• Energy Research

Model predictive control (MPC) is widely used in resource optimization problems because it naturally deals with bounded controls and states and allows predictive information to be included. However, at each sampling instant, an optimization problem must be solved. Resource optimization problems with switching control actions naturally lead to optimization problems with integer decision variables, which are computationally costly, particularly when the number of variables is large. As a result, the approach of directly discretizing (DD) the problem to derive a mixed-integer linear program (MILP) sets fundamental limitations on the MPC sampling rate owing to the computational time required to solve the optimization problem. In this paper, we propose a two-scale optimization algorithm (TSOA) for MPC. On the first-scale, the entire prediction horizon is considered and the algorithm provides the optimal resources to be used at each interval with a constant weighting cost. This optimization problem may be cast as a linear program (LP); thus, it is computationally tractable even for a large number of variables and constraints. In the secondscale, the switching nature of the decision variable is recovered by posing an MILP to deploy the optimal resources computed in the previous scale. In this manner, the MILP is solved for a shorter time interval than the entire prediction horizon, thus reducing the number of variables in the optimization problem. The simulation results demonstrate the computational advantages of the proposed algorithm compared to direct problem discretization and optimization.

The development of photovoltaic (PV) technology is now a reality. The inclusion of lithium-ion batteries in grid-connected PV systems is growing, and the sharp drop in prices for these batteries will enable their use in applications such as PV water pumping schemes (PVWPS). A technical solution for the monitoring and tracking of PV systems is shown in this work, and a novel quasi-real-time monitoring system for a PVWPS with a Li-ion battery is proposed in which open-source Internet of Things (IoT) tools are used. The purpose of the monitoring system is to provide a useful tool for the operation, management, and development of these facilities. The experimental facility used to test the monitoring system includes a 2.4 kWpk photovoltaic field, a 3.6 kVA hybrid inverter, a 3.3 kWh/3 kW lithium-ion battery, a 2.2 kVA variable speed driver, and a 1.5 kW submersible pump. To address this study, data acquisition is performed using commercial hardware solutions that communicate using a Modbus-RTU protocol over an RS485 bus and open software. A Raspberry Pi is used in the data gateway stage, including a PM2 free open-source process manager to increase the robustness and reliability of the monitoring system. Data storage is performed in a server using InfluxDB for open-source database storage and Grafana as open-source data visualization software. Data processing is complemented with a configurable data exporter program that enables users to select and copy the data stored in InfluxDB. Excel or .csv files can be created that include the desired variables with a defined time interval and with the desired data granularity. Finally, the initial results of the monitoring system are presented, and the possible uses of the acquired data and potential users of the system are identified and described.

This paper proposes a new on-site technique for the experimental characterization of small wind systems by emulating the behavior of a wind tunnel facility. Due to the high cost and complexity of these facilities, many manufacturers of small wind systems do not have a well knowledge of the characteristic λ - C p curve of their turbines. Therefore, power electronics converters connected to the wind generator are usually programmed with speed/power control curves that do not optimize the power generation. The characteristic λ - C p curves obtained through the proposed method will help manufacturers to obtain optimized speed/power control curves. In addition, a low cost small wind emulator has been designed. Programmed with the experimental λ - C p curve, it can validate, improve, and develop new control algorithms to maximize the energy generation. The emulator is completed with a new graphic user interface that monitors in real time both the value of the λ - C p coordinate and the operating point on the 3D working surface generated with the characteristic λ - C p curve obtained from the real small wind system. The proposed method has been applied to a small wind turbine commercial model. The experimental results demonstrate that the point of operation obtained with the emulator is always located on the 3D surface, at the same coordinates (rotor speed/wind speed/power) as the ones obtained experimentally, validating the designed emulator.