Hasil untuk "Renewable energy sources"

H. Zabed, J. Sahu, A. Suely et al.

Emre Akusta, Raif Cergibozan

Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar et al.

Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines.

Dongwei Zhao, Stefanos Delikaraogloub, Vladimir Dvorkin Alberto J. Lamadrid L. et al.

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome.

Julien Allard, Noé Diffels, François Vallée et al.

Driven by the ongoing energy transition, shared mobility service providers are emerging actors in electrical power systems which aim to shift combustion-based mobility to electric paradigm. In the meantime, Energy Communities are deployed to enhance the local usage of distributed renewable production. As both ators share the same goal of satisfying the demand at the lowest cost, they could take advantage of their complementarity and coordinate their decisions to enhance each other operation. This paper presents an original Mixed-Integer Second Order Cone Programming long-term Electric Vehicle fleet planning optimization problem that integrates the coordination with a Renewable Energy Community and Vehicle-to-Grid capability. This model is used to assess the economic, energy, and grid performances of their collaboration in a 21 buses low-voltage distribution network. Key results show that, both actors coordination can help reducing the yearly cost up to 11.3 % compared to their stand-alone situation and that it may reduce the stress on the substation transformer by 46 % through the activation of the inherent EVs flexibility when subject to peak penalties from the grid operator.

H. Abele, J. Amaral, W. R. Anthony et al.

Fundamental neutron and neutrino physics at neutron sources, combining precision measurements and theory, can probe new physics at energy scales well beyond the highest energies probed by the LHC and possible future high energy collider facilities. The European Spallation Source (ESS) will in the not too far future be a most powerful pulsed neutron source and simultaneously the world's brightest pulsed neutrino source. The ESS, and neutron sources in general, can provide unprecedented and unique opportunities to contribute to the search for the missing elements in the Standard Model of particle physics. Currently there are no strong indications where hints of the origin of the new physics will emerge. A multi-pronged approach will provide the fastest path to fill the gaps in our knowledge and neutron sources have a pivotal role to play. To survey the ongoing and proposed physics experiments at neutron sources and assess their potential impact, a workshop was held at Lund University in January, 2025. This report is a summary of that workshop and has been prepared as input to the European Strategy Update.

Sigma Ray, Kumari Kasturi, Samarjit Patnaik et al.

Alireza Azarhooshang, Alireza Rezazadeh

Abstract Virtual power plants (VPP) with resources and storages are able to control the active power of the network. They are also connected to the network through an inverter, which is capable of controlling reactive power. Therefore, it is expected that the optimal use of inverter‐based VPP can play an effective role in improving the economic and technical status of the distribution network. So, the operation of a smart distribution system is presented in this paper by considering inverter‐based VPPs constrained to the operator's measures. The weighted sum of expected energy loss (EEL) and voltage security index (VSI) is minimized while considering AC optimal power flow equations, restrictions of network's security, and operating model of the inverter‐based VPPs. Uncertainties with an origin of the amount of demand, renewable energy, and parameters of mobile energy storage are also discussed. The uncertainties are modelled using a stochastic optimization approach relying on the unscented transformation (UT). Evaluating inverter‐based VPP performance, providing models of flexible resources such as responsive loads and mobile storages, checking network voltage security status, and modelling uncertainties using the UT method are among the innovations of this study. According to the results, it is demonstrated that the technical situation of the distribution system is improved with the help of optimal management of the VPP. With energy management of the inverter‐based VPP, the suggested design has succeeded to enhance the operating status (voltage security) of the system by approximately 33–73% (12%) in comparison to power flow studies.

Shangpeng Zhong, Xiaoming Wang, Bin Xu et al.

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic (PV) power prediction that arises due to insufficient data samples for new PV plants. First, a time-series generative adversarial network (TimeGAN) is used to learn the distribution law of the original PV data samples and the temporal correlations between their features, and these are then used to generate new samples to enhance the training set. Subsequently, a hybrid network model that fuses bi-directional long-short term memory (BiLSTM) network with attention mechanism (AM) in the framework of deep & cross network (DCN) is constructed to effectively extract deep information from the original features while enhancing the impact of important information on the prediction results. Finally, the hyperparameters in the hybrid network model are optimized using the whale optimization algorithm (WOA), which prevents the network model from falling into a local optimum and gives the best prediction results. The simulation results show that after data enhancement by TimeGAN, the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Mohammad Dolatabadi, Alberto Borghetti, Pierluigi Siano

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection (CP) and linear programming (LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Srimannarayana Kovvali, Nakka Jayaram, Satya Venkata Kishore Pulavarthi et al.

Conventional energy sources will not be sufficient to meet future electrical demands, and also pollute the environment. Therefore, to meet their electrical energy needs, and maintain clean and green environmental conditions, people are focusing more on renewable energy sources. Small-scale PV solar standalone AC loads or grid integration applications need high voltage at a desired level, transformer/inductor less operation, high gain DC-DC front-end converters, and DC-AC converters. To achieve all the above objectives, this paper proposes a step-up quadruple boost nine-level inverter, it works on switched capacitor technique with a reduced count of components for the application of renewable energy systems. The proposed topology balances the capacitor voltages with the control scheme itself without using any sensors. A level-shifted pulse-width modulation (LPWM) technique can be used in the control strategy of the proposed topology. This paper covers the operational modes of the proposed topology, voltage stress calculations, capacitors calculations, and losses calculations at various stages and compared with recent literature, that reveals this topology is more advantageous in terms of less total standing voltage, switch count, cost factor, better efficiency and the number of gate driver circuits. The theoretical performance can be validated through MATLAB/Simulink-based simulation and their results are validated through prototype experimentation. Further, the experimental results contain modulation index variations, frequency modulation, switching frequency variations, input voltage variations, and load variations. Finally, the max efficiency of 96.5% is achieved for the experimental prototype of the proposed topology.

Andrew Kirby, François‐Xavier Briol, Thomas D. Dunstan et al.

Abstract Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so‐called farm blockage effect. To better predict these effects with low computational costs, we develop data‐driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient CT∗ as a function of turbine layout. We train the model using a multi‐fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high‐fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low‐fidelity data speeds up the learning process and the high‐fidelity data ensures a high accuracy. The trained multi‐fidelity GP model is shown to give more accurate predictions of CT∗ compared to a standard (single‐fidelity) GP regression applied only to a limited set of high‐fidelity data. We also use the multi‐fidelity GP model of CT∗ with the two‐scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.

Jonas P. Pereira, Carlos H. Coimbra-Araújo, Rita C. dos Anjos et al.

Binary coalescences are known sources of gravitational waves (GWs) and they encompass combinations of black holes (BHs) and neutron stars (NSs). Here we show that when BHs are embedded in magnetic fields ($B$s) larger than approximately $10^{10}$ G, charged particles colliding around their event horizons can easily have center-of-mass energies in the range of ultra-high energies ($\gtrsim 10^{18}$ eV) and escape. Such B-embedding and high-energy particles can take place in BH-NS binaries, or even in BH-BH binaries with one of the BHs being charged (with charge-to-mass ratios as small as $10^{-5}$, which do not change GW waveforms) and having a residual accretion disk. Ultra-high center-of-mass energies for particle collisions arise for basically any rotation parameter of the BH when $B \gtrsim 10^{10}$ G, meaning that it should be a common aspect in binaries, especially in BH-NS ones given the natural presence of a $B$ onto the BH and charged particles due to the NS's magnetosphere. We estimate that up to millions of ultra-high center-of-mass collisions may happen before the merger of BH-BH and BH-NS binaries. Thus, binary coalescences can also be efficient sources of ultra-high energy cosmic rays (UHECRs) and constraints to NS/BH parameters would be possible if UHECRs are detected along with GWs.

Marwan Mostafa, Daniela Vorwerk, Johannes Heise et al.

In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carrier. While complete electrification of the energy supply of households and the transportation sector may be the ultimate goal, a transitional phase is necessary as such massive as well as rapid expansion of the electrical distribution grid is infeasible. Additionally, German industries have expressed their plans to use hydrogen as their primary strategy in meeting carbon targets. This poses challenges to the existing electrical, gas, and heating distribution grids. It becomes necessary to integrate the planning and developing procedures for these grids to maximize efficiencies and guarantee security of supply during the transition. The aim of this paper is thus to highlight those challenges and present novel concepts for the integrated planning of the three grids as one multi-energy grid.

Finn Vehlhaber, Mauro Salazar

Electric airplanes are expected to take to the skies soon, finding first use cases in small networks within hardly accessible areas, such as island communities. In this context, the environmental footprint of such airplanes will be strongly determined by the energy sources employed when charging them. This paper presents a framework to optimize aircraft assignment, routing and charge schedules explicitly accounting for the energy availability at the different airports, which are assumed to be equipped with renewable energy sources and stationary batteries. Specifically, considering the daily travel demand and weather conditions forecast in advance, we first capture the aircraft operations within a time-expanded directed acyclic graph, and combine it with a dynamic energy model of the individual airports. Second, aiming at minimizing grid-dependency, we leverage our models to frame the optimal electric aircraft and airport operational problem as a mixed-integer linear program that can be solved with global optimality guarantees. Finally, we showcase our framework in a real-world case-study considering one week of operations on the Dutch Leeward Antilles. Our results show that, depending on weather conditions and compared to current schedules, optimizing flights and operations in a renewable-energy-aware manner can reduce grid dependency from 18 to 100%, whilst significantly shrinking the operational window of the airplanes.

Suresh G., Prasad D., Gopila M.

Yoganand Kumaravelu, Vasanthkumar Periyathambi, Poongundran Udhayanan et al.

A study into pool boiling heat transfer with nanofluids particularly aluminum silicate and cerium (IV) oxide was used to prepare nanofluids. A review of existing nanofluid implementations done previously in multiple literature and research journals was taken into consideration while determining their effects as nanoparticles in necessary base fluids. The nanofluids were prepared with two-step method by dispersing Al2SiO5 and CeO2 nanopowders in water and were analyzed at base temperatures of 50-75°C and peak flux readings taken at saturation temperature. An inference between these and surface modifications due to settlement of nanoparticles on heater surface was studied by SEM imaging, and dispersion was studied with TEM imaging. The volume concentrations of Al2SiO5 and CeO2 nanofluids are varied from 0.1%≤φ≤0.3%. Readings taken at temperatures varied by 5°C between 50°C to 75°C and at 100°C. The improvement of q″ for Al2SiO5/H2O and CeO2/H2O nanofluids is about 120.5±0.6% in PHF over water as base fluids for 0.3% volume concentration solutions.

Gugulethu Nogaya, Nnamdi I. Nwulu, Saheed Lekan Gbadamosi

South Africa is one of the most carbon-intensive economies in the world, but it is presently experiencing an energy crisis, as its utility company cannot meet the country’s energy demands. The use of renewable energy sources and retiring of coal-fired power stations are two important ways of alleviating this problem, as well as decarbonizing the grid. Repurposing retiring coal-fired power stations for renewable energy generation (RCP-RES) while maintaining energy sustainability and reliability has rarely been researched. This paper proposes macro- and microelements for repurposing retiring coal-fired power stations for renewable energy generation in Camden with the aim of improving power generation through a low-carbon system. In this model, concentrated solar power (CSP) and solar photovoltaics (SPV), in combination with storage technologies (STs), were employed for RCP-RES, owing to their excellent levels of availability in the retiring fleet regions. The simulation results show that the power densities of CSP and SPV are significantly lower compared with retiring a coal-fired power plant (CFPP). Both are only able to generate 8.4% and 3.84% rated capacity of the retired CFPP, respectively. From an economic perspective, the levelized cost of electricity (LCOE) analysis indicates that CSP is significantly cheaper than coal technology, and even cheaper when considering SPV with a storage system.

Nicola Cantisani, Tobias K. S. Ritschel, Christian A. Thilker et al.

This paper presents models for renewable energy systems with storage, and considers its optimal operation. We model and simulate wind and solar power production using stochastic differential equations as well as storage of the produced power using batteries, thermal storage, and water electrolysis. We formulate an economic optimal control problem, with the scope of controlling the system in the most efficient way, while satisfying the power demand from the electric grid. Deploying multiple storage systems allows flexibility and higher reliability of the renewable energy system.