Real-time application of a demand-side management strategy using optimization algorithms

Non-linear loads driven by sinusoidal sources can cause harmonic pollution in residential distribution networks. International standards (IEEE 519 and IEC61000) require harmonics in distribution networks to be kept under control, as determined by the total harmonic distortion (THD) and total demand distortion (TDD) levels, to comply with the concept of energy conservation. The implementation of demand-side management (DSM) strategies can improve system performance in the smart grid concept. This study was conducted in Trabzon, Türkiye, and involved taking real harmonic measurements of 26 different appliances in a household of four people. The active power, active energy, and harmonic values of each appliance were obtained through real measurements with a 1-second resolution. The energy management concept utilized the DPSO and DTLBO optimization methods to perform multi-objective optimization of household loads. Load scheduling using these optimization methods was performed using high-resolution data and optimization time intervals of 1 min. The load-shifting strategies obtained through various optimization methods to meet power quality requirements were investigated. Real-time measurements were taken in a house with actual appliances with a 1-second resolution for 24 h for each case study. The study presents curves, tables, and analyses of high-resolution active power, active energy, TDD, and THD data from the measurements. The load-shifting method effectively reduced both the PAR value and billing costs. The study also showed how DSM strategies worked in real-world scenarios by taking measurements with real appliances in a real house. The findings suggest are the first in the literature to show that DSM methods can mitigate the TDD value, which indicates the effect of harmonic distortion on the system according to IEEE standard limits, through real-time measurements. This contribution to the literature highlights the potential benefits of DSM methods for reducing energy costs and improving system efficiency.