Distributed $Q$ -Learning-Based Online Optimization Algorithm for Unit Commitment and Dispatch in Smart Grid

2020 ◽  
Vol 50 (9) ◽  
pp. 4146-4156 ◽  
Author(s):  
Fangyuan Li ◽  
Jiahu Qin ◽  
Wei Xing Zheng
Author(s):  
Errong Pei ◽  
Lineng Zhou ◽  
Bingguang Deng ◽  
Xun Lu ◽  
Zhizhong Zhang ◽  
...  

Energies ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 319 ◽  
Author(s):  
Nadeem Javaid ◽  
Sakeena Javaid ◽  
Wadood Abdul ◽  
Imran Ahmed ◽  
Ahmad Almogren ◽  
...  

2014 ◽  
Vol 3 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Vikram Kumar Kamboj ◽  
S.K. Bath

Biogeography Based Optimization (BBO) algorithm is a population-based algorithm based on biogeography concept, which uses the idea of the migration strategy of animals or other spices for solving optimization problems. Biogeography Based Optimization algorithm has a simple procedure to find the optimal solution for the non-smooth and non-convex problems through the steps of migration and mutation. This research paper presents the solution to Economic Load Dispatch Problem for IEEE 3, 4, 6 and 10-unit generating model using Biogeography Based Optimization algorithm. It also presents the mathematical formulation of scalar and multi-objective unit commitment problem, which is a further extension of economic load dispatch problem.


2017 ◽  
Vol 32 (6) ◽  
pp. 4696-4707 ◽  
Author(s):  
Chaoyi Peng ◽  
Yunhe Hou ◽  
Nanpeng Yu ◽  
Weisheng Wang
Keyword(s):  

Author(s):  
M. Suresh ◽  
R. Meenakumari

An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.


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