Integrated Fuzzy-Knapsack Based Demand Response Energy Management System for Smart Grid Buildings

Abstract

Demand response schemes play a vital role in managing the load demand. However, the demand response applicability is pre-descriptive where loads to be managed are pre-selected majorly based on the availability of renewable energy and lower tariff rates. However, in hospitality buildings such as hotels, user comfort cannot be compromised by the cost of energy. The arrival of guests is a unique parameter that drives the load consumption regardless of the availability of free energy or lower tariff rates. During higher guest arrivals, pre-descriptive loads meant to be scheduled during low renewable energy availability and higher tariff rates cannot be compromised over guest comfort. Similarly, pre-descriptive loads that are already not in operation at the time of low guest arrivals will result in wastage of green power at times of its availability. There is a need to develop an automated demand response that has the liberty to select any load for shifting to renewable energy based on the power they consume to utilize maximum resources without compromising guest comfort. In this research, a novel automated demand response scheme is developed that intelligently selects any load from the building in real time while mapping it with the available capacity of renewable power. A cascaded fuzzy integrated knapsack algorithm is designed for intelligent selection of loads participation in demand response. Based on the availability of solar PV power, grid rates, and load operations, fuzzy designates values to the random operational loads. In the second step, the designated values are given to the Knapsack algorithm to find the best optimal responsive loads to be operated at that time. In the proposed approach, random loads were selected for shifting to renewable power without any prior load selection, which enhances the operation and usability of solar PV power. It was found that 88%–100% of solar PV power was utilized under all simulated scenarios of operation.