A bibliometric analysis was conducted to examine the trends and developments in the field of Virtual Power Plants (VPPs) from 2000 to June 2022. The selection and identification of data involved a systematic search resulting in 1245 articles for bibliometric analysis after applying the inclusion and exclusion criteria. Strategic diagrams, overlay graphs, and evolution maps were used to analyze the trends and themes in different periods. The analysis reveals the emergence and evolution of various themes and their interconnections. In the early periods, the focus was on energy market issues, distributed generation, and the control of Distributed Energy Resources. Themes such as microgrids, renewable energy, electric vehicles, and economic analysis have gained prominence over time. The present study also identified the introduction of new concepts such as prosumers, collaborative networks, and dynamic power plants in later periods. The performance analysis for the last period (2022) highlighted the centrality and density of themes such as power plants, renewable power plants, battery energy storage systems, and robust optimization. These themes are considered both fundamental and transverse in the research field. This study discusses the importance of VPPs and battery energy storage systems in addressing grid intermittency issues and providing auxiliary market services. The analysis also emphasized the management of the demand side and the integration of electric vehicles and Building Energy Management Systems in VPPs. Therefore, future directions for VPP research include innovative structures and topologies, big-data analysis, and diversified optimization techniques. This study provides insights into the evolution and current state of research in the field of VPPs and identifies areas for further exploration and development.

The variability of renewable energy sources, storage limitations, and fluctuations in residential demand affect the reliability of sustainable energy systems, resulting in energy deficits and the risk of service interruptions. Given this situation, the objective of this study is to diagnose and optimize the reliability of a residential microgrid based on photovoltaic and wind power generation and battery energy storage systems (BESSs). To this end, genetic algorithms (GAs) and ant colony optimization (ACO) are used to evaluate the performance of the system using metrics such as loss of load probability (LOLP), loss of supply probability (LPSP), and availability. The test system consists of a 3.25 kW photovoltaic (PV) system, a 1 kW wind turbine, and a 3 kWh battery. The evaluation is performed using Python-based simulations with real consumption, solar irradiation, and wind speed data to assess reliability under different optimization strategies. The initial diagnosis shows limitations in the reliability of the system with an availability of 77% and high values of LOLP (22.7%) and LPSP (26.6%). Optimization using metaheuristic algorithms significantly improves these indicators, reducing LOLP to 11% and LPSP to 16.4%, and increasing availability to 89%. Furthermore, optimization achieves a better balance between generation and consumption, especially in periods of low demand, and the ACO manages to distribute wind and photovoltaic generation more efficiently. In conclusion, the use of metaheuristics is an effective strategy for improving the reliability and efficiency of autonomous microgrids, optimizing the energy balance and operating costs.

Solar energy is one of the most important renewable energy sources due to its wide availability and applicability. One way to use this resource is by building-integrated photovoltaics (BIPV). Therefore, it is essential to develop a scientific map of BIPV systems and a comprehensive review of the scientific literature that identifies future research directions. For that reason, the bibliometric research methodology enables the quantification and evaluation of the performance, quality and influence of the generated maps and their elements. In this regard, an analysis of the scientific production related to BIPV, indexed from 2001 to 2022, was carried out using the Scopus database. This was done using a scientific mapping approach via the SciMAT tool to analyze the co-occurrence of terms through clustering techniques. The BIPV was integrated with the themes of buildings, investments, numerical models, office buildings, photovoltaic modules, roofs, solar cells and zero-energy buildings. As photovoltaic technology progresses, the production of flexible PV elements is increasing in lieu of silicon substrate-based PV elements, and this is of current scientific interest. The evaluations of BIPVs in various climatic contexts are encouraging in warm and sunny climates. BIPVs demonstrated high-energy generation, while in temperate climates, BIPV windows exhibited a reduction in heating and cooling loads, indicating notable efficiency. Despite significant benefits, BIPVs face challenges such as upfront costs, integration complexities and durability concerns. Therefore, silicon solar cells are considered a cross-cutting theme within the BIPV research field. It is highlighted that this study provides a comprehensive scientific mapping and critical review of the literature in the field of BIPV systems. This bibliometric analysis not only quantifies the performance and quality of the generated maps but also identifies key thematic areas that have evolved.

Forecast models play a fundamental role in anticipating the effects of the energy demand in buildings to addressing the energy crisis. A forecast model for anticipating from one to three days every 30 min of the building energy demand is presented. In this model, a feed-forward artificial neural network (ANN) is combined with bootstrap aggregation techniques, using a Box–Cox transformation, seasonal and trend decomposition using loess, and a moving block bootstrap technique. An analysis was conducted using the data provided by a building’s energy demand; the data were collected during a period of four months, with readings every 10 s and averages of the values obtained every 30 min. The feed-forward neural-network method combined with bootstrap aggregation techniques consistently outperformed the forecasting accuracy of the original feed-forward neural network through cross-validation in the root mean square error (RMSE) and the mean absolute percentage error. From cross-validation in-sample period, used for the initial parameter estimation and model selection, it is concluded that a feed-forward neural network with the original data gives a slightly lower RMSE compared with the data generated by bootstrapped versions. However, in the cross-validation out-of-sample period used to evaluate forecasting performance, it has better consistency throughout all horizons for the ANN model combined with bootstrap aggregation techniques than for the ANN original model. The results are statistically significant according to the Ljung–Box test, which verifies that the forecast errors are not correlated and validates the proposed model.