Optimal design of geothermal power plants
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- Chủ đề:
- Geothermal Power Plant Optimization: A New Design Paradigm
- Số trang:
- 204 trang
- Trường:
- Virginia Commonwealth University
- Chuyên ngành:
- Mechanical and Nuclear Engineering
- Tác giả:
- Joshua Geiger Clarke
- Năm:
- 2014
Tóm tắt nội dung luận án
I.Geothermal Power Plant Optimization A New Design Paradigm
Optimal design of geothermal power plants is crucial for sustainable energy production. This research explores methodologies to enhance plant performance and economic viability. Geothermal energy offers a consistent, renewable baseload power source. However, its effective utilization demands precise engineering and optimization. This study addresses the complex interplay of resource characteristics, plant configurations, and environmental factors. The goal is to maximize energy extraction while minimizing operational costs and environmental impact. Advanced modeling and simulation techniques are employed to achieve these objectives, guiding future developments in geothermal energy systems.
1.1. Geothermal Energy An Overview of Potentials
Geothermal energy provides a reliable, clean power source derived from Earth's heat. Power generation from this resource reduces reliance on fossil fuels. Various plant types exist: dry steam, flash steam, and binary cycle. Each type suits different geothermal fluid conditions. Understanding their fundamental operating principles is essential for selecting and optimizing a plant design. This research focuses on improving the efficiency and design of these diverse geothermal power plants.
1.2. Key Objectives for Optimal Plant Design Strategies
Optimal plant design aims to maximize specific work output and improve overall thermodynamic efficiency. Another critical objective is to reduce the levelized cost of energy (LCOE). Achieving these goals requires balancing multiple design variables and constraints. The design process must consider resource temperature, pressure, fluid composition, and ambient conditions. This study develops frameworks to evaluate these interdependencies and drive performance enhancements in geothermal energy systems.
1.3. Addressing Challenges in Geothermal System Design
Geothermal system design faces inherent challenges. Resource variability, potential silica scaling, and stringent re-injection temperature limits impact operational efficiency and longevity. Environmental factors, particularly local climate, further influence plant performance. Overcoming these obstacles necessitates advanced design methodologies and robust optimization tools. This research directly tackles these issues through comprehensive modeling and an integrated optimization approach for geothermal power plants.
II.Optimizing Double Flash Geothermal Plant Design for Efficiency
Double-flash geothermal power plants are a common choice for high-temperature resources. This section delves into the design space and optimization strategies for these systems. The process involves flashing high-temperature geothermal fluid into steam at two different pressure levels. This sequential flashing maximizes steam generation and power output. A detailed analysis of operating parameters is performed to identify configurations that yield optimal specific work. Constraints like silica saturation are rigorously applied to ensure practical and sustainable designs.
2.1. Principles of Double Flash Technology Operation
Double-flash geothermal power plants harness energy from high-temperature geothermal brine. The fluid is directed through a high-pressure separator, producing the first flash of steam. The remaining liquid then enters a low-pressure separator for a second flash. Both steam streams drive turbines to generate electricity. This method improves upon single-flash systems by extracting more energy from the geothermal fluid, enhancing the overall thermodynamic efficiency of the flash steam power plant.
2.2. Constrained Design Space Analysis for Efficiency
A constrained design space analysis is fundamental for optimizing double-flash plants. This analysis maps specific work output against critical parameters like brine and condenser temperatures. Silica saturation limits are incorporated as a crucial constraint, preventing scaling in pipes and heat exchanger design. Understanding these boundaries helps identify viable operating regions. Exergy analysis further aids in pinpointing areas for thermodynamic efficiency improvements within the defined constraints.
2.3. Achieving Optimum Specific Work Output in Plants
The primary objective for double-flash systems is to achieve optimum specific work output. This is accomplished by carefully tuning flash pressures and re-injection temperatures. The research identifies specific combinations of brine and condenser temperatures that lead to maximum power generation per unit of geothermal fluid. These findings provide critical guidelines for designing and operating high-performance flash steam power plant configurations, maximizing thermodynamic efficiency.
III.Binary Cycle Geothermal Plant Design Efficiency Gains
Binary cycle power plants represent a key technology for lower-temperature geothermal resources. This section explores the design considerations and efficiency potential of these systems. Unlike flash plants, binary cycles use a secondary working fluid with a low boiling point to drive a turbine. This closed-loop system allows for efficient heat extraction and prevents direct contact between the geothermal fluid and the turbine. Comprehensive analysis considers various working fluids, operating temperatures, and re-injection constraints to optimize performance.
3.1. Fundamentals of Binary Cycle Power Plants Explained
Binary cycle power plants are designed for geothermal resources ranging from low to moderate temperatures. Heat from the geothermal fluid is transferred to an organic working fluid, such as isobutane or pentane, via a heat exchanger. This working fluid then vaporizes and expands through a turbine. After generating power, it is condensed and recycled. This system minimizes scaling and corrosion issues by isolating the geothermal fluid, ensuring environmental compliance and full re-injection.
3.2. Performance Under Various Brine Condenser Temps
The performance of a binary cycle power plant is heavily influenced by both brine and condenser temperatures. Higher brine temperatures generally increase the power output, while lower condenser temperatures enhance thermodynamic efficiency. The selection of the optimal working fluid is paramount for maximizing specific work output under these varying conditions. This study thoroughly investigates these interactions, providing insights into ideal operating points and working fluid choices for specific thermal environments.
3.3. Effect of Re injection Temperature on Design Choices
Re-injection temperature plays a significant role in binary cycle plant design and sustainability. Regulatory and resource management requirements often mandate minimum re-injection temperatures. This constraint directly affects the amount of heat extractable from the geothermal fluid and impacts the heat exchanger design. The research quantifies this effect on specific work output and overall system efficiency. Proper re-injection temperature management is vital for long-term reservoir health and operational sustainability.
IV.Advanced Optimization Algorithms for Geothermal Design
The intricate nature of geothermal power plant design necessitates the use of advanced optimization algorithms. This section examines various heuristic techniques employed to navigate complex design spaces. Traditional optimization methods often fall short when dealing with non-linear objectives and numerous interdependent variables. Algorithms like simulated binary crossover and particle swarm optimization offer robust solutions, enabling the identification of truly optimal or near-optimal plant configurations. These methods are crucial for maximizing specific work output and achieving cost optimization.
4.1. Selecting Heuristic Algorithms for Design Challenges
Complex geothermal power plant design problems require sophisticated optimization techniques. Heuristic algorithms provide powerful tools for exploring large, multi-dimensional design spaces where traditional methods are inefficient. This research evaluates the efficacy of different heuristic algorithms, including genetic algorithms and particle swarm optimization. The selection criteria prioritize robustness, convergence speed, and the ability to handle non-linear objectives inherent in geothermal energy system design.
4.2. Understanding Simulated Binary Crossover Functionality
Simulated Binary Crossover (SBX) is a prominent genetic algorithm operator. It facilitates the creation of new candidate solutions by combining information from parent solutions. SBX is particularly effective for continuous optimization problems common in engineering design. This algorithm efficiently explores the design space, maintaining diversity while converging towards optimal solutions. Its application aids in identifying novel and high-performing configurations for geothermal power plants.
4.3. Particle Swarm Optimization for Complex Systems
Particle Swarm Optimization (PSO) is a powerful metaheuristic algorithm inspired by social behavior. PSO operates by iteratively improving candidate solutions, called particles, which 'fly' through the search space. Each particle adjusts its trajectory based on its own best-found position and the global best-found position. PSO excels in addressing high-dimensional and non-linear optimization problems, making it highly suitable for multi-objective cost optimization and exergy analysis in geothermal plant design.
V.Climate Impact on Geothermal Plant Performance Design
Climate significantly influences the performance of geothermal power plants, particularly those utilizing air-cooled condensers. This section investigates the impact of ambient temperature and other meteorological factors on plant efficiency and output. Air-cooled condensers, while conserving water, are directly affected by external air conditions. Understanding these relationships is vital for accurate performance prediction and robust plant design. The study leverages typical meteorological year data to simulate realistic operational scenarios and quantify climate-induced performance variations.
5.1. Role of Air Cooled Condensers in Design Choices
Air-cooled condensers are increasingly adopted in geothermal power plants, especially in arid regions, to conserve water. However, their performance is directly tied to ambient air temperature. Higher ambient temperatures lead to reduced heat rejection efficiency and elevated condenser pressures. This subsequently diminishes the turbine's specific work output. Optimal design of air-cooled systems balances water conservation with maintaining high thermodynamic efficiency for the geothermal energy plant.
5.2. Analysis of Typical Meteorological Year Data Usage
Plant performance varies dynamically with local climatic conditions. Typical Meteorological Year (TMY) data provides a standardized set of hourly weather information, including ambient temperature, humidity, and wind speed. Utilizing TMY data allows for realistic simulations of plant operation over an entire year. This analysis reveals seasonal and diurnal performance fluctuations, crucial for accurate energy production forecasting and economic evaluations of geothermal energy projects.
5.3. Effect of Ambient Temperature on Output and EGS
Ambient temperature has a profound effect on geothermal plant output, particularly for binary cycle power plant designs with air-cooled condensers. As ambient temperature increases, the heat rejection temperature also rises, reducing the effective temperature differential for power generation. This leads to a decrease in specific work output and overall thermodynamic efficiency. These insights are critical for site selection and for developing resilient control strategies for enhanced geothermal systems (EGS) operating in diverse climates.
VI.Multi Objective Optimization for Geothermal Plants LCOE
Designing geothermal power plants often involves conflicting objectives, such as maximizing power output and minimizing cost. Multi-objective optimization provides a robust framework to address these trade-offs. This section focuses on simultaneously optimizing specific work output and the levelized cost of energy (LCOE). The approach generates a Pareto-optimal front, illustrating the spectrum of best possible solutions where improving one objective necessitates compromising another. This methodology guides stakeholders in making informed design decisions that balance technical performance with economic viability, crucial for any geothermal energy project.
6.1. Balancing Work Output and Cost Optimization Goals
Optimal geothermal plant design frequently involves conflicting goals. Maximizing specific work output might lead to increased capital expenditures. Conversely, solely minimizing the levelized cost of energy (LCOE) could compromise thermodynamic efficiency. Multi-objective optimization techniques address these inherent trade-offs. They help identify design solutions that offer the best possible balance, ensuring both high performance and economic feasibility through effective cost optimization.
6.2. Pareto Optimal Front for Critical Design Decisions
The Pareto-optimal front represents a set of non-dominated solutions. For any solution on this front, no objective can be improved without degrading at least one other objective. In geothermal plant design, this front graphically depicts the trade-off between specific work output and LCOE. Visualizing this front empowers designers and investors to make informed decisions, understanding the compromises involved in achieving different performance and cost objectives.
6.3. Comparing Basic vs. Advanced Binary Cycles Efficiency
This research compares the multi-objective optimal design of basic binary cycle power plant configurations against advanced ones, incorporating components like superheaters and recuperators. These advanced features aim to boost thermodynamic efficiency and exergy analysis performance. The multi-objective framework evaluates whether the increased complexity and capital investment for these additions are justified by improved energy output and reduced LCOE. This comparison provides critical insights for developing next-generation geothermal energy systems, including enhanced geothermal systems (EGS).
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Tải xuống để đọc toàn bộVirginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2014 Optimal design of geothermal power plants Joshua Clarke Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.edu/etd Part of the Energy Systems Commons © The Author Downloaded from https://scholarscompass.edu/etd/3472 This Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact libcompass@vcu. Optimal design of geothermal power plants A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University by Joshua Geiger Clarke B.
Aerospace Engineering, Georgia Institute of Technology, 2002 M. Mechanical and Nuclear Engineering, Virginia Commonwealth University, 2011 Director: Dr. Associate Professor, Mechanical and Nuclear Engineering ©2014 Joshua Clarke Virginia Commonwealth University Richmond, Virginia May 2014 ii Contents List of Figures v List of Tables viii Abstract x 1 Introduction 1 2 Objectives 9 2.1 Geothermal power plants .2 Double-flash plants .1 Geothermal power plants .2 Other power plants of interest .3 Summary and basis for new research. 21 4 Double-flash design space 26 4.4 Results and discussion .1 Constrained design space .2 Optimum specific work output.
45 iii 5 Binary design space 48 5.4 Inputs and constants .4 Results and discussion .1 Constrained design space .2 Optimum specific work output .3 Effect of constrained re-injection temperature. 75 6 Heuristic algorithm selection 79 6.2 Simulated binary crossover .3 Parameter-based mutation .5 Particle swarm optimization .1 Optimum objective function .7 Sensitivity to algorithm control parameters .8 Algorithm validation for binary power plant. 99 7 Effect of climate on plant performance 102 7.2 Air-cooled condensers .1 Principles of operation .1 Typical meteorological year data .2 Climates of interest .4 Impact on plant performance .1 Effect of condenser temperature on turbine efficiency .2 Effect of ambient temperature on specific work output .3 Effect of condenser temperature on plant operations .4 Inputs and constants .7 Results and discussion. 122 8 Binary plant multi-objective optimization 124 8.4 Multi-objective particle swarm optimization .5 Inputs and constants .4 Results and discussion.
153 9 Optimal plant design 155 9.2 Basic binary plant vs.3 Binary plant with superheater and recuperator vs.1 Multi-objective double-flash plant model .2 Pareto-optimal front .3 Maximum specific work output. 180 10 Conclusions 182 Bibliography 185 Vita 192 v List of Figures 1.1 Geothermal power plant .2 Worldwide installed geothermal electric capacity .3 Double flash geothermal power plant .4 Binary geothermal power plant .1 Scope of existing literature .2 Scope of this work compared to existing literature: double-flash plants .3 Scope of this work compared to existing literature: binary plants .1 Double flash geothermal power plant model .2 Double flash temperature entropy diagram .3 Equilibrium solubility of quartz and amorphous silica .4 Constrained design space: Contours of specific work output, w [kJ/kg], vs. T2 and T6 ; Tbrine = 260 o C and Tcond = 30 o C .5 Constrained design space: Contours of specific work output, w [kJ/kg], vs. T2 and T6 , with woptimum indicated by a triangle; Tbrine = 200 − 240 o C, Tcond = 30 − 60 o C 41 4.6 Constrained design space: Contours of specific work output, w [kJ/kg], vs.
T2 and T6 , with woptimum indicated by a triangle; Tbrine = 260 − 300 o C, Tcond = 30 − 60 o C 42 4.7 Optimum specific work output vs. brine temperature at Tcond = 30 − 60 o C .8 Constrained design space for relaxed silica constraint .9 Optimum specific work output vs. brine temperature for two different silica con- straints at Tcond = 30 − 60 o C .1 Binary power plant schematic .2 Binary power plant temperature-entropy diagram .3 Equilibrium solubility of quartz and amorphous silica .4 Constrained design space: Tbrine = 120o C, Tcond = 20o C .5 Effect of increasing condenser temperature; Constrained design space: Tbrine = 120o C, Tcond = 40o C .6 Effect of increasing brine temperature; Constrained design space: Tbrine = 180o C, Tcond = 20o C .7 Effect of increasing brine and condenser temperature; Constrained design space: Tbrine = 180o C, Tcond = 40o C .8 Condenser temperature affects optimum working fluid .9 Constrained design space: Normalized specific work output (w/woptimum ) vs. Tevap /Tcrit and working fluid, with woptimum indicated by a triangle; Tbrine = 80 − 120o C, Tcond = 30 − 60o C .10 Constrained design space: Normalized specific work output (w/woptimum ) vs.
Tevap /Tcrit and working fluid, with woptimum indicated by a triangle; Tbrine = 140 − 180o C, Tcond = 30 − 60o C .11 Optimum working fluid and specific work output: Tbrine = 70 − 200o C, Tcond = 10 − 60o C .12 Optimum working fluid and specific work output: Tbrine = 70 − 200o C, Tcond = 10 − 60o C, Tin j,min = 70o C .13 Optimum specific work output with and without re-injection temperature con- straint: Tbrine = 70 − 200o C, Tcond = 10 − 60o C .1 Double-flash geothermal power plant model .2 Genetic algorithm decision variable progression. White circles indicate infeasible solutions, gray to black are feasible with increasing value of w.3 Particle swarm optimization decision variable progression. White circles indicate infeasible solutions, gray to black are feasible with increasing value of w.4 GA objective function progression .5 PSO objective function progression .1 Air-cooled condenser .2 Condenser temperature vs. dry-bulb temperature for an air-cooled condenser .3 Hourly dry-bulb temperature from Santa Rosa, CA TMY3 data .4 Representative annual profile of hourly dry-bulb temperature data for Santa Rosa, CA.
climates of interest .6 Dry-bulb temperature data for 8 U. climates of interest .7 Semi-qualitative example of turbine efficiency vs.8 Specific work output of an example plant vs. dry-bulb temperature .9 Example unmodified annual hourly condenser temperature profile .10 Example annual hourly profile indicating feasible and infeasible condenser tem- peratures .11 Example modified annual hourly condenser temperature profile .12 Annual hourly dry-bulb temperature profile: Fairbanks, AK .13 Annual hourly dry-bulb temperature profile: Medford, OR .14 Annual hourly dry-bulb temperature profile: Imperial, CA .15 Annual hourly dry-bulb temperature profile: Honolulu, HI .1 Binary power plant with superheater and recuperator schematic .2 Binary power plant with superheater and recuperator temperature-entropy diagram 127 8.3 Swarm of objective vectors and Pareto-optimal front: Tbrine = 160o C, Tdry = 15o C .4 Pareto-optimal front: Tbrine = 160o C, Tdry = 15o C .5 Power output per heat exchanger area: Tbrine = 160o C, Tdry = 15o C .6 Pareto-optimal front with various Tin j,min constraints: Tbrine = 160o C, Tdry = 15o C .7 Pareto-optimal front with various Tin j,min constraints: Tbrine = 80 − 120o C, Tdry = 5 − 25o C .8 Pareto-optimal front with various Tin j,min constraints: Tbrine = 140 − 180o C, Tdry = 5 − 25o C .1 Optimum specific work output vs. brine temperature for double-flash and basic binary plants at Tcond = 10 − 60 o C; no re-injection temperature constraint .2 Optimum specific work output vs.
brine temperature for double-flash and basic binary plants at Tcond = 10 − 60 o C; Tin j,Min = 70 o C .3 Pareto-optimal front binary and double-flash: Tbrine = 160o C, Tdry = 15o C .4 Pareto-optimal front binary and double-flash: Tbrine = 80 − 120o C, Tdry = 5 − 25o C 163 9.5 Pareto-optimal front binary and double-flash: Tbrine = 140 − 180o C, Tdry = 5 − 25o C164 9.6 Pareto-optimal front binary and double-flash: Tbrine = 200 − 240o C, Tdry = 5 − 25o C165 9.7 Pareto-optimal front binary and double-flash: Tbrine = 260 − 300o C, Tdry = 5 − 25o C166 9.8 Maximum specific work output, binary and double-flash: Tbrine = 80 − 300o C, Tdry = 5o C .9 Maximum specific work output, binary and double-flash: Tbrine = 80 − 300o C, Tdry = 15o C .10 Maximum specific work output, binary and double-flash: Tbrine = 80 − 300o C, Tdry = 25o C. 179 viii List of Tables 1.1 Comparison of gaseous emissions from typical power plants .2 Comparison of land requirements for typical power plants .1 Double-flash power plant optimization literature review .2 Binary power plant optimization literature review .1 Double-flash power plant optimization literature review .2 Double-flash power plant model validation .1 Binary power plant optimization literature review .2 Binary power plant model validation .3 Binary power plant potential working fluids .4 Binary power plant model inputs and constants .5 Binary power plant optimum decision variables .1 Number of runs (of 30) converged within ε of highest achieved objective function value and optimum objective function value (w) statistics .2 Median radius of convergence .3 Computational runtime for 300 objective function evaluations and median number of objective function evaluations required to converge within δ of highest known objective function value .4 Number of runs (of 30) converged within ε of highest achieved objective function value for varying GA control parameters .5 Number of runs (of 30) converged within ε of highest achieved objective function value for varying PSO control parameters .6 Comparison of algorithm control parameter cases: t-test results .7 Particle swarm optimization validation .1 Air-cooled condenser energy ratio .2 Binary power plant model inputs and constants .3 Effect of utilizing annual hourly condenser temperature profile vs. annual mean condenser temperature on optimum annual average specific work output .1 Binary power plant model decision variables; multi-objective optimization .2 Binary power plant model inputs and constants; multi-objective optimization .3 Optimal plant design for maximizing specific work output: Binary with no Tin j constraint; Tdry = 5 − 25 o C, Tbrine = 80 − 180 o C .4 Optimal plant design for maximizing specific work output: Binary with Tin j,min = 70 o C; Tdry = 5 − 25 o C, Tbrine = 100 − 180 o C .5 Optimal plant design for maximizing specific work output: Binary with Tin j,min = 90 o C; Tdry = 5 − 25 o C, Tbrine = 120 − 180 o C .6 Optimal plant design at ≈ 0.8 · wmax : Binary with no Tin j constraint; Tdry = 5 − 25 o C, T o brine = 80 − 180 C .7 Optimal plant design at ≈ 0.8 · wmax : Binary with Tin j,min = 70 o C; Tdry = 5 − 25 o C, T o brine = 100 − 180 C .8 Optimal plant design at ≈ 0.8 · wmax : Binary with Tin j,min = 90 o C; Tdry = 5 − 25 o C, T o brine = 120 − 180 C .1 Optimal plant design for maximizing specific work output: Binary with no Tin j constraint; Tdry = 5 − 25 o C, Tbrine = 80 − 300 o C .2 Optimal plant design for maximizing specific work output: Binary with Tin j,min = 70 o C; Tdry = 5 − 25 o C, Tbrine = 100 − 300 o C .3 Optimal plant design for maximizing specific work output: Binary with Tin j,min = 90 o C; Tdry = 5 − 25 o C, Tbrine = 120 − 300 o C .4 Optimal plant design for maximizing specific work output: Double-flash; Tdry = 5 − 25 o C, Tbrine = 120 − 300 o C .5 Optimal plant design at ≈ 0.8 · wmax : Binary with no Tin j constraint; Tdry = 5 − 25 o C, T o brine = 80 − 300 C .6 Optimal plant design at ≈ 0.8 · wmax : Binary with Tin j,min = 70 o C; Tdry = 5 − 25 o C, T o brine = 100 − 300 C .7 Optimal plant design at ≈ 0.8 · wmax : Binary with Tin j,min = 90 o C; Tdry = 5 − 25 o C, T o brine = 120 − 300 C .
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Joshua Geiger Clarke (2014). Optimal design of geothermal power plants [Luận án tiến sĩ, Virginia Commonwealth University]. LuanAn.net. https://luanan.net/nang-luong-moi-truong/nang-luong-tai-tao/optimal-design-of-geothermal-power-plants
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