Comparative pso optimisation of microgrid management models in island operation to minimise cost




HIGHLIGHTS
  • What: The aim is to minimise production costs while ensuring a reliable energy supply. In this study the performance of both algorithms is evaluated through simulation in different operating scenarios. By conducting a series of simulations, the authors evaluate the effectiveness of these algorithms in minimising production costs and improving energy management. In this paper, a detailed simulation model for centralised microgrid management is developed considering all relevant components such as photovoltaic panels, battery storage systems, diesel generators and bidirectional converters.
  • Who: Dubravko Žigman and colleagues from the Department of Electrical Engineering, Zagreb University of Applied Sciences, Konavoska , have published the research: Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost, in the Journal: Energies 2024, 3901 of /2024/
  • How: This paper presents a comparative analysis of two optimisation algorithms P and M70 used for the optimal control of the operation of microgrids in islanded mode. The authors have decided to use PSO in the research to take advantage of its fast and efficient optimisation capabilities for energy management in microgrids . Although the approach also requires careful parameter tuning and can be computationally intensive it offers significant improvements in terms of responsiveness and overall efficiency setting it apart from other methods investigated. This paper presents a comprehensive analysis of existing microgrid management models focusing on their optimisation methods.
  • Future: Section 5 concludes the paper and outlines directions for future research. Future research aims to further develop the predictive algorithm to minimise external influences on the management and operation of the microgrid.
SUMMARY

    Mathematical Modelling algorithms for determining the optimal management model presents and performing simulations to determine the optimal management of a This section new management algorithms for determining the optimal manmicrogrid in islanded operation based on costs. In the first step, the microgrid for each of the developed management algorithms (P, M70) was modelled to obtain the optimal value of each management algorithm using parallel processing and discrete input data together with the developed PSO management algorithms. A comparative economic analysis at the optimal points of each algorithm shows that the P model, which ultimately leads to a higher total project cost. The utilisation of the diesel generator by the M70 model and the optimised balance between PV and battery capacities resulted in lower overall costs and better energy management. The analysis and systematisation of optimisation methods, especially the PSO method, provided a comprehensive understanding of their performance and led to the selection of the M70 algorithm as the optimal model for centralised microgrid management in islanded operation, based on the minimisation of production costs. The M70 model had lower maintenance and fuel costs due to the efficient operation of the diesel generator and the optimised balance of PV and battery capacities. The dynamic adjustment of the limit up to which the DG charges the battery (chrLim) by the M70 algorithm using PSO significantly improves the cost efficiency and operational performance compared to the traditional P algorithm. Of 19 Based on the results, it is recommended to integrate dynamic and adaptive optimisation techniques, as used in the M70 algorithm, into the management of microgrids to improve economic efficiency and operational performance. @@

LAY DEFINITIONS
  • Diesel generator: A diesel generator is the combination of a diesel engine with an electric generator to generate electrical energy. This is a specific case of engine-generator
  • smart grids: A smart grid is an electrical grid which includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources, and energy efficiency resources. Electronic power conditioning and control of the production and distribution of electricity are important aspects of the smart grid
  • Cost: Absolute, comparative, or differential costs pertaining to services, institutions, resources, etc., or the analysis and study of these costs.
  • energy management: Energy management includes planning and operation of energy production and energy consumption units. Objectives are resource conservation, climate protection and cost savings, while the users have permanent access to the energy they need
  • renewable energy: Forms of energy that are constantly and rapidly renewed by natural processes such as solar, ocean wave, and wind energy. (from McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
  • Genetic algorithm: In computer science and operations research, a genetic algorithm is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection
  • Robust optimisation: Robust optimization is a field of optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and-or its solution.

Licence: cc-by

Site reference: https://www.mdpi.com/1996-1073/17/16/3901/pdf?version=1723028266

DOI reference: https://www.doi.org/10.3390/en17163901

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