Collaborative Optimization of Electricity-Gas Energy Flow Using Improved Consensus Algorithm with Teaching-Learning Based Optimization

In the context of integrated energy network, it is necessary to consider distributed collaborative optimization of multiple energy flow. Based on the characteristics of multi-agent distributed autonomy of decision-making for electric power and natural gas, an optimization model of multi-flow local area network with new energy is established considering the relationship of electricity and gas coupling. And a reasonable energy planning mechanism is established based on energy routers to ensure the orderly flow of energy between individual energy local area networks. With the improved consensus optimization algorithm using teaching-learning based optimization, the optimization problem is solved and the distributed collaborative optimization of the electricity-gas coupling system is realized. Energy router is not only an energy device but also an information node, in which different forms of energy are organically integrated and coordinately optimized. The improved consensus algorithm with teaching-learning based optimization has better exploration and exploitation ability, and can obtain better optimization results and calculation efficiency. The IEEE 118-GAS90 electricity-gas interconnected network system is tested as an example and the results verify the effectiveness and feasibility of the proposed method. © 2019 Automation of Electric Power Systems Press.