Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid

Zahoor Ali Khan,Nadeem Javaid,Tanzila Saba,Abdul Haseeb,Muhammad Shafiq,Adia Khalid

doi:10.1109/access.2019.2942813

Abstract

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.

Highlights

The integration of Information and Communication Technology (ICT) with electricity infrastructure revolutionize the traditional grid into smart grid [1]
The meta-heuristic algorithms are widely used for finding a sub-optimal solution instead of locating an optimal solution from the given search space. This is the reason, we propose new nature-inspired optimization techniques: (1) Multi-objective Binary Bird Swarm Optimization (MBBSO) algorithm, which is the multi-objective version of BSO, its four searching strategies makes it efficient and effective during exploitation and exploration of the search space [23], (2) Multi-objective Binary Hybrid of BSO and Cuckoo search Optimization (MBHBCO) algorithm, which is a hybrid technique because combining two or more meta-heuristic algorithms enhance the performance [24]
We study the effect of coordination on electricity cost, Peak to Average Ratio (PAR) and waiting time

Summary

Introduction

The integration of Information and Communication Technology (ICT) with electricity infrastructure revolutionize the traditional grid into smart grid [1]. The exchange of data between the smart utility (service provider) and end user is achieved using Advanced Metering Infrastructure (AMI). This provides bi-directional communication paradigm and enables the consumers to customize the execution of appliance operations based on the electricity prices in particular time. This supports the end users to become active from passive consumers in the smart grid using AMI [1]. The variation in the pricing tariffs depends on the peak load because of high power demand in a particular time period [2]. The consumer is encouraged to shift load from On-peak (high prices) hours to Off-peak (low prices) hours to avoid blackouts [2]

Methods

Results

Conclusion