Abstract
The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points was shown to be feasible with corresponding savings in coal consumption of 184.8 tons/day and a carbon footprint decrease of 0.035 kg/kWh.
Highlights
The excessive emissions of CO2 from fossil-fueled power plants contribute to the greenhouse effect and global warming
The generic formulation of the dynamic optimization problem solved for the power plant of Figure 1 is presented in Equation (3), where f is the system of differential algebraic equations describing the conservation of mass and energy; sp x is the vector of temporal state variables; x0 is the vector of initial state variables; yO is the temporal set points of the optimization controllers; y are the temporal system’s outputs; tn is the vector of control action time points with a constant interval, τn ; τ is the optimization horizon; and t is the time
The static optimization of the power plant operating at full load with the optimization formulation of Equation (2) is discussed first, followed by the dynamic optimization of the power plant operating under a time-varying power load with the optimization formulation of Equation (3)
Summary
The excessive emissions of CO2 from fossil-fueled power plants contribute to the greenhouse effect and global warming. Supervisory control is a promising approach for the efficiency optimization of power plants, wherein there exists a large number of regulatory controllers that must be maintained for safety and performance reasons In one such effort, Sáez et al [26] developed a supervisory algorithm based on adaptive predictive control to optimize the operation of the gas turbine of a combined cycle power plant in Chile. Fuel savings of 1.7–3.7% were shown to be feasible through the manipulation of set points of the regulatory controllers of the steam pressure, gas turbine power and steam turbine power These efforts focused mostly on the optimization of a few power plant components instead of solving a problem that maximizes the power plant efficiency by using all or most of the degrees of freedom. This can enable plant operators to operate power plants efficiently at variable load demands which becomes increasingly important with the higher grid-penetration of renewables
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