Price-based demand response for household load management with interval uncertainty

In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. Robust optimization is a technique that is increasingly used in home energy management systems, where it is applied in the scheduling of household loads through demand side control. In this work, interruptible loads and thermostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris’ hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties. [Display omitted] •Time-shiftable and thermostatically controlled loads are considered for optimal scheduling under dynamic pricing scheme.•Robust optimization is proposed to handle uncertainties associated with thermal loads such as heating and air conditioning.•The uncertain parameters are converted into different intervals, and these parameters are divided into various robust levels to control the degree of conservatism.•Harris’ Hawk optimization together with integer linear programming is used to solve the uncertainty problem.•Cost and trade-off schemes are analyzed for the user satisfaction and financial benefits of several robust levels.•Additionally, three time-shiftable appliances with operational constraints are considered for optimal scheduling.