scholarly journals Real-time optimisation of emergency demand response and HVDC power modulation to improve short-term frequency stability of the receiving-end power systems

2019 ◽  
Vol 2019 (16) ◽  
pp. 1952-1957 ◽  
Author(s):  
Luping Wang ◽  
Xiaorong Xie ◽  
Long Peng ◽  
Yinghong Hu ◽  
Yuan Zhao
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Frequency Stability ◽  
Short Term ◽  
Term Frequency ◽  
Power Modulation

Real‐time optimisation of short‐term frequency stability controls for a power system with renewables and multi‐infeed HVDCs

2018 ◽  
Vol 12 (13) ◽  
pp. 1462-1469 ◽  
Author(s):  
Luping Wang ◽  
Xiaorong Xie ◽  
Xiaoliang Dong ◽  
Ying Liu ◽  
Hongming Shen
Keyword(s):  
Power System ◽  
Real Time ◽  
Frequency Stability ◽  
Short Term ◽  
Term Frequency

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
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

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.

Real-time price-based demand response model for combined heat and power systems

Energy ◽  
2019 ◽  
Vol 168 ◽  
pp. 1119-1127 ◽  
Author(s):  
Manijeh Alipour ◽  
Kazem Zare ◽  
Heresh Seyedi ◽  
Mehdi Jalali
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Combined Heat And Power ◽  
Response Model ◽  
Time Price
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