scholarly journals Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 107937-107951 ◽  
Author(s):  
M. A. Hannan ◽  
M. G. M. Abdolrasol ◽  
M. Faisal ◽  
P. J. Ker ◽  
R. A. Begum ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plant ◽  
Energy Saving ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Virtual Power ◽  
Swarm Optimization ◽  
Virtual Power Plant

scholarly journals Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization

2019 ◽  
Vol 9 (2) ◽  
pp. 292 ◽  
Author(s):  
Jiahui Zhang ◽  
Zhiyu Xu ◽  
Weisheng Xu ◽  
Feiyu Zhu ◽  
Xiaoyu Lyu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Power Plant ◽  
Short Term Memory ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Practical Case ◽  
Virtual Power ◽  
Swarm Optimization ◽  
Virtual Power Plant

This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.

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