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Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%.

The fuzzy proposed system is a multigeneration system capable of producing different multi-products including power, cooling, heating, and fresh water. The energy required for the operation of the system is geothermal renewable energy, which is a very serious issue in the green transient. To analyze the function of the suggested subsystems, such thermodynamic rules have been considered as an important tool. Another important parameter that is evaluated is the entransy parameter that evaluates the function of thermodynamic cycles. In this study, the rate of G ˙ l o s s is achieved at 699699 KW.K. The results of the suggested system demonstrate the rate of electrical power is 226.1 KW, the rate of cooling load is 24.5 KW, freshwater production is 0.235 kg/s, and the heating load is 418.5 KW of the system output products. In addition, energy efficiency and exergy are calculated as 59.51% and 51.5%, respectively. To provide an optimal performance state of the system, a multi-objective optimization method with a genetic algorithm method is used to optimize the objective functions ( G ˙ l o s s and η I I ) of Matlab software. In this research, it has been attempted to reduce entransy and increase exergy efficiency using algorithm genetic (GA). [ABSTRACT FROM AUTHOR]

Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the environment. The efficiency of these systems depends on choosing the right combination of renewable sources, their sizes, and proper scheduling of the generating units. This paper suggests an optimisation method for sizing a geothermal/PV/wind/diesel system, both in off-grid and grid-connected configurations. In the grid-connected mode, the possibility of selling surplus energy generated by renewable resources to the network is considered. Investigations reveal that without accurate control of geothermal reservoirs, they may become depleted. In this study, a strategy is suggested for unit commitment; moreover, the harmony search algorithm is used to find the optimal size of the hybrid system in both configurations. The effectiveness of the proposed approach is represented by simulating an HRES for Ferdows/Iran.

Binary geothermal power plants (GPP) always attract researchers' attention as they are renewable-energy operated, low-temperature, high-performance, environmentally-friendly, and baseload power plants. In addition, they need to be monitored, controlled and optimized due to their complex structure and functioning. This article presents the application of the Simulated Annealing (SA) algorithm for the thermodynamic performance optimization on the verified thermodynamic model of the SINEM GPP operating in Aydın, Turkey. This algorithm is also compared to the Gravitational Search Algorithm (GSA). By using these methods, 17 optimization parameters in the plant model are simultaneously optimized for maximum exergy efficiency. Study results show that the exergy analysis, gravitational search algorithm and simulated annealing algorithm respectively determined the exergy efficiency of the plant as 14.48%, 30.62%, and 38.49%. The SA algorithm has a better performance compared to the other two methods. System components such as condensers, vaporizers, and pumps are made more efficient using the SA algorithm. In addition, the most effective parameters of the plant are evaporator pressure differences and the mass flow of ORC's working fluid. By using GSA and SA algorithm, the gross electricity generation in the power plant can be increased by 2.11 MW and 3.15 MW, respectively. While GSA uses the procedure of reducing the amount of component exergy destruction, the SA algorithm uses the procedure of reducing the amount of electricity spent in the operation of the plant equipment. The rate of non-condensing gas (NCG) outlet, which is harmful to the environment, can be reduced by using SA algorithm. In this way, a power plant can be operated more economically and in a more environmentally friendly manner. • It is used a simulated annealing (SA) algorithm for maximizing performance of a real GPP. • A comprehensive research and real-world constraints are conducted for design and off-design. • Simulation programs (SA and GSA) are compared with each other for a parametric study. • The gross electricity generation in GPP is increased by 2.11 MW and 3.15 MW with GSA and SA. • The release of non-condensing gas (NCG) can be reduced by using SA algorithm. [ABSTRACT FROM AUTHOR]

Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the environment. The efficiency of these systems depends on choosing the right combination of renewable sources, their sizes, and proper scheduling of the generating units. This paper suggests an optimisation method for sizing a geothermal/PV/wind/diesel system, both in off-grid and grid-connected configurations. In the grid-connected mode, the possibility of selling surplus energy generated by renewable resources to the network is considered. Investigations reveal that without accurate control of geothermal reservoirs, they may become depleted. In this study, a strategy is suggested for unit commitment; moreover, the harmony search algorithm is used to find the optimal size of the hybrid system in both configurations. The effectiveness of the proposed approach is represented by simulating an HRES for Ferdows/Iran. [ABSTRACT FROM AUTHOR]

The Afyon Geothermal Power Plant is modeled using the Multi-Layer Feed-Forward Artificial Neural Network. The 100 × 8 data set obtained from the real Binary Geothermal Power Plant is divided into two parts: 80 × 8 training data and 20 × 8 test data. Geothermal Power Plant system modeling has been performed numerically on Matlab with three inputs and five outputs. There are ten neurons in the hidden layer in the Artificial Neural Network-based system, and the logarithmic sigmoid transfer function is used as the transfer function in each neuron. The neurons in the output layer have the purelin transfer function. As a result of the training process, the 3.06 × 10E-2 mean square error value was obtained from the ANN-based Binary Geothermal Power Plant system. The main point of the study is the optimization of the binary geothermal power plant. The genetic algorithm method with Artificial Neural Network-based is used for this purpose. The results obtained from the outputs of the Artificial Neural Network-based Binary Geothermal Power Plant system are presented. The plant's geothermal water temperature and mass flow rates are 110 °C and 150 kg/s. Energy and exergy efficiencies of the plant are calculated as 10.4% and 29.7%. The optimized simple payback period and exergy cost of the electricity generated in the plant is calculated as 2.87 years and 0.0176 $/kWh, respectively. Schematic configuration of Afyon Geothermal Power Plant. Image 1 • Modelling of Afyon Geothermal Power Plant is performed numerically based on ANN. • Geothermal temperature and mass flow rate of the plant are 110 °C, and 150 kg/s. • Energy and exergy efficiencies of the plant are calculated as 10.4%, and 29.7%. • The exergetic cost of the electricity from the plant is calculated as 0.0176 $/kWh. [ABSTRACT FROM AUTHOR]

Geothermal power plants have been in operation for decades in many parts of the world. Different thermodynamic cycles can be used for producing power from geothermal resources. Binary cycle plants use the geothermal water from liquid-dominated resources at relatively low temperatures. These plants operate on a Rankine cycle with a binary working fluid (isobutane, pentane, isopentane, R-114, etc.) that has a low boiling temperature. A case study on an existing binary geothermal power plant is available in this study. Thermoeconomic performance evaluation and optimization of 2.7 MW binary organic Rankine cycle (ORC) design geothermal power plant in western Turkey is conducted using actual plant operating data, and potential improvements are identified. Afyon Geothermal Power Plant (AFJES) is thermodynamically modeled in a computer environment using current working parameters in a comprehensive way for the use of geothermal energy in electricity generation. Geothermal water temperature and mass flow rate of the plant are 110°C, and 150 kg/s, respectively. Energy and exergy efficiencies of the plant are calculated as 10.4% and 29.7%. The potential annual revenue of geothermal electricity is calculated to be 2,880,277 $/yr with a simple payback period of 3.36 years. The exergetic cost of the electricity from the plant is calculated as 0.0233 $/kWh. The optimized simple payback period and exergy cost of the electricity generated in the plant is calculated as 2.87 years and 0.0176 $/kWh, respectively. [ABSTRACT FROM AUTHOR]

Highlights • It is focused on improving thermodynamic performance for a binary geothermal power plant. • An accurate modelled system simulating a real operating system is developed. • It is used the gravitational search algorithm to maximize its exergy efficiency. • System exergy efficiency can be maximized from 14% to 31% using gravitational search algorithm. • Thus, a more compatible operating strategy between system components is ensured. Abstract The limited energy resources globally, low efficiency of renewable energies, complicated and costly energy conversion systems and environmental pollution have significantly increased scholar's interest in innovative and efficient systems and their improvement studies. Therefore, it is necessary to increase the efficiency of power generation systems used in geothermal sources of medium or low enthalpy. This study aims to improve the thermodynamic performance of an existing binary geothermal system with organic Rankine cycle and its system components while trying to comprehend the physical events/changes during these improvement processes. A model has been developed that simulates the system completely and accurately. Seventeen system parameters which were considered as crucial to maximize the exergy efficiency of the system like turbine inlet, condenser temperature and so on, are optimized using a gravitational search algorithm. The results of the study show that the exergy efficiency of the system is 14% and thus it can be maximized to 31% with optimization. During the optimization process, the pressure of work fluid on the evaporator line is increased and thus 2.1 MW more power is produced compared to normal power production. The condenser, with the highest exergy destruction in the system, has performance improvements of 75%. As a result, with the optimization process, a more compatible operating strategy between system components is ensured. This will allow the system and its components to run for longer and without failures. [ABSTRACT FROM AUTHOR]

Finding an appropriate solution to find stable and continuous green energy is one of the significant problems of scientists in the field of renewable energy. In this study, a mixed type of system implementing two geothermal gas turbine energy combined using an innovative method, the suggested system continuously merits productivity of different production. Also, it implements renewable energies to provide the required power to generate conducts which is considered important in the green transient of governments. In addition, thermodynamic rules were used to analyze the suggested system. The results obtained from analysis and equations in the EES software show that power production and thermal load and cooling equal 1432.3kw, 937.9kw and 128.8kw, respectively. the total energy and exergy efficiency is calculated at 58.31 % and 42.17 %. The system's return on investment is calculated in twenty years, with the electricity price of 0.15$/GJ, 6.02 years and with the fuel price of C fuel = 21$/GJ, 7.15 years, and the system reaches profitability. to improve system function, the multiple optimized genetic algorithm is used. The objective function in optimizing the system is exergy efficiency and total cost, which is considered optimization MATLAB software, which means a decrease of total cost and an increase of exergy efficiency. [ABSTRACT FROM AUTHOR]

The design of plant-wide control system to optimize electricity production in geothermal power plant is proposed in this research. The objective is to overcome the deficiency due to changes in the characteristics of production well and fluctuation in electricity demand load. The proposed plant-wide control system has two main tasks; to maintain production process at optimum value and to increase efficiency. The pressure in separator and condenser is maintained at the respective set points under electrical load fluctuations in order to ensure optimum efficiency. The control system also reduce the usage of auxialiary electrical power and increase efficiency. The task was performed by controlling inlet cooling water temperatures to the condenser. It was concluded that the proposed control structure was able to increase efficiency and maintain production.

Abstract Optimizing a complex system/problem under real working conditions with optimization methods means ensuring that they operate more efficiently, economical, and eco-friendly. For this purpose, in order to maximize the exergy efficiency of a thermodynamic model of a real operated geothermal power plant (GPP), two optimization methods, namely Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC), have been comparatively evaluated in this study. The selected thermodynamic model is a problem that is highly complex, non-linear and unsolvable through mathematical methods. In order to solve the problem, 17 optimization parameters have been selected on the model. In addition, the selected parameters have been divided into 11 groups according to the system equipment specifications to reduce time loss. The results of the study reported that GSA and ABC maximized the exergy efficiency of the real system from 14.52% to 26.31% and 23.92% respectively. The effects of the optimized parameters on the model are observed, and it has been verified by GPP operators, engineers and researchers that no contrariety to logic and engineering discipline existed. Hence, the results of GSA method for the engineering problem addressed in this study are better than those of ABC method and they responded in a much shorter time. The most effective group in both methods is the G3 group related to the turbines. Besides, the most effective optimization parameters on the system performance are the pressure differences in evaporators and mass flow of the geothermal fluid. Highlights • It is focused on comparative evaluation of two optimization methods, GSA and ABC. • Thermodynamic model of a geothermal power plant (REAL) is chosen as a complex and non-linear engineering problem. • To solve the problem, 17 optimization parameters in 11 groups have been selected on the model. • Maximum exergy efficiency is found as 26.31% and 23.92% for the GSA and ABC, respectively. • Results of GSA are better than those of ABC and most effective group is one related to turbines. [ABSTRACT FROM AUTHOR]

In this study, the thermodynamic performance of a binary geothermal power plant (GPP) is comparatively evaluated using the exergy analysis and optimization method. Thus, in addition to routes to improve the thermodynamic performance of the system, the thermodynamic relationships between the system components and improvement performances of the components are determined. With this aim, the Sinem GPP located in Aydın province in Turkey as a real system is selected. All data from the system are collected and a numerical model simulating the real system is developed. On the developed model, the conventional and advanced exergy analyses for exergy analysis and the artificial bee colony (ABC) method for optimization process are performed. The results of the study show that total exergy efficiencies of the conventional exergy analysis, advanced exergy analysis and artificial bee colony are determined as 39.1%, 43.1% and 42.8%, respectively. The exergy efficiency obtained from advanced exergy analysis is higher compared to the other two methods. This is due to the fact that theoretical and unavoidable operation assumptions in advanced exergy analysis are arbitrary as a single value depending on the decision maker. However, decision variables in the ABC method are within certain constraints chosen by the decision maker. It is better to select constraint limits instead of an arbitrary single value selection. Therefore, its arbitrary values should be confirmed with any optimization method. Additionally, the highest exergy destruction identified in the three methods is occurred in heat exchangers as the condenser and vaporizer. [ABSTRACT FROM AUTHOR]

In this paper, the design of an Organic Rankine Cycle (ORC) is optimized by means of numerical simulations. The systems of interest are the subcritical and transcritical thermodynamic cycles. Optimizations are performed with the objective of determining the design that maximizes the specific power output. The design variables include the operating parameters (pressures, mass flow rates), and the best working fluid is determined by comparing the performance of 36 refrigerants. Optimization runs are performed for a wide range of geofluid temperatures (from 80 to 180 °C), and for a wide range of condenser temperature (from 0.1 to 50 °C). The results are summarized in charts that may be used as efficient tools for designing optimal geothermal power plants. Finally, an approximate analysis allowed to develop a new correlation for predicting the maximal specific power output of an ORC. [ABSTRACT FROM AUTHOR]

In this study, geothermal resources in Kutahya-Simav region having geothermal water at a temperature suitable for power generation is considered. The study is aimed to yield the method of the most effective use of the geothermal resource and a rational thermodynamic comparison of various cycles for a given resource. Maximum first law efficiencies vary between 6.9 to 10.6% while the second law efficiencies vary between 38.5 to 59.3% depending on the cycle considered. The maximum power output, the first law, and the second law efficiencies are obtained for Kalina cycle followed by combined cycle and binary cycle. [ABSTRACT FROM PUBLISHER]

Highlights: [•] We analyze the exergy simulation of the geothermal power plant in Dieng, Indonesia. [•] We also optimize the power plant to consider the improvement of power output. [•] The total available exergy of the geothermal fluid is estimated to be 59.52MW. [•] It is found that the second law efficiency of the power plant is 36.48%. [•] With optimization, the power output increases by lowering the separator pressure. [Copyright &y& Elsevier]

Highlights: [•] We conduct the thermodynamic and economic analysis of various geothermal power cycles. [•] The optimization process was performed to minimize the exergy losses. [•] Kalina cycle is a new technology compared to flash and binary cycles. [•] It is shown that Kalina cycle presents a viable choice for both thermodynamically and economically. [Copyright &y& Elsevier]

• Enhancing the performance of geothermal power plant through biomass combustion. • Utilizing the exhaust gasses of biomass combustion to desalinate the seawater. • Comparing the environmental impact of the system in different scenarios. • Determining the optimal design of the system through three-objective optimization. In the current research, a comprehensive thermodynamic study of an innovative biomass-geothermal power plant combined with a desalination system is presented and analyzed. The suggested system is a geothermal based cascaded steam/organic Rankine cycle benefiting from municipal solid waste combustion in order to enhance its performance. Besides, the exhaust gasses of municipal solid waste combustion are utilized as the primary energy source for driving a multi-effect desalination subsystem which converts the seawater into low salinity water. A comprehensive approach including energy and exergy analyses along with thermoeconomic evaluation is applied to investigate the viability of the plant. First of all, validation of the presented model has been tested by means of comparing the results with published data, through which a good agreement has achieved. The energy and exergy efficiencies can be reached to 13.9% and 19.4% respectively while the total product cost rate of the system is estimated to be 285.3 $/h. Moreover, environmental analysis is conducted in terms of estimation of CO 2 and NO x emissions to address the environmental benefits of utilizing municipal solid waste combustion instead of coal for improving the performance of the geothermal power plant. Results indicate that municipal solid waste utilization saves 8,092 tonnes of CO 2 emission and 36 tonnes of NO x emission annually in relative to coal utilization. Finally, a three-objective optimization is performed regarding exergy efficiency, total product cost rate, and CO 2 emission rate as objective functions through applying the Genetic Algorithm in order to figure out the optimum performance of the system and the Pareto frontier is extracted. The results of optimization indicate that in the optimum case, exergy efficiency of 20.72%, total product cost rate of 306.1 $/h and CO 2 emission rate of 1967.7 tonnes/y are achievable. [ABSTRACT FROM AUTHOR]

• Dual pressure transcritical ORC is usually the best in the range of conditions tested. • Specific work output between 5 and 70 kJ/kg were obtained from simulations. • Five fluids (R218, R115, R227ea, RC318, RE245cb2) stand out in the optimization. • Optimal values of cooling tower range are lower at colder brine and hotter cold sink. • Parasitic loads (pumps, cooling) range from 15 to 45% of the gross power produced. Four variants of the Organic Rankine Cycle (ORC) applied to geothermal power plants are optimized by means of numerical tools. These variants are: (i) the subcritical ORC with single-pressure heater (ORC/S/SC), (ii) the transcritical ORC with single-pressure heater (ORC/S/TC), (iii) the subcritical ORC with dual-pressure heater (ORC/D/SC), and (iv) the transcritical ORC with dual-pressure heater (ORC/D/TC). All the systems are recuperative and include a wet cooling tower. The objective function is the specific work output and design variables include operating pressures, mass flow ratios between the brine and the working fluid, superheaters effectiveness and cooling tower range. The systems are optimized for 20 different potential working fluids. The optimization is performed for inlet brine temperatures from 80 to 180 °C, and for ambient air wet bulb temperatures from 10 to 32 °C. The results show: (i) the superiority of ORC/D/TC for most of the cases, (ii) the relevance of using a dual-pressure heater at high sink temperature and low brine temperature, and (iii) the importance of choosing the right cooling tower range for an optimal power plant design. [ABSTRACT FROM AUTHOR]