The rapid growth of wind energy has increased the need for advanced condition monitoring (CM), predictive maintenance, and remaining useful life (RUL) estimation strategies for wind turbines. In this context, digital twins (DTs) have emerged as a key tool for improving reliability, availability, and operational efficiency by integrating physical models, operational data, and artificial intelligence (AI). This paper presents a systematic literature review (SLR) aimed at analyzing the state of the art, classifying the main applications, and identifying research gaps. A rigorous search protocol was applied across scientific databases, considering inclusion and exclusion criteria and analysis categories aligned with four research questions. The results show a high concentration of studies on critical wind turbine components, a predominance of hybrid physics-based and data-driven approaches, and an increasing use of deep learning (DL) models. However, several research gaps remain, including the predominance of component-level digital twin implementations rather than system-level architectures, the lack of standardized datasets and benchmarking frameworks, and challenges related to SCADA data heterogeneity and real-time scalability. It is concluded that DTs are evolving toward more autonomous and prescriptive systems; however, they still require further maturation for widespread industrial adoption.

On-site photovoltaic (PV) and battery systems intend to improve buildings energy performance, however battery costs and monetary incentives are a major drawback for the introduction of these technologies into the electricity grids. This paper proposes an energy refurbishment of an office building based on multi-objective simulations. An innovative demand-side management approach is analyzed through the PV and battery control with the purpose of reducing grid power peaks and grid imported energy, as well as improving the project economy. Optimization results of load matching and grid interaction parameters, complemented with an economic analysis, are investigated in different scenarios. By means of battery use, the equivalent use of the grid connection is reduced by 12%, enhancing the grid interaction potential, and 10% of load matching rates can be increased. Project improvements indicate the grid connection capacity can be reduced by 13% and significant savings of up to 48% are achieved on yearly bills. The economy demonstrates the grid parity is only achieved for battery costs below 100 €/kWh and the payback period is large: 28 years. In the case with only PV system, the grid parity achieves better outcomes and the payback time is reduced by a half, making this a more attractive option.