Intelligent operation of photovoltaic generators in isolated AC microgrids to reduce costs and improve operating conditions

Abstract

This paper addresses the challenges associated with optimizing the operation of photovoltaic distributed generators in isolated electrical microgrids. With the aim of reducing energy production and system maintenance costs and improving the microgrid operating conditions, a master--slave methodology is proposed. In the master stage, the problem of intelligently injecting active power from photovoltaic generators is solved using the continuous versions of four optimization techniques: the Monte Carlo method, the Chu \& Beasley genetic algorithm, the population genetic algorithm, and the particle swarm optimizer. Meanwhile, the slave stage evaluates the solutions proposed by the master stage by solving an hourly power flow problem based on the successive approximations method. The proposed solution methodologies are validated in two test scenarios of 10 and 27 buses to select the one with the best performance. Then, the most efficient methodology is implemented in a real isolated grid located in Huatacondo, Chile. This validation aims to assess its ability to optimize the operation of photovoltaic generators in isolated microgrids, considering variations in power generation and demand across the different seasons of the year. The study underscores the importance of financial considerations in achieving an efficient and economically viable operation of photovoltaic generation systems. Furthermore, it provides valuable input to successfully integrate non-conventional renewable energy sources into isolated electrical microgrids.