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Turton, Richard

Publications and source records attributed to Turton, Richard.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multiobjective Dynamic Optimization for Optimal Load-Following of Natural Gas Combined Cycle Power Plants under Stress Constraints

With the increasing penetration of intermittent renewable energy sources into the electric grid, there is an associated need for conventional thermal power plants that are designed to operate at base-load conditions to cycle their load rapidly and frequently and operate under low-load conditions. Fast load-following operations lead to plant efficiency loss and the reduction of equipment life. A high-fidelity dynamic model of a natural gas combined cycle power plant, with rigorous equipment level submodels, is developed to capture the plant transient performance and the thermomechanical stress evolution at the high-pressure drum is computed to assess the drum life consumption. Stress evolution is modeled for the location of the edge at the drum/downcomer junction that experiences high circumferential stress amplitude during the fast load-following operation, leading to higher fatigue damage than locations considered under existing design standards. A dynamic optimization problem is solved for optimal load-following operation. Depending on the value of the stress constraint and the desired ramp rate, satisfying the desired stress constraint may be infeasible without relaxing the ramp rate. A multiobjective dynamic optimization problem is solved using a lexicographic approach that minimizes ramp rate relaxation and maximizes efficiency while satisfying stress constraints, avoiding spraying to saturation, and maintaining main steam and reheat steam temperature within bounds. It was observed that the optimal ramp rate can be highly nonlinear as opposed to the industry-standard linear ramp rate. Nonlinear optimal ramp-rate profiles not only help to avoid stress constraints that may be unavoidable by using the linear profile but also result in higher efficiency than the linear profile. Furthermore, the study shows that there is a strong tradeoff between the relaxation in the plant ramp rate and the time-average thermal efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dynamic Optimal Dispatch of Energy Systems with Intermittent Renewables and Damage Model

With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic mixed-integer linear programming optimization approach that integrates coal-fired and natural gas-fired power plants, NaS batteries for energy storage, and solar/wind energy to supply the demand. This optimization approach considers an economic goal and constraints to provide power balance while maintaining the overall damage of the natural gas combined cycle (NGCC) power plant drum under a maximum stress as well as avoiding the overheating of the NGCC superheater and reheater. Renewable curtailment levels are also retained at minimum levels. Case studies are analyzed considering different loads and renewable penetration levels. The results show that the demand was met for all cases. Grid flexibility was mostly provided by the NGCC, while the batteries were used sparingly. In addition, considering a CO 2 equivalent analysis, the environmental performance was intrinsically connected to grid flexibility and the level of renewable penetration. Stress analysis results reinforced the necessity for an equipment health-related constraint.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants

The objective of the project entitled “Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition, and Decision Capabilities for Control of Advanced Energy Plants” is to develop algorithms and methodologies for designing a biomimetic control system for optimal control of advanced energy plants. Traditionally, control systems have been designed based on assumed a priori knowledge of the process system. A number of variables are manipulated to accomplish disturbance rejection and/or servo control performance. While it is possible to adapt the process model based on available data, the control structure and the controllers rarely change. Thus, the knowledge learnt during process operation is lost or remains underutilized. In addition, the business models of today, especially for power plants, are rapidly changing. The power plant operators are being pushed to the edge due to the penetration of renewable energy derived power to the grid, the demand for high efficiency, ever-tightening environmental regulations, and requirements for increased plant availability. Thus, power plant operations need to be agile and should adapt quickly to the dynamic requirements. A number of characteristics distinguish biological systems from the traditional process control systems. Self-organization, distributed intelligence, adaptability, intelligent monitoring, cognition, and decision capabilities are some of the powerful characteristics of the biological world that can be effectively utilized in process control. At the top, the central nervous system (CNS) integrates the information from and coordinates the activities of all parts of the bodies (for bilaterian animals). Inspired by these distinct characteristics, this project seeks to develop methodologies and algorithms to accomplish: (i) self-organization of the control structure for maximizing the plant’s operating profit by mimicking the function of the cortical areas in the human brain, (ii) distributed and adaptive controllers that mimic the rule of pursuit present in ants, (iii) intelligent monitoring of the controllers powered with cognition and decision capabilities that mimic the artificial immune systems, and (iv) seamless coordination and integration in the control system that mimics the CNS.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Optimal scheduling and its Lyapunov stability for advanced load-following energy plants with CO 2 capture

In this study, a two-level control methodology consisting of an upper-level scheduler and a lower-level supervisory controller is proposed for an advanced load-following energy plant with CO 2 capture. With the use of an economic objective function that considers fluctuation in electricity demand and price at the upper level, optimal scheduling of energy plant electricity production and carbon capture with respect to several carbon tax scenarios is implemented. The optimal operational profiles are then passed down to corresponding lower-level supervisory controllers designed using a methodological approach that balances control complexity with performance. Finally, it is shown how optimal carbon capture and electricity production rate profiles for an energy plant such as the integrated gasification combined cycle (IGCC) plant are affected by electricity demand and price fluctuations under different carbon tax scenarios. As a result, the paper also presents a Lyapunov stability analysis of the proposed scheme.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimization problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.

Lexicographic optimization↗
DOE OSTI2016

Dynamic model-based sensor network design algorithm for system efficiency maximization

Here, a dynamic model-based sensor network design (DMSND) algorithm has been developed for maximizing system efficiency for an estimator-based control system. The algorithm synthesizes the optimal sensor network in the face of disturbances or set point changes. Computational expense of the large-scale combinatorial optimization problem is significantly reduced by parallel computing and by using combination of three novel strategies: multi-rate sampling frequency, model order reduction, and use of an incumbent solution that enables early termination of evaluation of infeasible sensor sets. The developed algorithm is applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with carbon capture. Even though there are more than thousand process states and more than hundred candidate sensor locations, the optimal sensor network design problem for maximizing process efficiency could be solved within couple of hours for a given budget.

47 OTHER INSTRUMENTATION↗