An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty. (1st November 2019)

• Record Type: Journal Article
• Authors: Xu, Lei; Wang, Shengwei; Xiao, Fu
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Building demand management -- Peak demand limiting -- Optimal threshold resetting -- Uncertain load forecast -- Uncertainty quantification; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113582
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An adaptive optimal monthly peak building demand limiting strategy is developed. An optimal resetting scheme is developed to optimize the limiting threshold. The optimal threshold is adaptive to the changing weather forecast and power use. Uncertain economic benefits of demand limiting in a billing cycle are quantified. Sensitivity analysis is done on the economic impacts of dominant factors. Abstract: Peak demand limiting is an efficient means to reduce the electricity cost during a billing cycle in cases where peak demand charge is applied. Most previous studies focus on the daily peak demand limiting without considering load uncertainty, which is a big challenge in making proper and reliable decisions in applications. This paper presents an adaptive optimal peak building demand limiting strategy in a month considering load uncertainty. The core element and major innovation of the strategy is the optimal threshold resetting scheme, which involves two major functions as follows. The uncertain economic benefits (i.e., gains and losses) of a demand limiting control are quantified on the basis of probabilistic load forecasts. The optimal monthly limiting threshold is identified using the expectation metric based on the quantified economic benefits. The strategy optimizes and updates the monthly limiting threshold by adapting it to the ever-changing weather forecast and actual peak power use. Case studies are conducted and the results show that this strategy can effectively reduce the monthly peak demand cost under load uncertainty in different seasons. In addition, sensitivity analysis on the cost benefits of the developed strategy using different means of demand limiting and under different electricity demand charges is conducted.