Demand Side Management Considering Household Appliances and EV

Abstract

Combination of the information technology and the power engineering is the feature of next-generation grid. Depending on bidirectional communications, demand side management (DSM) aims at optimizing the electricity usage pattern of customers to improve energy efficiency and alleviate environmental impact. In this study, a DSM optimization algorithm is designed, which can perform load shifting on a household level based on the Time-of-Use strategy. Several flexible appliances, plug-in hybrid electric vehicle (EV) charging and rooftop photovoltaic (PV), are considered. Results show that the daily electricity cost has declined by 19% after the optimization. A 12% reduction of the domestic carbon emission is also achieved from the variation of grid carbon intensity and energy provided by rooftop PV. It is validated that with the growing penetration rate of EVs and renewable energy generation, smart scheduling of household load can greatly benefit grid stability and energy efficiency.

Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

“…In addition to lowering the carbon intensity of the grid and providing energy, rooftop PV reduces domestic carbon emissions by 12%. With the growing use of electric vehicles and renewable energy, the smart scheduling of household loads has a substantial impact on grid resilience and energy efficiency [20] of the charging facility was the first step, followed by the relocation of vehicles in the second step. In addition, both supply-side and demand-side uncertainties were taken into account and approximated into a form that was manageable by using a sample average approximation.…”