Renewable Energy Farms Research Articles

Utilizing Fuzzy DANP to Rank Factors for Optimal Siting of Renewable Energy Farms

Energy consumption, much higher than the global average in developing countries, witnessed a growing and alarming trend in past years. An accepted reality for human communities is that the world’s energy demand is hastily increasing, and cheap fossil energy resources will gradually, yet certainly, be exhausted in future decades. To conserve these worthy fossil resources for future generations, prevent environmental damages due to burning these fuels, and respond to the increasing demand for energy, humans should use clean and renewable energies and discover proper locations to generate renewable energies. Today, locating studies prevail to determine the sites of renewable energy farms and reach industrial development. These studies consider various criteria to select the best location for establishing renewable energy farms. The present research attempted to identify and rank factors influencing the location of renewable energy farms using the fuzzy DANP approach.

Feasibility and optimal sizing analysis of hybrid PV/Wind powered seawater desalination system: A case study of four ports, Egypt

This research aims to investigate A novel and complete system consists of hybrid renewable energy farm with high-energy-consuming seawater desalination in fourth locations in Egypt. This paper proposes fuzzy-based multi-criteria decision-making model for optimal sizing of a hybrid PV/Wind/Storage system to power the reverse osmosis (RO) desalination process in order to increase freshwater availability and meet the electric load requirement in selected area. Firstly, according to collected data and cost of electricity (COE) for various renewable energy resources, specific procedures to determining the optimal system design and optimal sizing (solar cell, wind turbine parameter and storage system units) are presented for selected site. The optimization based on finding the minimum net present cost (NPC) feeds the load demand and maintains the system's reliability. Secondly, the fuzzy analytic hierarchy process (FAHP) is implemented to select the optimal system considering ten performance criteria. The results show that the feasible design consists of 16 × 100-kWwind turbines, 4127-kW photovoltaic array, 153 PH245 storages and 710-kW converter. The feasible design has the optimal economic values among all criteria with least NPC, COE, and payback-period (PBP) of $9,08,046, 0.091$/kWh and 1.1 yr, respectively. Besides, it has a 100 % renewable energy system (RES).In contrast to recent studies that concentrated on incorporating renewable energy sources into desalination systems at specific Egyptian location. This approach is applied to different case studies with diverse renewable energy resources in multiple locations across Egypt, thus providing valuable insights for decision-makers tackling electricity and water shortages throughout Egypt. This research fills a gap in the optimization of hybrid renewable energy systems (HRES) by taking into account a variety of sustainability criteria, including technological feasibility, cost-effectiveness, emissions reduction, and socio-political considerations, in contrast to other studies that concentrated on single aims. Additionally, the research presents fuzzy logic and fuzzy-AHP and fuzzy-VIKOR decision-making techniques, enabling a comprehensive assessment of system design alternatives while taking uncertainties into consideration. In general, this study makes a significant addition to the field of renewable energy integration and gives decision-makers a framework for choosing the best answers to Egypt's water and electricity problems.

Development of pyramid neural networks for prediction of significant wave height for renewable energy farms

Significant wave height (Hs) is a critical parameter in the design, operation, and maintenance of nearshore and offshore wind and wave farms. In this study, the original pyramid neural network (PNN) was introduced and applied to predict Hs for North Sea renewable energy farms. The performance of PNN was compared with state-of-the-art algorithms. The results show that the machine learning (ML) models trained by the network datasets can accurately predict Hs at any point of nearshore or offshore energy farms, significantly reducing wave monitoring costs. The PNN models predict Hs with high accuracy. The results predicted by PNN, with SRMSE of 0.256 and R2 of 0.873, are more accurate than the corresponding results obtained by the deep neural network (DNN) model, which show that PNNs with their far simpler structures and lower number of parameters are attractive alternatives for complex and time-consuming DNN models. The ensemble of regular models, with SRMSE of 0.247, is ranked as the most accurate method. The results also show that the applied state-of-the-art algorithms including MLR, SNN, DNN, ANFIS, and SVR models predict non-physical negative values of Hs. The PNNs developed in this study predict Hs with positive values. Moreover, the analysis shows that the scatter of the results obtained by the derived nonlinear models is less pronounced than the scatter of corresponding results obtained by other methods. The results also show that to evaluate the performance of nonlinear ML methods for large datasets, the analysis of standard statistical indices is not sufficient. For large datasets standard statistical indices provide inadequate or insufficient information and additional calculations, and statistical analysis are required.

Techno-economic assessment of offshore wind and hybrid wind–wave farms with energy storage systems

Ocean renewables (such as offshore wind and wave) are abundant and essential energy resources for supporting future emission-free targets. However, their energy intermittency and high cost have hindered commercialization and wide-scale implementations of these ocean energy technologies. This paper focuses on both issues and aims to increase the dispatchability of ocean energy farms by investigating the potential of a hybrid wind and wave energy platform with energy storage systems (ESS). This research introduces an innovative method to assess the necessary ESS capacity for offshore renewable energy farms, ensuring energy dispatchability to the local demand. In addition, the key criteria and the economic feasibility of deploying ESS in offshore energy farms are also discussed. A life-cycle cost model is presented that quantitatively compares different farm configurations, specifically a standalone offshore wind farm and a hybrid configuration incorporating both wind and wave generation systems. The results indicate that the combined wind and wave energy farm significantly reduces the ESS requirement and provides competitive lifecycle costs compared to the stand-alone wind energy farm, though the amount of these benefits vary on the local resource characteristics. The quantitative assessment presented in this study also provides realistic and practical justifications for combined energy farms that complement statistical conclusions derived from variability indexes in previous studies.

Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions

The offshore wind industry is rapidly developing in a hurricane prone area off the US East Coast. For assessment of extreme environmental conditions, or hazard intensity measures, such as wind and wave loads (e.g., 50-year or 500-year wind speed) various methodologies including univariate (e.g., Generalized Extreme Value distribution; GEV) and multivariate (e.g., Inverse First Order Reliability Method, IFORM) analysis have been recommended. Further, due to lack of long-term observed data at a site, available observed/hindcast at nearby stations are commonly used which can lead to errors. The objective of this study is to better understand and quantify the level of uncertainty in extreme value analysis of wind/wave data for marine renewable energy sites in hurricane-prone regions. An area off the northeast of the US where several large projects have been planned was selected as a case study. Univariate and bivariate analyses of wind/wave data at several stations were carried out and results were compared.For this study, Bivariate (IFORM) and univariate (GPD) methods resulted in an average difference of 4% and 6% for extreme wind and wave data, respectively. It has been demonstrated that these discrepancies still fall within the 95% confidence intervals of the standard GEV method.

UNRAVELING MULTIMODAL NEARSHORE WIND-WAVE FIELDS ON THE DUTCH SHOREFACE

Changing (wind) climate might influence the magnitude, direction, and frequency of wave systems (Lobeto et al., 2021). However, in coastal engineering applications, generalized wave parameters are commonly used in climate change assessments with the risk of, for example, misrepresenting the nearshore transformation of wind-driven wave climates (Hegermiller et al., 2017). In consequence, these uncertainties in the nearshore (wind) climate will affect, amongst others, ship navigation, the implementation of marine renewable energy farms, the feasibility of coastal infrastructure and defences, or the efficiency of sandy coastal maintenance, and thus the decision-making of long-term, multidecadal coastal strategies (Rijksoverheid, 2013), especially when they are designed accounting for the Building with Nature concept (de Vriend et al., 2015). This study analyses the importance and application of considering multiple coexisting wave trains on the Dutch shoreface.

A Techno-Economic Study for Off-Grid Green Hydrogen Production Plants: The Case of Chile

In this study, we present a pre-feasibility analysis that examines the viability of implementing autonomous green hydrogen production plants in two strategic regions of Chile. With abundant renewable energy resources and growing interest in decarbonization in Chile, this study aims to provide a comprehensive financial analysis from the perspective of project initiators. The assessment includes determining the optimal sizing of an alkaline electrolyzer stack, seawater desalination system, and solar and wind renewable energy farms and the focus is on conducting a comprehensive financial analysis from the perspective of project initiators to assess project profitability using key economic indicators such as net present value (NPV). The analyses involve determining appropriate sizing of an alkaline electrolyzer stack, a seawater desalination system, and solar and wind renewable energy farms. Assuming a base case production of 1 kiloton per year of hydrogen, the capital expenditures (CAPEX) and operating expenses (OPEX) are determined. Then, the manufacturing and production costs per kilogram of green hydrogen are calculated, resulting in values of USD 3.53 kg−1 (utilizing wind energy) and USD 5.29 kg−1 (utilizing photovoltaic solar energy). Cash flows are established by adjusting the sale price of hydrogen to achieve a minimum expected return on investment of 4% per year, yielding minimum prices of USD 7.84 kg−1 (with wind energy) and USD 11.10 kg−1 (with photovoltaic solar energy). Additionally, a sensitivity analysis is conducted to assess the impact of variations in investment and operational costs. This research provides valuable insights into the financial feasibility of green hydrogen production in Chile, contributing to understanding renewable energy-based hydrogen projects and their potential economic benefits. These results can provide a reference for future investment decisions and the global development of green hydrogen production plants.

ACCESSIBLE SOLAR ENERGY TECHNOLOGY FOR DOMESTIC APPLICATIONS IN THE UK: EDGE SOLAR

AbstractRenewable energy is increasingly used and promoted. In the UK, for example, large scale renewable energy farms have been used to supply electricity with great effect. Given the large number of homes, there is considerable impact to be made by small scale residential renewable energy systems. Despite solar panels being the most common form of residential renewable energy technology, only 4% of buildings in the UK support solar technology of any kind. For direct electricity generation, silicon-based photovoltaic (PV) arrays are the most utilised, and when used in a residential setting, they are typically mounted on the sloped roofs. This is where the problem lies. The technology comes with a high cost, and there is further financial burden of installation and maintenance, making solar energy inaccessible for many UK homeowners. This paper presents a research and design innovation project to make PV technology more accessible in the UK. Edge Solar, the innovative, affordable, new PV system concept for UK homes may become a promising solution to significantly improve the accessibility to the PV technology and renewable energy at the household level in the UK and beyond with further development and commercialisation.

ALFRED reactor and hybrid systems: A test case

Future electricity generation will likely rely on renewable energy sources with consequent power intermittency and grid instability issues that will need to be compensated for. In this work, a model of a Generation IV small modular reactor has been developed in order to study its capabilities as a grid stabilizer. The plant model has been developed in Dymola and exported in the Simulink environment for the control strategy. The hybrid system considered couples a lead-cooled fast reactor to a desalination plant, an energy storage system, renewable energy farms, a variable electrical load, a local grid, and an external interconnected grid. Different desalination plants have been considered. Reverse osmosis has proven to be the most suitable option when coupled with energy production systems. Different configurations of the hybrid system have been considered showing a higher degree of load-following capabilities for the nuclear plant studied.

Offshore wind farm layouts designer software's

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

Site Selection of Combined Offshore Wind and Wave Energy Farms: A Systematic Review

Growing energy demand worldwide and onshore limitations have increased interest in offshore renewable energy exploitation. A combination of offshore renewable energy resources such as wind and wave energy can produce stable power output at a lower cost compared to a single energy source. Consequently, identifying the best locations for constructing combined offshore renewable energy farms is crucial. This paper investigates the technical, economic, social, and environmental aspects of Combined Offshore Wind and Wave Energy Farm (COWWEF) site selection. Past literature was evaluated using a systematic review method to synthesize, criticize, and categorize study regions, dataset characteristics, constraints, evaluation criteria, and methods used for the site selection procedure. The results showed that most studied regions belong to European countries, and numerical model outputs were mainly used in the literature as met-ocean data due to the limited coverage and low spatiotemporal resolution of buoy and satellite observations. Environmental and marine usage are the main constraints in the site selection process. Among all constraints, shipping lanes, marine protected areas, and military exercise areas were predominately considered to be excluded from the potential sites for COWWEF development. The technical viability and economic feasibility of project deployment are emphasized in the literature. Resource assessment and distance to infrastructures were mostly evaluated among techno-economic criteria. Wind and wave energy power are the most important criteria for evaluating feasibility, followed by water depth, indicators of variability and correlation of the energy resources, and distance to the nearest port. Multi-Criteria Decision-Making (MCDM) methods and resource-based analysis were the most-used evaluation frameworks. Resource-based studies mainly used met-ocean datasets to determine site technical and operational performance (i.e., resource availability, variability, and correlation), while MCDM methods were applied when a broader set of criteria were evaluated. Based on the conducted review, it was found that the literature lacks evaluation of seabed conditions (seabed type and slope) and consideration of uncertainty involved in the COWWEF site selection process. In addition, the market analysis and evaluation of environmental impacts of COWWEF development, as well as impacts of climate change on combined exploitation of offshore wind and wave energy, have rarely been investigated and need to be considered in future studies. Finally, by providing a comprehensive repository of synthesized and categorized information and research gaps, this study represents a road map for decision-makers to determine the most suitable locations for COWWEF developments.

Experimental and Modeling Investigation for Slugging Pressure under Zero Net Liquid Flow in Underwater Compressed Gas Energy Storage Systems

As an emerging flexible-scale energy storage technology, underwater compressed gas energy storage (UW-CGES) is regarded as a promising energy storage option for offshore platforms, offshore renewable energy farms, islands, coastal cities, etc. Liquid accumulation often occurs in underwater gas transmission pipelines, which is a challenge to overcome. In this study, an experimental investigation is carried out on the pressure distribution characteristics of liquid accumulation flow in hilly terrain under the condition of Zero Net Liquid Flow. A slug flow pressure model with different inclination angles at four times is established and verified, and its error range is within ±20%. Analysis revealed that reduction and growth in pressure difference are related to the outflow of slug in an inclined pipe. A high-speed camera is used to capture the movement of liquid accumulation under Zero Net Liquid Flow (ZNLF) and record the associated dynamic parameters. By imaging the motion of liquid accumulation and detecting the pressure changes in the pipeline at various times, the pressure fluctuation in the pipeline at the slug flow cause is studied. Outcomes from this work can be leveraged to help further the development of underwater compressed gas energy storage technology.

Investigation on green hydrogen generation devices dedicated for integrated renewable energy farm: Solar and wind

This study presents a comprehensive methodology to evaluate plants that integrate renewable energy sources and hydrogen generation devices. The paper focuses on presenting the methods for devices’ operation assessment taking into account the annual operation. Multiple effectiveness indices have been presented. On the basis of experimental investigation with the hydrogen generator, the methods for assessing its operation during start-up phase and sudden change in the supply current were proposed. The results of the experiments and the provided mathematical models show that dynamics of the hydrogen generator should be taken into account when selecting the suitable device for cooperation with variable renewable energy. It is especially important for multiple start-ups throughout the day due to significant differences in the amount of hydrogen produced by devices characterized by the same efficiency, yet various time constants. Methodology for selecting the optimal nominal power for hydrogen generator to cooperate with given renewable sources was developed. It was proven the optimal power depends on the type of the renewable source and minimal load of the hydrogen generator. Several case studies, including the integration of wind and solar energy farms to yield a 10 MW renewable energy farm were considered and the minimal load of the hydrogen generator impacts the annual operation of the device has been presented. The paper provides a set of tools to contribute to the development of sustainable energy plants. The methods proposed in this paper are universal and can be used for various renewable energy sources.

Importance of the fluctuations of the steel price in the economic feasibility of a hybrid offshore platform in the West of the Iberian Peninsula

The aim of this paper is to study the suitability of different floating offshore energy technologies in a particular location in economic terms. In this context, their main initial investments and expenses have been taken into account in order to calculate the economic indicators of the economic feasibility study. These indicators are Internal Rate of Return, Net Present Value and Levelized Cost Of Energy. The case study has evaluated the Canary Islands (Spain) and three types of floating offshore renewable energies: offshore wind, wave energy and hybrid systems. The method created generates economic maps, which facilitates the election of the best area where install offshore renewable energy farms in the location selected. In addition, it also allows to select what is the best marine technology to be exploited in this area.

Quantifying the Air Pollution Mortality Impacts of Renewable Energy Farms Across 150 U.S. Cities

Quantifying the Air Pollution Mortality Impacts of Renewable Energy Farms Across 150 U.S. Cities

Design of combined stationary and mobile battery energy storage systems.

To minimize the curtailment of renewable generation and incentivize grid-scale energy storage deployment, a concept of combining stationary and mobile applications of battery energy storage systems built within renewable energy farms is proposed. A simulation-based optimization model is developed to obtain the optimal design parameters such as battery capacity and power ratings by solving a multi-objective optimization problem that aims to maximize the economic profitability, the energy provided for transportation electrification, the demand peak shaving, and the renewable energy utilized. Two applications considered for the stationary energy storage systems are the end-consumer arbitrage and frequency regulation, while the mobile application envisions a scenario of a grid-independent battery-powered electric vehicle charging station network. The charging stations receive supplies from the energy storage system that absorbs renewable energy, contributing to a sustained DC demand that helps with revenues. Representative results are presented for two operation modes and different sets of weights assigned to the objectives. Substantial improvement in the profitability of combined applications over single stationary applications is shown. Pareto frontier of a reduced dimensional problem is obtained to show the trade-off between design objectives. This work could pave the road for future implementations of the new form of energy storage systems.

A spatial cost-benefit-externality modelling framework for siting of variable renewable energy farms: A case in Bohol, Philippines

Site selection is an integral component of energy planning. For renewable technologies, Geographic Information System (GIS) and multi-criteria decision-making became the most prevalent approach for such a data-intensive task. However, site selection can also be carried out using GIS and Cost-Benefit Analysis (CBA). This paper aims to introduce a four-stage energy modelling framework for siting of Variable Renewable Energy (VRE) farms using GIS and CBA. Adopting a well-established framework, the first three stages are dedicated to finding suitable sites and estimating spatio-technical inputs. In the fourth stage, CBA is performed from the developer's perspective, and the viability of VRE technologies is assessed using the benefit-cost ratio. The model output is a viability map, and externalities are separately calculated to assist planning and policymaking. We implement this framework to assess the viability and estimate the externalities of utility-scale solar PV and wind farms in Bohol and find a modest potential for viable VREs in this province. The compensation effect, which is inherent to linear combination models, is unavoidable and is manifested in our results. We have also evaluated the model sensitivity to the model variables and parameters. This framework can be implemented in a study area of any geographic scale.

The decarbonisation of Galicia using renewable marine energy

The objective of this paper is to examine the importance of independent arrays in the offshore renewable energy farms. In this context, several scenarios have been contemplated for a floating offshore renewable energy farm: a farm only using floating wave energy; a farm only using floating offshore wind energy; and a farm composed by floating wave energy and floating offshore wind energy installed in independent arrays. The article proposes a method to calculate the main economic parameters and decide their economic feasibility. A hypothetic offshore renewable energy farm located in the Galicia region (Spain) has been studied as case of study. Results show which of the scenarios has the best economic results. This method is worthwhile to compare different floating offshore renewable energy technologies in economic terms and help in the decision making of this new emerging sector that can help to rebuild Europe in the post-pandemic period.

Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review

Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.

Potential assessment of multi-renewable energy farms establishment using spatial multi-criteria decision analysis: A case study and mapping in Iran

Many parts of the world may have suitable conditions and potential to establish two or more renewable energy farms. Given the pervasive use of renewable energy globally, assessing the potential of regions to establish a multi-renewable energy farm is of great importance. This study aimed to assess the potential for the establishment of renewable energy farms (solar, wind, biomass, and geothermal) in the eastern regions of Iran. For the first time, the potential for establishing multi-renewable energy farms in an area has been assessed. For this purpose, a series of environmental and economic criteria were addressed and investigated. Respectively, Analytical Network Process (ANP) and Fuzzy logic were employed for obtaining the required weights as well as accounting for the element of uncertainty among the different criteria. The final suitability maps for identification of the most optimal locations for the institution of renewable energy farms were obtained using Weighted Linear Combination (WLC) method. The study area was classified as highly suitable for the establishment of renewable energy farms, as maintained by the final results, wherein 5, 13, 23, and 19% of the entire study area were selected as eligible placements for the institution of biomass, geothermal, solar, and wind power plants, respectively. In consonance with the final map obtained using a combination of individual suitability maps, a total of 5465 km2 worth of area was categorized as highly suitable for the establishment of renewable energy farms. Results also were indicative of the prominence of the different weights assigned to each criterion on identifying the optimal choices of the region concerning the establishment of renewable energy farms. The results can be further used for and are highly advantageous to various managerial, planning, and decision-making procedures in connection with the development of prospective renewable energy sources.