Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

Abstract

In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer's electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

“…Mostly, researchers focus on appliance scheduling to reduce the load on utility and balance supply and load. However, with the appliance scheduling, the user comfort is compromised [10,11]. Therefore, Short-Term Load Forecasting (STLF) is important.…”

Multi-Searcher Optimization for the Optimal Energy Dispatch of Combined Heat and Power-Thermal-Wind-Photovoltaic Systems

“…In order to test the optimization effect of the MSO for the OED of combined heat and power-thermal-wind-photovoltaic systems, this paper introduces nine commonly used heuristic algorithms which are highly independent from a specific mathematical model, including biogeography-based optimization (BBO) [27], cultural algorithms (CA) [28], firefly algorithms (FA) [29], genetic algorithms (GA) [11], the grey wolf optimizer (GWO) [14], moth-flame optimization (MFO) [30], particle swarm optimization (PSO) [12], simulated annealing (SA) [31] and a teach-learn based optimization algorithm (TLBO) [32].…”

Methodology for the Analysis of Electrical Consumption

“…Many researchers around the world worked to make an optimal electric consumption system in different research lines smart meter, smart grid, neural network, metaheuristic, IoT, Genetic algorithm and big data. Nadeem et al in [16] considered to develop a DS to reduce consumption under a predefined level, costs and waiting time using hybrid metaheuristics schemes based on Teaching-Learning techniques. The metaheuristics used were Optimization Stopping Rule (OSR), Genetic Algorithm (GA) and Firefly Algorithm (FA); in this work is combined OSR-GA, OSR-TLBO, and OSR-FA.…”

“…Interruptible devices are defined like devices that could be used in anytime, and the time of use varies according to the user needs [5,7,10,13,17]. Not-interruptible devices stop their function once they are finished, their consume could be variable or constant [7,10,13,16,17]. Flexible devices, their functions could be stopped and continue at another time, could be on standby too [3,7,13,17] Not-flexible devices, they could not be turned off because is necessary that they have a constant function [7,13].…”