Optimization-based estimation of power capacity profiles for activity-based residential loads

Abstract

This paper proposes a framework to determine capacity profiles in smart buildings. In this scheme the users choose a level of power capacity to account for their stochastic demand while paying the corresponding electricity prices through a flexible time-and-level-of-use pricing policy. We formulate a two-stage stochastic optimization model that minimizes the total cost of booking a power capacity level and meeting the energy demand for the planning horizon. We present two approaches to select the scenarios for the stochastic optimization. In the first approach, we assume that the probability distributions of the start times of the loads are known, and the scenarios are generated using those distributions. In the second approach, we assume that only historical consumption data is available and we propose a new algorithm to build the scenarios using this data. Our simulation experiments validate the performance of both approaches and report cost savings of up to 16%.

Optimal collaborative demand-response planner for smart residential buildings

“…First, observe that the BU is similar in every case; it depends on the capacity of the battery and the number of time frames where using the battery makes sense. Observe also that the instances with equation (25) report a better PR. In fact, the peak demand increases considerably for the instances with equation (26).…”

“…We include a time and level of use (TLOU) pricing structure in which the price varies time-wise and level-wise [25]. This is an extension of the time of use (TOU) pricing that is widely used.…”

“…Equations (25) and (26) show two possible cost structures for a cheaper and a more expensive period (off-peak and on-peak). The following reasoning can be extended to the other combinations: off-peak and mid-peak, and mid-peak and on-peak.…”

“…A cost structure based on equation (25) will encourage consumption of energy from the lower capacities before going to the higher level. On the other hand, a cost structure based on equation (26) will encourage using all the off-peak resources before moving to more expensive time frames.…”

Smart grid lab research in Europe and beyond

“…The research initiative Optimization for Smart Grids (OSG) was founded by a consortium of research institutes and the Polytechnique Montreal and is focused on the application of mathematical optimization techniques to the planning and operation of modern power systems. In particular, the areas in which the initiative is conducting research are system load management and DR, modeling, management and operation of smart buildings, maintenance scheduling in hydropower systems, location and pricing of EV charging stations, and the optimal provision of reactive power in a large‐scale grid …”