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Special Issue "Optimum Choice of Energy System Configuration and Storages for a Proper Match between Energy Conversion and Demands"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (30 December 2018).

Special Issue Editors

Guest Editor
Prof. Andrea Lazzaretto

Department of Industrial Engineering, University of Padova, Via Venezia 1, 35131 Padova, Italy
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Phone: +39-049-8276747
Interests: simulation; optimization; environmental/economic aspects and malfunction analysis of energy conversion systems for power, heating or cooling generation
Co-Guest Editor
Prof. Andrea Toffolo

Department of Engineering Sciences and Mathematics, Luleå University of Technology, S-97187 Lulea, Sweden
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Phone: 0920-493416
Interests: Energy Engineering; Thermal Turbomachines and Steam Boilers; Aircraft Engine Technology; Internal Combustion Engines

Special Issue Information

Dear Colleagues,

The basic problem for the designer of an energy conversion system is the so-called “synthesis/design problem”, i.e., the definition of the system configuration and design parameters.

This general problem has a different level of complexity depending on the design requirements and other boundary constraints. The system is generally asked to supply energy to the users in the required form and time variability. On the other hand, it operates in a specific location where the availability of energy sources may be limited by different constraints such as market prices, weather conditions (especially in case of renewables) or others. Other constraints are given by the external environment, which limits for instance the room available for the system construction, availability of water for condensation processes, emissions of toxic substances, etc.

Energy systems are considered here as assembly of components (single plants) or assembly of plants (group of plants). In both cases the goal is to fulfil the users’ demands (electricity, heat, cooling, fuels, etc.) utilizing the available sources and operating in a specified location. These systems can work in isolation or be connected to an electric grid or other heat or fuel networks and can take advantage of the presence of storage capacities of various types to properly decouple users’ demands from energy generation.

This Special Issue aims to address the general problem of the design and operation of the system configuration both for single or group of plants, which involves decisions about thermodynamic cycles or processes involved, type, number and design parameters of components/plants and storage capacities, and their interconnections.

The availability of easy-to-use and more powerful built-in software, or the possibility to create new software to simulate (and therefore predict) and optimize the system performance taking into account all possible external constraints (e.g., grid capacity restrictions, stochastic availability of renewable sources, energy prices and costs, etc.) widen the possibility of creating “smart” system configurations, e.g., able to optimally adapt to requirements and other constraints.

Original manuscripts focusing on the search for new energy systems (as described above) configurations are welcome. New concepts, modelling approaches, optimization algorithms and practical applications aimed at simplifying and making more efficient, less costly, more environmental friendly this search are distinguishing factors.

Prof. Andrea Lazzaretto
Guest Editor

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 papers will be 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. Energies is an international peer-reviewed open access semimonthly 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 1800 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

  • automatic generation of energy systems superstructures or alternative concepts
  • smart interaction between generation units, grids or other heating, cooling or fuel networks
  • mixed integer linear/non-linear optimization approaches
  • multi-objective optimization approaches
  • energy market structures
  • decision making tools and offering strategies for producers, users and prosumers
  • stochastic predictions of renewable sources availability, energy prices and costs
  • modelling approaches of generation units and constraints

Published Papers (12 papers)

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Research

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Open AccessArticle
Smart Energy Systems: Guidelines for Modelling and Optimizing a Fleet of Units of Different Configurations
Energies 2019, 12(7), 1320; https://doi.org/10.3390/en12071320
Received: 7 February 2019 / Revised: 2 April 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
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Abstract
The need to reduce fossil fuels consumption and polluting emissions pushes towards the search of systems that combine traditional and renewable energy conversion units efficiently. The design and management of such systems are not easy tasks because of the high level of integration [...] Read more.
The need to reduce fossil fuels consumption and polluting emissions pushes towards the search of systems that combine traditional and renewable energy conversion units efficiently. The design and management of such systems are not easy tasks because of the high level of integration between energy conversion units of different types and the need of storage units to match the availability of renewables with users’ requirements properly. This paper summarizes the basic theoretical and practical concepts that are required to simulate and optimize the design and operation of fleet of energy units of different configurations. In particular, the paper presents variables and equations that are required to simulate the dynamic behavior of the system, the operational constraints that allow each unit to operate correctly, and a suitable objective function based on economic profit. A general Combined Heat-and-Power (CHP) fleet of units is taken as an example to show how to build the dynamic model and formulate the optimization problem. The goal is to provide a “recipe” to choose the number, type, and interconnection of energy conversion and storage units that are able to exploit the available sources to fulfill the users’ demands in an optimal, and therefore “smart”, way. Full article
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Open AccessArticle
Intertemporal Static and Dynamic Optimization of Synthesis, Design, and Operation of Integrated Energy Systems of Ships
Energies 2019, 12(5), 893; https://doi.org/10.3390/en12050893
Received: 3 January 2019 / Revised: 15 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
Cited by 2 | PDF Full-text (5184 KB) | HTML Full-text | XML Full-text
Abstract
Fuel expenses constitute the largest part of the operating cost of a merchant ship. Integrated energy systems that cover all energy loads with low fuel consumption, while being economically feasible, are increasingly studied and installed. Due to the large variety of possible configurations, [...] Read more.
Fuel expenses constitute the largest part of the operating cost of a merchant ship. Integrated energy systems that cover all energy loads with low fuel consumption, while being economically feasible, are increasingly studied and installed. Due to the large variety of possible configurations, design specifications, and operating conditions that change with time, the application of optimization methods is imperative. Designing the system for nominal conditions only is not sufficient. Instead, intertemporal optimization needs to be performed that can be static or dynamic. In the present article, intertemporal static and dynamic optimization problems for the synthesis, design, and operation (SDO) of integrated ship energy systems are stated mathematically and the solution methods are presented, while case studies demonstrate the applicability of the methods and also reveal that the optimal solution may defer significantly from the solutions suggested with the usual practice. While in other works, the SDO optimization problems are usually solved by two- or three-level algorithms; single-level algorithms are developed and applied here, which tackle all three aspects (S, D, and O) concurrently. The methods can also be applied on land installations, e.g., power plants, cogenerations systems, etc., with proper modifications. Full article
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Open AccessArticle
A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage
Energies 2019, 12(5), 875; https://doi.org/10.3390/en12050875
Received: 30 December 2018 / Revised: 28 February 2019 / Accepted: 2 March 2019 / Published: 6 March 2019
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Abstract
Cogeneration systems are widely acknowledged as a viable solution to reduce energy consumption and costs, and CO2 emissions. Nonetheless, their performance is highly dependent on their capacity and operational strategy, and optimization methods are required to fully exploit their potential. Among the [...] Read more.
Cogeneration systems are widely acknowledged as a viable solution to reduce energy consumption and costs, and CO2 emissions. Nonetheless, their performance is highly dependent on their capacity and operational strategy, and optimization methods are required to fully exploit their potential. Among the available technical possibilities to maximize their performance, the integration of thermal energy storage is recognized as one of the most effective solutions. The introduction of a storage device further complicates the identification of the optimal equipment capacity and operation. This work presents a cutting-edge methodology for the optimal design and operation of cogeneration systems with thermal energy storage. A two-level algorithm is proposed to reap the benefits of the mixed integer linear programming formulation for the optimal operation problem, while overcoming its main drawbacks by means of a genetic algorithm at the design level. Part-load effects on nominal efficiency, variation of the unitary cost of the components in relation to their size, and the effect of the storage volume on its thermal losses are considered. Moreover, a novel formulation of the optimization problem is proposed to better characterize the heat losses and operation of the thermal energy storage. A rolling-horizon technique is implemented to reduce the computational time required for the optimization, without affecting the quality of the results. Furthermore, the proposed methodology is adopted to design a cogeneration system for a secondary school in San Francisco, California, which is optimized in terms of the equivalent annual cost. The results show that the optimally sized cogeneration unit directly meets around 70% of both the electric and thermal demands, while the thermal energy storage additionally covers 16% of the heat demands. Full article
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Open AccessFeature PaperArticle
Thermo-Electric Energy Storage with Solar Heat Integration: Exergy and Exergo-Economic Analysis
Energies 2019, 12(4), 648; https://doi.org/10.3390/en12040648
Received: 17 January 2019 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 17 February 2019
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Abstract
A Thermo-Electric Energy Storage (TEES) system is proposed to provide peak-load support (1–2 daily hours of operation) for distributed users using small/medium-size photovoltaic systems (4 to 50 kWe). The purpose is to complement the PV with a reliable storage system that cancompensate the [...] Read more.
A Thermo-Electric Energy Storage (TEES) system is proposed to provide peak-load support (1–2 daily hours of operation) for distributed users using small/medium-size photovoltaic systems (4 to 50 kWe). The purpose is to complement the PV with a reliable storage system that cancompensate the produc tivity/load mismatch, aiming at off-grid operation. The proposed TEES applies sensible heat storage, using insulated warm-water reservoirs at 120/160 °C, and cold storage at −10/−20 °C (water and ethylene glycol). The power cycle is a trans-critical CO2 unit including recuperation; in the storage mode, a supercritical heat pump restores heat to the hot reservoir, while a cooling cycle cools the cold reservoir; both the heat pump and cooling cycle operate on photovoltaic (PV) energy, and benefit from solar heat integration at low–medium temperatures (80–120 °C). This allows the achievement of a marginal round-trip efficiency (electric-to-electric) in the range of 50% (not considering solar heat integration).The TEES system is analysed with different resource conditions and parameters settings (hot storage temperature, pressure levels for all cycles, ambient temperature, etc.), making reference to standard days of each month of the year; exergy and exergo-economic analyses are performed to identify the critical items in the complete system and the cost of stored electricity. Full article
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Open AccessArticle
Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks
Energies 2019, 12(3), 546; https://doi.org/10.3390/en12030546
Received: 4 December 2018 / Revised: 24 January 2019 / Accepted: 31 January 2019 / Published: 10 February 2019
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Abstract
Details are presented of the development and incorporation of a generation and transmission reliability approach in an upper-level sustainability assessment framework for power system planning. This application represents a quasi-stationary, multiobjective optimization problem with nonlinear constraints, load uncertainties, stochastic effects for renewable energy [...] Read more.
Details are presented of the development and incorporation of a generation and transmission reliability approach in an upper-level sustainability assessment framework for power system planning. This application represents a quasi-stationary, multiobjective optimization problem with nonlinear constraints, load uncertainties, stochastic effects for renewable energy producers, and the propagation of uncertainties along the transmission lines. The Expected Energy Not Supplied (EENS) accounts for generation and transmission reliability and is based on a probabilistic as opposed to deterministic approach. The optimization is developed for three scenarios. The first excludes uncertainties in the load demand, while the second includes them. The third scenario accounts not only for these uncertainties, but also for the stochastic effects related to wind and photovoltaic producers. The sustainability-reliability approach is applied to the standard IEEE Reliability Test System. Results show that using a Mixture of Normals Approximation (MONA) for the EENS formulation makes the reliability analysis simpler, as well as possible within a large-scale optimization. In addition, results show that the inclusion of renewable energy producers has some positive impact on the optimal synthesis/design of power networks under sustainability considerations. Also shown is the negative impact of renewable energy producers on the reliability of the power network. Full article
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Open AccessArticle
Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use
Energies 2019, 12(3), 533; https://doi.org/10.3390/en12030533
Received: 31 December 2018 / Revised: 30 January 2019 / Accepted: 31 January 2019 / Published: 7 February 2019
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Abstract
Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah [...] Read more.
Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah is used as a case study for the analysis. The proposed DES incorporates solar photovoltaics (PV) and wind turbines for power generation along with using the existing electrical grid. A combined heat and power (CHP) system provides the DES with power generation and thermal energy for heating. Natural gas boilers supply the remaining heating demand and electricity is used to run all of the cooling equipment. A Monte Carlo study is used to analyze the stochastic power generation from the renewable energy resources in the DES. The optimization of the DES is performed with the Particle Swarm Optimization (PSO) algorithm based on a day-ahead model. The objective of the optimization is to minimize the operating cost of the DES. The results of the study suggest that the proposed DES can achieve operating cost reductions (approximately 10% reduction with respect to the current system). The uncertainty of energy loads and power generation from renewable energy resources heavily affects the operating cost. The statistical approach shows the potential to identify probable operating costs at different time periods, which can be useful for facility managers to evaluate the operating costs of their DES. Full article
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Open AccessArticle
Development of Complex Energy Systems with Absorption Technology by Combining Elementary Processes
Energies 2019, 12(3), 495; https://doi.org/10.3390/en12030495
Received: 27 December 2018 / Revised: 31 January 2019 / Accepted: 2 February 2019 / Published: 4 February 2019
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Abstract
Optimal design of energy systems ultimately aims to develop a methodology to realize an energy system that utilizes available resources to generate maximum product with minimum components. For this aim, several researches attempt to decide the optimal system configuration as a problem of [...] Read more.
Optimal design of energy systems ultimately aims to develop a methodology to realize an energy system that utilizes available resources to generate maximum product with minimum components. For this aim, several researches attempt to decide the optimal system configuration as a problem of decomposing each energy system into primitive process elements. Then, they search the optimal combination sequentially from the minimum number of constituent elements. This paper proposes a bottom-up procedure to define and explore configurations by combining elementary processes for energy systems with absorption technology, which is widely applied as a heat driven technology and important for improving system’s energy efficiency and utilizing alternative energy resources. Two examples of application are presented to show the capability of the proposed methodology to find basic configurations that can generate the maximum product. The demonstration shows that the existing absorption systems, which would be calculated based on the experience of designers, could be derived by performing optimization with the synthesis methodology automatically under the simplified/idealized operating conditions. The proposed bottom-up methodology is significant for realizing an optimized absorption system. With this methodology, engineers will be able to predict all possible configurations and identify a simple yet feasible optimal system configuration. Full article
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Open AccessArticle
Weptos Wave Energy Converters to Cover the Energy Needs of a Small Island
Energies 2019, 12(3), 423; https://doi.org/10.3390/en12030423
Received: 25 November 2018 / Revised: 17 January 2019 / Accepted: 21 January 2019 / Published: 29 January 2019
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Abstract
This paper presents the details of a study performed to investigate the feasibility of a wave energy system made up of a number of Weptos wave energy converters (WECs) and sets of batteries, to provide the full energy demands of a small island [...] Read more.
This paper presents the details of a study performed to investigate the feasibility of a wave energy system made up of a number of Weptos wave energy converters (WECs) and sets of batteries, to provide the full energy demands of a small island in Denmark. Two different configurations with 2 and 4 Weptos machines respectively with a combined installed power of 750 kW (and a capacity factor of 0.2) are presented. One full year simulation, based a detailed hourly analysis of the power consumption and wave energy resource assessment in the surrounding sea, is used to demonstrate that both configurations, supplemented by a 3 MWh battery bank and a backup generator, can provide the energy needs of the island. The proposed configurations are selected on the basis of a forecast optimization of price estimates for the individual elements of the solutions. The simulations show that Weptos WECs actually deliver 50% more than average consumption over the year, but due to the imbalance between consumption and production, this is not enough to cover all situations, which necessitates a backup generator that must cover 5–7% of consumption, in situations where there are too few waves and the battery bank is empty. Full article
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Open AccessArticle
Opportunities to Optimize the Palm Oil Supply Chain in Sumatra, Indonesia
Energies 2019, 12(3), 420; https://doi.org/10.3390/en12030420
Received: 17 December 2018 / Revised: 22 January 2019 / Accepted: 25 January 2019 / Published: 29 January 2019
Cited by 1 | PDF Full-text (8564 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Significant amounts of biomass residues were generated in Indonesia. While untreated, residues emit greenhouse gases during the decomposition process. On the other hand, if efficiently utilized, these residues could be used to produce value-added products. This study investigates opportunities for harnessing the full [...] Read more.
Significant amounts of biomass residues were generated in Indonesia. While untreated, residues emit greenhouse gases during the decomposition process. On the other hand, if efficiently utilized, these residues could be used to produce value-added products. This study investigates opportunities for harnessing the full potential of palm oil residues (i.e., empty fruit bunches, kernel shells, fiber, and mill effluent). As far as we are aware, the study is the first attempt to model the palm oil supply chain in a geographically explicit way while considering regional infrastructures in Sumatra Island, Indonesia. The BeWhere model, a mixed integer linear programming model for energy system optimization, was used to assess the costs and benefits of optimizing the regional palm oil supply chain. Different scenarios were investigated, considering current policies and new practices leading to improved yields in small-scale plantations and power grid connectivity. The study shows that a more efficient palm oil supply chain can pave the way for the country to meet up to 50% of its national bioenergy targets by 2025, and emission reductions of up to 40 MtCO2eq/year. As much as 50% of the electricity demand in Sumatra could be met if residues are efficiently used and grid connections are available. We recommend that system improvements be done in stages. In the short to medium term, improving the smallholder plantation yield is the most optimal way to maximize regional economic gains from the palm oil industry. In the medium to long term, improving electricity grid connection to palm oil mills could bring higher economic value as excess electricity is commercialized. Full article
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Open AccessArticle
Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm
Energies 2019, 12(3), 377; https://doi.org/10.3390/en12030377
Received: 31 October 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 24 January 2019
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Abstract
Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. [...] Read more.
Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. Sometimes, the design parameters used for the simulation of the new technique are randomly chosen, making their actual construction difficult or impossible. That is why, in this paper, a small-scale CAES without fossil fuel is proposed, analyzed, and optimized to identify the set of its optimal design parameters maximizing its performances. The performance of the system is investigated by global exergy efficiency obtained from energy and exergy analyses methods and used as an objective function for the optimization process. A modified Real Coded Genetic Algorithm (RCGA) is used to maximize the global exergy efficiency depending on thirteen design parameters. The results of the optimization indicate that corresponding to the optimum operating point, the consumed compressor electric energy is 103.83 kWh and the electric energy output is 25.82 kWh for the system charging and discharging times of about 8.7 and 2 h, respectively. To this same optimum operating point, a global exergy efficiency of 24.87% is achieved. Moreover, if the heat removed during the compression phase is accounted for in system efficiency evaluation based on the First Law of Thermodynamics, an optimal round-trip efficiency of 79.07% can be achieved. By systematically analyzing the variation of all design parameters during evolution in the optimization process, we conclude that the pneumatic motor mass flow rate can be set as constant and equal to its smallest possible value. Finally, a sensitivity analysis performed with the remaining parameters for the change in the global exergy efficiency shows the impact of each of these parameters. Full article
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Open AccessArticle
A Model of Optimal Gas Supply to a Set of Distributed Consumers
Energies 2019, 12(3), 351; https://doi.org/10.3390/en12030351
Received: 30 November 2018 / Revised: 10 January 2019 / Accepted: 16 January 2019 / Published: 23 January 2019
Cited by 1 | PDF Full-text (6134 KB) | HTML Full-text | XML Full-text
Abstract
A better design of gas supply chains may lead to a more efficient use of locally available resources, cost savings, higher energy efficiency and lower impact on the environment. In optimizing the supply chain of liquefied natural gas (LNG), compressed natural gas (CNG) [...] Read more.
A better design of gas supply chains may lead to a more efficient use of locally available resources, cost savings, higher energy efficiency and lower impact on the environment. In optimizing the supply chain of liquefied natural gas (LNG), compressed natural gas (CNG) or biogas for smaller regions, the task is to find the best supplier and the most efficient way to transport the gas to the customers to cover their demands, including the design of pipeline networks, truck transportation and storage systems. The analysis also has to consider supporting facilities, such as gasification units, truck loading lines and CNG tanking and filling stations. In this work a mathematical model of a gas supply chain is developed, where gas may be supplied by pipeline, as compressed gas in containers or as LNG by tank trucks, with the goal to find the solution that corresponds to lowest overall costs. In order to efficiently solve the combinatorial optimization problem, it is linearized and tacked by mixed integer linear programming. The resulting model is flexible and can easily be adapted to tackle local supply chain problems with multiple gas sources and distributed consumers of very different energy demands. The model is illustrated by applying it on a local gas distribution problem in western Finland. The dependence of the optimal supply chain on the conditions is demonstrated by a sensitivity analysis, which reveals how the model can be used to evaluate different aspects of the resulting supply chains. Full article
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Review

Jump to: Research

Open AccessReview
A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants
Energies 2019, 12(1), 73; https://doi.org/10.3390/en12010073
Received: 16 September 2018 / Revised: 10 December 2018 / Accepted: 26 December 2018 / Published: 27 December 2018
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Abstract
To reach optimal/better conceptual designs of energy systems, key design variables should be optimized/adapted with system layouts, which may contribute significantly to system improvement. Layout improvement can be proposed by combining system analysis with engineers’ judgments; however, optimal flowsheet synthesis is not trivial [...] Read more.
To reach optimal/better conceptual designs of energy systems, key design variables should be optimized/adapted with system layouts, which may contribute significantly to system improvement. Layout improvement can be proposed by combining system analysis with engineers’ judgments; however, optimal flowsheet synthesis is not trivial and can be best addressed by mathematical programming. In addition, multiple objectives are always involved for decision makers. Therefore, this paper reviews progressively the methodologies of system evaluation, optimization, and synthesis for the conceptual design of energy systems, and highlights the applications to thermal power plants, which are still supposed to play a significant role in the near future. For system evaluation, both conventional and advanced exergy-based analysis methods, including (advanced) exergoeconomics are deeply discussed and compared methodologically with recent developments. The advanced analysis is highlighted for further revealing the source, avoidability, and interactions among exergy destruction or cost of different components. For optimization and layout synthesis, after a general description of typical optimization problems and the solving methods, the superstructure-based and -free concepts are introduced and intensively compared by emphasizing the automatic generation and identification of structural alternatives. The theoretical basis of the most commonly-used multi-objective techniques and recent developments are given to offer high-quality Pareto front for decision makers, with an emphasis on evolutionary algorithms. Finally, the selected analysis and synthesis methods for layout improvement are compared and future perspectives are concluded with the emphasis on considering additional constraints for real-world designs and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice. Full article
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