Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

This review provides the state of the art of energy management systems (EMS) and organizational structures of prosumers. Integration of renewable energy sources (RES) into the household brings new challenges in optimal operation, power quality, participation in the electricity market and power system stability. A common solution to these challenges is to develop an EMS with different prosumer organizational structures. EMS development is a multidisciplinary process that needs to involve several aspects of observation. This paper provides an overview of the prosumer organizational and control structures, types and elements, prediction methods of input parameters, optimization frameworks, optimization methods, objective functions, constraints and the market environment. Special attention is given to the optimization framework and prediction of input parameters, which represents room for improvement, that mitigate the impact of uncertainties associated with RES-based generation, consumption and market prices on optimal operation.

The high expansion of a variable and intermittent nature of distributed generation, such as photovoltaics (PV), can cause technical issues in existing distribution networks (DN). In addition to producing electrical energy, PVs are inverter-based sources, and can help conventional control mechanisms in mitigating technical issues. This paper proposes a multi-stage optimal power flow (OPF)-based mixed-integer non-linear programming (MINLP) model for improving an operation state in LV PV-rich DN. A conventional control mechanism such as on load tap changer (OLTC) is used in the first stage to mitigate overvoltage caused by PVs. The second stage is related to reducing losses in DN using reactive power capabilities from PVs, which defines the optimization problem as a fully centralized observed from the distribution system operator’s (DSO) point of view. The optimization problem is realized under the co-simulation approach in which the power system analyzer and computational intelligence (CI) optimization method interact through an interface. This approach allows keeping the original MINLP model without approximations and using any computational intelligence method. OpenDSS is used as a power system analyzer, while particle swarm optimization (PSO) is used as a CI optimization method in this paper. Detailed case studies are performed and analyzed over a single-day period. To study validation and feasibility, the proposed model is evaluated on the IEEE LV European distribution feeder. The obtained results suggest that a combination of conventional control mechanisms (OLTC) and inverter-based sources (PVs) represent a promising solution for DSO and can serve as an alternative control method in active distribution networks.

Infrared thermography, in the analysis of photovoltaic (PV) power plants, is a mature technical discipline. In the event of a hailstorm that leaves the PV system without the support of the power grid (and a significant portion of the generation potential), thermography is the easiest way to determine the condition of the modules and revive the existing system with the available resources. This paper presents research conducted on a 30 kW part of a 420 kW PV power plant, and demonstrates the procedure for inspecting visually correct modules that have suffered from a major natural disaster. The severity of the disaster is shown by the fact that only 14% of the PV modules at the test site remained intact. Following the recommendations of the standard IEC TS 62446-3, a thermographic analysis was performed. The thermographic analysis was preceded by an analysis of the I-V curve, which was presented in detail using two characteristic modules as examples. I-V curve measurements are necessary to relate the measured values of the radiation and the measured contact temperature of the module to the thermal patterns. The analysis concluded that soiled modules must be cleaned, regardless of the degree of soiling. The test results clearly indicated defective module elements that would result in a safety violation if reused. The research shows that the validity criterion defined on the basis of the analysis of the reference module can be supplemented, but can also be replaced by a statistical analysis of several modules. The comparison between the thermographic analysis and the visual inspection clearly confirmed thermography as a complementary method for testing PV-s.