Power capacity profile estimation for building heating and cooling in demand-side management

Abstract

This paper presents a new methodology for the estimation of power capacity profiles for smart buildings. The capacity profile can be used within a demand-side management system in order to guide the building temperature operation. It provides a trade-off between the quality of service perceived by the end user and the requirements from the grid in a demand-response context. We use a data-fitting approach and a multiclass classifier to compute the required profile to run a set of electric heating and cooling units via an admission control module. Simulation results validate the performance of the proposed methodology under various conditions, and we compare our approach with neural networks in a real-world-based scenario.

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

“…If such a c does not exist, it is optimal to retain the TOU pricing K 0 . Because inequality (14) depends strongly on the tariffs, and the tariffs vary depending on the TOU, we observe different behavior for different time frames. We can see this situation in Figure 6(b), where the user buys capacity at t = 8, an on-peak period, and not at t = 13, a mid-peak period, despite the similar expected demands.…”

“…where we find a clear relationship: the net expected savings must be less than the fixed cost from booking capacity c. The net expected savings are the savings from the lower tariff and the extra cost of consumption at the higher tariff. The optimization seeks a c that violates the condition in inequality (14). If such a c does not exist, it is optimal to retain the TOU pricing K 0 .…”

“…DSM normally deals with user's costs and demand satisfaction. In [12], [13], and [14], the user preferences are typically hard constraints and are met while optimizing the energy consumption or peak reduction.…”

Design and implementation of UPnP-based energy gateway for demand side management in smart grid

“…Efficient demand-response management is a key factor revolutionizing legacy grids into smart grids. Authors in [8] proposed a methodology for estimating power capacity and managing building temperature operation within the DSM system. The system meets demand-response requirements of grid while ensuring quality of service for endusers.…”

“…Amongst the most innovative and challenging opportunities brought by the smart grid, there is the design of smart home and building automation that can be deployed in both residential and commercial buildings [7,8]. Such smart systems efficiently control all the appliances e.g., air conditioning, heating, refrigerators, washing machines, TV, computers, etc without requiring any physical presence [2].…”