INSPECTORMAP: A Spatial Data Infrastructure Applied to the Maintenance of Solar Plants Using Free Satellite Imagery

Photovoltaic solar plants are one of the main facilities away from urban centers for the generation of clean energy. Since its appropriate maintenance ensures its suitable operation, optimizing their maintenance tasks in a preventive way is key. This article presents a spatial data infrastructure called INSPECTORMAP that, based on the analysis of free satellite images within the optical spectrum, can detect unusual vegetation and bodies of water in the vicinity of photovoltaic plants that can affect their correct operation. Thanks to the implementation of a monitoring and alert system, it is possible to know and map the status of the photovoltaic plant in terms of unusual coverages appearing, both natural and artificial, at any moment. Thus, maintenance workers would travel to the solar plant to carry out their maintenance tasks in this regard only when the system detects a risk.

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

Power Grid Frequency Regulation Strategy for Photovoltaic Plant Based on Multi-Objective Harris Hawks Optimization

To settle the issue of balance between two objectives, i.e., photovoltaic (PV) power station output power maximization and frequency regulation (FR) signals response, a novel PV reconfiguration strategy is proposed in this work, which maximizes the output power through PV reconfiguration, and meanwhile utilizes the energy storage system (ESS) to decrease the PV plant generated power’ deviation from FR signals. Above all, a model of PV-storage power station reconfiguration is designed to minimize the power bias of both rated power and FR signals. Then, the multi-objective Harris hawks optimization (MHHO) is used to obtain the Pareto front which can optimize the above two objectives due to its high optimization efficiency and speed. Subsequently, the optimal compromise solution is selected by the decision-making method of VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Aiming to substantiate the efficacy of the proposed technique, the case studies are carried out under partial shading condition (PSC) with constant and time-varying FR signals. The simulation results show that, compared with the situation without optimization, the power deviations of the two objectives are reduced by 25.11 and 75.76% under constant FR signals and 23.27 and 55.81% under time-varying FR signals by proposed method, respectively.

Design methodology of smart photovoltaic plant

In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.

A Renewable Energy Community of DC Nanogrids for Providing Balancing Services

The massive expansion of Distributed Energy Resources and schedulable loads have forced a variation of generation, transmission, and final usage of electricity towards the paradigm of Smart Communities microgrids and of Renewable Energy Communities. In the paper, the use of multiple DC microgrids for residential applications, i.e., the nanogrids, in order to compose and create a renewable energy community, is hypothesized. The DC Bus Signaling distributed control strategy for the power management of each individual nanogrid is applied to satisfy the power flow requests sent from an aggregator. It is important to underline that this is an adaptive control strategy, i.e., it is used when the nanogrid provides a service to the aggregator and when not. In addition, the value of the DC bus voltage of each nanogrid is communicated to the aggregator. In this way, the aggregator is aware of the regulation capacity that each nanogrid can provide and which flexible resources are used to provide this capacity. The effectiveness of the proposed control strategy is demonstrated via numerical experiments. The energy community considered in the paper consists of five nanogrids, interfaced to a common ML-LV substation. The nanogrids, equipped with a photovoltaic plant and a set of lithium-ion batteries, participate in the balancing service depending on its local generation and storage capacity.