Energy Systems Planning, Operation and Optimization in Net-Zero Emissions

Topic Information

Dear Colleagues,

Decarbonization energy transition is one of the most important measures to mitigate climate change and improve sustainability. These main investment projects for future net-zero emissions include renewables, energy storage systems (ESSs), electric vehicles (EVs), charging infrastructure, hydrogen production, recycling, etc. High penetration of renewables and large-scale deployment of EV and charging infrastructure can significantly affect the operations of energy systems, even rendering them unstable and unreliable. Therefore, revolutionary energy systems call for the advancement of smart technologies in planning, operation, and optimization. This includes highly resilient energy system architecture enabled by the Internet of Energy (IoE).

This Topic on “Energy Systems Planning, Operation and Optimization in Net-Zero Emissions” invites contributions on the most advanced and latest research developments, focusing in particular on the planning, operation, and optimization for energy system integration with high penetration of renewable energy and EVs for net-zero emissions. The topics include but are not limited to:

• Government roadmap of energy system transition for net-zero emission;
• Design, planning, and optimization of smart technologies for resilient energy system architecture and net-zero energy systems;
• Energy system operation and control under highly variable and uncertain energy sources;
• Application and optimization for the integration of EVs and ESSs in energy systems;
• Integration of IoE in net-zero energy systems;
• Environment and industry issues from the transition of net-zero energy systems and solutions thereof;
• Optimization modeling, simulation, and solution techniques for design, simulation, analysis, and operation of energy systems achieving net-zero emission;
• Hybrid microgrid design and development for net-zero energy systems;
• Energy markets enabling net-zero energy systems;
• AI and cyberphysical-system-enabled net-zero energy systems.

Prof. Dr. Jen-Hao Teng
Dr. Kin-Cheong Sou
Prof. Dr. Alfeu J. Sguarezi Filho
Dr. Lakshmanan Padmavathi
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	4.8	2020	27.2 Days	CHF 1000	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit
Systems systems	2.3	2.8	2013	17.3 Days	CHF 2400	Submit

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Published Papers (21 papers)

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21 pages, 3206 KiB  

Open AccessArticle

Economic Appraisal and Enhanced Efficiency Optimization for Liquid Methanol Production Process

by Alireza Khatamijouybari and Adrian Ilinca

Sustainability 2024, 16(5), 1993; https://doi.org/10.3390/su16051993 - 28 Feb 2024

Cited by 1 | Viewed by 779

Abstract

The presented study examines the economic viability and optimization of a previously designed integrated process for producing liquid methanol. The annualized cost of the system method is applied for economic analysis. The optimization method includes a robust hybrid approach that combines the NSGA-II [...] Read more.

The presented study examines the economic viability and optimization of a previously designed integrated process for producing liquid methanol. The annualized cost of the system method is applied for economic analysis. The optimization method includes a robust hybrid approach that combines the NSGA-II multi-objective optimization algorithm with artificial intelligence. Decision variables for the optimization are taken from a sensitivity analysis to optimize the exergy and energy efficiencies and the investment return period. Decision-making methodologies, including LINMAP, fuzzy, and TOPSIS, are utilized to identify the optimal outcomes, effectively identifying points along the Pareto-optimal front. Compared with the original design, the research outcomes demonstrate an over 38% reduction in the process’s investment return period post optimization, as evaluated through the TOPSIS and LINMAP methodologies. Additionally, the highest level of thermal efficiency achieved through optimization stands at 79.9%, assessed using the LINMAP and TOPSIS methods, and 79.2% using the fuzzy Bellman–Zadeh method. The process optimization in the presented research, coupled with the improved economic feasibility, mitigates energy consumption through maximizing efficiency, thereby fostering sustainable and environmentally friendly development. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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27 pages, 5736 KiB  

Open AccessReview

Challenges, Roadmaps and Smart Energy Transition towards 100% Renewable Energy Markets in American Islands: A Review

by Daniel Icaza, David Vallejo-Ramirez, Carlos Guerrero Granda and Edwin Marín

Energies 2024, 17(5), 1059; https://doi.org/10.3390/en17051059 - 23 Feb 2024

Cited by 2 | Viewed by 1305

Abstract

There is no doubt that the transition towards renewable energies is generating many changes on different continents, some with greater impacts than others, but the development that has occurred is recognized and widely accepted. The progress has been significant but it is necessary [...] Read more.

There is no doubt that the transition towards renewable energies is generating many changes on different continents, some with greater impacts than others, but the development that has occurred is recognized and widely accepted. The progress has been significant but it is necessary to analyze the roadmaps that have been proposed so far at the island level so that decision makers have sufficient tools to commit the much-needed economic resources to transform their energy systems into 100% renewable ones. These approaches are not simple and the hard work of the authors who have disseminated their research is recognized. The roadmaps are planned based on the energy potential available in the territories and the future energy demand. Within countries, it is important to increase the economic resources to allocate to investments in environmentally friendly renewable energies. In this review of 100% renewable smart systems on islands, the situation of the American continent, its challenges and its long-term approaches in the different geographical areas facing 2050 are analyzed. This article shows that research into the design of 100% renewable energy systems in scientific articles is fairly new but has gained more and more attention in recent years. In total, 175 articles published since 2002 were identified and analyzed. Many of these articles have a predominant focus on the electricity sector. As a general result, it has been determined that although there has been significant progress towards an orderly energy transition, this has not been consistent with the international agreements signed since the Paris Summit, which is a real challenge in complying with the new commitment of the COP28 of Dubai in tripling the participation of renewables. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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28 pages, 3827 KiB  

Open AccessArticle

Development and Analysis of Optimization Algorithm for Demand-Side Management Considering Optimal Generation Scheduling and Power Flow in Grid-Connected AC/DC Microgrid

by Abdulwasa Bakr Barnawi

Sustainability 2023, 15(21), 15671; https://doi.org/10.3390/su152115671 - 6 Nov 2023

Cited by 1 | Viewed by 1110

Abstract

The world energy sector is experiencing many challenges, such as maintaining a demand–supply balance with continuous increases in demand, reliability issues, and environmental concerns. Distributed energy resources (DERs) that use renewable energy sources (RESs) have become more prevalent due to environmental challenges and [...] Read more.

The world energy sector is experiencing many challenges, such as maintaining a demand–supply balance with continuous increases in demand, reliability issues, and environmental concerns. Distributed energy resources (DERs) that use renewable energy sources (RESs) have become more prevalent due to environmental challenges and the depletion of fossil fuel reserves. An increased penetration of RESs in a microgrid system facilitates the establishment of a local independent system. However, these systems, due to the uncertainties of RESs, still encounter major issues, like increased operating costs or operating constraint violations, optimal power management, etc. To solve these issues, this paper proposes a stochastic programming model to minimize the total operating cost and emissions and improve the operational reliability with the help of a generalized normal distribution optimization (GNDO). A day-ahead demand response is scheduled, aiming to shift loads to enhance RES utilization efficiency. Demand-side management (DSM) with RESs is utilized, and battery energy storage systems in low-voltage and medium-voltage microgrids are shown. Mathematical formulations of each element in the microgrids were performed. Optimal and consumer-friendly solutions were found for all the cases. Environmental concerns based on the amount of harmful emissions were also analyzed. The importance of demand response is demonstrated vividly. The aim is to optimize energy consumption and achieve optimum cost of operation via DSM, considering several security constraints. A comparative analysis of operating costs, emission values, and the voltage deviation was carried out to prove and justify their potential to solve the optimal scheduling and power flow problem in AC/DC microgrids. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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17 pages, 4857 KiB  

Open AccessArticle

Fast Power System Transient Stability Simulation

by Teshome Lindi Kumissa and Fekadu Shewarega

Energies 2023, 16(20), 7157; https://doi.org/10.3390/en16207157 - 19 Oct 2023

Viewed by 1185

Abstract

Power system transient stability simulation is of critical importance for utilities to assess dynamic security. Most of the commercially available tools use the traditional numerical integration method to simulate power system transient stability, which is computationally intensive and has low simulation speed. This [...] Read more.

Power system transient stability simulation is of critical importance for utilities to assess dynamic security. Most of the commercially available tools use the traditional numerical integration method to simulate power system transient stability, which is computationally intensive and has low simulation speed. This makes it difficult to identify any insecure contingency before it happens. It is already proven that power system transient stability simulation achieved using the differential transformation method (DTM) requires less computational effort and has improved simulation speed, but it still requires further improvement regarding its accuracy and performance efficiency. This paper introduces a novel power system transient stability simulation method based on the adaptive step-size differential transformation method. Using the proposed method, the step size is varied based on the estimated local solution error at each time step. The accuracy and speed of the proposed simulation approach are investigated in comparison with the classical differential transformation method and the traditional numerical integration method using the IEEE 9 bus and 39 bus test systems. The simulation results reveal that the proposed method increases the simulation speed by 20–44.57% and 83–92% when compared with the classical DTM and traditional numerical-integration-based simulation methods, respectively. It is also proved that compared with the DTM-based simulation, the proposed method provides 45.27% to 58.85% and more than 90% accurate simulation results for IEEE 9 and IEEE 39 test systems, respectively. Therefore the proposed power system transient stability simulation method is faster and relatively more accurate and can be applied for online transient stability monitoring of power system networks. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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20 pages, 4772 KiB  

Open AccessArticle

Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features

by Kai-Rong Lin, Chien-Chung Huang and Kin-Cheong Sou

Energies 2023, 16(20), 7066; https://doi.org/10.3390/en16207066 - 12 Oct 2023

Cited by 1 | Viewed by 1423

Abstract

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) [...] Read more.

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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22 pages, 2853 KiB  

Open AccessArticle

Stochastic Optimization Model of Capacity Configuration for Integrated Energy Production System Considering Source-Load Uncertainty

by Ankang Miao, Yue Yuan, Yi Huang, Han Wu and Chao Feng

Sustainability 2023, 15(19), 14247; https://doi.org/10.3390/su151914247 - 26 Sep 2023

Cited by 1 | Viewed by 947

Abstract

China’s carbon neutrality strategy has expedited a transition towards greener and lower-carbon integrated energy systems. Faced with the problem that the central position of thermal power cannot be transformed quickly, utilizing traditional thermal power units in a low-carbon and efficient manner is the [...] Read more.

China’s carbon neutrality strategy has expedited a transition towards greener and lower-carbon integrated energy systems. Faced with the problem that the central position of thermal power cannot be transformed quickly, utilizing traditional thermal power units in a low-carbon and efficient manner is the premise to guarantee green energy development. This study focuses on the integrated energy production system (IEPS) and a stochastic optimization model for capacity configuration that integrates carbon capture storage and power-to-gas while considering source-load uncertainty. Firstly, carbon capture storage and power-to-gas technologies are introduced, and the architecture and models of the IEPS are established. The carbon and hydrogen storage equipment configuration enhances the system’s flexibility. Also, source-load uncertainty is considered, and a deterministic transformation is applied using the simultaneous backward reduction algorithm combined with K-means clustering. The paper simulates the optimal capacity configuration of the IEPS in a park energy system in Suzhou, China. Furthermore, the research performs a sensitivity analysis on coal, natural gas, and carbon tax prices. Case studies verified that IEPS can realize the recycling of electricity, gas, hydrogen, and carbon, with remarkable characteristics of low-carbon, flexibility, and economical. Stochastic optimized capacity allocation results considering source-load uncertainty are more realistic. Sensitivity intervals for energy prices can reference pricing mechanisms in energy markets. This study can provide ideas for the transition of China’s energy structure and offer directions to the low-carbon sustainable development of the energy system. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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19 pages, 1407 KiB  

Open AccessArticle

Energy Storage Sharing for Multiple Services Provision: A Computable Combinatorial Auction Design

by Bo Wei, Wenfei Liu, Chong Shao, Yong Yang, Yanbing Su and Zhaoyuan Wu

Sustainability 2023, 15(16), 12314; https://doi.org/10.3390/su151612314 - 12 Aug 2023

Cited by 1 | Viewed by 726

Abstract

Given the profound integration of the sharing economy and the energy system, energy storage sharing is promoted as a viable solution to address the underutilization of energy storage and the challenges associated with cost recovery. While energy storage sharing offers various services for [...] Read more.

Given the profound integration of the sharing economy and the energy system, energy storage sharing is promoted as a viable solution to address the underutilization of energy storage and the challenges associated with cost recovery. While energy storage sharing offers various services for system operation, a significant question remains regarding the development of an optimal allocation model for shared energy storage in diverse application scenarios and the proposal of efficient solving algorithms. This paper presents the design of a computable combinatorial mechanism aimed at facilitating energy storage sharing. Leveraging the distinct characteristics of buyers and sellers engaged in energy storage sharing, we propose a combinatorial auction solving algorithm that prioritizes and incorporates the offers of shared energy storage, accounting for temporal variations in the value of energy resources. The numerical results demonstrate that the proposed solving algorithm achieves a computation time reduction of over 95%, adequately meeting the practical requirements of industrial applications. Importantly, the proposed method maintains a high level of computational accuracy, ranging from 92% to 98%, depending on the participants and application scenarios. Hopefully, our work is able to provide a useful reference for the further mechanism design for energy storage sharing. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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19 pages, 9582 KiB  

Open AccessArticle

Parameter Optimization of Magnetic Components for Phase-Shifted Full-Bridge Converters Using a Digital Twin

by Jen-Hao Teng, Chia-Wei Chao, Hong-Wen Song and Shi-Wei Huang

Energies 2023, 16(15), 5773; https://doi.org/10.3390/en16155773 - 2 Aug 2023

Viewed by 1035

Abstract

Due to their significant performance, Phase-Shifted Full-Bridge Converters (PSFBCs) have gained widespread adoption in medium- and high-power applications. The performance of a PSFBC is greatly influenced by its magnetic components, namely the transformer and resonance inductor. To address these challenges, this paper proposes [...] Read more.

Due to their significant performance, Phase-Shifted Full-Bridge Converters (PSFBCs) have gained widespread adoption in medium- and high-power applications. The performance of a PSFBC is greatly influenced by its magnetic components, namely the transformer and resonance inductor. To address these challenges, this paper proposes a parameter optimization of magnetic components for PSFBCs, specifically the transformer turns ratio and resonance inductor value, to enhance conversion efficiency and minimize operational loss. A digital twin of PSFBCs enabling a more accurate loss estimation is proposed to achieve this objective. The proposed loss estimation method precisely calculates the effective and circulation intervals and the corresponding current points of the primary-side transformer current, resulting in improved accuracy. By leveraging the digital twin, the effects of transformer turns ratio and resonant inductor value on the conversion efficiency of a PSFBC can be efficiently simulated. This facilitates the parameter optimization of magnetic components, thereby minimizing operational losses across different application scenarios. This paper also designs and implements a PSFBC prototype with a rated input voltage of 380 V, output voltage of 24 V, and output current of 20 A. The experimental results show the influences of transformer turns ratio and resonant inductor value on the PSFBC and validate the proposed digital twin. The proposed parameter optimization of magnetic components is further evaluated across two application scenarios with varying utilization rates. The simulation results indicate a reduction of approximately 14% in operational loss per hour after applying the parameter optimization of magnetic components for the PSFBC used as a battery charger. The results demonstrate the effectiveness and practicality of the proposed digital twin in designing PSFBCs. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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17 pages, 6034 KiB  

Open AccessArticle

Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model

by Qifan Guan

Sustainability 2023, 15(12), 9681; https://doi.org/10.3390/su15129681 - 16 Jun 2023

Cited by 2 | Viewed by 1034

Abstract

To deal with global warming and fulfil China’s commitment to carbon neutrality by 2060, reducing carbon emissions has become a necessary requirement. As one of China’s three major economic circles, the Beijing–Tianjin–Hebei region (B–T–H) has a great responsibility. This paper measures energy-related carbon [...] Read more.

To deal with global warming and fulfil China’s commitment to carbon neutrality by 2060, reducing carbon emissions has become a necessary requirement. As one of China’s three major economic circles, the Beijing–Tianjin–Hebei region (B–T–H) has a great responsibility. This paper measures energy-related carbon emissions of B–T–H from 2005 to 2019 and uses the extended Logarithmic Mean Division Index (LMDI) to decompose the carbon emission effect factors. Then, a Tapio index model was constructed to analyse the contribution of each effect factor. The results showed that: (1) the total carbon emissions of B–T–H increased by 1.5 times, with Hebei having the highest proportion, followed by Tianjin and Beijing. Coal was the biggest emitter in all three regions. Natural gas emissions in Tianjin and Beijing were growing rapidly. (2) Consistent with most studies, economic development promoted carbon emissions, while energy intensity and energy structure inhibited them. It was found that innovative factors also have significant impacts: research and development efficiency was the primary emission inhibition factor in Hebei and the secondary inhibition factor in Tianjin and Beijing. The effects of investment intensity and research and development intensity differed between regions. (3) Beijing took the lead in achieving strong decoupling, followed by Tianjin. Hebei maintained weak decoupling. Innovative factors also played an important role in decoupling, which cannot be ignored in achieving emission reduction targets. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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20 pages, 3218 KiB  

Open AccessArticle

Multi-Agent Optimal Control for Central Chiller Plants Using Reinforcement Learning and Game Theory

by Shunian Qiu, Zhenhai Li, Zhihong Pang, Zhengwei Li and Yinying Tao

Systems 2023, 11(3), 136; https://doi.org/10.3390/systems11030136 - 3 Mar 2023

Cited by 4 | Viewed by 2376

Abstract

To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. [...] Read more.

To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. Classic optimal control methods for central chiller plants are mostly based on system performance models which takes much effort and cost to establish. In addition, inevitable model error could cause control risk to the applied system. To mitigate the model dependency of HVAC optimal control, reinforcement learning (RL) algorithms have been drawing attention in the HVAC control domain due to its model-free feature. Currently, the RL-based optimization of central chiller plants faces several challenges: (1) existing model-free control methods based on RL typically adopt single-agent scheme, which brings high training cost and long training period when optimizing multiple controllable variables for large-scaled systems; (2) multi-agent scheme could overcome the former problem, but it also requires a proper coordination mechanism to harmonize the potential conflicts among all involved RL agents; (3) previous agent coordination frameworks (identified by distributed control or decentralized control) are mainly designed for model-based control methods instead of model-free controllers. To tackle the problems above, this article proposes a multi-agent, model-free optimal control approach for central chiller plants. This approach utilizes game theory and the RL algorithm SARSA for agent coordination and learning, respectively. A data-driven system model is set up using measured field data of a real HVAC system for simulation. The simulation case study results suggest that the energy saving performance (both short- and long-term) of the proposed approach (over 10% in a cooling season compared to the rule-based baseline controller) is close to the classic multi-agent reinforcement learning (MARL) algorithm WoLF-PHC; moreover, the proposed approach’s nature of few pending parameters makes it more feasible and robust for engineering practices than the WoLF-PHC algorithm. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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17 pages, 7242 KiB  

Open AccessArticle

Accurate and Efficient SOH Estimation for Retired Batteries

by Jen-Hao Teng, Rong-Jhang Chen, Ping-Tse Lee and Che-Wei Hsu

Energies 2023, 16(3), 1240; https://doi.org/10.3390/en16031240 - 23 Jan 2023

Cited by 7 | Viewed by 1973

Abstract

There will be an increasing number of retired batteries in the foreseeable future. Retired batteries can reduce pollution and be used to construct a battery cycle ecosystem. To use retired batteries more efficiently, it is critical to be able to determine their State [...] Read more.

There will be an increasing number of retired batteries in the foreseeable future. Retired batteries can reduce pollution and be used to construct a battery cycle ecosystem. To use retired batteries more efficiently, it is critical to be able to determine their State of Health (SOH) precisely and speedily. SOH can be estimated accurately through a comprehensive and inefficient charge-and-discharge procedure. However, the comprehensive charge and discharge is a time-consuming process and will make the SOH assessment for many retired batteries unrealistic. This paper proposes an accurate and efficient SOH Estimation (SOH-E) method using the actual data of retired batteries. A battery data acquisition system is designed to acquire retired batteries’ comprehensive discharge and charge data. The acquired discharge data are separated into various time interval-segregated sub-data. Then, the specially designed features for SOH-E are extracted from the sub-data. Neural Networks (NNs) are trained using these sub-data. The retired batteries’ SOH levels are then estimated after the NNs’ training. The experiments described herein use retired lead–acid batteries. The batteries’ rated voltage and capacity are 12 V and 90 Ah, respectively. Different feature value extractions and time intervals that might affect the SOH-E accuracy and are tested. The Backpropagation NN (BPNN) and Long-Short-Term-Memory NN (LSTMNN) are designed to estimate SOH in this paper. The experimental results indicate that SOH can be calculated in 30 min. The Root-Mean-Square Errors (RMSEs) are less than 3%. The proposed SOH-E can help decrease pollution, extend the life cycle of a retired battery, and establish a battery cycle ecosystem. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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26 pages, 3878 KiB  

Open AccessArticle

Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles

by Vishnu P. Sidharthan, Yashwant Kashyap and Panagiotis Kosmopoulos

Energies 2023, 16(3), 1214; https://doi.org/10.3390/en16031214 - 22 Jan 2023

Cited by 9 | Viewed by 2480

Abstract

The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery [...] Read more.

The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1%, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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13 pages, 826 KiB  

Open AccessArticle

Optimization Model for the Energy Supply Chain Management Problem of Supplier Selection in Emergency Procurement

by Jiseong Noh and Seung-June Hwang

Systems 2023, 11(1), 48; https://doi.org/10.3390/systems11010048 - 16 Jan 2023

Cited by 2 | Viewed by 3005

Abstract

In energy supply chain management (ESCM), the supply chain members try to make long-term contracts for supplying energy stably and reducing the cost. Currently, optimizing ESCM is a complex problem with two social issues: environmental regulations and uncertainties. First, environmental regulations have been [...] Read more.

In energy supply chain management (ESCM), the supply chain members try to make long-term contracts for supplying energy stably and reducing the cost. Currently, optimizing ESCM is a complex problem with two social issues: environmental regulations and uncertainties. First, environmental regulations have been tightened in countries around the world, leading to eco-friendly management. As a result, it has become imperative for the energy buyer to consider not only the total operating cost but also carbon emissions. Second, the uncertainties, such as pandemics and wars, have had a serious impact on handling ESCM. Since the COVID-19 pandemic disrupted the supply chain, the supply chain members adopted emergency procurement for sustainable operations. In this study, we developed an optimization model using mixed-integer linear programming to solve ESCM with supplier selection problems in emergency procurement. The model considers a single thermal power plant and multiple fossil fuel suppliers. Because of uncertainties, energy demand may suddenly change or may not be supplied on time. To better manage these uncertainties, we developed a rolling horizon method (RHM), which is a well-known method for solving deterministic problems in mathematical programming models. To test the model and the RHM, we conducted three types of numerical experiments. First, we examined replenishment strategies and schedules under uncertain demands. Second, we conducted a supplier selection experiment within a limited budget and carbon emission regulations. Finally, we conducted a sensitivity analysis of carbon emission limits. The results show that our RHM can handle ESCM under uncertain situations effectively. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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17 pages, 842 KiB  

Open AccessArticle

Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control

by Timothé Gronier, William Maréchal, Christophe Geissler and Stéphane Gibout

Energies 2023, 16(1), 123; https://doi.org/10.3390/en16010123 - 22 Dec 2022

Cited by 1 | Viewed by 1422

Abstract

With the increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. However, small-scale energy storage is still an expensive [...] Read more.

With the increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. However, small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper, a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which however is difficult to use when the learning data is limited, as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. In some cases, they even show that it is worth punctually missing the H${}_2$ production requirement in exchange for higher economic performance. However, they require fine-tuning as to not deteriorate the system performance. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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19 pages, 3179 KiB  

Open AccessFeature PaperArticle

Solar Energy Production Planning in Antikythera: Adequacy Scenarios and the Effect of the Atmospheric Parameters

by Panagiotis G. Kosmopoulos, Marios T. Mechilis and Panagiota Kaoura

Energies 2022, 15(24), 9406; https://doi.org/10.3390/en15249406 - 12 Dec 2022

Cited by 2 | Viewed by 2983

Abstract

The National Observatory of Athens intends to operate a European Climate Change Observatory (ECCO) on the island of Antikythera, which meets the criteria to become a first-class research infrastructure. This project requires electricity that is unprofitable to get from the thermal units of [...] Read more.

The National Observatory of Athens intends to operate a European Climate Change Observatory (ECCO) on the island of Antikythera, which meets the criteria to become a first-class research infrastructure. This project requires electricity that is unprofitable to get from the thermal units of this small island (20 km²). Solar energy is the subject that was examined in case it can give an environmentally and economically viable solution, both for the observatory and for the whole island. Specifically, observational and modeled data were utilized relevant to solar dynamic and atmospheric parameters in order to simulate the solar energy production by photovoltaics (PV) and Concentrated Solar Power (CSP) plant technologies. To this direction, a synergy of aerosol and cloud optical properties from the Copernicus Atmosphere Monitoring Service (CAMS) and the Eumetsat’s support to nowcasting and very short range forecasting (NWC SAF) with Radiative Transfer Model (RTM) techniques was used in order to quantify the solar radiation and energy production as well as the effect of the atmospheric parameters and to demonstrate energy adequacy scenarios and financial analysis. The ultimate goal is to highlight the opportunity for energy transition and autonomy for both the island itself and the rest of the community with the operation of ECCO, and hence to tackle climate change. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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28 pages, 2459 KiB  

Open AccessReview

Planning Sustainable Energy Systems in the Southern African Development Community: A Review of Power Systems Planning Approaches

by Constantino Dário Justo, José Eduardo Tafula and Pedro Moura

Energies 2022, 15(21), 7860; https://doi.org/10.3390/en15217860 - 23 Oct 2022

Cited by 2 | Viewed by 4549

Abstract

Southern Africa has a huge potential for renewable energy sources such as hydro, solar, wind, biomass, and geothermal. However, electricity access remains a key policy issue for most member states, with a global average access to electricity of only 54% in 2019. This [...] Read more.

Southern Africa has a huge potential for renewable energy sources such as hydro, solar, wind, biomass, and geothermal. However, electricity access remains a key policy issue for most member states, with a global average access to electricity of only 54% in 2019. This low electrification rate is a strong motivation for member states to increase renewable energy use and improve access to electricity for all. The goal of this paper was to present a literature review of methodologies, energy plans, and government programs that have been implemented by the Southern African Development Community member states to address the region’s low average electrification rate and greenhouse gas emission reduction targets. The study presents the most commonly used methodologies for the integration of renewable energies into electrical systems, considering the main grid and distributed generation systems. LCOE minimization methodologies and software options, such as GIS, HOMER, LEAP, and EnergyPLAN, are the most common among the identified studies. The traditional method of electrifying by expanding the grid has not contributed to the eradication of energy poverty in rural areas. Therefore, to improve electricity access in Southern Africa, it is essential to consider off-grid solutions based on renewable energy sources. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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20 pages, 3893 KiB  

Open AccessArticle

Planning and Energy–Economy–Environment–Security Evaluation Methods for Municipal Energy Systems in China under Targets of Peak Carbon Emissions and Carbon Neutrality

by Weiwei Chen, Yibo Wang, Jia Zhang, Wei Dou and Yaxuan Jiao

Energies 2022, 15(19), 7443; https://doi.org/10.3390/en15197443 - 10 Oct 2022

Cited by 1 | Viewed by 1835

Abstract

In order to mitigate the negative effects of global climate change, the Chinese government has committed to achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Since municipal cities are the bottom administrative level for drawing up development plans, it is [...] Read more.

In order to mitigate the negative effects of global climate change, the Chinese government has committed to achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Since municipal cities are the bottom administrative level for drawing up development plans, it is necessary and important to conduct decarbonization pathway research on municipal energy systems (MESs). However, there is little research on decarbonization at the municipal level, and the impact of development paths in each forecast scenario is mostly based on expert evaluation and qualitative assessment. Therefore, this study established a complete decarbonization framework for MESs, including general research procedures, models, and a sustainable evaluation method. The models of energy consumption and carbon emission were adapted and improved for MESs. In order to quantitatively evaluate the energy system development for each scenario, we proposed an energy–economy–environment–security (3E–S) evaluation method, in which principal component analysis (PCA) was adopted for multi-criterion decision making. According to the analysis results of the case city in Guangdong, this evaluation method was proved to be an effective way to identify the factors that may influence coordinated development. By adjusting the relevant parameters and factors in the model, the optimal decarbonization pathway can be found to promote sustainable and coordinated development, thus helping government decision makers to quantitatively evaluate planning paths. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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21 pages, 896 KiB  

Open AccessArticle

Eliciting Stakeholders’ Requirements for Future Energy Systems: A Case Study of Heat Decarbonisation in the UK

by Lai Fong Chiu and Robert John Lowe

Energies 2022, 15(19), 7248; https://doi.org/10.3390/en15197248 - 2 Oct 2022

Cited by 1 | Viewed by 1860

Abstract

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. However, recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of [...] Read more.

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. However, recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder’s priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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32 pages, 6326 KiB  

Open AccessArticle

Development of Demand Factors for Electric Car Charging Points for Varying Charging Powers and Area Types

by Shawki Ali, Patrick Wintzek and Markus Zdrallek

Electricity 2022, 3(3), 410-441; https://doi.org/10.3390/electricity3030022 - 1 Sep 2022

Cited by 3 | Viewed by 3021

Abstract

With the increasing number of electric vehicles, the required charging infrastructure is increasing rapidly. The lack of historical data for the charging infrastructure compromises a challenge for distribution system operators to forecast the corresponding increase in the load demand. This challenge is characterised [...] Read more.

With the increasing number of electric vehicles, the required charging infrastructure is increasing rapidly. The lack of historical data for the charging infrastructure compromises a challenge for distribution system operators to forecast the corresponding increase in the load demand. This challenge is characterised by two main uncertainties, namely, the charging power of the charging infrastructure and its location. Expectedly, the charging infrastructure is going to include varying charging powers and is going to be installed country-wide in different area types. Hence, this contribution sets to tackle these two uncertainties by developing demand factors for the charging infrastructure according to the area type. In order to develop the demand factors, a stochastic simulation tool for the charging profiles has been run for a simulation period of 5200 weeks (100 years) for six main charging powers and seven area types for up to 500 charging points. Thus, compromising a total of over 2.1 million simulated charging profiles. The resulting demand factor curves cover the charging powers between 3.7 kW and 350 kW with 1 kW steps for a total of 348 kW steps. Furthermore, they differ according to seven area types ranging from an urban metropolis to a rural village and are developed for up to 500 charging points. Consequently, the demand factor curves serve as a base to be used for the strategic grid planning of distribution power grids while taking the future development of the charging infrastructure into account. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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21 pages, 4346 KiB  

Open AccessArticle

A Load-Independent Output Current Method for Wireless Power Transfer Systems with Optimal Parameter Tuning

by Leila Yarmohammadi, S. Mohammad Hassan Hosseini, Javad Olamaei and Babak Mozafari

Sustainability 2022, 14(15), 9391; https://doi.org/10.3390/su14159391 - 31 Jul 2022

Viewed by 1819

Abstract

In current study, a method for achieving a load-independent output current with the ability to optimize the parameter tuning, which is applied to wireless power transfer (WPT) systems, is analyzed. The proposed technique is based on the immittance property in a passive resonant [...] Read more.

In current study, a method for achieving a load-independent output current with the ability to optimize the parameter tuning, which is applied to wireless power transfer (WPT) systems, is analyzed. The proposed technique is based on the immittance property in a passive resonant network (PRN) with the purpose of transforming a voltage/current resource into a current/voltage resource. This study determines an immittance conditions-qualified family of PRN, which is associated with a more appropriate topological description in WPT applications. Considering the resource and sink type, a comprehensive specification of the coupling coefficient-based design condition and operating point is carried out. Moreover, the parameters of each proposed topology are reconfigured by adjusting the proportion of active power to reactive power ratios as an index to optimize the topology size as well as a reduction of voltage/current stresses on their elements without changing the specified system-level parameters, such as the loosely coupled transformer operating frequency, and specified constant current outputs. The sample topology selection is also carried out with respect to the absorption of the parasitic components and achieving the inherent dc-blocked transformer. Zero-voltage-switching (ZVS) operation of the switches, minimum conduction losses of the rectifier diodes within an extensive variety of load variations, and capability of consistent generation of stable load-regulated current are also achieved. Analytical results show that the proposed compensation has the minimum output current fluctuation versus variations of the coupling coefficient and other parameters. Finally, the effectiveness of the proposed methodology is evaluated through simulations, and practical experiments, and compared with the conventional design method. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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15 pages, 2958 KiB  

Open AccessArticle

Modeling and Analysis of Load Growth Expected for Electric Vehicles in Pakistan (2021–2030)

by Naveed Ahmed Unar, Nayyar Hussain Mirjat, Bilal Aslam, Muneer Ahmed Qasmi, Maha Ansari and Kush Lohana

Energies 2022, 15(15), 5426; https://doi.org/10.3390/en15155426 - 27 Jul 2022

Cited by 5 | Viewed by 3401

Abstract

The world is facing severe environmental challenges as it heavily relies on a USD 100 trillion fossil-fuel-based economy. Its transition from a fuel-intensive to a material-intensive economy is not well understood. The conventional energy resources are responsible for the excessive generation of Green [...] Read more.

The world is facing severe environmental challenges as it heavily relies on a USD 100 trillion fossil-fuel-based economy. Its transition from a fuel-intensive to a material-intensive economy is not well understood. The conventional energy resources are responsible for the excessive generation of Green House Gas (GHG) emissions resulting in increased environmental degradation owing to climate change. The human impact has been cited as highly indisputable in this respect. Pakistan is one of the most climate-vulnerable countries highly suffering from such increased impact of climate change and, thus, has been warned against the excessive use of conventional resources. As such, in the premises of Pakistan, conventional products are being excessively utilized in both power generation and transport sectors. Apart from the electrical power sector, the transport sector is also one of the main contributors to GHG emissions. In this context, the automobile industry has emerged as an environmentally friendly solution, which presents Electric Vehicles (EVs) as an efficient and feasible alternative to mitigate the GHG footprint. The transition from fossil-fuel-based vehicles (FFVs) to EVs is, therefore, considered as a potential way to decarbonize the transport sector, where the socio-economic conditions may be improved to a significant extent. A major prerequisite under planning and implementation in Pakistan is forecasting of load growth of EVs in Pakistan. Therefore, this paper proposes a load growth model (load forecast), used to forecast the load growth expected for electric vehicles in Pakistan from 2021 to 2030. This paper discusses in detail the original and revised models. According to the revised model, total EV energy demand stood at 24.61 GWh in 2020 and increased up to 2862.54 GWh in 2030. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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