The Experts below are selected from a list of 483 Experts worldwide ranked by ideXlab platform
David R Mills - One of the best experts on this subject based on the ideXlab platform.
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Solar Thermal Electricity - FULL STEAM AHEAD
2020Co-Authors: David R MillsAbstract:This paper summarises progress in Solar Thermal Electricity development up to the present, and suggests productive avenues for technical and commercial development in the future. Solar Thermal Electricity makes use of heat engines to take Solar heat and convert it to mechanical energy and thence to Electricity using a conventional generator. The history of Solar Thermal Electricity development is briefly described. Six advancing technologies are then described and cost estimates are presented for some cases. Market prospects are discussed with regard to current Australian and international support mechanisms. The issue of cross-subsidy to fossil fuel is also addressed.
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appropriate market strategy for Solar Thermal Electricity
2016Co-Authors: David R MillsAbstract:Direct Solar Electricity is unlikely to contribute significantly to global emissions reduction before 2035, but becomes essential to meeting climate goals after this time. To fulfil this long term role, rapid growth must ensue now and be continued for decades so that full market share can be achieved. With the above background, in this chapter we argue for a new industry market approach has important differences from current STE industry visions for the future. The STE industry advocates installation of ISCCS and Solar/gas hybrids as Global Environmental Facility (GEF) projects in developing countries. These are not the most emissions effective modes of installation, nor do they address the very large emissions 'debt' in developed nations. The main features of the suggested industry approach outlined in the chapter can be summarised as follows: • The primary market for STE should mostly be in developed countries, rather than in developing countries under GEF projects. • Until 2035, four transition markets which use hybridisation but not Thermal or chemical storage are recommended to create strong STE industry growth. These applications can all use low cost line focus technology. They are: 1. Solar biomass hybrids with a significant Solar fraction to provide firm capacity in both developed and developing countries 2. Solar fossil (mainly coal) hybrids with a significant Solar fraction for use in developing countries 3. Solar coal savers supplying main boiler steam for developed nations at a low Solar fraction 4. Solar coal savers supplying reheat Thermal energy for developed nations at a low Solar fraction Evidence presented in this paper suggests that Solar Thermal Electricity and other renewable energy options are likely to be less expensive as a total societal cost than conventional fuel, and may be highly competitive against pure wind and biomass technology without the invocation of storage. The main bounds on future growth will come from the performance of renewable energy competitors rather than fossil fuel. Future storage options applied to line focus technology may improve CO2 avoided cost, as will large scale production. This paper does not address policy issues, but it is essential to the development of the STE industry that full life cycle costing information be adequately developed as an essential energy policy input so that equitable societal costing of different energy options can be performed.
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screening of high melting point phase change materials pcm in Solar Thermal concentrating technology based on clfr
Solar Energy, 2005Co-Authors: Akira Hoshi, Antoine Bittar, David R Mills, Takeo S SaitohAbstract:Abstract We have investigated the suitability of high melting point phase change materials for use in new, large scale Solar Thermal Electricity plants. Candidate materials for latent heat Thermal energy storage are identified and their operating parameters modeled and analysed. The mathematical characteristics of charging and discharging these storage materials are discussed. Several high melting point, high conductivity materials are shown to be suitable and advantageous for use with Solar Thermal Electricity plants, such as Sydney University’s novel, low cost CLFR and MTSA collector systems, as well as existing parabolic trough and tower technologies.
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Screening of high melting point phase change materials (PCM) in Solar Thermal concentrating technology based on CLFR
Solar Energy, 2005Co-Authors: Akira Hoshi, Antoine Bittar, David R Mills, Takeo S SaitohAbstract:We have investigated the suitability of high melting point phase change materials for use in new, large scale Solar Thermal Electricity plants. Candidate materials for latent heat Thermal energy storage are identified and their operating parameters modeled and analysed. The mathematical characteristics of charging and discharging these storage materials are discussed. Several high melting point, high conductivity materials are shown to be suitable and advantageous for use with Solar Thermal Electricity plants, such as Sydney University's novel, low cost CLFR and MTSA collector systems, as well as existing parabolic trough and tower technologies. © 2004 Elsevier Ltd. All rights reserved.
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advances in Solar Thermal Electricity technology
Solar Energy, 2004Co-Authors: David R MillsAbstract:Abstract Various advanced Solar Thermal Electricity technologies are reviewed with an emphasis on new technology and new market approaches. In single-axis tracking technology, the conventional parabolic trough collector is the mainstream established technology and is under continued development but is soon to face competition from two linear Fresnel reflector (LFR) technologies, the CLFR and Solarmundo. A Solarmundo prototype has been built in Belgium, and a CLFR prototype is awaiting presale of Electricity as a commercial plant before it can be constructed in Queensland. In two-axis tracking technologies, dish/Stirling technologies are faced with high Stirling engine costs and emphasism may shift to Solarised gas micro-turbines, which are adapted from the small stationary gas turbine market and will be available shortly at a price in the US$1 ppW range. ANU dish technology, in which steam is collected across the field and run through large steam turbines, has not been commercialised. Emphasis in Solar Thermal Electricity applications in two-axis tracking systems seems to be shifting to tower technology. Two central receiver towers are planned for Spain, and one for Israel. Our own multi-tower Solar array (MTSA) technology has gained Australian Research Council funding for an initial single tower prototype in Australia of approximately 150 kW(e) and will use combined microturbine and PV receivers. Non-tracking systems are described of two diverse types, Chimney and evacuated tubes. Solar chimney technology is being proposed for Australia based upon German technology. Air is heated underneath a large glass structure of about 5 km in diameter, and passes up a large chimney through a wind turbine near the base as it rises. A company Enviromission Ltd. has been listed in Australia to commercialise the concept. Evacuated tubes are growing rapidly for domestic hot water heating in Europe and organic rankine cycle engines such as the Freepower 6 kW are being considered for operation with Thermal energy developed by evacuated tube and trough systems. These may replace some PV in medium sized applications as they offer potential for inexpensive pressurised water storage for 24 h operation, and backup by fuels instead of generators. In the medium term there is a clear trend to creation of smaller sized systems which can operate on a retail Electricity cost offset basis near urban and industrial installations. In the longer term large low cost plants will be necessary for large scale Electricity and fuels production. Retrofit central generation Solar plants offer a cost effective transition market which allows increased production rates and gradual cost reduction for large Solar Thermal plant. In the paper the author describes current funding systems in Europe, Australia, and the USA, and makes suggestions for more effective programmes of support.
Rahul Tewari - One of the best experts on this subject based on the ideXlab platform.
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on the sizing of a Solar Thermal Electricity plant for multiple objectives using evolutionary optimization
Applied Soft Computing, 2012Co-Authors: Francisco Ruiz, Jose M. Cabello, Mariano Luque, Rahul Tewari, Jose M. CejudoAbstract:Design, implementation and operation of Solar Thermal Electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.
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optimization of the size of a Solar Thermal Electricity plant by means of genetic algorithms
Renewable Energy, 2011Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Solar Thermal Electricity technologies are attractive alternatives to produce Electricity by means of a renewable source. One of these technologies is parabolic trough collectors. In Spain, the sale of Solar Electricity to the national grid is primed. There are three main parameters that affect the behaviour of these plants: area of Solar collector field, capacity of Thermal storage tanks and power of the auxiliary system. In this paper, a simplified model of the plant is used to optimize the size of its components that produces the maximum yearly profit. The use of traditional methods of optimization is not possible and genetic algorithms have been used. An important feature of the model is that the minimum level of the Electricity production of the block of power can be fixed. Once the optimization has been performed, the traditional parameters that characterize the dimension of the plant are analysed (the Solar multiple and the capacity factor). For a gross power of 50 MW, the optimum collector area varies between 583,000 m2 and 749,860 m2 with a Thermal storage between 6.55 h and 13.46 h respectively. The economic benefit is always higher than 19.30 M€ per year and the cost of the Electricity produced is about 18.5 c€/kWh.
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Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.
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IEEE Congress on Evolutionary Computation - Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.
Amir Faghri - One of the best experts on this subject based on the ideXlab platform.
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high temperature latent heat Thermal energy storage using heat pipes
International Journal of Heat and Mass Transfer, 2010Co-Authors: Hamidreza Shabgard, Nourouddin Sharifi, Theodore L. Bergman, Amir FaghriAbstract:Abstract A Thermal network model is developed and used to analyze heat transfer in a high temperature latent heat Thermal energy storage unit for Solar Thermal Electricity generation. Specifically, the benefits of inserting multiple heat pipes between a heat transfer fluid and a phase change material (PCM) are of interest. Two storage configurations are considered; one with PCM surrounding a tube that conveys the heat transfer fluid, and the second with the PCM contained within a tube over which the heat transfer fluid flows. Both melting and solidification are simulated. It is demonstrated that adding heat pipes enhances Thermal performance, which is quantified in terms of dimensionless heat pipe effectiveness.
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High temperature latent heat Thermal energy storage using heat pipes
International Journal of Heat and Mass Transfer, 2010Co-Authors: Hamidreza Shabgard, Nourouddin Sharifi, Theodore L. Bergman, Amir FaghriAbstract:A Thermal network model is developed and used to analyze heat transfer in a high temperature latent heat Thermal energy storage unit for Solar Thermal Electricity generation. Specifically, the benefits of inserting multiple heat pipes between a heat transfer fluid and a phase change material (PCM) are of interest. Two storage configurations are considered; one with PCM surrounding a tube that conveys the heat transfer fluid, and the second with the PCM contained within a tube over which the heat transfer fluid flows. Both melting and solidification are simulated. It is demonstrated that adding heat pipes enhances Thermal performance, which is quantified in terms of dimensionless heat pipe effectiveness. © 2010 Elsevier Ltd. All rights reserved.
Jose M. Cabello - One of the best experts on this subject based on the ideXlab platform.
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on the sizing of a Solar Thermal Electricity plant for multiple objectives using evolutionary optimization
Applied Soft Computing, 2012Co-Authors: Francisco Ruiz, Jose M. Cabello, Mariano Luque, Rahul Tewari, Jose M. CejudoAbstract:Design, implementation and operation of Solar Thermal Electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.
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optimization of the size of a Solar Thermal Electricity plant by means of genetic algorithms
Renewable Energy, 2011Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Solar Thermal Electricity technologies are attractive alternatives to produce Electricity by means of a renewable source. One of these technologies is parabolic trough collectors. In Spain, the sale of Solar Electricity to the national grid is primed. There are three main parameters that affect the behaviour of these plants: area of Solar collector field, capacity of Thermal storage tanks and power of the auxiliary system. In this paper, a simplified model of the plant is used to optimize the size of its components that produces the maximum yearly profit. The use of traditional methods of optimization is not possible and genetic algorithms have been used. An important feature of the model is that the minimum level of the Electricity production of the block of power can be fixed. Once the optimization has been performed, the traditional parameters that characterize the dimension of the plant are analysed (the Solar multiple and the capacity factor). For a gross power of 50 MW, the optimum collector area varies between 583,000 m2 and 749,860 m2 with a Thermal storage between 6.55 h and 13.46 h respectively. The economic benefit is always higher than 19.30 M€ per year and the cost of the Electricity produced is about 18.5 c€/kWh.
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Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.
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IEEE Congress on Evolutionary Computation - Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.
Jose M. Cejudo - One of the best experts on this subject based on the ideXlab platform.
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on the sizing of a Solar Thermal Electricity plant for multiple objectives using evolutionary optimization
Applied Soft Computing, 2012Co-Authors: Francisco Ruiz, Jose M. Cabello, Mariano Luque, Rahul Tewari, Jose M. CejudoAbstract:Design, implementation and operation of Solar Thermal Electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.
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optimization of the size of a Solar Thermal Electricity plant by means of genetic algorithms
Renewable Energy, 2011Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Solar Thermal Electricity technologies are attractive alternatives to produce Electricity by means of a renewable source. One of these technologies is parabolic trough collectors. In Spain, the sale of Solar Electricity to the national grid is primed. There are three main parameters that affect the behaviour of these plants: area of Solar collector field, capacity of Thermal storage tanks and power of the auxiliary system. In this paper, a simplified model of the plant is used to optimize the size of its components that produces the maximum yearly profit. The use of traditional methods of optimization is not possible and genetic algorithms have been used. An important feature of the model is that the minimum level of the Electricity production of the block of power can be fixed. Once the optimization has been performed, the traditional parameters that characterize the dimension of the plant are analysed (the Solar multiple and the capacity factor). For a gross power of 50 MW, the optimum collector area varies between 583,000 m2 and 749,860 m2 with a Thermal storage between 6.55 h and 13.46 h respectively. The economic benefit is always higher than 19.30 M€ per year and the cost of the Electricity produced is about 18.5 c€/kWh.
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Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.
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IEEE Congress on Evolutionary Computation - Optimization of the sizing of a Solar Thermal Electricity plant: Mathematical programming versus genetic algorithms
2009 IEEE Congress on Evolutionary Computation, 2009Co-Authors: Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Francisco Ruiz, Rahul TewariAbstract:Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a Solar Thermal Electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties - black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.