Research on Optimization of Demand Response Characteristics Based on MCMC Sampling and Considering User Production Characteristics

Author(s):  
Li Bingjie
Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xuesong Shao ◽  
Gaoying Cui ◽  
Xiao Chen ◽  
Xinrong Ji ◽  
Yongxian Yi

In recent years, with the continuous growth of China’s power peak load and the rapid development of renewable energy, a large number of renewable energy sources are connected to the power grid, increasing the uncertainty of power grid operation and posing new major challenges to the power system regulation capacity. Flexible load has the characteristics of wide distribution, fast response, and high economy, which is an important control resource for the future power system. Based on the flexible load of commercial buildings and residential users, this paper studies the resource characteristics and response characteristics, clarifies the resource characteristics and demand response characteristic indexes of commercial and residential users, and establishes the response characteristics model of commercial buildings and residential users. Considering the influence of weather, holidays, incentive mechanism, and other factors on the response of flexible load, the quantitative analysis method of flexible load resource regulation potential for regional power grid dispatching was studied, and the feasibility of flexible load resources directly participating in the load control system was analyzed. Based on the uncertainty and mathematical characterization method of the active response of flexible loads, the optimal combination control strategy of demand response resources was proposed to eliminate the problems of heavy load and overload of regional power grid equipment by using the active response ability of flexible loads. Finally, the IEEE 14-node system is selected for simulation verification, which provides a theoretical basis for alleviating the power grid operation pressure in the peak load period of the power grid in the urban core area, improving the safety and economic operation level of regional power grid dispatching and the utilization rate of power grid equipment assets.


2020 ◽  
Vol 8 (1) ◽  
pp. 15-26
Author(s):  
Zhengqi Chen ◽  
Yingyun Sun ◽  
Ai Xin ◽  
Sarmad Majeed Malik ◽  
Liping Yang

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.


1997 ◽  
Vol 36 (04/05) ◽  
pp. 237-240
Author(s):  
P. Hammer ◽  
D. Litvack ◽  
J. P. Saul

Abstract:A computer model of cardiovascular control has been developed based on the response characteristics of cardiovascular control components derived from experiments in animals and humans. Results from the model were compared to those obtained experimentally in humans, and the similarities and differences were used to identify both the strengths and inadequacies of the concepts used to form the model. Findings were confirmatory of some concepts but contrary to some which are firmly held in the literature, indicating that understanding the complexity of cardiovascular control probably requires a combination of experiments and computer models which integrate multiple systems and allow for determination of sufficiency and necessity.


2017 ◽  
Vol 137 (5) ◽  
pp. 372-380 ◽  
Author(s):  
Yutaka Iino ◽  
Tsutomu Fujikawa ◽  
Saori Kaneko ◽  
Gaku Shimoda ◽  
Kazuto Kataoka ◽  
...  
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