Optimal control framework for cost-effective, intelligent, renewable energy-driven communities in Norway, enhanced by ANN and grey wolf method

The present work introduces a novel yet smart energy system to decarbonize the energy mix, provide cost-effective green energy, and help to achieve sustainable energy production/storage/usage. Smart hydronic units and multiple controllers are the backbone of this idea, which aims to monitor energy conversion among components, the grid, and users through an innovative rule-based framework. This clever integration results in smaller components, bidirectional grid connection, and increased penetration of renewable energy sources into the local energy network. The system is driven by photovoltaic thermal panels and a biomass heater integrated with a heat pump and internal combustion engine. The TRNSYS-MATLAB framework evaluates the system's practicality from all aspects. Multi-objective optimization based on the grey wolf approach equipped with artificial intelligence is added to find the most optimal state. Compared to the conventional system in Norway (hydropower + wind), the proposed smart integration achieves up to 55 % and 5.4 % reduction in energy costs and emission index. In particular, the carbon emission index is lowered to 36.3 g/kWh, and the energy cost is decreased to 38.4 $/MWh. Also, the parametric analysis indicates a conflictive change among key metrics when altering a variable, necessitating the multi-objective optimization to achieve a balancing trade-off. The optimization reduces the emission index, investment cost, and energy cost by about 1.8 g/kWh (5 %), 156,000 $/year (2.5 %), and 1.5 $/MWh (4 %) while improving the efficiency by about 1.7 % compared to the design condition. The suggested system generates 17,830 MW/year of clean energy and can mitigate carbon dioxide emissions by 552,000 kg per year compared to the Norwegian energy mix.