scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

Energy ◽  
2019 ◽  
Vol 168 ◽  
pp. 1119-1127 ◽  
Author(s):  
Manijeh Alipour ◽  
Kazem Zare ◽  
Heresh Seyedi ◽  
Mehdi Jalali

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 796
Author(s):  
Ramasamy Govindarasu ◽  
Solaiappan Somasundaram

A dynamic model of a Direct Methanol Fuel Cell is developed in the MATLAB platform. A newly proposed Coefficient Diagram based Proportional Integral Controller (CD-PIC) is designed and its parameters are calculated. The newly designed CD-PIC is implemented in a real time Direct Methanol Fuel Cell (DMFC) experimental setup. Performances in real time operation of the Direct Methanol Fuel Cell (DMFC) are evaluated. The performance of CD-PIC is obtained under tracking of set point changes. In order to evaluate the CD-PIC performances, most popular tuning rules based Conventional PI Controllers (C-PIC) are also designed and analyzed. Set point tracking is carried out for the step changes of ±10% and ±15% at two different operational points. The controller performances are analyzed in terms of Controller Performance Measuring (CPM) indices. The said performance measures indicate that the proposed CD-PIC gives the superior performances for set point changes and found very much robust in controlling DMFC.


2019 ◽  
Vol 13 (12) ◽  
pp. 2195-2206 ◽  
Author(s):  
Yulong Jia ◽  
Zengqiang Mi ◽  
Yang Yu ◽  
Zhuoliang Song ◽  
Hui Fan

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xuanyu Wang ◽  
Xudong Qi ◽  
Ping Wang ◽  
Jingwen Yang

AbstractWith the development of autonomous car, a vehicle is capable to sense its environment more precisely. That allows improved drving behavior decision strategy to be used for more safety and effectiveness in complex scenarios. In this paper, a decision making framework based on hierarchical state machine is proposed with a top-down structure of three-layer finite state machine decision system. The upper layer classifies the driving scenario based on relative position of the vehicle and its surrounding vehicles. The middle layer judges the optimal driving behavior according to the improved energy efficiency function targeted at multiple criteria including driving efficiency, safety and the grid-based lane vacancy rate. The lower layer constructs the state transition matrix combined with the calculation results of the previous layer to predict the optimal pass way in the region. The simulation results show that the proposed driving strategy can integrate multiple criteria to evaluate the energy efficiency value of vehicle behavior in real time, and realize the selection of optimal vehicle driving strategy. With popularity of automatic vehicles in future, the driving strategy can be used as a reference to provide assistance for human drive or even the real-time decision-making of autonomous driving.


Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1413 ◽  
Author(s):  
Arsalan Najafi ◽  
Mousa Marzband ◽  
Behnam Mohamadi-Ivatloo ◽  
Javier Contreras ◽  
Mahdi Pourakbari-Kasmaei ◽  
...  

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.


Author(s):  
Fantail Meng ◽  
Boyin Ding ◽  
Nataliia Y Sergiienko ◽  
Hao Chen ◽  
Hao Xu ◽  
...  

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