The decarbonization of the energy system is one of the most complex and consequential challenges of the 21st century. Meeting this challenge will require the deployment of existing low carbon technologies at unprecedented scales and rates and will necessitate the development of new technologies that have the ability to transform variable renewable energy into high energy density products. Reversible Solid Oxide Cells (RSOCs) are electrochemical devices that can function both as fuel cells or electrolyzers: in fuel cell mode, RSOCs consume a chemical fuel (H₂, CO, CH₄, etc.) to produce electrical power, while in electrolysis mode they consume electric power and chemical inputs (H₂O, CO₂) to produce a chemical fuel (H₂, CO, CH₄, etc.). As such, RSOC systems can be thought of as flexible “energy hubs” that have unique potential to bridge the low power density renewable infrastructure with that of high energy density fuels in an efficient, dynamic, and bidirectional fashion. This dissertation explores the different operational sensitivities and design trade-offs of a methane based RSOC system, investigates the optimum operating strategies for a system that adapts to variations in the hourly spot electricity and fuel prices in Western Denmark, and provides an economic analysis of the system under a wide variety of design assumptions, operational strategies, and fuel and electricity market structures.
In order to perform such comprehensive analyses, a 0-D computational model of a methane based RSOC system was developed in Python. In fuel cell mode, the system generates power by consuming natural gas, while in electrolysis mode the system generates synthetic natural gas (SNG) by electrolyzing steam and catalytically hydrogenating recycled CO₂ into CH₄ downstream of the RSOC. The model's flexibility enables the simulation of “part-load” operation, allowing the user to assess the changes in output, efficiency, and operating cost as the system is operated across multiple points. The model has the ability to evaluate the impact that changes in design choices and operating parameters (Area Specific Resistance, temperatures, current density, etc.) have on the system as it interfaces with time varying exogenous factors such as fuel and electricity prices. As such, one of the main contributions of this model is the ability to run simulations in which the operating strategy of the RSOC system responds and adapts to varying market signals.
The computational model is used to develop a series of hourly optimizations for finding the optimal operating strategy for an RSOC system that can buy or sell electricity and gas in the spot electricity and natural gas markets in Western Denmark. After receiving an electricity and gas price signal, the optimization determines the operating mode (fuel cell, electrolysis or idle) and operating point (e.g., current density) that maximize the operating profits every hour for the given electricity and gas price pair. In order to avoid the speculation associated with traditional energy storage simulations, the system is “opened” at both ends, allowing it to instantaneously buy and sell any electricity or gas that is generated. Thus, the system never stores any of the products and it buys and sells them at the instantaneously available market price. By assuming that market prices reflect all existing information, this design choice removes the necessity of having to speculate about the future in order to determine the optimum operating strategy. This approach is one of the innovations presented in this work.
The optimizations aim at maximizing the operating profits at each hour of the year, and decisions of operating mode and point are based on marginal operating costs for each electricity and natural gas price pair. The full economic analysis, however, requires the understanding of how design choices (e.g. operating limits, heat management, gas recycling systems, etc.) affect the investment costs, and therefore a Total Plant Cost (TPC) model is developed. For each design choice, the TPC model is used to compute a cost of the system per m² of active electrode area or kW of output. This value, assumed to be a sunk cost that does not affect the operating decision, together with the operating profits resulting from the optimization is used to assess the overall profitability of the system. For a system with 100m² of active electrode area, conventional costing metrics suggest that the balance of plant (BoP) components for managing the system's heat (Heat exchangers, evaporators, condensers) are the main cost drivers and represent roughly 50% of the TPC. The cost of the electrochemical RSOC stack, assembly, power inverter and piping represent 35% of the cost, with the other 15% coming from pumps, compressors and the methanation system.
Twenty different optimization scenarios are developed in order to quantify the effect that system design choices, operating limits, and market prices have on the operating profile and on the overall economics of the system. The first 12 case studies are based on real hourly spot electricity and natural gas prices for the years 2009-2014 in Western Denmark. For the last 8 scenarios, a forecasted hourly time-series for electricity in the Danish grid for the year 2050 and two fixed SNG prices (high and a low) are used. The 2050 prices, which assume a fossil fuel free system, are used to understand the role and value that RSOC systems can offer in deeply decarbonized energy systems. For each optimization, different parameters such as the initial ASR and the operating limits (maximum current densities for each mode of operation) are varied in order to find the impact that these changes have on the system's design (balance of plant components), hourly operating mode, investment costs, hourly operating profits, and overall plant profits.
For the 2009-2014 optimizations, it is found that the sale of electricity (fuel cell mode) and fuel (electrolysis mode) is not large enough to cover the fixed costs associated with the plant. Fuel cell mode dominates the operation (61% of the time) with electrolysis representing only ~ 4% of the operating hours. ASR is found to have an important impact on the system's economics, due to the fact that a lowering of the ASR leads to a reduction in the size of the heat management system, which in turn reduces the Total Plant Cost.
For the 2050 dataset, it is found that under the high gas price scenario electrolysis mode dominates (50% of the time), and fuel cell operation represents 15% of the hours in the year. For the low SNG price, electrolysis still dominates (48% of the time), and fuel cell operation increases to 30% of the operating hours. Furthermore, for the high SNG scenario, the sale of fuel and electricity are large enough to cover the system's fixed cost, making the system attractive from an investment perspective. For the low SNG price, the system also becomes profitable when using ASR values of 0.4 ASR or below.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8V988P6 |
| Date | January 2017 |
| Creators | Villarreal, Diego |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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