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Processes
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Process Design and Modeling of Low-Carbon Energy Systems
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Process Design and Modeling of Low-Carbon Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 10750

Special Issue Editors

Dr. Chenyu Wu

E-Mail
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: integrated energy system; frequency regulation; electricity market; optimization; game theory
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongkai Yi

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Guest Editor
School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: convex optimization; machine learning; virtual power plant
Special Issues, Collections and Topics in MDPI journals
Dr. Chenhui Lin

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: power system operation; distributed optimization; economic dispatch; machine learning

Special Issue Information

Dear Colleagues,

With the rapid promotion of renewable energy technologies and the trend to a low-carbon society, the positive impacts of a low-carbon energy system that realizes various forms of energy-utilizing improvement and carbon reduction have fully emerged. The technologies involved vary widely, such as synthetic and alternative fuels such as alcohols and ethers, nuclear energy, fuel cells, renewables such as wind and solar, and energy storage technologies of wide varieties. The carbon market is also one of the most cost-effective ways of incentivizing CO2 reductions which put a price on carbon and can be accomplished through either a carbon tax or a cap-and-trade program. All of these are essential components of the future of energy systems.

This Special Issue on “Process Design and Modeling of Low-Carbon Energy Systems” will curate novel advances in research which use modeling, planning, and optimization as essential tools to design energy systems or construct effective electricity markets and carbon markets for pricing carbon dioxide. Topics include, but are not limited to, methods and/or applications in the following areas:

  1. Integrated energy system;
  2. Wind power/ Photovoltaic power generation;
  3. Carbon markets;
  4. Carbon capture technology;
  5. Hydrogen energy systems;
  6. Fuel cell systems.

Dr. Chenyu Wu
Dr. Zhongkai Yi
Dr. Chenhui Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integrated energy system
  • wind power/photovoltaic power generation
  • carbon markets
  • carbon capture technology
  • hydrogen energy systems
  • fuel cell systems

Published Papers (13 papers)

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Research

14 pages, 1911 KiB  
Article
Leveraging Transformer-Based Non-Parametric Probabilistic Prediction Model for Distributed Energy Storage System Dispatch
by Xinyi Chen, Yufan Ge, Yuanshi Zhang and Tao Qian
Processes 2024, 12(4), 779; https://doi.org/10.3390/pr12040779 - 12 Apr 2024
Viewed by 559
Abstract
In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex [...] Read more.
In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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19 pages, 598 KiB  
Article
Interdependent Expansion Planning for Resilient Electricity and Natural Gas Networks
by Weiqi Pan, Yang Li, Zishan Guo and Yuanshi Zhang
Processes 2024, 12(4), 775; https://doi.org/10.3390/pr12040775 - 12 Apr 2024
Viewed by 395
Abstract
This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy [...] Read more.
This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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17 pages, 3095 KiB  
Article
Bi-Level Inverse Robust Optimization Dispatch of Wind Power and Pumped Storage Hydropower Complementary Systems
by Xiuyan Jing, Liantao Ji and Huan Xie
Processes 2024, 12(4), 729; https://doi.org/10.3390/pr12040729 - 4 Apr 2024
Viewed by 456
Abstract
This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for [...] Read more.
This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the minimum generation cost associated with each non-dominated solution in the optimal load allocation. To demonstrate its economic viability and effectiveness, we simulate the proposed approach using real power system operation data and conduct a comparative analysis. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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17 pages, 2801 KiB  
Article
Thermoeconomic Modeling as a Tool for Internalizing Carbon Credits into Multiproduct System Analysis
by José Joaquim C. S. Santos, Pedro Rosseto de Faria, Igor Chaves Belisario, Rodrigo Guedes dos Santos and Marcelo Aiolfi Barone
Processes 2024, 12(4), 705; https://doi.org/10.3390/pr12040705 - 30 Mar 2024
Viewed by 607
Abstract
In the context of emissions, carbon dioxide constitutes a predominant portion of greenhouse gases (GHGs), leading to the use of the term “carbon” interchangeably with these gases in climate-related discussions. The carbon market has emerged as a pivotal mechanism for emission regulation, allowing [...] Read more.
In the context of emissions, carbon dioxide constitutes a predominant portion of greenhouse gases (GHGs), leading to the use of the term “carbon” interchangeably with these gases in climate-related discussions. The carbon market has emerged as a pivotal mechanism for emission regulation, allowing industries that struggle to meet emission reduction targets to acquire credits from those who have successfully curbed their emissions below stipulated levels. Thermoeconomics serves as a tool for analyzing multiproduct systems prevalent in diverse sectors, including sugarcane and alcohol mills, paper and pulp industries, steel mills, and cogeneration plants. These systems necessitate frameworks for equitable cost/emission allocation. This study is motivated by the need to expand the scope of thermoeconomic modeling to encompass expenses or revenues linked with the carbon market. By utilizing a cogeneration system as a representative case, this research aims to demonstrate how such modeling can facilitate the allocation of carbon market costs to final products. Moreover, it underscores the adaptability of this approach for internalizing other pertinent costs, encompassing expenses associated with environmental control devices, licenses, and permits. Although certain exergy disaggregation models depict an environmental component within diagrams, which is integral for addressing environmental burdens, even models without explicit environmental devices can effectively internalize carbon credits and allocate them to final products. The integration of carbon credits within thermoeconomic modeling introduces the capability to assess both the financial and environmental implications of emissions. This integration further incentivizes the reduction in GHGs and supports optimization endeavors concerning system design and operation. In summary, this study delves into the incorporation of carbon market dynamics into thermoeconomic modeling. It demonstrates the potential to allocate carbon-related costs, facilitates comprehensive cost analysis, encourages emission reduction, and provides a platform for enhancing system efficiency across industrial sectors. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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12 pages, 396 KiB  
Article
The Value of Energy Storage in Facilitating Renewables: A Northeast Area Analysis
by Meng Zhu, Yong Sun, Yu Lu, Linwei Sang, Zhongkai Yi, Ying Xu and Kerui Ma
Processes 2023, 11(12), 3449; https://doi.org/10.3390/pr11123449 - 18 Dec 2023
Viewed by 748
Abstract
The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind [...] Read more.
The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind and solar power poses challenges for the stable bundled dispatch of new energy. Leveraging the regulation flexibility of energy storage offers a potential solution to mitigate new energy fluctuations, enhance the flexibility of the hybrid energy systems, and promote bundled dispatch of new energy for external transmission. This paper takes energy storage as an example and proposes a capacity configuration optimization method for a hybrid energy system. The system is composed of wind power, solar power, and energy storage, denoted by the wind–solar–energy storage hybrid energy systems. The objective is to quantify the support provided by energy storage to bundled dispatch of new energy, namely determining the new energy transmission capacity that can be sustained per unit of energy storage. The results demonstrate that the proposed method effectively improves the bundled dispatch capacity of new energy. Moreover, the obtained configuration results can be tailored based on different wind–solar ratios, allowable fluctuation rates, and transmission channel capacities, rendering the approach highly valuable for engineering practicality. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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24 pages, 3527 KiB  
Article
A Novel Hybrid Deep Learning Model for Forecasting Ultra-Short-Term Time Series Wind Speeds for Wind Turbines
by Jianzan Yang, Feng Pang, Huawei Xiang, Dacheng Li and Bo Gu
Processes 2023, 11(11), 3247; https://doi.org/10.3390/pr11113247 - 18 Nov 2023
Viewed by 1133
Abstract
Accurate forecasting of ultra-short-term time series wind speeds (UTSWS) is important for improving the efficiency and safe and stable operation of wind turbines. To address this issue, this study proposes a VMD-AOA-GRU based method for UTSWS forecasting. The proposed method utilizes variational mode [...] Read more.
Accurate forecasting of ultra-short-term time series wind speeds (UTSWS) is important for improving the efficiency and safe and stable operation of wind turbines. To address this issue, this study proposes a VMD-AOA-GRU based method for UTSWS forecasting. The proposed method utilizes variational mode decomposition (VMD) to decompose the wind speed data into temporal mode components with different frequencies and effectively extract high-frequency wind speed features. The arithmetic optimization algorithm (AOA) is then employed to optimize the hyperparameters of the model of the gated recurrent unit (GRU), including the number of hidden neurons, training epochs, learning rate, learning rate decay period, and training data temporal length, thereby constructing a high-precision AOA-GRU forecasting model. The AOA-GRU forecasting model is trained and tested using different frequency temporal mode components obtained from the VMD, which achieves multi-step accurate forecasting of the UTSWS. The forecasting results of the GRU, VMD-GRU, VMD-AOA-GRU, LSTM, VMD-LSTM, PSO-ELM, VMD-PSO-ELM, PSO-BP, VMD-PSO-BP, PSO-LSSVM, VMD-PSO-LSSVM, ARIMA, and VMD-ARIMA are compared and analyzed. The calculation results show that the VMD algorithm can accurately mine the high-frequency components of the time series wind speed, which can effectively improve the forecasting accuracy of the forecasting model. In addition, optimizing the hyperparameters of the GRU model using the AOA can further improve the forecasting accuracy of the GRU model. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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28 pages, 5729 KiB  
Article
Maximum Power Point Tracking Constraint Conditions and Two Control Methods for Isolated Photovoltaic Systems
by Jingxun Fan, Shaowu Li, Sanjun Liu, Xiaoqing Deng and Xianping Zhu
Processes 2023, 11(11), 3245; https://doi.org/10.3390/pr11113245 - 17 Nov 2023
Viewed by 584
Abstract
A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation [...] Read more.
A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation transformer into a basic PV system allows for moderate values of the converter duty cycle and electrical isolation. However, there is no comprehensive research on MPPT (maximum power point tracking) constraint conditions for different isolated PV systems, which seriously hinders the application of isolated PV systems and the development of a related linear control theory. Therefore, in this paper, the overall mathematical models of different isolated PV systems are first established based on the PV cell engineering model and the MPP linear model, and then, two sets of constraint conditions are found for the successful realization of MPPT. These MPPT constraint conditions (MCCs) describe in detail the direct mathematical relationships between PV cell parameters, weather conditions and circuit parameters. Finally, based on the MPP linear model and MCCs, two new MPPT methods are designed for isolated PV systems. Considering the MCCs proposed in this paper, a suitable range of load and transformer ratios can be estimated from the measured data of irradiance and temperature in a certain area, and the range of MPPs existing in PV systems with different structures can be estimated, which is a good guide for circuit design, theoretical derivation and product selection for PV systems. Meanwhile, comparative experiments confirm the rapidity and accuracy of the two proposed MPPT methods, with the MPPT time improving from 0.23 s to 0.03 s, and they have the advantages of a simple program, small computational volume and low hardware cost. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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13 pages, 1022 KiB  
Article
Reinforcement Learning and Stochastic Optimization with Deep Learning-Based Forecasting on Power Grid Scheduling
by Cheng Yang, Jihai Zhang, Wei Jiang, Li Wang, Hanwei Zhang, Zhongkai Yi and Fangquan Lin
Processes 2023, 11(11), 3188; https://doi.org/10.3390/pr11113188 - 8 Nov 2023
Cited by 1 | Viewed by 951
Abstract
The emission of greenhouse gases is a major contributor to global warming. Carbon emissions from the electricity industry account for over 40% of the total carbon emissions. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and [...] Read more.
The emission of greenhouse gases is a major contributor to global warming. Carbon emissions from the electricity industry account for over 40% of the total carbon emissions. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable environment is challenging. To address the issue, we propose a grid dispatching technique that combines deep learning-based forecasting technology, reinforcement learning, and optimization technology. Deep learning-based forecasting can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the optimization method, we simplify the complex electricity environment to speed up the solution. The combination of proposed deep learning-based forecasting and stochastic optimization with online data augmentation is used to address the uncertainty of the dispatch system. A multi-agent reinforcement learning method is proposed to utilize team reward among energy storage devices. At last, we achieved the best results by combining reinforcement and optimization strategies. Comprehensive experiments demonstrate the effectiveness of our proposed framework. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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17 pages, 5207 KiB  
Article
Analysis of Heat Transfer of the Gas Head Cover of Diaphragm Compressors for Hydrogen Refueling Stations
by Shengdong Ren, Xiaohan Jia, Jiatong Zhang, Dianbo Xin and Xueyuan Peng
Processes 2023, 11(8), 2274; https://doi.org/10.3390/pr11082274 - 28 Jul 2023
Cited by 3 | Viewed by 1165
Abstract
The inadequate ability to dissipate heat of the gas head cover of the diaphragm compressor will result in its excessive temperature, which will put the operation of the hydrogen filling station at risk for safety issues and raise operating costs. This paper analyzed [...] Read more.
The inadequate ability to dissipate heat of the gas head cover of the diaphragm compressor will result in its excessive temperature, which will put the operation of the hydrogen filling station at risk for safety issues and raise operating costs. This paper analyzed the structure and the heat transfer characteristics of the gas head cover, along with the relevant heat transfer boundaries, based on which a finite element simulation model of the temperature distribution was established. A test rig for the temperature test of a 22 MPa diaphragm compressor was built to validate this simulation model. The results indicated that the simulated temperatures agree well with the measured values, and the deviation is within 9.1%. Further, this paper proposed two head cover structures for enhancing the heat transfer according to the temperature field distribution characteristics, and the simulation and experimental verification were carried out, respectively. The findings demonstrate that the method of enhancing heat transfer around the centre area is more effective, reducing the highest temperature by 14.1 °C, because it greatly lowers thermal conduction resistance, which is the principal impediment to the heat dissipation of the gas head cover. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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17 pages, 2323 KiB  
Article
Optimal Allocation Method of Circuit Breakers and Switches in Distribution Networks Considering Load Level Variation
by Guodong Huang, Yi Zhou, Chen Yang, Qiong Zhu, Li Zhou, Xiaofeng Dong, Junting Li and Junpeng Zhu
Processes 2023, 11(8), 2235; https://doi.org/10.3390/pr11082235 - 25 Jul 2023
Viewed by 713
Abstract
Reliability is a fundamental concept for power systems, and the optimal placement of switchable devices is a valuable tool for improvements in this area. The goal of this paper is to propose an optimal allocation method for circuit breakers and switches that can [...] Read more.
Reliability is a fundamental concept for power systems, and the optimal placement of switchable devices is a valuable tool for improvements in this area. The goal of this paper is to propose an optimal allocation method for circuit breakers and switches that can break the cost–reliability dilemma and simultaneously achieve reliability and economic improvement in terms of the distribution network. Moreover, in view of the fact that variations in the load level can affect the reliability of the distribution network, the variations of different load level scenarios are considered in this paper, where a mixed integer linear programming (MILP) model based on fictitious fault flows is established to derive the optimal allocation scheme that can adapt to the changes of multiple scenarios regarding the load. Meanwhile, due to the constraints of reliability indices, the post-fault reconfiguration scheme of a distribution network under different load level scenarios can also be obtained to enhance its overall reliability. Finally, the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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17 pages, 2936 KiB  
Article
Distributed Shared Energy Storage Double-Layer Optimal Configuration for Source-Grid Co-Optimization
by Meng Yang, Yihan Zhang, Junhui Liu, Shuo Yin, Xing Chen, Lihui She, Zhixin Fu and Haoming Liu
Processes 2023, 11(7), 2194; https://doi.org/10.3390/pr11072194 - 21 Jul 2023
Cited by 1 | Viewed by 829
Abstract
Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. [...] Read more.
Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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18 pages, 8961 KiB  
Article
Investigation of the Interface Effects and Frosting Mechanism of Nanoporous Alumina Sheets
by Song He, Heyun Liu, Yuan Zhang, Haili Liu and Wang Chen
Processes 2023, 11(7), 2019; https://doi.org/10.3390/pr11072019 - 6 Jul 2023
Viewed by 622
Abstract
Nanoporous alumina sheets can inhibit the growth of the frost layer in a low-temperature environment, which has been widely used in air-conditioning heat exchangers. In this study, nanoporous alumina sheets with pore diameters of 30 nm, 100 nm, 200 nm, 300 nm, and [...] Read more.
Nanoporous alumina sheets can inhibit the growth of the frost layer in a low-temperature environment, which has been widely used in air-conditioning heat exchangers. In this study, nanoporous alumina sheets with pore diameters of 30 nm, 100 nm, 200 nm, 300 nm, and 400 nm were prepared by using the anodic oxidation method with the conventional polished aluminum sheet as the reference. A comprehensive and in-depth analysis of the frosting mechanism has been proposed based on the contact angle, specific surface area, and fractal dimension. It was found that compared with the polished aluminum sheet, the nanoporous alumina sheets had good anti-frost properties. Due to its special interface effects, the porous alumina sheet with a 100 nm pore diameter had strong anti-frost performance under low temperatures and high humidity. In an environment with low surface temperature and high relative humidity, it is recommended to use hydrophilic aluminum fins with large specific areas and small fractal dimensions for the heat exchange fins of air source heat pump air conditioning systems. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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18 pages, 3797 KiB  
Article
Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm
by Liqun Liu and Yang Li
Processes 2023, 11(7), 1957; https://doi.org/10.3390/pr11071957 - 28 Jun 2023
Cited by 5 | Viewed by 1056
Abstract
With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, [...] Read more.
With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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