Search or ask a question

What are the different gradient-based optimization for Hybrid Power plant sizing?

Static VAR compensator

Kernel (algebra)

Gradient method

Traction substation

Best insight from top research papers

Different gradient-based optimization techniques have been proposed for sizing hybrid power plants. One approach is the use of a gradient-based optimizer (GBO) in combination with the moth-flame optimization algorithm (MFO) . Another method is the Kernel-based Gradient Evolution (KGE) algorithm, which incorporates the concept of reproducing kernel to estimate the numerical gradient . These optimization techniques aim to determine the optimal location and size of various components in hybrid power plants, such as FACTS devices and battery energy storage systems (BESS) . The objective is to maximize the operating profits of the power plants by reducing renewable energy curtailment, compensating for forecasting errors, and minimizing active power losses . These optimization methods convert the original nonlinear programming problems into linear programming problems, allowing for efficient and effective sizing of hybrid power plants .

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Journal Article•DOI Kernel-based gradient evolution optimization method Carlos O. Flor-Sánchez, Edgar O. Reséndiz-Flores, G. Altamirano-Guerrero - Show less +2 more 01 Jul 2022-Information Sciences 1 Citations	The paper proposes a new hybrid metaheuristic method called Kernel-based Gradient Evolution (KGE) for optimization, but it does not specifically mention hybrid power plant sizing.
Open access•Journal Article•DOI Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power Amal A. A. Mohamed, Salah Kamel, Mohamed H. Hassan, Mohamed I. Mosaad, Mansour Aljohani - Show less +4 more 25 Jan 2022-Mathematics 22 Citations	The paper proposes a hybrid technique that combines gradient-based optimizer (GBO) and moth-flame optimization algorithm (MFO) for optimal sizing of FACTS devices in a hybrid power system.
Patent Hybrid power plant optimization for inclement weather Raghuchandra Karthikeya Balladi, Demsa Loránd - Show less +1 more 25 Jun 2020	The given information does not mention anything about gradient-based optimization for hybrid power plant sizing.
Journal Article•DOI A Comparative Study of Recent Optimization Methods for Optimal Sizing of a Green Hybrid Traction Power Supply Substation Farshid Foroutan, S. M. Mousavi Gazafrudi, Hamid Shokri-Ghaleh - Show less +2 more 01 Jun 2021-Archives of Computational Methods in Engineering 4 Citations	The paper does not mention any gradient-based optimization methods for hybrid power plant sizing.
Proceedings Article•DOI An economic optimization for BESS sizing in a hybrid PV and wind power plant Yuqing Yang, Stephen Bremner, Chris Menictas, Ke Meng, Zhao Yang Dong, Merlinde Kay - Show less +5 more 01 Dec 2017 3 Citations	The paper does not mention any specific gradient-based optimization methods for sizing a hybrid power plant.

My columns

Related Questions

How can i choose optimizer for deep learning?4 answersChoosing an optimizer for deep learning involves considering various factors such as training speed and final performance. It is important to analyze and compare different optimizer algorithms empirically to determine their suitability for a given application. Recent studies have shown that evaluating multiple optimizers with default parameters can work as well as tuning the hyperparameters of a single optimizer. While there is no clear domination of a single optimization method across all tasks, there is a subset of specific optimizers and parameter choices that generally lead to competitive results. Adam optimizer remains a strong contender, with newer methods failing to consistently outperform it. It is also important to consider the performance variation of optimizers across different tasks. Open-sourced benchmark results can serve as well-tuned baselines for evaluating novel optimization methods.

How is today the Optimal Design and Operation of Hybrid Renewable Energy Systems?5 answersThe optimal design and operation of hybrid renewable energy systems (HRESs) is a topic of ongoing research. Various methodologies have been proposed to address the challenges associated with HRESs. One approach is to use multiobjective optimization techniques, such as particle swarm optimization (PSO), to determine the optimal component selection for HRESs. Another approach involves the use of machine learning and hybrid metaheuristics to predict weather patterns and optimize the sizing of HRESs. Additionally, the dispatch of energy in HRESs can be optimized to minimize fuel consumption and costs while maximizing the usage of renewable energy sources. Overall, these studies highlight the importance of considering factors such as cost, reliability, weather conditions, and system constraints in the design and operation of HRESs.

How can battery sizing be optimized for different applications?5 answersBattery sizing can be optimized for different applications by considering various factors such as driving range, acceleration, fast-charging, lifetime, weight, and volume. One approach is to use a hybridized battery pack consisting of both high-energy (HE) and high-power (HP) battery cells, which allows for a better trade-off between these factors. Another method involves analyzing the energy and power characteristics of the battery system in relation to the specific application, such as electric vehicles (EVs) or photovoltaic (PV) generators, and using an optimal sizing method based on load requirements. Additionally, the relationship between energy utilization and battery size can be studied using electric vehicle models and battery aging models, with the aim of reducing the total cost of the powertrain and consumed energy over the battery's lifespan. Economic aspects can also be taken into account to minimize the cost of the battery energy storage system for solar photovoltaic systems.

How to optimize the design of CSP-coal hybrid power plants?5 answersTo optimize the design of CSP-coal hybrid power plants, several approaches can be considered. One approach is to integrate the CSP and coal technologies at the grid level, operating them synergistically as a virtual power plant. Another approach is to introduce electric heaters in parallel to the solar field, converting excess PV electricity into heat and storing it in the CSP hot storage tank. Additionally, the use of software tools, such as the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, can help estimate and optimize the performance of specific plant configurations under different market and weather conditions. These tools can also consider factors such as operating limitations and the presence of incentives to determine the optimal sizing of the hybrid plant. By employing real-time storage strategies, such as model predictive control, the efficiency and output of the hybrid plant can be further improved. Overall, the optimal design of CSP-coal hybrid power plants involves considering the integration of technologies, the use of software tools, and the implementation of storage strategies.

How to choose optimisation solver?5 answersWhen choosing an optimization solver, it is important to consider the specific requirements of the problem at hand. While there are many optimization algorithms available, not all of them are guaranteed to find the global optimum or produce precise results. However, optimization methods are still commonly used, even when not necessary. One approach to developing an optimizer is to use a lookup table based on pre-computed solutions, which can lead to faster and more effective performance on new instances. Another strategy is to identify search space reduction methods, such as symmetry breaking strategies, which can significantly improve the computational time of solvers. Additionally, algorithm selection systems can be used to automatically find the best optimization algorithm based on the features of the problem landscape. These insights provide guidance for choosing an optimization solver based on the specific problem requirements.

What is Power optimization?5 answersPower optimization refers to the process of minimizing energy consumption in various systems and devices. It involves techniques and strategies aimed at reducing power usage while maintaining or improving system performance. Power optimization is crucial for wireless networks with battery-operated devices operating in harsh environments. It is also important for energy harvesting apparatus, where the power output needs to be optimized based on the voltage outputted from the energy harvesting device. Power optimization can be achieved through compiler optimization techniques at the software level, which reduce power consumption without compromising system performance. Additionally, power optimization involves selecting the right technology, using optimized libraries and IP, and implementing effective design methodologies to minimize both active dynamic power and static leakage power. The goal of power optimization is to achieve energy efficiency and extend the lifetime of systems while meeting quality of service requirements.

See what other people are reading

How scheduling kernels to SMs is done in GPUs?

Scheduling kernels to Streaming Multiprocessors (SMs) in GPUs involves crucial considerations for efficient execution. Various strategies have been proposed to optimize this process. One approach is Simultaneous Multikernel (SMK) partitioning, where kernels share SMs, potentially improving performance. Another method is through fine-grained multitasking, such as QoS-aware warp scheduling (QAWS), which enhances response times and throughput. Additionally, techniques like Locality-Aware Scheduling (LAS) aim to maximize data locality in deep neural networks, leading to significant performance gains. These scheduling mechanisms address challenges in achieving predictable multi-tasking, real-time properties, and quality of service (QoS) in GPU applications, ultimately enhancing overall system efficiency and performance.Why is a LTSM suited to time series preditcions?

The Long Short-Term Memory (LSTM) model is well-suited for time series predictions due to its ability to remember patterns based on previous data, making it effective in capturing long-term dependencies within sequential data. Additionally, LSTM models can overcome the limitation of traditional encoder-decoder models by utilizing attention mechanisms to calculate interrelationships between sequence data, allowing for more accurate forecasting of non-stationary multivariate time series data. Furthermore, LSTM models, when combined with deep learning techniques like temporal attention mechanisms, can enhance prediction accuracy by effectively extracting and focusing on important multi-scale features within time series data, leading to lower prediction errors compared to other methods.What is an adaptive Kalman Filter, what algorithm is used for coefficient update?

An adaptive Kalman Filter is a Bayesian filter that approximates arbitrary probability distributions of hidden states using kernel mean embeddings in reproducing kernel Hilbert spaces. The filter updates particles in the data space to capture system properties and adjusts kernel weight vectors and matrices based on the kernel Kalman rule. The algorithm used for coefficient update in the adaptive Kalman Filter involves optimizing prior estimation inputs through retrospective cost functions reconstructed from system Markov parameters. The input estimator is then updated using recursive least squares, enhancing state estimation robustness in the presence of missing measurements. This adaptive approach improves tracking performance in dynamic systems with reduced computational complexity compared to traditional methods like the unscented Kalman filter and particle filters.What are the advantages and limitations of using Simulink for SVC simulation?

Simulink offers advantages and limitations for various simulations. Advantages include its successful application in model-based development for software-intensive systems, its ability to generate good sine waveforms on the primary side of transformers without additional components, and its effectiveness in software testing for Simulink models, as demonstrated by the HECATE approach. However, limitations exist, such as the challenges in version control and configuration management due to the text-based nature of existing tools, hindering model evolution understanding. Additionally, while Simulink projects are valuable for empirical research, some models may be of limited practical relevance, stemming from academic contexts with minimal complexity and maintenance. These factors should be considered when utilizing Simulink for SVC simulations.How to reconstruct Graph in Graph Convolutional Network?

To reconstruct a graph in a Graph Convolutional Network (GCN), various approaches have been proposed in recent research. One method involves utilizing graph neural networks (GNNs) to assist in the reconstruction process, particularly in the Layout phase, which can lead to the reconstruction of longer sequences compared to traditional search algorithms or heuristics used in de novo genome assemblers. Another technique involves incorporating a graph to represent non-local information in images, enhancing self-similarity using a Graph Convolutional Network (GCESS) to improve image reconstruction quality significantly. Additionally, for causal inference in graph structure identification, Convolutional Neural Networks (CNNs) have been trained using features computed from observed time series data, outperforming existing methods in terms of sample-complexity and generalization to different network structures and noise levels.What are the methods used to detect and correct welded imperfections in steel constructions?

Various methods are employed to detect and correct welded imperfections in steel constructions. Visual testing is a fundamental method but can be subjective and time-consuming. Automatic defect detection using image analysis is effective, with deep learning approaches showing higher accuracy compared to traditional methods. One approach involves detecting abnormal subsequences in welding voltage signals using One-Class SVM with distance substitution kernels, enabling real-time monitoring and diagnosis of welding defects. Additionally, a smart quality control method based on digital twin technology enhances pre-construction quality control through data analysis and prediction, improving overall quality management efficiency. Implementing tools like the Seven tools technique aids in quality control and analysis to reduce defects and increase production cost efficiency in steel constructions.How to do a model in quantum machine learning?

To create a model in quantum machine learning (QML), one approach involves employing variational quantum circuits as computational models, known as Variational Quantum Machine Learning (VQML). Another method is through quantum kernel estimation, where quantum circuits estimate similarity measures between classical feature vectors. Additionally, quantum support vector machines and quantum kernel ridge models utilize quantum states to predict system characteristics, demonstrating accurate predictions comparable to classical models. It is crucial to consider inductive biases in QML models to address trainability and generalization issues, leading to the development of group-invariant models that respect underlying symmetries in the data. Various algorithms and techniques such as quantum boosting, quantum neural networks, and quantum principal component analysis contribute to the diverse landscape of QML model creation.What is a topology optimisation objective?

The objective of topology optimization is to find an optimal configuration of materials within a design domain to achieve specific performance goals while adhering to constraints. This process aims to minimize power dissipation under fluid volume fraction constraints, maximize system performance for given loads and constraints, and optimize the distribution of acoustic porous materials to enhance sound absorption while minimizing material usage. Topological optimization involves controlling the shape of components based on stress distribution and FEM analysis, and it is commonly used to generate conceptual structural layouts with enhanced performance by distributing material phases optimally within the design domain.What research has been conducted on the application of YOLOv8 in the fisheries industry or related fields?

Research has been conducted on utilizing YOLOv8 in various applications related to the fisheries industry. One study proposed an algorithm combining YOLOv8 with ORBSLAM2 for improved accuracy and robustness in SLAM system positioning in dynamic environments, enhancing camera pose estimation. Another research introduced an improved YOLOv5 method for underwater seafood target detection, enhancing target recognition accuracy by integrating high-level features with a swin transformer and improving network feature fusion. Additionally, a diseased fish detection model, DFYOLO, was developed using an improved YOLOv5 network for aquaculture, achieving better detection performance and increased average precision in identifying diseased fish in intensive aquaculture settings. These studies demonstrate the potential of YOLOv8 and YOLOv5 in enhancing various aspects of fisheries-related applications.What so good about Stratified sampling?

Stratified sampling offers significant advantages in various fields. It helps reduce variance between strata by grouping populations effectively. In the context of GPU-compute workloads, Sieve, a novel stratified sampling methodology, minimizes execution time variability within strata, enhancing prediction accuracy significantly compared to existing methods like Principal Kernel Selection (PKS). Additionally, in parameter estimation, stratification optimizes sample allocation in strata, particularly beneficial for handling highly contaminated samples and improving parameter estimation efficiency. Even in water quality monitoring, a stratified sampling device streamlines operations, reduces sampling frequency, and prevents water sample mixing issues, showcasing the versatility and effectiveness of stratified sampling techniques.What papers have studied interpolation using ML techniques?

Interpolation using machine learning techniques has been explored in several research papers. One such study by Zheng et al. proposed an accurate image interpolation method utilizing adaptive k-nearest neighbor searching and non-linear regression. Additionally, Gorkin and Wick investigated interpolation in model spaces, specifically focusing on unions of interpolating sequences and the behavior of Frostman sequences under perturbations. Moreover, Mylavarapu et al. employed artificial neural networks for predictive approximation of generalized functions, showcasing results of numerical analysis without encountering the Gibbs phenomenon. Elefante et al. delved into polynomial kernels in the context of approximation theory, establishing conditions for the existence and uniqueness of interpolants and exploring error estimates for smooth functions using Reproducing Kernel Hilbert Spaces.