Pumped Hydroelectric Storage System Design: Maximizing Efficiency and Capacity, Assignments of Mechanical Engineering

Download Pumped Hydroelectric Storage System Design: Maximizing Efficiency and Capacity and more Assignments Mechanical Engineering in PDF only on Docsity! Pumped Storage Hydroelectricity HW2: Planning Your Simulation-Based Design Study ME6105 Kyle Azevedo Ryan Demars Tommaso Gomez Parichit Kumar Task 1: Identify the Decision Situation The application domain chosen for this project is stationary energy storage and return. The power demands for a power plant fluctuate depending on the time of day and season in a particular geographical region. Some power plants (e.g. nuclear or fossil fuel) cannot modulate their output quickly and easily or can only operate efficiently over a narrower power output range. These types of power plants may be forced to produce excess power during off-peak hours in order to maintain an efficient operating point. Thus, the purpose of this energy storage system is to store excess power generated in off-peak hours and return this power to meet the higher demands of on-peak hours, thereby leveling the load on the power plant. The system being considered by our team is a hydroelectric pumped-storage solution. Hydroelectric pumped storage stores potential energy in the form of hydrostatic water pressure by pumping water from a low elevation reservoir to a higher one, as shown in Figure 1 below. Figure 1: Illustration of a Hydroelectric Pumped-Storage System1 The pumping occurs during off-peak hours of the demand cycle, meaning that the plant can use inexpensive surplus power to pump water and then reclaim the stored energy to match higher demand during peak periods. The specific design decisions for this project involve (a) optimizing the system to maximize the overall power storage efficiency (the ratio of power generated to power consumed by the system) and (b) meeting required demand level when the system returns power to the grid. Relevant variables that require specification include 1 http://www.tva.gov/power/pumpstorart.htm 2 Means Objectives Network: Minimize wasted energy from traditional power generation Maximize storage Minimize energy capability losses Optimize differential Maximize key Appropriate choice Figure 3: Means Objectives Network Some of the objectives in the network above are easier to model than others, especially in the context of the Dymola software package. Although this is a fluid system, we want to model its performance at a level that does not require CFD or finite element approaches. Keeping this in mind while emphasizing our fundamental goal leads us to the following key objectives: - Maximize storage efficiency - Maximize storage capacity - Maximize power output These objectives are reasonable to pursue using Dymola energy-based modeling systems, and provide a general scale for the project. The energy storage efficiency can be measured by taking the ratio of power stored to power returned (or the ratio of energy stored to energy returned over a given time integral). The maximum power output will determine whether or not the system can meet the demands of power return during peak hours. The capacity of the storage tanks will determine the quantity of energy that can be stored by the system. reservoir height component efficiencies Regular maintenance Environmental constraints of fuel Quality control 5 Task 3: Identify the Design Alternatives Integrating a hydroelectric pumped-storage system into any existing full-scale power generation cycle is an extremely complex problem. Therefore, this project will focus solely on several key parameters that affect the outcome of the design process. The chosen design alternatives should contribute to a reasonable level of accuracy within the model while remaining within the scope of Dymola’s modeling capability. The initial alternatives that will be examined include four key variables: - Pump size - Turbine size - Main pipe diameter - Elevation delta between reservoirs - Reservoir volume Other relevant parameters include properties of the working fluid within the system, environmental variables such as temperature and humidity, as well as more detailed turbine and pump specifications (turbine type, blade profile, generator conversion efficiency, etc.). These will be included in the model as time permits, but will not be examined initially in an effort to limit scope. 6 Task 4: Identify the Structure of the Design Problem Reservoir height differential decision Turbine size decision Pump size decision Pipe diameter decision Pump power Power demand Turbine speed Hydrostatic pressure differential Upstream flow-rate Downstream flow-rate Frictional losses Frictional losses in pump in pipes Frictional losses in turbine Seismic activity Drought Power storage cycle efficiency Power generation cycle efficiency Decision Calculation Outcome Chance Event Utility 7 Legend: Task 6: Assess Your Plan There are several sources of uncertainty that arise when modeling our energy storage system with the above assumptions. First, several of the assumptions that reduce overall system complexity will reduce our confidence level and introduce uncertainty. For example, by using a lumped parameter turbine model, we do not account for detailed blade characteristics and internal flow dynamics and we introduce considerable uncertainty in measuring turbine performance and efficiency. Once we understand more about the component design characteristics required for this system, we may add complexity to the model by altering these component models. Also, chance events, such as weather or component fatigue and failure, can also reduce accuracy. As seen in Task 4, this encompasses a wide range of factors. Finally, unknown design constraints reduce our ability to tailor the model to a specific situation. In an actual hydroelectric design study, site properties such as topography or the characteristics of a chosen natural reservoir would greatly affect simulation outcome. If our model proves to be unnecessarily complicated, we will consider adding further simplifications. Our initial model will attempt to model viscosity as a function of temperature. This variability will affect turbine and pump performance as well as the resistance through fluid elements. If this degree of complexity exceeds the capability of Dymola, we may simplify the model by using a fixed viscosity at a mean temperature. 10 Task 7: Articulate Your Learning Objectives Kyle Azevedo: As a Master’s student, my thesis work deals with systems engineering and high level modeling of transportation solutions. Simulation is an extremely applicable domain for this work. The course will help me gain a better understanding of how changing parameters affects an overall system, and will help me make better, more informed decisions over the course of my research project. Also, as an advanced multiple-domain modeling tool, Dymola is something that interests me as a different approach to thermal and power systems within transportation. It provides an additional viewpoint to complement other traditional modeling approaches such as FEM. Ryan Demars: In my learning experience during this course, I would like to emphasize decision making based on objectives and alternatives, and apply them to simulation based design. I am really interested in performing these simulations through Dymola’s modeling environment and learning the Modelica language. I have seen projects from this class in the past, which intrigued me and stimulated my interest in taking this class. I believe that learning these new and cutting-edge techniques and applying them to a semester-long project will be beneficial and exciting both now and in the future. Tommaso Gomez: In my most recent industry position as systems engineer, I gained some exposure to dynamic system modeling as it applied to a geothermal Rankine cycle. I developed an interest in energy system modeling and simulation, and I hope to strengthen my modeling fundamentals in this course. Specifically, I would like to improve my intuition and understanding of dynamic models so that I can determine which variables and parameters are most influential, and which can be neglected to a certain extent. This implies developing a better understanding of the analogous circuits and systems of equations that result from a model. Parichit Kumar: The learning experience in this course for me is to make sound design decisions based on the ability to create appropriate models. One of my key goals would be to learn how to critically evaluate analysis results in an efficient manner, as well as to make key tradeoff decisions between cost and effectiveness of a given system. This class to me is an introduction to engineering decision making, taught through Dymola. 11