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Abstract

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.
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Authors and Affiliations

G. K. Jabash Samuel
1
ORCID: ORCID
M. S. Sivagama Sundari
2
R. Bhavani
3
A. Jasmine Gnanamalar
4

  1. Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari, India
  2. Department of Electrical and Electronics Engineering, Amrita College of Engineering and Technology, Nagercoil, India
  3. Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi-626004, India
  4. Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Anna University, India
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Abstract

In promoting the construction of prefabricated residential buildings in Yunnan villages and towns, the use of precast concrete elements is unstoppable. Due to the dense arrangement of steel bars at the joints of precast concrete elements, collisions are prone to occur, which can affect the stress of the components and even pose certain safety hazards for the entire construction project. Because the commonly used the steel bar obstacle avoidance method based on building information modeling has low adaptation rate and cannot change the trajectory of the steel bar to avoid collision, a multi-agent reinforcement learning-based model integrating building information modeling is proposed to solve the steel bar collision in reinforced concrete frame. The experimental results show that the probability of obstacle avoidance of the proposed model in three typical beam-column joints is 98.45%, 98.62% and 98.39% respectively, which is 5.16%, 12.81% and 17.50% higher than that of the building information modeling. In the collision-free path design of the same object, the research on the path design of different types of precast concrete elements takes about 3–4 minutes, which is far less than the time spent by experienced structural engineers on collision-free path modeling. The experimental results indicate that the model constructed by the research institute has good performance and has certain reference significance.
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Authors and Affiliations

Hong Chai
1
ORCID: ORCID
Junchao Guo
1
ORCID: ORCID

  1. YellowRiver Conservancy Technical Institute, Department of Civil Engineering and Transportation Engineering, 475000 Kaifeng, China

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