This work presents some preliminary results of a project aimed at the application of industrial Model Predictive Control (MPC) to thermal Power Plants. The rationale which motivates this research is the need to improve the efficiency of power plants to cope with the high levels of competitions induced by the liberalization of the energy market. A detailed plant simulator is coupled to an indust...

This chapter deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high-level MPC controller, a second level of so-called aggregators, which reduces the computational and communicationrelated load on the high-level control, and a lower level of autonomous consumers. The control system is tasked with balancing electric power production and consump...

This paper deals with the design and evaluation of the performance of fuzzy predictive controller in supervisory mode for a solar-concentration-based power plant, with cylindrical-parabolic solar collectors. For the design of the controller, the inner control loops present in this solar platform are included in the model of the plant. That is, a control loop consisting of a PI plus a feed-forwa...

This paper presents an integral control strategy which integrates data from phase measurement units to damp inter-area oscillations. The proposed decentralized model predictive control method has one control unit for each controllable device (Generators, FACTS, HVDC) and coordinates their behavior after a fault. Each unit is designed by applying a systematic controller synthesis. The decentrali...

• In Section 2.4.4. “Parallel versus serial schemes”, in the paragraph “Parallel implementation”, the following text: 2 (b) For all agents i ∈ {1, . . . , n}, at the same time, agent i solves the problem (2.21) to determine x̃i(k + 1) , ũi(k) , w̃in,ji(k) , w̃out,ji(k) , and sends to agent j ∈ Ni the computed values w̃out,ji(k) . should be replaced by 2 (b) For all agents i ∈ {1, . . . , n}, at the...

The present paper investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the neural network model predictive controller (NN-MPC) on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission li...

One of the main difficulties in large-scale implementation of renewable energy in exisiting power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As ...

In this brief, constrained multi variable model predictive control (MPC) strategy is investigated for a GE9001E gas turbine power plant. So the rotor speed and exhaust gas temperature are controlled manipulating the fuel command and compressor inlet guide vanes position. A nonlinear model is introduced using conventional mathematical models and ARX identification procedure as gas turbine plant ...

A closed-loop power control (CLPC) scheme with a multistep (indicating multiple prediction steps) linear autoregressive predictor is presented. The proposed CLPC relies on low-rate sample vector based autoregressive prediction. Compared to currently available predictive CLCP schemes, it demonstrates particularly robust performance in the presence of large loop delays and channel estimation errors.