Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

Abstract

Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems.

The Online Frequency Security Assessment of a Power System Considering the Time-Varying Characteristics of Renewable Energy Inertia

“…The genetic algorithm refers to a metaheuristic algorithm influenced by genetics and natural selection principles. This works well for searching [26,27]. Based on the unified VSG model shown in Figure 4, the genetic algorithm is used to identify the inertia parameters of the renewable energy station.…”

Energy Management of Multi-Area Islanded Hybrid Microgrids: A Stochastic Approach

“…The quadratic fuel cost function of active and reactive power for DUs are calculated by (7) & (8). The relation between active power and reactive power cost coefficients are addressed in ( 9), (10), and (11). It should be noted that the RESs are assumed to operate at unity power factor.…”

“…Regarding HMGs, they consist of groups of AC and DC lines and buses connected using a bi-directional converter (BDCs). It is more economical and efficient to supply AC loads from AC generators and DC loads from DC generators rather than using multiple inverters and rectifiers [6]- [10]. Accordingly, HMGs might be a desirable approach for providing the optimal accommodation of AC and DC demand and sources.…”

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

“…With the widespread diffusion of approaches based on artificial intelligence (AI), scientific production in the DC-MG field has recently achieved important results thanks to the strong peculiarity of these techniques in the processing of large quantities of data for the process of decision making. In fact, machine learning techniques have opened wide frontiers, especially in the study of stability (regression [26,46,47], random forest tree [26,48,49], convolutional neural networks [26,50,51] and others [52][53][54]). However, these techniques, although promising in performance and results obtained, have the flaw of being black-box type procedures so, on the one hand, they are difficult to understand by non-experts and, on the other, they do not allow updates except in the case of substantial interventions.…”