Yana D. Severina, Vladislav A. Shakirov

Melentiev energy systems institute SB RAS, Irkutsk national research technical university

At present, the use of stand-alone hybrid renewable energy systems (HRES) combining diesel generators and renewable energy sources is an effective way to improve the efficiency of electricity supply to consumers in isolated and hard-to-teach areas. Designing of HRES associated with the need to solve an optimization problem, in which it is necessary to determine the optimal equipment configuration and their installed capacities in the context of multi-criteria. In the case of multi-criteria problem solving, a two-level approach is used in most studies: at the top level, the optimal Pareto configurations of HRES are formed using heuristic algorithms of multi-criteria optimization, at the bottom-level, the simulation of each HRES configuration are considered for detailed evaluation of each solution by a number of criteria. There is a large number of heuristic algorithms applied at the top level for planning the development of energy systems and HRES, which have both advantages and disadvantages, which creates difficulties in choosing an algorithm. This study presents an evaluation of heuristic multi-criteria optimization algorithms based on evolutionary algorithms such as NSGA-II, NSGA-III, AGE-MOEA and MOEA/D using Python and Pymoo package. To compare the algorithms, indicators were used that evaluate the Pareto set uniformity; distance between the true Pareto set and the Pareto-set formed by the heuristic algorithm; efficiency of the algorithm to achieving the best criteria evaluations; the time required to form the Pareto set. Evaluation of optimization algorithms was carried out on the example of solving the problem of development of a hybrid renewable energy system in the remote area of Sakhalin region. According to the results of evaluation of algorithms of multi-criteria sizing of HRES at the top-level of the two-level approach should be chosen heuristic algorithm NSGA-II, as it allows to obtain the Pareto set of high quality, ensure the achievement the minimum estimates on criteria during less time than other algorithms.

heuristic algorithms, multi-criteria optimization, hybrid renewable energy systems, Pareto set