Dynamic Optimization of Startup and Load-Increasing Processes in Power Plants—Part I: Method

Abstract

The fast startup and load-increasing process of power plants is a complex task involving several restrictions that have to be fulfilled simultaneously. An important restriction is the maximum allowed thermal stress of the steam generator pipes and the steam turbines caused by temperature gradients. In this paper the startup process is treated as a dynamic optimization problem. Any appropriate objective function can be used in this optimization problem. Examples include the minimization of fuel consumption or the minimization of the time required to reach the desired load. The maximum allowable temperature and pressure gradients in major plant components appear as additional constraints. In this paper a general method for solving these problems is presented: The dynamic process model, consisting of first-order ordinary differential equations (ODEs) and algebraic equations, is discretized over the time horizon using well established methods for the solution of ODEs. Thus, the continuous dynamic optimization problem is transformed into a large-scale non-linear parameter optimization problem with up to 20,000 optimization parameters and constraints. Such parameter optimization problems can be solved with appropriate sequential quadratic programming (SQP) methods that have become available lately. An application of this method is presented in the second part of this study by optimizing the process of rapid load increase in a single-pressure combined-cycle power plant on the basis of a simplified model.

Intelligent Start-Up Schedule Optimization System for a Thermal Power Plant

“…The steam turbine increases its speed again toward the rated speed (3600 rpm), and maintains this speed during the high-speed heat soak time, 3 x . After synchronizing, the steam turbine goes to an initial load of 3%, and maintains this load during the initial-load heat soak time, 4 x . Before completing the high-speed heat soak operation, the first gas turbine increases its load to a predefined load of 40% so that the steam flow rate is secured to drive the steam turbine.…”

“…This is due to the thermal stress that develops in the steam turbine rotors, which is a factor particularly decisive in reducing the start-up time and fuel consumption rate. Therefore, four schedule variables are selected, i.e., the steam turbine acceleration rate, 1 x , low-speed heat soak time, 2 x , high-speed heat soak time, 3 x , and initial-load heat soak time, 4 x ; these are shown in Fig. 2.…”

“…A genetic algorithm with enforcement operation to search along the edges of the operational constraints has been proposed (3) . Furthermore, a dynamic optimization technique using a simultaneous nonlinear programming formulation has been applied (4), (5) . However, all these studies have aimed to optimize a single-objective function (e.g., only the start-up time is minimized under the operational constraints).…”

An Improved Finite Element Meshing Strategy for Dynamic Optimization Problems

“…When using Radau points to configure, the algebraic precision is lower than the Gauss point, 2 − 2. The discretization proposition based on Radau point has better stability [15]. Therefore, this paper uses Radau points to configure.…”

Real-Time Operation Management of CHP System in Manufacturing Industry

“…This is usually formulated as a deterministic optimization problem whereas there is a possibility that operating environment conditions deviate from what is expected at the operation stage (Mitra et al, 2012;Gamou et al, 2002). Consequently, the operational management scheme should be capable of including not only the current system condition but also potential future dynamics along the rolling control time horizon (CTH) (Bausa & Tsatsaronis, 1999;Douglass, 2002;Mayhorn et al, 2012;Vallianou & Frangopoulos, 2012).…”