Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

2022 ◽  
Vol 134 ◽  
pp. 107124
Author(s):  
Bingjie He ◽  
Junxiang Li ◽  
Dongjun Li ◽  
Jingxin Dong ◽  
Liting Zhu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Quadratic Function ◽  
Price Adjustment ◽  
Adjustment Strategy ◽  
Monitoring Process

Monitoring of power consumption requirement load process and price adjustment for smart grid

2019 ◽  
Vol 137 ◽  
pp. 106068
Author(s):  
Bingjie He ◽  
Junxiang Li ◽  
Fugee Tsung ◽  
Yan Gao ◽  
Jingxin Dong ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Price Adjustment

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

A technique for Effective Metering of Power Consumption in Smart Grid Environment

2021 ◽  
Author(s):  
T Margaret Mary ◽  
G Ramanathan ◽  
K Soumya ◽  
G Clinton
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Grid Environment

Retailer’s decision selection with dual supply uncertainties under different reliability levels of serving the market

2020 ◽  
Vol 54 (3) ◽  
pp. 883-911 ◽  
Author(s):  
Zhibing Liu ◽  
Geni Xu ◽  
Chi Zhou ◽  
Huiru Chen
Keyword(s):  
Demand Management ◽  
Wholesale Price ◽  
Price Adjustment ◽  
Optimal Decision ◽  
Market Demand ◽  
Supply Uncertainty ◽  
Reliability Level ◽  
Adjustment Strategy ◽  
Uncertain Supply ◽  
Selection Of

The main reason why supply uncertainty reduces supply chain performance is that it is difficult to estimate whether uncertain supply matches demand. Seldom do papers study retailers’ decision-selection problems according to the reliability of uncertain supply in satisfying demand. This paper considers the optimal decision selection of a retailer working with a main supplier facing supply uncertainty and a backup supplier whose yield is infinite or uncertain. The retailer can enforce demand management by adjusting prices, seeking the backup supplier to make up for the lack of products or mixing the two decisions. We provide the definition called the reliability level of serving the market (RLSM) to characterize the reliability of uncertain supply in satisfying market demand. Under different RLSMs, the participants maximize their profits based on a confidence level in three scenarios: benchmark, infinite backup supply and uncertain backup supply. Whether the main supplier determines the wholesale price or not, we find that in the benchmark, the retailer orders from the main supplier if the RLSM is low; otherwise, the retailer gives up purchasing the product. In the latter two scenarios, our results show that the particular order strategy chosen by the retailer depends on the values of the RLSM and that the retailer’s order quantity follows threshold rules. It is interesting that for different RLSMs, the retailer chooses either a price adjustment strategy, a backup supply strategy or neither of them but does not choose the mixed one, which is counterintuitive. We also derive the particular scenario that is good for the retailer by comparing the results in the three scenarios. Finally, a proper RLSM is suggested for the retailer to balance the reliability of serving the market and her profit.

scholarly journals Integrated model predictive control of water resource recovery facilities and sewer systems in a smart grid: example of full-scale implementation in Kolding

2020 ◽  
Vol 81 (8) ◽  
pp. 1766-1777 ◽  
Author(s):  
P. A. Stentoft ◽  
L. Vezzaro ◽  
P. S. Mikkelsen ◽  
M. Grum ◽  
T. Munk-Nielsen ◽  
...  
Keyword(s):  
Smart Grid ◽  
Model Predictive Control ◽  
Power Consumption ◽  
Water Resource ◽  
Predictive Control ◽  
Storage Capacity ◽  
Resource Recovery ◽  
Integrated Model ◽  
Full Scale ◽  
Sewer System

Abstract An integrated model predictive control (MPC) strategy to control the power consumption and the effluent quality of a water resource recovery facility (WRRF) by utilizing the storage capacity from the sewer system was implemented and put into operation for a 7-day trial period. This price-based MPC reacted to electricity prices and forecasted pollutant loads 24 hours ahead. The large storage capacity available in the sewer system directly upstream from the plant was used to control the incoming loads and, indirectly, the power consumption of the WRRF during dry weather operations. The MPC balances electricity costs and treatment quality based on linear dynamical models and predictions of storage capacity and effluent concentrations. This article first shows the modelling results involved in the design of this MPC. Secondly, results from full-scale MPC operation of the WRRF are shown. The monetary savings of the MPC strategy for the specific plant were quantified around approximately 200 DKK per day when fully exploiting the allowed storage capacity. The developed MPC strategy provides a new option for linking WRRFs to smart grid electricity systems.

Sign in / Sign up

Export Citation Format

Share Document