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The paper presents the prototype of the first Romanian Compressed Air Energy Storage (CAES) installation. The relatively small scale facility consists of a twin-screw compressor, driven by a 110 kW threephase asynchronous motor, which supplies pressurized air into a 50m3 reservoir, of 20 bar maximum pressure. The air from the vessel is released into a twin-screw expander, whose shaft spins a 132 kW electric generator. The demonstrative model makes use of a 5m3 water tank acting as heat transfer unit, for minimising losses and increasing efficiency and the electric power generated. Air compression and decompression induce energy losses, resulting in a low efficiency, mainly caused by air heating during compression, waste heat being released into the atmosphere. A similar problem is air cooling during decompression, lowering the electric power generated. Thus, using a thermal storage unit plays an essential role in the proper functioning of the facility and in generating maximum electric power. Supervisory control and data acquisition is performed from the automation cabinets. During commissioning tests, a constant stable power of around 50 kW with an 80 kW peak was recorded.

The proposed concept presents an innovative test cell control system, compatible with an existing APU (Auxiliary Power Unit) test cell. The system is essentially a Non-Propulsive Energy (NPE) Power Management Unit that needs to efficiently distribute power among an aircraft’s pneumatic and electrical loads, based on key parameters read from: loads (electrical, pneumatical), a real APU and real-time models of main engines representative to the aircraft. For this, the concept suggests a hardware & software solution, based on the approach of Artificial Neural Network (ANN). The ANN processes all inputs according to a mathematical law trained from existing data sets, such that minimal power loss is considered, given all safety levels are achieved. Development of the neural network is made such that the fastest response time and best performance consist as general goals, and the resulting control system is tested via Hardware-in-the-Loop simulation. Thus, the neural network is also designed to be safe and stable given maximum performance. The hardware solution describes all the equipment included to fulfil the objectives of the concept.

The paper presents some theoretical considerations and the related deliverable results obtained within a research approach developed for analytically inventorying the process variables for a wood pellets micro-production activity integrated with self-providing crops of energetic willow. The study is part of a larger research approach intended to accurately define the appropriate specifications for developing a sustainable small business in this area. Within the particular research approach being discussed in the paper, some conceptual models have been structured and are proposed, based on process systematic analysis, as following: a model of the research general hypotheses; a model of the general conceptual research frame; a process flow model for the considered integrated processes, respectively wood pellets micro-production and energetic willow crops; a model for sustainability groups of indicators to be considered; two models for process variables inventorying and classification, one for each of the two considered integrated processes. After the research hypotheses are simplified by considering some particular implementing conditions, a final model of the investigated variables is submitted to discussion. Some adequate conclusions revealed by the research approach, together with some directions of further research development are finally identified and presented in the paper.