Demand For Renewable Energy Research Articles

A hybrid robust-interval optimization approach for integrated energy systems planning under uncertainties

The presence of multiple uncertainties in demand response and renewable energy generation significantly impeded the planning of integrated energy systems (IES). To address this challenge, a novel hybrid robust-interval optimization (HRIO) framework was proposed in this study to facilitate flexible and robust uncertainty planning while reducing IES operation costs. The framework integrated robust optimization and interval analysis to account for the uncertainties associated with renewable energy generation output and demand response. To model IES planning as a deterministic bi-objective optimization problem with investment operation cost and robustness as the optimization objectives, a constrained multi-objective transition algorithm was developed to solve the problem. Simulation results obtained from a typical IES planning case demonstrated the efficacy of the proposed HRIO method in effectively coordinating the relationship between the system's economy, robustness, and operation reliability. This study provided a theoretical guideline for IES operators and exhibited significant potential for engineering applications.

The topology-based approach to leakage current suppression on transformerless photovoltaic inverters

With the increase in demand for renewable energy, photovoltaic power generation is gradually becoming the most promising new energy source. However, the electrical connection of PV cells to the grid, and parasitic capacitance between the PV cell and the ground can introduce severe leakage currents into the grid. The scientific community has developed various topology-based techniques to suppress leakage currents. This paper is dedicated to a review of these techniques. Firstly the H5, HERIC, FB-DCBP, oH5, H8, and H6 are analyzed. These techniques are then reviewed more fully in terms of quality, efficiency, size, and complexity. All six of these topologies can meet the requirements for leakage current suppression, and there is an increasing trend toward reducing the size and simplifying topologies. Finally, future trends in topology-based techniques for suppressing leakage currents are predicted. The significance of this paper is that it provides a comprehensive review and summary of the various topology-based techniques for suppressing leakage currents.

Stochastic analysis of load-transfer mechanism of energy piles by random finite difference model

The surge in demand for renewable energy to combat the ever-escalating climate crisis promotes development of the energy-saving, carbon saving and reduction technologies. Shallow ground-source heat pump (GSHP) system is a promising carbon reduction technology that can stably and effectively exploit subsurface geothermal energy by taking advantage of load-bearing structural elements as heat transfer medium. However, the transformation of conventional geo-structures (e.g. piles) into heat exchangers between the ground and superstructures can potentially induce variable thermal axial stresses and displacements in piles. Traditional energy pile analysis methods often rely on deterministic and homogeneous soil parameter profiles for investigating thermo-mechanical soil-structure interaction, without consideration of soil spatial variability, model uncertainty or statistical uncertainty associated with interpolation of soil parameter profiles from limited site-specific measurements. In this study, a random finite difference model (FDM) is proposed to investigate the thermo-mechanical load-transfer mechanism of energy piles in granular soils. Spatially varying soil parameter profile is interpreted from limited site-specific measurements using Bayesian compressive sensing (BCS) with proper considering of soil spatial variability and other uncertainties in the framework of Monte Carlo simulation (MCS). Performance of the proposed method is demonstrated using an illustrative example. Results indicate that the proposed method enables an accurate evaluation of thermally induced axial stress/displacement and variation in null point (NP) location with quantified uncertainty. A series of sensitivity analyses are also carried out to assess effects of the pile-superstructure stiffness and measurement data number on the performance of the proposed method, leading to useful insights.

Changes in the Vegetable Oil Market, with Particular Emphasis on Market Instability in Relation to the War in Ukraine

In the 21st century, the global production and consumption of oilseeds and their processing products are growing dynamically as a result of the globally growing demand for food and renewable energy. Globally, supply and demand factors have changed the edible vegetable oils industry. The production of vegetable oils is characterized by one of the highest dynamics among all agricultural raw materials, which results from the growing demand for vegetable oils for food, industrial purposes and the growing demand for protein feed. The cultivation of oilseeds plays an important role in Polish agriculture. On an industrial scale, the production of rapeseed oil dominates in Poland, and is a strategic product of the Polish agri-food sector. Sunflower and soybean oils are also of market importance. The scale of crude rapeseed oil production ranks Poland third in the European Union (after Germany and France) and sixth in the world. The country's share in EU rapeseed oil production is about 13%. Poland, despite the dynamic development of rapeseed production and processing which took place after 2004, has low self-sufficiency in the field of oilseed products, especially low in the field of oilseed meals (self-sufficiency at the average level of approx. 43% in 2018-2020) and oils vegetable crops (self-sufficiency at the average level of approx. 63%); therefore, it remains a permanent, large net importer.

Clean Hydrogen Portfolio Standard 기반 Virtual Clean Hydrogen Plant 플랫폼 설계 및 운영 전략

In Korea, there are separate areas for renewable energy concentration and power load concentration. As both renewable energy and power demand increase, transmission constraints lead to saturation of renewable energy. This phenomenon has adverse effects on the benefits of Independent Power Producers (IPPs) and renewable energy acceptance. Policymakers and individual power generation operators must solve this essential problem. This paper proposes a new energy operation system that converts surplus energy generated by transmission constraints into hydrogen and supplies it to load-intensive areas. This system utilizes PV complexes in renewable energy-intensive areas through the Virtual Clean Hydrogen Plant (VCHP) platform. South Korea plans to introduce the Clean Hydrogen Portfolio Standard (CHPS), which provides a fixed premium (FP) to power generators using clean hydrogen. This paper derives a marginal fixed premium that can be used as an indicator of securing the economy of the VCHP platform. The VCHP platform design and operation algorithm consists of two optimizations. The first optimization analyzes the behavior of Photovoltaic (PV) in renewable energy concentration areas, and the second optimization supplies power and heat to load concentration areas through optimal scheduling of Fuel Cell (FC).

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap.

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.

System analysis and experimental investigation of a pendulum-based wave energy converter

Global economic development and environmental protection requirements are increasing the demand for renewable energy rapidly. Wave energy converters are a hotspot in research, and many designs have been proposed. Pendulum-based wave energy converters, because of their sealed structure and high reliability, are drawing researchers' attention. This article considers the concept of a Vertical Axis Parametric Pendulum Wave Energy Converter (VAPPWEC) and focuses mainly on the characteristics of the pendulum system. The dynamic function of the pendulum is proposed. A new evaluation indicator, Cumulative Frequency curves of Angular Velocity (CFAV) is introduced to analyze pendulum motion accurately and evaluate the harvesting performance conveniently. The model test shows the parametric resonance zone of the pendulum with a notable boundary and the pendulum's stability under power take-off (PTO) damping. Additionally, a large-range analysis method based on the Prescribed Excitation Model (PEM) is proposed, and the output power is assessed. Linear damping performs better than constant damping, with a maximum capture width ratio of 39.05%.

Sector Coupling through Vehicle to Grid: A Case Study for Electric Vehicles and Households in Berlin, Germany

A key factor in limiting global warming is the conversion of conventional electricity generation to renewable energy sources. However, a major obstacle is that renewable energy generation and energy demand often do not coincide in time, and energy must therefore be stored temporarily. Vehicle to grid (V2G) can be used to store excess renewable energy in battery electric vehicles (BEVs) and feed it back into the electric grid when needed. For effective V2G operation, the grid may have to be expanded, as the energy needs to be transported to BEVs. However, the grid should only be strengthened where renewable energy demand exceeds current grid capacity due to high grid expansion costs. This requires a method that determines the spatial distribution of V2G potential at a high resolution. Since such a method has not yet been reported in the existing literature, and so is developed in this paper. The method is demonstrated for the city of Berlin and its 448 sub-districts. For each sub-district, the method allows determining the percentage of residential and BEV energy demand that can be met by renewables if V2G is deployed, and answers the question of whether a full renewable supply is possible. The results show that BEVs can be effectively used as intermediate storage for renewable energy. If 30% of the BEVs participate in V2G, more than 99% of the energy demand of households and BEVs in Berlin can be covered by renewables on certain days. On the other hand, V2G deployment increases the average peak load in the districts by up to 100% and results in a nearly double load on vehicle batteries. High shares of renewable energy can be observed in districts with a high degree of motorization, which are predominantly found in the outskirts of the city.

Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2-6-monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions' quality.

Moving toward Net Zero Carbon Buildings to Face Global Warming: A Narrative Review

The increase in global surface temperatures will surpass the 2 °C target set by the Paris Agreement unless carbon emissions are lowered to zero by 2050. To date, the building sector is responsible for 38% of all carbon emissions, thus one of the main targets is represented by the development of building strategies that can facilitate the transition toward carbon-neutral buildings. The main strategies are today represented by nearly zero energy buildings (nZEBs), zero energy buildings (ZEBs)/net zero energy buildings (NZEBs) and net zero carbon buildings (NZCBs). Particularly, NZCBs completely target zero operational and embodied carbon during their life cycles, fulfilling the leadership role in the decarbonization of the construction sector. Moreover, adopting the European Standard EN 15978:2011, carbon emissions can be precisely classified to enhance strategies aimed at reducing them. Commercial viability remains a fundamental economic driver, but the higher initial capital costs hinder the NZCBs. In addition, legislative, socio-cultural, technological, professional and geographical barriers hold back its diffusion. NZCBs can be met by a four-steps program: embodied carbon reduction, operational carbon reduction, increase in renewable energy supply and offset and carbon storage. Circular economy principles are strictly connected to design for disassembly and for adaptability to reduce embodied carbon, while passive design and solar and geothermal energy production can satisfy the renewable energy demand of the building. The aim of this narrative review is to determine and describe which is the current state of the art for NZCB definition, the drivers and barriers toward its application in a broader context and which strategies are eligible to meet the ambitious goal of zero operational and zero embodied carbon emissions.

Peer-to-Peer Energy Trading Using Prediction Intervals of Renewable Energy Generation

The rapid development of renewable energy generation and demand side flexible resource makes the operation of distribution network and the organisation of power market facing greater uncertainty challenges. This paper proposes a novel receding horizon peer-to-peer energy transaction model based on the prediction intervals of renewable energy generation to manage the volatility in the range of a distribution network. A peer-to-peer energy interval matching algorithm is proposed to fully explore the flexibility in demand side for mitigating the output fluctuation of renewable energy generation locally. Then the responsibilities of undertaking the uncertainty risk from renewable generations are assigned to the counter-part consumers who have been matched with the renewable energy generations in a peer-to-peer market. The autonomy energy management problem under distribution network of each consumer is formulated as a cooperative gaming problem using the Nash bargaining theory. The uncertainty risk is considered into the Nash bargaining problem by utilizing voltage chance constraints and conditional value at risk based return-risk utility, of which the quantile connotations are consistent with the quantile results of the probability prediction of renewable energy generations. Moreover, an alternating direction method of multipliers algorithm based distributed methodology is developed to solve the Nash bargaining problem in a distributed manner. Numerical results demonstrate the effectiveness of the presented peer-to-peer energy trading model.

Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response

Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response

Multi-domain optimization of cast iron components in wind turbines

The continuously rising demand for renewable energies leads to increased installations of wind turbines with higher power. While the current power-to-weight ratio of up to 20 metric tons of cast iron per megawatt is stagnating, cast iron components of modern wind turbines are facing new challenges in terms of weight, manufacturability, and castability. These challenges can be addressed by systematically using multi-domain optimization approaches to reduce component weight and increase local component utilization.In order to meet the requirements for modern cast iron components, this multi-domain approach must employ methods from casting simulation, micromechanical analysis, topology optimization, and strength assessment. Here, casting simulation is used to determine local microstructure descriptors, which are subsequently used in micromechanical shakedown analysis to estimate the local microstructure-dependent fatigue strength. In parallel to the fatigue strength estimation, topology optimization is performed iteratively in combination with a castability analysis. The component strength is evaluated using a strength assessment approach based on the previously determined local material properties in combination with the topology optimized component.In this study, the overall concept of the proposed multi-domain approach is presented and requirements for the application of such an approach are formulated. The use case of this study is a planet carrier of a wind turbine gearbox manufactured from austempered ductile cast iron ADI-GJS-1050‑6. For this use case, a weight reduction of 17% was achieved while maintaining the required stiffness, such that the microstructure variance along the component was significantly reduced. Furthermore, the potentials and limitations of the presented approach are outlined and discussed in the context of the design of heavy-section castings.

Numerical investigation of a shell-and-tube thermochemical reactor with thermal bridges: Structurale optimization and performance evaluation

Thermochemical heat storage is a key technology to solve the mismatch between supply and demand of renewable energy and realize long-term energy storage. This work is concerned about a shell-and-tube thermochemical energy storage reactor and its aim is to address a challenge associated the low reaction rate from the tube centre of the reactor due to low thermal conductivity of the storage material. We proposed the integration of porous copper thermal bridges into the reactor and examined the performance enhancement with a validated mathematical model. By optimizing the structural parameters (numbers, thickness, and porosity) of the thermal bridge, the discharging rate and water temperature of the reactor can be maximized while the fan's electricity consumption is reduced. The results show that, with the addition of the thermal bridges with optimal structural parameters, the total heat release and water peak temperature lift can be increased by 11.33% and 39.75%, respectively, with the peak outlet water temperature reaching 59.02 °C. Reducing air velocity from 0.428 m/s to 0.107 m/s, the fan's electricity consumption can be reduced by 85.07% and 91.72% in the charging and discharging processes, which can be reduced by another 62.34% and 16.55%, respectively, after adding the thermal bridge.

An integrative and prospective approach to regional material flow analysis: Modeling the decarbonization of the North Rhine‐Westphalian steel industry

AbstractNorth‐Rhine Westphalia is the center of the German and European steel production. Its steel industry is heavily based on the primary production route and emits up to 30 Mt CO2 annually. One possible and increasingly prominent alternative to reduce these emissions is the hydrogen‐based direct reduction. While this technology allows for a near climate‐neutral production of primary steel, it poses substantial impacts on regional energy and material flows. Hence, the aim of this paper is to quantify the alterations in energy and material flows over time via integrating top‐down energy and material flow models with bottom‐up process models. The resulting values of emissions, energy, and material flows are then used to develop prospective scenarios that depict the requirements and consequences of potential pathways toward a climate‐neutral steel production by 2045. The outcomes show that decarbonizing the North Rhine‐Westphalian steel industry leads to an additional demand for renewable energies of up to 52.5 TWh per year, which represents 10% of the current electricity production in Germany. As securing the green electricity demand is a large challenge, the study also analyzes the impact of a partial recourse to natural gas as a reducing agent in combination with other measures like carbon capture and utilization/storage. The results show that such a recourse would reduce the electricity demand to 36.8 TWh. Hence, the paper illustrates relevant implications of the different scenarios, which can be used by policymakers to develop more realistic and resilient strategies for reaching carbon neutrality.

Geothermal energy production adversely affects a sensitive indicator species within sagebrush ecosystems in western North America

Growing demand for renewable energy has resulted in expansion of energy infrastructure across sagebrush ecosystems of western North America. Geothermal power is an increasingly popular renewable energy source, especially within remote areas, but little is known about the impacts it may have on local wildlife populations. Investigations are warranted given similarities to more conventional surface disturbance activities with well-documented impacts. Using a novel 2-pronged analytical approach, we estimated effects of geothermal energy production activities (hereafter, geothermal) on populations of greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), a species of high conservation concern. First, we applied a before-after-control-impact paired series design at two geothermal sites in Nevada, USA, to estimate absence rates of male sage-grouse from lek sites (breeding grounds) and changes in predicted apparent abundance (λ̂) in relation to geothermal energy infrastructure. We then estimated effects of geothermal energy infrastructure and other environmental covariates on demographic rates. We found λ̂ declined ∼24 % within 5 km of geothermal sites, while lek absence rates (e.g., indicating local extirpation) increased by ∼730 % within 2 km. Our demographic models revealed decreased nest survival in association with proximity to geothermal infrastructure, reduced topographic impedance surface (TIS; a proxy for sound and light dispersion emanating from geothermal infrastructure), and increased density of common ravens (Corvus corax), an effective nest predator. Models also estimated decreases in adult survival in relation to TIS. Finally, we applied population matrix models within a quantitative decision support framework to help guide locations for future geothermal development that balance the need for domestic energy production while minimizing adverse effects on wildlife populations.

Customised Multi-Energy Pricing: Model and Solutions

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated.

Parametric Optimization and Numerical Analysis of GaAs Inspired Highly Efficient I-Shaped Metamaterial Solar Absorber Design for Visible and Infrared Regions

Renewable energy demand is increasing as fossil fuels are limited and pollute the environment. The solar absorber is an efficient renewable energy source that converts solar radiation into heat energy. We have proposed a gallium arsenide-backed solar absorber design made with a metamaterial resonator and SiO2 substrate. The metamaterial resonator is investigated with thin wire metamaterial and I-shaped metamaterial designs. The I-shape metamaterial design outperforms the thin wire metamaterial design and gives 96% average absorption with a peak absorption of 99.95%. Structure optimization is applied in this research paper using parametric optimization. Nonlinear parametric optimization is used because of the nonlinear system results. The optimization method is used to optimize the design and improve the efficiency of the solar absorber. The gallium arsenide and silicon dioxide thicknesses are modified to see how they affect the absorption response of the solar absorber design. The optimized parameter values for SiO2 and GaAs thicknesses are 2500 nm and 1000 nm, respectively. The effect of the change in angles is also investigated in this research. The absorption is high for such a wide angle of incidence. The angle of 30° only shows a lower absorption of about 30–50%. The effect of the change in angles is also investigated in this research. The design results are verified by presenting the E-field results for different wavelengths. The optimized solar absorber design applies to renewable energy applications.