Load frequency control (LFC) is one of the most challenging problems in multi-area power systems. In this paper, we consider power system formed of distinct control areas with identical dynamics which are interconnected via weak tie-lines. We then formulate a disturbance rejection problem of power-load step variations for the interconnected network system. We follow a top-down method to approximate a centralized linear quadratic regulator (LQR) optimal controller by a distributed scheme. Overall network stability is guaranteed via a stability test applied to a convex combination of Hurwitz matrices, the validity of which leads to stable network operation for a class of network topologies. The efficiency of the proposed distributed load frequency controller is illustrated via simulation studies involving a six-area power system and three interconnection schemes. In the study, apart from the nominal parameters, significant parametric variations have been considered in each area. The obtained results suggest that the proposed approach can be extended to the non-identical case.

This article is concerned with modelling, controllability analysis and the design of aggressive robust controllers for interconnected electric power systems. The proposed Load Frequency Control (LFC), relying on $H_{\mathrm{\infty }}$ methodologies enhanced with integral action and pole clustering, can provide frequency regulation and fast transient response despite the presence of unknown disturbance load application. The inherent saturation constraints are handled by the combination of a controller gain minimization framework and an anti-windup enhanced controller design which provides stability guarantees, while avoiding frequency and tie-line power oscillations. For this scheme, particular attention should be paid on the modelling and controllability aspects of the power system, where it is shown that due to the singularity of the Laplacian matrix of the network's graph, a reduction of the state vector is necessary. Simulation studies are offered to illustrate the effectiveness of the suggested scheme.

Exothermic chemical reaction taking place in continuous stirred tank reactor is considered. Heat release from the chemical reaction, non-linear dynamic behavior of the process and uncertainty in parameters are the main factors motivating the use of robust control design. Viewing temperature and molar concentration as variables both accessible in real time, PI and optimal state-feedback controllers driven by temperature and concentration error signals are proposed to regulate the system over reactor’s steady-state working points by counteracting undesired disturbances. Since access to concentration value has proved beneficial for the reactor’s performance, estimation techniques are examined to compensate for the problematic nature of the concentration’s measurement. A linear reduced-order observer is first proposed to estimate the concentration value using temperature measurements. In addition, assuming concentration measurement is available with a relatively short delay via sample analysis, a linear and non-linear discrete-time predictor is constructed to estimate the concentration’s real-time value. A linear combination of the two estimation schemes (observer, predictor) is proposed resulting in a combined estimator, in which the emphasis between the two individual schemes can be controlled via a scalar parameter. The work presented in this paper was supported by the GLOW project – New weather-stable low gloss powder coatings based on bifunctional acrylic solid resins and nanoadditives – as part of the development of novel and efficient processing technologies regarding the production of new families of powder coatings, responding to industrial requirements for quality improvement at lower cost and shorter development cycles.

Two well-established complementary distributed linear quadratic regulator (LQR) methods applied to networks of identical agents are extended to the non-identical dynamics case. The first uses a top-down approach where the centralized optimal LQR controller is approximated by a distributed control scheme whose stability is guaranteed by the stability margins of LQR control. The second consists of a bottom-up approach in which optimal interactions between self-stabilizing agents are defined so as to minimize an upper bound of the global LQR criterion. In this paper, local state-feedback controllers are designed by solving model-matching type problems and mapping all the agents in the network to a target system specified a priori. Existence conditions for such schemes are established for various families of systems. The single-input and then the multi-input case relying on the controllability indices of the plants are first considered followed by an LMI approach combined with LMI regions for pole clustering. Then, the two original top-down and bottom-up methods are adapted to our framework and the stability problem for networks of non-identical dynamical agents is solved. The applicability of our approach for distributed network control is illustrated via a simple example.

The paper proposes a distributed LQR method for the solution to regulator problems of networks composed of dynamically dependent agents. It is assumed that these dynamical couplings among agents can be expressed in a state-space form of a certain structure. Following a top-down approach we approximate a centralized LQR optimal controller by a distributed scheme the stability of which is guaranteed via a stability test applied to convex combination of Hurwitz matrices. The method is applied to N-identical-area power grid where a distributed state-feedback Load Frequency Controller (LFC) is proposed to achieve frequency regulation under power demand variations. An illustrative numerical example demonstrates the applicability of the method.

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size.