Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.

The impact of PV generation distributed in a low voltage transmission line depends on many factors: The distribution lines and PV generators characteristics, its location, operational control, local meteorological conditions, electricity consumption profile, and the electricity cost variation. An atypical and challenging behavior of photovoltaic distributed generation (DG) insertion in consumer units (CUs), implies in some circumstances, as the reverse directionality of the power flow between the load equipped with a photovoltaic system generator and the electrical grid, when a CU contains a distributed generation and low power consumption, the power flow will be directed to the power electric grid. In this work, the modeling of a low-voltage real feeder was performed, setting the variables of the system under real operating conditions. As result, voltage levels variability throughout the feeder, the electrical losses, and the asymmetry between the phases were observed. Through simulation scenarios, the occurrence of voltage increase under different penetration scenarios of distributed generation was verified and there was a 10% increase in reference voltage as well as the occurrence of higher electrical losses by reverse current, reaching 1200% more with a DG penetration, in the massive presence of the photovoltaic generator. The mitigatory action used in this work was able to attenuate the negative impacts to the feeder circuit, ensuring the integrity grid and the consumer unit.

Among the electrical problems observed from the solar irradiation variability, the electrical energy quality and the energetic dispatch guarantee stand out. The great revolution in batteries technologies has fostered its usage with the installation of photovoltaic system (PVS). This work presents a proposition for voltage regulation for residential prosumers using a set of scalable power batteries in passive mode, operating as a consumer device. The mitigation strategy makes decisions acting directly on the demand, for a storage bank, and the power of the storage element is selected in consequence of the results obtained from the power flow calculation step combined with the prediction of the solar radiation calculated by a recurrent neural network Long Short-Term Memory (LSTM) type. The results from the solar radiation predictions are used as subsidies to estimate, the state of the power grid, solving the power flow and evidencing the values of the electrical voltages 1-min enabling the entry of the storage device. In this stage, the OpenDSS (Open distribution system simulator) software is used, to perform the complete modeling of the power grid where the study will be developed, as well as simulating the effect of the overvoltages mitigation system. The clear sky day stored 9111 Wh/day of electricity to mitigate overvoltages at the supply point; when compared to other days, the clear sky day needed to store less electricity. On days of high variability, the energy stored to regulate overvoltages was 84% more compared to a clear day. In order to maintain a constant state of charge (SoC), it is necessary that the capacity of the battery bank be increased to meet the condition of maximum accumulated energy. Regarding the total loading of the storage system, the days of low variability consumed approximately 12% of the available capacity of the battery, considering the SoC of 70% of the capacity of each power level.

The objective of this paper was to analyze the impacts caused by the operation of voltage regulators in electrical distribution networks and to evidence the number of operations in the face of short-duration voltage variation caused by the high intermittency of the connected PV generators. A real LV and MV feeder was used, modeled in OpenDSS software, based on normative standards, adjustments, and technical maneuvers strategically used by the local utility. The analyses considered the temporal variations for the photovoltaic generators and different load demand profiles connected to the feeder. The feeder was submitted to the demand curves varying the load percentage, framing it in high and conventional (nominal) load according to the profiles of consumers and prosumers connected. The simulations made it possible to observe the exacerbated elevation in the number of maneuvers performed by the voltage regulators of the network. The single-phase voltage regulators stood out by the elevation of control operations, causing premature wear of the PV generation equipment connected to the most loaded phase. It was observed that discrepancies in the power flow in the lines and the voltage levels at the busbars. The creation of strategies and decisions to correct these impacts caused to transformers and regulators is possible.

The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Maceió (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.