Abstract

The work is devoted to the development of a methodology that allows combining qualitative and quantitative assessments of different risk factors to make an integrated decision when renewable energy sources connect to the electricity supply system. The integration of quantitative information and qualitatively presented knowledge through the creation of appropriate algorithms can significantly expand the possibilities of risk analysis of renewable energy sources, established energy systems and justification of measures to manage them.
 The key features of large-scale and complex systems are the diversity and interdependence of the elements included in them, the large number of decision-makers in conditions of high uncertainty and the uniqueness of these objects. Therefore, the study describes the methods and approaches that can be used to analyze the risks of complex systems. In conditions of high uncertainty and complexity of large-scale systems, many solutions can be obtained by using qualitative assessments based on the knowledge and experience of experts.
 Maintaining the reliability, stability and efficiency of the electrical system is becoming a difficult issue for distribution networks with variable energy resources. Further movement in the direction of increasing the share of solar power plants (SPP) in the generation of the power system is a great challenge for effective management of the distribution network and a serious threat to its normal functioning.
 The article identifies the main factors that lead to the problems of integration of SPP. Also, the probability of occurrence and the level of possible consequences of the implementation of each of the identified risk factors were assessed by using the method of expert assessments.
 According to the results of the study, the main problems in integrating SPP into the power supply system are increasing shunting power, deteriorating electricity quality, the need to modernize lines, increasing debt on the "green" tariff, higher harmonics and reactive power flow.

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