Topic Editors

Dr. Xiandong Ma
School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Prof. Dr. Mohamed Benbouzid
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Dr. Sinisa Durovic
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Prof. Dr. Hao Chen
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Integration of Renewable Energy

Abstract submission deadline
30 September 2024
Manuscript submission deadline
31 December 2024
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Topic Information

Dear Colleagues,

The energy supply chain is changing rapidly, driven by a societal and environmental push towards clean and renewable resources. However, renewable resources such as solar, wind, tide, and wave energy are inherently uncontrollable as their availability is governed by the often challenging-to-predict natural cycles. This in turn poses a great challenge for the grid system to match the supply to the constantly changing demand and maintain network stability. Without a viable solution to the plethora of underlying technical issues, we will not be able to integrate and utilize renewable energy resources and systems in an economic and environmentally affordable way.

The aim of this Topic is to collect the latest developments and applications in these interdisciplinary fields related to "Integration of Renewable Energy". Topics of interest include but are not limited to:

  • Wind energy;
  • Solar power;
  • Tidal and wave energy;
  • Hybrid renewable energy systems;
  • Microgrids;
  • Modelling, simulation, optimization, and control;
  • Condition monitoring and control, including advanced control for optimized exploitation;
  • Energy management and demand-side management;
  • Power electronic converters and systems;
  • Storage technologies and systems;
  • Vehicle to grid and grid to vehicle;
  • Inertia and frequency control strategies;
  • HVAC and HVDC interconnection systems;
  • Smart metering and data management solution;
  • Energy security;
  • Artificial-intelligence-enabled techniques and applications.

Dr. Xiandong Ma
Prof. Dr. Mohamed Benbouzid
Dr. Sinisa Durovic
Prof. Dr. Hao Chen
Topic Editors

Keywords

  • renewable energy systems
  • smart grids
  • microgrids
  • energy storage
  • electric vehicle
  • power conversion
  • energy management
  • monitoring and control

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electricity
electricity
- - 2020 24.2 Days 1000 CHF Submit
Electronics
electronics
2.690 3.7 2012 14.4 Days 2000 CHF Submit
Energies
energies
3.252 5.0 2008 15.5 Days 2200 CHF Submit
Processes
processes
3.352 3.5 2013 12.7 Days 2000 CHF Submit
Sustainability
sustainability
3.889 5.0 2009 17.7 Days 2200 CHF Submit

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Published Papers (5 papers)

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Article
Active Disturbance Rejection Control of an Interleaved High Gain DC-DC Boost Converter for Fuel Cell Applications
Energies 2023, 16(3), 1019; https://doi.org/10.3390/en16031019 - 17 Jan 2023
Viewed by 283
Abstract
In this paper, a simplified and robust control strategy of an interleaved high gain DC/DC boost converter (IHGBC) is proposed in order to enhance DC bus voltage regulation in proton exchange membrane fuel cell (PEMFC) applications. The fluctuation of the energy source voltage [...] Read more.
In this paper, a simplified and robust control strategy of an interleaved high gain DC/DC boost converter (IHGBC) is proposed in order to enhance DC bus voltage regulation in proton exchange membrane fuel cell (PEMFC) applications. The fluctuation of the energy source voltage and external load, and the change in system parameters lead to the instability of output voltage. Based on the creation of an average state space model of the DC/DC boost converter, the proposed controller is designed based on a linear active disturbance rejection control (LADRC), which has an external voltage loop and an internal current loop to meet the output voltage requirements under parameters uncertainties and disturbances. The effectiveness of the proposed approach strategy and its superiority were examined under different operating conditions and scenarios. Simulation and experiment results showed the efficiency and robustness of the suggested approach and the great effectiveness in the reference tracking and disturbance rejection. Full article
(This article belongs to the Topic Integration of Renewable Energy)
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Article
Optimal Scheduling of a Hydrogen-Based Energy Hub Considering a Stochastic Multi-Attribute Decision-Making Approach
Energies 2023, 16(2), 631; https://doi.org/10.3390/en16020631 - 05 Jan 2023
Viewed by 310
Abstract
Nowadays, the integration of multi-energy carriers is one of the most critical matters in smart energy systems with the aim of meeting sustainable energy development indicators. Hydrogen is referred to as one of the main energy carriers in the future energy industry, but [...] Read more.
Nowadays, the integration of multi-energy carriers is one of the most critical matters in smart energy systems with the aim of meeting sustainable energy development indicators. Hydrogen is referred to as one of the main energy carriers in the future energy industry, but its integration into the energy system faces different open challenges which have not yet been comprehensively studied. In this paper, a novel day-ahead scheduling is presented to reach the optimal operation of a hydrogen-based energy hub, based on a stochastic multi-attribute decision-making approach. In this way, the energy hub model is first developed by providing a detailed model of Power-to-Hydrogen (P2H) facilities. Then, a new multi-objective problem is given by considering the prosumer’s role in the proposed energy hub model as well as the integrated demand response program (IDRP). The proposed model introduces a comprehensive approach from the analysis of the historical data to the final decision-making with the aim of minimizing the system operation cost and carbon emission. Moreover, to deal with system uncertainty, the scenario-based method is applied to model the renewable energy resources fluctuation. The proposed problem is defined as mixed-integer non-linear programming (MINLP), and to solve this problem, a simple augmented e-constrained (SAUGMECON) method is employed. Finally, the simulation of the proposed model is performed on a case study and the obtained results show the effectiveness and benefits of the proposed scheme. Full article
(This article belongs to the Topic Integration of Renewable Energy)
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Review
A Review of Power Co-Generation Technologies from Hybrid Offshore Wind and Wave Energy
Energies 2023, 16(1), 550; https://doi.org/10.3390/en16010550 - 03 Jan 2023
Viewed by 411
Abstract
Renewable energy resources such as offshore wind and wave energy are environmentally friendly and omnipresent. A hybrid offshore wind-wave energy system produces a more sustainable form of energy that is not only eco-friendly but also economical and efficient as compared to use of [...] Read more.
Renewable energy resources such as offshore wind and wave energy are environmentally friendly and omnipresent. A hybrid offshore wind-wave energy system produces a more sustainable form of energy that is not only eco-friendly but also economical and efficient as compared to use of individual resources. The objective of this paper is to give a detailed review of co-generation technologies for hybrid offshore wind and wave energy. The proposed area of this review paper is based on the power conversions techniques, response coupling, control schemes for co-generation and complimentary generation, and colocation and integrated conversion systems. This paper aims to offer a systematic review to cover recent research and development of novel hybrid offshore wind-wave energy (HOWWE) systems. The current hybrid wind-wave energy structures lack efficiency due to their design and AC-DC-AC power conversion that need to be improved by applying an advanced control strategy. Thus, using different power conversion techniques and control system methodologies, the HOWWE structure can be improved and will be transferrable to the other hybrid models such as hybrid solar and wind energy. The state-of-the-art HOWWE systems are reviewed. Critical analysis of each method is performed to evaluate the best possible combination for development of a HOWWE system. Full article
(This article belongs to the Topic Integration of Renewable Energy)
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Article
A Study on the Wind Power Forecasting Model Using Transfer Learning Approach
Electronics 2022, 11(24), 4125; https://doi.org/10.3390/electronics11244125 - 10 Dec 2022
Viewed by 609
Abstract
Recently, wind power plants that generate wind energy with electricity are attracting a lot of attention thanks to their smaller installation area and cheaper power generation costs. In wind power generation, it is important to predict the amount of generated electricity because the [...] Read more.
Recently, wind power plants that generate wind energy with electricity are attracting a lot of attention thanks to their smaller installation area and cheaper power generation costs. In wind power generation, it is important to predict the amount of generated electricity because the power system would be unstable due to uncertainty in supply. However, it is difficult to accurately predict the amount of wind power generation because the power varies due to several causes, such as wind speed, wind direction, temperature, etc. In this study, we deal with a mid-term (one day ahead) wind power forecasting problem with a data-driven approach. In particular, it is intended to solve the problem of a newly completed wind power generator that makes it very difficult to predict the amount of electricity generated due to the lack of data on past power generation. To this end, a deep learning based transfer learning model was proposed and compared with other models, such as a deep learning model without transfer learning and Light Gradient Boosting Machine (LGBM). As per the experimental results, when the proposed transfer learning model was applied to a similar wind power complex in the same region, it was confirmed that the low predictive performance of the newly constructed generator could be supplemented. Full article
(This article belongs to the Topic Integration of Renewable Energy)
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Article
Distributionally Robust Optimization of an Integrated Energy System Cluster Considering the Oxygen Supply Demand and Multi-Energy Sharing
Energies 2022, 15(22), 8723; https://doi.org/10.3390/en15228723 - 20 Nov 2022
Viewed by 526
Abstract
Regional integrated energy systems (IESs) have emerged to satisfy the increasing diversified energy demand in Tibet. However, limited resource allocation of a given IES can occur because of the uncertainty in the output and prediction error of distributed renewable energy (DRE). [...] Read more.
Regional integrated energy systems (IESs) have emerged to satisfy the increasing diversified energy demand in Tibet. However, limited resource allocation of a given IES can occur because of the uncertainty in the output and prediction error of distributed renewable energy (DRE). A distributionally robust optimization (DRO) model was proposed for the joint operation of multiple regional IESs, and multi-energy sharing and multi-energy flow coupling of electricity, heat, and oxygen were considered. The probability distribution of the DRE output was described using 1 norm and norm constraints, and the minimum operating cost under adverse scenarios was determined through DRO. Furthermore, on the premise of ensuring cluster profit, a pricing mechanism of the energy supply within the cluster was proposed. Finally, a typical model involving eight cases was established and analyzed. The results revealed that multi-energy sharing and multi-energy flow coupling improved the economy of IES cluster operation and realized the coordination of robustness and economy. The energy supply price within the cluster enhanced enthusiasm on the demand side. Full article
(This article belongs to the Topic Integration of Renewable Energy)
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