Enhanced exergy-based malfunction diagnosis of an integrated solar combined cycle system: parametric study and multi-objective optimisation

The integrated solar combined cycle (ISCC) system is a proven solution for grid-connected power generation from solar energy. How to further improve the ISCC system efficiency and propose a more efficient system solution has become a research focus. A novel gas turbine combined cycle (GTCC) benchmark system is proposed by replacing the conventional steam Rankine bottom cycle with a supercritical CO2 Brayton cycle, whose output power and efficiency are increased by 9.07 MW and 1.3%, respectively, compared to those of the conventional GTCC system. Furthermore, the novel ISCC systems are established with the parabolic trough solar collector (PTC) and the solar tower (ST) collector coupled to the novel GTCC system. Thermal performance analysis, exergy performance analysis, and the sensitivity analysis of the ISCC systems have been performed, and the results show that the system efficiencies of both ISCC systems are lower than that of the GTCC system, at 57.1% and 57.5%, respectively, but the power generation of the ISCC system with PTC is greater than that of the benchmark system, while that of the ISCC system with ST is less than that of the benchmark system. The photoelectric efficiency of the ISCC system with PTC is 27.6%, which is 2.1% greater than that of ISCC system with ST. In the ISCC system with PTC, the components with the highest exergy destruction and the lowest exergy efficiency are the combustion chamber, and PTC, respectively. ST is the component with the highest exergy destruction and the lowest exergy efficiency in the ISCC system with ST. With the increase in direct normal irradiance (DNI), the total output power, solar energy output power, and photoelectric efficiency of the ISCC system with PTC increase, while the system efficiency decreases; the solar energy output power and photoelectric efficiency of the ISCC system with ST increase, while the total output power and system efficiency decrease. The photoelectric efficiency of the ISCC system with PTC is greater when the DNI is greater than 600 W/m2; conversely, the photoelectric efficiency of the ISCC system with ST is greater. After sensitivity analysis, the optimal intercooler pressure for the ISCC system is 11.3 MPa.

Parabolic trough power plants have several concepts for production energy, these concepts are developed by Solar Electric Generating System (SEGS) and Direct Solar Steam (DISS) project for research and development, the SEGS is developed the integrated solar combined cycle system (ISCCS), however in recent years the DISS project with direct steam generation (DSG) technology proves the evaluation from the cost, the process control and the easy erection in construction. There are many studies developed in the solar hybrid power plants system for ISCCS and DSG, in this paper will be evaluated these models of solar hybrid system compound a natural gas combined-cycle backup for solar thermal energy, taken into account the thermodynamic analyses and techno-economic assessment for each concept of the ISCCS and DSG plants. These models evaluated in this work will be simulated with TRNSYS program and SAM System Advisor Model. This analysis was conducted to get the performance in these concepts and shows the lower levelized energy cost (LEC), so to increase to the electricity net efficiency in solar contribution of the power plants.

Solar Trough Integration Into Combined Cycle Systems

General performance evaluation method of integrated solar combined cycle (ISCC) system

Comprehensive evaluation of integrated solar combined cycle system regarding fuel-savability under unified framework

Modeling and numerical simulation of an Integrated Solar Combined Cycle System in Algeria

Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system

Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm

Multi-objective optimization using MS Excel with an application to design of a falling-film evaporator system

The parameter estimation tuning is one of the procedures of system identification. One of the identification problems is to find an optimal model structure especially for representing the complex multivariable dynamic systems. Therefore, this problem needs to be solved by using multi-objective optimization with two objective functions namely minimum predictive error and model complexity. In this paper, the tuning of control parameters is studied before comparing multi-objective evolutionary algorithms. The framework is based on the system identification problems. In parameter tuning, two main qualitative parameters of evolutionary algorithms are focused on crossover rate and mutation rate. The multi-objective evolutionary algorithms used for this study are an elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective optimization using differential evolution (MOODE). The different parameter settings are compared in order to obtain good parameter setting for NSGA-II and MOODE. Then, the performance for both algorithms is also compared using Pareto front. The plotted graphs of two objective functions show the non-dominated fronts achieved by the algorithms. The results proved that MOODE shows an advantage in solving system identification problems in a multivariable nonlinear dynamic system.

PurposeThis paper presents a computer‐aided design (CAD) tool for the design of isolated dc‐dc converters.Design/methodology/approachThis tool, developed in Matlab environment, is based on multiobjective optimization (MO) using genetic algorithms. The Elitist Nondominated Sorting Genetic Algorithm is used to perform search and optimization whereas analytical models are used to model the power converters. The design problem requires minimizing the weight, losses and cost of the converter while ensuring the satisfaction of a number of constraints. The optimization variables are, as for them, the operating frequency, the current density, the maximum flux density, the transformer dimensions, the wire diameter, the core material, the conductor material, the converter topology (among Flyback, Forward, Push‐Pull, half‐bridge and full‐bridge topologies), the number of semiconductor devices associated in parallel, the number of cells associated in series or parallel as well as the kinds of input and output connections (serial or parallel) of these cells. Finally, the design of an auxiliary railway power supply is presented and discussed.FindingsThe results show that such tool to design dc‐dc power converters presents several advantages. In particular, it proposes to the designer a set of solutions – instead of a single one – so that he can choose a posteriori which solution best fits the application under consideration. Moreover, interesting solutions not considered a priori can be found with this tool.Originality/valueTo the best of the authors’ knowledge, such a CAD tool including a MO procedure taking several topologies into account has not been suggested so far.

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

Multiobjective optimization (MOO) has been successfully used to improve the process design and operation, by finding trade-offs among conflicting objectives such as energy, capital cost, and profit. In this work, the cumene process design is modified to decrease the raw materials and product losses and to facilitate better energy integration. Here, two slightly different cumene process designs are presented and evaluated. One process design uses a column to vent off the undesired chemicals, whereas the other uses two flash tanks. Additionally, vapor recompression is applied in both the designs to recover energy. Then, MOO of both modified process designs is carried out to examine two trade-offs: total capital cost (TCC) versus material loss and TCC versus utility cost. For this, an Excel-based MOO program is used; it is based on the elitist nondominated sorting genetic algorithm. The cumene process design with column is found to be superior for the first trade-off, whereas the design with two flash tanks is better for the other trade-off. Further, both the designs are compared based on their cumene production capacities; column design is found to be overall superior. Finally, energy requirements of the developed cumene process designs are compared with those reported in recent studies.

Integrated solar combined cycle (ISCC) system, which integrates solar thermal energy into traditional gas turbine combined cycle (GTCC) system, has become an efficient way to reduce the levelized cost of electricity (LCOE) and improve the thermal efficiency of the GTCC system. However, the volatility of solar energy usually results in low ISCC system thermal efficiency when no or low direct normal irradiance (DNI) is available because the ISCC system is usually not equipped with thermal energy storage system. In this paper, the ISCC system with a new operation strategy of changeable integration mode under different DNI conditions is proposed. The ISCC system model is constructed using the EBSILON software. Performance of ISCC system using the new operation strategy is investigated deeply. The results show that the ISCC system using the new operation strategy has higher annual solar to electricity efficiency (19.08%), 1.1% higher than that of the traditional ISCC system. Due to different DNI distributions in a whole year, the larger the distribution proportion of the low‐value DNI is, the greater the thermodynamic performance advantage of ISCC system with the optimization strategy is. Through the economic performance analysis, the LCOE of the ISCC system with the new optimization strategy is 0.223 $/kW·h, 0.01 $/kW·h lower than that of the traditional ISCC system. The LCOE decreases at first and then increases with the increase of the number of the parabolic trough collector. The optimal number of the parabolic trough collector corresponding to the new optimization strategy is 500. So this new operation strategy provides a new way for adequately utilizing the solar energy in ISCC system.

Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm