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Operational Scheduling of Power Plants with Flexible Carbon Capture under Uncertain Electricity Prices
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The use of fossil fuels to meet the global energy demand has led to a significant increase in CO2 emissions. CO2 emissions from coal-fired power plants, in particular, constitute a major part of the global greenhouse gas (GHG) emissions. Capturing CO2 from power plant flue gas using solvent-based absorption process is one of the most effective options of reducing emissions. However, the high energy requirement of solvent regeneration and CO2 compression prevents widespread deployment of the technology. This can be mitigated by flexible operation of the capture process in response to dynamic variation in electricity prices. The literature in the dynamic scheduling of power plants with flexible carbon capture systems mostly assumes perfect foreknowledge of electricity prices. However, electricity markets exhibit high uncertainty in reality, and it is necessary to account for price uncertainty in optimal decision-making. In this work, we consider a pulverized coal-fired power plant retrofitted with a carbon capture unit, which varies its load with variation in electricity price. We first pose a deterministic problem that aims to maximize profit assuming complete knowledge of prices in the day-ahead electricity market. This is then extended to incorporate price uncertainty. We apply a multi-stage stochastic programming approach to determine an optimal hourly schedule of power production and carbon capture operations, while meeting a strict regulation on CO2 emissions. Since hourly electricity prices can assume a range of values, we need to consider a large number of price scenarios. To reduce the resulting computational complexity in the optimization framework, we develop low-complexity surrogate models for optimal action policy at each stage. These models are then used to determine total optimal profit for different real-time scenarios of electricity price. Our approach is able to obtain solutions with the expected value of perfect information under uncertainty within 25% of the maximum achievable profit, while keeping CO2 emissions sufficiently below the threshold limit.
Zantye, Manali Sunil (2019). Operational Scheduling of Power Plants with Flexible Carbon Capture under Uncertain Electricity Prices. Master's thesis, Texas A&M University. Available electronically from