Multi-objective particle swarm optimization of binary geothermal power plants

Thermodynamic analysis and performance improvement of Irem geothermal power plant in Turkey: A case study of organic Rankine cycle

“…Thermodynamic properties calculated for 6 different refrigerants and waste heat (reinjected geothermal fluid) are shown in Tables (9)(10)(11)(12)(13)(14) to generate 2.5 MWe of electricity depending upon the selected dead state properties.…”

“…The calina cycle is suitable for reservoir temperatures below 1208C and is available in the cycles using waste heat (1208C-2008C). In such cycles, ammonia and water mixture are used [9][10][11][12][13][14][15][16]. Power plants using these cycles as well as the reclamation of the existing geothermal reservoirs have been an important research topic.…”

Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

“…Multi-objective evolutionary algorithms (MOEAs) are heuristics stochastic methods for solving multi-objective problems. Recently, inspired by the famous non-dominated sorting genetic algorithm-II (NSGA-II) [24], some MOEAs, such as multiobjective differential evolution (MODE) [4,25], multi-objective particle swarm optimization (MOPSO) [26], multi-objective gravitational search algorithm (MOGSA) [27], multi-objective artificial bee colony optimization (MOABC) [11], multi-objective ant colony optimization (MOACO) [28], have been proposed to deal with the complex multi-objective dispatch problems with practical modeling of operation constraints efficiently. However, the premature convergence caused by sharply decrease of the population diversity during evolutionary process, as well as shortcomings in balancing global exploration and local exploitation still exist in these stochastic searching algorithms.…”

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

“…Both works study waste heat recovery on an offshore platform to illustrate the approach. [69] have proposed a multi-objective optimization approach for ORC system design. Their approach allows to simultaneously consider two objective functions, the specific work output of the ORC and the specific heat exchanger area, and six decision variables: The choice of alternative working fluids out of a predefined set of 17 candidates, the evaporation temperature, the minimum approach temperature, the effectiveness of the superheater, the effectiveness of the recuperator and the temperature difference in the condenser.…”