Application Of Stable Nonlinear Economic Model Predictive Control In Power Generation Systems

Due to continuous energy consumption growth and despite the environmental protection efforts,thermal power plants are still essential for a reliable power supply to fulfill variable load requests although renewable energy output has quickly increased.Besides,wind energy is generally recognized as one of the most nature-friendly energy resources and has the fastest growth rate compared to other renewable generation resources.Therefore,enhancing the power generation systems(thermal and wind power generation systems)efficiency has become more crucial to meet the growing energy demand.The optimal control design of these power generation systems is necessary to ensure feasibility and high response capability under demand load variations and wind speed variations.Using the traditional linear control techniques realization of this task is difficult,as these power generation systems have strong nonlinearities.With the gradual increase in the installed capacity of these power generation systems,more and more attention has been paid to the economy of power plants.Nowadays,the major concern for the power plants’ operation has been shifted from entirely tracking control problems to environmental and economic issues.The economic performance of these power generation systems is generally managed in a hierarchical model predictive control architecture,wherein the low layer fulfills the dynamic tracking performance while the upper layer fulfills the economic performance.Nevertheless,this architecture achieves steady-state optimization,but it may ignore the dynamic optimization of the process.In contrast,the input constraints of the system are forced by physical limitations,whereas the state constraint cannot be fulfilled all time due to unanticipated disturbances that may place the states aside from the feasible region.Considering the earlier efforts made to appropriately constitute the economic MPC(EMPC)framework for these power generation systems,four additional issues need to be addressed for the effective performance of these power generation control systems:(1)Although the previous modeling techniques have a good steady-state control performance,they are based on modeling approximations,which may cause deterioration of the dynamic control performance;(2)An appropriate technique(e.g.,input-output feedback linearization(IOFL)method)can be utilized for overcoming modeling approximations,however,a constraint mapping algorithm should be constructed to convert the original input constraints into a new form based on the controller output variable;(3)The input-rate constraints of these power generation systems have not been considered in the previous stable MPC schemes;(4)The state constraints and disturbances of these power generation systems should be considered in the EMPC scheme design.Therefore,the main work of the thesis is as follows:(1)Proposes an optimal EMPC scheme based on the IOFL method for the boilerturbine system.By employing the IOFL method,the nonlinear boiler-turbine unit is decoupled into a new linear system,which is used for developing the proposed IOFL EMPC problem.The proposed controller is formulated in an economic quadratic programming form that considers the input and input-rate limits of the boiler-turbine unit.In addition,an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the whole prediction horizon.(2)Proposes a stable EMPC scheme based on the IOFL method for the boiler-turbine system.Here,a constraint mapping strategy that converts the actual input constraints to a new form based on the controller output variable is applied.Moreover,the stability of the EMPC scheme is ensured using a min-max MPC problem in the form of linear matrix inequality with realizing the input and inputrate constraints.Further,the IOFL EMPC scheme incorporates economic optimization and dynamic boiler-turbine unit tracking into one online framework.(3)Proposes a fuzzy soft-constrained EMPC scheme of the boiler-turbine system to demonstrate the stability and dynamic economic performance with persistent disturbance.Online linear feedback control is designed to guarantee the proposed controller’s stability and feasibility through a soft-constrained MPC problem in linear matrix inequalities form with considering the input and soft-state constraints.The proposed approach combines economic optimization and the dynamic tracking of the fuzzy control system into one online framework.(4)Proposes a partial offline strategy,with a trade-off between computation burden and optimality,for practical wind turbine control.The quasi-min-max strategy is improved by proposing a way to handle the input rate using linear matrix inequality constraints with guaranteeing the closed-loop stability of the wind turbine system.Then,a stable EMPC scheme is proposed for wind-turbine pitch control based on multi-models to provide dynamic economic performance with guaranteeing stability.A min-max MPC approach is involved to ensure the feasibility and stability of the wind-turbine system via solving an online linear matrix inequality problem with fulfilling the input and state constraints.