Multi-Bit Incremental Converters with Optimal Power Consumption and Mismatch Error

2020 ◽  
Author(s):  
W. A. Qureshi ◽  
A. Salimath ◽  
E. Bonizzoni ◽  
F. Maloberti
Keyword(s):  
Power Consumption ◽  
Mismatch Error ◽  
Optimal Power

Optimal Power Consumption Method Based on Household Load Interaction Ability

2021 ◽  
Author(s):  
Baoxing Shen ◽  
Mingkai Yu ◽  
Lin Lin ◽  
Gengyin Li ◽  
Daohong Liu
Keyword(s):  
Power Consumption ◽  
Optimal Power

scholarly journals An External Archive-Based Constrained State Transition Algorithm for Optimal Power Dispatch

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaojun Zhou ◽  
Jianpeng Long ◽  
Chongchong Xu ◽  
Guanbo Jia
Keyword(s):  
Power Consumption ◽  
Current Density ◽  
Optimization Problem ◽  
State Transition ◽  
Optimal Power ◽  
Power Dispatch ◽  
External Archive ◽  
State Transition Algorithm ◽  
Dispatch Optimization ◽  
The Cost

This paper proposes an external archive-based constrained state transition algorithm (EA-CSTA) with a preference trade-off strategy for solving the power dispatch optimization problem in the electrochemical process of zinc (EPZ). The optimal power dispatch problem aims to obtain the optimal current density schedule to minimize the cost of power consumption with some rigorous technology and production constraints. The current density of each production equipment in different power stages is restricted by technology and production requirements. In addition, electricity price and current density are considered comprehensively to influence the cost of power consumption. In the process of optimization, technology and production restrictions are difficult to be satisfied, which are modeled as nonconvex equality constraints in the power dispatch optimization problem. Moreover, multiple production equipment and different power supply stages increase the amount of decision variables. In order to solve this problem, an external archive-based constrained state transition algorithm (EA-CSTA) is proposed. The external archive strategy is adopted for maintaining the diversity of solutions to increase the probability of finding the optima of power dispatch optimization problem. Moreover, a preference trade-off strategy is designed to improve the global search performance of EA-CSTA, and the translation transformation in state transition algorithm is modified to improve the local search ability of EA-CSTA. Finally, the experimental results indicate that the proposed method is more efficient compared with other approaches in previous papers for the optimal power dispatch. Furthermore, the proposed method significantly reduces the cost of power consumption, which not only guides the production process of zinc electrolysis but also alleviates the pressure of the power grid load.

The average speed of motion and optimal power consumption in biped robots

2019 ◽  
Vol 34 ◽  
Author(s):  
Vida Shams Esfanabadi ◽  
Mostafa Rostami ◽  
Seyed Mohammadali Rahmati ◽  
Jacky Baltes ◽  
Soroush Sadeghnejad
Keyword(s):  
Power Consumption ◽  
Empirical Evaluation ◽  
Biped Robot ◽  
Walking Ability ◽  
Average Speed ◽  
Optimal Power ◽  
Minimum Power Consumption ◽  
Maximum Stability ◽  
Motion Speed ◽  
The Stability

Abstract One of the issues that have garnered little attention, but that is nevertheless important for developing practical robots, is optimal walking conditions like power consumption during walking. The main contribution of this research is to prepare a correct walking pattern for humans who have a problem with their walking and also study the effect of average motion speed on optimal power consumption. In this study, we firstly optimize the stability and minimize the power consumption of the robot during the single support phase using parameter optimization. Our approach is based on the well-known Zero Moment Point method to calculate the stability of the proposed biped robot. Secondly, we performed experiments on healthy male, age 29 years, to analyze human walking by placing 28 markers, attached to anatomical positions and two power plates for a distance of more than one gait cycle at an average speed of 1.23 ± 0.1 m s−1 validate our results for motion analysis of correct walking ability. Our model was continuously validated by comparing the results of our empirical evaluation against the prediction of our model. The errors between experimental test and our prediction were about 4%–11% for the joint trajectories and about 0.2%–0.5% for the ground reaction forces which is acceptable for our prediction. Due to the presented model and optimized issue and predicted path, the robot can move like a person in a way that has maximum stability along with the minimum power consumption. Finally, the robot was able to walk like a specific person that we considered. This study is a case study and also can be generalized to all samples and can perform these procedures to another person’s with different features.

scholarly journals Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bingjie He ◽  
Qiaorong Dai ◽  
Aijuan Zhou ◽  
Jinxiu Xiao
Keyword(s):  
Power Consumption ◽  
Power Supply ◽  
Electric Energy ◽  
Power Market ◽  
Data Driven ◽  
The Novel ◽  
Real Power ◽  
The Real ◽  
Optimal Power ◽  
Adjustment Strategy

Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to control the users’ real power consumption. With the repeated-feedback adjustment, we adjust the real-time prices according to changes in the power discrepancy between the optimal power supply and the users’ real power consumption. If and only if the power discrepancy deviates the preset range, the real power consumption in different periods will be adjusted through the change of the price, so the adjustment times is the least. Numerical results on real power market show that the novel inverse proportional function-based repeated-feedback adjustment strategy brought forward in the article achieves better effect than the linear one, that is to say, the adjustments times and standard error of the residuals are less. Meanwhile, profit and whole social welfare are more. The proposed strategy can obtain more steady and dependable consumption load close to the optimal power supply, which is conducive to the balanced supply of electric energy.

scholarly journals Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

2019 ◽  
Vol 11 (4) ◽  
pp. 997 ◽  
Author(s):  
Wenquan Jin ◽  
Israr Ullah ◽  
Shabir Ahmad ◽  
Dohyeun Kim
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Prediction Model ◽  
Optimization Model ◽  
Indoor Environment ◽  
Energy Optimization ◽  
Comfort Index ◽  
Optimal Power ◽  
Temperature And Humidity ◽  
Occupant Comfort

Occupant comfort management is an important feature of a smart home, which requires achieving a high occupant comfort level as well as minimum energy consumption. Based on a large amount of data, learning models enable us to predict factors of a mathematical model for deriving the optimal result without expensive experiments. Comfort management supports high-level comfort to the occupant in the individual indoor environment, using the optimal power consumption to run home appliances. In this paper, we propose occupant comfort management based on energy optimization, using an environment prediction model. The proposed energy optimization model provides optimal power consumption based on the proposed objective function, which requires temperature and comfort index data as the input parameters. For the input requirement, temperature prediction model and humidity prediction model are presented based on a recurrent neural network with a pre-collected dataset, including indoor and outdoor temperature and humidity sensing data. Using the predicted temperature and humidity data, the comfort index model derives the predicted mean vote value to be used in the energy optimization model with the predicted temperature data. The experimental results present an 8.43% reduction of the optimized power consumption compared to the actual power consumption using mean absolute percentage error to calculate. Moreover, the emulation of an indoor environment using optimal energy consumption presents as approximately similar to the actual data.

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