Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Most current model order reduction methods offer limited interpretability with regards to the nonreduced system and are not in general applicable for varying flow rates, multiple producers, or changing flow directions. This article presents a graph perspective on modeling of district energy networks. Based on spectral graph theory, a novel method for model order reduction of district energy systems is proposed. The method approximates the solution of an optimization problem, minimizing the coefficients of the local truncation error for the advection equation. Furthermore, a method for calculating the intracluster temperature distribution is presented. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution, with a significant decrease in simulation time. The relative root-mean-square error (rRMSE) was 2.5% for the temperature in the evaluation scenario, comparing the reduced-order system with the nonreduced system at the instances of the coarser time step.

This article proposes an online solution to address the problem of closed-loop system identification using multiple recursive least squares estimation protocols. Some control systems cannot be analysed in an open-loop form for stability reasons or the requirement for online control system operation. So, it is necessary to identify plant dynamics and controller parameters based on input–output data from the feedback structure. The presented method identifies real-time parameters of plant dynamics and controller parameters by utilising a series of recursive least square estimation algorithms that estimate open-loop data from noisy input–output data measured from the closed-loop feedback structure. The proposed method can effectively identify abrupt variations in both the controller parameters and plant dynamics. This capability makes it valuable for deployment as a supervisory component, enabling the detection of any faults that may arise in operating systems. Mathematical formulations and theorems are developed, and two numerical case studies are presented to examine the feasibility and performance of the presented closed-loop system identification protocol.

The supply temperature in district heating systems has traditionally been controlled using feedforward – a robust and well-validated approach for district heating networks with few producers and relatively high supply temperatures. The transition towards lower temperature district heating networks allows for efficient reuse of excess heat from, e.g., industrial processes and data centers. Excess heat is often intermittent, cannot always be assumed to be possible to control with a centralized controller, and can cause temperature disturbances in the grid. Closing the loop using PID control is challenging due to the process’s time-varying nature and long time delays. Model Predictive Control (MPC) suffers from a higher complexity, long computational times, and the need for a well-validated and maintained centralized model. The paper suggests a semi-decentralized approach using the Smith predictor with an event-driven assignment of active controllers and sensors. A reduced order model based on a more comprehensive state space model is derived and used for gain scheduling and input–output pairing using the normalized relative gain array. The focus is on temperature disturbance rejection, and appropriate tuning rules and controller structures are suggested. Simulation results show that the proposed control structure can handle various types of temperature disturbances, even in the presence of model estimation errors.

This paper presents an adaptive control allocation technique for over-actuated systems. The online actuator selection algorithm is used to select the best group of actuators. Also, a multi-objective cost function is utilized for the allocation unit. The virtual and actual control signals in the control allocation methodologies are linked by the effectiveness matrix. In many practical systems, the elements of the effectiveness matrix may vary due to changing operating conditions, nonlinearities, ageing, disturbances and faults. Hence, an online algorithm for estimation of the entries of the effectiveness matrix is presented in this paper. Estimation of the effectiveness matrix will be used for the proposed adaptive actuator selection strategy, employing the Actuator Effectiveness Index (AEI). The AEI is calculated for all the actuators, and the best group of actuators will be subsequently selected. Finally, simulation results are used to show the effectiveness of the proposed methodology.

This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.

Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Current model order reduction methods offer limited interpretability with regards to the nonreduced system, and are not in general applicable for e.g., varying flow rates, multiple producers, or changing flow directions. This article presents a novel method based on graph theory that approximates the solution of an optimization problem that minimizes the local truncation error for heat transport in the grid. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution. The relative root mean square error was 2.3 % for the temperature in the evaluation scenario, comparing the reduced-order system with the nonreduced system at the instances of the coarser time-step.

This paper investigates the security issue of the data replay attacks on the control systems. The attacker is assumed to interfere with the control system’s process in a steady-state case. The problem is presented as the standard way to attack, which is storing measurements and replaying them in further times to the system. The controller is assumed to be the LQG controller. The main novelty in this paper can be stated as proposing a different attack detection criterion by using the Kullback-Leibler (K-L) divergence method to cover more general control system problems with these attacks and with higher-order dynamics. Also, there exists a packet-dropout feature in transmitting the data as another contribution of the paper. Formulations and numerical simulations prove the effectiveness of the newly proposed attack detection procedure by having a quick response to occurred attacks with various values of the considered packet-dropout coefficient. Although, in previous approaches, the trade-off between attack detection delay or LQG the performance was significant, in this approach it is proved that the difference in this trade-off is not considered in early moments when the attack happens since the attack detection rate is rapid and thus, these attacks can be stopped with defense strategies in the first moments with the proposed attack detection criterion.

Decentralized control of MIMO system is still the dominating control strategy in industry for its ease in design and maintenance, despite its known shortcomings in performance. In this article, an online decoupling scheme of MIMO processes using an extremum-seeking approach is proposed. By applying the developed method, at first, the system's loop interactions are estimated in real-time using phasor extremum seeking, and then, a pre-compensator matrix is calculated and augmented to the process by minimizing the loop interactions. The presented online decoupling approach is a model-free method, and only the plant's inputs and outputs are analyzed to provide the decentralized control protocol. Numerical results provide the effectiveness of the developed method for a two-input, two-output (TITO) control system.