measures. 3) Streamlining approval processes to mitigate delays and cancellations in project implementation. 4) Expanding domestic market size and enhancing efficient power interconnection to address supply chain deficiencies.

Electricity Generation From Cow Manure Compared to Wind and Photovoltaic Electric Power Considering Load Uncertainty and Renewable Generation Variability

Electricity Generation From Cow Manure Compared to Wind and Photovoltaic Electric Power Considering Load Uncertainty and Renewable Generation Variability

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

(Invited) Materials Challenges for the Green Hydrogen Economy at Scale

Research and development projects over the last 30 years have successfully shown the value of hydrogen as a fuel and energy storage medium, and now hydrogen is seen as a key solution to decarbonization. Green hydrogen can be made by electrolysis anywhere that there is ample hydroelectric, solar and wind electric power. The hydrogen energy can be converted back to useful work in a fuel cell, turbine, or engine, or directly to manufacture products such as ammonia, cement, and steel. However, world decarbonization at scale also requires the demonstration of hydrogen at scale, and the widescale adoption of hydrogen for a green economy requires a reimagining of how energy is created, stored and used.Leading technologies for the green hydrogen economy include proton exchange membrane fuel cells (PEMFCs) and PEM water electrolyzers (PEMWE), both which operate under acidic conditions. In addition to high electrocatalytic activity, the electrode materials must be designed for two- phase flow of water and gas. All of the materials should be durable, low cost, manufacturable, recyclable, and have the appropriate electronic and thermal properties.This talk will describe some of the materials challenges needed to meet commercialization goals for PEMFCs and PEMWEs, and some of the constraints of manufacturing and recycling.

Overview of Photovoltaic and Wind Electrical Power Hybrid Systems

The overexploitation of non-renewable fossil resources has led to dangerous warming of our planet due to greenhouse gas emissions. The main reason for this problem is the increase in global energy demand. The rising prices of oil and gas have pushed governments around the world to turn to renewable energy, especially solar and wind power. For this reason, the present paper aimed to focus on photovoltaic and wind energy systems. However, exploitation of these two sources individually is not always easy because of their intermittent and irregular characters. Therefore, the obvious solution is the hybridisation of these two sources, which, when used alongside other systems such as batteries, increases the reliability, availability, and efficiency of these renewable sources. The main objective of this paper is to give an overview of different configurations of hybrid solar and wind energy conversion systems. First, the behaviour of each system, as well as their mathematical models, characteristics, and existing topologies, is presented. Then, the control strategies, optimal configurations, and sizing techniques, as well as different energy management strategies, of these hybrid PV–wind systems are presented.

Nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy

The heat demand for industrial processes is often provided in the form of steam generated by various fossil fueled equipment. In order to reduce CO2 emissions, the heat demand has to be covered by renewable energy sources. Electrified steam generation relies on complex energy systems, that can be operated according to energy availability and cost developments. However, such a multi component industrial energy system poses a challenge in modeling and determining the cost- or emission-optimal operation of the system. This study develops a methodology to model a multi component industrial energy system on the basis of a case study. By optimal system operation, either costs or emissions are minimized in response to fluctuating renewable wind energy and electricity prices.A high temperature heat pump (HTHP), a sensible thermal energy storage (TES) and a wind turbine are combined to create an electrified energy system to supply super-heated steam. During periods of low wind speed, additional grid electricity is purchased to ensure a steady heat supply. The HTHP offers a high operational flexibility and thus, enables the charging and discharging of the TES. A model of the closed reverse Brayton cycle HTHP, which is able to simulate part load behavior, is created in a process simulation software and consolidated in nonlinear surrogate models. The component behavior of a TES is represented by a combination of equations based on heat exchanger relations. Finally, the resulting algebraic nonconvex, nonlinear optimization problem based on the proposed system is solved using the local interior point optimizer (IPOPT) solver equipped with a multi-start approach to determine an optimal operation over a reference week with respect to the current wind power generation, grid emissions and electricity prices.The results of the optimization show, that optimal operating strategies enable a high potential to decarbonize future industries at minimum operational costs or emissions.

A wind power smoothing strategy based on two-layer model algorithm control

The inherent uncertainty of wind power generation leads to a mismatch between actual wind power generation and electric grid demand power, while the occurrence of sudden loads makes real-time scheduling of wind farms difficult. To address these issues, a cooperative compensation strategy of battery is proposed to compensate for the unbalanced power and sudden loads. Positive unbalanced power is used directly to compensate for sudden loads, and the energy storage absorbs the remaining positive unbalanced power to indirectly compensate for negative unbalanced power and sudden loads when the energy storage has excess power. The fluctuation rate of wind power after compensation is considered, and a smoothing model based on two-layer model algorithm control (MAC) is established. A variable mode decomposition (VMD) adaptive frequency division method is proposed to decompose the compensated power in order to smooth the high-frequency and low-frequency parts of the compensated power using different components of the hybrid energy storage to achieve accurate compensation.

Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

• Day-ahead and real-time market bidding and scheduling strategy for wind power participation. • Shared energy storage is used to reduce the real-time market deviation penalty of wind power. • Analyze the influence of deviation penalty coefficient on wind power bidding. • Analyze the impact of energy storage capacity on the income of wind farms and energy storage operator. In order to reduce the impact of wind power output and electricity price uncertainty on the income of wind power participating in the electricity market, this paper proposes a day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage. In the first stage, considering the uncertainty of wind power output and electricity price, aiming at the maximum income of wind farms in the day-ahead market, the optimal bidding power of each wind farm in the day-ahead market is obtained by using quantum genetic algorithm. In the second stage, based on the day-ahead market winning bid volume and actual output, by coordinating each wind farm to use the charging and discharging services of the shared energy storage power station, the net income of multiple wind farms in the day-ahead and real-time market is maximized. Case studies are carried out with three wind farms and one shared energy storage operator in this paper. Compared with the modes that wind farms not configuring energy storage or configuring energy storage independently, it is verified that the shared energy storage can significantly reduce the deviation penalty of wind farms in the real-time market and improve wind farm income.

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties

Hydrogen is believed as a promising energy carrier that contributes to deep decarbonization, especially for the sectors hard to be directly electrified. A grid-connected wind/hydrogen system is a typical configuration for hydrogen production. For such a system, a critical barrier lies in the poor cost-competitiveness of the produced hydrogen. Researchers have found that flexible operation of a wind/hydrogen system is possible thanks to the excellent dynamic properties of electrolysis. This finding implies the system owner can strategically participate in day-ahead power markets to reduce the hydrogen production cost. However, the uncertainties from imperfect prediction of the fluctuating market price and wind power reduce the effectiveness of the offering strategy in the market. In this paper, we proposed a decision-making framework, which is based on data-driven robust chance constrained programming (DRCCP). This framework also includes multi-layer perception neural network (MLPNN) for wind power and spot electricity price prediction. Such a DRCCP-based decision framework (DDF) is then applied to make the day-ahead decision for a wind/hydrogen system. It can effectively handle the uncertainties, manage the risks and reduce the operation cost. The results show that, for the daily operation in the selected 30 days, offering strategy based on the framework reduces the overall operation cost by 24.36%, compared to the strategy based on imperfect prediction. Besides, we elaborate the parameter selections of the DRCCP to reveal the best parameter combination to obtain better optimization performance. The efficacy of the DRCCP method is also highlighted by the comparison with the chance-constrained programming method.

Reliability Parameter Evaluation of Wind Turbine Based on Minimization of Dynamc Loads Acting on Power Plant Structural Components

An evaluation method of minimization of dynamic loads of big wind wheels with horizontal axis of rotation by replacing a steel lattice tower with a concrete tube was considered as the basic approach to obtain the calculated data of wind power plant reliability evaluation parameters based on dynamic loads minimization acting on power plant structural components. A certain degree of dynamic load minimization acting on vanes based on duly system preparation for external disturbances due to prediction of wind speed and power of electric energy input resulted in load minimization per a vane by 20 %. The problem of parameter reliability evaluation of wind power plant based on minimization of dynamic loads acting on power plant vanes under the condition of duly system preparation for external disturbances was solved enabling increasing of run-to-failure mean time by 8 %. Evaluation analysis of dynamic load minimization acting on power plant structural elements based on optimization model that accounts drive loading conditions at various operation conditions of wind electrical power plant was performed, as the result of rotor system load (“wind wheel - the driving gear”, “gear - generator”) decrease constitutes 20 %. The problem of reliability evaluation parameters of wind electric power plant based on dynamic load minimization acting on power plant structural elements was solved by implementing optimization model of drive loading conditions at various operational conditions of wind electric power plant increasing the rotor system (“wind wheel - driving gear”; “gear - generator”) run-to-failure mean time by 18 %. It was found that the proposed methods of efficiency improvement of wind power plant control enable to increase the operation lifecycle of wind power plant by 26 %.

Energy Cooperation for Wind Farm and Hydrogen Refueling Stations: A RO-Based and Nash-Harsanyi Bargaining Solution

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms.

Exploitable wind power potential in Turkey with technological advancement and resulting environmental-economic aspects

This work's focus is determining how the improvements in wind power technology alter current onshore wind power potential and exploitable electrical energy in Turkey. Then, consequent environmental and economic benefits through substitution of imported hard coal, natural gas and local lignite resources in the Turkish electricity mix have been analyzed. Lack of potential, environmental and economic data kept offshore wind calculations out of the scope of this work. Increasing hub height and power coefficient due to better blade designs of wind turbines can increase 114 thousand MW of onshore wind power potential at 50 m up to 418 thousand MW at 300 m. Consequent wind electricity generation can change in the range of 2.3 × 105–1.95 × 106 GWh. Acknowledging several different conditions operating wind turbines can be subject to and that the whole capacity may not be used, mean value of 1.09 × 106 GWh wind electricity generation is assessed as the limit providable to the Turkish electricity grid. Installable wind power capacity (IWPC) and electricity generation potential (EGP) are both most sensitive to the changes in spacing followed by CP and aging. The level of sensitivity increases at higher hub heights for both IWPC and EGP. Environmentally, substitution of lignite and after 2040 hard coal and lignite by wind power is the most feasible option in improving environmental performance of Turkish electricity mix compared to sole substitution of hard coal, natural gas or lignite. In 2040, wind can replace all lignite hence in the years following 2040, wind can substitute lignite and a second fossil resource. Economically, hard coal and natural gas substitution result in 10.85 billion $ and 7.93 billion $ gain from 2021to 2050. Local job creation and carbon tax mechanisms to be employed in favor of wind power can create additional economic and social benefits for the country.

EXPERIMENTAL STUDY OF CIRCULAR CUT OFF ENDPLATE EFFECT ON THE SAVONIUS TURBINE

The Savonius turbine is widely used because it has a simple design. However, the Savonius turbine has a lower efficiency than the Horizontal Axis Wind Turbine (HAWT). Several studies have been conducted to increase the efficiency of the Savonius turbine. Turbine performance can be improved by reducing the negative vortex by creating a flushing effect. This experiment was conducted to determine the impact of circular cut-off end plates on the Savonius turbine. The research results are shown through turbine performance data on wind turbine angular speed, electric power, static torque, and efficiency. The wind turbines with circular cut-off endplates had higher angular velocities than conventional Savonius turbines. The angular velocity measurement is carried out when the turbine is unloading. Meanwhile, the electric power and static torque of a conventional turbine are more significant than a modified turbine. The measurement of electric power and static torque is carried out when the turbine is in a generator-loaded condition (loading).

Down-Hole Electromagnetic Heating of Deep Aquifers for Renewable Energy Storage

Electromagnetic (EM) heating is an emerging method for storing renewable energy, such as photovoltaic solar and wind electric power, into aquifers. We investigate how the captured energy increases the temperature of a prototypical deep aquifer for a six-month period and then to which extent the stored energy can be recovered during the consecutive six months. Water injected at a constant flow rate is simultaneously heated using a high-frequency electromagnetic microwave emitter operating at the water natural resonance frequency of 2.45 GHz. The coupled reservoir flow and EM heating are described using Darcy’s and the energy balance equations. The latter includes a source term accounting for the EM wave propagation and absorption, modeled separately using Maxwell’s equations. The equations are solved numerically by the Galerkin least-squares finite element method. The approach was validated using EM-heating input data obtained from controlled laboratory experiments and then was applied to the aquifer. We found that after six years of alternate storage and recovery, up to 77% of the injected energy is recovered when considering realistic heat losses estimated from field data. Even when heat losses are increased by a factor of two, up to 69% of the injected energy is recovered in this case. This shows that down-hole EM heating is a highly effective method for storing renewable energies, capable of helping to solve their inherent intermittency.

A DFT‐based method for estimating interharmonics in wind power generation

This paper introduces a discrete Fourier transform (DFT)-based method for estimating the amplitudes and frequencies of interharmonics, which arise from the interaction between wind power generators and electric power systems. This method exploits the synergy of choosing signal enhancement, mains frequency estimation, and re-sampling techniques. Consequently, it accomplishes a cost-effective estimation process. The performance results show that signal enhancement and re-sampling techniques are necessary to establish suitable conditions for performing amplitude and frequency estimation. Furthermore, numerical results show that the proposed method, relying on low-cost digital signal processing techniques, offers competitive performance and significant computational complexity reduction compared to a previous DFT-based method.

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants’ behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters.

ASSESSMENT OF THE PROSPECTS FOR THE USE OF WIND POWER IN THE TURKESTAN REGION

The article considers proposals on the prospects of wind power in various regions of the Turkestan region based on statistical data on wind speed. The average wind speed in the Turkestan region was determined by means of Internet programming, methods of analysis and evaluation of promising proposals. Based on the results of the examination, it was found that the wind speed in the Turkestan region is the most effective when using alternative energy sources, including wind energy. A physical comparative analysis of the amount of power that can be obtained from a wind power plant and electricity sources was also carried out. The results of the study can be used as a theoretical basis for the application of wind power in the Turkestan region and in the preparation of state programs. In the course of our research work, the Disa A300, Terex TW 600, Direct wind 900/52 wind turbines were considered, taking into account that the average annual wind speed ranges from 3-6 m/s. It was found that the average wind speed in May, June, July is 5-6 m/s. It was found that the amount of energy received from wind turbines during this period of time is higher than in other months. The introduction of wind power in the Turkestan region can become an incentive to improve the social situation of the population, reduce prices.

Environmental impact of the main household cooking systems—A survey

The food cooking energy may represent the primary hotspot in the cradle-to-grave life cycle of several foods and drinks. It is mainly affected by the type of food and its cookery method, cooking appliance and the fuel selected as well as the number of portions to be cooked. The primary aim of this survey was to demonstrate the basic characteristics of the main cooking methods, appliances, and fuels as well as energy required for some key foods. The secondary aim was to assess the environmental impacts of a generic cooking system as a function of few household cookers fueled by different fuels (i.e., firewood, charcoal, coal, natural gas, liquefied petroleum gas, kerosene and biogas) and electricity in the Italian scenario by using the ReCiPe 2016 and product environmental footprint (PEF) standard methods and Ecoinvent v. 3.7 database. A functional unit equal to per capita useful energy delivered to the pot for cooking (1.41 gigajoule [GJ]) in 27 European Union countries in 2019 was used as the basis of comparison. The use of natural gas resulted in minimum impact in nine of the 18 mid-point impact categories of ReCiPe 2016 method and two damage categories (human health and ecosystem quality) with a minimum overall weighted damage score (OWDSR) of ~5 Pt. Thus, such a cookstove appeared to be more apt to minimize both indoor and outdoor air pollution. Even if the electric cookstove yielded a greater OWDSR (8.6 Pt) because the Italian electricity grid mix was mainly based on fossil sources, it was possible to forecast that new-generation, smart cooktops driven by hydro- and wind-power electricity would minimize OWDSR to as low as 0.9 and 1.4 Pt, respectively, thus not only avoiding the consumption of any fossil energy source but also improving people’s health.

Economic Feasibility Study on PV/Wind Hybrid Microgrids for Indonesia Remote Island Application

This paper presents the economic feasibility of hybrid microgrid power system for three remote islands of Sumatra, Indonesia. The microgrid system simulated and analysed using Homer Pro software. Optimization results showed that the combination of photovoltaic (PV), diesel generation (G) and batteries (Batt) for microgrid power system in Mandeh and Lagundri Island area were the most economical configuration. Meanwhile, for Mentawai area, the combination of PV, Wind Turbine (WT), G, Batt was the most optimal since it has higher wind speed then the other two areas. The Mandeh area has the highest solar radiation compared to the other two areas, resulting in the lowest CoE of $0.096/kWh as well as the lowest investment and operational costs. For the fixed PV 100 kW scenario, the optimal configuration is obtained with 86 kW supplied by WT for the Lagundri location, and 67 kW supplied by WT for the Mentawai area, while the WT installation area is not recommended for Mandeh location. The power management analysis showed that the average and patterns of weather parameters including solar radiation and wind speed effect both PV and Wind electrical power production.