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Control and Optimization Technologies in Renewable Energy and Integrated Energy...
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Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 9114

Special Issue Editors

Dr. Temitayo Olowu

E-Mail Website
Guest Editor
Energy & Environment Science & Technology, Idaho National Laboratory, Idaho Falls, ID 83415, USA
Interests: renewable energy systems integration; power systems' control and optimization; power electronics control; machine learning
Dr. Raghav Khanna

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA
Interests: renewable energy systems; power electronics; transactive energy; wide bandgap semiconductor device modeling and characterization

Special Issue Information

Dear Colleagues,

With increasing penetration of renewable energy resources, there has been growing concern over their impacts on the stability, reliability, and resiliency of the grid, especially during extreme events. Consequently, there is a growing need to develop novel control and optimization techniques to address these issues. This could involve leveraging the flexibility and the controllability of several renewable energy resources as well as developing hybrid power sources, such as solar PVs, energy storage, hydro power plants, nuclear power plants, fuel cells, amongst others. This Special Issue aims to inform the community about recent advancements in these and other areas. Topics of interest include but are not limited to:

  • Power electronics controls for renewable energy systems
  • Integrated energy system optimal dispatch
  • Optimal power flow in smart grids
  • Hydrogen generation in integrated energy systems
  • Thermal energy systems
  • Grid integration of electric vehicles
  • Inverter-based resource controls and optimization
  • Hybrid energy storage systems
  • Machine learning applications in smart grids

Dr. Temitayo Olowu
Dr. Raghav Khanna
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. Electronics is an international peer-reviewed open access semimonthly 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

  • renewable energy systems
  • optimization
  • power electronics control
  • integrated energy systems

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Published Papers (11 papers)

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Research

30 pages, 2746 KiB  
Article
Optimizing Microgrid Performance: Integrating Unscented Transformation and Enhanced Cheetah Optimization for Renewable Energy Management
by Ali S. Alghamdi
Electronics 2024, 13(22), 4563; https://doi.org/10.3390/electronics13224563 - 20 Nov 2024
Viewed by 258
Abstract
The increased integration of renewable energy sources (RESs), such as photovoltaic and wind turbine systems, in microgrids poses significant challenges due to fluctuating weather conditions and load demands. To address these challenges, this study introduces an innovative approach that combines Unscented Transformation (UT) [...] Read more.
The increased integration of renewable energy sources (RESs), such as photovoltaic and wind turbine systems, in microgrids poses significant challenges due to fluctuating weather conditions and load demands. To address these challenges, this study introduces an innovative approach that combines Unscented Transformation (UT) with the Enhanced Cheetah Optimization Algorithm (ECOA) for optimal microgrid management. UT, a robust statistical technique, models nonlinear uncertainties effectively by leveraging sigma points, facilitating accurate decision-making despite variable renewable generation and load conditions. The ECOA, inspired by the adaptive hunting behaviors of cheetahs, is enhanced with stochastic leaps, adaptive chase mechanisms, and cooperative strategies to prevent premature convergence, enabling improved exploration and optimization for unbalanced three-phase distribution networks. This integrated UT-ECOA approach enables simultaneous optimization of continuous and discrete decision variables in the microgrid, efficiently handling uncertainty within RESs and load demands. Results demonstrate that the proposed model significantly improves microgrid performance, achieving a 10% reduction in voltage deviation, a 10.63% decrease in power losses, and an 83.32% reduction in operational costs, especially when demand response (DR) is implemented. These findings validate the model’s efficacy in enhancing microgrid reliability and efficiency, positioning it as a viable solution for optimized performance under uncertain renewable inputs. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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29 pages, 11145 KiB  
Article
Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm
by Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink and Aaron Werth
Electronics 2024, 13(20), 4055; https://doi.org/10.3390/electronics13204055 - 15 Oct 2024
Viewed by 740
Abstract
In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially [...] Read more.
In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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16 pages, 12121 KiB  
Article
Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms
by Li Yang and Qiaoni Zhao
Electronics 2024, 13(18), 3610; https://doi.org/10.3390/electronics13183610 - 11 Sep 2024
Viewed by 483
Abstract
Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and [...] Read more.
Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and time-consuming to repeatedly debug the system on-site. To save research costs and shorten research cycles, a hardware-in-the-loop (HIL) testing system was built to provide a convenient testing environment for the research of FESSs on wind farms. The focus of this study is the construction of mathematical models in the HIL testing system. Firstly, a mathematical model of the FESS main circuit is established using a hierarchical method. Secondly, the principle of the permanent magnet synchronous motor (PMSM) is analyzed, and a nonlinear dq mathematical model of the PMSM is established by referring to the relationship among d-axis inductance, q-axis inductance, and permanent magnet flux change with respect to the motor’s current. Then, the power grid and wind farm test models are established. Finally, the established mathematical models are applied to the HIL testing system. The experimental results indicated that the HIL testing system can provide a convenient testing environment for the optimization of FESS control algorithms. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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22 pages, 25802 KiB  
Article
The Robust Optimization of Low-Carbon Economic Dispatching for Regional Integrated Energy Systems Considering Wind and Solar Uncertainty
by Mingguang Zhang, Bo Wang and Juan Wei
Electronics 2024, 13(17), 3480; https://doi.org/10.3390/electronics13173480 - 2 Sep 2024
Viewed by 593
Abstract
In this paper, a two-stage robust optimization approach is employed to address the variability in renewable energy output by accounting for the uncertainties associated with wind and solar energy. The model aims to achieve a balanced system that is both low-carbon and economically [...] Read more.
In this paper, a two-stage robust optimization approach is employed to address the variability in renewable energy output by accounting for the uncertainties associated with wind and solar energy. The model aims to achieve a balanced system that is both low-carbon and economically efficient while also being resilient to uncertainties. Initially, a regional integrated energy system model is developed, integrating electricity, gas, and heat. The variability of wind and photovoltaic power outputs is represented using a modifiable uncertainty set. A resilient optimal scheduling model is formulated in two stages, with the objective of minimizing costs under worst-case scenarios. This model is solved iteratively through a column and constraint generation approach. Additionally, the scheduling model incorporates horizontal time shifts and vertical complementary substitutions for carbon trading costs and demand-side loads to avoid excessive conservatism and to manage carbon emissions and energy trading in the regional integrated energy system (RIES). Results show that the two-stage robust optimization approach significantly enhances the system’s resilience to risks and minimizes economic losses. The inclusion of carbon trading mechanisms and the demand response prevents the system from becoming overly robust, which could impede economic growth, while also reducing carbon emissions. The proposed method effectively achieves balanced optimal scheduling for a robust, economical, and low-carbon system. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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14 pages, 2817 KiB  
Article
A Prosumer Hydro Plant Network as a Sustainable Distributed Energy Depot
by Michał Morawski and Przemysław Ignaciuk
Electronics 2024, 13(15), 3043; https://doi.org/10.3390/electronics13153043 - 1 Aug 2024
Viewed by 529
Abstract
The shortage of efficient, low-cost storage depots inhibits the large-scale adoption of volatile-by-nature, renewable sources of energy (RSEs). In this paper, we outline how to utilize prosumer-owned hydro plants of a few to several kW as a distributed, short-term energy storage solution that [...] Read more.
The shortage of efficient, low-cost storage depots inhibits the large-scale adoption of volatile-by-nature, renewable sources of energy (RSEs). In this paper, we outline how to utilize prosumer-owned hydro plants of a few to several kW as a distributed, short-term energy storage solution that is deployable with little investment and a low operational expenditure. The proposed solution is a system of interconnected hydro depots with an active water-flow control algorithm that reduces the grid’s load variability and benefits prosumers. According to the tests conducted, prosumer revenue grows from several percent to over 30 percent, depending on weather conditions, in comparison to the free-flow case. In turn, the cushioning effect of the distributed energy buffer balances the fluctuations introduced by other RSEs, e.g., photovoltaic- or wind-based ones. Hence, while benefitting the involved parties, it also facilitates the inclusion of RSEs within the power distribution system. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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13 pages, 1134 KiB  
Article
Mitigation Strategy of Neutral-Point DC for Transformer Caused by Metro Stray Currents
by Aimin Wang, Sheng Lin, Guoxing Wu and Xiaopeng Li
Electronics 2024, 13(13), 2467; https://doi.org/10.3390/electronics13132467 - 24 Jun 2024
Viewed by 623
Abstract
Metro stray currents flowing into neutral-point-grounded transformers can cause serious direct current (DC) bias. Affected by both metro train and urban power grid operations, transformer neutral-point DC caused by metro stray current is complex, dynamic, and time-varying, which changes the DC bias risk [...] Read more.
Metro stray currents flowing into neutral-point-grounded transformers can cause serious direct current (DC) bias. Affected by both metro train and urban power grid operations, transformer neutral-point DC caused by metro stray current is complex, dynamic, and time-varying, which changes the DC bias risk level of transformers. Thus, just installing blocking devices (BDs) at transformer neutral points with high instantaneous DC may make it difficult to comprehensively mitigate DC bias and lead to increased BD installation. To solve this, through optimizing BD installation placements, a mitigation method for transformer DC bias is proposed. In the proposed method, the DC bias risk level and BD installation number are included in the constraint and objective functions. To evaluate the risk level of transformer DC bias, four indicators are proposed considering the distribution characteristics of neutral DC. To optimize the BD installation placements, the effects of both the metro train dynamic operation and the operation topology of the urban power grid on neutral DCs are considered. The Monte Carlo method is used to sample the train operation conditions and a relation matrix among transformers is established. Applying the method to a certain power system, the result of BD installation placements shows that the transformers supplying the metro system must have BDs installed at their neutral points. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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29 pages, 2164 KiB  
Article
Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting
by Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, Dimitrios Bargiotas and Lefteri H. Tsoukalas
Electronics 2024, 13(10), 1996; https://doi.org/10.3390/electronics13101996 - 20 May 2024
Cited by 1 | Viewed by 1032
Abstract
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial [...] Read more.
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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16 pages, 3285 KiB  
Article
Adaptive Virtual Inertial Control and Virtual Droop Control Coordinated Control Strategy for Hybrid Energy Storage Taking into Account State of Charge Optimization
by Chao Xing, Jiajie Xiao, Peiqiang Li, Xinze Xi, Yunhe Chen and Qi Guo
Electronics 2024, 13(7), 1228; https://doi.org/10.3390/electronics13071228 - 26 Mar 2024
Cited by 2 | Viewed by 725
Abstract
For energy-storage-assisting conventional units to participate in the primary frequency regulation of a power system, firstly, based on the frequency regulation mechanism of virtual inertial control (VIC) and virtual droop control (VDC) of energy storage, we analyze the effect of the action timing [...] Read more.
For energy-storage-assisting conventional units to participate in the primary frequency regulation of a power system, firstly, based on the frequency regulation mechanism of virtual inertial control (VIC) and virtual droop control (VDC) of energy storage, we analyze the effect of the action timing of energy storage on the frequency deviation of the grid under two control methods and put forward a reasonable combination of the two control methods; on this basis, we also put forward hybrid energy storage adaptive VIC and VDC based on the demand of VIC and VDC on the power and capacity of energy storage. On this basis, based on the demand of VIC and VDC on the power and capacity of energy storage, a hybrid energy storage adaptive VIC and VDC coordinated control strategy based on supercapacitor–lithium batteries is proposed, whereby a high-power storage supercapacitor responds to inertial control signals to rapidly suppress a drop in frequency, and the high-capacity lithium battery responds to droop control signals to perform long-time droop control. The high-capacity lithium battery responds to the sagging control signal and is used to perform a long-time sagging power response; finally, in order to avoid the state of charge (SOC) of energy storage falling into a low/high working condition and losing the subsequent frequency regulation ability, an adaptive power control strategy of energy storage based on the improved logistic function is proposed. The simulation results show that under typical load disturbance, the SOC level of the proposed strategy increases by 19.17% and 30.16%, respectively, compared with that of the single-lithium strategy and no energy storage, and the SOC level of the supercapacitor and lithium battery increases by 29.4% and 2.1%, respectively, compared with that of logistic optimization. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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21 pages, 5497 KiB  
Article
A Fast Repetitive Control Strategy for a Power Conversion System
by Jinghua Zhou, Yifei Sun, Shasha Chen and Tianfeng Lan
Electronics 2024, 13(7), 1186; https://doi.org/10.3390/electronics13071186 - 23 Mar 2024
Viewed by 1105
Abstract
With the expansion of renewable energy sources, the stable and high-quality operation of microgrids composed of new energy sources has attracted widespread attention. Among them, the power conversion system (PCS), as an important part of microgrids, plays a crucial role in their operation [...] Read more.
With the expansion of renewable energy sources, the stable and high-quality operation of microgrids composed of new energy sources has attracted widespread attention. Among them, the power conversion system (PCS), as an important part of microgrids, plays a crucial role in their operation and management. The PCS operation modes are classified into grid-connected and off-grid modes. However, in off-grid mode, due to the access of nonlinear and unbalanced loads, the output voltage quality of a PCS is worse, and the voltage waveform distortion is serious. To solve these problems, a fast repetitive control (FRC) strategy is proposed for a power conversion system with an Active Neutral Point Clamped (ANPC) architecture of three levels. The voltage loop control strategy can be applied to the voltage/frequency (V/f) mode and the grid-forming mode. The control strategy can effectively realize the suppression of the harmonics of the output voltage and has a 100% capability to carry unbalanced loads. Finally, a 1725 kVA PCS prototype is developed, and the proposed control strategy is verified using the MT3200 HIL semiphysical simulator of ModelingTech in the V/f mode as an example. This practically verifies the feasibility and validity of the proposed control strategy, which has a certain degree of engineering practicability and reference due to the simplicity of the design and the ease of realization. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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17 pages, 5029 KiB  
Article
Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems
by Tao Shi, Zeyan Xu, Libo Gu and Hangyu Zhou
Electronics 2024, 13(5), 956; https://doi.org/10.3390/electronics13050956 - 1 Mar 2024
Cited by 1 | Viewed by 1100
Abstract
With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power [...] Read more.
With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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17 pages, 2524 KiB  
Article
A Novel Fractional Delay Proportional–Integral Multi-Resonant-Type Repetitive Control Based on a Farrow-Structure Filter for Grid-Tied Inverters
by Fen Liang, Ho-Joon Lee and Qiangsong Zhao
Electronics 2023, 12(19), 4010; https://doi.org/10.3390/electronics12194010 - 23 Sep 2023
Viewed by 1009
Abstract
The integer-order delay of proportional–integral multi-resonant-type repetitive control (PIMR-RC) cannot provide excellent control performance for grid-tied inverters when the grid frequency fluctuates. To address this issue and reduce control errors, a fractional delay PIMR-RC (FD-PIMR-RC) scheme is proposed. In addition, to reduce the [...] Read more.
The integer-order delay of proportional–integral multi-resonant-type repetitive control (PIMR-RC) cannot provide excellent control performance for grid-tied inverters when the grid frequency fluctuates. To address this issue and reduce control errors, a fractional delay PIMR-RC (FD-PIMR-RC) scheme is proposed. In addition, to reduce the computational load and memory consumption, a Farrow-structure fractional delay (FFD) filter is adopted. The digital filter with the Farrow structure is flexibly and efficiently used for fractional delay. For each new fractional delay, a large number of calculations and storage for the FFD filter coefficients are avoided, which significantly reduces the computational load and memory consumption. The parameter design of the FD-PIMR-RC scheme is provided in detail, including the implementation of fractional delay based on the Farrow structure. Then, a system stability analysis and parameter optimization are presented. Finally, simulations for the steady-state and dynamic responses are presented, and the validity of the proposed method is demonstrated. Full article
(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)
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