Novel modular multilevel converter-based five-terminal MV/LV hybrid AC/DC microgrids with improved operation capability under unbalanced power distribution

Applied Energy

Published On 2022/1/15

Conventionally, the multilevel converter-based multi-terminal hybrid microgrids require a large number of power switches and have a limited operation capability under unbalanced power distribution in medium and low voltage (MV/LV) AC/DC microgrids. To solve this issue, this paper proposes the novel modular multilevel converter (MMC)-based five-terminal MV/LV hybrid AC/DC microgrids. The proposed hybrid microgrids realize the interconnection between the medium-voltage AC (MVAC), MVDC, low voltage AC (LVAC), and two LVDC terminals. In addition, the MVAC grid is connected to the AC terminal of MMC, and the MVDC microgrid is connected to the DC terminal of MMC through a dual active bridge (DAB) converter. Based on MMC, the compact interlinking converters are established, providing three LVDC terminals, which are connected to two LVDC microgrids and one LVAC microgrid through a DC …

Journal

Applied Energy

Published On

2022/1/15

Volume

306

Page

118140

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Yunfei Mu

Yunfei Mu

Tianjin University

Position

School of Electrical Engineering&Automation

H-Index(all)

39

H-Index(since 2020)

38

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power system stability and control

New energy application

Electric vehicle

University Profile Page

Qian Xiao

Qian Xiao

Tianjin University

Position

Assitant Professor

H-Index(all)

15

H-Index(since 2020)

15

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Microgrids

DC Distribution Network

Multilevel Converters

BESS

Energy Router

University Profile Page

Yu Jin

Yu Jin

Harbin Institute of Technology

Position

H-Index(all)

10

H-Index(since 2020)

10

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Multilevel converters

Battery energy storage system

FACTs

University Profile Page

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Applied Energy

Investigating the fast energy-related carbon emissions growth in African countries and its drivers

Efforts to avoid the acceleration of global warming have tended to focus on countries with high CO2 emissions levels and large populations, with a high level of economic development or industrialization. African countries, which often do not conform to such criteria, are more vulnerable to climate change due to their dependence on climate-sensitive industries and their limited infrastructure and technological capacity to cope with its impacts. The long-term economic growth rates projected for Africa's rapid development period will, further, make Africa a potential emission hotspot in the near future. Here, for the first time, we built an energy-related emissions inventory for 19 African countries for 2010–2019, which addresses emissions from 47 economic sectors and 5 energy types, making it the most comprehensive of its kind. The degree of decoupling of economy and emissions, and drivers of CO2 emission changes …

HE Hongwen 何洪文

HE Hongwen 何洪文

Beijing Institute of Technology

Applied Energy

Towards a fossil-free urban transport system: An intelligent cross-type transferable energy management framework based on deep transfer reinforcement learning

Deep reinforcement learning (DRL) is now a research focus for the energy management of fuel cell vehicles (FCVs) to improve hydrogen utilization efficiency. However, since DRL-based energy management strategies (EMSs) need to be retrained when the types of FCVs are changed, it is a laborious task to develop DRL-based EMSs for different FCVs. Given that, this article introduces transfer learning (TL) into DRL to design a novel deep transfer reinforcement learning (DTRL) method and then innovatively proposes an intelligent transferable energy management framework between two different urban FCVs based on the designed DTRL method to achieve the reuse of well-trained EMSs. To begin, an enhanced soft actor-critic (SAC) algorithm integrating prioritized experience replay (PER) is formulated to be the studied DRL algorithm in this article. Then, an enhanced-SAC based EMS of a light fuel cell hybrid …

RJ BARTHELMIE

RJ BARTHELMIE

Cornell University

Applied Energy

Corrigendum to “Wind shadows impact planning of large offshore wind farms”[Applied Energy 359 (2024) 122755]

This work is supported by the US Department of Energy (DoE)(DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE)(allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231. None of the funding agencies have reviewed the information contained here and the opinions in this manuscript do not necessarily reflect those of any of these parties.

Dayong Zhang

Dayong Zhang

Southwestern University of Finance and Economics

Applied Energy

On the interactive effects of climate policies: Insights from a stock-flow consistent model

Climate policies such as carbon tax and green finance policy are critical measures to promote low-carbon transition worldwide. These policies are, however, likely to interact with each other and create unintentional consequences. This paper adopts a stock-flow consistent model to theoretically explore the effects of policy interactions. Specifically, commercial banks, as the key credit creators and critical players in providing source of finance, are included. Based on the numerical simulations of the dynamic model, we find that both carbon tax and green finance policies render positive impacts on low-carbon transition. In addition, green credit incentives, if imposed simultaneously with carbon tax, can enhance the positive effects. Although low-carbon transition could generate higher net government revenues, our results show that banking sector tends to expose to higher financial risks when firms’ debt-equity ratio rises …

Badong Chen

Badong Chen

Xi'an Jiaotong University

Applied Energy

A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique

Accurate cluster photovoltaic power prediction (CPPP) is crucial for the operation and control of renewable energy grid-connected power systems. The traditional modeling strategies for CPPP such as direct aggregation (DA) and statistical upscaling (SU) have limitations such as error accumulation and upscaling factor uncertainty. To address these issues, this paper proposed a novel hybrid approach for CPPP by combining machine learning models with an improved SU technique. Firstly, a robust broad learning system (BLS) model, in which the Generalized Maximum Correntropy Criterion (GMCC) is used to replace the original mean square error (MSE) loss in BLS, is proposed to solve the problem of multiple outliers affecting the prediction accuracy of regional cluster stations, and it is called GBLS. Then, the Relevance Vector Machine (RVM) as an effective nonlinear regression model is further utilized to …

Xianming Ye

Xianming Ye

University of Pretoria

Applied Energy

Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP

This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection process for national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), both XAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying and eliminating irrelevant features. Comparative analysis revealed Grad-CAM’s exceptional computational efficiency in high-dimensional applications and SHAP’s superior ability in revealing features that degrade forecast accuracy. However, limitations are found in both methods, with Grad-CAM including features that decrease model stability, and SHAP inaccurately ranking significant features. Future research should focus on refining these XAI methods to overcome these limitations …

Sharifah Rafidah Wan Alwi

Sharifah Rafidah Wan Alwi

Universiti Teknologi Malaysia

Applied Energy

A comparative life cycle assessment of solar combined cooling, heating, and power systems based on RESHeat technology

Switching to renewable energy is key to reducing Greenhouse Gas (GHG) emissions from building energy systems. The Renewable Energy System for Residential Building Heating and Electricity Production (RESHeat) uses solar irradiation, integrating underground thermal energy storage and high-performance heat pumps. This is a new system ongoing prototype demonstration, and its environmental impact has to be evaluated on a life-cycle basis. This study provides a comprehensive analysis of the monthly and annual environmental impacts of RESHeat systems in Limanowa and Cracow, comparing them with traditional gas boilers and Combined Cooling, Heating, and Power system (CCHP) systems. Compared to traditional gas boilers, global warming and fossil resource scarcity are reduced by exceeding 60%. In the end-of-life of systems, reuse decreases mineral resource scarcity by 38.73% in Limanowa …

Chukwuma John Okolie

Chukwuma John Okolie

University of Lagos

Applied Energy

Technical and performance assessments of wind turbines in low wind speed areas using numerical, metaheuristic and remote sensing procedures

The application of innovative technologies in the manufacture of wind turbines (WT) has produced more efficient WT that can operate successfully in low wind speed (LWS) environments. This technology has not been implemented in many LWS parts of the world due to the paucity of enabling technical information (wind resource availability and wind turbine configuration). This study uses ten years wind speed data from twelve Nigerian cities and their population densities, remote sensing, and the configuration of some commercially available LWS turbines in generating technical information suitable for data-backed decision-making on low-speed turbine deployability, operational conditions, and energy yield at 50 and 400 m. Five different numerical and metaheuristic procedures were randomly selected and utilized to estimate Weibull parameters used in computing wind energy development (WEDP) parameters …