Siting Renewable Energy Research Articles

Supporting energy policy research with large language models: A case study in wind energy siting ordinances

The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90 % accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.

Unveiling and explaining the procedural justice in the policy design of renewable energy siting process in the United States

Unveiling and explaining the procedural justice in the policy design of renewable energy siting process in the United States

Ripple effects of coal phaseout on employment in China: From mining to coal consumption sectors

China's stringent decarbonisation, marked by an uncompromising coal phaseout, has induced significant ripple effects across the coal industry chain's employment structure. By utilising a composite methodology that integrates the Global Change Assessment Model and Input‒Output Model, this study systematically quantifies the impact on employment, revealing a loss of 1.6 million jobs in mining and 8.2 million in the entire chain. Concurrently, a labour shift to the renewable sector has resulted in an 18.5% increase in employment, equivalent to creating 508,000 new positions from 2017 to 2020. Projections suggest that renewable energy initiatives could address 25% of the employment gap for precision by 2060 under carbon neutrality scenarios. Nonetheless, the coal sector is expected to lose more than 20 million jobs, indicating the need for a 2.3 trillion-yuan compensation fund. This study highlights the need for policy interventions to facilitate labour transitions through comprehensive retraining programs and strategic investments. Promoting economic diversification and clean coal technologies can improve resource utilization and create new jobs. Additionally, repurposing abandoned coal mines into energy storage facilities or renewable energy sites can prevent stranded assets and generate employment. These findings provide a robust framework for addressing the socioeconomic challenges of energy transitions, offering critical insights for other coal-dependent countries aiming for sustainable development.

Weather window analysis for the deployment, operation, and maintenance of marine renewable energy devices in Irish coastal waters

AbstractThis study presents a detailed analysis of weather window accessibility for marine renewable energy (MRE) sites along Ireland's coast, utilizing a robust 12 year met-ocean dataset. The research focuses on key test sites—the atlantic marine energy test site (AMETS), the galway bay test site (GBTS), and the Westwave Demonstration Site—and expands to a broader spatial analysis of Irish coastal waters. By integrating significant wave height and wind data, the study evaluates site accessibility, emphasizing the paramount role of wave height in determining access. Findings reveal substantial spatial variability in accessibility, with high-resource areas like AMETS facing greater access challenges due to harsher conditions, as opposed to the more accessible GBTS. The study underscores the need for a nuanced, region-specific approach to MRE development in Ireland, highlighting how strategic planning and technological advancements are crucial in exploiting the country's significant MRE potential. The results also stress the importance of long-term data for accurate environmental variability assessment, offering vital insights for future MRE site viability and strategy development.

Evaluating Renewable Energy Sites in the Green Hydrogen Supply Chain with Integrated Multi-Criteria Decision Analysis

Evaluating Renewable Energy Sites in the Green Hydrogen Supply Chain with Integrated Multi-Criteria Decision Analysis

Evaluating Taiwan’s Geothermal Sites: A Bounded Rationality Data Envelopment Analysis Approach

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan’s transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan’s strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research.

Geological survey techniques and carbon storage: Optimizing renewable energy site selection and carbon sequestration

Geological survey techniques play a crucial role in optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage to mitigate climate change. This abstract provides an overview of how geological survey techniques can be used to achieve these objectives. Renewable energy development, particularly solar and wind power, requires careful site selection to maximize energy generation efficiency and minimize environmental impacts. Geological surveys are instrumental in assessing factors such as subsurface geology, topography, soil composition, and hydrological conditions. These surveys help identify suitable locations with optimal wind or solar resources and geologic conditions for infrastructure development. Additionally, geological surveys are essential for identifying suitable sites for carbon storage, a critical component of carbon capture and storage (CCS) technologies aimed at reducing greenhouse gas emissions. Geological formations, such as deep saline aquifers, depleted oil and gas reservoirs, and unmineable coal seams, can serve as storage reservoirs for captured carbon dioxide (CO2). Geological surveys help characterize these formations to assess their suitability for long-term CO2 storage, considering factors such as porosity, permeability, and sealing integrity. Optimizing site selection for renewable energy projects and carbon storage requires a comprehensive understanding of subsurface geology and environmental conditions. Advanced geological survey techniques, such as seismic imaging, remote sensing, and geophysical surveys, are essential for acquiring detailed subsurface data. These techniques enable scientists and engineers to assess site suitability, evaluate risks, and design effective mitigation measures. In conclusion, geological survey techniques are invaluable tools for optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage. By leveraging these techniques, stakeholders can make informed decisions that promote sustainable energy development and mitigate the impacts of climate change.

Participation 3.0 in the implementation of the energy transition-Components and effectiveness of an interactive dialogue tool (Vision:En 2040).

The allocation of renewable energy plants, especially wind turbines, is stagnating in Germany. Although the citizens approve of the energy transition, they resist concrete local projects. In recent years, research has shown that interactive map applications support participatory planning through motivation, social interaction, and knowledge transfer. We aim to reduce biases against renewable energy (RE) and support informed decision making while accepting local responsibility. We hypothesized that finding a new gamified participation format, based on behavioral mechanisms, would strengthen the empowerment of people. To this end, we designed a dialogue tool and participation format, 'Vision:En 2040', which combines: (i) a precise target electricity yield, (ii) an interactive map showing results of people's actions, information about environmental impacts, and (iii) game rules which foster cooperation. In facilitated workshops, participants simulate the allocation of wind and photovoltaic power plants in their municipality to achieve a target electricity yield. The developed tool is based on methods of environmental planning and geoinformatics. 'Vision:En 2040' was systematically tested with a technical test and a pre-test. In addition, its impact on participants was assessed through surveys and qualitative content analysis. The evaluation results show that the tool can influence the acceptance of the energy transition in terms of attitude. Through 'Vision:En 2040', participants became aware of the community's responsibility in the energy transition and expanded their knowledge. In addition, decision makers used the workshop results to plan RE sites. Our results indicate that 'Vision:En 2040' is helpful for informal citizen participation in accelerating the energy transition.

Applications of q-Spherical Fuzzy Rough CODAS to the Assessment of a Problem Involving Renewable Energy Site Selection

Applications of q-Spherical Fuzzy Rough CODAS to the Assessment of a Problem Involving Renewable Energy Site Selection

Global transcontinental power pools for low-carbon electricity

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

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.

On the Definition of a Comprehensive Technology-Informed Accessibility Metric for Offshore Renewable Energy Site Selection

Despite the important role of offshore renewable energies (OREs) in the energy transition, the economical viability is still unclear. Therefore, an appropriate site selection is crucial. Besides the energy potential, the impact of operation and maintenance (O&M) aspects on the location can be critical. Accessibility is one of the most relevant aspects for O&M, assessing the feasibility to access the ORE farm. However, traditional accessibility assessment metrics do not allow a comprehensive evaluation. Therefore, the present paper suggests a novel, technology-informed metric, incorporating the overall set of most critical aspects, i.e., metocean conditions, visibility due to sunlight and sea fog, system failures, and O&M logistics. Among the different aspects, limited visibility is shown to be highly relevant with a reduction of up to 60% in accessibility. The study assesses accessibility in five different locations across Europe. On the one hand, accessibility is shown to be less sensitive to long-term resource variations with a reduction of 5% in the last six decades. On the other hand, accessibility is overall shown to be inversely proportional to the energy potential, meaning that as the energy potential increases, accessibility is reduced, increasing downtime, reducing the energy generation and increasing the cost of energy. As a consequence, site selection should combine the energy potential and accessibility assessments, which is enabled by the technology-informed metric presented here.

A coordinated control scheme for VSC-MTDC system with multiple renewable energy sites based on voltage margin and adaptive drooping control

This paper designs a novel coordinated control scheme to improve the control performance of voltage source converters (VSC) based multi-terminal high voltage direct current (MTDC) transmission network by merging the benefits of adaptive drooping and voltage margin controllers. In this proposed scheme, the droop coefficient is used to manage the DC voltage and maintain power sharing by shifting the control modes of the rectifiers and inverters in the presence of considerable power disturbances. The droop coefficient is planned based on the stability limit of DC voltage. The performance of the designed control scheme is compared to that of existing coordinated control and voltage margin droop control approaches. A ±400 kV five-terminal VSC-MTDC network is developed in PSCAD to verify the planned control method. The simulation results indicate that the proposed control scheme can achieve good performance for controlling the DC voltage and maintaining active power in the MTDC system.

Energy distributive injustices: Assessing the demographics of communities surrounding renewable and fossil fuel power plants in the United States

As the United States transitions away from fossil fuel to renewable energy sources, questions abound as to whether this is occurring in an equitable way. Are renewable energy power plants sited near privileged or marginalized communities, and how does this compare with non-renewable power plants? To answer this question, this article contrasts the sociodemographic profiles of host versus non-host communities for utility scale power plants at a national and state level. Findings demonstrate that the transition is not occurring equitably, and there are important differences between types of power plants. Hosts of coal plants and wind farms are Whiter but poorer while oil, natural gas and solar are located in communities with higher percentages of people of color but that are bifurcated in economic and education variables. State-level analysis reveals that as the diversity of the racial/ethnic and economic makeup of the state increases, so does the propensity for power plants to be located in communities of color and lower income. This article offers a timely and novel assessment of distributive injustices related to renewable energy siting, an updated analysis of fossil fuel host communities, and presents the information in a comparative manner that allows national and state-level trends to be made apparent. This analysis suggests that inequities are occurring because certain marginalized communities do not have the ability to access or to repel opportunities. It raises important considerations for practitioners and researchers about what an equitable distribution of power plants entails, and offers data to spark conversations.

Land use trade-offs in decarbonization of electricity generation in the American West

Land-use conflicts may constrain the unprecedented rates of renewable energy deployment required to meet the decarbonization goals of the Inflation Reduction Act (IRA). This paper employs geospatially resolved data and a detailed electricity system capacity expansion model to generate 160 affordable, zero-carbon electricity supply portfolios for the American west and evaluates the land use impacts of each portfolio. Less than 4% of all sites suitable for solar development and 17% of all wind sites appear in this set of portfolios. Of these sites, 53% of solar and 85% of wind sites exhibit higher development risk and potential for land-related conflict. We thus find that clean electricity goals cannot be achieved affordably without substantial renewable development on sites with potential for land use conflict. However, this paper identifies significant flexibility across western U.S. states to site renewable energy or alter the composition of the electricity supply portfolio to ameliorate potential conflicts.

Site Selection of Solar Power Plants Using Hybrid MCDM Models: A Case Study in Indonesia

Among developing countries in Asia, Indonesia has realized the importance of transitioning from fossil fuels to renewable energy sources such as solar power. Careful consideration must be given to the strategic placement of solar power installations to fully leverage the benefits of solar energy. This study proposes a methodology to optimize the site selection of solar power plants in Indonesia by integrating Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (F-MARCOS) models. The proposed methodology considers quantitative and qualitative criteria to evaluate potential locations for solar power plants. In the first stage, DEA is used to identify the most efficient locations based on quantitative measures such as solar radiation, land availability, and grid connectivity. In the second stage, qualitative factors such as technological, economic, environmental, and socio-political aspects are evaluated using F-AHP to prioritize the most important criteria for site selection. Finally, F-MARCOS ranks potential locations based on the selected criteria. The methodology was tested using data from Indonesia as a case study. The results show that the proposed hybrid model optimizes Indonesia’s solar power plant site selection. The optimal locations can contribute to a cost-effective long-term renewable energy supply nationwide. The findings from this study are relevant to policymakers, industry stakeholders, and researchers interested in renewable energy development and site selection. However, to promote sustainable solar energy development, governments and local authorities must also enact supportive policies and mechanisms that encourage the adoption and growth of renewable energy technologies in Indonesia.

Two-step approach for quasi-optimization of energy storage and transportation at renewable energy site

In this paper, the authors present a two-step model to obtain quasi-optimal solutions for the storage and transportation of renewable energy as its installation becomes more widespread in remote areas. The model is formulated as a total cost minimization linear programming problem and considers both renewable energy facilities and the energy supply and demand of the entire country. The first step of the model optimizes the entire country, while the second step determines the marginal capacity of energy storage and transportation facilities when the excess production capacity of remote renewable energy is added. This approximation for reducing computational complexity is shown to be sufficiently accurate under realistic conditions. The authors apply the model to analyze energy storage and hydrogen production at renewable energy power plants in Japan in 2050. The study shows that photovoltaic power generation uses storage batteries attached to the power plant for electricity transmission. In contrast, wind power generation transmits most electricity directly without storage batteries and converts some into hydrogen for consumers. The proposed lightweight model allows for numeral sensitivity analyses, which provide valuable insight into optimizing energy storage and transport of renewable energy.

Characterizing seabed sediments at contrasting offshore renewable energy sites

Due to the impacts of climate change, there is an urgent need to scale up existing, and develop novel, renewable energy technologies. Although there are many types of renewable energy technology, ocean renewable energy, including established offshore wind, and novel wave and tidal energy converters, offers many opportunities due to the abundance of the resource, availability of sea space, and (for tidal) predictability. However, the extraction of energy from the ocean environment will influence sediment dynamics and morphodynamics at various temporal and spatial scales. Detailed knowledge of seabed properties is also important for device installation, affecting foundation design and cabling. In this study, 36 seabed sediment samples were collected across a region of the Irish Sea extending from the west of Anglesey into Liverpool Bay up to a maximum distance of around 35 km offshore – a region where there are many existing and planned ocean renewable energy projects. Particle size analysis at quarter phi intervals was used to calculate the statistical properties of the seabed sediment samples, including Mean grain size, Sorting, Skewness and Kurtosis. These properties were compared against the outputs of wave (SWAN) and tidal (TELEMAC) models of the region to investigate the relationship between environmental variables and sediment characteristics, and to determine the impact and challenges of renewable energy technologies deployed in the region. Most of the sediments in the study area are medium sand, polymodal, very poorly sorted, coarse skewed, and very platykurtic. We found that mean water depth and peak current speed have the largest influence on Median grain size, and Sorting can be affected by tidal range, in addition to water depth and peak current speed. Moreover, minimal influence of wave climate was found on the sediments. A thorough discussion based on a literature review of the environmental issues of various energy converters (tidal energy converter (both individual and arrays), tidal barrage/lagoons, and wind turbines) was used to determine how devices in the study region, and at other sites throughout the world, would interact with sediment dynamics. We make recommendations on ways to minimize environmental impacts of ocean energy technologies.

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Abstract The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large-scale projects, deployed in ever more challenging environments. This inherently increases both cost and complexity and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions to support critical decision-making associated with marine operations. Here, an attention-based long short-term memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in situ observations. This is then integrated with an existing, low computational cost spatial nowcasting model to develop a complete framework for spatiotemporal forecasting. The framework addresses the challenge of filling gaps in the in situ observations and undertakes feature selection, with seasonal training datasets embedded. The full spatiotemporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the United Kingdom’s national weather service). For these two example locations, the spatiotemporal forecast is found to have an accuracy of R2 = 0.9083 and 0.7409 in forecasting 1-h-ahead significant wave height and R2 = 0.8581 and 0.6978 in 12-h-ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources. Significance Statement Spectral wave models, based on modeling the underlying physics and physical processes, are traditionally used to generate wave forecasts but require significant computational cost. In this study, we propose a machine learning forecasting framework developed using both in situ buoy observations and a surrogate regional numerical wave model. The proposed framework is validated against in situ measurements at two renewable energy sites and found to have very similar 12-h forecasting errors when benchmarked against the Met Office’s physics-based forecasting model but requires far less computational power. The proposed framework is highly flexible and has the potential for offering a low-cost, low computational resource approach for the provision of short-term forecasts and can operate with other types of observations and other machine learning algorithms to improve the availability and accuracy of the prediction.

Siting Renewable Energy Facilities Using a Matching Algorithm: A Case Study in Japan

Finding an optimal match between installation sites and renewable energy (RE) facilities while ensuring that private initiatives meet local socio-environmental needs is a significant albeit complicated task. Different sites may need diverse considerations, such as landscape conservation, while information on the true local preferences and costs of RE facilities is unknown to the planner, causing information asymmetry and inefficiency. This study explores how a matching model can be utilised for empirically planning RE siting using an illustrative case study. It employs the so-called ‘college admission problem’ of the matching model. The matching algorithm enables the matching of sites and RE specifications, reflecting the true preferences of local people regarding facility siting. The matching result would ensure the most desirable choice for local people, as adopting the ‘student-optimal matching’ algorithm generates desirable matching patterns for the locals among the stable matching patterns.