Integration of solar energy by nature-inspired optimization in the context of circular economy

The purpose of this paper is to make a research on solar power plants integration in an electric power system, taking into account all costs arising from circular economy criteria. Four scenarios of solar power plant installation are analyzed. The methodology relies on nature-inspired optimization. The evolutionary multiobjective genetic algorithm is applied. The optimization is based on the costs of electricity production, encompassing not only the costs of technology, but also the costs of environmental protection and sustainable development. These costs are included in the model and the objectives are formulated accordingly. The objectives are based on maximization of electricity production up to the level of demand, minimization of total electricity costs and minimization of greenhouse gas emissions. In addition, grid losses as well as other limitations and constraints of the electric power system are included into the model. The loss distribution is calculated in proportion to the distance between the supplier and the end user. The solar power plant losses equal zero, because it is assumed that the plant is located in a local community, close to consumers. The results show that the emissions and costs are higher without an introduced solar power plant. Introduction of solar capacities reduces the costs and emissions to a certain level. The developed model may be useful as a decision-support system generator.