Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving

M A Hannan,M Faisal,A Hussain,M G M Abdolrasol,P J Ker,R A Begum

doi:10.1109/access.2019.2933010

Abstract

This paper introduces a novel optimal schedule controller to manage renewable energy resources (RESs) in virtual power plant (VPP) using binary particle swarm optimization (BPSO) algorithm. It is crucial to minimize the costs giving priority for sustainable resources use instead of purchasing from the national grid. The effectiveness of the proposed approach is examined by the IEEE 14 bus system containing microgrids (MGs) integrated with RESs in the form of VPP. Real load demand recorded is used to model and simulate the test case studies of the system for 24 h in Perlis, Malaysia. Moreover, weather data collected from the Malaysian Meteorological Department such as wind, solar, fuel, and battery status data are used in the BPSO to find the best ON and OFF schedules. The results found that the developed BPSO algorithm is robust in reducing energy consumption and emissions of the VPP. This study contributes to the development of an optimization algorithm for an optimal scheduling controller of MG integrated VPP in order to reduce carbon emissions and manage sustainable energy. Finally, a comparative analysis of the optimal algorithms over conventional justifies the use of RESs integration and validates the developed BPSO for sustainable energy management and emissions reduction.

Highlights

Empirical research on global warming and climate change identifies the CO2 emission as the key factor for increasing the atmospheric GHG emissions [1]
The objective of this study is achieved in developing a novel binary particle swarm optimization (BPSO) algorithm based scheduling controller for the energy management system of an IEEE14 bus system which includes multi MGs integrated with RESs to form virtual power plant
The virtual power plant (VPP) system is modeled and simulated with real data of load recorded in February 2016 at Perlis, Malaysia

Summary

Introduction

Empirical research on global warming and climate change identifies the CO2 emission as the key factor for increasing the atmospheric GHG emissions [1]. Climate change threatens the quality of life and the habitability of planet earth for many species, which significantly reduce the prospect of ensuring energy, environmental sustainability, and economic effectiveness (3E) of energy supply. Energy efficiency and renewable sources have become the focus of GHG emission reduction efforts to the researchers. In Malaysia, 85% of total electricity is harvested through fossil fuel. Generating electricity from renewable sources increased remarkably from 2012 to 2017, and expected to increase by 11% of the total generated power in 2020 [6], [7]. It is clear that per capita CO2 emission in Malaysia is expected to reach at 9.5 metric

Objectives

Results

Conclusion