Back to results list
Please use this identifier to cite or link to this item:
|Title:||Adaptive optimal monthly peak building demand limiting strategy considering load uncertainty||Authors:||Xu, Lei||Advisors:||Wang, Shengwei (BSE)
Xiao, Fu (BSE)
Electric power consumption
|Issue Date:||2019||Publisher:||The Hong Kong Polytechnic University||Abstract:||Peak demand limiting is an efficient means to reduce the electricity cost over a billing cycle (typically a month) in cases where peak demand charge is applied. However, 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. The daily peak demand limiting might not achieve the maximum cost saving in a month due to the partial/complete offset between the peak demand cost reduction and the costs of associated limiting efforts. Therefore, a monthly peak demand limiting strategy is needed. Besides, load prediction is usually an inevitable part of peak demand limiting strategy. For individual buildings, there exists great load uncertainty, mainly due to uncertain peak loads and uncertain weather conditions. As for load uncertainty, probabilistic load forecasting can provide more comprehensive information for decision-making compared with the traditional deterministic load forecasting and it draws increasing attention recently.
This study presents an adaptive optimal monthly peak demand limiting strategy considering load uncertainty. The developed strategy includes the probabilistic load forecasting model, the optimal threshold resetting scheme, and the proactive-adaptive demand limiting control scheme. A new probabilistic building load forecasting model is proposed considering uncertainties in weather forecasts and abnormal peak load. Two basic function components are developed, including the probabilistic normal load forecasting and the probabilistic uncertain peak load (or abnormal peak load) forecasting. The probabilistic normal load forecasting model is built using the artificial neural network (ANN) and the probabilistic temperature forecasts. The probabilistic abnormal peak load forecasting model consists of two models quantifying the probabilistic occurrence and magnitude of the peak abnormal differential load, respectively. Based on the building load model, an optimal threshold resetting scheme is proposed for monthly peak demand limiting considering load uncertainty, 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 scheme optimizes and updates the monthly limiting threshold by adapting it to the ever-changing weather forecast and actual peak power use. Based on the building load model and the optimal threshold resetting scheme, a proactive-adaptive demand limiting control scheme is developed for online demand limiting threshold reset and online demand limiting control, which is especially effective when only small-scale storages are available. Validation tests are conducted on the probabilistic load forecasting model, the optimal threshold resetting scheme, and the proactive-adaptive demand limiting control scheme, respectively. The test results show that the ANN deterministic load forecasting model can achieve satisfactory performance. The probabilistic occurrence model can forecast the occurrence frequency of the peak abnormal differential load with the satisfactory agreement, and the probabilistic magnitude model can well forecast the magnitudes of the peak abnormal differential load. Furthermore, real-time application case studies are conducted by different means of using probabilistic weather forecasts. Results show that the probabilistic normal load forecasts have satisfactory accuracies and the load forecasts based on the one-day-ahead probabilistic weather forecasts are the best. In validating the optimal threshold resetting scheme, case studies are conducted, and the results show that demand limiting based on this scheme can effectively reduce the monthly peak demand cost under load uncertainty in different seasons. Moreover, sensitivity analysis of the means of demand limiting and electricity demand charge on the cost benefits of demand limiting with the resetting scheme is conducted. In validating the proactive-adaptive demand limiting control scheme, case studies are conducted, and the results show that this proactive-adaptive demand limiting control scheme can effectively reduce the monthly peak demand cost for buildings with small-scale thermal storages under load uncertainty. In summary, an adaptive optimal monthly peak demand limiting strategy is proposed to reduce building peak demands over a month (or a billing cycle) considering load uncertainties, which includes the following three main components. A building load model is developed to quantify load uncertainties from weather forecasting uncertainty and uncertain peak loads. An optimal threshold resetting scheme is developed to identify an adaptive optimal monthly limiting threshold. A proactive-adaptive demand limiting control scheme is developed to conduct proactively online demand limiting control, even when small-scale thermal storages are available. Validation tests are conducted, and the test results show the adaptive optimal monthly peak demand limiting strategy can achieve significant peak demand reduction under load uncertainties, even when only small-scale active thermal storages are available.
|Description:||xx, 151 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P BSE 2019 Xu
|URI:||http://hdl.handle.net/10397/81474||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
Show full item record
Files in This Item:
|991022270852703411_link.htm||For PolyU Users||168 B||HTML||View/Open|
|991022270852703411_pira.pdf||For All Users (Non-printable)||3.91 MB||Adobe PDF||View/Open|
Citations as of Feb 19, 2020
Citations as of Feb 19, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.