An Intelligent Load Management System with Renewable Energy Integration for Smart Homes

Abstract

Demand Side Management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via Home Energy Management (HEM) systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, Evolutionary Algorithms (EAs) (Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA) and Cuckoo search) based DSM model for scheduling the appliances of residential users is presented. The model is simulated in Time of Use (ToU) pricing environment for three cases: (i) traditional homes, (ii) smart homes, and (iii) smart homes with Renewable Energy Sources (RES).

Existing System

Residential Electricity Optimization in Dynamic Pricing Environments.

Proposed System

In this paper, we present a cost efficient appliance scheduling model for residential users. Our appliance scheduling model aims at optimizing the operation time of electrical appliances. The model also takes into account the RES generated energy jointly with grid generated energy. The model uses EAs (GA, BPSO and Cuckoo) for generating the optimized schedules and it is simulated in ToU pricing environment. Results validate that the proposed model performs well in scheduling the household electrical appliances and provides benefits to the users by significantly reducing their electricity bills. The contributions of work are listed as follows: 1) We propose a model for different types of users and loads and a simple way to model user preferences with the aim at cost and peak reductions. Then cost reduction objective function is formulated, mathematically. 2) BPSO, GA and Cuckoo search algorithms are used to solve centralized optimization problem. Control parameters of these algorithms are selected in such a way that an optimal solution is found within acceptable processing time. 3) To avoid the usage of peaking power plants during high demanding hours, on-site renewable energy and backup storage systems are used which further reduce electricity cost.

Architecture

Proposed Model