Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids

Abstract

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS). The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model, equipped by renewable sources and an energy storage system, aiming to maximize the accounting profit in energy trading with the main-grid. In particular this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set as the core inference engine of an an EMS. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes, applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. A performance comparison is performed with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem, yielding at the same time a simpler RB

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

“…De Santis et al [107] introduced an energy management system for an interconnected MG using fuzzy logic based on the Mamdani algorithm. The main objective is to take decisions on the management tasks of the energy flow in the MG model, which is composed of renewable energy sources and energy storage elements.…”

An advanced neural network based solution to enforce dispatch continuity in smart grids

“…The literature presents only a few approaches that deal with intelligent decision-making to manage a power plant and the energy distribution [21].…”

“…Thirdly, many approaches simply react to the fluctuations of energy production and demand, without planning in advance [21,22]. Often their main goal has been an economical balance [21,23], and in some cases an energy balance has been considered [22,24].…”

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

“…It was found that the proposed multi-objective PSO could be validated for multi variable EMS of a factory. The optimal power control strategy for a standalone microgrid was developed and presented in [20] where optimized gains of a proportional-integrator controller of inverter-based DGs using hybrid big bang-big crunch (BB-BC) algorithm were found.With the aim of adjusting microgrid generation to energy demand, numerous technical studies have been carried out to improve energy management with penetration of renewable energy sources combined with energy storage devices in microgrids [21][22][23][24][25]. Alvarez et al [26] conducted an optimization of micro sources in a DG microgrid in terms of emission and fuel consumption costs.…”

“…With the aim of adjusting microgrid generation to energy demand, numerous technical studies have been carried out to improve energy management with penetration of renewable energy sources combined with energy storage devices in microgrids [21][22][23][24][25]. Alvarez et al [26] conducted an optimization of micro sources in a DG microgrid in terms of emission and fuel consumption costs.…”