Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value

Power plants based on renewable sources offer environmental, technical and economic advantages. Of particular importance is the reduction in greenhouse gas emissions compared to conventional power plants. Despite the advantages, people are often opposed to the construction of these facilities due to their high visual impact, particularly if they are close to places with a great cultural and/or landscape value. This paper proposes a new methodology for identifying the most suitable geographical areas for the construction of new photovoltaic (PV) power plants in zones of special scenic or cultural interest, helping to keep the environment free from the visual intrusions caused by these facilities. From several repeated analyses, the degree of visibility of the new PV plant, the potential observation time of passing visitors, considering the route they follow and their speed, and the increase in visibility of the plants when seen totally or partially with the sky as background, are determined. The result obtained is a map showing the ranking of the geographical areas based on a variable calculated in such analyses: the Global Accumulated Perception Time (GAPT). The application of this methodology can help the different agents involved in the decision-making process for the installation of new PV plant by providing them with an objective visibility criterion

Net demand short-term forecasting in a distribution substation with PV power generation

The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared

Probabilistic photovoltaic power forecasting model based on deterministic forecasts

This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models

Electric power distribution planning tool based on geographic information systems and evolutionary algorithms

The expansion of electric distribution networks in new geographic areas is a tedious task. Once the position of the low voltage power substations has been decided, the planning engineers need to select the routes for the new power lines ensuring more efficient connections among the substations. This paper presents the methodology followed to plan the set of overhead power lines which achieves the optimal distribution network with the minimum installation and maintenance costs. The methodology is based on the use of Geographic Information Systems, which provide the needed functions to find feasible and economic routes for the new overhead power lines linking the substations, and an evolutionary algorithm which selects the optimal links. The application of the proposed methodology allows finding the optimal solution under an economic perspective in an automatic manner

Day-ahead probabilistic photovoltaic power forecasting models based on quantile regression neural networks

This paper presents the results obtained in the development of probabilistic short-term forecasting models of the power production in a photovoltaic power plant for the day-ahead. The probabilistic models are based on quantile regression neural networks. The structure of such neural networks is optimized with a genetic algorithm which selects the values for the main parameters of the neural network and the variables used as inputs. These input variables are selected among a set of variables which includes chronological, astronomical and forecasted weather variables related to the location of the power plant. The forecasts correspond to quantiles of the hourly power generation in the photovoltaic power plant for the daytime hours of the day-ahead. The forecasts are obtained in the first hours of the day, allowing their use for preparing bid offers for the day-ahead in electricity markets

Short-term net load forecast in distribution networks with PV penetration behind the meter

In recent years there has been a strong expansion of photovoltaic (PV) distributed generation systems. A high PV penetration level can cause uncertainty in the operation and management processes carried out by electric utilities, since most meters register the net load, i.e., the actual load minus the power generated by the PV systems behind the meter. The goal of this paper was to analyze the difference in the net load forecasting error achieved by models using or not using behind-the-meter PV generation data. The PV plant is connected to the lower voltage side of the power substation, representing a penetration level of more than 35% of the total load. The study shows that the best forecasting results are obtained with an indirect approach using two forecasting models, one for the total load and the other for the PV generation. However, the difference with respect to the results obtained with a unique net load forecasting model is almost negligible, which may be of special interest for power system distributors or other agents who do not have access to behind-the-meter generation data