(117c) Flexible Technoeconomic Analysis Tools for Evaluating Emerging Power Generation Technologies in Hourly Electricity Markets Using Idaes and Pyomo

As global electricity demands increase, the capacity must increase to meet new customer load [1] while simultaneously decreasing the carbon intensity of the power sector [2]. These emerging technologies must be competitive with respect to power generation efficiency and carbon efficiency. From a design perspective, economic analysis to compare emerging and mature technologies is required to inform research and development investment. Traditional analysis of power generation technologies relies on levelized cost of electricity (LCOE) [3]. However, LCOE oversimplifies technology performance within a complex power grid and wholesale market into a single metric. This assumption fails to take advantage of the complex hourly dynamics of the real-time electricity price within a locational market. Also, as variable renewable energy sources (i.e., wind, solar) increase potential capacity, these hourly price markets are experiencing drastic real-time price spikes or even multimodal behavior, and subsequently, considering dynamic prices will have a higher impact on the viability of candidate technologies within that market. To address this shortcoming, the price-taker assumption allows a generator to sell electricity for the hourly price at which it was generated within a locational market, enabling a more holistic operational analysis of emerging technologies [4]. However, the mathematical model required to perform price-taker analysis on a specific technology within a yearly electricity market with hourly price values is significantly more complex.

In this work, we present a framework developed to automate the construction of the price-taker model. The general model components required for price-taker analysis are: (a) cost functions (including capital costs, operating and management costs, fuel costs, CO₂ transportation costs, startup and shutdown costs), (b) revenue functions (i.e., revenue from hourly power production), and (c) operational constraints (i.e., when to turn the unit on or off, when to produce a different product, etc.). The cost estimation is conducted using the NETL QGESS approach [5]. Costs and revenues (i.e., (a) and (b)) associated with equipment operation and producing power, respectively, are combined into an objective function to either maximize a measure of net profit or minimize the cost of operation. For each technology, the functional form of (a) will change due to technology-specific attributes, including the configuration and operation of the entire system. The key performance metrics assumed, such as power generation efficiency and carbon capture efficiency, are based on the state-of-the-art processes. Also, for each market in which the technology is analyzed, the market price signal will change. However, for (c), the general form is consistent across all technologies. Within the framework, IDAES [6] and Pyomo [7] are used to organize locational marginal price data, structuring it automatically for the user, as well as constructing common operational constraints. Also, common objective functions forms (i.e., net present value and gross profit) can be constructed automatically.

To demonstrate the framework's viability, we reimplement a large-scale analysis workflow of six technologies among 61 markets (drawn from historical, present, and future yearly market price signals). Since the framework is general, we can also consider additional or alternative commodity production, in this case, hydrogen. In this way, we can rapidly analyze and compare new and emerging technologies with existing ones. We show that solid oxide fuel cell (SOFC)-based technologies present an economically viable route for greener power production and should be further explored for scale-up and industrial adoption. Also, including hydrogen production units in a generating system can create flexible energy systems that can take advantage of both high (produce power) and low (produce hydrogen) electricity prices, especially in projected markets with high renewables penetration.

The framework also facilitates important modeling questions, such as what model fidelity is sufficient to capture system costs appropriately (i.e., linear, quadratic, cubic surrogates); what granularity of price data is required to exploit the price signals to a sufficient degree; and what modifications to the price-taker model can enable more accurate consideration of how the price fluctuates due to the generator’s participation within that market. Since the tool is automated, only the cost model, the price data, and the functional form of revenue would have to change to address these questions. We demonstrate that this general price-taker tool facilitates rapid comparison of technologies, model formats, market price signals, and more.

Disclaimer: This project was funded by the Department of Energy, National Energy Technology Laboratory, an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Acknowledgment: This work was conducted as part of the Institute for the Design of Advanced Energy Systems (IDAES) with support through the Simulation-Based Engineering program within the U.S. Department of Energy’s Office of Fossil Energy and Carbon Management.

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