This paper proposes a parallelized online optimization of low voltage distribution network (LVDN) operation. It is performed on a graphics processing unit (GPU) by combining the optimization procedure with the load flow method. In the case study, performed for the test LVDN with distributed generators (DGs) and controllable loads, differential evolution optimization based on a backward–forward sweep load flow method was parallelized on GPU. The goal of online optimization is to keep the LVDN voltage profile within the prescribed limits, to minimize LVDN losses, and to enable demand response functionality. This is achieved by the optimization determined reference values for the controllable load’s operation, and the reactive power generation, and active power curtailment of DGs. The results show that the parallelized GPU implemented optimization can be significantly faster than similar implementation on a central processing unit and is, therefore, suitable for the online optimization of the presented LVDN.
COBISS.SI-ID: 20280342
Proliferation of distributed generation units, integrated within the distribution network requires increased attention to their proper placements. In urban areas, buildings' rooftops are expected to have greater involvement in the deployment of PV (photovoltaic) systems. This paper proposes a novel procedure for determining roof surfaces suitable for their installation. The PV potential of roof surfaces is assessed based on Light Detection And Ranging (LiDAR) data and pyranometer measurements. Then, the time-dependent PV generation profiles, electricity distribution network configuration, and time dependent loading profiles are used together over time-steps for selecting those roof surfaces with the highest PV potential, which would lead to the highest reduction of network losses per year. The presented procedure was implemented within a real urban area distribution network. The results obtained confirmed that PV potential assessment could be an insufficient criterion when selecting those roof surfaces suitable for the installation of PV systems. In order to obtain relevant results, network configuration and time-dependent loading and generation profiles must be considered as well.
COBISS.SI-ID: 19317526
The paper investigates a control approach for achieving reliable zerovoltage switching transitions within the entire operating range of a conventional nonisolated bidirectional dc-dc converter that utilizes synchronous rectification. The approach is based on operation in the discontinuous conduction mode with a constant reversed current of sufficient amplitude, which is achieved by load-dependent variation of the switching frequency. This paper focuses on the obtained resonant voltage transitions and provides analytical models for determining the reversed current and timing parameters that would ensure safe, reliable, and highly efficient operation of the converter. In addition, the proposed approach solves the synchronous transistor's spurious turn-on and body diode reverse recovery induced issues, does not require any additional components or circuitry for its realization, and can be entirely implemented within a digital signal controller. The effectiveness and performance of the presented control approach was confirmed in a 1-kW experimental bidirectional dc-dc converter that achieved 97% efficiency over a wide range of output powers at switching frequencies above 100 kHz.
COBISS.SI-ID: 18799382
This paper describes a complete digitally controlled dc-dc buck converter performed by field-programmable gate array (FPGA) circuitry. The voltage and current-mode control is based on a voltage control oscillator (VCO) performed measurements regarding output-voltage and inductor current, respectively. This measurement principle also uses digital-counters (digital-integrators) in order to obtain integral values for the outputvoltage and inductor current. In analog current-mode control, the instantaneous inductor current-value-measurement is used for the switching action. When the VCO is used for the inductor current measurement, the integral is measured during the switching-on time set as an observation interval and the switching action occurs based on this measurement. Such a principle enables full digitalization of the voltageand current-control loop and also the used measurement principle is capable of rejecting the switching disturbances during current and voltage measurements. All the tasks for current and voltage control were implemented within the FPGA. The algorithm was verified by simulation and experimentation at a switching-frequency of 25kHz.
COBISS.SI-ID: 17671446
One of the major challenges today is assessing the suitability of PV (photovoltaic) systems' installations on buildings' roofs regarding the received solar irradiance. The availability of aerial laser-scanning, namely LiDAR (Light Detection And Ranging), means that assessment can be performed automatically over large-scale urban areas in high accuracy by considering surfaces' topographies, long-term direct and diffuse irradiance measurements, and influences of shadowing. The solar potential metric was introduced for this purpose, however it fails to provide any insights into the production of electrical energy by a specific PV system. Hence, the PV potential metric can be used that integrates received instantaneous irradiance which is then multiplied by the PV system's efficiency characteristics. Many existing PV potential metrics over LiDAR data consider the PV modules' efficiencies to be constant, when in reality they are nonlinear. This paper presents a novel PV potential estimation over LiDAR data, where the PV modules' and solar inverter's nonlinear efficiency characteristics are approximated by modelled functions. The estimated electrical energy production from buildings' roofs within an urban area was extensively analysed by comparing the constant and nonlinear efficiency characteristics of different PV module types and solar inverters. The obtained results were confirmed through measurements performed on an existing PV system.
COBISS.SI-ID: 17543702