Scheduling smart home appliances using mixed integer linear programming

Abstract

Abstract-This paper considers the minimum electricity cost scheduling problem of smart home appliances. Operation characteristics, such as expected duration and peak power consumption of the smart appliances, can be adjusted through a power profile signal. The optimal power profile signal minimizes cost, while satisfying technical operation constraints and consumer preferences. Constraints such as enforcing uninterruptible and sequential operations are modeled in the proposed framework using mixed integer linear programming (MILP). Several realistic scenarios based on actual spot price are considered, and the numerical results provide insight into tariff design. Computational issues and extensions of the proposed scheduling framework are also discussed.

Optimal scheduling of a household refrigerator using adaptive model predictive technique

“…C a (n − 1). The number of necessary iterations of the above bisection algorithm depends on the desired tolerance ε in the following form N Ca = log 2 10 C amin ε C amin = log 2 10 ε .…”

“…However, in the past few years (or decade) low power micro-controllers (MCU) with high computational potential have revolutionized the field of embedded systems thus opening the way to apply advanced control methods in this area, too. In [2] a mixed integer linear programming based approach is used for the optimal scheduling of domestic Price signals for the days of a week in a day-ahead market. Source: [1] appliancess in a smart environment.…”

Bayesian Learning of Consumer Preferences for Residential Demand Response

“…Two sorts of optimization criteria are met in the literature on automatic scheduling of home appliances: in Conejo et al (2010), Ferreira et al (2012), Lujano-Rojas et al (2012), Volkova et al (2014) user preferences over home appliance use schedules are represented with a utility function, while in Bradac et al (2014), Mohsenian-Rad and Leon-Garcia (2010), and in Sou et al (2011) electricity bill is minimized while user preferences impose constraints on feasible schedules. Most papers assume a priori knowledge of the utility function, whereas in the present paper it is learned from consumer's actions.…”

Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

“…Nevertheless, solving such MILPs efficiently can only be done for relatively small instances of appliances [25]. Algorithms such as cutting plane methods and the branch and bound method [26] can also be used to reduce the average execution time complexity.…”