Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Abstract

Demand Side Management (DSM) provides a better solution in order to manage increased electricity demand in the power system network. The DSM program relieves the stress on the electrical network for maintaining power system reliability during peak hours. This work proposes a new scheduling approach based on an Adaptive Differential Evolution algorithm (ADEA) by considering a new recombination probability factor (CP) and real mutation factor (F) for analysis. This proposed method is analyzed for industrial, commercial and residential network loads. The main aim of this demand control technique is to reduce the difference between the target curve and total load consumption curve. This paper provides a better solution compared to other algorithms like Evolutionary Approach (EA) and Symbiotic Organism Search (SOS) to reduce the peak load and electricity cost in industrial, commercial and residential distribution networks. The proposed method gives 8.2 % peak load reduction compared to EA for residential area and 2.28% reduction in peak load when compared to SOS for commercial sector. Also it reduces the electricity cost at 17.25%, 20.76% and 21.15 % for residential, commercial and industrial sectors when compared to without DSM. The participation factor of a consumer may be increased by price factor and the less violation in their scheduled demand. The described concept provides a relatively accurate fit to the target curve after load shifting. This concept will increase consumer participation in the DR Program.

Energy management strategy with smart building control system to reduction electrical load using ANN

“…This co-simulation architecture's major goal is to mimic occupant behavior in buildings and implement an intelligent building energy management system (IBEMS). In [15], [16] considers a novel recombination probability component and a real mutation factor for analysis, and proposes a new scheduling strategy based on an adaptive differential evolution method. Pallante et al [17] described a new technique for cutting building energy costs that relied on a combination of optimization and simulation produced in the MATLAB/Simulink environment.…”

“…In the summer season VRF must be (ON) in (Tcold) at (23 ℃) set if the place is occupied (14). When the room is unoccupied the VRF should be (Thot) at (26 ℃) (15), If the amount of electricity used by the VRF system exceeds a certain level value threshold (Th), the HVAC must be 𝑂𝑁 in the (Tmedium) at (25 ℃) mode ( 16), with feedback output power to permit the controller to take another resolution,…”