Renewable Energy Generation Research Articles

Does energy innovation heterogeneously affect renewable energy production?

Achieving long-term sustainable development is a critical global imperative, and the adoption of renewable energy sources plays a pivotal role in this endeavor, aided by the advancements of the Fourth Industrial Revolution. As a result, energy innovation has emerged as a crucial factor in realizing sustainable development goals. This study utilizes panel quantile regression analysis to investigate the relationship between energy innovation and renewable energy production in the Middle East and North Africa (MENA) region from 1985 to 2019. The findings suggest that energy innovation can effectively enhance renewable energy production, particularly in countries with higher proportions of renewable energy generation, such as Egypt, Morocco, and Tunisia. However, countries like Saudi Arabia, which have lower ratios of renewable energy production, may not experience a significant impact from energy innovation. Consequently, the study recommends implementing systematic and integrated measures that focus on raising awareness and improving conditions for the adoption of renewable energy. These measures can drive the transition towards a more sustainable energy landscape in the MENA region. Policymakers should prioritize research and development investment and incentives in countries with higher levels of renewable energy generation, while simultaneously addressing barriers and fostering knowledge sharing between countries with differing levels of renewable energy production.

Local Energy Community to Support Hydrogen Production and Network Flexibility

This paper deals with the optimal scheduling of the resources of a renewable energy community, whose coordination is aimed at providing flexibility services to the electrical distribution network. The available resources are renewable generation units, battery energy storage systems, dispatchable loads, and power-to-hydrogen systems. The main purposes behind the proposed strategy are enhancement of self-consumption and hydrogen production from local resources and the maximization of the economic benefits derived from both the selling of hydrogen and the subsidies given to the community for the shared energy. The proposed approach is formulated as an economic problem accounting for the perspectives of both community members and the distribution system operator. In more detail, a mixed-integer constrained non-linear optimization problem is formulated. Technical constraints related to the resources and the power flows in the electrical grid are considered. Numerical applications allow for verifying the effectiveness of the procedure. The results show that it is possible to increase self-consumption and the production of green hydrogen while providing flexibility services through the exploitation of community resources in terms of active and reactive power support. More specifically, the application of the proposed strategy to different case studies showed that daily revenues of up to EUR 1000 for each MW of renewable energy generation installed can be obtained. This value includes the benefit obtained thanks to the provision of flexibility services, which contribute about 58% of the total.

Development & Validation of a Packed Bed Thermal Energy Storage Model

The rapid growth of economies and population around the world have exponentially increased the demand for energy. This demand has invoked increased annual global CO2 emissions and necessitated a transition to clean energy technologies. Reliable and affordable energy storage technologies are paramount for increasing renewable energy penetration onto the grid, supporting periods with reduced or no renewable energy generation. This project is aimed at developing a Thermal Energy Storage (TES) solution that can deliver heat to a heat engine for power production or to an industrial process for prolonged periods. TES long duration energy storage (LDES) presents many potential benefits, including 1) Low Cost 2) scalability 3) high energy density 4) low carbon footprint and 5) resiliency via ability to produce synchronous power (i.e., spinning turbomachinery). The broader objective of this project is to build, test, and model the TES concept at a 2 MWh scale to determine the economic and physics-based practicality of the system. The work presented here describes the first phase objectives of the project, including benchtop scale testing, model development, and Model Validation.

A Dual-Mode Hybrid System Combining Solar Thermal With Pumped Thermal Energy Storage

A hybrid system that delivers renewable electricity generation and electricity storage capabilities is introduced. This dual-mode hybrid system is based on Pumped Thermal Energy Storage (PTES) which uses a heat pump to convert electricity into thermal energy that is transferred to silica particles which are stored in concrete silos. The stored heat is later converted back to electricity in a heat engine. The heat pump and heat engine use a closed Joule-Brayton cycle and fluidized bed heat exchangers. If the PTES system is co-located with an array of concentrating solar mirrors and a particle receiver, then the silica particles may also be heated up by the concentrating solar power (CSP) system. The PTES heat engine may also be used to convert the stored solar heat into electricity, thereby sharing this system between the PTES and CSP systems and reducing costs. However, this requires careful management of the heat engine off-design parameters. A thermodynamic model is used to evaluate the performance of the dual mode PTES-CSP hybrid system. The model accounts for turbomachinery efficiency, and approach temperature and pressure loss in heat exchangers, as well as other sources of inefficiency, such as motor-generator losses, and air fan power. The hybrid system also requires the evaluation of the turbomachinery efficiency and pressure ratio at off-design conditions since the CSP system operates over different temperature ratios than the Carnot Battery. Several design variables and design modifications are investigated, such as pressure ratios and maximum temperatures.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Molecular dynamics simulation of arc ablation for CuCr contact materials and improvement method of ablation resistance

Renewable energy generation is a development trend worldwide due to sustainability and environmental protection. High-voltage direct current (HVDC) transmission is indispensable in the renewable energy system due to its efficient power transmission capability and adaptability to complex grid structures. The DC vacuum circuit breaker (VCB) is essential for system control and fault protection. However, with the development of DC systems towards high voltage and large current, the ablation resistance of CuCr contact materials in VCBs has become the leading technical bottleneck. In order to enhance the ablation resistance of CuCr contact materials, this paper investigates a new molecular dynamics (MD) simulation model for arc ablation of CuCr contacts; therefore, it proposes a design method for contact materials measured by characteristic parameters of ablation resistance. Results are as follows: Firstly, the Cu particles cause a severe ablation on CuCr contacts with the effect of arcs. Subsequently, the model’s accuracy is verified by the influence of Cr content on ablation resistance. Furthermore, Mo and W metal elements were used to dope and strengthen the CuCr10 contact material, and CuCr8Mo2 and CuCr7Mo3 have an apparent improvement in resistance to arc ablation, which are alternatives for reinforced CuCr10 contact materials.

Flexible photovoltaic systems for renewable energy integration in Lazio region, Italy

Italy is expanding its renewable electricity generation to meet European energy and environmental targets. The aim of this work is to study and implement a strategy to promote a high self-production of non-programmable renewable energies (solar and wind) in the electricity mix of an Italian region (Lazio), to reach the target of 100 % Renewable Energy Source (RES) by 2050 set in the Regional Energy Plan (PER). This can be achieved through the innovative concept of "flexible PV" that has the potential to make solar power generation 24/365. The results showed that by oversizing the photovoltaic in relation to the annual electricity demand and using optimized batteries, 90 % of the electricity demand of the Lazio region can be satisfied by photovoltaic and the remaining 10 % by hydro and wind power. To reach this target, Lazio would need to install 34.73 GWp of photovoltaic capacity and 42.34 GWh of batteries at an optimum cost of 92.21 €/MWh (costs estimated at 2050). In addition, the integration of wind energy into the grid was studied to reduce the photovoltaic capacity.

Building-Integrated Photovoltaics in Existing Buildings: A Novel PV Roofing System

Among renewable energy generation technologies, photovoltaics has a pivotal role in reaching the EU’s decarbonization goals. In particular, building-integrated photovoltaic (BIPV) systems are attracting increasing interest since they are a fundamental element that allows buildings to abate their CO2 emissions while also performing functions typical of traditional building components, such as sealing against water. In such a context, since one of the main challenges to decarbonizing the building sector lies in the retrofitting of existing buildings, the current paper is focused on the design, development, and testing of a novel roofing BIPV system. The entire research was carried out as part of the Horizon 2020 HEART project. In more detail, the research analyzed the requirements of typical pitched tile roofs, which are currently the most common type in Europe, and developed a universal photovoltaic tile that can be easily and quickly integrated into such a type of roof. The research was also aimed at minimizing the embodied energy of the component and promoting disassembly and recycling at the end of life, fully in line with a circular economy perspective. The adopted design and development processes are described in detail in the present paper, along with the results of several tests performed in the field. In addition, further development prospects of the component, aimed at meeting the integration requirements in historic buildings, are finally presented.

Understanding Business Models in the Brazilian Context of Electric Mobility: A Proposed Framework

In the transition to electric mobility (EM), business model innovation plays a crucial role in expanding the use of electric vehicles and increasing acceptance of this technology. The transition pathways differ between countries in the largest economies and those in Latin America. Brazil presents a unique scenario, benefiting from an early start with biofuels, the production of flex-fuel vehicles, predominantly renewable electric energy generation (>80%), and the absence of a structured national policy aligned with local governments. This study introduces a framework that surveys and categorizes businesses in EM, relating them to sustainable development aspects and regulatory maturity. It builds a solid conceptual foundation, incorporating data from technical and commercial events, as well as interviews with Brazilian specialists for validation. The proposed framework aids in understanding the Brazilian context, identifying regulatory gaps, and developing a common language to advance studies on business model innovation, contributing to electromobility studies in Latin America. Additionally, it can guide the construction of regional and local public policies and help identify more sustainable projects.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

Identification and application of indicators for the assessment of pumped hydro storage projects with renewable energy generation

Pumped hydro storage is crucial in Europe's energy strategy to achieve a 100% renewable energy-based electricity system by 2050. As a consequence of the large number of factors involved and the variety of criteria included in a proper assessment of renewable generation projects with pumped hydro storage, this paper aims to identify and calculate technical, economic, environmental and social indicators in order to provide a tool to improve the decision-making process in this type of large-scale projects.

Concurrent PV production and consumption load forecasting using CT‐Transformer deep learning to estimate energy system flexibility

AbstractThe integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

Developments and Issues in Renewable Ecofuels and Feedstocks

Ecofuels and their feedstock come in three main product classes: electrofuels (e-Fuels), biofuels, and non-biowaste-derived fuels. Ecofuels originate from non-fossil sources, derived from circular raw materials such as non-food organic waste, renewable hydrogen, and captured CO2 through a rapid process of carbon fixation. Proposed regulation drafts under discussion indicate that new fuels would need to reach a substantial degree of climate neutrality. The manufacture of all ecofuels, however, requires energy input to accomplish the conversion of the initial feedstock; their climate neutrality claims stem from the use of renewable electric energy and/or biomasses in the production process, but fossil fuels are still the main primary sources of global (and the EU’s) electric power, and most biofuels consumed in the EU transport mix are still crop-based, with potential conflicts with food and land use. Furthermore, entirely neglecting GHG emissions from renewable energy generation is scientifically debatable, as the impact of the energy (and the related GHG emissions) embedded in the materials used to build renewable energy facilities is small, but not nil. The paper reports ecofuel trends according to the above-mentioned originating technologies and discusses the issues related to their development.

TOWARDS CARBON-NEUTRAL FISH COLD STORAGE: A SUSTAINABLE APPROACH THROUGH RENEWABLE ENERGY

Preserving perishable foods, particularly fish, demands meticulous attention throughout the entire supply chain, from capture to consumption. The critical role of cold storage facilities in maintaining optimal conditions, encompassing temperature and humidity regulation, is indispensable. Nevertheless, these facilities pose a considerable challenge due to their substantial electrical energy consumption, which significantly contributes to operational costs. In addressing this challenge, the implementation of energy-efficient strategies, including the utilization of advanced equipment and real-time monitoring facilitated by automation technologies, becomes imperative. This research investigates an innovative integrated cold storage system situated in Tarrafal, Santiago, Cape Verde, which harmoniously incorporates wind, solar, and tidal energy sources. The primary objectives are to evaluate the energy efficiency, financial viability, and environmental impact across four distinct hypothesis. Hypothesis 1 entails the deployment of two R134a refrigeration units powered by the conventional public grid, while Hypothesis 2 adopts a transcritical CO2 system reliant on grid electricity, Hypothesis 3 integrates CO2 technology with autonomous renewable energy sources and Hypothesis 4 leverages CO2 for refrigeration coupled with seawater heat exchange and autonomous renewable energy generation. The research findings strongly favor Hypothesis 4, demonstrating emissions of 15880 kg CO2eq with an impressive 5-year return on investment. The autonomous electricity production associated with this hypothesis markedly reduces emissions by more than 90%. Despite an initial higher investment, Hypothesis 3 showcases financial viability, contributing significantly to long-term energy sustainability. The autonomous energy production in this scenario results in a noteworthy reduction of more than 600000 kgCO2 compared to conventional systems, underscoring the positive impact of integrating local renewable energy sources.

Three network design problems for community energy storage

AbstractIn this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

The role of renewable energy and carbon dioxide emissions on the ESG market in European Union

AbstractIn view of global climate problems, public interest in the environment has recently evolved over decision economics. Accordingly, this study assesses the impact of carbon emission allowances (CEA), information technology (IT), renewable energy generation (REG), and carbon dioxide (CO2) on the environmental, social, governance (ESG) in the European Union (EU) by applying quantile‐based models from January 2, 2019 to February 29, 2024. The outcomes demonstrate that CEA and IT have an increasing effect on the ESG with the moderating effect of economic policy uncertainty (EPU). REG has a declining effect on the ESG, while the EPU moderates the effect and makes the effect increasing across higher quantiles.

Probabilistic assessment of short‐term voltage stability under load and wind uncertainty

AbstractContemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (>80%) and dynamic load (>75%), as well as encountering a large disturbance under uncertain circumstances.

Synthesis of Cs3Cu2I5 Nanocrystals in a Continuous Flow System.

Achieving the goal of generating all of the world's energy via renewable sources and significantly reducing the energy usage will require the development of novel, abundant, nontoxic energy conversion materials. Here, a cost-efficient and scalable continuous flow synthesis of Cs3Cu2I5 nanocrystals is developed as a basis for the rapid advancement of novel nanomaterials. Ideal precursor solutions are obtained through a novel batch synthesis, whose product served as a benchmark for the subsequent flow synthesis. Realizing this setup enabled a reproducible fabrication of Cs3Cu2I5 nanocrystals. The effect of volumetric flow rate and temperature on the final product's morphology and optical properties are determined, obtaining 21% quantum yield with the optimal configuration. Consequently, the size and morphology of the nanocrystals can be tuned with far more precision and in a much broader range than previously achievable. The flow setup is readily applicable to other relevant nanomaterials. It should enable a rapid determination of a material's potential and subsequently optimize its desired properties for renewable energy generation or efficient optoelectronics.