• Energy Research

Abstract This study considers the optimization of operations for an integrated fossil-renewable energy system with CO2 capture. The system treated consists of a coal-fired power station, a temperature-swing absorption CO2 capture facility powered by a natural gas combustion turbine, and wind generation. System components are represented in a modular fashion using energy and mass balances. Optimization is applied to determine hourly system dispatch to maximize operating profit given energy prices and wind generation data. A CO2 emission constraint, modeled after a California law, is enforced. Idealized and realistic scenarios are considered, along with several different system specifications. For a year of operation, simulated using available wind and energy price data, operating profit for optimized operation is shown to be approximately 20% greater than profit using a heuristic procedure. The benefit from optimization is positively correlated with electricity price variability and mean wind generation. The impact of different component specifications and different CO2 absorption solvents on the optimal operation of the energy system is also assessed. In total, this study demonstrates that the effective operating cost of an integrated energy system operating under a CO2 emission constraint can be substantially reduced via optimal flexible operation.

Abstract This study considers the optimization of operations for an integrated fossil-renewable energy system with CO2 capture. The system treated consists of a coal-fired power station, a temperature-swing absorption CO2 capture facility powered by a natural gas combustion turbine, and wind generation. System components are represented in a modular fashion using energy and mass balances. Optimization is applied to determine hourly system dispatch to maximize operating profit given energy prices and wind generation data. A CO2 emission constraint, modeled after a California law, is enforced. Idealized and realistic scenarios are considered, along with several different system specifications. For a year of operation, simulated using available wind and energy price data, operating profit for optimized operation is shown to be approximately 20% greater than profit using a heuristic procedure. The benefit from optimization is positively correlated with electricity price variability and mean wind generation. The impact of different component specifications and different CO2 absorption solvents on the optimal operation of the energy system is also assessed. In total, this study demonstrates that the effective operating cost of an integrated energy system operating under a CO2 emission constraint can be substantially reduced via optimal flexible operation.

Abstract Computational optimization is used to simultaneously determine the design and planned operating profile of a flexible coal–natural gas power station with CO2 capture, under a CO2 emission performance standard. The facility consists of a coal-fired power station undergoing retrofit with CO2 capture. The CO2 capture energy demand is provided by a specially designed combined cycle gas turbine (CCGT). The heat recovery steam generator (HRSG) component of the CCGT is modeled and optimized in detail, with explicit treatment of the discrete aspects of the HRSG configuration, including the number and sequential arrangement of HRSG internal components. Variable facility operations are represented by discrete operating modes selected based on the electricity price–duration curve. Two objectives, the minimization of capital requirement and the maximization of net present value, are considered in a bi-objective mixed-integer nonlinear programming formulation. Pareto frontiers, which define the optimal tradeoffs between these two objectives, are generated for six scenarios constructed from recent historical data from West Texas, the United Kingdom, and India. For a 440 MW coal plant in a scenario based on 2011 West Texas data, the minimum effective net present cost required for the retrofit (which meets the CO2 emission performance standard) varies from $278 to 383 million, and the minimum total capital investment requirement ranges from $346 to 517 million. The variations in these optimized values correspond to the range of the Pareto frontier within the bounds of the problem. The net present cost of the retrofit is less than the present value of the existing coal plant, $476 million, indicating that a retrofit is preferred over decommissioning. In the case of very low energy prices, however, decommissioning is shown to be the preferred option. The UK and India scenarios demonstrate that optimal designs can vary greatly depending upon location-specific economic conditions.

Abstract Computational optimization is used to simultaneously determine the design and planned operating profile of a flexible coal–natural gas power station with CO2 capture, under a CO2 emission performance standard. The facility consists of a coal-fired power station undergoing retrofit with CO2 capture. The CO2 capture energy demand is provided by a specially designed combined cycle gas turbine (CCGT). The heat recovery steam generator (HRSG) component of the CCGT is modeled and optimized in detail, with explicit treatment of the discrete aspects of the HRSG configuration, including the number and sequential arrangement of HRSG internal components. Variable facility operations are represented by discrete operating modes selected based on the electricity price–duration curve. Two objectives, the minimization of capital requirement and the maximization of net present value, are considered in a bi-objective mixed-integer nonlinear programming formulation. Pareto frontiers, which define the optimal tradeoffs between these two objectives, are generated for six scenarios constructed from recent historical data from West Texas, the United Kingdom, and India. For a 440 MW coal plant in a scenario based on 2011 West Texas data, the minimum effective net present cost required for the retrofit (which meets the CO2 emission performance standard) varies from $278 to 383 million, and the minimum total capital investment requirement ranges from $346 to 517 million. The variations in these optimized values correspond to the range of the Pareto frontier within the bounds of the problem. The net present cost of the retrofit is less than the present value of the existing coal plant, $476 million, indicating that a retrofit is preferred over decommissioning. In the case of very low energy prices, however, decommissioning is shown to be the preferred option. The UK and India scenarios demonstrate that optimal designs can vary greatly depending upon location-specific economic conditions.

Abstract The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

Abstract The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

Abstract Future energy systems will inevitably rely much more on variable renewable energy. This transition has implications for capital equipment in the energy gathering, processing, and end-use sectors. We define a “flexible energy strategy” (FES) as an energy capital investment and associated operating strategy that can increase usage of variable renewable energy. The literature on FES options is vast and many options have been explored, such as electrochemical storage, demand management, or flexible manufacturing. However, FESs have been difficult to compare to date because of large variation in the details of technology characteristics and possible operating strategies. We develop a purposely simplified framework for consistent inter-comparison of FESs that uses the levelized cost of peak energy (LCPE) – energy provided at times of high electricity prices. We show that various FESs which differ in many details can be represented at a more abstract level with a small number of common terms (e.g., $ per W). We develop analytical solutions for LCPE for four broad classes of FESs. We evaluate these equations for four template variability cycles with empirical FES data. Our simple framework recreates intuitive and oft-cited results from the literature (i.e., challenges of seasonal-scale variability) and points to concrete targets for energy storage technologies.

Abstract Future energy systems will inevitably rely much more on variable renewable energy. This transition has implications for capital equipment in the energy gathering, processing, and end-use sectors. We define a “flexible energy strategy” (FES) as an energy capital investment and associated operating strategy that can increase usage of variable renewable energy. The literature on FES options is vast and many options have been explored, such as electrochemical storage, demand management, or flexible manufacturing. However, FESs have been difficult to compare to date because of large variation in the details of technology characteristics and possible operating strategies. We develop a purposely simplified framework for consistent inter-comparison of FESs that uses the levelized cost of peak energy (LCPE) – energy provided at times of high electricity prices. We show that various FESs which differ in many details can be represented at a more abstract level with a small number of common terms (e.g., $ per W). We develop analytical solutions for LCPE for four broad classes of FESs. We evaluate these equations for four template variability cycles with empirical FES data. Our simple framework recreates intuitive and oft-cited results from the literature (i.e., challenges of seasonal-scale variability) and points to concrete targets for energy storage technologies.

Abstract We optimize the design and time-varying operation of a CO 2 -capture-enabled power station burning coal and natural gas, subject to a CO 2 emission intensity constraint. The facility consists of a coal-fired power plant and an amine CO 2 capture system, which is powered by a combined-heat-and-power auxiliary gas-fired subsystem. The detailed design of the CO 2 capture system, the detailed heat integration of the facility, and time-varying operations schedule across all hours of the year are determined in a single optimization problem. This problem is formulated as a bi-objective mixed integer nonlinear program: objectives include minimizing total capital requirement (TCR) and maximizing net present value (NPV). Because the Aspen Plus model used for the CO 2 capture system is too computationally intensive to use directly in optimization runs, we develop a statistical proxy model of the capture system that reproduces Aspen Plus results but is several hundred times faster. The integrated proxy model includes statistical submodels for the CO 2 absorption and solvent regeneration blocks, as well as simple physical models of other system components. Incorporating the detailed CO 2 capture system in the optimization provides important design information such as the optimal number of CO 2 capture trains required. Two scenarios are considered, based on historical data for Texas and India. Results show that the choice of objective function can have a strong effect on planned operating profile (constant or variable operations). Similarly, hourly electricity price variability strongly affects design and plant scheduling. In the West Texas scenario, which has high price variability, the maximum NPV objective favors variable operations, with a CO 2 capture system utilization factor of 65.9% (out of a maximum of 85%), while the minimum TCR objective favors constant operations. In contrast, because of low electricity price variability in the India scenario, there is little value in time-shifting the demand for capture heat, so constant operations are favored in this case for both objectives.