A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Application of PMUs to Coordinate Hydroelectric and Wind Power Plant Operation

It is acknowledged that wind power is a stochastic energy source compared to hydroelectric generation which is easily scheduled. In this paper a scheme for coordinating wind power plant and hydroelectric power plant is presented by using PMUs to measure and control the state of wind and hydro power plants. Hydroelectric generation is proposed as a method of energy reserve and compensation in the context of wind power fluctuation in order to avoid full or partial curtailment of wind generation to benefit wind providers. The feasibility of this proposed scheme is investigated by power flow calculation and stability analysis using the IEEE 30-bus power system model.

Process simulation of co-firing torrefied biomass in a 220 MWe coal-fired power plant

Torrefaction based co-firing in a pulverized coal boiler has been proposed for large percentage of biomass co-firing. A 220 MWe pulverized coal-power plant is simulated using Aspen Plus for full understanding the impacts of an additional torrefaction unit on the efficiency of the whole power plant, the studied process includes biomass drying, biomass torrefaction, mill systems, biomass/coal devolatilization and combustion, heat exchanges and power generation. Palm kernel shells (PKS) were torrefied at same residence time but 4 different temperatures, to prepare 4 torrefied biomasses with different degrees of torrefaction. During biomass torrefaction processes, the mass loss properties and released gaseous components have been studied. In addition, process simulations at varying torrefaction degrees and biomass co-firing ratios have been carried out to understand the properties of CO₂ emission and electricity efficiency in the studied torrefaction based co-firing power plant. According to the experimental results, the mole fractions of CO ₂ and CO account for 69-91% and 4-27% in torrefied gases. The predicted results also showed that the electrical efficiency reduced when increasing either torrefaction temperature or substitution ratio of biomass. A deep torrefaction may not be recommended, because the power saved from biomass grinding is less than the heat consumed by the extra torrefaction process, depending on the heat sources.