Design and Performance Optimization of Renewable Energy Systems

__Design and Performance Optimization of Renewable Energy Systems__ provides an integrated discussion of issues relating to renewable energy performance design and optimization using advanced thermodynamic analysis with modern methods to configure major renewable energy plant configurations (solar, geothermal, wind, hydro, PV). Vectors of performance enhancement reviewed include thermodynamics, heat transfer, exergoeconomics and neural network techniques. Source technologies studied range across geothermal power plants, hydroelectric power, solar power towers, linear concentrating PV, parabolic trough solar collectors, grid-tied hybrid solar PV/Fuel cell for freshwater production, and wind energy systems. Finally, nanofluids in renewable energy systems are reviewed and discussed from the heat transfer enhancement perspective. Title-page_2021_Design-and-Performance-Optimization-of-Renewable-Energy-Syst Design and Performance Optimization of Renewable Energy Systems Copyright_2021_Design-and-Performance-Optimization-of-Renewable-Energy-Syste Copyright Dedication_2021_Design-and-Performance-Optimization-of-Renewable-Energy-Syst Dedication Contents_2021_Design-and-Performance-Optimization-of-Renewable-Energy-System Contents List-of-contribut_2021_Design-and-Performance-Optimization-of-Renewable-Ener List of contributors Preface_2021_Design-and-Performance-Optimization-of-Renewable-Energy-Systems Preface Chapter-1---Applications-of-rene_2021_Design-and-Performance-Optimization-of 1 Applications of renewable energy sources 1.1 Introduction 1.2 Solar energy 1.2.1 Solar electricity generation 1.2.2 Heating and cooling 1.2.3 Desalination 1.3 Wind energy 1.4 Geothermal energy 1.4.1 Geothermal electricity 1.4.2 Geothermal heating 1.4.3 Geothermal cooling 1.5 Hydro energy 1.6 Bioenergy Conclusions Acknowledgments References Chapter-2---Renewable-energy-and-_2021_Design-and-Performance-Optimization-o 2 Renewable energy and energy sustainability 2.1 Introduction 2.2 Sustainability 2.3 Energy 2.4 Societal energy use and energy sustainability 2.5 Energy sustainability: interpretations, definitions, and needs 2.5.1 Interpretations and definitions of energy sustainability 2.5.2 Needs for energy sustainability 2.5.2.1 Need 1: Obtain sustainable energy resources 2.5.2.2 Need 2: Employ advantageous energy carriers 2.5.2.3 Need 3: Boost efficiencies of energy systems 2.5.2.4 Need 4: Mitigate lifetime environmental impacts of energy systems 2.5.2.5 Need 5: Address nontechnical aspects of energy sustainability 2.5.3 Reflection 2.6 Selected measures relating to renewable energy for enhancing energy sustainability 2.7 Illustrative example: net-zero energy buildings 2.8 Closure References Chapter-3---Heat-exchangers-_2021_Design-and-Performance-Optimization-of-Ren 3 Heat exchangers and nanofluids 3.1 Introduction 3.2 Heat exchanger classification 3.3 Effectiveness concept 3.4 Nanofluids 3.5 Applications of nanofluids in heat exchangers used in renewable energy technologies 3.6 Exergy analysis of nanofluidic heat exchangers Conclusions Acknowledgement References Chapter-4---Exergy-an_2021_Design-and-Performance-Optimization-of-Renewable- 4 Exergy analysis 4.1 Introduction 4.2 Exergy 4.3 Procedure for energy and exergy analyses 4.4 Conventional balances: mass, energy, and entropy 4.5 Exergy balance 4.6 Exergy consumption 4.7 Exergy of heat, work, and electricity interactions 4.7.1 Exergy of heat 4.7.2 Exergy of work and electricity 4.8 Exergy of matter 4.8.1 Exergy of matter in a closed system 4.8.2 Exergy of a matter flow 4.8.3 Properties of materials for energy and exergy analyses 4.9 Reference environment 4.10 Efficiencies and other measures of merit 4.10.1 Efficiency conceptually 4.10.2 Energy efficiencies and their deficiencies 4.10.3 Exergy and exergy-based efficiencies 4.11 Applications and implications 4.11.1 Thermodynamic applications of exergy analysis 4.11.2 Other applications of exergy analysis 4.11.3 Implications of results of exergy analyses 4.11.4 Exergy, renewable energy, and sustainability 4.12 Illustrative examples 4.12.1 Illustrative example 1: thermal energy storage 4.12.2 Illustrative example 2: heat pump versus electrical resistance heating 4.13 Closing remarks Nomenclature Greek letters Subscripts References Chapter-5---Solar-power-to_2021_Design-and-Performance-Optimization-of-Renew 5 Solar power tower system 5.1 Introduction 5.2 Case study 5.3 Solar power tower direct steam system 5.3.1 Technology overview 5.3.2 Heliostat field 5.3.3 Central receiver 5.3.4 Rankine cycle component 5.3.5 The heliostat field 5.3.6 Receiver 5.4 Intelligent methods 5.4.1 The proposed methodology 5.4.2 Adaptive neurofuzzy inference system 5.4.3 Biogeography-based optimization algorithm 5.4.4 ANFIS-BBO 5.5 Result and discussion 5.5.1 Receiver power loss 5.5.2 Power absorbed by the receiver 5.5.2.1 Sensitivity analysis 5.5.3 Receiver thermal efficiency 5.5.4 Field simulation 5.5.5 Cycle electrical power output Conclusions Acknowledgment References Chapter-6---Parabolic-trough-s_2021_Design-and-Performance-Optimization-of-R 6 Parabolic trough solar collectors 6.1 Introduction 6.2 Parabolic trough solar collectors: a summary 6.3 Theoretical formulations 6.3.1 Governing equation of the CFD model 6.3.2 Properties of the Ferrofluid 6.3.3 Thermal performance 6.4 Parabolic trough solar collector analysis: a case study Conclusions References Chapter-7---Benefit-cost-analysis-and-paramet_2021_Design-and-Performance-Op 7 Benefit-cost analysis and parametric optimization using Taguchi method for a solar water heater 7.1 Introduction 7.2 Economic analysis of solar water heating system 7.2.1 Concept of time value of money 7.2.2 Opportunity cost of capital or discount rate 7.2.3 Risks associated with the cash flows of an SWH 7.2.4 SWH investment evaluation criteria 7.2.5 Simple payback period 7.2.6 Discounted payback period 7.2.7 Benefit-cost ratio 7.2.8 DCF break-even analysis 7.3 Results and discussion of economic analysis 7.3.1 Simple and discounted payback period 7.3.2 Effects of various parameters on benefit–cost ratio 7.3.3 DCF break-even profile 7.4 Optimization of input parameters using Taguchi method 7.5 Signal-to-noise ratio 7.6 Data analysis and parameter optimization 7.6.1 Analysis and optimization for scenario 1 7.6.2 Analysis and optimization for scenario 2 Conclusions Nomenclatures Appendix References Chapter-8---Fundamentals-and-performa_2021_Design-and-Performance-Optimizati 8 Fundamentals and performance of solar photovoltaic systems 8.1 Introduction 8.2 The pn junction model for solar cells 8.2.1 Electrostatic analysis in the depletion region 8.2.2 Solution for the quasineutral regions 8.2.3 Current–voltage characteristics 8.3 Photovoltaic modules 8.3.1 Module components and characterizations 8.3.2 Environmental effects on module performance 8.4 Photovoltaic systems 8.4.1 System components 8.4.2 Design for stand-alone systems 8.4.3 Design for grid-connected systems Conclusion References Chapter-9---Cooling-systems-for-linear-_2021_Design-and-Performance-Optimiza 9 Cooling systems for linear concentrating photovoltaic (LCPV) system 9.1 Introduction 9.2 Linear concentrating photovoltaic system 9.2.1 Solar concentrator 9.2.2 Photovoltaic cell 9.3 Cooling system 9.3.1 Photovoltaic cell cooler 9.3.2 Water mechanical pumped loop system 9.3.3 Two-phase mechanical pumped loop (TMPL) system 9.3.3.1 Condenser 9.3.3.2 Water tank 9.3.3.3 Accumulator 9.3.3.4 Two-phase mechanical pumped simulation 9.3.4 Vapor compression refrigeration (VCR) system 9.3.4.1 Compressor 9.3.4.2 Expansion device 9.3.4.3 Vapor compression refrigeration simulation Conclusion Acknowledgments Nomenclatures Abbreviations Subscripts References Chapter-10---Geothermal-po_2021_Design-and-Performance-Optimization-of-Renew 10 Geothermal power plants 10.1 Introduction 10.2 Dry steam power plant 10.2.1 Thermodynamic analysis 10.2.2 Exergy analysis 10.3 Single-flash steam power plant 10.3.1 Mass balance 10.3.2 Energy balance 10.3.3 Exergy analysis 10.4 Double-flash steam power plant 10.4.1 Mass balance 10.4.2 Energy balance 10.4.3 Exergy analysis 10.5 Binary power plant (ORC) 10.5.1 Energy balance 10.5.2 Exergy analysis 10.6 Illustrative examples 10.6.1 Example 1 10.6.2 Solution 10.6.3 Example 2 10.6.4 Solution 10.7 Exercises References Chapter-11---Heat-pumps-and-ab_2021_Design-and-Performance-Optimization-of-R 11 Heat pumps and absorption chillers 11.1 Introduction 11.2 Types of heat pumps and their advantages 11.3 Geothermal heat pumps 11.3.1 Site evaluation for geothermal heat pumps 11.3.1.1 Geology 11.3.1.2 Hydrology 11.3.1.3 Land availability 11.3.2 Benefits of geothermal heat pump systems 11.3.3 Basic operating principles of geothermal heat pumps 11.3.3.1 Heating mode 11.3.3.2 Cooling mode 11.4 Conventional heat pump for cooling 11.5 Illustrative examples 11.5.1 Example 1 11.5.2 Solution 11.5.3 Example 2 11.5.4 Solution 11.5.5 Example 3 11.5.6 Solution 11.6 Absorption chillers 11.6.1 Thermodynamic analysis 11.6.2 Illustrative example 11.6.2.1 Example 4 11.6.2.2 Solution 11.7 Closing remarks References Chapter-12---Hydrop_2021_Design-and-Performance-Optimization-of-Renewable-En 12 Hydropower 12.1 Introduction 12.2 Hydropower technology 12.2.1 Classification 12.2.2 Turbine types and their classifications 12.2.3 Large hydropower components 12.2.4 Small hydropower components 12.2.5 Micro hydropower components 12.3 Revaluation concepts for hydroelectric energy storage 12.4 Pumped storage 12.5 Modeling of micro hydroelectric power plants 12.5.1 Flow duration curve 12.5.2 Flow rate measurement 12.5.2.1 Cross sectional area (Ar) 12.5.2.2 Velocity (Vr) 12.5.3 Weir and open channel 12.5.4 Penstock design 12.5.5 Head measurement 12.5.6 Turbine power 12.5.7 Turbine speed 12.5.8 Turbine selection 12.5.8.1 Pelton turbine 12.5.8.2 Francis turbine 12.5.8.3 For Kaplan turbine 12.5.8.4 Cross-flow turbine 12.6 Hydroelectric optimization problem Conclusion Acknowledgment References Chapter-13---Energy-and-exergy-an_2021_Design-and-Performance-Optimization-o 13 Energy and exergy analyses of wind turbines 13.1 Introduction 13.2 Energy analysis of wind turbines 13.3 Exergy analysis of wind turbines 13.4 Numerical example Conclusions References Chapter-14---Energy-s_2021_Design-and-Performance-Optimization-of-Renewable- 14 Energy storage 14.1 Introduction 14.2 Electrochemical energy storage 14.2.1 Nickel–cadmium (Ni–Cd) batteries 14.2.2 Nickel–zinc batteries 14.2.3 Lead–acid batteries 14.2.4 Lithium-ion batteries 14.3 Hydrogen energy storage 14.4 Mechanical energy storage 14.4.1 Flywheel electric energy storage 14.4.2 Compressed air energy storage 14.5 Electromagnetic energy storage 14.5.1 Super capacitor energy storage 14.5.2 Superconducting magnetic energy storage 14.6 Fuel cells 14.6.1 Thermodynamic analysis 14.6.2 Illustrative example 14.6.3 Solution 14.7 Thermal energy storage Conclusions Acknowledgment References Chapter-15---Use-of-nanofluids-in_2021_Design-and-Performance-Optimization-o 15 Use of nanofluids in solar energy systems 15.1 Nanofluid: a new generation of heat transfer fluids 15.1.1 Nanofluid preparation 15.1.1.1 Single-step 15.1.1.2 Two-step 15.1.2 Type of nanofluids 15.1.3 Thermophysical properties 15.1.3.1 Viscosity 15.1.3.2 Thermal conductivity 15.1.4 Other properties 15.1.4.1 Density 15.1.5 Mathematical modeling convection heat transfer through nanofluids 15.1.5.1 Single-phase approach 15.1.5.2 Two-phase approach 15.1.5.3 Mixture model 15.1.6 Natural convection 15.1.7 Forced and mixed convection 15.2 Renewable energy versus nonrenewable energy 15.3 Solar energy 15.3.1 Solar collectors 15.3.1.1 Different types of solar collectors 15.3.1.2 Nonconcentrating solar collectors 15.3.1.2.1 Flat-plate solar collectors 15.3.1.2.2 Compound parabolic collector 15.3.1.3 Concentrating solar collectors 15.3.1.3.1 Parabolic trough collector 15.3.1.3.1.1 Modeling of energy transfer 15.3.1.3.2 Parabolic dish collector 15.3.1.3.3 Linear Fresnel collector 15.3.3.4 Central receiver of heliostat field collectors 15.4 Simulation of nanofluid flow through solar absorbers 15.4.1 Role of nanofluid in absorbing solar energy 15.5 Solar stills 15.6 Concluding remarks References Chapter-16---Artificial-Intelligence-ap_2021_Design-and-Performance-Optimiza 16 Artificial Intelligence applications in renewable energy systems 16.1 What is Artificial Intelligence? 16.2 Artificial Intelligence and renewable energy 16.3 Artificial Intelligence examples for a photovoltaic solar cell: case study 16.3.1 Artificial Neural Network 16.3.2 Fuzzy Logic 16.3.3 Metaheuristic techniques 16.3.3.1 Particle Swarm Optimization 16.3.3.2 Salp Swarm Algorithm 16.3.3.3 Grey Wolf Optimizer 16.3.3.4 Genetic Algorithm 16.3.3.5 Simulated Annealing algorithm 16.3.4 Case study: numerical example 16.3.4.1 Black-box model 16.3.4.1.1 Artificial Neural Network 16.3.4.1.2 Fuzzy Logic 16.3.4.2 Grey-box model 16.3.4.2.1 Particle Swarm Optimization 16.3.4.2.2 Salp Swarm Algorithm 16.3.4.2.3 Grey Wolf Optimizer 16.3.4.2.4 Genetic Algorithm 16.3.4.2.5 Simulated Annealing References Index_2021_Design-and-Performance-Optimization-of-Renewable-Energy-Systems Index Design and Performance Optimization of Renewable Energy Systems provides an integrated discussion of issues relating to renewable energy performance design and optimization using advanced thermodynamic analysis with modern methods to configure major renewable energy plant configurations (solar, geothermal, wind, hydro, PV). Vectors of performance enhancement reviewed include thermodynamics, heat transfer, exergoeconomics and neural network techniques. Source technologies studied range across geothermal power plants, hydroelectric power, solar power towers, linear concentrating PV, parabolic trough solar collectors, grid-tied hybrid solar PV/Fuel cell for freshwater production, and wind energy systems. Finally, nanofluids in renewable energy systems are reviewed and discussed from the heat transfer enhancement perspective. Reviews the fundamentals of thermodynamics and heat transfer concepts to help engineers overcome design challenges for performance maximization Explores advanced design and operating principles for solar, geothermal and wind energy systems with diagrams and examples Combines detailed mathematical modeling with relevant computational analyses, focusing on novel techniques such as artificial neural network analyses Demonstrates how to maximize overall system performance by achieving synergies in equipment and component efficiency