Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

Jinhong Feng,Ruizheng Jiang,Chuan Wang,Jundong Zhang,Mingyi Xu

doi:10.1109/access.2020.2988530

Abstract

With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem.

Highlights

For most conventional cargo ships, in which Diesel Generators (DG) are the main power sources, DGs can be well controlled to meet the required power demands on board
To show the significance between the presented method and another competitor, we conducted the Wilcoxon rank sum test at 0.05 level [69], [70], regarding SaCIDE-r vs. another one as ‘‘+’’, ‘‘−’’, ‘‘≈’’. ‘‘+’’ means the proposed method is significantly better than another algorithm, ‘‘−’’ represents the proposed method is significantly worse than another algorithm, and ‘‘≈’’ denotes the proposed method is significantly equal to another algorithm
With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in ship microgrids

Summary

INTRODUCTION

For most conventional cargo ships, in which Diesel Generators (DG) are the main power sources, DGs can be well controlled to meet the required power demands on board. It is very important for DEs to avoid meeting a stagnation which means no better solutions generated [52] To address these issues, we would like to propose a new Self-adaptive Collective Intelligence (CI) Differential Evolution algorithm with a restart mechanism (SaCIDE-r) for optimizing the economic scheduling of a shipboard microgrid. A day-ahead scheduling in 24 hours is the most widely used mode, which is different from some researches on optimizing economic dispatch of microgrids in seconds or minutes Those investigations usually focus on the performances of controllers of DC/AC inverters. The EMS will forecast the output power of PV, WT and loads required in the day Based on these information and the State of Charge (SOC) of the battery system, the outputs of all dispatchable sources are calculated to obtain the total losses of the shipboard microgrid.

BATTERY SYSTEM MODEL

EVOLUTIONARY OPERATIONS OF SaCIDE-R

RE-INITIALIZATION

CASE 2

Findings

CONCLUSION