Renewable Energy Sources (RESs) are characterized by high unevenness, cyclicality, and seasonality of energy production. Due to the trends in the production of electricity itself and the utilization of hydrogen, distributed generation systems are preferred. They can be connected to the energy distribution network or operate without its participation (off-grid). However, in both cases, such distributed energy sources should be balanced in terms of power generation. According to the authors, it is worth combining different RESs to ensure the stability of energy production from such a mix. Within the mix, the sources can complement and replace each other. According to the authors, an effective system for generating energy from RESs should contain at least two different sources and energy storage. The purpose of the analyses and calculations performed is to determine the characteristics of energy generation from a photovoltaic system and a wind turbine with a specific power and geographical location in the Lublin region in Poland. Another important goal is to determine the substitutability of the sources studied. Probabilistic analysis will be used to determine the share of given energy sources in the energy mix and will allow us to estimate the size of the stationary energy storage. The objective of these procedures is to strive for the highest possible share of renewable energy in the total energy required to charge electric vehicle fleets and to produce low-emission hydrogen for transportation. The article proves that the appropriately selected components of the photovoltaic and wind energy mix located in the right place lead to the self-balancing of the local energy network using a small energy storage. The conclusions drawn from the conducted research can be used by RES developers who intend to invest in new sources of power generation to produce low-emission hydrogen. This is in line with the current policy of the European Union aimed at climate and energy transformation of many companies using green hydrogen.

Smart Grid, integrating modern information and communication technologies with traditional power infrastructure, is already widely used in many countries around the world. Its domain is generating large amounts of energy and, at the same time, measuring data from various sources, especially Renewable Energy Sources. Acquiring measurement data from generators and power receivers requires appropriate infrastructure and tools. An even greater challenge is the effective processing of measurement data in order to obtain information helpful in energy management in Smart Grid. The article will present an effective method of acquiring and processing measurement data from a photovoltaic system with a peak power of 50 kWp supplying the administrative building of the university. Unsupervised clustering will be used to create signatures of both generated and consumed power. Analysis of the relationships between measured network parameters in the three-state space allows for a quick determination of the power generated by the photovoltaic system and the power needed to power the building. The applied approach can have a wide practical application, both in Energy Management in institutional buildings. It can also be successfully used to train AI algorithms to categorize operating states in Smart Grid. The traditional and AI-assisted algorithms used by the authors are used to obtain practical information about the operation of Smart Grid. Such expert-validated knowledge is highly desirable in Advanced Process Control, which aims to optimize processes in real time.