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Prof. Dr. Jen-Hao Teng

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Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan

Dr. Kin-Cheong Sou

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Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan

Prof. Dr. Alfeu J. Sguarezi Filho

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Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, Brazil

Dr. Lakshmanan Padmavathi

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Central Electronic Engineering Research Institute (CEERI), Pilani, Rajasthan, India

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

Abstract submission deadline

31 May 2025

Manuscript submission deadline

31 July 2025

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51011

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

decarbonization energy transition
net-zero emission
renewable energy
energy storage system
electric vehicle
resilient energy system architecture
Internet of Energy

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 (22 papers)

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15 pages, 451 KiB

Open AccessArticle

Three Duopoly Game-Theoretic Models for the Smart Grid Demand Response Management Problem

by Slim Belhaiza

Systems 2024, 12(10), 401; https://doi.org/10.3390/systems12100401 - 28 Sep 2024

Viewed by 608

Demand response management (DRM) significantly influences the prospective advancement of electricity smart grids. This paper introduces three distinct game-theoretic duopoly models for the smart grid demand response management problem. It delineates several rational assumptions regarding the model variables, functions, and parameters. The first [...] Read more.

Demand response management (DRM) significantly influences the prospective advancement of electricity smart grids. This paper introduces three distinct game-theoretic duopoly models for the smart grid demand response management problem. It delineates several rational assumptions regarding the model variables, functions, and parameters. The first model adopts a Cournot duopoly form, offering a unique closed-form equilibrium solution. The second model adopts a Stackelberg duopoly structure, also providing a unique closed-form equilibrium solution. Following a comparison of the economic viability of the two model equilibria and an assessment of their sensitivity to parametric changes, the paper proposes a third model with a Cartel structure and discusses its advantages over the earlier models. Finally, the paper examines how demand forecasting affects the equilibrium quantities and pricing solutions of each model. Full article

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

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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 990

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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<p>Block flow diagram of the process under study [<a href="#B14-sustainability-16-01993" class="html-bibr">14</a>].</p> Full article ">Figure 2
<p>The effect of changes in the methanol price on (<b>a</b>) net annual benefit and additive value; (<b>b</b>) period of return.</p> Full article ">Figure 3
<p>The effect of changes in the crude feed gas cost on (<b>a</b>) additive value, prime cost, and levelized cost of product; (<b>b</b>) period of return.</p> Full article ">Figure 4
<p>The effect of fluctuation in the value of fuel gas price on (<b>a</b>) additive value, prime cost, and levelized cost of product; (<b>b</b>) net annual benefit.</p> Full article ">Figure 5
<p>The effect of changes in the electricity cost on (<b>a</b>) additive value, prime cost, and levelized cost of product; (<b>b</b>) net annual benefit.</p> Full article ">Figure 6
<p>The effect of changes in the liquid nitrogen cost on (<b>a</b>) additive value, prime cost, and levelized cost of product; (<b>b</b>) liquid nitrogen cost.</p> Full article ">Figure 7
<p>The optimization process considered to develop the design.</p> Full article ">Figure 8
<p>Pareto frontier resulting from utilizing the NSGAII.</p> Full article ">Figure 9
<p>The ultimate optimal solution derived from each decision-making method.</p> Full article ">

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 5 | Viewed by 1703

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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Figure 1
<p>America seen from satellite, adapted from [<a href="#B50-energies-17-01059" class="html-bibr">50</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Map with the islands that are part of the American countries. (<b>b</b>) Detail of the level of penetration of renewable energies by country regarding electrified islands.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) Map with the islands that are part of the American countries. (<b>b</b>) Detail of the level of penetration of renewable energies by country regarding electrified islands.</p> Full article ">Figure 3
<p>(<b>a</b>) Map with the location of the islands on the American continent. (<b>b</b>) Bar diagram detailing the level of penetration of renewable energies on the islands. (<b>c</b>) Pie diagram of renewable energy component by island (GWh, %).</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Map with the location of the islands on the American continent. (<b>b</b>) Bar diagram detailing the level of penetration of renewable energies on the islands. (<b>c</b>) Pie diagram of renewable energy component by island (GWh, %).</p> Full article ">Figure 4
<p>Solar photovoltaic power potential in America, analyzed using the free software Global Solar Atlas [<a href="#B95-energies-17-01059" class="html-bibr">95</a>].</p> Full article ">Figure 5
<p>Wind energy potential focused on the islands of the American continent, analyzed using the free software Global Wind Atlas [<a href="#B106-energies-17-01059" class="html-bibr">106</a>].</p> Full article ">Figure 6
<p>Mapping of American land use for biomass exploitation purposes using the Land Cover Viewer tool [<a href="#B110-energies-17-01059" class="html-bibr">110</a>].</p> Full article ">Figure 7
<p>Approaches identified in the review that point to a 100% renewable system on American islands.</p> Full article ">Figure 8
<p>Identification of islands that have long-term roadmaps and the number of studies carried out.</p> Full article ">Figure 9
<p>Bibliometric analysis with the support of the VOSviewer tool. (<b>a</b>) Bibliometric links by keywords for the Galapagos Islands. (<b>b</b>) Collaborative links between authors referring to research in Galapagos. (<b>c</b>) Bibliometric links by keywords for the island of Cuba. (<b>d</b>) Collaborative links between authors referring to research on the island of Cuba.</p> Full article ">Figure 9 Cont.
<p>Bibliometric analysis with the support of the VOSviewer tool. (<b>a</b>) Bibliometric links by keywords for the Galapagos Islands. (<b>b</b>) Collaborative links between authors referring to research in Galapagos. (<b>c</b>) Bibliometric links by keywords for the island of Cuba. (<b>d</b>) Collaborative links between authors referring to research on the island of Cuba.</p> Full article ">Figure 10
<p>Tools used in the designs of energy transition systems in the American islands.</p> Full article ">

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 1768

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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<p>AC/DC microgrid system.</p> Full article ">Figure 2
<p>Normal distribution with varied <math display="inline"><semantics> <mi>μ</mi> </semantics></math> and <math display="inline"><semantics> <mi>σ</mi> </semantics></math>.</p> Full article ">Figure 3
<p>Framework and search strategies of GNDO.</p> Full article ">Figure 4
<p>The GNDO algorithm flowchart.</p> Full article ">Figure 5
<p>DSM results pertaining to low-load scenario in AC/DC MG.</p> Full article ">Figure 6
<p>DSM results pertaining to high-load scenario in AC/DC MG.</p> Full article ">Figure 7
<p>Hourly generation scheduling of the AC/DC MG according to cumulative output power from SPV, BESS, DE-SG, and utility for high-load scenario.</p> Full article ">Figure 8
<p>Hourly generation scheduling of the AC/DC MG by cumulative output power from SPV, BESS, DE-SG, and utility for low-load scenario.</p> Full article ">

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 1437

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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Figure 1
<p>Flowchart of AsDTM-based fast power system transient stability simulation method.</p> Full article ">Figure 2
<p>Recursive process to solve power series coefficients (source: [<a href="#B20-energies-16-07157" class="html-bibr">20</a>]), where X(k) represents <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>j</mi> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msub> <mi>ω</mi> <mi>j</mi> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msubsup> <mi>E</mi> <mrow> <mi>q</mi> <mi>j</mi> </mrow> <mo>′</mo> </msubsup> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msubsup> <mi>E</mi> <mrow> <mi>d</mi> <mi>j</mi> </mrow> <mo>′</mo> </msubsup> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msub> <mi>V</mi> <mrow> <mi>r</mi> <mi>j</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msub> <mi>V</mi> <mrow> <mi>f</mi> <mi>j</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msub> <mi>E</mi> <mrow> <mi>f</mi> <mi>j</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>j</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> <mo>,</mo> <mtext> </mtext> <mi>and</mi> <mtext> </mtext> <msub> <mi>P</mi> <mrow> <mi>s</mi> <mi>v</mi> <mi>j</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and j = 1, 2, 3 … m.</p> Full article ">Figure 3
<p>One-line diagram of (<b>a</b>) New England 39 bus system; (<b>b</b>) IEEE 9 bus system [<a href="#B29-energies-16-07157" class="html-bibr">29</a>].</p> Full article ">Figure 4
<p>Step-size variations during simulation for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 5
<p>Simulation time cost for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 6
<p>Number of iterations for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 7
<p>Rotor angle error for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 8
<p>Rotor speed error for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 9
<p>Rotor speed simulation for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">Figure 10
<p>Rotor angle simulation for IEEE (<b>a</b>) 9 bus and (<b>b</b>) 39 bus test systems.</p> Full article ">

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 3 | Viewed by 1716

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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Figure 1
<p>A typical Q–V curve during the charging operation of a lithium-ion battery. The zoomed-in part shows the quantization error in the voltage measurement.</p> Full article ">Figure 2
<p>Secant approximation method.</p> Full article ">Figure 3
<p>A comparison between the proposed secant approximation (yellow line) and a similar method in [<a href="#B27-energies-16-07066" class="html-bibr">27</a>] (orange line).</p> Full article ">Figure 4
<p>Zero-phase filtering, where r represents the time-reversal operator of a sequence.</p> Full article ">Figure 5
<p>ICA curve before smoothing (blue) and after smoothing using zero-phase filtering (red) or radial basis function regression (yellow).</p> Full article ">Figure 6
<p>ICA curves of an LIB after different numbers of cycles of full discharge and recharge.</p> Full article ">Figure 7
<p>ICA curve and the chosen peaks and valley.</p> Full article ">Figure 8
<p>Illustration of piecewise-linear-interpolation-based SOH estimation involving a single feature. The blue dots (training data) indicate the SOH-feature pairs from offline data. The interpolation grid consists of SOH values <span class="html-italic">s</span>1′, <span class="html-italic">s</span>2′, and <span class="html-italic">s</span>′<span class="html-italic">N</span>′, where extra feature values are generated via interpolation if necessary (cf. (5)). The vertical coordinate of the red dot is the value of the measurement-derived feature (i.e., <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>y</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> </semantics></math>), while the horizontal coordinate is the true SOH of the battery. The method of minimum residual in (6) identifies the SOH estimate from the interpolation grid <span class="html-italic">s</span>1′, <span class="html-italic">s</span>2′, and <span class="html-italic">s</span>′<span class="html-italic">N</span>′, the feature value of which is closest to the vertical coordinate of the red dot, illustrated as “SOH estimate” in the figure.</p> Full article ">Figure 9
<p>The structure of a neural network.</p> Full article ">Figure 10
<p>ICA curve for different SOC ranges.</p> Full article ">Figure 11
<p>Battery data including measurement errors. (<b>a</b>) Voltage curve. (<b>b</b>) ICA curve.</p> Full article ">Figure 11 Cont.
<p>Battery data including measurement errors. (<b>a</b>) Voltage curve. (<b>b</b>) ICA curve.</p> Full article ">Figure 12
<p>Measurement error of different battery numbers at 400 cycles.</p> Full article ">

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 2 | Viewed by 1144

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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<p>The architecture of the IEPS.</p> Full article ">Figure 2
<p>Flow chart of combining K-means clustering and SBR algorithm.</p> Full article ">Figure 3
<p>Annual solar irradiance and load curve.</p> Full article ">Figure 4
<p>Typical daily predictions of solar irradiance and load for different seasons.</p> Full article ">Figure 5
<p>Optimal power scheduling in Scenario 1.</p> Full article ">Figure 6
<p>CO<sub>2</sub> optimal scheduling and carbon storage in Scenario 2.</p> Full article ">Figure 7
<p>H<sub>2</sub> ptimal scheduling and carbon storage in Scenario 2.</p> Full article ">Figure 8
<p>Global horizontal irradiance clustering results for scenarios 3 and 4.</p> Full article ">Figure 9
<p>Power load clustering results for scenarios 3 and 4.</p> Full article ">Figure 10
<p>Sensitivity analysis of coal price.</p> Full article ">Figure 11
<p>Sensitivity analysis of natural gas price.</p> Full article ">Figure 12
<p>Sensitivity analysis of carbon tax prices.</p> Full article ">

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 874

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 1229

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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<p>Circuit topology of a conventional PSFBC.</p> Full article ">Figure 2
<p>Theoretical waveforms of a PSFBC.</p> Full article ">Figure 3
<p>Effects of resonant inductor values on <span class="html-italic">i<sub>p</sub></span>. (<b>a</b>) Effect on transition region; (<b>b</b>) Effect on circulation region.</p> Full article ">Figure 4
<p>Potential conversion efficiencies of a PSFBC with different resonance inductor values.</p> Full article ">Figure 5
<p>Effects of transformer turns ratios on the <span class="html-italic">D<sub>eff</sub></span> and <span class="html-italic">i<sub>p</sub></span>. (<b>a</b>) Transformer turns ratio of 13; (<b>b</b>) Transformer turns ratio of 10.</p> Full article ">Figure 6
<p>Potential conversion efficiencies of a PSFBC with different transformer turns ratios.</p> Full article ">Figure 7
<p>Flowchart of proposed accurate loss estimation.</p> Full article ">Figure 8
<p>Digital twin of a PSFBC.</p> Full article ">Figure 9
<p>Concepts of parameter optimization using a digital twin.</p> Full article ">Figure 10
<p>Potential utilization histograms of different application scenarios. (<b>a</b>) Server power supply; (<b>b</b>) Battery charger.</p> Full article ">Figure 11
<p>Measured waveforms with different resonance inductor values. (<b>a</b>) Resonance Inductance of 34 µH; (<b>b</b>) Resonance Inductance of 83 µH.</p> Full article ">Figure 12
<p>Measured waveforms with different transformer turns ratios. (<b>a</b>) Transformer turns ratio of 10:1; (<b>b</b>) Transformer turns ratio of 13:1.</p> Full article ">Figure 13
<p>Comparisons of measured and estimated waveforms. (<b>a</b>) Output current of 1 A; (<b>b</b>) Output current of 20 A.</p> Full article ">Figure 14
<p>Measured and estimated conversion efficiencies. (<b>a</b>) Transformer turns ratio of 10; (<b>b</b>) Transformer turns ratio of 13.</p> Full article ">Figure 15
<p>Conversion efficiency comparisons with and without the proposed magnetic design.</p> Full article ">

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 4 | Viewed by 1204

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 5 | Viewed by 2774

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 2321

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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<p>Possible battery cycle ecosystem.</p> Full article ">Figure 2
<p>Configuration of proposed battery data acquisition system.</p> Full article ">Figure 3
<p>Hardware of proposed battery data acquisition system.</p> Full article ">Figure 4
<p>HMI of proposed battery data acquisition system.</p> Full article ">Figure 5
<p>Complete CHA-DISCH waveforms.</p> Full article ">Figure 6
<p>Discharge voltage profiles of various SOH levels.</p> Full article ">Figure 7
<p>Basic division concept of sub-data.</p> Full article ">Figure 8
<p>Basic concept of feature value extraction.</p> Full article ">Figure 9
<p>Two feature value extractions proposed in this paper. (<b>a</b>) First feature value extraction, (<b>b</b>) Second feature value extraction.</p> Full article ">Figure 10
<p>Architecture of BPNN for the proposed SOH-E.</p> Full article ">Figure 11
<p>Correlation of SOH (%) and R (mΩ).</p> Full article ">Figure 12
<p>Number of retired batteries used in training and estimation. (<b>a</b>) SOH (%), (<b>b</b>) R (mΩ).</p> Full article ">Figure 13
<p>SOH-E of BPNN under 3 hidden layers and 300 neurons.</p> Full article ">Figure 14
<p>SOH-E of LSTMNN under 2 hidden layers and 100 neurons.</p> Full article ">

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 10 | Viewed by 2865

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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<p>Power train of hybrid source system in electric 3W vehicle.</p> Full article ">Figure 2
<p>Schematic structure of hybrid-source energy management in electric three-wheeler.</p> Full article ">Figure 3
<p>Dynamics of electric three-wheeler vehicle.</p> Full article ">Figure 4
<p>Velocity profile of NYCC, Artemis Urban, and WLTP class-1.</p> Full article ">Figure 5
<p>Circuit diagram of proposed EMS of hybrid-source electric vehicle.</p> Full article ">Figure 6
<p>Irradiance (blue), ambient temperature (red).</p> Full article ">Figure 7
<p>Input and output membership functions of fuzzy controller.</p> Full article ">Figure 8
<p>Flowchart of absolute energy-sharing algorithm.</p> Full article ">Figure 9
<p>Absolute energy sharing profiles.</p> Full article ">Figure 10
<p>Cut-off frequency derived for the CDP.</p> Full article ">Figure 11
<p>Comparison of battery, SC, PV, and load power (BEV) with IHSEMS.</p> Full article ">Figure 12
<p>Comparison of source energy consumption of IHSEMS under CDP.</p> Full article ">Figure 13
<p>PV energy generation (kWh). (<b>a</b>) Map of selected location for the analysis (Bangalore-12.9716° N, 77.5946° E). (<b>b</b>) PV energy generation (kWh) at Bangalore, India throughout the year.</p> Full article ">Figure 14
<p>Power allocation of IHSEMS for sudden variation in solar irradiance (<b>a</b>) at zero PV power; (<b>b</b>) under NYCC driving cycle.</p> Full article ">Figure 15
<p>Comparison of BEV, SMC, FDS, and IHSEMS in terms of battery current (<b>a</b>) and battery capacity loss (<b>b</b>) under NYCC driving cycle.</p> Full article ">Figure 16
<p>Comparison of BEV, SMC, FDS, and proposed IHSEMS under NYCC driving cycle. (<b>a</b>) Comparison of peak battery power for NYCC driving cycle (510 s–599 s). (<b>b</b>) Comparison of power fluctuations for NYCC driving cycle (400 s–520 s).</p> Full article ">Figure 17
<p>Comparison of <math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>C</mi> <mrow> <mi>S</mi> <mi>O</mi> <mi>C</mi> </mrow> </msub> </mrow> </semantics></math> with SMC, FDS, and IHSEMS under NYCC driving cycle.</p> Full article ">Figure 18
<p>Comparison of <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mi>B</mi> <mi>V</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> bus voltage fluctuations with SMC, FDS, and IHSEMS under NYCC driving cycle.</p> Full article ">Figure 19
<p>Economic analysis of EV with SMC, FDS, BEV, and IHSEMS.</p> Full article ">

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 3352

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 1607

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 4 | Viewed by 3305

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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<p>Study region and the specific location of Antikythera.</p> Full article ">Figure 2
<p>Annual solar energy potential of Antikythera.</p> Full article ">Figure 3
<p>Monthly panels plot for (<b>a</b>) average of AOD and specific types of aerosols, (<b>b</b>) average of CMF and AMF, (<b>c</b>) average of GHI and DNI and (<b>d</b>) percentage attenuation of DNI and GHI because of CMF and AMF.</p> Full article ">Figure 4
<p>Mean solar energy input to the PV and CSP systems in a monthly time horizon (in kWh/m<sup>2</sup>). The inset values represent the annual total energy.</p> Full article ">Figure 5
<p>Monthly average production and consumption for scenario zero.</p> Full article ">Figure 6
<p>Monthly average energy production and consumption based on scenario 1.</p> Full article ">Figure 7
<p>Column bars of monthly production and consumption based on scenario 2.</p> Full article ">Figure 8
<p>Column bars of monthly production and consumption based on scenario 3.</p> Full article ">Figure 9
<p>Contour plots of hourly energy production for each day of the year (<b>a</b>) scenario 0 of 200 KW PV (<b>b</b>) scenarios 1 and 2 of 500 KW PV and (<b>c</b>) scenario 3 of 500 KW PV and 200 KW CSP.</p> Full article ">Figure 10
<p>Annual financial analysis for PV and CSP plants with (<b>a</b>) 200 KW PV, (<b>b</b>) 500 KW PV and (<b>c</b>) 500 KW PV combined with 200 KW CSP energy production depicted in different colors. The corresponding energy and financial losses due to aerosol and cloud presence was included.</p> Full article ">

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 3 | Viewed by 5047

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 2 | Viewed by 2231

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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<p>The procedures of energy system decarbonization pathways (simulation).</p> Full article ">Figure 2
<p>The modeling framework of municipal decarbonization pathway planning.</p> Full article ">Figure 3
<p>The primary energy demand and energy intensity from 2010 to 2020.</p> Full article ">Figure 4
<p>Sankey diagram of energy flow in 2020. Services industry consists of transportation and commercial sectors.</p> Full article ">Figure 5
<p>The CO<sub>2</sub> emissions and CO<sub>2</sub> emission intensity from 2010 to 2020.</p> Full article ">Figure 6
<p>The fuel energy demand forecast for the three scenarios: (<b>a</b>) conservative scenario; (<b>b</b>) carbon neutrality scenario; (<b>c</b>) carbon-negative scenario.</p> Full article ">Figure 7
<p>The CO<sub>2</sub> emission forecast for the three scenarios.</p> Full article ">Figure 8
<p>The CO<sub>2</sub> emissions and net emissions forecast for the sectors and carbon sink in the three scenarios: (<b>a</b>) conservative scenario; (<b>b</b>) carbon neutrality scenario; (<b>c</b>) carbon-negative scenario.</p> Full article ">Figure 9
<p>The overall coordination degree of MES from 2011 to 2060.</p> Full article ">Figure 10
<p>The coordination degree of each dimension for the three scenarios: (<b>a</b>) conservative scenario; (<b>b</b>) carbon neutrality scenario; (<b>c</b>) carbon-negative scenario.</p> Full article ">

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 2053

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 3403

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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<p>Planning perspectives considering the respective demand factor (DF) [<xref ref-type="bibr" rid="B25-electricity-03-00022">25</xref>].</p> Full article ">Figure 2
<p><bold>Left</bold>: Probability distribution of the number of routes per day and vehicle. <bold>Right</bold>: Percentage of the purpose of the daily routes for different weekdays based on [<xref ref-type="bibr" rid="B28-electricity-03-00022">28</xref>].</p> Full article ">Figure 3
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Urban Region: Metropolis”.</p> Full article ">Figure 4
<p>Geographical representation of the seven area types (RegioStar 7) in Germany for the research in mobility and transportation sectors [<xref ref-type="bibr" rid="B29-electricity-03-00022">29</xref>], with permission from Federal Ministry of Transport and Digital Infrastructure, 2018.</p> Full article ">Figure 5
<p>Simulation algorithm for generating probabilistic driving and load profiles [<xref ref-type="bibr" rid="B28-electricity-03-00022">28</xref>].</p> Full article ">Figure 6
<p>Exemplary weekly charging profile for an electric vehicle and the accumulated charging profile for ten electric vehicles [<xref ref-type="bibr" rid="B28-electricity-03-00022">28</xref>].</p> Full article ">Figure 7
<p>Example of calculated demand factors for six charging powers.</p> Full article ">Figure 8
<p>Process diagram for the simulation of charging profiles for different area types and charging powers.</p> Full article ">Figure 9
<p>Example of curve fitting (CF) for charging powers between 3.7 kW and 350 kW for (10, 50, 100 and 500) charging points (CP).</p> Full article ">Figure 10
<p>Example for the newly generated demand factors.</p> Full article ">Figure 11
<p>Demand factors (ordinate) for the area type “Urban Region: Metropolis” and six charging powers.</p> Full article ">Figure 12
<p>Demand factors (ordinate) for the area type “Urban Region: Regiopolis, Large City” and six charging powers.</p> Full article ">Figure 13
<p>Demand factors (ordinate) for the area type “Urban Region: Medium-sized City, Urbanised Area” and six charging powers.</p> Full article ">Figure 14
<p>Demand factors (ordinate) for the area type “Urban Region: Small-town Area, Village Area” and six charging powers.</p> Full article ">Figure 15
<p>Demand factors (ordinate) for the area type “Rural Region: Central City” and six charging powers.</p> Full article ">Figure 16
<p>Demand factors (ordinate) for the area type “Rural Region: Medium-sized City, Urbanised Area” and six charging powers.</p> Full article ">Figure 17
<p>Demand factors (ordinate) for the area type “Rural Region: Small-town Area, Village Area” and six charging powers.</p> Full article ">Figure 18
<p>Demand factors (ordinate) for 350 kW charging power and seven different area types.</p> Full article ">Figure 19
<p>Demand factors (ordinate) for 150 kW charging power and seven different area types.</p> Full article ">Figure 20
<p>Demand factors (ordinate) for 50 kW charging power and seven different area types.</p> Full article ">Figure 21
<p>Demand factors (ordinate) for 22 kW charging power and seven different area types.</p> Full article ">Figure 22
<p>Demand factors (ordinate) for 11 kW charging power and seven different area types.</p> Full article ">Figure 23
<p>Demand factors (ordinate) for 3.7 kW charging power and seven different area types.</p> Full article ">Figure 24
<p>Demand factors (ordinate) for 22 kW charging power with seven different area types, six area types according to <italic>FNN</italic> and the results from <italic>PuBStadt</italic> for 150 charging points.</p> Full article ">Figure 25
<p>Demand factors (ordinate) for 11 kW charging power with seven different area types, six area types according to <italic>FNN</italic> and the results from <italic>PuBStadt</italic> for 150 charging points.</p> Full article ">Figure 26
<p>Accumulated charging power (ordinate) for 22 kW charging power with seven different area types, six area types according to <italic>FNN</italic> and the results from <italic>PuBStadt</italic> for 150 charging points.</p> Full article ">Figure 27
<p>Accumulated charging power (ordinate) for 11 kW charging power with seven different area types, six area types according to <italic>FNN</italic> and the results from <italic>PuBStadt</italic> for 150 charging points.</p> Full article ">Figure A1
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Urban Region: Regiopolis, Large City”.</p> Full article ">Figure A2
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Urban Region: Medium-sized City, Urbanised Area”.</p> Full article ">Figure A3
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Urban Region: Small-town Area, Village Area”.</p> Full article ">Figure A4
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Rural Region: Central City”.</p> Full article ">Figure A5
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Rural Region: Medium-sized City, Urbanised Area”.</p> Full article ">Figure A6
<p><bold>Left</bold>: Probability distribution of the length of the routes per vehicle according to the purpose of the route. <bold>Right</bold>: Probability distribution of the time of departure of a vehicle according to the purpose of the route based on data published in [<xref ref-type="bibr" rid="B27-electricity-03-00022">27</xref>] for “Rural Region: Small-town Area, Village Area”.</p> Full article ">Figure A7
<p>Accumulated charging power in kW for 350 kW charging points up to 500 charging points for seven area types.</p> Full article ">Figure A8
<p>Accumulated charging power in kW for 150 kW charging points up to 500 charging points for seven area types.</p> Full article ">Figure A9
<p>Accumulated charging power in kW for 50 kW charging points up to 500 charging points for seven area types.</p> Full article ">Figure A10
<p>Accumulated charging power in kW for 22 kW charging points up to 500 charging points for seven area types.</p> Full article ">Figure A11
<p>Accumulated charging power in kW for 11 kW charging points up to 500 charging points for seven area types.</p> Full article ">Figure A12
<p>Accumulated charging power in kW for 3.7 kW charging points up to 500 charging points for seven area types.</p> Full article ">

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

Cited by 1 | Viewed by 2014

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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<p>A general structure of WPT system block diagram.</p> Full article ">Figure 2
<p>The RN block diagram based on the source and sink types; (<b>a</b>) voltage-type source, (<b>b</b>) current-type source, (<b>c</b>) voltage-type sink, and (<b>d</b>) current-type sink.</p> Full article ">Figure 3
<p>Block diagram of bipolar immittance network.</p> Full article ">Figure 4
<p>Block diagram of T-type PRN network.</p> Full article ">Figure 5
<p>Fifth-order (T1~T12) T-type PRN in WPT systems.</p> Full article ">Figure 6
<p>Circuit diagrams of proposed T1-IPRN with (<b>a</b>) reactive components, and (<b>b</b>) integrated magnetic components.</p> Full article ">Figure 7
<p>Normalized voltage and current stresses on the proposed IPRN elements versus <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>a</b>) Currents; (<b>b</b>) voltages.</p> Full article ">Figure 7 Cont.
<p>Normalized voltage and current stresses on the proposed IPRN elements versus <math display="inline"><semantics> <mi>β</mi> </semantics></math>. (<b>a</b>) Currents; (<b>b</b>) voltages.</p> Full article ">Figure 8
<p>Quality factor diagram in terms of the physical size of elements for different values of <math display="inline"><semantics> <mi mathvariant="sans-serif">β</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="sans-serif">α</mi> </semantics></math>.</p> Full article ">Figure 9
<p>The constant current in the design condition. (<b>a</b>) Output current (I<sub>RL</sub>); (<b>b</b>) output voltage (V<sub>RL</sub>); (<b>c</b>) phase angle between the input current and voltage (θ).</p> Full article ">Figure 9 Cont.
<p>The constant current in the design condition. (<b>a</b>) Output current (I<sub>RL</sub>); (<b>b</b>) output voltage (V<sub>RL</sub>); (<b>c</b>) phase angle between the input current and voltage (θ).</p> Full article ">Figure 10
<p>IPRN built circuit prototype.</p> Full article ">Figure 11
<p>IPRN waveforms in the maximum load. (<b>a</b>) Transformer secondary current and voltage; (<b>b</b>) waveforms of voltage across switches and current through it.</p> Full article ">Figure 12
<p>IPRN waveforms in 5% of the maximum load. (<b>a</b>) Transformer secondary current and voltage; (<b>b</b>) waveforms of voltage across switches, as well as including current.</p> Full article ">Figure 13
<p>IPRN output waveforms for the varying load.</p> Full article ">Figure 14
<p>Experimental efficiency of the system in various loads.</p> Full article ">Figure 15
<p>Contribution of system losses: system losses analysis under different power conditions.</p> Full article ">

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 6 | Viewed by 3949

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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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
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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
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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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