Traditionally, proportional-integral-derivative (PID) based hydro turbine governors are mainly used due to their robustness and simple implementation. However, the weakness of these controllers is their design based on linear models and fixed parameters structure. This implies that the controller's parameters are usually calculated for the critical operating point leading to the controller's underperformance when operating away from the critical operating point. This paper introduces a Model Predictive Control (MPC) based hydro turbine's governor load/frequency controller whose linear prediction model parameters are updated depending on the operating point. The main intention of the paper is to reduce the gap between theoretical contributions and industrial practice. The experimental validation is achieved by implementing the introduced MPC algorithm to a laboratory hydro power plant's governor Programmable Logic Controller (PLC) and comparing its response with the responses of the gain-scheduled PI (GS-PI) controller, PI controller based on Particle Swarm Optimization (PSO-PI) and the controller based on exponential control law (EXP).

Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non‐linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non‐linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

One of the most important objectives of the European Union is the creation of an Internal Electricity Market (IEM) in Europe. Various national electricity market designs represent one of the major obstacles to the creation of IEM and it is necessary to coordinate national market designs among Member States in order to achieve that. Therefore, this paper gives a comprehensive overview of the electricity market design according to laws related to the electricity sector that are in force in Croatia. Additionally, the paper identifies key obstacles that hamper the development of a well-functioning electricity market in Croatia towards its further integration into the IEM. Since Croatian Power Exchange (CROPEX) is not coupled with any other power exchange, special attention was given to necessary prerequisites for market coupling with the Slovenian Power Exchange (BSP SouthPool) and with the Hungarian Power Exchange (HUPX).

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers.

The purpose of this paper is to establish a supervisory battery management system which collects measurements from the installed battery storage units and enables various battery management functions. In addition, an HMI user interface was designed to allow the user to monitor and control the batteries. The batteries communicate with the PLC via Modbus TCP/IP protocol. The communication enables the exchange of measured power and state of charge values between the PLC and batteries, as well as setting the desired setpoint values. The PLC and HMI communicate via PROFINET. The battery storage units are installed in Smart Grid Laboratory (SGLab) at the University of Zagreb Faculty of Electrical Engineering and Computing.