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Processes
Special Issues
Multi-Period Optimization of Sustainable Energy Systems
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Multi-Period Optimization of Sustainable Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 15494

Special Issue Editors

Dr. Jui-Yuan Lee

E-Mail Website1 Website2
Guest Editor
Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: chemical process design and integration; energy system design and planning; biomass supply chain network synthesis
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Dr. Adeniyi Jide Isafiade

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Guest Editor
Department of Chemical Engineering, University of Cape Town, Rondebosch 7701, South Africa
Interests: process synthesis, integration, and optimisation for sustainable development; safety and environmental engineering
Prof. Dr. Yongzhong Liu

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Guest Editor
Department of Chemical Engineering, Xi’an Jiaotong University, Xi'an 710049, China
Interests: process systems engineering; modelling and optimisation in chemical engineering; integration and optimization on energy storage systems; mitigation and control technique of greenhouse gases

Special Issue Information

Dear Colleagues,

Sustainable energy systems are an essential response to climate change challenges. Important measures include energy efficiency enhancement, increased use of non-fossil energy (e.g., renewables) and carbon capture and storage. A holistic management system would thus be necessary to integrate these initiatives for a low-carbon emission society for climate resilient economic growth. Systematic methods for the optimal synthesis, design and operation of efficient, low-carbon energy systems have been developed. Efficient and multi-functional energy systems are considered an important engineering solution to reduce carbon emissions. For example, polygeneration systems can take the opportunity for process integration from simultaneous production of multiple products, thereby achieving improved fuel efficiency and reduced carbon emissions. Integrating renewables into the energy mix can achieve similar benefits. Process systems engineering (PSE) methods can be applied to the synthesis of such sustainable energy systems, which should normally be designed with multi-period consideration to account for variations in product demand and resource availability, as well as changes in external factors such as electricity price.

This Special Issue on “Multi-Period Optimization of Sustainable Energy Systems” aims to curate novel advances in the development and application of PSE and alternative tools to address longstanding challenges in the synthesis and design of sustainable energy systems for multi-period operations. Topics include but are not limited to:

  • Energy-related resource conservation networks;
  • Distributed multi-functional energy systems (e.g., trigeneration and polygeneration systems);
  • Regional or sectoral low-carbon energy systems;
  • Bioenergy supply chain networks;
  • Penetration of renewable energy through energy storage; and
  • Power-to-X (P2X) systems.

Dr. Jui-Yuan Lee
Prof. Dr. Adeniyi Jide Isafiade
Prof. Dr. Yongzhong Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process systems engineering
  • process integration
  • energy recovery
  • hydrogen management
  • distributed generation
  • renewable energy systems
  • bioenergy
  • waste management

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

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Editorial

Jump to: Research

2 pages, 176 KiB  
Editorial
Special Issue on “Multi-Period Optimization of Sustainable Energy Systems”
by Jui-Yuan Lee, Adeniyi Jide Isafiade and Yongzhong Liu
Processes 2022, 10(7), 1386; https://doi.org/10.3390/pr10071386 - 15 Jul 2022
Viewed by 1119
Abstract
Sustainable energy systems are an essential response to climate change challenges [...] Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)

Research

Jump to: Editorial

20 pages, 3946 KiB  
Article
Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results
by Simone Polimeni, Alfredo Nespoli, Sonia Leva, Gianluca Valenti and Giampaolo Manzolini
Processes 2021, 9(2), 323; https://doi.org/10.3390/pr9020323 - 9 Feb 2021
Cited by 20 | Viewed by 2470
Abstract
Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The [...] Read more.
Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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19 pages, 5749 KiB  
Article
Conceptual Design of a Negative Emissions Polygeneration Plant for Multiperiod Operations Using P-Graph
by Jean Pimentel, Ákos Orosz, Kathleen B. Aviso, Raymond R. Tan and Ferenc Friedler
Processes 2021, 9(2), 233; https://doi.org/10.3390/pr9020233 - 27 Jan 2021
Cited by 12 | Viewed by 2884
Abstract
Reduction of CO2 emissions from industrial facilities is of utmost importance for sustainable development. Novel process systems with the capability to remove CO2 will be useful for carbon management in the future. It is well-known that major determinants of performance in [...] Read more.
Reduction of CO2 emissions from industrial facilities is of utmost importance for sustainable development. Novel process systems with the capability to remove CO2 will be useful for carbon management in the future. It is well-known that major determinants of performance in process systems are established during the design stage. Thus, it is important to employ a systematic tool for process synthesis. This work approaches the design of polygeneration plants with negative emission technologies (NETs) by means of the graph-theoretic approach known as the P-graph framework. As a case study, a polygeneration plant is synthesized for multiperiod operations. Optimal and alternative near-optimal designs in terms of profit are identified, and the influence of network structure on CO2 emissions is assessed for five scenarios. The integration of NETs is considered during synthesis to further reduce carbon footprint. For the scenario without constraint on CO2 emissions, 200 structures with profit differences up to 1.5% compared to the optimal design were generated. The best structures and some alternative designs are evaluated and compared for each case. Alternative solutions prove to have additional practical features that can make them more desirable than the nominal optimum, thus demonstrating the benefits of the analysis of near-optimal solutions in process design. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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16 pages, 2748 KiB  
Article
Optimal Design of a Hydrolysis Sugar Membrane Purification System Using a Superstructure-Based Approach
by Chien-Yuan Su, Bo-Yan Ji, Pei-Jung Yu, Ming-Hua Wang, Wei-Chun Hung, Ying-Hsi Chang and Jui-Yuan Lee
Processes 2021, 9(1), 168; https://doi.org/10.3390/pr9010168 - 18 Jan 2021
Cited by 1 | Viewed by 2289
Abstract
As an alternative to gasoline, bioethanol can be produced from lignocellulosic biomass through hydrolysis using an ionic solution containing zinc chloride (ZnCl2). This method allows for a high yield of glucose from lignocellulose, but entails the removal of ZnCl2 from [...] Read more.
As an alternative to gasoline, bioethanol can be produced from lignocellulosic biomass through hydrolysis using an ionic solution containing zinc chloride (ZnCl2). This method allows for a high yield of glucose from lignocellulose, but entails the removal of ZnCl2 from the hydrolysate using multiple nanofiltration membranes before the fermentation of glucose. This paper presents a mathematical technique for designing such a multistage membrane separation system. The optimization model for the synthesis of membrane networks is based on a superstructure with all feasible interconnections between the membrane units, and consists of mass balances, logical constraints and product specifications. A case study of the separation of a bagasse hydrolysis solution is used to demonstrate the application of the proposed model. Results show that using both types of nanofiltration membranes allows higher ZnCl2 removal ratios at each membrane unit, hence a decrease in the number of membrane units required and a reduction of about 35% in capital cost compared to the cases in which only one membrane type is used. Further analysis is performed to examine the effect of membrane performance on the economics of the separation system. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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18 pages, 1954 KiB  
Article
Multi-Objective Coordinated Optimal Allocation of DG and EVCSs Based on the V2G Mode
by Lijun Liu, Feng Xie, Zonglong Huang and Mengqi Wang
Processes 2021, 9(1), 18; https://doi.org/10.3390/pr9010018 - 23 Dec 2020
Cited by 21 | Viewed by 2534
Abstract
With the vigorous promotion of new energy sources and the development of vehicle-to-grid (V2G) technology, the influence of the V2G mode should be considered in the joint optimal allocation of Distributed Generation (DG) and electric vehicle charging stations (EVCSs). The timing characteristics of [...] Read more.
With the vigorous promotion of new energy sources and the development of vehicle-to-grid (V2G) technology, the influence of the V2G mode should be considered in the joint optimal allocation of Distributed Generation (DG) and electric vehicle charging stations (EVCSs). The timing characteristics of the intermittent output of DG, conventional demand for load, and charging load of the electric vehicle (EV) are considered, as is its participation in grid interaction to examine the construction of typical scenarios and the EV cluster dispatching strategy. From the perspective of comprehensively planning the coordination of the distribution network, a DG-EVCSs bi-level joint planning model is established under the peak and valley price mechanism, with the sub-objectives of obtaining a comprehensive profit and high quality of voltage, curbing system load fluctuations, and satisfactorily charging the EV. An improved harmony particle swarm optimization algorithm is proposed to solve the bi-level model. The proposed method was tested on the IEEE-33 and the PG&E-69 (Pacific Gas and Electric Company) bus distribution systems, and the results show that the optimized configuration model that considers the V2G mode can improve the overall performance of the planning scheme, promote the use of clean energy, smoothen the load fluctuations of the system, and improve the quality of voltage and charging satisfaction of EV users. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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25 pages, 8489 KiB  
Article
Synthesis of European Union Biorefinery Supply Networks Considering Sustainability Objectives
by Sanja Potrč, Lidija Čuček, Mariano Martin and Zdravko Kravanja
Processes 2020, 8(12), 1588; https://doi.org/10.3390/pr8121588 - 1 Dec 2020
Cited by 15 | Viewed by 2807
Abstract
Increasing the use of renewable energy sources is one of the most important goals of energy policies in several countries to build a sustainable energy future. This contribution proposes the synthesis of a biorefinery supply network for a case study of the European [...] Read more.
Increasing the use of renewable energy sources is one of the most important goals of energy policies in several countries to build a sustainable energy future. This contribution proposes the synthesis of a biorefinery supply network for a case study of the European Union (EU-27) under several scenarios based on a mathematical programming approach. Several biomass and waste sources, such as grains, waste oils, and lignocellulosics, are proposed to be utilized, and various biofuels including first, second, and third generations are produced such as bioethanol, green gasoline, biodiesel, Fischer Tropsch (FT) diesel, and hydrogen. The aim of this study is to evaluate the capabilities of EU-27 countries to be able to meet the Renewable Energy Directive (RED II) target regarding the share of renewable energy in the transport sector by 2030 in each Member State while not compromising the current production of food. A generic mathematical model has been developed for the multi-period optimization of a biorefinery supply network with the objective of maximizing sustainability profit. The solutions obtained show that biomass and waste are promising raw materials to reach and go beyond the EU’s renewable energy target in the transport sector for the year 2030. However, some countries would need to provide additional subsidies for their companies to achieve at least a non-negative economic performance of biofuel production. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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19 pages, 1378 KiB  
Article
Optimization of an Inter-Plant Hydrogen Network: A Simultaneous Approach to Solving Multi-Period Optimization Problems
by Rusong Han, Lixia Kang, Yinghua Jiang, Jing Wang and Yongzhong Liu
Processes 2020, 8(12), 1548; https://doi.org/10.3390/pr8121548 - 26 Nov 2020
Cited by 11 | Viewed by 1952
Abstract
Inter-plant hydrogen integration can reduce the consumption of hydrogen utility in petrochemical parks. However, the fluctuation of operating conditions will lead to complex multi-period problems of hydrogen network integration. This work develops a simultaneous optimization approach to solving multi-period optimization problems for the [...] Read more.
Inter-plant hydrogen integration can reduce the consumption of hydrogen utility in petrochemical parks. However, the fluctuation of operating conditions will lead to complex multi-period problems of hydrogen network integration. This work develops a simultaneous optimization approach to solving multi-period optimization problems for the inter-plant hydrogen network. To do this, we consider the inter-plant hydrogen integration and the fluctuation of operating conditions in each plant at the same time, and aim to minimize the total annualized cost of the entire hydrogen system of all plants involved. An industrial case study of a three-plant hydrogen network with seven subperiods was adopted to verify the effectiveness of the proposed method. Results show that the optimal structure and the corresponding scheduling scheme can be obtained when the lowest cost of the system is targeted. Compared with the stepwise methods, the proposed approach features taking the characteristics of all subperiods into account simultaneously and making the structure of the hydrogen network much more effective and economical. For the scheduling schemes, the utilization efficiency of the internal hydrogen sources is increased by hydrogen exchange among the plants. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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14 pages, 2078 KiB  
Article
Development of an Oxygen Pressure Estimator Using the Immersion and Invariance Method for a Particular PEMFC System
by Ángel Hernández-Gómez, Victor Ramirez and Belem Saldivar
Processes 2020, 8(9), 1095; https://doi.org/10.3390/pr8091095 - 3 Sep 2020
Cited by 1 | Viewed by 2280
Abstract
The fault detection method has been used usually to give a diagnosis of the performance and efficiency in the proton exchange membrane fuel cell (PEMFC) systems. To be able to use this method a lot of sensors are implemented in the PEMFC to [...] Read more.
The fault detection method has been used usually to give a diagnosis of the performance and efficiency in the proton exchange membrane fuel cell (PEMFC) systems. To be able to use this method a lot of sensors are implemented in the PEMFC to measure different parameters like pressure, temperature, voltage, and electrical current. However, despite the high reliability of the sensors, they can fail or give erroneous measurements. To address this problem, an efficient solution to replace the sensors must be found. For this reason, in this work, the immersion and invariance method is proposed to develop an oxygen pressure estimator based on the voltage, electrical current density, and temperature measurements. The estimator stability region is calculated by applying Lyapunov’s Theorem and constraints to achieve stability are established for the oxygen pressure, electrical current density, and temperature. Under these estimator requirements, oxygen pressure measurements of high reliability are obtained to fault diagnosis without the need to use an oxygen sensor. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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15 pages, 2560 KiB  
Article
Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification
by Pei Zhang, Yanling Wang, Likai Liang, Xing Li and Qingtian Duan
Processes 2020, 8(2), 157; https://doi.org/10.3390/pr8020157 - 27 Jan 2020
Cited by 31 | Viewed by 3087
Abstract
Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to [...] Read more.
Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction. Full article
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
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