Sustainability and more intense implementation of solar energy by nature inspired optimization

The subject of this paper is the research on wider solar energy implementation with the aim to meet the criteria of ecological management and sustainable development. In order to analyze and increase the economic and energy benefits, while at the same time respecting and improving the quality of the environment, on the local and global level, nature inspired optimization models are applied. New Energy Policy of the 2050 European Union (New Energy Policy, EU 2050) requires achieving 80-95% greenhouse gases emissions reductions, referring to 1990, which presents great challenges for mankind, taking all conflicting interests into account. This research includes development of possible optimization models which are inspired by the nature. The optimal operation of different degree of solar power plants installation was considered using evolutionary multi objective optimization algorithm. The numerous solutions are selected as the nature selects the best results in the evolution process. The bed solutions are punished and excluded, as the nature punished weakness and insufficiency. The proposed research has a wider context and applicability because the optimal scope of the installation for the implementation of solar energy, as well as the effects of carbon dioxide reduction in the entire community is calculated. The operation of the system is simulated on the base of real energy suppliers, with their minimum and maximum possible production. The defined target in production was to reach the consumption, including losses in grid from the transport of the energy. 24 hours daily pattern from the most cold winter day is selected to target the consumption requirements. Their shapes of the peaks, which are noticeable in this period, are followed. Time base is 2018 with the prognoses on 2050. Scenarios for 2050 are developed according to the literature review, assumption of possible consumption l, assumption relating to the non-renewable resources limitations and requirements in accordance with New EU Energy Policy 2050, relating to CO2 emission reduction. Optimal electricity system operation is defined by evolutionary multiple objective genetic algorithm GANetXL, as a decision-support system generator. Additional algorithms are developed with the purpose of graphical user interface that allows easy visualization of enormous number of results and better understanding and checking of decision-support applications. The results suggest that higher installation of solar energy provides the economic and environmental benefits. Counting the limitation of non-renewable coal reserves and EU Energy Policy 2050 requirements about emission reduction, the results and conclusion on justification of investment in more intense solar energy implementation in 2050 are even more on the side of feasibility